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bottom-up-attention | bottom-up-attention-master/caffe/tools/extra/resize_and_crop_images.py | #!/usr/bin/env python
from mincepie import mapreducer, launcher
import gflags
import os
import cv2
from PIL import Image
# gflags
gflags.DEFINE_string('image_lib', 'opencv',
'OpenCV or PIL, case insensitive. The default value is the faster OpenCV.')
gflags.DEFINE_string('input_folder', '',
'The folder that contains all input images, organized in synsets.')
gflags.DEFINE_integer('output_side_length', 256,
'Expected side length of the output image.')
gflags.DEFINE_string('output_folder', '',
'The folder that we write output resized and cropped images to')
FLAGS = gflags.FLAGS
class OpenCVResizeCrop:
def resize_and_crop_image(self, input_file, output_file, output_side_length = 256):
'''Takes an image name, resize it and crop the center square
'''
img = cv2.imread(input_file)
height, width, depth = img.shape
new_height = output_side_length
new_width = output_side_length
if height > width:
new_height = output_side_length * height / width
else:
new_width = output_side_length * width / height
resized_img = cv2.resize(img, (new_width, new_height))
height_offset = (new_height - output_side_length) / 2
width_offset = (new_width - output_side_length) / 2
cropped_img = resized_img[height_offset:height_offset + output_side_length,
width_offset:width_offset + output_side_length]
cv2.imwrite(output_file, cropped_img)
class PILResizeCrop:
## http://united-coders.com/christian-harms/image-resizing-tips-every-coder-should-know/
def resize_and_crop_image(self, input_file, output_file, output_side_length = 256, fit = True):
'''Downsample the image.
'''
img = Image.open(input_file)
box = (output_side_length, output_side_length)
#preresize image with factor 2, 4, 8 and fast algorithm
factor = 1
while img.size[0]/factor > 2*box[0] and img.size[1]*2/factor > 2*box[1]:
factor *=2
if factor > 1:
img.thumbnail((img.size[0]/factor, img.size[1]/factor), Image.NEAREST)
#calculate the cropping box and get the cropped part
if fit:
x1 = y1 = 0
x2, y2 = img.size
wRatio = 1.0 * x2/box[0]
hRatio = 1.0 * y2/box[1]
if hRatio > wRatio:
y1 = int(y2/2-box[1]*wRatio/2)
y2 = int(y2/2+box[1]*wRatio/2)
else:
x1 = int(x2/2-box[0]*hRatio/2)
x2 = int(x2/2+box[0]*hRatio/2)
img = img.crop((x1,y1,x2,y2))
#Resize the image with best quality algorithm ANTI-ALIAS
img.thumbnail(box, Image.ANTIALIAS)
#save it into a file-like object
with open(output_file, 'wb') as out:
img.save(out, 'JPEG', quality=75)
class ResizeCropImagesMapper(mapreducer.BasicMapper):
'''The ImageNet Compute mapper.
The input value would be the file listing images' paths relative to input_folder.
'''
def map(self, key, value):
if type(value) is not str:
value = str(value)
files = [value]
image_lib = FLAGS.image_lib.lower()
if image_lib == 'pil':
resize_crop = PILResizeCrop()
else:
resize_crop = OpenCVResizeCrop()
for i, line in enumerate(files):
try:
line = line.replace(FLAGS.input_folder, '').strip()
line = line.split()
image_file_name = line[0]
input_file = os.path.join(FLAGS.input_folder, image_file_name)
output_file = os.path.join(FLAGS.output_folder, image_file_name)
output_dir = output_file[:output_file.rfind('/')]
if not os.path.exists(output_dir):
os.makedirs(output_dir)
feat = resize_crop.resize_and_crop_image(input_file, output_file,
FLAGS.output_side_length)
except Exception, e:
# we ignore the exception (maybe the image is corrupted?)
print line, Exception, e
yield value, FLAGS.output_folder
mapreducer.REGISTER_DEFAULT_MAPPER(ResizeCropImagesMapper)
mapreducer.REGISTER_DEFAULT_READER(mapreducer.FileReader)
mapreducer.REGISTER_DEFAULT_WRITER(mapreducer.FileWriter)
if __name__ == '__main__':
launcher.launch()
| 4,541 | 40.290909 | 99 | py |
bottom-up-attention | bottom-up-attention-master/caffe/tools/extra/parse_log.py | #!/usr/bin/env python
"""
Parse training log
Evolved from parse_log.sh
"""
import os
import re
import extract_seconds
import argparse
import csv
from collections import OrderedDict
def parse_log(path_to_log):
"""Parse log file
Returns (train_dict_list, test_dict_list)
train_dict_list and test_dict_list are lists of dicts that define the table
rows
"""
regex_iteration = re.compile('Iteration (\d+)')
regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)')
regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)')
regex_learning_rate = re.compile('lr = ([-+]?[0-9]*\.?[0-9]+([eE]?[-+]?[0-9]+)?)')
# Pick out lines of interest
iteration = -1
learning_rate = float('NaN')
train_dict_list = []
test_dict_list = []
train_row = None
test_row = None
logfile_year = extract_seconds.get_log_created_year(path_to_log)
with open(path_to_log) as f:
start_time = extract_seconds.get_start_time(f, logfile_year)
last_time = start_time
for line in f:
iteration_match = regex_iteration.search(line)
if iteration_match:
iteration = float(iteration_match.group(1))
if iteration == -1:
# Only start parsing for other stuff if we've found the first
# iteration
continue
try:
time = extract_seconds.extract_datetime_from_line(line,
logfile_year)
except ValueError:
# Skip lines with bad formatting, for example when resuming solver
continue
# if it's another year
if time.month < last_time.month:
logfile_year += 1
time = extract_seconds.extract_datetime_from_line(line, logfile_year)
last_time = time
seconds = (time - start_time).total_seconds()
learning_rate_match = regex_learning_rate.search(line)
if learning_rate_match:
learning_rate = float(learning_rate_match.group(1))
train_dict_list, train_row = parse_line_for_net_output(
regex_train_output, train_row, train_dict_list,
line, iteration, seconds, learning_rate
)
test_dict_list, test_row = parse_line_for_net_output(
regex_test_output, test_row, test_dict_list,
line, iteration, seconds, learning_rate
)
fix_initial_nan_learning_rate(train_dict_list)
fix_initial_nan_learning_rate(test_dict_list)
return train_dict_list, test_dict_list
def parse_line_for_net_output(regex_obj, row, row_dict_list,
line, iteration, seconds, learning_rate):
"""Parse a single line for training or test output
Returns a a tuple with (row_dict_list, row)
row: may be either a new row or an augmented version of the current row
row_dict_list: may be either the current row_dict_list or an augmented
version of the current row_dict_list
"""
output_match = regex_obj.search(line)
if output_match:
if not row or row['NumIters'] != iteration:
# Push the last row and start a new one
if row:
# If we're on a new iteration, push the last row
# This will probably only happen for the first row; otherwise
# the full row checking logic below will push and clear full
# rows
row_dict_list.append(row)
row = OrderedDict([
('NumIters', iteration),
('Seconds', seconds),
('LearningRate', learning_rate)
])
# output_num is not used; may be used in the future
# output_num = output_match.group(1)
output_name = output_match.group(2)
output_val = output_match.group(3)
row[output_name] = float(output_val)
if row and len(row_dict_list) >= 1 and len(row) == len(row_dict_list[0]):
# The row is full, based on the fact that it has the same number of
# columns as the first row; append it to the list
row_dict_list.append(row)
row = None
return row_dict_list, row
def fix_initial_nan_learning_rate(dict_list):
"""Correct initial value of learning rate
Learning rate is normally not printed until after the initial test and
training step, which means the initial testing and training rows have
LearningRate = NaN. Fix this by copying over the LearningRate from the
second row, if it exists.
"""
if len(dict_list) > 1:
dict_list[0]['LearningRate'] = dict_list[1]['LearningRate']
def save_csv_files(logfile_path, output_dir, train_dict_list, test_dict_list,
delimiter=',', verbose=False):
"""Save CSV files to output_dir
If the input log file is, e.g., caffe.INFO, the names will be
caffe.INFO.train and caffe.INFO.test
"""
log_basename = os.path.basename(logfile_path)
train_filename = os.path.join(output_dir, log_basename + '.train')
write_csv(train_filename, train_dict_list, delimiter, verbose)
test_filename = os.path.join(output_dir, log_basename + '.test')
write_csv(test_filename, test_dict_list, delimiter, verbose)
def write_csv(output_filename, dict_list, delimiter, verbose=False):
"""Write a CSV file
"""
if not dict_list:
if verbose:
print('Not writing %s; no lines to write' % output_filename)
return
dialect = csv.excel
dialect.delimiter = delimiter
with open(output_filename, 'w') as f:
dict_writer = csv.DictWriter(f, fieldnames=dict_list[0].keys(),
dialect=dialect)
dict_writer.writeheader()
dict_writer.writerows(dict_list)
if verbose:
print 'Wrote %s' % output_filename
def parse_args():
description = ('Parse a Caffe training log into two CSV files '
'containing training and testing information')
parser = argparse.ArgumentParser(description=description)
parser.add_argument('logfile_path',
help='Path to log file')
parser.add_argument('output_dir',
help='Directory in which to place output CSV files')
parser.add_argument('--verbose',
action='store_true',
help='Print some extra info (e.g., output filenames)')
parser.add_argument('--delimiter',
default=',',
help=('Column delimiter in output files '
'(default: \'%(default)s\')'))
args = parser.parse_args()
return args
def main():
args = parse_args()
train_dict_list, test_dict_list = parse_log(args.logfile_path)
save_csv_files(args.logfile_path, args.output_dir, train_dict_list,
test_dict_list, delimiter=args.delimiter)
if __name__ == '__main__':
main()
| 7,114 | 32.720379 | 86 | py |
bottom-up-attention | bottom-up-attention-master/caffe/examples/web_demo/app.py | import os
import time
import cPickle
import datetime
import logging
import flask
import werkzeug
import optparse
import tornado.wsgi
import tornado.httpserver
import numpy as np
import pandas as pd
from PIL import Image
import cStringIO as StringIO
import urllib
import exifutil
import caffe
REPO_DIRNAME = os.path.abspath(os.path.dirname(os.path.abspath(__file__)) + '/../..')
UPLOAD_FOLDER = '/tmp/caffe_demos_uploads'
ALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif'])
# Obtain the flask app object
app = flask.Flask(__name__)
@app.route('/')
def index():
return flask.render_template('index.html', has_result=False)
@app.route('/classify_url', methods=['GET'])
def classify_url():
imageurl = flask.request.args.get('imageurl', '')
try:
string_buffer = StringIO.StringIO(
urllib.urlopen(imageurl).read())
image = caffe.io.load_image(string_buffer)
except Exception as err:
# For any exception we encounter in reading the image, we will just
# not continue.
logging.info('URL Image open error: %s', err)
return flask.render_template(
'index.html', has_result=True,
result=(False, 'Cannot open image from URL.')
)
logging.info('Image: %s', imageurl)
result = app.clf.classify_image(image)
return flask.render_template(
'index.html', has_result=True, result=result, imagesrc=imageurl)
@app.route('/classify_upload', methods=['POST'])
def classify_upload():
try:
# We will save the file to disk for possible data collection.
imagefile = flask.request.files['imagefile']
filename_ = str(datetime.datetime.now()).replace(' ', '_') + \
werkzeug.secure_filename(imagefile.filename)
filename = os.path.join(UPLOAD_FOLDER, filename_)
imagefile.save(filename)
logging.info('Saving to %s.', filename)
image = exifutil.open_oriented_im(filename)
except Exception as err:
logging.info('Uploaded image open error: %s', err)
return flask.render_template(
'index.html', has_result=True,
result=(False, 'Cannot open uploaded image.')
)
result = app.clf.classify_image(image)
return flask.render_template(
'index.html', has_result=True, result=result,
imagesrc=embed_image_html(image)
)
def embed_image_html(image):
"""Creates an image embedded in HTML base64 format."""
image_pil = Image.fromarray((255 * image).astype('uint8'))
image_pil = image_pil.resize((256, 256))
string_buf = StringIO.StringIO()
image_pil.save(string_buf, format='png')
data = string_buf.getvalue().encode('base64').replace('\n', '')
return 'data:image/png;base64,' + data
def allowed_file(filename):
return (
'.' in filename and
filename.rsplit('.', 1)[1] in ALLOWED_IMAGE_EXTENSIONS
)
class ImagenetClassifier(object):
default_args = {
'model_def_file': (
'{}/models/bvlc_reference_caffenet/deploy.prototxt'.format(REPO_DIRNAME)),
'pretrained_model_file': (
'{}/models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'.format(REPO_DIRNAME)),
'mean_file': (
'{}/python/caffe/imagenet/ilsvrc_2012_mean.npy'.format(REPO_DIRNAME)),
'class_labels_file': (
'{}/data/ilsvrc12/synset_words.txt'.format(REPO_DIRNAME)),
'bet_file': (
'{}/data/ilsvrc12/imagenet.bet.pickle'.format(REPO_DIRNAME)),
}
for key, val in default_args.iteritems():
if not os.path.exists(val):
raise Exception(
"File for {} is missing. Should be at: {}".format(key, val))
default_args['image_dim'] = 256
default_args['raw_scale'] = 255.
def __init__(self, model_def_file, pretrained_model_file, mean_file,
raw_scale, class_labels_file, bet_file, image_dim, gpu_mode):
logging.info('Loading net and associated files...')
if gpu_mode:
caffe.set_mode_gpu()
else:
caffe.set_mode_cpu()
self.net = caffe.Classifier(
model_def_file, pretrained_model_file,
image_dims=(image_dim, image_dim), raw_scale=raw_scale,
mean=np.load(mean_file).mean(1).mean(1), channel_swap=(2, 1, 0)
)
with open(class_labels_file) as f:
labels_df = pd.DataFrame([
{
'synset_id': l.strip().split(' ')[0],
'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]
}
for l in f.readlines()
])
self.labels = labels_df.sort('synset_id')['name'].values
self.bet = cPickle.load(open(bet_file))
# A bias to prefer children nodes in single-chain paths
# I am setting the value to 0.1 as a quick, simple model.
# We could use better psychological models here...
self.bet['infogain'] -= np.array(self.bet['preferences']) * 0.1
def classify_image(self, image):
try:
starttime = time.time()
scores = self.net.predict([image], oversample=True).flatten()
endtime = time.time()
indices = (-scores).argsort()[:5]
predictions = self.labels[indices]
# In addition to the prediction text, we will also produce
# the length for the progress bar visualization.
meta = [
(p, '%.5f' % scores[i])
for i, p in zip(indices, predictions)
]
logging.info('result: %s', str(meta))
# Compute expected information gain
expected_infogain = np.dot(
self.bet['probmat'], scores[self.bet['idmapping']])
expected_infogain *= self.bet['infogain']
# sort the scores
infogain_sort = expected_infogain.argsort()[::-1]
bet_result = [(self.bet['words'][v], '%.5f' % expected_infogain[v])
for v in infogain_sort[:5]]
logging.info('bet result: %s', str(bet_result))
return (True, meta, bet_result, '%.3f' % (endtime - starttime))
except Exception as err:
logging.info('Classification error: %s', err)
return (False, 'Something went wrong when classifying the '
'image. Maybe try another one?')
def start_tornado(app, port=5000):
http_server = tornado.httpserver.HTTPServer(
tornado.wsgi.WSGIContainer(app))
http_server.listen(port)
print("Tornado server starting on port {}".format(port))
tornado.ioloop.IOLoop.instance().start()
def start_from_terminal(app):
"""
Parse command line options and start the server.
"""
parser = optparse.OptionParser()
parser.add_option(
'-d', '--debug',
help="enable debug mode",
action="store_true", default=False)
parser.add_option(
'-p', '--port',
help="which port to serve content on",
type='int', default=5000)
parser.add_option(
'-g', '--gpu',
help="use gpu mode",
action='store_true', default=False)
opts, args = parser.parse_args()
ImagenetClassifier.default_args.update({'gpu_mode': opts.gpu})
# Initialize classifier + warm start by forward for allocation
app.clf = ImagenetClassifier(**ImagenetClassifier.default_args)
app.clf.net.forward()
if opts.debug:
app.run(debug=True, host='0.0.0.0', port=opts.port)
else:
start_tornado(app, opts.port)
if __name__ == '__main__':
logging.getLogger().setLevel(logging.INFO)
if not os.path.exists(UPLOAD_FOLDER):
os.makedirs(UPLOAD_FOLDER)
start_from_terminal(app)
| 7,793 | 33.184211 | 105 | py |
bottom-up-attention | bottom-up-attention-master/caffe/examples/web_demo/exifutil.py | """
This script handles the skimage exif problem.
"""
from PIL import Image
import numpy as np
ORIENTATIONS = { # used in apply_orientation
2: (Image.FLIP_LEFT_RIGHT,),
3: (Image.ROTATE_180,),
4: (Image.FLIP_TOP_BOTTOM,),
5: (Image.FLIP_LEFT_RIGHT, Image.ROTATE_90),
6: (Image.ROTATE_270,),
7: (Image.FLIP_LEFT_RIGHT, Image.ROTATE_270),
8: (Image.ROTATE_90,)
}
def open_oriented_im(im_path):
im = Image.open(im_path)
if hasattr(im, '_getexif'):
exif = im._getexif()
if exif is not None and 274 in exif:
orientation = exif[274]
im = apply_orientation(im, orientation)
img = np.asarray(im).astype(np.float32) / 255.
if img.ndim == 2:
img = img[:, :, np.newaxis]
img = np.tile(img, (1, 1, 3))
elif img.shape[2] == 4:
img = img[:, :, :3]
return img
def apply_orientation(im, orientation):
if orientation in ORIENTATIONS:
for method in ORIENTATIONS[orientation]:
im = im.transpose(method)
return im
| 1,046 | 25.175 | 51 | py |
bottom-up-attention | bottom-up-attention-master/caffe/examples/pycaffe/caffenet.py | from __future__ import print_function
from caffe import layers as L, params as P, to_proto
from caffe.proto import caffe_pb2
# helper function for common structures
def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
num_output=nout, pad=pad, group=group)
return conv, L.ReLU(conv, in_place=True)
def fc_relu(bottom, nout):
fc = L.InnerProduct(bottom, num_output=nout)
return fc, L.ReLU(fc, in_place=True)
def max_pool(bottom, ks, stride=1):
return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
def caffenet(lmdb, batch_size=256, include_acc=False):
data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
# the net itself
conv1, relu1 = conv_relu(data, 11, 96, stride=4)
pool1 = max_pool(relu1, 3, stride=2)
norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
pool2 = max_pool(relu2, 3, stride=2)
norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
pool5 = max_pool(relu5, 3, stride=2)
fc6, relu6 = fc_relu(pool5, 4096)
drop6 = L.Dropout(relu6, in_place=True)
fc7, relu7 = fc_relu(drop6, 4096)
drop7 = L.Dropout(relu7, in_place=True)
fc8 = L.InnerProduct(drop7, num_output=1000)
loss = L.SoftmaxWithLoss(fc8, label)
if include_acc:
acc = L.Accuracy(fc8, label)
return to_proto(loss, acc)
else:
return to_proto(loss)
def make_net():
with open('train.prototxt', 'w') as f:
print(caffenet('/path/to/caffe-train-lmdb'), file=f)
with open('test.prototxt', 'w') as f:
print(caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True), file=f)
if __name__ == '__main__':
make_net()
| 2,112 | 36.732143 | 91 | py |
bottom-up-attention | bottom-up-attention-master/caffe/examples/pycaffe/tools.py | import numpy as np
class SimpleTransformer:
"""
SimpleTransformer is a simple class for preprocessing and deprocessing
images for caffe.
"""
def __init__(self, mean=[128, 128, 128]):
self.mean = np.array(mean, dtype=np.float32)
self.scale = 1.0
def set_mean(self, mean):
"""
Set the mean to subtract for centering the data.
"""
self.mean = mean
def set_scale(self, scale):
"""
Set the data scaling.
"""
self.scale = scale
def preprocess(self, im):
"""
preprocess() emulate the pre-processing occurring in the vgg16 caffe
prototxt.
"""
im = np.float32(im)
im = im[:, :, ::-1] # change to BGR
im -= self.mean
im *= self.scale
im = im.transpose((2, 0, 1))
return im
def deprocess(self, im):
"""
inverse of preprocess()
"""
im = im.transpose(1, 2, 0)
im /= self.scale
im += self.mean
im = im[:, :, ::-1] # change to RGB
return np.uint8(im)
class CaffeSolver:
"""
Caffesolver is a class for creating a solver.prototxt file. It sets default
values and can export a solver parameter file.
Note that all parameters are stored as strings. Strings variables are
stored as strings in strings.
"""
def __init__(self, testnet_prototxt_path="testnet.prototxt",
trainnet_prototxt_path="trainnet.prototxt", debug=False):
self.sp = {}
# critical:
self.sp['base_lr'] = '0.001'
self.sp['momentum'] = '0.9'
# speed:
self.sp['test_iter'] = '100'
self.sp['test_interval'] = '250'
# looks:
self.sp['display'] = '25'
self.sp['snapshot'] = '2500'
self.sp['snapshot_prefix'] = '"snapshot"' # string within a string!
# learning rate policy
self.sp['lr_policy'] = '"fixed"'
# important, but rare:
self.sp['gamma'] = '0.1'
self.sp['weight_decay'] = '0.0005'
self.sp['train_net'] = '"' + trainnet_prototxt_path + '"'
self.sp['test_net'] = '"' + testnet_prototxt_path + '"'
# pretty much never change these.
self.sp['max_iter'] = '100000'
self.sp['test_initialization'] = 'false'
self.sp['average_loss'] = '25' # this has to do with the display.
self.sp['iter_size'] = '1' # this is for accumulating gradients
if (debug):
self.sp['max_iter'] = '12'
self.sp['test_iter'] = '1'
self.sp['test_interval'] = '4'
self.sp['display'] = '1'
def add_from_file(self, filepath):
"""
Reads a caffe solver prototxt file and updates the Caffesolver
instance parameters.
"""
with open(filepath, 'r') as f:
for line in f:
if line[0] == '#':
continue
splitLine = line.split(':')
self.sp[splitLine[0].strip()] = splitLine[1].strip()
def write(self, filepath):
"""
Export solver parameters to INPUT "filepath". Sorted alphabetically.
"""
f = open(filepath, 'w')
for key, value in sorted(self.sp.items()):
if not(type(value) is str):
raise TypeError('All solver parameters must be strings')
f.write('%s: %s\n' % (key, value))
| 3,457 | 27.344262 | 79 | py |
bottom-up-attention | bottom-up-attention-master/caffe/examples/pycaffe/layers/pascal_multilabel_datalayers.py | # imports
import json
import time
import pickle
import scipy.misc
import skimage.io
import caffe
import numpy as np
import os.path as osp
from xml.dom import minidom
from random import shuffle
from threading import Thread
from PIL import Image
from tools import SimpleTransformer
class PascalMultilabelDataLayerSync(caffe.Layer):
"""
This is a simple synchronous datalayer for training a multilabel model on
PASCAL.
"""
def setup(self, bottom, top):
self.top_names = ['data', 'label']
# === Read input parameters ===
# params is a python dictionary with layer parameters.
params = eval(self.param_str)
# Check the parameters for validity.
check_params(params)
# store input as class variables
self.batch_size = params['batch_size']
# Create a batch loader to load the images.
self.batch_loader = BatchLoader(params, None)
# === reshape tops ===
# since we use a fixed input image size, we can shape the data layer
# once. Else, we'd have to do it in the reshape call.
top[0].reshape(
self.batch_size, 3, params['im_shape'][0], params['im_shape'][1])
# Note the 20 channels (because PASCAL has 20 classes.)
top[1].reshape(self.batch_size, 20)
print_info("PascalMultilabelDataLayerSync", params)
def forward(self, bottom, top):
"""
Load data.
"""
for itt in range(self.batch_size):
# Use the batch loader to load the next image.
im, multilabel = self.batch_loader.load_next_image()
# Add directly to the caffe data layer
top[0].data[itt, ...] = im
top[1].data[itt, ...] = multilabel
def reshape(self, bottom, top):
"""
There is no need to reshape the data, since the input is of fixed size
(rows and columns)
"""
pass
def backward(self, top, propagate_down, bottom):
"""
These layers does not back propagate
"""
pass
class BatchLoader(object):
"""
This class abstracts away the loading of images.
Images can either be loaded singly, or in a batch. The latter is used for
the asyncronous data layer to preload batches while other processing is
performed.
"""
def __init__(self, params, result):
self.result = result
self.batch_size = params['batch_size']
self.pascal_root = params['pascal_root']
self.im_shape = params['im_shape']
# get list of image indexes.
list_file = params['split'] + '.txt'
self.indexlist = [line.rstrip('\n') for line in open(
osp.join(self.pascal_root, 'ImageSets/Main', list_file))]
self._cur = 0 # current image
# this class does some simple data-manipulations
self.transformer = SimpleTransformer()
print "BatchLoader initialized with {} images".format(
len(self.indexlist))
def load_next_image(self):
"""
Load the next image in a batch.
"""
# Did we finish an epoch?
if self._cur == len(self.indexlist):
self._cur = 0
shuffle(self.indexlist)
# Load an image
index = self.indexlist[self._cur] # Get the image index
image_file_name = index + '.jpg'
im = np.asarray(Image.open(
osp.join(self.pascal_root, 'JPEGImages', image_file_name)))
im = scipy.misc.imresize(im, self.im_shape) # resize
# do a simple horizontal flip as data augmentation
flip = np.random.choice(2)*2-1
im = im[:, ::flip, :]
# Load and prepare ground truth
multilabel = np.zeros(20).astype(np.float32)
anns = load_pascal_annotation(index, self.pascal_root)
for label in anns['gt_classes']:
# in the multilabel problem we don't care how MANY instances
# there are of each class. Only if they are present.
# The "-1" is b/c we are not interested in the background
# class.
multilabel[label - 1] = 1
self._cur += 1
return self.transformer.preprocess(im), multilabel
def load_pascal_annotation(index, pascal_root):
"""
This code is borrowed from Ross Girshick's FAST-RCNN code
(https://github.com/rbgirshick/fast-rcnn).
It parses the PASCAL .xml metadata files.
See publication for further details: (http://arxiv.org/abs/1504.08083).
Thanks Ross!
"""
classes = ('__background__', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
class_to_ind = dict(zip(classes, xrange(21)))
filename = osp.join(pascal_root, 'Annotations', index + '.xml')
# print 'Loading: {}'.format(filename)
def get_data_from_tag(node, tag):
return node.getElementsByTagName(tag)[0].childNodes[0].data
with open(filename) as f:
data = minidom.parseString(f.read())
objs = data.getElementsByTagName('object')
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, 21), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
# Make pixel indexes 0-based
x1 = float(get_data_from_tag(obj, 'xmin')) - 1
y1 = float(get_data_from_tag(obj, 'ymin')) - 1
x2 = float(get_data_from_tag(obj, 'xmax')) - 1
y2 = float(get_data_from_tag(obj, 'ymax')) - 1
cls = class_to_ind[
str(get_data_from_tag(obj, "name")).lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'index': index}
def check_params(params):
"""
A utility function to check the parameters for the data layers.
"""
assert 'split' in params.keys(
), 'Params must include split (train, val, or test).'
required = ['batch_size', 'pascal_root', 'im_shape']
for r in required:
assert r in params.keys(), 'Params must include {}'.format(r)
def print_info(name, params):
"""
Output some info regarding the class
"""
print "{} initialized for split: {}, with bs: {}, im_shape: {}.".format(
name,
params['split'],
params['batch_size'],
params['im_shape'])
| 6,846 | 30.552995 | 78 | py |
bottom-up-attention | bottom-up-attention-master/caffe/examples/pycaffe/layers/pyloss.py | import caffe
import numpy as np
class EuclideanLossLayer(caffe.Layer):
"""
Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
to demonstrate the class interface for developing layers in Python.
"""
def setup(self, bottom, top):
# check input pair
if len(bottom) != 2:
raise Exception("Need two inputs to compute distance.")
def reshape(self, bottom, top):
# check input dimensions match
if bottom[0].count != bottom[1].count:
raise Exception("Inputs must have the same dimension.")
# difference is shape of inputs
self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
# loss output is scalar
top[0].reshape(1)
def forward(self, bottom, top):
self.diff[...] = bottom[0].data - bottom[1].data
top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.
def backward(self, top, propagate_down, bottom):
for i in range(2):
if not propagate_down[i]:
continue
if i == 0:
sign = 1
else:
sign = -1
bottom[i].diff[...] = sign * self.diff / bottom[i].num
| 1,223 | 31.210526 | 79 | py |
bottom-up-attention | bottom-up-attention-master/caffe/examples/finetune_flickr_style/assemble_data.py | #!/usr/bin/env python
"""
Form a subset of the Flickr Style data, download images to dirname, and write
Caffe ImagesDataLayer training file.
"""
import os
import urllib
import hashlib
import argparse
import numpy as np
import pandas as pd
from skimage import io
import multiprocessing
# Flickr returns a special image if the request is unavailable.
MISSING_IMAGE_SHA1 = '6a92790b1c2a301c6e7ddef645dca1f53ea97ac2'
example_dirname = os.path.abspath(os.path.dirname(__file__))
caffe_dirname = os.path.abspath(os.path.join(example_dirname, '../..'))
training_dirname = os.path.join(caffe_dirname, 'data/flickr_style')
def download_image(args_tuple):
"For use with multiprocessing map. Returns filename on fail."
try:
url, filename = args_tuple
if not os.path.exists(filename):
urllib.urlretrieve(url, filename)
with open(filename) as f:
assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1
test_read_image = io.imread(filename)
return True
except KeyboardInterrupt:
raise Exception() # multiprocessing doesn't catch keyboard exceptions
except:
return False
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Download a subset of Flickr Style to a directory')
parser.add_argument(
'-s', '--seed', type=int, default=0,
help="random seed")
parser.add_argument(
'-i', '--images', type=int, default=-1,
help="number of images to use (-1 for all [default])",
)
parser.add_argument(
'-w', '--workers', type=int, default=-1,
help="num workers used to download images. -x uses (all - x) cores [-1 default]."
)
parser.add_argument(
'-l', '--labels', type=int, default=0,
help="if set to a positive value, only sample images from the first number of labels."
)
args = parser.parse_args()
np.random.seed(args.seed)
# Read data, shuffle order, and subsample.
csv_filename = os.path.join(example_dirname, 'flickr_style.csv.gz')
df = pd.read_csv(csv_filename, index_col=0, compression='gzip')
df = df.iloc[np.random.permutation(df.shape[0])]
if args.labels > 0:
df = df.loc[df['label'] < args.labels]
if args.images > 0 and args.images < df.shape[0]:
df = df.iloc[:args.images]
# Make directory for images and get local filenames.
if training_dirname is None:
training_dirname = os.path.join(caffe_dirname, 'data/flickr_style')
images_dirname = os.path.join(training_dirname, 'images')
if not os.path.exists(images_dirname):
os.makedirs(images_dirname)
df['image_filename'] = [
os.path.join(images_dirname, _.split('/')[-1]) for _ in df['image_url']
]
# Download images.
num_workers = args.workers
if num_workers <= 0:
num_workers = multiprocessing.cpu_count() + num_workers
print('Downloading {} images with {} workers...'.format(
df.shape[0], num_workers))
pool = multiprocessing.Pool(processes=num_workers)
map_args = zip(df['image_url'], df['image_filename'])
results = pool.map(download_image, map_args)
# Only keep rows with valid images, and write out training file lists.
df = df[results]
for split in ['train', 'test']:
split_df = df[df['_split'] == split]
filename = os.path.join(training_dirname, '{}.txt'.format(split))
split_df[['image_filename', 'label']].to_csv(
filename, sep=' ', header=None, index=None)
print('Writing train/val for {} successfully downloaded images.'.format(
df.shape[0]))
| 3,636 | 35.737374 | 94 | py |
bottom-up-attention | bottom-up-attention-master/caffe/src/caffe/test/test_data/generate_sample_data.py | """
Generate data used in the HDF5DataLayer and GradientBasedSolver tests.
"""
import os
import numpy as np
import h5py
script_dir = os.path.dirname(os.path.abspath(__file__))
# Generate HDF5DataLayer sample_data.h5
num_cols = 8
num_rows = 10
height = 6
width = 5
total_size = num_cols * num_rows * height * width
data = np.arange(total_size)
data = data.reshape(num_rows, num_cols, height, width)
data = data.astype('float32')
# We had a bug where data was copied into label, but the tests weren't
# catching it, so let's make label 1-indexed.
label = 1 + np.arange(num_rows)[:, np.newaxis]
label = label.astype('float32')
# We add an extra label2 dataset to test HDF5 layer's ability
# to handle arbitrary number of output ("top") Blobs.
label2 = label + 1
print data
print label
with h5py.File(script_dir + '/sample_data.h5', 'w') as f:
f['data'] = data
f['label'] = label
f['label2'] = label2
with h5py.File(script_dir + '/sample_data_2_gzip.h5', 'w') as f:
f.create_dataset(
'data', data=data + total_size,
compression='gzip', compression_opts=1
)
f.create_dataset(
'label', data=label,
compression='gzip', compression_opts=1,
dtype='uint8',
)
f.create_dataset(
'label2', data=label2,
compression='gzip', compression_opts=1,
dtype='uint8',
)
with open(script_dir + '/sample_data_list.txt', 'w') as f:
f.write('src/caffe/test/test_data/sample_data.h5\n')
f.write('src/caffe/test/test_data/sample_data_2_gzip.h5\n')
# Generate GradientBasedSolver solver_data.h5
num_cols = 3
num_rows = 8
height = 10
width = 10
data = np.random.randn(num_rows, num_cols, height, width)
data = data.reshape(num_rows, num_cols, height, width)
data = data.astype('float32')
targets = np.random.randn(num_rows, 1)
targets = targets.astype('float32')
print data
print targets
with h5py.File(script_dir + '/solver_data.h5', 'w') as f:
f['data'] = data
f['targets'] = targets
with open(script_dir + '/solver_data_list.txt', 'w') as f:
f.write('src/caffe/test/test_data/solver_data.h5\n')
| 2,104 | 24.670732 | 70 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/draw_net.py | #!/usr/bin/env python
"""
Draw a graph of the net architecture.
"""
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from google.protobuf import text_format
import caffe
import caffe.draw
from caffe.proto import caffe_pb2
def parse_args():
"""Parse input arguments
"""
parser = ArgumentParser(description=__doc__,
formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('input_net_proto_file',
help='Input network prototxt file')
parser.add_argument('output_image_file',
help='Output image file')
parser.add_argument('--rankdir',
help=('One of TB (top-bottom, i.e., vertical), '
'RL (right-left, i.e., horizontal), or another '
'valid dot option; see '
'http://www.graphviz.org/doc/info/'
'attrs.html#k:rankdir'),
default='LR')
parser.add_argument('--phase',
help=('Which network phase to draw: can be TRAIN, '
'TEST, or ALL. If ALL, then all layers are drawn '
'regardless of phase.'),
default="ALL")
args = parser.parse_args()
return args
def main():
args = parse_args()
net = caffe_pb2.NetParameter()
text_format.Merge(open(args.input_net_proto_file).read(), net)
print('Drawing net to %s' % args.output_image_file)
phase=None;
if args.phase == "TRAIN":
phase = caffe.TRAIN
elif args.phase == "TEST":
phase = caffe.TEST
elif args.phase != "ALL":
raise ValueError("Unknown phase: " + args.phase)
caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir,
phase)
if __name__ == '__main__':
main()
| 1,934 | 31.79661 | 81 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/detect.py | #!/usr/bin/env python
"""
detector.py is an out-of-the-box windowed detector
callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
Note that this model was trained for image classification and not detection,
and finetuning for detection can be expected to improve results.
The selective_search_ijcv_with_python code required for the selective search
proposal mode is available at
https://github.com/sergeyk/selective_search_ijcv_with_python
TODO:
- batch up image filenames as well: don't want to load all of them into memory
- come up with a batching scheme that preserved order / keeps a unique ID
"""
import numpy as np
import pandas as pd
import os
import argparse
import time
import caffe
CROP_MODES = ['list', 'selective_search']
COORD_COLS = ['ymin', 'xmin', 'ymax', 'xmax']
def main(argv):
pycaffe_dir = os.path.dirname(__file__)
parser = argparse.ArgumentParser()
# Required arguments: input and output.
parser.add_argument(
"input_file",
help="Input txt/csv filename. If .txt, must be list of filenames.\
If .csv, must be comma-separated file with header\
'filename, xmin, ymin, xmax, ymax'"
)
parser.add_argument(
"output_file",
help="Output h5/csv filename. Format depends on extension."
)
# Optional arguments.
parser.add_argument(
"--model_def",
default=os.path.join(pycaffe_dir,
"../models/bvlc_reference_caffenet/deploy.prototxt"),
help="Model definition file."
)
parser.add_argument(
"--pretrained_model",
default=os.path.join(pycaffe_dir,
"../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel"),
help="Trained model weights file."
)
parser.add_argument(
"--crop_mode",
default="selective_search",
choices=CROP_MODES,
help="How to generate windows for detection."
)
parser.add_argument(
"--gpu",
action='store_true',
help="Switch for gpu computation."
)
parser.add_argument(
"--mean_file",
default=os.path.join(pycaffe_dir,
'caffe/imagenet/ilsvrc_2012_mean.npy'),
help="Data set image mean of H x W x K dimensions (numpy array). " +
"Set to '' for no mean subtraction."
)
parser.add_argument(
"--input_scale",
type=float,
help="Multiply input features by this scale to finish preprocessing."
)
parser.add_argument(
"--raw_scale",
type=float,
default=255.0,
help="Multiply raw input by this scale before preprocessing."
)
parser.add_argument(
"--channel_swap",
default='2,1,0',
help="Order to permute input channels. The default converts " +
"RGB -> BGR since BGR is the Caffe default by way of OpenCV."
)
parser.add_argument(
"--context_pad",
type=int,
default='16',
help="Amount of surrounding context to collect in input window."
)
args = parser.parse_args()
mean, channel_swap = None, None
if args.mean_file:
mean = np.load(args.mean_file)
if mean.shape[1:] != (1, 1):
mean = mean.mean(1).mean(1)
if args.channel_swap:
channel_swap = [int(s) for s in args.channel_swap.split(',')]
if args.gpu:
caffe.set_mode_gpu()
print("GPU mode")
else:
caffe.set_mode_cpu()
print("CPU mode")
# Make detector.
detector = caffe.Detector(args.model_def, args.pretrained_model, mean=mean,
input_scale=args.input_scale, raw_scale=args.raw_scale,
channel_swap=channel_swap,
context_pad=args.context_pad)
# Load input.
t = time.time()
print("Loading input...")
if args.input_file.lower().endswith('txt'):
with open(args.input_file) as f:
inputs = [_.strip() for _ in f.readlines()]
elif args.input_file.lower().endswith('csv'):
inputs = pd.read_csv(args.input_file, sep=',', dtype={'filename': str})
inputs.set_index('filename', inplace=True)
else:
raise Exception("Unknown input file type: not in txt or csv.")
# Detect.
if args.crop_mode == 'list':
# Unpack sequence of (image filename, windows).
images_windows = [
(ix, inputs.iloc[np.where(inputs.index == ix)][COORD_COLS].values)
for ix in inputs.index.unique()
]
detections = detector.detect_windows(images_windows)
else:
detections = detector.detect_selective_search(inputs)
print("Processed {} windows in {:.3f} s.".format(len(detections),
time.time() - t))
# Collect into dataframe with labeled fields.
df = pd.DataFrame(detections)
df.set_index('filename', inplace=True)
df[COORD_COLS] = pd.DataFrame(
data=np.vstack(df['window']), index=df.index, columns=COORD_COLS)
del(df['window'])
# Save results.
t = time.time()
if args.output_file.lower().endswith('csv'):
# csv
# Enumerate the class probabilities.
class_cols = ['class{}'.format(x) for x in range(NUM_OUTPUT)]
df[class_cols] = pd.DataFrame(
data=np.vstack(df['feat']), index=df.index, columns=class_cols)
df.to_csv(args.output_file, cols=COORD_COLS + class_cols)
else:
# h5
df.to_hdf(args.output_file, 'df', mode='w')
print("Saved to {} in {:.3f} s.".format(args.output_file,
time.time() - t))
if __name__ == "__main__":
import sys
main(sys.argv)
| 5,734 | 31.95977 | 88 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/classify.py | #!/usr/bin/env python
"""
classify.py is an out-of-the-box image classifer callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
"""
import numpy as np
import os
import sys
import argparse
import glob
import time
import caffe
def main(argv):
pycaffe_dir = os.path.dirname(__file__)
parser = argparse.ArgumentParser()
# Required arguments: input and output files.
parser.add_argument(
"input_file",
help="Input image, directory, or npy."
)
parser.add_argument(
"output_file",
help="Output npy filename."
)
# Optional arguments.
parser.add_argument(
"--model_def",
default=os.path.join(pycaffe_dir,
"../models/bvlc_reference_caffenet/deploy.prototxt"),
help="Model definition file."
)
parser.add_argument(
"--pretrained_model",
default=os.path.join(pycaffe_dir,
"../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel"),
help="Trained model weights file."
)
parser.add_argument(
"--gpu",
action='store_true',
help="Switch for gpu computation."
)
parser.add_argument(
"--center_only",
action='store_true',
help="Switch for prediction from center crop alone instead of " +
"averaging predictions across crops (default)."
)
parser.add_argument(
"--images_dim",
default='256,256',
help="Canonical 'height,width' dimensions of input images."
)
parser.add_argument(
"--mean_file",
default=os.path.join(pycaffe_dir,
'caffe/imagenet/ilsvrc_2012_mean.npy'),
help="Data set image mean of [Channels x Height x Width] dimensions " +
"(numpy array). Set to '' for no mean subtraction."
)
parser.add_argument(
"--input_scale",
type=float,
help="Multiply input features by this scale to finish preprocessing."
)
parser.add_argument(
"--raw_scale",
type=float,
default=255.0,
help="Multiply raw input by this scale before preprocessing."
)
parser.add_argument(
"--channel_swap",
default='2,1,0',
help="Order to permute input channels. The default converts " +
"RGB -> BGR since BGR is the Caffe default by way of OpenCV."
)
parser.add_argument(
"--ext",
default='jpg',
help="Image file extension to take as input when a directory " +
"is given as the input file."
)
args = parser.parse_args()
image_dims = [int(s) for s in args.images_dim.split(',')]
mean, channel_swap = None, None
if args.mean_file:
mean = np.load(args.mean_file)
if args.channel_swap:
channel_swap = [int(s) for s in args.channel_swap.split(',')]
if args.gpu:
caffe.set_mode_gpu()
print("GPU mode")
else:
caffe.set_mode_cpu()
print("CPU mode")
# Make classifier.
classifier = caffe.Classifier(args.model_def, args.pretrained_model,
image_dims=image_dims, mean=mean,
input_scale=args.input_scale, raw_scale=args.raw_scale,
channel_swap=channel_swap)
# Load numpy array (.npy), directory glob (*.jpg), or image file.
args.input_file = os.path.expanduser(args.input_file)
if args.input_file.endswith('npy'):
print("Loading file: %s" % args.input_file)
inputs = np.load(args.input_file)
elif os.path.isdir(args.input_file):
print("Loading folder: %s" % args.input_file)
inputs =[caffe.io.load_image(im_f)
for im_f in glob.glob(args.input_file + '/*.' + args.ext)]
else:
print("Loading file: %s" % args.input_file)
inputs = [caffe.io.load_image(args.input_file)]
print("Classifying %d inputs." % len(inputs))
# Classify.
start = time.time()
predictions = classifier.predict(inputs, not args.center_only)
print("Done in %.2f s." % (time.time() - start))
# Save
print("Saving results into %s" % args.output_file)
np.save(args.output_file, predictions)
if __name__ == '__main__':
main(sys.argv)
| 4,262 | 29.669065 | 88 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/train.py | #!/usr/bin/env python
"""
Trains a model using one or more GPUs.
"""
from multiprocessing import Process
import caffe
def train(
solver, # solver proto definition
snapshot, # solver snapshot to restore
gpus, # list of device ids
timing=False, # show timing info for compute and communications
):
# NCCL uses a uid to identify a session
uid = caffe.NCCL.new_uid()
caffe.init_log()
caffe.log('Using devices %s' % str(gpus))
procs = []
for rank in range(len(gpus)):
p = Process(target=solve,
args=(solver, snapshot, gpus, timing, uid, rank))
p.daemon = True
p.start()
procs.append(p)
for p in procs:
p.join()
def time(solver, nccl):
fprop = []
bprop = []
total = caffe.Timer()
allrd = caffe.Timer()
for _ in range(len(solver.net.layers)):
fprop.append(caffe.Timer())
bprop.append(caffe.Timer())
display = solver.param.display
def show_time():
if solver.iter % display == 0:
s = '\n'
for i in range(len(solver.net.layers)):
s += 'forw %3d %8s ' % (i, solver.net._layer_names[i])
s += ': %.2f\n' % fprop[i].ms
for i in range(len(solver.net.layers) - 1, -1, -1):
s += 'back %3d %8s ' % (i, solver.net._layer_names[i])
s += ': %.2f\n' % bprop[i].ms
s += 'solver total: %.2f\n' % total.ms
s += 'allreduce: %.2f\n' % allrd.ms
caffe.log(s)
solver.net.before_forward(lambda layer: fprop[layer].start())
solver.net.after_forward(lambda layer: fprop[layer].stop())
solver.net.before_backward(lambda layer: bprop[layer].start())
solver.net.after_backward(lambda layer: bprop[layer].stop())
solver.add_callback(lambda: total.start(), lambda: (total.stop(), allrd.start()))
solver.add_callback(nccl)
solver.add_callback(lambda: '', lambda: (allrd.stop(), show_time()))
def solve(proto, snapshot, gpus, timing, uid, rank):
caffe.set_mode_gpu()
caffe.set_device(gpus[rank])
caffe.set_solver_count(len(gpus))
caffe.set_solver_rank(rank)
caffe.set_multiprocess(True)
solver = caffe.SGDSolver(proto)
if snapshot and len(snapshot) != 0:
solver.restore(snapshot)
nccl = caffe.NCCL(solver, uid)
nccl.bcast()
if timing and rank == 0:
time(solver, nccl)
else:
solver.add_callback(nccl)
if solver.param.layer_wise_reduce:
solver.net.after_backward(nccl)
solver.step(solver.param.max_iter)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--solver", required=True, help="Solver proto definition.")
parser.add_argument("--snapshot", help="Solver snapshot to restore.")
parser.add_argument("--gpus", type=int, nargs='+', default=[0],
help="List of device ids.")
parser.add_argument("--timing", action='store_true', help="Show timing info.")
args = parser.parse_args()
train(args.solver, args.snapshot, args.gpus, args.timing)
| 3,145 | 30.148515 | 85 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/net_spec.py | """Python net specification.
This module provides a way to write nets directly in Python, using a natural,
functional style. See examples/pycaffe/caffenet.py for an example.
Currently this works as a thin wrapper around the Python protobuf interface,
with layers and parameters automatically generated for the "layers" and
"params" pseudo-modules, which are actually objects using __getattr__ magic
to generate protobuf messages.
Note that when using to_proto or Top.to_proto, names of intermediate blobs will
be automatically generated. To explicitly specify blob names, use the NetSpec
class -- assign to its attributes directly to name layers, and call
NetSpec.to_proto to serialize all assigned layers.
This interface is expected to continue to evolve as Caffe gains new capabilities
for specifying nets. In particular, the automatically generated layer names
are not guaranteed to be forward-compatible.
"""
from collections import OrderedDict, Counter
from .proto import caffe_pb2
from google import protobuf
import six
def param_name_dict():
"""Find out the correspondence between layer names and parameter names."""
layer = caffe_pb2.LayerParameter()
# get all parameter names (typically underscore case) and corresponding
# type names (typically camel case), which contain the layer names
# (note that not all parameters correspond to layers, but we'll ignore that)
param_names = [f.name for f in layer.DESCRIPTOR.fields if f.name.endswith('_param')]
param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]
# strip the final '_param' or 'Parameter'
param_names = [s[:-len('_param')] for s in param_names]
param_type_names = [s[:-len('Parameter')] for s in param_type_names]
return dict(zip(param_type_names, param_names))
def to_proto(*tops):
"""Generate a NetParameter that contains all layers needed to compute
all arguments."""
layers = OrderedDict()
autonames = Counter()
for top in tops:
top.fn._to_proto(layers, {}, autonames)
net = caffe_pb2.NetParameter()
net.layer.extend(layers.values())
return net
def assign_proto(proto, name, val):
"""Assign a Python object to a protobuf message, based on the Python
type (in recursive fashion). Lists become repeated fields/messages, dicts
become messages, and other types are assigned directly. For convenience,
repeated fields whose values are not lists are converted to single-element
lists; e.g., `my_repeated_int_field=3` is converted to
`my_repeated_int_field=[3]`."""
is_repeated_field = hasattr(getattr(proto, name), 'extend')
if is_repeated_field and not isinstance(val, list):
val = [val]
if isinstance(val, list):
if isinstance(val[0], dict):
for item in val:
proto_item = getattr(proto, name).add()
for k, v in six.iteritems(item):
assign_proto(proto_item, k, v)
else:
getattr(proto, name).extend(val)
elif isinstance(val, dict):
for k, v in six.iteritems(val):
assign_proto(getattr(proto, name), k, v)
else:
setattr(proto, name, val)
class Top(object):
"""A Top specifies a single output blob (which could be one of several
produced by a layer.)"""
def __init__(self, fn, n):
self.fn = fn
self.n = n
def to_proto(self):
"""Generate a NetParameter that contains all layers needed to compute
this top."""
return to_proto(self)
def _to_proto(self, layers, names, autonames):
return self.fn._to_proto(layers, names, autonames)
class Function(object):
"""A Function specifies a layer, its parameters, and its inputs (which
are Tops from other layers)."""
def __init__(self, type_name, inputs, params):
self.type_name = type_name
self.inputs = inputs
self.params = params
self.ntop = self.params.get('ntop', 1)
# use del to make sure kwargs are not double-processed as layer params
if 'ntop' in self.params:
del self.params['ntop']
self.in_place = self.params.get('in_place', False)
if 'in_place' in self.params:
del self.params['in_place']
self.tops = tuple(Top(self, n) for n in range(self.ntop))
def _get_name(self, names, autonames):
if self not in names and self.ntop > 0:
names[self] = self._get_top_name(self.tops[0], names, autonames)
elif self not in names:
autonames[self.type_name] += 1
names[self] = self.type_name + str(autonames[self.type_name])
return names[self]
def _get_top_name(self, top, names, autonames):
if top not in names:
autonames[top.fn.type_name] += 1
names[top] = top.fn.type_name + str(autonames[top.fn.type_name])
return names[top]
def _to_proto(self, layers, names, autonames):
if self in layers:
return
bottom_names = []
for inp in self.inputs:
inp._to_proto(layers, names, autonames)
bottom_names.append(layers[inp.fn].top[inp.n])
layer = caffe_pb2.LayerParameter()
layer.type = self.type_name
layer.bottom.extend(bottom_names)
if self.in_place:
layer.top.extend(layer.bottom)
else:
for top in self.tops:
layer.top.append(self._get_top_name(top, names, autonames))
layer.name = self._get_name(names, autonames)
for k, v in six.iteritems(self.params):
# special case to handle generic *params
if k.endswith('param'):
assign_proto(layer, k, v)
else:
try:
assign_proto(getattr(layer,
_param_names[self.type_name] + '_param'), k, v)
except (AttributeError, KeyError):
assign_proto(layer, k, v)
layers[self] = layer
class NetSpec(object):
"""A NetSpec contains a set of Tops (assigned directly as attributes).
Calling NetSpec.to_proto generates a NetParameter containing all of the
layers needed to produce all of the assigned Tops, using the assigned
names."""
def __init__(self):
super(NetSpec, self).__setattr__('tops', OrderedDict())
def __setattr__(self, name, value):
self.tops[name] = value
def __getattr__(self, name):
return self.tops[name]
def __setitem__(self, key, value):
self.__setattr__(key, value)
def __getitem__(self, item):
return self.__getattr__(item)
def to_proto(self):
names = {v: k for k, v in six.iteritems(self.tops)}
autonames = Counter()
layers = OrderedDict()
for name, top in six.iteritems(self.tops):
top._to_proto(layers, names, autonames)
net = caffe_pb2.NetParameter()
net.layer.extend(layers.values())
return net
class Layers(object):
"""A Layers object is a pseudo-module which generates functions that specify
layers; e.g., Layers().Convolution(bottom, kernel_size=3) will produce a Top
specifying a 3x3 convolution applied to bottom."""
def __getattr__(self, name):
def layer_fn(*args, **kwargs):
fn = Function(name, args, kwargs)
if fn.ntop == 0:
return fn
elif fn.ntop == 1:
return fn.tops[0]
else:
return fn.tops
return layer_fn
class Parameters(object):
"""A Parameters object is a pseudo-module which generates constants used
in layer parameters; e.g., Parameters().Pooling.MAX is the value used
to specify max pooling."""
def __getattr__(self, name):
class Param:
def __getattr__(self, param_name):
return getattr(getattr(caffe_pb2, name + 'Parameter'), param_name)
return Param()
_param_names = param_name_dict()
layers = Layers()
params = Parameters()
| 8,048 | 34.45815 | 88 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/classifier.py | #!/usr/bin/env python
"""
Classifier is an image classifier specialization of Net.
"""
import numpy as np
import caffe
class Classifier(caffe.Net):
"""
Classifier extends Net for image class prediction
by scaling, center cropping, or oversampling.
Parameters
----------
image_dims : dimensions to scale input for cropping/sampling.
Default is to scale to net input size for whole-image crop.
mean, input_scale, raw_scale, channel_swap: params for
preprocessing options.
"""
def __init__(self, model_file, pretrained_file, image_dims=None,
mean=None, input_scale=None, raw_scale=None,
channel_swap=None):
caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
# configure pre-processing
in_ = self.inputs[0]
self.transformer = caffe.io.Transformer(
{in_: self.blobs[in_].data.shape})
self.transformer.set_transpose(in_, (2, 0, 1))
if mean is not None:
self.transformer.set_mean(in_, mean)
if input_scale is not None:
self.transformer.set_input_scale(in_, input_scale)
if raw_scale is not None:
self.transformer.set_raw_scale(in_, raw_scale)
if channel_swap is not None:
self.transformer.set_channel_swap(in_, channel_swap)
self.crop_dims = np.array(self.blobs[in_].data.shape[2:])
if not image_dims:
image_dims = self.crop_dims
self.image_dims = image_dims
def predict(self, inputs, oversample=True):
"""
Predict classification probabilities of inputs.
Parameters
----------
inputs : iterable of (H x W x K) input ndarrays.
oversample : boolean
average predictions across center, corners, and mirrors
when True (default). Center-only prediction when False.
Returns
-------
predictions: (N x C) ndarray of class probabilities for N images and C
classes.
"""
# Scale to standardize input dimensions.
input_ = np.zeros((len(inputs),
self.image_dims[0],
self.image_dims[1],
inputs[0].shape[2]),
dtype=np.float32)
for ix, in_ in enumerate(inputs):
input_[ix] = caffe.io.resize_image(in_, self.image_dims)
if oversample:
# Generate center, corner, and mirrored crops.
input_ = caffe.io.oversample(input_, self.crop_dims)
else:
# Take center crop.
center = np.array(self.image_dims) / 2.0
crop = np.tile(center, (1, 2))[0] + np.concatenate([
-self.crop_dims / 2.0,
self.crop_dims / 2.0
])
crop = crop.astype(int)
input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
# Classify
caffe_in = np.zeros(np.array(input_.shape)[[0, 3, 1, 2]],
dtype=np.float32)
for ix, in_ in enumerate(input_):
caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
out = self.forward_all(**{self.inputs[0]: caffe_in})
predictions = out[self.outputs[0]]
# For oversampling, average predictions across crops.
if oversample:
predictions = predictions.reshape((len(predictions) / 10, 10, -1))
predictions = predictions.mean(1)
return predictions
| 3,537 | 34.737374 | 78 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/coord_map.py | """
Determine spatial relationships between layers to relate their coordinates.
Coordinates are mapped from input-to-output (forward), but can
be mapped output-to-input (backward) by the inverse mapping too.
This helps crop and align feature maps among other uses.
"""
from __future__ import division
import numpy as np
from caffe import layers as L
PASS_THROUGH_LAYERS = ['AbsVal', 'BatchNorm', 'Bias', 'BNLL', 'Dropout',
'Eltwise', 'ELU', 'Log', 'LRN', 'Exp', 'MVN', 'Power',
'ReLU', 'PReLU', 'Scale', 'Sigmoid', 'Split', 'TanH',
'Threshold']
def conv_params(fn):
"""
Extract the spatial parameters that determine the coordinate mapping:
kernel size, stride, padding, and dilation.
Implementation detail: Convolution, Deconvolution, and Im2col layers
define these in the convolution_param message, while Pooling has its
own fields in pooling_param. This method deals with these details to
extract canonical parameters.
"""
params = fn.params.get('convolution_param', fn.params)
axis = params.get('axis', 1)
ks = np.array(params['kernel_size'], ndmin=1)
dilation = np.array(params.get('dilation', 1), ndmin=1)
assert len({'pad_h', 'pad_w', 'kernel_h', 'kernel_w', 'stride_h',
'stride_w'} & set(fn.params)) == 0, \
'cropping does not support legacy _h/_w params'
return (axis, np.array(params.get('stride', 1), ndmin=1),
(ks - 1) * dilation + 1,
np.array(params.get('pad', 0), ndmin=1))
def crop_params(fn):
"""
Extract the crop layer parameters with defaults.
"""
params = fn.params.get('crop_param', fn.params)
axis = params.get('axis', 2) # default to spatial crop for N, C, H, W
offset = np.array(params.get('offset', 0), ndmin=1)
return (axis, offset)
class UndefinedMapException(Exception):
"""
Exception raised for layers that do not have a defined coordinate mapping.
"""
pass
def coord_map(fn):
"""
Define the coordinate mapping by its
- axis
- scale: output coord[i * scale] <- input_coord[i]
- shift: output coord[i] <- output_coord[i + shift]
s.t. the identity mapping, as for pointwise layers like ReLu, is defined by
(None, 1, 0) since it is independent of axis and does not transform coords.
"""
if fn.type_name in ['Convolution', 'Pooling', 'Im2col']:
axis, stride, ks, pad = conv_params(fn)
return axis, 1 / stride, (pad - (ks - 1) / 2) / stride
elif fn.type_name == 'Deconvolution':
axis, stride, ks, pad = conv_params(fn)
return axis, stride, (ks - 1) / 2 - pad
elif fn.type_name in PASS_THROUGH_LAYERS:
return None, 1, 0
elif fn.type_name == 'Crop':
axis, offset = crop_params(fn)
axis -= 1 # -1 for last non-coordinate dim.
return axis, 1, - offset
else:
raise UndefinedMapException
class AxisMismatchException(Exception):
"""
Exception raised for mappings with incompatible axes.
"""
pass
def compose(base_map, next_map):
"""
Compose a base coord map with scale a1, shift b1 with a further coord map
with scale a2, shift b2. The scales multiply and the further shift, b2,
is scaled by base coord scale a1.
"""
ax1, a1, b1 = base_map
ax2, a2, b2 = next_map
if ax1 is None:
ax = ax2
elif ax2 is None or ax1 == ax2:
ax = ax1
else:
raise AxisMismatchException
return ax, a1 * a2, a1 * b2 + b1
def inverse(coord_map):
"""
Invert a coord map by de-scaling and un-shifting;
this gives the backward mapping for the gradient.
"""
ax, a, b = coord_map
return ax, 1 / a, -b / a
def coord_map_from_to(top_from, top_to):
"""
Determine the coordinate mapping betweeen a top (from) and a top (to).
Walk the graph to find a common ancestor while composing the coord maps for
from and to until they meet. As a last step the from map is inverted.
"""
# We need to find a common ancestor of top_from and top_to.
# We'll assume that all ancestors are equivalent here (otherwise the graph
# is an inconsistent state (which we could improve this to check for)).
# For now use a brute-force algorithm.
def collect_bottoms(top):
"""
Collect the bottoms to walk for the coordinate mapping.
The general rule is that all the bottoms of a layer can be mapped, as
most layers have the same coordinate mapping for each bottom.
Crop layer is a notable exception. Only the first/cropped bottom is
mappable; the second/dimensions bottom is excluded from the walk.
"""
bottoms = top.fn.inputs
if top.fn.type_name == 'Crop':
bottoms = bottoms[:1]
return bottoms
# walk back from top_from, keeping the coord map as we go
from_maps = {top_from: (None, 1, 0)}
frontier = {top_from}
while frontier:
top = frontier.pop()
try:
bottoms = collect_bottoms(top)
for bottom in bottoms:
from_maps[bottom] = compose(from_maps[top], coord_map(top.fn))
frontier.add(bottom)
except UndefinedMapException:
pass
# now walk back from top_to until we hit a common blob
to_maps = {top_to: (None, 1, 0)}
frontier = {top_to}
while frontier:
top = frontier.pop()
if top in from_maps:
return compose(to_maps[top], inverse(from_maps[top]))
try:
bottoms = collect_bottoms(top)
for bottom in bottoms:
to_maps[bottom] = compose(to_maps[top], coord_map(top.fn))
frontier.add(bottom)
except UndefinedMapException:
continue
# if we got here, we did not find a blob in common
raise RuntimeError('Could not compute map between tops; are they '
'connected by spatial layers?')
def crop(top_from, top_to):
"""
Define a Crop layer to crop a top (from) to another top (to) by
determining the coordinate mapping between the two and net spec'ing
the axis and shift parameters of the crop.
"""
ax, a, b = coord_map_from_to(top_from, top_to)
assert (a == 1).all(), 'scale mismatch on crop (a = {})'.format(a)
assert (b <= 0).all(), 'cannot crop negative offset (b = {})'.format(b)
assert (np.round(b) == b).all(), 'cannot crop noninteger offset ' \
'(b = {})'.format(b)
return L.Crop(top_from, top_to,
crop_param=dict(axis=ax + 1, # +1 for first cropping dim.
offset=list(-np.round(b).astype(int))))
| 6,721 | 35.139785 | 79 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/detector.py | #!/usr/bin/env python
"""
Do windowed detection by classifying a number of images/crops at once,
optionally using the selective search window proposal method.
This implementation follows ideas in
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik.
Rich feature hierarchies for accurate object detection and semantic
segmentation.
http://arxiv.org/abs/1311.2524
The selective_search_ijcv_with_python code required for the selective search
proposal mode is available at
https://github.com/sergeyk/selective_search_ijcv_with_python
"""
import numpy as np
import os
import caffe
class Detector(caffe.Net):
"""
Detector extends Net for windowed detection by a list of crops or
selective search proposals.
Parameters
----------
mean, input_scale, raw_scale, channel_swap : params for preprocessing
options.
context_pad : amount of surrounding context to take s.t. a `context_pad`
sized border of pixels in the network input image is context, as in
R-CNN feature extraction.
"""
def __init__(self, model_file, pretrained_file, mean=None,
input_scale=None, raw_scale=None, channel_swap=None,
context_pad=None):
caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
# configure pre-processing
in_ = self.inputs[0]
self.transformer = caffe.io.Transformer(
{in_: self.blobs[in_].data.shape})
self.transformer.set_transpose(in_, (2, 0, 1))
if mean is not None:
self.transformer.set_mean(in_, mean)
if input_scale is not None:
self.transformer.set_input_scale(in_, input_scale)
if raw_scale is not None:
self.transformer.set_raw_scale(in_, raw_scale)
if channel_swap is not None:
self.transformer.set_channel_swap(in_, channel_swap)
self.configure_crop(context_pad)
def detect_windows(self, images_windows):
"""
Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net.
Parameters
----------
images_windows: (image filename, window list) iterable.
context_crop: size of context border to crop in pixels.
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.
"""
# Extract windows.
window_inputs = []
for image_fname, windows in images_windows:
image = caffe.io.load_image(image_fname).astype(np.float32)
for window in windows:
window_inputs.append(self.crop(image, window))
# Run through the net (warping windows to input dimensions).
in_ = self.inputs[0]
caffe_in = np.zeros((len(window_inputs), window_inputs[0].shape[2])
+ self.blobs[in_].data.shape[2:],
dtype=np.float32)
for ix, window_in in enumerate(window_inputs):
caffe_in[ix] = self.transformer.preprocess(in_, window_in)
out = self.forward_all(**{in_: caffe_in})
predictions = out[self.outputs[0]]
# Package predictions with images and windows.
detections = []
ix = 0
for image_fname, windows in images_windows:
for window in windows:
detections.append({
'window': window,
'prediction': predictions[ix],
'filename': image_fname
})
ix += 1
return detections
def detect_selective_search(self, image_fnames):
"""
Do windowed detection over Selective Search proposals by extracting
the crop and warping to the input dimensions of the net.
Parameters
----------
image_fnames: list
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.
"""
import selective_search_ijcv_with_python as selective_search
# Make absolute paths so MATLAB can find the files.
image_fnames = [os.path.abspath(f) for f in image_fnames]
windows_list = selective_search.get_windows(
image_fnames,
cmd='selective_search_rcnn'
)
# Run windowed detection on the selective search list.
return self.detect_windows(zip(image_fnames, windows_list))
def crop(self, im, window):
"""
Crop a window from the image for detection. Include surrounding context
according to the `context_pad` configuration.
Parameters
----------
im: H x W x K image ndarray to crop.
window: bounding box coordinates as ymin, xmin, ymax, xmax.
Returns
-------
crop: cropped window.
"""
# Crop window from the image.
crop = im[window[0]:window[2], window[1]:window[3]]
if self.context_pad:
box = window.copy()
crop_size = self.blobs[self.inputs[0]].width # assumes square
scale = crop_size / (1. * crop_size - self.context_pad * 2)
# Crop a box + surrounding context.
half_h = (box[2] - box[0] + 1) / 2.
half_w = (box[3] - box[1] + 1) / 2.
center = (box[0] + half_h, box[1] + half_w)
scaled_dims = scale * np.array((-half_h, -half_w, half_h, half_w))
box = np.round(np.tile(center, 2) + scaled_dims)
full_h = box[2] - box[0] + 1
full_w = box[3] - box[1] + 1
scale_h = crop_size / full_h
scale_w = crop_size / full_w
pad_y = round(max(0, -box[0]) * scale_h) # amount out-of-bounds
pad_x = round(max(0, -box[1]) * scale_w)
# Clip box to image dimensions.
im_h, im_w = im.shape[:2]
box = np.clip(box, 0., [im_h, im_w, im_h, im_w])
clip_h = box[2] - box[0] + 1
clip_w = box[3] - box[1] + 1
assert(clip_h > 0 and clip_w > 0)
crop_h = round(clip_h * scale_h)
crop_w = round(clip_w * scale_w)
if pad_y + crop_h > crop_size:
crop_h = crop_size - pad_y
if pad_x + crop_w > crop_size:
crop_w = crop_size - pad_x
# collect with context padding and place in input
# with mean padding
context_crop = im[box[0]:box[2], box[1]:box[3]]
context_crop = caffe.io.resize_image(context_crop, (crop_h, crop_w))
crop = np.ones(self.crop_dims, dtype=np.float32) * self.crop_mean
crop[pad_y:(pad_y + crop_h), pad_x:(pad_x + crop_w)] = context_crop
return crop
def configure_crop(self, context_pad):
"""
Configure crop dimensions and amount of context for cropping.
If context is included, make the special input mean for context padding.
Parameters
----------
context_pad : amount of context for cropping.
"""
# crop dimensions
in_ = self.inputs[0]
tpose = self.transformer.transpose[in_]
inv_tpose = [tpose[t] for t in tpose]
self.crop_dims = np.array(self.blobs[in_].data.shape[1:])[inv_tpose]
#.transpose(inv_tpose)
# context padding
self.context_pad = context_pad
if self.context_pad:
in_ = self.inputs[0]
transpose = self.transformer.transpose.get(in_)
channel_order = self.transformer.channel_swap.get(in_)
raw_scale = self.transformer.raw_scale.get(in_)
# Padding context crops needs the mean in unprocessed input space.
mean = self.transformer.mean.get(in_)
if mean is not None:
inv_transpose = [transpose[t] for t in transpose]
crop_mean = mean.copy().transpose(inv_transpose)
if channel_order is not None:
channel_order_inverse = [channel_order.index(i)
for i in range(crop_mean.shape[2])]
crop_mean = crop_mean[:, :, channel_order_inverse]
if raw_scale is not None:
crop_mean /= raw_scale
self.crop_mean = crop_mean
else:
self.crop_mean = np.zeros(self.crop_dims, dtype=np.float32)
| 8,541 | 38.364055 | 80 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/__init__.py | from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, set_multiprocess, Layer, get_solver
from ._caffe import __version__
from .proto.caffe_pb2 import TRAIN, TEST
from .classifier import Classifier
from .detector import Detector
from . import io
from .net_spec import layers, params, NetSpec, to_proto
| 561 | 61.444444 | 225 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/pycaffe.py | """
Wrap the internal caffe C++ module (_caffe.so) with a clean, Pythonic
interface.
"""
from collections import OrderedDict
try:
from itertools import izip_longest
except:
from itertools import zip_longest as izip_longest
import numpy as np
from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
import caffe.io
import six
# We directly update methods from Net here (rather than using composition or
# inheritance) so that nets created by caffe (e.g., by SGDSolver) will
# automatically have the improved interface.
@property
def _Net_blobs(self):
"""
An OrderedDict (bottom to top, i.e., input to output) of network
blobs indexed by name
"""
if not hasattr(self, '_blobs_dict'):
self._blobs_dict = OrderedDict(zip(self._blob_names, self._blobs))
return self._blobs_dict
@property
def _Net_blob_loss_weights(self):
"""
An OrderedDict (bottom to top, i.e., input to output) of network
blob loss weights indexed by name
"""
if not hasattr(self, '_blobs_loss_weights_dict'):
self._blob_loss_weights_dict = OrderedDict(zip(self._blob_names,
self._blob_loss_weights))
return self._blob_loss_weights_dict
@property
def _Net_params(self):
"""
An OrderedDict (bottom to top, i.e., input to output) of network
parameters indexed by name; each is a list of multiple blobs (e.g.,
weights and biases)
"""
if not hasattr(self, '_params_dict'):
self._params_dict = OrderedDict([(name, lr.blobs)
for name, lr in zip(
self._layer_names, self.layers)
if len(lr.blobs) > 0])
return self._params_dict
@property
def _Net_inputs(self):
if not hasattr(self, '_input_list'):
keys = list(self.blobs.keys())
self._input_list = [keys[i] for i in self._inputs]
return self._input_list
@property
def _Net_outputs(self):
if not hasattr(self, '_output_list'):
keys = list(self.blobs.keys())
self._output_list = [keys[i] for i in self._outputs]
return self._output_list
def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
"""
Forward pass: prepare inputs and run the net forward.
Parameters
----------
blobs : list of blobs to return in addition to output blobs.
kwargs : Keys are input blob names and values are blob ndarrays.
For formatting inputs for Caffe, see Net.preprocess().
If None, input is taken from data layers.
start : optional name of layer at which to begin the forward pass
end : optional name of layer at which to finish the forward pass
(inclusive)
Returns
-------
outs : {blob name: blob ndarray} dict.
"""
if blobs is None:
blobs = []
if start is not None:
start_ind = list(self._layer_names).index(start)
else:
start_ind = 0
if end is not None:
end_ind = list(self._layer_names).index(end)
outputs = set([end] + blobs)
else:
end_ind = len(self.layers) - 1
outputs = set(self.outputs + blobs)
if kwargs:
if set(kwargs.keys()) != set(self.inputs):
raise Exception('Input blob arguments do not match net inputs.')
# Set input according to defined shapes and make arrays single and
# C-contiguous as Caffe expects.
for in_, blob in six.iteritems(kwargs):
if blob.shape[0] != self.blobs[in_].shape[0]:
raise Exception('Input is not batch sized')
self.blobs[in_].data[...] = blob
self._forward(start_ind, end_ind)
# Unpack blobs to extract
return {out: self.blobs[out].data for out in outputs}
def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
"""
Backward pass: prepare diffs and run the net backward.
Parameters
----------
diffs : list of diffs to return in addition to bottom diffs.
kwargs : Keys are output blob names and values are diff ndarrays.
If None, top diffs are taken from forward loss.
start : optional name of layer at which to begin the backward pass
end : optional name of layer at which to finish the backward pass
(inclusive)
Returns
-------
outs: {blob name: diff ndarray} dict.
"""
if diffs is None:
diffs = []
if start is not None:
start_ind = list(self._layer_names).index(start)
else:
start_ind = len(self.layers) - 1
if end is not None:
end_ind = list(self._layer_names).index(end)
outputs = set([end] + diffs)
else:
end_ind = 0
outputs = set(self.inputs + diffs)
if kwargs:
if set(kwargs.keys()) != set(self.outputs):
raise Exception('Top diff arguments do not match net outputs.')
# Set top diffs according to defined shapes and make arrays single and
# C-contiguous as Caffe expects.
for top, diff in six.iteritems(kwargs):
if diff.shape[0] != self.blobs[top].shape[0]:
raise Exception('Diff is not batch sized')
self.blobs[top].diff[...] = diff
self._backward(start_ind, end_ind)
# Unpack diffs to extract
return {out: self.blobs[out].diff for out in outputs}
def _Net_forward_all(self, blobs=None, **kwargs):
"""
Run net forward in batches.
Parameters
----------
blobs : list of blobs to extract as in forward()
kwargs : Keys are input blob names and values are blob ndarrays.
Refer to forward().
Returns
-------
all_outs : {blob name: list of blobs} dict.
"""
# Collect outputs from batches
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
for batch in self._batch(kwargs):
outs = self.forward(blobs=blobs, **batch)
for out, out_blob in six.iteritems(outs):
all_outs[out].extend(out_blob.copy())
# Package in ndarray.
for out in all_outs:
all_outs[out] = np.asarray(all_outs[out])
# Discard padding.
pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
if pad:
for out in all_outs:
all_outs[out] = all_outs[out][:-pad]
return all_outs
def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
"""
Run net forward + backward in batches.
Parameters
----------
blobs: list of blobs to extract as in forward()
diffs: list of diffs to extract as in backward()
kwargs: Keys are input (for forward) and output (for backward) blob names
and values are ndarrays. Refer to forward() and backward().
Prefilled variants are called for lack of input or output blobs.
Returns
-------
all_blobs: {blob name: blob ndarray} dict.
all_diffs: {blob name: diff ndarray} dict.
"""
# Batch blobs and diffs.
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
all_diffs = {diff: [] for diff in set(self.inputs + (diffs or []))}
forward_batches = self._batch({in_: kwargs[in_]
for in_ in self.inputs if in_ in kwargs})
backward_batches = self._batch({out: kwargs[out]
for out in self.outputs if out in kwargs})
# Collect outputs from batches (and heed lack of forward/backward batches).
for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}):
batch_blobs = self.forward(blobs=blobs, **fb)
batch_diffs = self.backward(diffs=diffs, **bb)
for out, out_blobs in six.iteritems(batch_blobs):
all_outs[out].extend(out_blobs.copy())
for diff, out_diffs in six.iteritems(batch_diffs):
all_diffs[diff].extend(out_diffs.copy())
# Package in ndarray.
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = np.asarray(all_outs[out])
all_diffs[diff] = np.asarray(all_diffs[diff])
# Discard padding at the end and package in ndarray.
pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
if pad:
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = all_outs[out][:-pad]
all_diffs[diff] = all_diffs[diff][:-pad]
return all_outs, all_diffs
def _Net_set_input_arrays(self, data, labels):
"""
Set input arrays of the in-memory MemoryDataLayer.
(Note: this is only for networks declared with the memory data layer.)
"""
if labels.ndim == 1:
labels = np.ascontiguousarray(labels[:, np.newaxis, np.newaxis,
np.newaxis])
return self._set_input_arrays(data, labels)
def _Net_batch(self, blobs):
"""
Batch blob lists according to net's batch size.
Parameters
----------
blobs: Keys blob names and values are lists of blobs (of any length).
Naturally, all the lists should have the same length.
Yields
------
batch: {blob name: list of blobs} dict for a single batch.
"""
num = len(six.next(six.itervalues(blobs)))
batch_size = six.next(six.itervalues(self.blobs)).shape[0]
remainder = num % batch_size
num_batches = num // batch_size
# Yield full batches.
for b in range(num_batches):
i = b * batch_size
yield {name: blobs[name][i:i + batch_size] for name in blobs}
# Yield last padded batch, if any.
if remainder > 0:
padded_batch = {}
for name in blobs:
padding = np.zeros((batch_size - remainder,)
+ blobs[name].shape[1:])
padded_batch[name] = np.concatenate([blobs[name][-remainder:],
padding])
yield padded_batch
def _Net_get_id_name(func, field):
"""
Generic property that maps func to the layer names into an OrderedDict.
Used for top_names and bottom_names.
Parameters
----------
func: function id -> [id]
field: implementation field name (cache)
Returns
------
A one-parameter function that can be set as a property.
"""
@property
def get_id_name(self):
if not hasattr(self, field):
id_to_name = list(self.blobs)
res = OrderedDict([(self._layer_names[i],
[id_to_name[j] for j in func(self, i)])
for i in range(len(self.layers))])
setattr(self, field, res)
return getattr(self, field)
return get_id_name
# Attach methods to Net.
Net.blobs = _Net_blobs
Net.blob_loss_weights = _Net_blob_loss_weights
Net.params = _Net_params
Net.forward = _Net_forward
Net.backward = _Net_backward
Net.forward_all = _Net_forward_all
Net.forward_backward_all = _Net_forward_backward_all
Net.set_input_arrays = _Net_set_input_arrays
Net._batch = _Net_batch
Net.inputs = _Net_inputs
Net.outputs = _Net_outputs
Net.top_names = _Net_get_id_name(Net._top_ids, "_top_names")
Net.bottom_names = _Net_get_id_name(Net._bottom_ids, "_bottom_names")
| 11,256 | 32.602985 | 89 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/draw.py | """
Caffe network visualization: draw the NetParameter protobuffer.
.. note::
This requires pydot>=1.0.2, which is not included in requirements.txt since
it requires graphviz and other prerequisites outside the scope of the
Caffe.
"""
from caffe.proto import caffe_pb2
"""
pydot is not supported under python 3 and pydot2 doesn't work properly.
pydotplus works nicely (pip install pydotplus)
"""
try:
# Try to load pydotplus
import pydotplus as pydot
except ImportError:
import pydot
# Internal layer and blob styles.
LAYER_STYLE_DEFAULT = {'shape': 'record',
'fillcolor': '#6495ED',
'style': 'filled'}
NEURON_LAYER_STYLE = {'shape': 'record',
'fillcolor': '#90EE90',
'style': 'filled'}
BLOB_STYLE = {'shape': 'octagon',
'fillcolor': '#E0E0E0',
'style': 'filled'}
def get_pooling_types_dict():
"""Get dictionary mapping pooling type number to type name
"""
desc = caffe_pb2.PoolingParameter.PoolMethod.DESCRIPTOR
d = {}
for k, v in desc.values_by_name.items():
d[v.number] = k
return d
def get_edge_label(layer):
"""Define edge label based on layer type.
"""
if layer.type == 'Data':
edge_label = 'Batch ' + str(layer.data_param.batch_size)
elif layer.type == 'Convolution' or layer.type == 'Deconvolution':
edge_label = str(layer.convolution_param.num_output)
elif layer.type == 'InnerProduct':
edge_label = str(layer.inner_product_param.num_output)
else:
edge_label = '""'
return edge_label
def get_layer_label(layer, rankdir):
"""Define node label based on layer type.
Parameters
----------
layer : ?
rankdir : {'LR', 'TB', 'BT'}
Direction of graph layout.
Returns
-------
string :
A label for the current layer
"""
if rankdir in ('TB', 'BT'):
# If graph orientation is vertical, horizontal space is free and
# vertical space is not; separate words with spaces
separator = ' '
else:
# If graph orientation is horizontal, vertical space is free and
# horizontal space is not; separate words with newlines
separator = '\\n'
if layer.type == 'Convolution' or layer.type == 'Deconvolution':
# Outer double quotes needed or else colon characters don't parse
# properly
node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
(layer.name,
separator,
layer.type,
separator,
layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size._values) else 1,
separator,
layer.convolution_param.stride[0] if len(layer.convolution_param.stride._values) else 1,
separator,
layer.convolution_param.pad[0] if len(layer.convolution_param.pad._values) else 0)
elif layer.type == 'Pooling':
pooling_types_dict = get_pooling_types_dict()
node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
(layer.name,
separator,
pooling_types_dict[layer.pooling_param.pool],
layer.type,
separator,
layer.pooling_param.kernel_size,
separator,
layer.pooling_param.stride,
separator,
layer.pooling_param.pad)
else:
node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
return node_label
def choose_color_by_layertype(layertype):
"""Define colors for nodes based on the layer type.
"""
color = '#6495ED' # Default
if layertype == 'Convolution' or layertype == 'Deconvolution':
color = '#FF5050'
elif layertype == 'Pooling':
color = '#FF9900'
elif layertype == 'InnerProduct':
color = '#CC33FF'
return color
def get_pydot_graph(caffe_net, rankdir, label_edges=True, phase=None):
"""Create a data structure which represents the `caffe_net`.
Parameters
----------
caffe_net : object
rankdir : {'LR', 'TB', 'BT'}
Direction of graph layout.
label_edges : boolean, optional
Label the edges (default is True).
phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
Include layers from this network phase. If None, include all layers.
(the default is None)
Returns
-------
pydot graph object
"""
pydot_graph = pydot.Dot(caffe_net.name if caffe_net.name else 'Net',
graph_type='digraph',
rankdir=rankdir)
pydot_nodes = {}
pydot_edges = []
for layer in caffe_net.layer:
if phase is not None:
included = False
if len(layer.include) == 0:
included = True
if len(layer.include) > 0 and len(layer.exclude) > 0:
raise ValueError('layer ' + layer.name + ' has both include '
'and exclude specified.')
for layer_phase in layer.include:
included = included or layer_phase.phase == phase
for layer_phase in layer.exclude:
included = included and not layer_phase.phase == phase
if not included:
continue
node_label = get_layer_label(layer, rankdir)
node_name = "%s_%s" % (layer.name, layer.type)
if (len(layer.bottom) == 1 and len(layer.top) == 1 and
layer.bottom[0] == layer.top[0]):
# We have an in-place neuron layer.
pydot_nodes[node_name] = pydot.Node(node_label,
**NEURON_LAYER_STYLE)
else:
layer_style = LAYER_STYLE_DEFAULT
layer_style['fillcolor'] = choose_color_by_layertype(layer.type)
pydot_nodes[node_name] = pydot.Node(node_label, **layer_style)
for bottom_blob in layer.bottom:
pydot_nodes[bottom_blob + '_blob'] = pydot.Node('%s' % bottom_blob,
**BLOB_STYLE)
edge_label = '""'
pydot_edges.append({'src': bottom_blob + '_blob',
'dst': node_name,
'label': edge_label})
for top_blob in layer.top:
pydot_nodes[top_blob + '_blob'] = pydot.Node('%s' % (top_blob))
if label_edges:
edge_label = get_edge_label(layer)
else:
edge_label = '""'
pydot_edges.append({'src': node_name,
'dst': top_blob + '_blob',
'label': edge_label})
# Now, add the nodes and edges to the graph.
for node in pydot_nodes.values():
pydot_graph.add_node(node)
for edge in pydot_edges:
pydot_graph.add_edge(
pydot.Edge(pydot_nodes[edge['src']],
pydot_nodes[edge['dst']],
label=edge['label']))
return pydot_graph
def draw_net(caffe_net, rankdir, ext='png', phase=None):
"""Draws a caffe net and returns the image string encoded using the given
extension.
Parameters
----------
caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
ext : string, optional
The image extension (the default is 'png').
phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
Include layers from this network phase. If None, include all layers.
(the default is None)
Returns
-------
string :
Postscript representation of the graph.
"""
return get_pydot_graph(caffe_net, rankdir, phase=phase).create(format=ext)
def draw_net_to_file(caffe_net, filename, rankdir='LR', phase=None):
"""Draws a caffe net, and saves it to file using the format given as the
file extension. Use '.raw' to output raw text that you can manually feed
to graphviz to draw graphs.
Parameters
----------
caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
filename : string
The path to a file where the networks visualization will be stored.
rankdir : {'LR', 'TB', 'BT'}
Direction of graph layout.
phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
Include layers from this network phase. If None, include all layers.
(the default is None)
"""
ext = filename[filename.rfind('.')+1:]
with open(filename, 'wb') as fid:
fid.write(draw_net(caffe_net, rankdir, ext, phase))
| 8,813 | 34.97551 | 120 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/io.py | import numpy as np
import skimage.io
from scipy.ndimage import zoom
from skimage.transform import resize
try:
# Python3 will most likely not be able to load protobuf
from caffe.proto import caffe_pb2
except:
import sys
if sys.version_info >= (3, 0):
print("Failed to include caffe_pb2, things might go wrong!")
else:
raise
## proto / datum / ndarray conversion
def blobproto_to_array(blob, return_diff=False):
"""
Convert a blob proto to an array. In default, we will just return the data,
unless return_diff is True, in which case we will return the diff.
"""
# Read the data into an array
if return_diff:
data = np.array(blob.diff)
else:
data = np.array(blob.data)
# Reshape the array
if blob.HasField('num') or blob.HasField('channels') or blob.HasField('height') or blob.HasField('width'):
# Use legacy 4D shape
return data.reshape(blob.num, blob.channels, blob.height, blob.width)
else:
return data.reshape(blob.shape.dim)
def array_to_blobproto(arr, diff=None):
"""Converts a N-dimensional array to blob proto. If diff is given, also
convert the diff. You need to make sure that arr and diff have the same
shape, and this function does not do sanity check.
"""
blob = caffe_pb2.BlobProto()
blob.shape.dim.extend(arr.shape)
blob.data.extend(arr.astype(float).flat)
if diff is not None:
blob.diff.extend(diff.astype(float).flat)
return blob
def arraylist_to_blobprotovector_str(arraylist):
"""Converts a list of arrays to a serialized blobprotovec, which could be
then passed to a network for processing.
"""
vec = caffe_pb2.BlobProtoVector()
vec.blobs.extend([array_to_blobproto(arr) for arr in arraylist])
return vec.SerializeToString()
def blobprotovector_str_to_arraylist(str):
"""Converts a serialized blobprotovec to a list of arrays.
"""
vec = caffe_pb2.BlobProtoVector()
vec.ParseFromString(str)
return [blobproto_to_array(blob) for blob in vec.blobs]
def array_to_datum(arr, label=None):
"""Converts a 3-dimensional array to datum. If the array has dtype uint8,
the output data will be encoded as a string. Otherwise, the output data
will be stored in float format.
"""
if arr.ndim != 3:
raise ValueError('Incorrect array shape.')
datum = caffe_pb2.Datum()
datum.channels, datum.height, datum.width = arr.shape
if arr.dtype == np.uint8:
datum.data = arr.tostring()
else:
datum.float_data.extend(arr.flat)
if label is not None:
datum.label = label
return datum
def datum_to_array(datum):
"""Converts a datum to an array. Note that the label is not returned,
as one can easily get it by calling datum.label.
"""
if len(datum.data):
return np.fromstring(datum.data, dtype=np.uint8).reshape(
datum.channels, datum.height, datum.width)
else:
return np.array(datum.float_data).astype(float).reshape(
datum.channels, datum.height, datum.width)
## Pre-processing
class Transformer:
"""
Transform input for feeding into a Net.
Note: this is mostly for illustrative purposes and it is likely better
to define your own input preprocessing routine for your needs.
Parameters
----------
net : a Net for which the input should be prepared
"""
def __init__(self, inputs):
self.inputs = inputs
self.transpose = {}
self.channel_swap = {}
self.raw_scale = {}
self.mean = {}
self.input_scale = {}
def __check_input(self, in_):
if in_ not in self.inputs:
raise Exception('{} is not one of the net inputs: {}'.format(
in_, self.inputs))
def preprocess(self, in_, data):
"""
Format input for Caffe:
- convert to single
- resize to input dimensions (preserving number of channels)
- transpose dimensions to K x H x W
- reorder channels (for instance color to BGR)
- scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
- subtract mean
- scale feature
Parameters
----------
in_ : name of input blob to preprocess for
data : (H' x W' x K) ndarray
Returns
-------
caffe_in : (K x H x W) ndarray for input to a Net
"""
self.__check_input(in_)
caffe_in = data.astype(np.float32, copy=False)
transpose = self.transpose.get(in_)
channel_swap = self.channel_swap.get(in_)
raw_scale = self.raw_scale.get(in_)
mean = self.mean.get(in_)
input_scale = self.input_scale.get(in_)
in_dims = self.inputs[in_][2:]
if caffe_in.shape[:2] != in_dims:
caffe_in = resize_image(caffe_in, in_dims)
if transpose is not None:
caffe_in = caffe_in.transpose(transpose)
if channel_swap is not None:
caffe_in = caffe_in[channel_swap, :, :]
if raw_scale is not None:
caffe_in *= raw_scale
if mean is not None:
caffe_in -= mean
if input_scale is not None:
caffe_in *= input_scale
return caffe_in
def deprocess(self, in_, data):
"""
Invert Caffe formatting; see preprocess().
"""
self.__check_input(in_)
decaf_in = data.copy().squeeze()
transpose = self.transpose.get(in_)
channel_swap = self.channel_swap.get(in_)
raw_scale = self.raw_scale.get(in_)
mean = self.mean.get(in_)
input_scale = self.input_scale.get(in_)
if input_scale is not None:
decaf_in /= input_scale
if mean is not None:
decaf_in += mean
if raw_scale is not None:
decaf_in /= raw_scale
if channel_swap is not None:
decaf_in = decaf_in[np.argsort(channel_swap), :, :]
if transpose is not None:
decaf_in = decaf_in.transpose(np.argsort(transpose))
return decaf_in
def set_transpose(self, in_, order):
"""
Set the input channel order for e.g. RGB to BGR conversion
as needed for the reference ImageNet model.
Parameters
----------
in_ : which input to assign this channel order
order : the order to transpose the dimensions
"""
self.__check_input(in_)
if len(order) != len(self.inputs[in_]) - 1:
raise Exception('Transpose order needs to have the same number of '
'dimensions as the input.')
self.transpose[in_] = order
def set_channel_swap(self, in_, order):
"""
Set the input channel order for e.g. RGB to BGR conversion
as needed for the reference ImageNet model.
N.B. this assumes the channels are the first dimension AFTER transpose.
Parameters
----------
in_ : which input to assign this channel order
order : the order to take the channels.
(2,1,0) maps RGB to BGR for example.
"""
self.__check_input(in_)
if len(order) != self.inputs[in_][1]:
raise Exception('Channel swap needs to have the same number of '
'dimensions as the input channels.')
self.channel_swap[in_] = order
def set_raw_scale(self, in_, scale):
"""
Set the scale of raw features s.t. the input blob = input * scale.
While Python represents images in [0, 1], certain Caffe models
like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
of these models must be 255.
Parameters
----------
in_ : which input to assign this scale factor
scale : scale coefficient
"""
self.__check_input(in_)
self.raw_scale[in_] = scale
def set_mean(self, in_, mean):
"""
Set the mean to subtract for centering the data.
Parameters
----------
in_ : which input to assign this mean.
mean : mean ndarray (input dimensional or broadcastable)
"""
self.__check_input(in_)
ms = mean.shape
if mean.ndim == 1:
# broadcast channels
if ms[0] != self.inputs[in_][1]:
raise ValueError('Mean channels incompatible with input.')
mean = mean[:, np.newaxis, np.newaxis]
else:
# elementwise mean
if len(ms) == 2:
ms = (1,) + ms
if len(ms) != 3:
raise ValueError('Mean shape invalid')
if ms != self.inputs[in_][1:]:
raise ValueError('Mean shape incompatible with input shape.')
self.mean[in_] = mean
def set_input_scale(self, in_, scale):
"""
Set the scale of preprocessed inputs s.t. the blob = blob * scale.
N.B. input_scale is done AFTER mean subtraction and other preprocessing
while raw_scale is done BEFORE.
Parameters
----------
in_ : which input to assign this scale factor
scale : scale coefficient
"""
self.__check_input(in_)
self.input_scale[in_] = scale
## Image IO
def load_image(filename, color=True):
"""
Load an image converting from grayscale or alpha as needed.
Parameters
----------
filename : string
color : boolean
flag for color format. True (default) loads as RGB while False
loads as intensity (if image is already grayscale).
Returns
-------
image : an image with type np.float32 in range [0, 1]
of size (H x W x 3) in RGB or
of size (H x W x 1) in grayscale.
"""
img = skimage.img_as_float(skimage.io.imread(filename, as_grey=not color)).astype(np.float32)
if img.ndim == 2:
img = img[:, :, np.newaxis]
if color:
img = np.tile(img, (1, 1, 3))
elif img.shape[2] == 4:
img = img[:, :, :3]
return img
def resize_image(im, new_dims, interp_order=1):
"""
Resize an image array with interpolation.
Parameters
----------
im : (H x W x K) ndarray
new_dims : (height, width) tuple of new dimensions.
interp_order : interpolation order, default is linear.
Returns
-------
im : resized ndarray with shape (new_dims[0], new_dims[1], K)
"""
if im.shape[-1] == 1 or im.shape[-1] == 3:
im_min, im_max = im.min(), im.max()
if im_max > im_min:
# skimage is fast but only understands {1,3} channel images
# in [0, 1].
im_std = (im - im_min) / (im_max - im_min)
resized_std = resize(im_std, new_dims, order=interp_order)
resized_im = resized_std * (im_max - im_min) + im_min
else:
# the image is a constant -- avoid divide by 0
ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]),
dtype=np.float32)
ret.fill(im_min)
return ret
else:
# ndimage interpolates anything but more slowly.
scale = tuple(np.array(new_dims, dtype=float) / np.array(im.shape[:2]))
resized_im = zoom(im, scale + (1,), order=interp_order)
return resized_im.astype(np.float32)
def oversample(images, crop_dims):
"""
Crop images into the four corners, center, and their mirrored versions.
Parameters
----------
image : iterable of (H x W x K) ndarrays
crop_dims : (height, width) tuple for the crops.
Returns
-------
crops : (10*N x H x W x K) ndarray of crops for number of inputs N.
"""
# Dimensions and center.
im_shape = np.array(images[0].shape)
crop_dims = np.array(crop_dims)
im_center = im_shape[:2] / 2.0
# Make crop coordinates
h_indices = (0, im_shape[0] - crop_dims[0])
w_indices = (0, im_shape[1] - crop_dims[1])
crops_ix = np.empty((5, 4), dtype=int)
curr = 0
for i in h_indices:
for j in w_indices:
crops_ix[curr] = (i, j, i + crop_dims[0], j + crop_dims[1])
curr += 1
crops_ix[4] = np.tile(im_center, (1, 2)) + np.concatenate([
-crop_dims / 2.0,
crop_dims / 2.0
])
crops_ix = np.tile(crops_ix, (2, 1))
# Extract crops
crops = np.empty((10 * len(images), crop_dims[0], crop_dims[1],
im_shape[-1]), dtype=np.float32)
ix = 0
for im in images:
for crop in crops_ix:
crops[ix] = im[crop[0]:crop[2], crop[1]:crop[3], :]
ix += 1
crops[ix-5:ix] = crops[ix-5:ix, :, ::-1, :] # flip for mirrors
return crops
| 12,729 | 32.151042 | 110 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/test/test_coord_map.py | import unittest
import numpy as np
import random
import caffe
from caffe import layers as L
from caffe import params as P
from caffe.coord_map import coord_map_from_to, crop
def coord_net_spec(ks=3, stride=1, pad=0, pool=2, dstride=2, dpad=0):
"""
Define net spec for simple conv-pool-deconv pattern common to all
coordinate mapping tests.
"""
n = caffe.NetSpec()
n.data = L.Input(shape=dict(dim=[2, 1, 100, 100]))
n.aux = L.Input(shape=dict(dim=[2, 1, 20, 20]))
n.conv = L.Convolution(
n.data, num_output=10, kernel_size=ks, stride=stride, pad=pad)
n.pool = L.Pooling(
n.conv, pool=P.Pooling.MAX, kernel_size=pool, stride=pool, pad=0)
# for upsampling kernel size is 2x stride
try:
deconv_ks = [s*2 for s in dstride]
except:
deconv_ks = dstride*2
n.deconv = L.Deconvolution(
n.pool, num_output=10, kernel_size=deconv_ks, stride=dstride, pad=dpad)
return n
class TestCoordMap(unittest.TestCase):
def setUp(self):
pass
def test_conv_pool_deconv(self):
"""
Map through conv, pool, and deconv.
"""
n = coord_net_spec()
# identity for 2x pool, 2x deconv
ax, a, b = coord_map_from_to(n.deconv, n.data)
self.assertEquals(ax, 1)
self.assertEquals(a, 1)
self.assertEquals(b, 0)
# shift-by-one for 4x pool, 4x deconv
n = coord_net_spec(pool=4, dstride=4)
ax, a, b = coord_map_from_to(n.deconv, n.data)
self.assertEquals(ax, 1)
self.assertEquals(a, 1)
self.assertEquals(b, -1)
def test_pass(self):
"""
A pass-through layer (ReLU) and conv (1x1, stride 1, pad 0)
both do identity mapping.
"""
n = coord_net_spec()
ax, a, b = coord_map_from_to(n.deconv, n.data)
n.relu = L.ReLU(n.deconv)
n.conv1x1 = L.Convolution(
n.relu, num_output=10, kernel_size=1, stride=1, pad=0)
for top in [n.relu, n.conv1x1]:
ax_pass, a_pass, b_pass = coord_map_from_to(top, n.data)
self.assertEquals(ax, ax_pass)
self.assertEquals(a, a_pass)
self.assertEquals(b, b_pass)
def test_padding(self):
"""
Padding conv adds offset while padding deconv subtracts offset.
"""
n = coord_net_spec()
ax, a, b = coord_map_from_to(n.deconv, n.data)
pad = random.randint(0, 10)
# conv padding
n = coord_net_spec(pad=pad)
_, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
self.assertEquals(a, a_pad)
self.assertEquals(b - pad, b_pad)
# deconv padding
n = coord_net_spec(dpad=pad)
_, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
self.assertEquals(a, a_pad)
self.assertEquals(b + pad, b_pad)
# pad both to cancel out
n = coord_net_spec(pad=pad, dpad=pad)
_, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
self.assertEquals(a, a_pad)
self.assertEquals(b, b_pad)
def test_multi_conv(self):
"""
Multiple bottoms/tops of a layer are identically mapped.
"""
n = coord_net_spec()
# multi bottom/top
n.conv_data, n.conv_aux = L.Convolution(
n.data, n.aux, ntop=2, num_output=10, kernel_size=5, stride=2,
pad=0)
ax1, a1, b1 = coord_map_from_to(n.conv_data, n.data)
ax2, a2, b2 = coord_map_from_to(n.conv_aux, n.aux)
self.assertEquals(ax1, ax2)
self.assertEquals(a1, a2)
self.assertEquals(b1, b2)
def test_rect(self):
"""
Anisotropic mapping is equivalent to its isotropic parts.
"""
n3x3 = coord_net_spec(ks=3, stride=1, pad=0)
n5x5 = coord_net_spec(ks=5, stride=2, pad=10)
n3x5 = coord_net_spec(ks=[3, 5], stride=[1, 2], pad=[0, 10])
ax_3x3, a_3x3, b_3x3 = coord_map_from_to(n3x3.deconv, n3x3.data)
ax_5x5, a_5x5, b_5x5 = coord_map_from_to(n5x5.deconv, n5x5.data)
ax_3x5, a_3x5, b_3x5 = coord_map_from_to(n3x5.deconv, n3x5.data)
self.assertTrue(ax_3x3 == ax_5x5 == ax_3x5)
self.assertEquals(a_3x3, a_3x5[0])
self.assertEquals(b_3x3, b_3x5[0])
self.assertEquals(a_5x5, a_3x5[1])
self.assertEquals(b_5x5, b_3x5[1])
def test_nd_conv(self):
"""
ND conv maps the same way in more dimensions.
"""
n = caffe.NetSpec()
# define data with 3 spatial dimensions, otherwise the same net
n.data = L.Input(shape=dict(dim=[2, 3, 100, 100, 100]))
n.conv = L.Convolution(
n.data, num_output=10, kernel_size=[3, 3, 3], stride=[1, 1, 1],
pad=[0, 1, 2])
n.pool = L.Pooling(
n.conv, pool=P.Pooling.MAX, kernel_size=2, stride=2, pad=0)
n.deconv = L.Deconvolution(
n.pool, num_output=10, kernel_size=4, stride=2, pad=0)
ax, a, b = coord_map_from_to(n.deconv, n.data)
self.assertEquals(ax, 1)
self.assertTrue(len(a) == len(b))
self.assertTrue(np.all(a == 1))
self.assertEquals(b[0] - 1, b[1])
self.assertEquals(b[1] - 1, b[2])
def test_crop_of_crop(self):
"""
Map coordinates through Crop layer:
crop an already-cropped output to the input and check change in offset.
"""
n = coord_net_spec()
offset = random.randint(0, 10)
ax, a, b = coord_map_from_to(n.deconv, n.data)
n.crop = L.Crop(n.deconv, n.data, axis=2, offset=offset)
ax_crop, a_crop, b_crop = coord_map_from_to(n.crop, n.data)
self.assertEquals(ax, ax_crop)
self.assertEquals(a, a_crop)
self.assertEquals(b + offset, b_crop)
def test_crop_helper(self):
"""
Define Crop layer by crop().
"""
n = coord_net_spec()
crop(n.deconv, n.data)
def test_catch_unconnected(self):
"""
Catch mapping spatially unconnected tops.
"""
n = coord_net_spec()
n.ip = L.InnerProduct(n.deconv, num_output=10)
with self.assertRaises(RuntimeError):
coord_map_from_to(n.ip, n.data)
def test_catch_scale_mismatch(self):
"""
Catch incompatible scales, such as when the top to be cropped
is mapped to a differently strided reference top.
"""
n = coord_net_spec(pool=3, dstride=2) # pool 3x but deconv 2x
with self.assertRaises(AssertionError):
crop(n.deconv, n.data)
def test_catch_negative_crop(self):
"""
Catch impossible offsets, such as when the top to be cropped
is mapped to a larger reference top.
"""
n = coord_net_spec(dpad=10) # make output smaller than input
with self.assertRaises(AssertionError):
crop(n.deconv, n.data)
| 6,894 | 34.725389 | 79 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/test/test_python_layer_with_param_str.py | import unittest
import tempfile
import os
import six
import caffe
class SimpleParamLayer(caffe.Layer):
"""A layer that just multiplies by the numeric value of its param string"""
def setup(self, bottom, top):
try:
self.value = float(self.param_str)
except ValueError:
raise ValueError("Parameter string must be a legible float")
def reshape(self, bottom, top):
top[0].reshape(*bottom[0].data.shape)
def forward(self, bottom, top):
top[0].data[...] = self.value * bottom[0].data
def backward(self, top, propagate_down, bottom):
bottom[0].diff[...] = self.value * top[0].diff
def python_param_net_file():
with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
f.write("""name: 'pythonnet' force_backward: true
input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
layer { type: 'Python' name: 'mul10' bottom: 'data' top: 'mul10'
python_param { module: 'test_python_layer_with_param_str'
layer: 'SimpleParamLayer' param_str: '10' } }
layer { type: 'Python' name: 'mul2' bottom: 'mul10' top: 'mul2'
python_param { module: 'test_python_layer_with_param_str'
layer: 'SimpleParamLayer' param_str: '2' } }""")
return f.name
@unittest.skipIf('Python' not in caffe.layer_type_list(),
'Caffe built without Python layer support')
class TestLayerWithParam(unittest.TestCase):
def setUp(self):
net_file = python_param_net_file()
self.net = caffe.Net(net_file, caffe.TRAIN)
os.remove(net_file)
def test_forward(self):
x = 8
self.net.blobs['data'].data[...] = x
self.net.forward()
for y in self.net.blobs['mul2'].data.flat:
self.assertEqual(y, 2 * 10 * x)
def test_backward(self):
x = 7
self.net.blobs['mul2'].diff[...] = x
self.net.backward()
for y in self.net.blobs['data'].diff.flat:
self.assertEqual(y, 2 * 10 * x)
| 2,031 | 31.774194 | 79 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/test/test_io.py | import numpy as np
import unittest
import caffe
class TestBlobProtoToArray(unittest.TestCase):
def test_old_format(self):
data = np.zeros((10,10))
blob = caffe.proto.caffe_pb2.BlobProto()
blob.data.extend(list(data.flatten()))
shape = (1,1,10,10)
blob.num, blob.channels, blob.height, blob.width = shape
arr = caffe.io.blobproto_to_array(blob)
self.assertEqual(arr.shape, shape)
def test_new_format(self):
data = np.zeros((10,10))
blob = caffe.proto.caffe_pb2.BlobProto()
blob.data.extend(list(data.flatten()))
blob.shape.dim.extend(list(data.shape))
arr = caffe.io.blobproto_to_array(blob)
self.assertEqual(arr.shape, data.shape)
def test_no_shape(self):
data = np.zeros((10,10))
blob = caffe.proto.caffe_pb2.BlobProto()
blob.data.extend(list(data.flatten()))
with self.assertRaises(ValueError):
caffe.io.blobproto_to_array(blob)
def test_scalar(self):
data = np.ones((1)) * 123
blob = caffe.proto.caffe_pb2.BlobProto()
blob.data.extend(list(data.flatten()))
arr = caffe.io.blobproto_to_array(blob)
self.assertEqual(arr, 123)
class TestArrayToDatum(unittest.TestCase):
def test_label_none_size(self):
# Set label
d1 = caffe.io.array_to_datum(
np.ones((10,10,3)), label=1)
# Don't set label
d2 = caffe.io.array_to_datum(
np.ones((10,10,3)))
# Not setting the label should result in a smaller object
self.assertGreater(
len(d1.SerializeToString()),
len(d2.SerializeToString()))
| 1,694 | 28.736842 | 65 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/test/test_solver.py | import unittest
import tempfile
import os
import numpy as np
import six
import caffe
from test_net import simple_net_file
class TestSolver(unittest.TestCase):
def setUp(self):
self.num_output = 13
net_f = simple_net_file(self.num_output)
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write("""net: '""" + net_f + """'
test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
display: 100 max_iter: 100 snapshot_after_train: false
snapshot_prefix: "model" """)
f.close()
self.solver = caffe.SGDSolver(f.name)
# also make sure get_solver runs
caffe.get_solver(f.name)
caffe.set_mode_cpu()
# fill in valid labels
self.solver.net.blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.solver.net.blobs['label'].data.shape)
self.solver.test_nets[0].blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.solver.test_nets[0].blobs['label'].data.shape)
os.remove(f.name)
os.remove(net_f)
def test_solve(self):
self.assertEqual(self.solver.iter, 0)
self.solver.solve()
self.assertEqual(self.solver.iter, 100)
def test_net_memory(self):
"""Check that nets survive after the solver is destroyed."""
nets = [self.solver.net] + list(self.solver.test_nets)
self.assertEqual(len(nets), 2)
del self.solver
total = 0
for net in nets:
for ps in six.itervalues(net.params):
for p in ps:
total += p.data.sum() + p.diff.sum()
for bl in six.itervalues(net.blobs):
total += bl.data.sum() + bl.diff.sum()
def test_snapshot(self):
self.solver.snapshot()
# Check that these files exist and then remove them
files = ['model_iter_0.caffemodel', 'model_iter_0.solverstate']
for fn in files:
assert os.path.isfile(fn)
os.remove(fn)
| 2,165 | 33.380952 | 76 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/test/test_layer_type_list.py | import unittest
import caffe
class TestLayerTypeList(unittest.TestCase):
def test_standard_types(self):
#removing 'Data' from list
for type_name in ['Data', 'Convolution', 'InnerProduct']:
self.assertIn(type_name, caffe.layer_type_list(),
'%s not in layer_type_list()' % type_name)
| 338 | 27.25 | 65 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/test/test_net.py | import unittest
import tempfile
import os
import numpy as np
import six
from collections import OrderedDict
import caffe
def simple_net_file(num_output):
"""Make a simple net prototxt, based on test_net.cpp, returning the name
of the (temporary) file."""
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write("""name: 'testnet' force_backward: true
layer { type: 'DummyData' name: 'data' top: 'data' top: 'label'
dummy_data_param { num: 5 channels: 2 height: 3 width: 4
num: 5 channels: 1 height: 1 width: 1
data_filler { type: 'gaussian' std: 1 }
data_filler { type: 'constant' } } }
layer { type: 'Convolution' name: 'conv' bottom: 'data' top: 'conv'
convolution_param { num_output: 11 kernel_size: 2 pad: 3
weight_filler { type: 'gaussian' std: 1 }
bias_filler { type: 'constant' value: 2 } }
param { decay_mult: 1 } param { decay_mult: 0 }
}
layer { type: 'InnerProduct' name: 'ip' bottom: 'conv' top: 'ip'
inner_product_param { num_output: """ + str(num_output) + """
weight_filler { type: 'gaussian' std: 2.5 }
bias_filler { type: 'constant' value: -3 } } }
layer { type: 'SoftmaxWithLoss' name: 'loss' bottom: 'ip' bottom: 'label'
top: 'loss' }""")
f.close()
return f.name
class TestNet(unittest.TestCase):
def setUp(self):
self.num_output = 13
net_file = simple_net_file(self.num_output)
self.net = caffe.Net(net_file, caffe.TRAIN)
# fill in valid labels
self.net.blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.net.blobs['label'].data.shape)
os.remove(net_file)
def test_memory(self):
"""Check that holding onto blob data beyond the life of a Net is OK"""
params = sum(map(list, six.itervalues(self.net.params)), [])
blobs = self.net.blobs.values()
del self.net
# now sum everything (forcing all memory to be read)
total = 0
for p in params:
total += p.data.sum() + p.diff.sum()
for bl in blobs:
total += bl.data.sum() + bl.diff.sum()
def test_forward_backward(self):
self.net.forward()
self.net.backward()
def test_clear_param_diffs(self):
# Run a forward/backward step to have non-zero diffs
self.net.forward()
self.net.backward()
diff = self.net.params["conv"][0].diff
# Check that we have non-zero diffs
self.assertTrue(diff.max() > 0)
self.net.clear_param_diffs()
# Check that the diffs are now 0
self.assertTrue((diff == 0).all())
def test_inputs_outputs(self):
self.assertEqual(self.net.inputs, [])
self.assertEqual(self.net.outputs, ['loss'])
def test_top_bottom_names(self):
self.assertEqual(self.net.top_names,
OrderedDict([('data', ['data', 'label']),
('conv', ['conv']),
('ip', ['ip']),
('loss', ['loss'])]))
self.assertEqual(self.net.bottom_names,
OrderedDict([('data', []),
('conv', ['data']),
('ip', ['conv']),
('loss', ['ip', 'label'])]))
def test_save_and_read(self):
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.close()
self.net.save(f.name)
net_file = simple_net_file(self.num_output)
# Test legacy constructor
# should print deprecation warning
caffe.Net(net_file, f.name, caffe.TRAIN)
# Test named constructor
net2 = caffe.Net(net_file, caffe.TRAIN, weights=f.name)
os.remove(net_file)
os.remove(f.name)
for name in self.net.params:
for i in range(len(self.net.params[name])):
self.assertEqual(abs(self.net.params[name][i].data
- net2.params[name][i].data).sum(), 0)
def test_save_hdf5(self):
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.close()
self.net.save_hdf5(f.name)
net_file = simple_net_file(self.num_output)
net2 = caffe.Net(net_file, caffe.TRAIN)
net2.load_hdf5(f.name)
os.remove(net_file)
os.remove(f.name)
for name in self.net.params:
for i in range(len(self.net.params[name])):
self.assertEqual(abs(self.net.params[name][i].data
- net2.params[name][i].data).sum(), 0)
class TestLevels(unittest.TestCase):
TEST_NET = """
layer {
name: "data"
type: "DummyData"
top: "data"
dummy_data_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
}
layer {
name: "NoLevel"
type: "InnerProduct"
bottom: "data"
top: "NoLevel"
inner_product_param { num_output: 1 }
}
layer {
name: "Level0Only"
type: "InnerProduct"
bottom: "data"
top: "Level0Only"
include { min_level: 0 max_level: 0 }
inner_product_param { num_output: 1 }
}
layer {
name: "Level1Only"
type: "InnerProduct"
bottom: "data"
top: "Level1Only"
include { min_level: 1 max_level: 1 }
inner_product_param { num_output: 1 }
}
layer {
name: "Level>=0"
type: "InnerProduct"
bottom: "data"
top: "Level>=0"
include { min_level: 0 }
inner_product_param { num_output: 1 }
}
layer {
name: "Level>=1"
type: "InnerProduct"
bottom: "data"
top: "Level>=1"
include { min_level: 1 }
inner_product_param { num_output: 1 }
}
"""
def setUp(self):
self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
self.f.write(self.TEST_NET)
self.f.close()
def tearDown(self):
os.remove(self.f.name)
def check_net(self, net, blobs):
net_blobs = [b for b in net.blobs.keys() if 'data' not in b]
self.assertEqual(net_blobs, blobs)
def test_0(self):
net = caffe.Net(self.f.name, caffe.TEST)
self.check_net(net, ['NoLevel', 'Level0Only', 'Level>=0'])
def test_1(self):
net = caffe.Net(self.f.name, caffe.TEST, level=1)
self.check_net(net, ['NoLevel', 'Level1Only', 'Level>=0', 'Level>=1'])
class TestStages(unittest.TestCase):
TEST_NET = """
layer {
name: "data"
type: "DummyData"
top: "data"
dummy_data_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
}
layer {
name: "A"
type: "InnerProduct"
bottom: "data"
top: "A"
include { stage: "A" }
inner_product_param { num_output: 1 }
}
layer {
name: "B"
type: "InnerProduct"
bottom: "data"
top: "B"
include { stage: "B" }
inner_product_param { num_output: 1 }
}
layer {
name: "AorB"
type: "InnerProduct"
bottom: "data"
top: "AorB"
include { stage: "A" }
include { stage: "B" }
inner_product_param { num_output: 1 }
}
layer {
name: "AandB"
type: "InnerProduct"
bottom: "data"
top: "AandB"
include { stage: "A" stage: "B" }
inner_product_param { num_output: 1 }
}
"""
def setUp(self):
self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
self.f.write(self.TEST_NET)
self.f.close()
def tearDown(self):
os.remove(self.f.name)
def check_net(self, net, blobs):
net_blobs = [b for b in net.blobs.keys() if 'data' not in b]
self.assertEqual(net_blobs, blobs)
def test_A(self):
net = caffe.Net(self.f.name, caffe.TEST, stages=['A'])
self.check_net(net, ['A', 'AorB'])
def test_B(self):
net = caffe.Net(self.f.name, caffe.TEST, stages=['B'])
self.check_net(net, ['B', 'AorB'])
def test_AandB(self):
net = caffe.Net(self.f.name, caffe.TEST, stages=['A', 'B'])
self.check_net(net, ['A', 'B', 'AorB', 'AandB'])
class TestAllInOne(unittest.TestCase):
TEST_NET = """
layer {
name: "train_data"
type: "DummyData"
top: "data"
top: "label"
dummy_data_param {
shape { dim: 1 dim: 1 dim: 10 dim: 10 }
shape { dim: 1 dim: 1 dim: 1 dim: 1 }
}
include { phase: TRAIN stage: "train" }
}
layer {
name: "val_data"
type: "DummyData"
top: "data"
top: "label"
dummy_data_param {
shape { dim: 1 dim: 1 dim: 10 dim: 10 }
shape { dim: 1 dim: 1 dim: 1 dim: 1 }
}
include { phase: TEST stage: "val" }
}
layer {
name: "deploy_data"
type: "Input"
top: "data"
input_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
include { phase: TEST stage: "deploy" }
}
layer {
name: "ip"
type: "InnerProduct"
bottom: "data"
top: "ip"
inner_product_param { num_output: 2 }
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip"
bottom: "label"
top: "loss"
include: { phase: TRAIN stage: "train" }
include: { phase: TEST stage: "val" }
}
layer {
name: "pred"
type: "Softmax"
bottom: "ip"
top: "pred"
include: { phase: TEST stage: "deploy" }
}
"""
def setUp(self):
self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
self.f.write(self.TEST_NET)
self.f.close()
def tearDown(self):
os.remove(self.f.name)
def check_net(self, net, outputs):
self.assertEqual(list(net.blobs['data'].shape), [1,1,10,10])
self.assertEqual(net.outputs, outputs)
def test_train(self):
net = caffe.Net(self.f.name, caffe.TRAIN, stages=['train'])
self.check_net(net, ['loss'])
def test_val(self):
net = caffe.Net(self.f.name, caffe.TEST, stages=['val'])
self.check_net(net, ['loss'])
def test_deploy(self):
net = caffe.Net(self.f.name, caffe.TEST, stages=['deploy'])
self.check_net(net, ['pred'])
| 9,722 | 27.101156 | 78 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/test/test_net_spec.py | import unittest
import tempfile
import caffe
from caffe import layers as L
from caffe import params as P
def lenet(batch_size):
n = caffe.NetSpec()
n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
dict(dim=[batch_size, 1, 1, 1])],
transform_param=dict(scale=1./255), ntop=2)
n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20,
weight_filler=dict(type='xavier'))
n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50,
weight_filler=dict(type='xavier'))
n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
n.ip1 = L.InnerProduct(n.pool2, num_output=500,
weight_filler=dict(type='xavier'))
n.relu1 = L.ReLU(n.ip1, in_place=True)
n.ip2 = L.InnerProduct(n.relu1, num_output=10,
weight_filler=dict(type='xavier'))
n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
return n.to_proto()
def anon_lenet(batch_size):
data, label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
dict(dim=[batch_size, 1, 1, 1])],
transform_param=dict(scale=1./255), ntop=2)
conv1 = L.Convolution(data, kernel_size=5, num_output=20,
weight_filler=dict(type='xavier'))
pool1 = L.Pooling(conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
conv2 = L.Convolution(pool1, kernel_size=5, num_output=50,
weight_filler=dict(type='xavier'))
pool2 = L.Pooling(conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
ip1 = L.InnerProduct(pool2, num_output=500,
weight_filler=dict(type='xavier'))
relu1 = L.ReLU(ip1, in_place=True)
ip2 = L.InnerProduct(relu1, num_output=10,
weight_filler=dict(type='xavier'))
loss = L.SoftmaxWithLoss(ip2, label)
return loss.to_proto()
def silent_net():
n = caffe.NetSpec()
n.data, n.data2 = L.DummyData(shape=dict(dim=3), ntop=2)
n.silence_data = L.Silence(n.data, ntop=0)
n.silence_data2 = L.Silence(n.data2, ntop=0)
return n.to_proto()
class TestNetSpec(unittest.TestCase):
def load_net(self, net_proto):
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write(str(net_proto))
f.close()
return caffe.Net(f.name, caffe.TEST)
def test_lenet(self):
"""Construct and build the Caffe version of LeNet."""
net_proto = lenet(50)
# check that relu is in-place
self.assertEqual(net_proto.layer[6].bottom,
net_proto.layer[6].top)
net = self.load_net(net_proto)
# check that all layers are present
self.assertEqual(len(net.layers), 9)
# now the check the version with automatically-generated layer names
net_proto = anon_lenet(50)
self.assertEqual(net_proto.layer[6].bottom,
net_proto.layer[6].top)
net = self.load_net(net_proto)
self.assertEqual(len(net.layers), 9)
def test_zero_tops(self):
"""Test net construction for top-less layers."""
net_proto = silent_net()
net = self.load_net(net_proto)
self.assertEqual(len(net.forward()), 0)
| 3,287 | 39.097561 | 77 | py |
bottom-up-attention | bottom-up-attention-master/caffe/python/caffe/test/test_python_layer.py | import unittest
import tempfile
import os
import six
import caffe
class SimpleLayer(caffe.Layer):
"""A layer that just multiplies by ten"""
def setup(self, bottom, top):
pass
def reshape(self, bottom, top):
top[0].reshape(*bottom[0].data.shape)
def forward(self, bottom, top):
top[0].data[...] = 10 * bottom[0].data
def backward(self, top, propagate_down, bottom):
bottom[0].diff[...] = 10 * top[0].diff
class ExceptionLayer(caffe.Layer):
"""A layer for checking exceptions from Python"""
def setup(self, bottom, top):
raise RuntimeError
class ParameterLayer(caffe.Layer):
"""A layer that just multiplies by ten"""
def setup(self, bottom, top):
self.blobs.add_blob(1)
self.blobs[0].data[0] = 0
def reshape(self, bottom, top):
top[0].reshape(*bottom[0].data.shape)
def forward(self, bottom, top):
pass
def backward(self, top, propagate_down, bottom):
self.blobs[0].diff[0] = 1
class PhaseLayer(caffe.Layer):
"""A layer for checking attribute `phase`"""
def setup(self, bottom, top):
pass
def reshape(self, bootom, top):
top[0].reshape()
def forward(self, bottom, top):
top[0].data[()] = self.phase
def python_net_file():
with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
f.write("""name: 'pythonnet' force_backward: true
input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
layer { type: 'Python' name: 'one' bottom: 'data' top: 'one'
python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
layer { type: 'Python' name: 'two' bottom: 'one' top: 'two'
python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
layer { type: 'Python' name: 'three' bottom: 'two' top: 'three'
python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""")
return f.name
def exception_net_file():
with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
f.write("""name: 'pythonnet' force_backward: true
input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top'
python_param { module: 'test_python_layer' layer: 'ExceptionLayer' } }
""")
return f.name
def parameter_net_file():
with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
f.write("""name: 'pythonnet' force_backward: true
input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top'
python_param { module: 'test_python_layer' layer: 'ParameterLayer' } }
""")
return f.name
def phase_net_file():
with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
f.write("""name: 'pythonnet' force_backward: true
layer { type: 'Python' name: 'layer' top: 'phase'
python_param { module: 'test_python_layer' layer: 'PhaseLayer' } }
""")
return f.name
@unittest.skipIf('Python' not in caffe.layer_type_list(),
'Caffe built without Python layer support')
class TestPythonLayer(unittest.TestCase):
def setUp(self):
net_file = python_net_file()
self.net = caffe.Net(net_file, caffe.TRAIN)
os.remove(net_file)
def test_forward(self):
x = 8
self.net.blobs['data'].data[...] = x
self.net.forward()
for y in self.net.blobs['three'].data.flat:
self.assertEqual(y, 10**3 * x)
def test_backward(self):
x = 7
self.net.blobs['three'].diff[...] = x
self.net.backward()
for y in self.net.blobs['data'].diff.flat:
self.assertEqual(y, 10**3 * x)
def test_reshape(self):
s = 4
self.net.blobs['data'].reshape(s, s, s, s)
self.net.forward()
for blob in six.itervalues(self.net.blobs):
for d in blob.data.shape:
self.assertEqual(s, d)
def test_exception(self):
net_file = exception_net_file()
self.assertRaises(RuntimeError, caffe.Net, net_file, caffe.TEST)
os.remove(net_file)
def test_parameter(self):
net_file = parameter_net_file()
net = caffe.Net(net_file, caffe.TRAIN)
# Test forward and backward
net.forward()
net.backward()
layer = net.layers[list(net._layer_names).index('layer')]
self.assertEqual(layer.blobs[0].data[0], 0)
self.assertEqual(layer.blobs[0].diff[0], 1)
layer.blobs[0].data[0] += layer.blobs[0].diff[0]
self.assertEqual(layer.blobs[0].data[0], 1)
# Test saving and loading
h, caffemodel_file = tempfile.mkstemp()
net.save(caffemodel_file)
layer.blobs[0].data[0] = -1
self.assertEqual(layer.blobs[0].data[0], -1)
net.copy_from(caffemodel_file)
self.assertEqual(layer.blobs[0].data[0], 1)
os.remove(caffemodel_file)
# Test weight sharing
net2 = caffe.Net(net_file, caffe.TRAIN)
net2.share_with(net)
layer = net.layers[list(net2._layer_names).index('layer')]
self.assertEqual(layer.blobs[0].data[0], 1)
os.remove(net_file)
def test_phase(self):
net_file = phase_net_file()
for phase in caffe.TRAIN, caffe.TEST:
net = caffe.Net(net_file, phase)
self.assertEqual(net.forward()['phase'], phase)
| 5,510 | 31.609467 | 81 | py |
bottom-up-attention | bottom-up-attention-master/caffe/scripts/cpp_lint.py | #!/usr/bin/python2
#
# Copyright (c) 2009 Google Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Does google-lint on c++ files.
The goal of this script is to identify places in the code that *may*
be in non-compliance with google style. It does not attempt to fix
up these problems -- the point is to educate. It does also not
attempt to find all problems, or to ensure that everything it does
find is legitimately a problem.
In particular, we can get very confused by /* and // inside strings!
We do a small hack, which is to ignore //'s with "'s after them on the
same line, but it is far from perfect (in either direction).
"""
import codecs
import copy
import getopt
import math # for log
import os
import re
import sre_compile
import string
import sys
import unicodedata
_USAGE = """
Syntax: cpp_lint.py [--verbose=#] [--output=vs7] [--filter=-x,+y,...]
[--counting=total|toplevel|detailed] [--root=subdir]
[--linelength=digits]
<file> [file] ...
The style guidelines this tries to follow are those in
http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml
Every problem is given a confidence score from 1-5, with 5 meaning we are
certain of the problem, and 1 meaning it could be a legitimate construct.
This will miss some errors, and is not a substitute for a code review.
To suppress false-positive errors of a certain category, add a
'NOLINT(category)' comment to the line. NOLINT or NOLINT(*)
suppresses errors of all categories on that line.
The files passed in will be linted; at least one file must be provided.
Default linted extensions are .cc, .cpp, .cu, .cuh and .h. Change the
extensions with the --extensions flag.
Flags:
output=vs7
By default, the output is formatted to ease emacs parsing. Visual Studio
compatible output (vs7) may also be used. Other formats are unsupported.
verbose=#
Specify a number 0-5 to restrict errors to certain verbosity levels.
filter=-x,+y,...
Specify a comma-separated list of category-filters to apply: only
error messages whose category names pass the filters will be printed.
(Category names are printed with the message and look like
"[whitespace/indent]".) Filters are evaluated left to right.
"-FOO" and "FOO" means "do not print categories that start with FOO".
"+FOO" means "do print categories that start with FOO".
Examples: --filter=-whitespace,+whitespace/braces
--filter=whitespace,runtime/printf,+runtime/printf_format
--filter=-,+build/include_what_you_use
To see a list of all the categories used in cpplint, pass no arg:
--filter=
counting=total|toplevel|detailed
The total number of errors found is always printed. If
'toplevel' is provided, then the count of errors in each of
the top-level categories like 'build' and 'whitespace' will
also be printed. If 'detailed' is provided, then a count
is provided for each category like 'build/class'.
root=subdir
The root directory used for deriving header guard CPP variable.
By default, the header guard CPP variable is calculated as the relative
path to the directory that contains .git, .hg, or .svn. When this flag
is specified, the relative path is calculated from the specified
directory. If the specified directory does not exist, this flag is
ignored.
Examples:
Assuing that src/.git exists, the header guard CPP variables for
src/chrome/browser/ui/browser.h are:
No flag => CHROME_BROWSER_UI_BROWSER_H_
--root=chrome => BROWSER_UI_BROWSER_H_
--root=chrome/browser => UI_BROWSER_H_
linelength=digits
This is the allowed line length for the project. The default value is
80 characters.
Examples:
--linelength=120
extensions=extension,extension,...
The allowed file extensions that cpplint will check
Examples:
--extensions=hpp,cpp
"""
# We categorize each error message we print. Here are the categories.
# We want an explicit list so we can list them all in cpplint --filter=.
# If you add a new error message with a new category, add it to the list
# here! cpplint_unittest.py should tell you if you forget to do this.
_ERROR_CATEGORIES = [
'build/class',
'build/deprecated',
'build/endif_comment',
'build/explicit_make_pair',
'build/forward_decl',
'build/header_guard',
'build/include',
'build/include_alpha',
'build/include_dir',
'build/include_order',
'build/include_what_you_use',
'build/namespaces',
'build/printf_format',
'build/storage_class',
'caffe/alt_fn',
'caffe/data_layer_setup',
'caffe/random_fn',
'legal/copyright',
'readability/alt_tokens',
'readability/braces',
'readability/casting',
'readability/check',
'readability/constructors',
'readability/fn_size',
'readability/function',
'readability/multiline_comment',
'readability/multiline_string',
'readability/namespace',
'readability/nolint',
'readability/nul',
'readability/streams',
'readability/todo',
'readability/utf8',
'runtime/arrays',
'runtime/casting',
'runtime/explicit',
'runtime/int',
'runtime/init',
'runtime/invalid_increment',
'runtime/member_string_references',
'runtime/memset',
'runtime/operator',
'runtime/printf',
'runtime/printf_format',
'runtime/references',
'runtime/string',
'runtime/threadsafe_fn',
'runtime/vlog',
'whitespace/blank_line',
'whitespace/braces',
'whitespace/comma',
'whitespace/comments',
'whitespace/empty_conditional_body',
'whitespace/empty_loop_body',
'whitespace/end_of_line',
'whitespace/ending_newline',
'whitespace/forcolon',
'whitespace/indent',
'whitespace/line_length',
'whitespace/newline',
'whitespace/operators',
'whitespace/parens',
'whitespace/semicolon',
'whitespace/tab',
'whitespace/todo'
]
# The default state of the category filter. This is overrided by the --filter=
# flag. By default all errors are on, so only add here categories that should be
# off by default (i.e., categories that must be enabled by the --filter= flags).
# All entries here should start with a '-' or '+', as in the --filter= flag.
_DEFAULT_FILTERS = [
'-build/include_dir',
'-readability/todo',
]
# We used to check for high-bit characters, but after much discussion we
# decided those were OK, as long as they were in UTF-8 and didn't represent
# hard-coded international strings, which belong in a separate i18n file.
# C++ headers
_CPP_HEADERS = frozenset([
# Legacy
'algobase.h',
'algo.h',
'alloc.h',
'builtinbuf.h',
'bvector.h',
'complex.h',
'defalloc.h',
'deque.h',
'editbuf.h',
'fstream.h',
'function.h',
'hash_map',
'hash_map.h',
'hash_set',
'hash_set.h',
'hashtable.h',
'heap.h',
'indstream.h',
'iomanip.h',
'iostream.h',
'istream.h',
'iterator.h',
'list.h',
'map.h',
'multimap.h',
'multiset.h',
'ostream.h',
'pair.h',
'parsestream.h',
'pfstream.h',
'procbuf.h',
'pthread_alloc',
'pthread_alloc.h',
'rope',
'rope.h',
'ropeimpl.h',
'set.h',
'slist',
'slist.h',
'stack.h',
'stdiostream.h',
'stl_alloc.h',
'stl_relops.h',
'streambuf.h',
'stream.h',
'strfile.h',
'strstream.h',
'tempbuf.h',
'tree.h',
'type_traits.h',
'vector.h',
# 17.6.1.2 C++ library headers
'algorithm',
'array',
'atomic',
'bitset',
'chrono',
'codecvt',
'complex',
'condition_variable',
'deque',
'exception',
'forward_list',
'fstream',
'functional',
'future',
'initializer_list',
'iomanip',
'ios',
'iosfwd',
'iostream',
'istream',
'iterator',
'limits',
'list',
'locale',
'map',
'memory',
'mutex',
'new',
'numeric',
'ostream',
'queue',
'random',
'ratio',
'regex',
'set',
'sstream',
'stack',
'stdexcept',
'streambuf',
'string',
'strstream',
'system_error',
'thread',
'tuple',
'typeindex',
'typeinfo',
'type_traits',
'unordered_map',
'unordered_set',
'utility',
'valarray',
'vector',
# 17.6.1.2 C++ headers for C library facilities
'cassert',
'ccomplex',
'cctype',
'cerrno',
'cfenv',
'cfloat',
'cinttypes',
'ciso646',
'climits',
'clocale',
'cmath',
'csetjmp',
'csignal',
'cstdalign',
'cstdarg',
'cstdbool',
'cstddef',
'cstdint',
'cstdio',
'cstdlib',
'cstring',
'ctgmath',
'ctime',
'cuchar',
'cwchar',
'cwctype',
])
# Assertion macros. These are defined in base/logging.h and
# testing/base/gunit.h. Note that the _M versions need to come first
# for substring matching to work.
_CHECK_MACROS = [
'DCHECK', 'CHECK',
'EXPECT_TRUE_M', 'EXPECT_TRUE',
'ASSERT_TRUE_M', 'ASSERT_TRUE',
'EXPECT_FALSE_M', 'EXPECT_FALSE',
'ASSERT_FALSE_M', 'ASSERT_FALSE',
]
# Replacement macros for CHECK/DCHECK/EXPECT_TRUE/EXPECT_FALSE
_CHECK_REPLACEMENT = dict([(m, {}) for m in _CHECK_MACROS])
for op, replacement in [('==', 'EQ'), ('!=', 'NE'),
('>=', 'GE'), ('>', 'GT'),
('<=', 'LE'), ('<', 'LT')]:
_CHECK_REPLACEMENT['DCHECK'][op] = 'DCHECK_%s' % replacement
_CHECK_REPLACEMENT['CHECK'][op] = 'CHECK_%s' % replacement
_CHECK_REPLACEMENT['EXPECT_TRUE'][op] = 'EXPECT_%s' % replacement
_CHECK_REPLACEMENT['ASSERT_TRUE'][op] = 'ASSERT_%s' % replacement
_CHECK_REPLACEMENT['EXPECT_TRUE_M'][op] = 'EXPECT_%s_M' % replacement
_CHECK_REPLACEMENT['ASSERT_TRUE_M'][op] = 'ASSERT_%s_M' % replacement
for op, inv_replacement in [('==', 'NE'), ('!=', 'EQ'),
('>=', 'LT'), ('>', 'LE'),
('<=', 'GT'), ('<', 'GE')]:
_CHECK_REPLACEMENT['EXPECT_FALSE'][op] = 'EXPECT_%s' % inv_replacement
_CHECK_REPLACEMENT['ASSERT_FALSE'][op] = 'ASSERT_%s' % inv_replacement
_CHECK_REPLACEMENT['EXPECT_FALSE_M'][op] = 'EXPECT_%s_M' % inv_replacement
_CHECK_REPLACEMENT['ASSERT_FALSE_M'][op] = 'ASSERT_%s_M' % inv_replacement
# Alternative tokens and their replacements. For full list, see section 2.5
# Alternative tokens [lex.digraph] in the C++ standard.
#
# Digraphs (such as '%:') are not included here since it's a mess to
# match those on a word boundary.
_ALT_TOKEN_REPLACEMENT = {
'and': '&&',
'bitor': '|',
'or': '||',
'xor': '^',
'compl': '~',
'bitand': '&',
'and_eq': '&=',
'or_eq': '|=',
'xor_eq': '^=',
'not': '!',
'not_eq': '!='
}
# Compile regular expression that matches all the above keywords. The "[ =()]"
# bit is meant to avoid matching these keywords outside of boolean expressions.
#
# False positives include C-style multi-line comments and multi-line strings
# but those have always been troublesome for cpplint.
_ALT_TOKEN_REPLACEMENT_PATTERN = re.compile(
r'[ =()](' + ('|'.join(_ALT_TOKEN_REPLACEMENT.keys())) + r')(?=[ (]|$)')
# These constants define types of headers for use with
# _IncludeState.CheckNextIncludeOrder().
_C_SYS_HEADER = 1
_CPP_SYS_HEADER = 2
_LIKELY_MY_HEADER = 3
_POSSIBLE_MY_HEADER = 4
_OTHER_HEADER = 5
# These constants define the current inline assembly state
_NO_ASM = 0 # Outside of inline assembly block
_INSIDE_ASM = 1 # Inside inline assembly block
_END_ASM = 2 # Last line of inline assembly block
_BLOCK_ASM = 3 # The whole block is an inline assembly block
# Match start of assembly blocks
_MATCH_ASM = re.compile(r'^\s*(?:asm|_asm|__asm|__asm__)'
r'(?:\s+(volatile|__volatile__))?'
r'\s*[{(]')
_regexp_compile_cache = {}
# Finds occurrences of NOLINT[_NEXT_LINE] or NOLINT[_NEXT_LINE](...).
_RE_SUPPRESSION = re.compile(r'\bNOLINT(_NEXT_LINE)?\b(\([^)]*\))?')
# {str, set(int)}: a map from error categories to sets of linenumbers
# on which those errors are expected and should be suppressed.
_error_suppressions = {}
# Finds Copyright.
_RE_COPYRIGHT = re.compile(r'Copyright')
# The root directory used for deriving header guard CPP variable.
# This is set by --root flag.
_root = None
# The allowed line length of files.
# This is set by --linelength flag.
_line_length = 80
# The allowed extensions for file names
# This is set by --extensions flag.
_valid_extensions = set(['cc', 'h', 'cpp', 'hpp', 'cu', 'cuh'])
def ParseNolintSuppressions(filename, raw_line, linenum, error):
"""Updates the global list of error-suppressions.
Parses any NOLINT comments on the current line, updating the global
error_suppressions store. Reports an error if the NOLINT comment
was malformed.
Args:
filename: str, the name of the input file.
raw_line: str, the line of input text, with comments.
linenum: int, the number of the current line.
error: function, an error handler.
"""
# FIXME(adonovan): "NOLINT(" is misparsed as NOLINT(*).
matched = _RE_SUPPRESSION.search(raw_line)
if matched:
if matched.group(1) == '_NEXT_LINE':
linenum += 1
category = matched.group(2)
if category in (None, '(*)'): # => "suppress all"
_error_suppressions.setdefault(None, set()).add(linenum)
else:
if category.startswith('(') and category.endswith(')'):
category = category[1:-1]
if category in _ERROR_CATEGORIES:
_error_suppressions.setdefault(category, set()).add(linenum)
else:
error(filename, linenum, 'readability/nolint', 5,
'Unknown NOLINT error category: %s' % category)
def ResetNolintSuppressions():
"Resets the set of NOLINT suppressions to empty."
_error_suppressions.clear()
def IsErrorSuppressedByNolint(category, linenum):
"""Returns true if the specified error category is suppressed on this line.
Consults the global error_suppressions map populated by
ParseNolintSuppressions/ResetNolintSuppressions.
Args:
category: str, the category of the error.
linenum: int, the current line number.
Returns:
bool, True iff the error should be suppressed due to a NOLINT comment.
"""
return (linenum in _error_suppressions.get(category, set()) or
linenum in _error_suppressions.get(None, set()))
def Match(pattern, s):
"""Matches the string with the pattern, caching the compiled regexp."""
# The regexp compilation caching is inlined in both Match and Search for
# performance reasons; factoring it out into a separate function turns out
# to be noticeably expensive.
if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
return _regexp_compile_cache[pattern].match(s)
def ReplaceAll(pattern, rep, s):
"""Replaces instances of pattern in a string with a replacement.
The compiled regex is kept in a cache shared by Match and Search.
Args:
pattern: regex pattern
rep: replacement text
s: search string
Returns:
string with replacements made (or original string if no replacements)
"""
if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
return _regexp_compile_cache[pattern].sub(rep, s)
def Search(pattern, s):
"""Searches the string for the pattern, caching the compiled regexp."""
if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
return _regexp_compile_cache[pattern].search(s)
class _IncludeState(dict):
"""Tracks line numbers for includes, and the order in which includes appear.
As a dict, an _IncludeState object serves as a mapping between include
filename and line number on which that file was included.
Call CheckNextIncludeOrder() once for each header in the file, passing
in the type constants defined above. Calls in an illegal order will
raise an _IncludeError with an appropriate error message.
"""
# self._section will move monotonically through this set. If it ever
# needs to move backwards, CheckNextIncludeOrder will raise an error.
_INITIAL_SECTION = 0
_MY_H_SECTION = 1
_C_SECTION = 2
_CPP_SECTION = 3
_OTHER_H_SECTION = 4
_TYPE_NAMES = {
_C_SYS_HEADER: 'C system header',
_CPP_SYS_HEADER: 'C++ system header',
_LIKELY_MY_HEADER: 'header this file implements',
_POSSIBLE_MY_HEADER: 'header this file may implement',
_OTHER_HEADER: 'other header',
}
_SECTION_NAMES = {
_INITIAL_SECTION: "... nothing. (This can't be an error.)",
_MY_H_SECTION: 'a header this file implements',
_C_SECTION: 'C system header',
_CPP_SECTION: 'C++ system header',
_OTHER_H_SECTION: 'other header',
}
def __init__(self):
dict.__init__(self)
self.ResetSection()
def ResetSection(self):
# The name of the current section.
self._section = self._INITIAL_SECTION
# The path of last found header.
self._last_header = ''
def SetLastHeader(self, header_path):
self._last_header = header_path
def CanonicalizeAlphabeticalOrder(self, header_path):
"""Returns a path canonicalized for alphabetical comparison.
- replaces "-" with "_" so they both cmp the same.
- removes '-inl' since we don't require them to be after the main header.
- lowercase everything, just in case.
Args:
header_path: Path to be canonicalized.
Returns:
Canonicalized path.
"""
return header_path.replace('-inl.h', '.h').replace('-', '_').lower()
def IsInAlphabeticalOrder(self, clean_lines, linenum, header_path):
"""Check if a header is in alphabetical order with the previous header.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
header_path: Canonicalized header to be checked.
Returns:
Returns true if the header is in alphabetical order.
"""
# If previous section is different from current section, _last_header will
# be reset to empty string, so it's always less than current header.
#
# If previous line was a blank line, assume that the headers are
# intentionally sorted the way they are.
if (self._last_header > header_path and
not Match(r'^\s*$', clean_lines.elided[linenum - 1])):
return False
return True
def CheckNextIncludeOrder(self, header_type):
"""Returns a non-empty error message if the next header is out of order.
This function also updates the internal state to be ready to check
the next include.
Args:
header_type: One of the _XXX_HEADER constants defined above.
Returns:
The empty string if the header is in the right order, or an
error message describing what's wrong.
"""
error_message = ('Found %s after %s' %
(self._TYPE_NAMES[header_type],
self._SECTION_NAMES[self._section]))
last_section = self._section
if header_type == _C_SYS_HEADER:
if self._section <= self._C_SECTION:
self._section = self._C_SECTION
else:
self._last_header = ''
return error_message
elif header_type == _CPP_SYS_HEADER:
if self._section <= self._CPP_SECTION:
self._section = self._CPP_SECTION
else:
self._last_header = ''
return error_message
elif header_type == _LIKELY_MY_HEADER:
if self._section <= self._MY_H_SECTION:
self._section = self._MY_H_SECTION
else:
self._section = self._OTHER_H_SECTION
elif header_type == _POSSIBLE_MY_HEADER:
if self._section <= self._MY_H_SECTION:
self._section = self._MY_H_SECTION
else:
# This will always be the fallback because we're not sure
# enough that the header is associated with this file.
self._section = self._OTHER_H_SECTION
else:
assert header_type == _OTHER_HEADER
self._section = self._OTHER_H_SECTION
if last_section != self._section:
self._last_header = ''
return ''
class _CppLintState(object):
"""Maintains module-wide state.."""
def __init__(self):
self.verbose_level = 1 # global setting.
self.error_count = 0 # global count of reported errors
# filters to apply when emitting error messages
self.filters = _DEFAULT_FILTERS[:]
self.counting = 'total' # In what way are we counting errors?
self.errors_by_category = {} # string to int dict storing error counts
# output format:
# "emacs" - format that emacs can parse (default)
# "vs7" - format that Microsoft Visual Studio 7 can parse
self.output_format = 'emacs'
def SetOutputFormat(self, output_format):
"""Sets the output format for errors."""
self.output_format = output_format
def SetVerboseLevel(self, level):
"""Sets the module's verbosity, and returns the previous setting."""
last_verbose_level = self.verbose_level
self.verbose_level = level
return last_verbose_level
def SetCountingStyle(self, counting_style):
"""Sets the module's counting options."""
self.counting = counting_style
def SetFilters(self, filters):
"""Sets the error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "+whitespace/indent").
Each filter should start with + or -; else we die.
Raises:
ValueError: The comma-separated filters did not all start with '+' or '-'.
E.g. "-,+whitespace,-whitespace/indent,whitespace/badfilter"
"""
# Default filters always have less priority than the flag ones.
self.filters = _DEFAULT_FILTERS[:]
for filt in filters.split(','):
clean_filt = filt.strip()
if clean_filt:
self.filters.append(clean_filt)
for filt in self.filters:
if not (filt.startswith('+') or filt.startswith('-')):
raise ValueError('Every filter in --filters must start with + or -'
' (%s does not)' % filt)
def ResetErrorCounts(self):
"""Sets the module's error statistic back to zero."""
self.error_count = 0
self.errors_by_category = {}
def IncrementErrorCount(self, category):
"""Bumps the module's error statistic."""
self.error_count += 1
if self.counting in ('toplevel', 'detailed'):
if self.counting != 'detailed':
category = category.split('/')[0]
if category not in self.errors_by_category:
self.errors_by_category[category] = 0
self.errors_by_category[category] += 1
def PrintErrorCounts(self):
"""Print a summary of errors by category, and the total."""
for category, count in self.errors_by_category.iteritems():
sys.stderr.write('Category \'%s\' errors found: %d\n' %
(category, count))
sys.stderr.write('Total errors found: %d\n' % self.error_count)
_cpplint_state = _CppLintState()
def _OutputFormat():
"""Gets the module's output format."""
return _cpplint_state.output_format
def _SetOutputFormat(output_format):
"""Sets the module's output format."""
_cpplint_state.SetOutputFormat(output_format)
def _VerboseLevel():
"""Returns the module's verbosity setting."""
return _cpplint_state.verbose_level
def _SetVerboseLevel(level):
"""Sets the module's verbosity, and returns the previous setting."""
return _cpplint_state.SetVerboseLevel(level)
def _SetCountingStyle(level):
"""Sets the module's counting options."""
_cpplint_state.SetCountingStyle(level)
def _Filters():
"""Returns the module's list of output filters, as a list."""
return _cpplint_state.filters
def _SetFilters(filters):
"""Sets the module's error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die.
"""
_cpplint_state.SetFilters(filters)
class _FunctionState(object):
"""Tracks current function name and the number of lines in its body."""
_NORMAL_TRIGGER = 250 # for --v=0, 500 for --v=1, etc.
_TEST_TRIGGER = 400 # about 50% more than _NORMAL_TRIGGER.
def __init__(self):
self.in_a_function = False
self.lines_in_function = 0
self.current_function = ''
def Begin(self, function_name):
"""Start analyzing function body.
Args:
function_name: The name of the function being tracked.
"""
self.in_a_function = True
self.lines_in_function = 0
self.current_function = function_name
def Count(self):
"""Count line in current function body."""
if self.in_a_function:
self.lines_in_function += 1
def Check(self, error, filename, linenum):
"""Report if too many lines in function body.
Args:
error: The function to call with any errors found.
filename: The name of the current file.
linenum: The number of the line to check.
"""
if Match(r'T(EST|est)', self.current_function):
base_trigger = self._TEST_TRIGGER
else:
base_trigger = self._NORMAL_TRIGGER
trigger = base_trigger * 2**_VerboseLevel()
if self.lines_in_function > trigger:
error_level = int(math.log(self.lines_in_function / base_trigger, 2))
# 50 => 0, 100 => 1, 200 => 2, 400 => 3, 800 => 4, 1600 => 5, ...
if error_level > 5:
error_level = 5
error(filename, linenum, 'readability/fn_size', error_level,
'Small and focused functions are preferred:'
' %s has %d non-comment lines'
' (error triggered by exceeding %d lines).' % (
self.current_function, self.lines_in_function, trigger))
def End(self):
"""Stop analyzing function body."""
self.in_a_function = False
class _IncludeError(Exception):
"""Indicates a problem with the include order in a file."""
pass
class FileInfo:
"""Provides utility functions for filenames.
FileInfo provides easy access to the components of a file's path
relative to the project root.
"""
def __init__(self, filename):
self._filename = filename
def FullName(self):
"""Make Windows paths like Unix."""
return os.path.abspath(self._filename).replace('\\', '/')
def RepositoryName(self):
"""FullName after removing the local path to the repository.
If we have a real absolute path name here we can try to do something smart:
detecting the root of the checkout and truncating /path/to/checkout from
the name so that we get header guards that don't include things like
"C:\Documents and Settings\..." or "/home/username/..." in them and thus
people on different computers who have checked the source out to different
locations won't see bogus errors.
"""
fullname = self.FullName()
if os.path.exists(fullname):
project_dir = os.path.dirname(fullname)
if os.path.exists(os.path.join(project_dir, ".svn")):
# If there's a .svn file in the current directory, we recursively look
# up the directory tree for the top of the SVN checkout
root_dir = project_dir
one_up_dir = os.path.dirname(root_dir)
while os.path.exists(os.path.join(one_up_dir, ".svn")):
root_dir = os.path.dirname(root_dir)
one_up_dir = os.path.dirname(one_up_dir)
prefix = os.path.commonprefix([root_dir, project_dir])
return fullname[len(prefix) + 1:]
# Not SVN <= 1.6? Try to find a git, hg, or svn top level directory by
# searching up from the current path.
root_dir = os.path.dirname(fullname)
while (root_dir != os.path.dirname(root_dir) and
not os.path.exists(os.path.join(root_dir, ".git")) and
not os.path.exists(os.path.join(root_dir, ".hg")) and
not os.path.exists(os.path.join(root_dir, ".svn"))):
root_dir = os.path.dirname(root_dir)
if (os.path.exists(os.path.join(root_dir, ".git")) or
os.path.exists(os.path.join(root_dir, ".hg")) or
os.path.exists(os.path.join(root_dir, ".svn"))):
prefix = os.path.commonprefix([root_dir, project_dir])
return fullname[len(prefix) + 1:]
# Don't know what to do; header guard warnings may be wrong...
return fullname
def Split(self):
"""Splits the file into the directory, basename, and extension.
For 'chrome/browser/browser.cc', Split() would
return ('chrome/browser', 'browser', '.cc')
Returns:
A tuple of (directory, basename, extension).
"""
googlename = self.RepositoryName()
project, rest = os.path.split(googlename)
return (project,) + os.path.splitext(rest)
def BaseName(self):
"""File base name - text after the final slash, before the final period."""
return self.Split()[1]
def Extension(self):
"""File extension - text following the final period."""
return self.Split()[2]
def NoExtension(self):
"""File has no source file extension."""
return '/'.join(self.Split()[0:2])
def IsSource(self):
"""File has a source file extension."""
return self.Extension()[1:] in ('c', 'cc', 'cpp', 'cxx')
def _ShouldPrintError(category, confidence, linenum):
"""If confidence >= verbose, category passes filter and is not suppressed."""
# There are three ways we might decide not to print an error message:
# a "NOLINT(category)" comment appears in the source,
# the verbosity level isn't high enough, or the filters filter it out.
if IsErrorSuppressedByNolint(category, linenum):
return False
if confidence < _cpplint_state.verbose_level:
return False
is_filtered = False
for one_filter in _Filters():
if one_filter.startswith('-'):
if category.startswith(one_filter[1:]):
is_filtered = True
elif one_filter.startswith('+'):
if category.startswith(one_filter[1:]):
is_filtered = False
else:
assert False # should have been checked for in SetFilter.
if is_filtered:
return False
return True
def Error(filename, linenum, category, confidence, message):
"""Logs the fact we've found a lint error.
We log where the error was found, and also our confidence in the error,
that is, how certain we are this is a legitimate style regression, and
not a misidentification or a use that's sometimes justified.
False positives can be suppressed by the use of
"cpplint(category)" comments on the offending line. These are
parsed into _error_suppressions.
Args:
filename: The name of the file containing the error.
linenum: The number of the line containing the error.
category: A string used to describe the "category" this bug
falls under: "whitespace", say, or "runtime". Categories
may have a hierarchy separated by slashes: "whitespace/indent".
confidence: A number from 1-5 representing a confidence score for
the error, with 5 meaning that we are certain of the problem,
and 1 meaning that it could be a legitimate construct.
message: The error message.
"""
if _ShouldPrintError(category, confidence, linenum):
_cpplint_state.IncrementErrorCount(category)
if _cpplint_state.output_format == 'vs7':
sys.stderr.write('%s(%s): %s [%s] [%d]\n' % (
filename, linenum, message, category, confidence))
elif _cpplint_state.output_format == 'eclipse':
sys.stderr.write('%s:%s: warning: %s [%s] [%d]\n' % (
filename, linenum, message, category, confidence))
else:
sys.stderr.write('%s:%s: %s [%s] [%d]\n' % (
filename, linenum, message, category, confidence))
# Matches standard C++ escape sequences per 2.13.2.3 of the C++ standard.
_RE_PATTERN_CLEANSE_LINE_ESCAPES = re.compile(
r'\\([abfnrtv?"\\\']|\d+|x[0-9a-fA-F]+)')
# Matches strings. Escape codes should already be removed by ESCAPES.
_RE_PATTERN_CLEANSE_LINE_DOUBLE_QUOTES = re.compile(r'"[^"]*"')
# Matches characters. Escape codes should already be removed by ESCAPES.
_RE_PATTERN_CLEANSE_LINE_SINGLE_QUOTES = re.compile(r"'.'")
# Matches multi-line C++ comments.
# This RE is a little bit more complicated than one might expect, because we
# have to take care of space removals tools so we can handle comments inside
# statements better.
# The current rule is: We only clear spaces from both sides when we're at the
# end of the line. Otherwise, we try to remove spaces from the right side,
# if this doesn't work we try on left side but only if there's a non-character
# on the right.
_RE_PATTERN_CLEANSE_LINE_C_COMMENTS = re.compile(
r"""(\s*/\*.*\*/\s*$|
/\*.*\*/\s+|
\s+/\*.*\*/(?=\W)|
/\*.*\*/)""", re.VERBOSE)
def IsCppString(line):
"""Does line terminate so, that the next symbol is in string constant.
This function does not consider single-line nor multi-line comments.
Args:
line: is a partial line of code starting from the 0..n.
Returns:
True, if next character appended to 'line' is inside a
string constant.
"""
line = line.replace(r'\\', 'XX') # after this, \\" does not match to \"
return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1
def CleanseRawStrings(raw_lines):
"""Removes C++11 raw strings from lines.
Before:
static const char kData[] = R"(
multi-line string
)";
After:
static const char kData[] = ""
(replaced by blank line)
"";
Args:
raw_lines: list of raw lines.
Returns:
list of lines with C++11 raw strings replaced by empty strings.
"""
delimiter = None
lines_without_raw_strings = []
for line in raw_lines:
if delimiter:
# Inside a raw string, look for the end
end = line.find(delimiter)
if end >= 0:
# Found the end of the string, match leading space for this
# line and resume copying the original lines, and also insert
# a "" on the last line.
leading_space = Match(r'^(\s*)\S', line)
line = leading_space.group(1) + '""' + line[end + len(delimiter):]
delimiter = None
else:
# Haven't found the end yet, append a blank line.
line = ''
else:
# Look for beginning of a raw string.
# See 2.14.15 [lex.string] for syntax.
matched = Match(r'^(.*)\b(?:R|u8R|uR|UR|LR)"([^\s\\()]*)\((.*)$', line)
if matched:
delimiter = ')' + matched.group(2) + '"'
end = matched.group(3).find(delimiter)
if end >= 0:
# Raw string ended on same line
line = (matched.group(1) + '""' +
matched.group(3)[end + len(delimiter):])
delimiter = None
else:
# Start of a multi-line raw string
line = matched.group(1) + '""'
lines_without_raw_strings.append(line)
# TODO(unknown): if delimiter is not None here, we might want to
# emit a warning for unterminated string.
return lines_without_raw_strings
def FindNextMultiLineCommentStart(lines, lineix):
"""Find the beginning marker for a multiline comment."""
while lineix < len(lines):
if lines[lineix].strip().startswith('/*'):
# Only return this marker if the comment goes beyond this line
if lines[lineix].strip().find('*/', 2) < 0:
return lineix
lineix += 1
return len(lines)
def FindNextMultiLineCommentEnd(lines, lineix):
"""We are inside a comment, find the end marker."""
while lineix < len(lines):
if lines[lineix].strip().endswith('*/'):
return lineix
lineix += 1
return len(lines)
def RemoveMultiLineCommentsFromRange(lines, begin, end):
"""Clears a range of lines for multi-line comments."""
# Having // dummy comments makes the lines non-empty, so we will not get
# unnecessary blank line warnings later in the code.
for i in range(begin, end):
lines[i] = '// dummy'
def RemoveMultiLineComments(filename, lines, error):
"""Removes multiline (c-style) comments from lines."""
lineix = 0
while lineix < len(lines):
lineix_begin = FindNextMultiLineCommentStart(lines, lineix)
if lineix_begin >= len(lines):
return
lineix_end = FindNextMultiLineCommentEnd(lines, lineix_begin)
if lineix_end >= len(lines):
error(filename, lineix_begin + 1, 'readability/multiline_comment', 5,
'Could not find end of multi-line comment')
return
RemoveMultiLineCommentsFromRange(lines, lineix_begin, lineix_end + 1)
lineix = lineix_end + 1
def CleanseComments(line):
"""Removes //-comments and single-line C-style /* */ comments.
Args:
line: A line of C++ source.
Returns:
The line with single-line comments removed.
"""
commentpos = line.find('//')
if commentpos != -1 and not IsCppString(line[:commentpos]):
line = line[:commentpos].rstrip()
# get rid of /* ... */
return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line)
class CleansedLines(object):
"""Holds 3 copies of all lines with different preprocessing applied to them.
1) elided member contains lines without strings and comments,
2) lines member contains lines without comments, and
3) raw_lines member contains all the lines without processing.
All these three members are of <type 'list'>, and of the same length.
"""
def __init__(self, lines):
self.elided = []
self.lines = []
self.raw_lines = lines
self.num_lines = len(lines)
self.lines_without_raw_strings = CleanseRawStrings(lines)
for linenum in range(len(self.lines_without_raw_strings)):
self.lines.append(CleanseComments(
self.lines_without_raw_strings[linenum]))
elided = self._CollapseStrings(self.lines_without_raw_strings[linenum])
self.elided.append(CleanseComments(elided))
def NumLines(self):
"""Returns the number of lines represented."""
return self.num_lines
@staticmethod
def _CollapseStrings(elided):
"""Collapses strings and chars on a line to simple "" or '' blocks.
We nix strings first so we're not fooled by text like '"http://"'
Args:
elided: The line being processed.
Returns:
The line with collapsed strings.
"""
if not _RE_PATTERN_INCLUDE.match(elided):
# Remove escaped characters first to make quote/single quote collapsing
# basic. Things that look like escaped characters shouldn't occur
# outside of strings and chars.
elided = _RE_PATTERN_CLEANSE_LINE_ESCAPES.sub('', elided)
elided = _RE_PATTERN_CLEANSE_LINE_SINGLE_QUOTES.sub("''", elided)
elided = _RE_PATTERN_CLEANSE_LINE_DOUBLE_QUOTES.sub('""', elided)
return elided
def FindEndOfExpressionInLine(line, startpos, depth, startchar, endchar):
"""Find the position just after the matching endchar.
Args:
line: a CleansedLines line.
startpos: start searching at this position.
depth: nesting level at startpos.
startchar: expression opening character.
endchar: expression closing character.
Returns:
On finding matching endchar: (index just after matching endchar, 0)
Otherwise: (-1, new depth at end of this line)
"""
for i in xrange(startpos, len(line)):
if line[i] == startchar:
depth += 1
elif line[i] == endchar:
depth -= 1
if depth == 0:
return (i + 1, 0)
return (-1, depth)
def CloseExpression(clean_lines, linenum, pos):
"""If input points to ( or { or [ or <, finds the position that closes it.
If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the
linenum/pos that correspond to the closing of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *past* the closing brace, or
(line, len(lines), -1) if we never find a close. Note we ignore
strings and comments when matching; and the line we return is the
'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
startchar = line[pos]
if startchar not in '({[<':
return (line, clean_lines.NumLines(), -1)
if startchar == '(': endchar = ')'
if startchar == '[': endchar = ']'
if startchar == '{': endchar = '}'
if startchar == '<': endchar = '>'
# Check first line
(end_pos, num_open) = FindEndOfExpressionInLine(
line, pos, 0, startchar, endchar)
if end_pos > -1:
return (line, linenum, end_pos)
# Continue scanning forward
while linenum < clean_lines.NumLines() - 1:
linenum += 1
line = clean_lines.elided[linenum]
(end_pos, num_open) = FindEndOfExpressionInLine(
line, 0, num_open, startchar, endchar)
if end_pos > -1:
return (line, linenum, end_pos)
# Did not find endchar before end of file, give up
return (line, clean_lines.NumLines(), -1)
def FindStartOfExpressionInLine(line, endpos, depth, startchar, endchar):
"""Find position at the matching startchar.
This is almost the reverse of FindEndOfExpressionInLine, but note
that the input position and returned position differs by 1.
Args:
line: a CleansedLines line.
endpos: start searching at this position.
depth: nesting level at endpos.
startchar: expression opening character.
endchar: expression closing character.
Returns:
On finding matching startchar: (index at matching startchar, 0)
Otherwise: (-1, new depth at beginning of this line)
"""
for i in xrange(endpos, -1, -1):
if line[i] == endchar:
depth += 1
elif line[i] == startchar:
depth -= 1
if depth == 0:
return (i, 0)
return (-1, depth)
def ReverseCloseExpression(clean_lines, linenum, pos):
"""If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
linenum/pos that correspond to the opening of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *at* the opening brace, or
(line, 0, -1) if we never find the matching opening brace. Note
we ignore strings and comments when matching; and the line we
return is the 'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
endchar = line[pos]
if endchar not in ')}]>':
return (line, 0, -1)
if endchar == ')': startchar = '('
if endchar == ']': startchar = '['
if endchar == '}': startchar = '{'
if endchar == '>': startchar = '<'
# Check last line
(start_pos, num_open) = FindStartOfExpressionInLine(
line, pos, 0, startchar, endchar)
if start_pos > -1:
return (line, linenum, start_pos)
# Continue scanning backward
while linenum > 0:
linenum -= 1
line = clean_lines.elided[linenum]
(start_pos, num_open) = FindStartOfExpressionInLine(
line, len(line) - 1, num_open, startchar, endchar)
if start_pos > -1:
return (line, linenum, start_pos)
# Did not find startchar before beginning of file, give up
return (line, 0, -1)
def CheckForCopyright(filename, lines, error):
"""Logs an error if a Copyright message appears at the top of the file."""
# We'll check up to line 10. Don't forget there's a
# dummy line at the front.
for line in xrange(1, min(len(lines), 11)):
if _RE_COPYRIGHT.search(lines[line], re.I):
error(filename, 0, 'legal/copyright', 5,
'Copyright message found. '
'You should not include a copyright line.')
def GetHeaderGuardCPPVariable(filename):
"""Returns the CPP variable that should be used as a header guard.
Args:
filename: The name of a C++ header file.
Returns:
The CPP variable that should be used as a header guard in the
named file.
"""
# Restores original filename in case that cpplint is invoked from Emacs's
# flymake.
filename = re.sub(r'_flymake\.h$', '.h', filename)
filename = re.sub(r'/\.flymake/([^/]*)$', r'/\1', filename)
fileinfo = FileInfo(filename)
file_path_from_root = fileinfo.RepositoryName()
if _root:
file_path_from_root = re.sub('^' + _root + os.sep, '', file_path_from_root)
return re.sub(r'[-./\s]', '_', file_path_from_root).upper() + '_'
def CheckForHeaderGuard(filename, lines, error):
"""Checks that the file contains a header guard.
Logs an error if no #ifndef header guard is present. For other
headers, checks that the full pathname is used.
Args:
filename: The name of the C++ header file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
cppvar = GetHeaderGuardCPPVariable(filename)
ifndef = None
ifndef_linenum = 0
define = None
endif = None
endif_linenum = 0
for linenum, line in enumerate(lines):
linesplit = line.split()
if len(linesplit) >= 2:
# find the first occurrence of #ifndef and #define, save arg
if not ifndef and linesplit[0] == '#ifndef':
# set ifndef to the header guard presented on the #ifndef line.
ifndef = linesplit[1]
ifndef_linenum = linenum
if not define and linesplit[0] == '#define':
define = linesplit[1]
# find the last occurrence of #endif, save entire line
if line.startswith('#endif'):
endif = line
endif_linenum = linenum
if not ifndef:
error(filename, 0, 'build/header_guard', 5,
'No #ifndef header guard found, suggested CPP variable is: %s' %
cppvar)
return
if not define:
error(filename, 0, 'build/header_guard', 5,
'No #define header guard found, suggested CPP variable is: %s' %
cppvar)
return
# The guard should be PATH_FILE_H_, but we also allow PATH_FILE_H__
# for backward compatibility.
if ifndef != cppvar:
error_level = 0
if ifndef != cppvar + '_':
error_level = 5
ParseNolintSuppressions(filename, lines[ifndef_linenum], ifndef_linenum,
error)
error(filename, ifndef_linenum, 'build/header_guard', error_level,
'#ifndef header guard has wrong style, please use: %s' % cppvar)
if define != ifndef:
error(filename, 0, 'build/header_guard', 5,
'#ifndef and #define don\'t match, suggested CPP variable is: %s' %
cppvar)
return
if endif != ('#endif // %s' % cppvar):
error_level = 0
if endif != ('#endif // %s' % (cppvar + '_')):
error_level = 5
ParseNolintSuppressions(filename, lines[endif_linenum], endif_linenum,
error)
error(filename, endif_linenum, 'build/header_guard', error_level,
'#endif line should be "#endif // %s"' % cppvar)
def CheckForBadCharacters(filename, lines, error):
"""Logs an error for each line containing bad characters.
Two kinds of bad characters:
1. Unicode replacement characters: These indicate that either the file
contained invalid UTF-8 (likely) or Unicode replacement characters (which
it shouldn't). Note that it's possible for this to throw off line
numbering if the invalid UTF-8 occurred adjacent to a newline.
2. NUL bytes. These are problematic for some tools.
Args:
filename: The name of the current file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
for linenum, line in enumerate(lines):
if u'\ufffd' in line:
error(filename, linenum, 'readability/utf8', 5,
'Line contains invalid UTF-8 (or Unicode replacement character).')
if '\0' in line:
error(filename, linenum, 'readability/nul', 5, 'Line contains NUL byte.')
def CheckForNewlineAtEOF(filename, lines, error):
"""Logs an error if there is no newline char at the end of the file.
Args:
filename: The name of the current file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
# The array lines() was created by adding two newlines to the
# original file (go figure), then splitting on \n.
# To verify that the file ends in \n, we just have to make sure the
# last-but-two element of lines() exists and is empty.
if len(lines) < 3 or lines[-2]:
error(filename, len(lines) - 2, 'whitespace/ending_newline', 5,
'Could not find a newline character at the end of the file.')
def CheckForMultilineCommentsAndStrings(filename, clean_lines, linenum, error):
"""Logs an error if we see /* ... */ or "..." that extend past one line.
/* ... */ comments are legit inside macros, for one line.
Otherwise, we prefer // comments, so it's ok to warn about the
other. Likewise, it's ok for strings to extend across multiple
lines, as long as a line continuation character (backslash)
terminates each line. Although not currently prohibited by the C++
style guide, it's ugly and unnecessary. We don't do well with either
in this lint program, so we warn about both.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Remove all \\ (escaped backslashes) from the line. They are OK, and the
# second (escaped) slash may trigger later \" detection erroneously.
line = line.replace('\\\\', '')
if line.count('/*') > line.count('*/'):
error(filename, linenum, 'readability/multiline_comment', 5,
'Complex multi-line /*...*/-style comment found. '
'Lint may give bogus warnings. '
'Consider replacing these with //-style comments, '
'with #if 0...#endif, '
'or with more clearly structured multi-line comments.')
if (line.count('"') - line.count('\\"')) % 2:
error(filename, linenum, 'readability/multiline_string', 5,
'Multi-line string ("...") found. This lint script doesn\'t '
'do well with such strings, and may give bogus warnings. '
'Use C++11 raw strings or concatenation instead.')
caffe_alt_function_list = (
('memset', ['caffe_set', 'caffe_memset']),
('cudaMemset', ['caffe_gpu_set', 'caffe_gpu_memset']),
('memcpy', ['caffe_copy']),
('cudaMemcpy', ['caffe_copy', 'caffe_gpu_memcpy']),
)
def CheckCaffeAlternatives(filename, clean_lines, linenum, error):
"""Checks for C(++) functions for which a Caffe substitute should be used.
For certain native C functions (memset, memcpy), there is a Caffe alternative
which should be used instead.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
for function, alts in caffe_alt_function_list:
ix = line.find(function + '(')
if ix >= 0 and (ix == 0 or (not line[ix - 1].isalnum() and
line[ix - 1] not in ('_', '.', '>'))):
disp_alts = ['%s(...)' % alt for alt in alts]
error(filename, linenum, 'caffe/alt_fn', 2,
'Use Caffe function %s instead of %s(...).' %
(' or '.join(disp_alts), function))
def CheckCaffeDataLayerSetUp(filename, clean_lines, linenum, error):
"""Except the base classes, Caffe DataLayer should define DataLayerSetUp
instead of LayerSetUp.
The base DataLayers define common SetUp steps, the subclasses should
not override them.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
ix = line.find('DataLayer<Dtype>::LayerSetUp')
if ix >= 0 and (
line.find('void DataLayer<Dtype>::LayerSetUp') != -1 or
line.find('void ImageDataLayer<Dtype>::LayerSetUp') != -1 or
line.find('void MemoryDataLayer<Dtype>::LayerSetUp') != -1 or
line.find('void WindowDataLayer<Dtype>::LayerSetUp') != -1):
error(filename, linenum, 'caffe/data_layer_setup', 2,
'Except the base classes, Caffe DataLayer should define'
+ ' DataLayerSetUp instead of LayerSetUp. The base DataLayers'
+ ' define common SetUp steps, the subclasses should'
+ ' not override them.')
ix = line.find('DataLayer<Dtype>::DataLayerSetUp')
if ix >= 0 and (
line.find('void Base') == -1 and
line.find('void DataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void ImageDataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void MemoryDataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void WindowDataLayer<Dtype>::DataLayerSetUp') == -1):
error(filename, linenum, 'caffe/data_layer_setup', 2,
'Except the base classes, Caffe DataLayer should define'
+ ' DataLayerSetUp instead of LayerSetUp. The base DataLayers'
+ ' define common SetUp steps, the subclasses should'
+ ' not override them.')
c_random_function_list = (
'rand(',
'rand_r(',
'random(',
)
def CheckCaffeRandom(filename, clean_lines, linenum, error):
"""Checks for calls to C random functions (rand, rand_r, random, ...).
Caffe code should (almost) always use the caffe_rng_* functions rather
than these, as the internal state of these C functions is independent of the
native Caffe RNG system which should produce deterministic results for a
fixed Caffe seed set using Caffe::set_random_seed(...).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
for function in c_random_function_list:
ix = line.find(function)
# Comparisons made explicit for clarity -- pylint: disable=g-explicit-bool-comparison
if ix >= 0 and (ix == 0 or (not line[ix - 1].isalnum() and
line[ix - 1] not in ('_', '.', '>'))):
error(filename, linenum, 'caffe/random_fn', 2,
'Use caffe_rng_rand() (or other caffe_rng_* function) instead of '
+ function +
') to ensure results are deterministic for a fixed Caffe seed.')
threading_list = (
('asctime(', 'asctime_r('),
('ctime(', 'ctime_r('),
('getgrgid(', 'getgrgid_r('),
('getgrnam(', 'getgrnam_r('),
('getlogin(', 'getlogin_r('),
('getpwnam(', 'getpwnam_r('),
('getpwuid(', 'getpwuid_r('),
('gmtime(', 'gmtime_r('),
('localtime(', 'localtime_r('),
('strtok(', 'strtok_r('),
('ttyname(', 'ttyname_r('),
)
def CheckPosixThreading(filename, clean_lines, linenum, error):
"""Checks for calls to thread-unsafe functions.
Much code has been originally written without consideration of
multi-threading. Also, engineers are relying on their old experience;
they have learned posix before threading extensions were added. These
tests guide the engineers to use thread-safe functions (when using
posix directly).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
for single_thread_function, multithread_safe_function in threading_list:
ix = line.find(single_thread_function)
# Comparisons made explicit for clarity -- pylint: disable=g-explicit-bool-comparison
if ix >= 0 and (ix == 0 or (not line[ix - 1].isalnum() and
line[ix - 1] not in ('_', '.', '>'))):
error(filename, linenum, 'runtime/threadsafe_fn', 2,
'Consider using ' + multithread_safe_function +
'...) instead of ' + single_thread_function +
'...) for improved thread safety.')
def CheckVlogArguments(filename, clean_lines, linenum, error):
"""Checks that VLOG() is only used for defining a logging level.
For example, VLOG(2) is correct. VLOG(INFO), VLOG(WARNING), VLOG(ERROR), and
VLOG(FATAL) are not.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
if Search(r'\bVLOG\((INFO|ERROR|WARNING|DFATAL|FATAL)\)', line):
error(filename, linenum, 'runtime/vlog', 5,
'VLOG() should be used with numeric verbosity level. '
'Use LOG() if you want symbolic severity levels.')
# Matches invalid increment: *count++, which moves pointer instead of
# incrementing a value.
_RE_PATTERN_INVALID_INCREMENT = re.compile(
r'^\s*\*\w+(\+\+|--);')
def CheckInvalidIncrement(filename, clean_lines, linenum, error):
"""Checks for invalid increment *count++.
For example following function:
void increment_counter(int* count) {
*count++;
}
is invalid, because it effectively does count++, moving pointer, and should
be replaced with ++*count, (*count)++ or *count += 1.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
if _RE_PATTERN_INVALID_INCREMENT.match(line):
error(filename, linenum, 'runtime/invalid_increment', 5,
'Changing pointer instead of value (or unused value of operator*).')
class _BlockInfo(object):
"""Stores information about a generic block of code."""
def __init__(self, seen_open_brace):
self.seen_open_brace = seen_open_brace
self.open_parentheses = 0
self.inline_asm = _NO_ASM
def CheckBegin(self, filename, clean_lines, linenum, error):
"""Run checks that applies to text up to the opening brace.
This is mostly for checking the text after the class identifier
and the "{", usually where the base class is specified. For other
blocks, there isn't much to check, so we always pass.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
pass
def CheckEnd(self, filename, clean_lines, linenum, error):
"""Run checks that applies to text after the closing brace.
This is mostly used for checking end of namespace comments.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
pass
class _ClassInfo(_BlockInfo):
"""Stores information about a class."""
def __init__(self, name, class_or_struct, clean_lines, linenum):
_BlockInfo.__init__(self, False)
self.name = name
self.starting_linenum = linenum
self.is_derived = False
if class_or_struct == 'struct':
self.access = 'public'
self.is_struct = True
else:
self.access = 'private'
self.is_struct = False
# Remember initial indentation level for this class. Using raw_lines here
# instead of elided to account for leading comments.
initial_indent = Match(r'^( *)\S', clean_lines.raw_lines[linenum])
if initial_indent:
self.class_indent = len(initial_indent.group(1))
else:
self.class_indent = 0
# Try to find the end of the class. This will be confused by things like:
# class A {
# } *x = { ...
#
# But it's still good enough for CheckSectionSpacing.
self.last_line = 0
depth = 0
for i in range(linenum, clean_lines.NumLines()):
line = clean_lines.elided[i]
depth += line.count('{') - line.count('}')
if not depth:
self.last_line = i
break
def CheckBegin(self, filename, clean_lines, linenum, error):
# Look for a bare ':'
if Search('(^|[^:]):($|[^:])', clean_lines.elided[linenum]):
self.is_derived = True
def CheckEnd(self, filename, clean_lines, linenum, error):
# Check that closing brace is aligned with beginning of the class.
# Only do this if the closing brace is indented by only whitespaces.
# This means we will not check single-line class definitions.
indent = Match(r'^( *)\}', clean_lines.elided[linenum])
if indent and len(indent.group(1)) != self.class_indent:
if self.is_struct:
parent = 'struct ' + self.name
else:
parent = 'class ' + self.name
error(filename, linenum, 'whitespace/indent', 3,
'Closing brace should be aligned with beginning of %s' % parent)
class _NamespaceInfo(_BlockInfo):
"""Stores information about a namespace."""
def __init__(self, name, linenum):
_BlockInfo.__init__(self, False)
self.name = name or ''
self.starting_linenum = linenum
def CheckEnd(self, filename, clean_lines, linenum, error):
"""Check end of namespace comments."""
line = clean_lines.raw_lines[linenum]
# Check how many lines is enclosed in this namespace. Don't issue
# warning for missing namespace comments if there aren't enough
# lines. However, do apply checks if there is already an end of
# namespace comment and it's incorrect.
#
# TODO(unknown): We always want to check end of namespace comments
# if a namespace is large, but sometimes we also want to apply the
# check if a short namespace contained nontrivial things (something
# other than forward declarations). There is currently no logic on
# deciding what these nontrivial things are, so this check is
# triggered by namespace size only, which works most of the time.
if (linenum - self.starting_linenum < 10
and not Match(r'};*\s*(//|/\*).*\bnamespace\b', line)):
return
# Look for matching comment at end of namespace.
#
# Note that we accept C style "/* */" comments for terminating
# namespaces, so that code that terminate namespaces inside
# preprocessor macros can be cpplint clean.
#
# We also accept stuff like "// end of namespace <name>." with the
# period at the end.
#
# Besides these, we don't accept anything else, otherwise we might
# get false negatives when existing comment is a substring of the
# expected namespace.
if self.name:
# Named namespace
if not Match((r'};*\s*(//|/\*).*\bnamespace\s+' + re.escape(self.name) +
r'[\*/\.\\\s]*$'),
line):
error(filename, linenum, 'readability/namespace', 5,
'Namespace should be terminated with "// namespace %s"' %
self.name)
else:
# Anonymous namespace
if not Match(r'};*\s*(//|/\*).*\bnamespace[\*/\.\\\s]*$', line):
error(filename, linenum, 'readability/namespace', 5,
'Namespace should be terminated with "// namespace"')
class _PreprocessorInfo(object):
"""Stores checkpoints of nesting stacks when #if/#else is seen."""
def __init__(self, stack_before_if):
# The entire nesting stack before #if
self.stack_before_if = stack_before_if
# The entire nesting stack up to #else
self.stack_before_else = []
# Whether we have already seen #else or #elif
self.seen_else = False
class _NestingState(object):
"""Holds states related to parsing braces."""
def __init__(self):
# Stack for tracking all braces. An object is pushed whenever we
# see a "{", and popped when we see a "}". Only 3 types of
# objects are possible:
# - _ClassInfo: a class or struct.
# - _NamespaceInfo: a namespace.
# - _BlockInfo: some other type of block.
self.stack = []
# Stack of _PreprocessorInfo objects.
self.pp_stack = []
def SeenOpenBrace(self):
"""Check if we have seen the opening brace for the innermost block.
Returns:
True if we have seen the opening brace, False if the innermost
block is still expecting an opening brace.
"""
return (not self.stack) or self.stack[-1].seen_open_brace
def InNamespaceBody(self):
"""Check if we are currently one level inside a namespace body.
Returns:
True if top of the stack is a namespace block, False otherwise.
"""
return self.stack and isinstance(self.stack[-1], _NamespaceInfo)
def UpdatePreprocessor(self, line):
"""Update preprocessor stack.
We need to handle preprocessors due to classes like this:
#ifdef SWIG
struct ResultDetailsPageElementExtensionPoint {
#else
struct ResultDetailsPageElementExtensionPoint : public Extension {
#endif
We make the following assumptions (good enough for most files):
- Preprocessor condition evaluates to true from #if up to first
#else/#elif/#endif.
- Preprocessor condition evaluates to false from #else/#elif up
to #endif. We still perform lint checks on these lines, but
these do not affect nesting stack.
Args:
line: current line to check.
"""
if Match(r'^\s*#\s*(if|ifdef|ifndef)\b', line):
# Beginning of #if block, save the nesting stack here. The saved
# stack will allow us to restore the parsing state in the #else case.
self.pp_stack.append(_PreprocessorInfo(copy.deepcopy(self.stack)))
elif Match(r'^\s*#\s*(else|elif)\b', line):
# Beginning of #else block
if self.pp_stack:
if not self.pp_stack[-1].seen_else:
# This is the first #else or #elif block. Remember the
# whole nesting stack up to this point. This is what we
# keep after the #endif.
self.pp_stack[-1].seen_else = True
self.pp_stack[-1].stack_before_else = copy.deepcopy(self.stack)
# Restore the stack to how it was before the #if
self.stack = copy.deepcopy(self.pp_stack[-1].stack_before_if)
else:
# TODO(unknown): unexpected #else, issue warning?
pass
elif Match(r'^\s*#\s*endif\b', line):
# End of #if or #else blocks.
if self.pp_stack:
# If we saw an #else, we will need to restore the nesting
# stack to its former state before the #else, otherwise we
# will just continue from where we left off.
if self.pp_stack[-1].seen_else:
# Here we can just use a shallow copy since we are the last
# reference to it.
self.stack = self.pp_stack[-1].stack_before_else
# Drop the corresponding #if
self.pp_stack.pop()
else:
# TODO(unknown): unexpected #endif, issue warning?
pass
def Update(self, filename, clean_lines, linenum, error):
"""Update nesting state with current line.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Update pp_stack first
self.UpdatePreprocessor(line)
# Count parentheses. This is to avoid adding struct arguments to
# the nesting stack.
if self.stack:
inner_block = self.stack[-1]
depth_change = line.count('(') - line.count(')')
inner_block.open_parentheses += depth_change
# Also check if we are starting or ending an inline assembly block.
if inner_block.inline_asm in (_NO_ASM, _END_ASM):
if (depth_change != 0 and
inner_block.open_parentheses == 1 and
_MATCH_ASM.match(line)):
# Enter assembly block
inner_block.inline_asm = _INSIDE_ASM
else:
# Not entering assembly block. If previous line was _END_ASM,
# we will now shift to _NO_ASM state.
inner_block.inline_asm = _NO_ASM
elif (inner_block.inline_asm == _INSIDE_ASM and
inner_block.open_parentheses == 0):
# Exit assembly block
inner_block.inline_asm = _END_ASM
# Consume namespace declaration at the beginning of the line. Do
# this in a loop so that we catch same line declarations like this:
# namespace proto2 { namespace bridge { class MessageSet; } }
while True:
# Match start of namespace. The "\b\s*" below catches namespace
# declarations even if it weren't followed by a whitespace, this
# is so that we don't confuse our namespace checker. The
# missing spaces will be flagged by CheckSpacing.
namespace_decl_match = Match(r'^\s*namespace\b\s*([:\w]+)?(.*)$', line)
if not namespace_decl_match:
break
new_namespace = _NamespaceInfo(namespace_decl_match.group(1), linenum)
self.stack.append(new_namespace)
line = namespace_decl_match.group(2)
if line.find('{') != -1:
new_namespace.seen_open_brace = True
line = line[line.find('{') + 1:]
# Look for a class declaration in whatever is left of the line
# after parsing namespaces. The regexp accounts for decorated classes
# such as in:
# class LOCKABLE API Object {
# };
#
# Templates with class arguments may confuse the parser, for example:
# template <class T
# class Comparator = less<T>,
# class Vector = vector<T> >
# class HeapQueue {
#
# Because this parser has no nesting state about templates, by the
# time it saw "class Comparator", it may think that it's a new class.
# Nested templates have a similar problem:
# template <
# typename ExportedType,
# typename TupleType,
# template <typename, typename> class ImplTemplate>
#
# To avoid these cases, we ignore classes that are followed by '=' or '>'
class_decl_match = Match(
r'\s*(template\s*<[\w\s<>,:]*>\s*)?'
r'(class|struct)\s+([A-Z_]+\s+)*(\w+(?:::\w+)*)'
r'(([^=>]|<[^<>]*>|<[^<>]*<[^<>]*>\s*>)*)$', line)
if (class_decl_match and
(not self.stack or self.stack[-1].open_parentheses == 0)):
self.stack.append(_ClassInfo(
class_decl_match.group(4), class_decl_match.group(2),
clean_lines, linenum))
line = class_decl_match.group(5)
# If we have not yet seen the opening brace for the innermost block,
# run checks here.
if not self.SeenOpenBrace():
self.stack[-1].CheckBegin(filename, clean_lines, linenum, error)
# Update access control if we are inside a class/struct
if self.stack and isinstance(self.stack[-1], _ClassInfo):
classinfo = self.stack[-1]
access_match = Match(
r'^(.*)\b(public|private|protected|signals)(\s+(?:slots\s*)?)?'
r':(?:[^:]|$)',
line)
if access_match:
classinfo.access = access_match.group(2)
# Check that access keywords are indented +1 space. Skip this
# check if the keywords are not preceded by whitespaces.
indent = access_match.group(1)
if (len(indent) != classinfo.class_indent + 1 and
Match(r'^\s*$', indent)):
if classinfo.is_struct:
parent = 'struct ' + classinfo.name
else:
parent = 'class ' + classinfo.name
slots = ''
if access_match.group(3):
slots = access_match.group(3)
error(filename, linenum, 'whitespace/indent', 3,
'%s%s: should be indented +1 space inside %s' % (
access_match.group(2), slots, parent))
# Consume braces or semicolons from what's left of the line
while True:
# Match first brace, semicolon, or closed parenthesis.
matched = Match(r'^[^{;)}]*([{;)}])(.*)$', line)
if not matched:
break
token = matched.group(1)
if token == '{':
# If namespace or class hasn't seen a opening brace yet, mark
# namespace/class head as complete. Push a new block onto the
# stack otherwise.
if not self.SeenOpenBrace():
self.stack[-1].seen_open_brace = True
else:
self.stack.append(_BlockInfo(True))
if _MATCH_ASM.match(line):
self.stack[-1].inline_asm = _BLOCK_ASM
elif token == ';' or token == ')':
# If we haven't seen an opening brace yet, but we already saw
# a semicolon, this is probably a forward declaration. Pop
# the stack for these.
#
# Similarly, if we haven't seen an opening brace yet, but we
# already saw a closing parenthesis, then these are probably
# function arguments with extra "class" or "struct" keywords.
# Also pop these stack for these.
if not self.SeenOpenBrace():
self.stack.pop()
else: # token == '}'
# Perform end of block checks and pop the stack.
if self.stack:
self.stack[-1].CheckEnd(filename, clean_lines, linenum, error)
self.stack.pop()
line = matched.group(2)
def InnermostClass(self):
"""Get class info on the top of the stack.
Returns:
A _ClassInfo object if we are inside a class, or None otherwise.
"""
for i in range(len(self.stack), 0, -1):
classinfo = self.stack[i - 1]
if isinstance(classinfo, _ClassInfo):
return classinfo
return None
def CheckCompletedBlocks(self, filename, error):
"""Checks that all classes and namespaces have been completely parsed.
Call this when all lines in a file have been processed.
Args:
filename: The name of the current file.
error: The function to call with any errors found.
"""
# Note: This test can result in false positives if #ifdef constructs
# get in the way of brace matching. See the testBuildClass test in
# cpplint_unittest.py for an example of this.
for obj in self.stack:
if isinstance(obj, _ClassInfo):
error(filename, obj.starting_linenum, 'build/class', 5,
'Failed to find complete declaration of class %s' %
obj.name)
elif isinstance(obj, _NamespaceInfo):
error(filename, obj.starting_linenum, 'build/namespaces', 5,
'Failed to find complete declaration of namespace %s' %
obj.name)
def CheckForNonStandardConstructs(filename, clean_lines, linenum,
nesting_state, error):
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
Complain about several constructs which gcc-2 accepts, but which are
not standard C++. Warning about these in lint is one way to ease the
transition to new compilers.
- put storage class first (e.g. "static const" instead of "const static").
- "%lld" instead of %qd" in printf-type functions.
- "%1$d" is non-standard in printf-type functions.
- "\%" is an undefined character escape sequence.
- text after #endif is not allowed.
- invalid inner-style forward declaration.
- >? and <? operators, and their >?= and <?= cousins.
Additionally, check for constructor/destructor style violations and reference
members, as it is very convenient to do so while checking for
gcc-2 compliance.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
"""
# Remove comments from the line, but leave in strings for now.
line = clean_lines.lines[linenum]
if Search(r'printf\s*\(.*".*%[-+ ]?\d*q', line):
error(filename, linenum, 'runtime/printf_format', 3,
'%q in format strings is deprecated. Use %ll instead.')
if Search(r'printf\s*\(.*".*%\d+\$', line):
error(filename, linenum, 'runtime/printf_format', 2,
'%N$ formats are unconventional. Try rewriting to avoid them.')
# Remove escaped backslashes before looking for undefined escapes.
line = line.replace('\\\\', '')
if Search(r'("|\').*\\(%|\[|\(|{)', line):
error(filename, linenum, 'build/printf_format', 3,
'%, [, (, and { are undefined character escapes. Unescape them.')
# For the rest, work with both comments and strings removed.
line = clean_lines.elided[linenum]
if Search(r'\b(const|volatile|void|char|short|int|long'
r'|float|double|signed|unsigned'
r'|schar|u?int8|u?int16|u?int32|u?int64)'
r'\s+(register|static|extern|typedef)\b',
line):
error(filename, linenum, 'build/storage_class', 5,
'Storage class (static, extern, typedef, etc) should be first.')
if Match(r'\s*#\s*endif\s*[^/\s]+', line):
error(filename, linenum, 'build/endif_comment', 5,
'Uncommented text after #endif is non-standard. Use a comment.')
if Match(r'\s*class\s+(\w+\s*::\s*)+\w+\s*;', line):
error(filename, linenum, 'build/forward_decl', 5,
'Inner-style forward declarations are invalid. Remove this line.')
if Search(r'(\w+|[+-]?\d+(\.\d*)?)\s*(<|>)\?=?\s*(\w+|[+-]?\d+)(\.\d*)?',
line):
error(filename, linenum, 'build/deprecated', 3,
'>? and <? (max and min) operators are non-standard and deprecated.')
if Search(r'^\s*const\s*string\s*&\s*\w+\s*;', line):
# TODO(unknown): Could it be expanded safely to arbitrary references,
# without triggering too many false positives? The first
# attempt triggered 5 warnings for mostly benign code in the regtest, hence
# the restriction.
# Here's the original regexp, for the reference:
# type_name = r'\w+((\s*::\s*\w+)|(\s*<\s*\w+?\s*>))?'
# r'\s*const\s*' + type_name + '\s*&\s*\w+\s*;'
error(filename, linenum, 'runtime/member_string_references', 2,
'const string& members are dangerous. It is much better to use '
'alternatives, such as pointers or simple constants.')
# Everything else in this function operates on class declarations.
# Return early if the top of the nesting stack is not a class, or if
# the class head is not completed yet.
classinfo = nesting_state.InnermostClass()
if not classinfo or not classinfo.seen_open_brace:
return
# The class may have been declared with namespace or classname qualifiers.
# The constructor and destructor will not have those qualifiers.
base_classname = classinfo.name.split('::')[-1]
# Look for single-argument constructors that aren't marked explicit.
# Technically a valid construct, but against style.
args = Match(r'\s+(?:inline\s+)?%s\s*\(([^,()]+)\)'
% re.escape(base_classname),
line)
if (args and
args.group(1) != 'void' and
not Match(r'(const\s+)?%s(\s+const)?\s*(?:<\w+>\s*)?&'
% re.escape(base_classname), args.group(1).strip())):
error(filename, linenum, 'runtime/explicit', 5,
'Single-argument constructors should be marked explicit.')
def CheckSpacingForFunctionCall(filename, line, linenum, error):
"""Checks for the correctness of various spacing around function calls.
Args:
filename: The name of the current file.
line: The text of the line to check.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Since function calls often occur inside if/for/while/switch
# expressions - which have their own, more liberal conventions - we
# first see if we should be looking inside such an expression for a
# function call, to which we can apply more strict standards.
fncall = line # if there's no control flow construct, look at whole line
for pattern in (r'\bif\s*\((.*)\)\s*{',
r'\bfor\s*\((.*)\)\s*{',
r'\bwhile\s*\((.*)\)\s*[{;]',
r'\bswitch\s*\((.*)\)\s*{'):
match = Search(pattern, line)
if match:
fncall = match.group(1) # look inside the parens for function calls
break
# Except in if/for/while/switch, there should never be space
# immediately inside parens (eg "f( 3, 4 )"). We make an exception
# for nested parens ( (a+b) + c ). Likewise, there should never be
# a space before a ( when it's a function argument. I assume it's a
# function argument when the char before the whitespace is legal in
# a function name (alnum + _) and we're not starting a macro. Also ignore
# pointers and references to arrays and functions coz they're too tricky:
# we use a very simple way to recognize these:
# " (something)(maybe-something)" or
# " (something)(maybe-something," or
# " (something)[something]"
# Note that we assume the contents of [] to be short enough that
# they'll never need to wrap.
if ( # Ignore control structures.
not Search(r'\b(if|for|while|switch|return|new|delete|catch|sizeof)\b',
fncall) and
# Ignore pointers/references to functions.
not Search(r' \([^)]+\)\([^)]*(\)|,$)', fncall) and
# Ignore pointers/references to arrays.
not Search(r' \([^)]+\)\[[^\]]+\]', fncall)):
if Search(r'\w\s*\(\s(?!\s*\\$)', fncall): # a ( used for a fn call
error(filename, linenum, 'whitespace/parens', 4,
'Extra space after ( in function call')
elif Search(r'\(\s+(?!(\s*\\)|\()', fncall):
error(filename, linenum, 'whitespace/parens', 2,
'Extra space after (')
if (Search(r'\w\s+\(', fncall) and
not Search(r'#\s*define|typedef', fncall) and
not Search(r'\w\s+\((\w+::)*\*\w+\)\(', fncall)):
error(filename, linenum, 'whitespace/parens', 4,
'Extra space before ( in function call')
# If the ) is followed only by a newline or a { + newline, assume it's
# part of a control statement (if/while/etc), and don't complain
if Search(r'[^)]\s+\)\s*[^{\s]', fncall):
# If the closing parenthesis is preceded by only whitespaces,
# try to give a more descriptive error message.
if Search(r'^\s+\)', fncall):
error(filename, linenum, 'whitespace/parens', 2,
'Closing ) should be moved to the previous line')
else:
error(filename, linenum, 'whitespace/parens', 2,
'Extra space before )')
def IsBlankLine(line):
"""Returns true if the given line is blank.
We consider a line to be blank if the line is empty or consists of
only white spaces.
Args:
line: A line of a string.
Returns:
True, if the given line is blank.
"""
return not line or line.isspace()
def CheckForFunctionLengths(filename, clean_lines, linenum,
function_state, error):
"""Reports for long function bodies.
For an overview why this is done, see:
http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Write_Short_Functions
Uses a simplistic algorithm assuming other style guidelines
(especially spacing) are followed.
Only checks unindented functions, so class members are unchecked.
Trivial bodies are unchecked, so constructors with huge initializer lists
may be missed.
Blank/comment lines are not counted so as to avoid encouraging the removal
of vertical space and comments just to get through a lint check.
NOLINT *on the last line of a function* disables this check.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
function_state: Current function name and lines in body so far.
error: The function to call with any errors found.
"""
lines = clean_lines.lines
line = lines[linenum]
raw = clean_lines.raw_lines
raw_line = raw[linenum]
joined_line = ''
starting_func = False
regexp = r'(\w(\w|::|\*|\&|\s)*)\(' # decls * & space::name( ...
match_result = Match(regexp, line)
if match_result:
# If the name is all caps and underscores, figure it's a macro and
# ignore it, unless it's TEST or TEST_F.
function_name = match_result.group(1).split()[-1]
if function_name == 'TEST' or function_name == 'TEST_F' or (
not Match(r'[A-Z_]+$', function_name)):
starting_func = True
if starting_func:
body_found = False
for start_linenum in xrange(linenum, clean_lines.NumLines()):
start_line = lines[start_linenum]
joined_line += ' ' + start_line.lstrip()
if Search(r'(;|})', start_line): # Declarations and trivial functions
body_found = True
break # ... ignore
elif Search(r'{', start_line):
body_found = True
function = Search(r'((\w|:)*)\(', line).group(1)
if Match(r'TEST', function): # Handle TEST... macros
parameter_regexp = Search(r'(\(.*\))', joined_line)
if parameter_regexp: # Ignore bad syntax
function += parameter_regexp.group(1)
else:
function += '()'
function_state.Begin(function)
break
if not body_found:
# No body for the function (or evidence of a non-function) was found.
error(filename, linenum, 'readability/fn_size', 5,
'Lint failed to find start of function body.')
elif Match(r'^\}\s*$', line): # function end
function_state.Check(error, filename, linenum)
function_state.End()
elif not Match(r'^\s*$', line):
function_state.Count() # Count non-blank/non-comment lines.
_RE_PATTERN_TODO = re.compile(r'^//(\s*)TODO(\(.+?\))?:?(\s|$)?')
def CheckComment(comment, filename, linenum, error):
"""Checks for common mistakes in TODO comments.
Args:
comment: The text of the comment from the line in question.
filename: The name of the current file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
match = _RE_PATTERN_TODO.match(comment)
if match:
# One whitespace is correct; zero whitespace is handled elsewhere.
leading_whitespace = match.group(1)
if len(leading_whitespace) > 1:
error(filename, linenum, 'whitespace/todo', 2,
'Too many spaces before TODO')
username = match.group(2)
if not username:
error(filename, linenum, 'readability/todo', 2,
'Missing username in TODO; it should look like '
'"// TODO(my_username): Stuff."')
middle_whitespace = match.group(3)
# Comparisons made explicit for correctness -- pylint: disable=g-explicit-bool-comparison
if middle_whitespace != ' ' and middle_whitespace != '':
error(filename, linenum, 'whitespace/todo', 2,
'TODO(my_username) should be followed by a space')
def CheckAccess(filename, clean_lines, linenum, nesting_state, error):
"""Checks for improper use of DISALLOW* macros.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum] # get rid of comments and strings
matched = Match((r'\s*(DISALLOW_COPY_AND_ASSIGN|'
r'DISALLOW_EVIL_CONSTRUCTORS|'
r'DISALLOW_IMPLICIT_CONSTRUCTORS)'), line)
if not matched:
return
if nesting_state.stack and isinstance(nesting_state.stack[-1], _ClassInfo):
if nesting_state.stack[-1].access != 'private':
error(filename, linenum, 'readability/constructors', 3,
'%s must be in the private: section' % matched.group(1))
else:
# Found DISALLOW* macro outside a class declaration, or perhaps it
# was used inside a function when it should have been part of the
# class declaration. We could issue a warning here, but it
# probably resulted in a compiler error already.
pass
def FindNextMatchingAngleBracket(clean_lines, linenum, init_suffix):
"""Find the corresponding > to close a template.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Current line number.
init_suffix: Remainder of the current line after the initial <.
Returns:
True if a matching bracket exists.
"""
line = init_suffix
nesting_stack = ['<']
while True:
# Find the next operator that can tell us whether < is used as an
# opening bracket or as a less-than operator. We only want to
# warn on the latter case.
#
# We could also check all other operators and terminate the search
# early, e.g. if we got something like this "a<b+c", the "<" is
# most likely a less-than operator, but then we will get false
# positives for default arguments and other template expressions.
match = Search(r'^[^<>(),;\[\]]*([<>(),;\[\]])(.*)$', line)
if match:
# Found an operator, update nesting stack
operator = match.group(1)
line = match.group(2)
if nesting_stack[-1] == '<':
# Expecting closing angle bracket
if operator in ('<', '(', '['):
nesting_stack.append(operator)
elif operator == '>':
nesting_stack.pop()
if not nesting_stack:
# Found matching angle bracket
return True
elif operator == ',':
# Got a comma after a bracket, this is most likely a template
# argument. We have not seen a closing angle bracket yet, but
# it's probably a few lines later if we look for it, so just
# return early here.
return True
else:
# Got some other operator.
return False
else:
# Expecting closing parenthesis or closing bracket
if operator in ('<', '(', '['):
nesting_stack.append(operator)
elif operator in (')', ']'):
# We don't bother checking for matching () or []. If we got
# something like (] or [), it would have been a syntax error.
nesting_stack.pop()
else:
# Scan the next line
linenum += 1
if linenum >= len(clean_lines.elided):
break
line = clean_lines.elided[linenum]
# Exhausted all remaining lines and still no matching angle bracket.
# Most likely the input was incomplete, otherwise we should have
# seen a semicolon and returned early.
return True
def FindPreviousMatchingAngleBracket(clean_lines, linenum, init_prefix):
"""Find the corresponding < that started a template.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Current line number.
init_prefix: Part of the current line before the initial >.
Returns:
True if a matching bracket exists.
"""
line = init_prefix
nesting_stack = ['>']
while True:
# Find the previous operator
match = Search(r'^(.*)([<>(),;\[\]])[^<>(),;\[\]]*$', line)
if match:
# Found an operator, update nesting stack
operator = match.group(2)
line = match.group(1)
if nesting_stack[-1] == '>':
# Expecting opening angle bracket
if operator in ('>', ')', ']'):
nesting_stack.append(operator)
elif operator == '<':
nesting_stack.pop()
if not nesting_stack:
# Found matching angle bracket
return True
elif operator == ',':
# Got a comma before a bracket, this is most likely a
# template argument. The opening angle bracket is probably
# there if we look for it, so just return early here.
return True
else:
# Got some other operator.
return False
else:
# Expecting opening parenthesis or opening bracket
if operator in ('>', ')', ']'):
nesting_stack.append(operator)
elif operator in ('(', '['):
nesting_stack.pop()
else:
# Scan the previous line
linenum -= 1
if linenum < 0:
break
line = clean_lines.elided[linenum]
# Exhausted all earlier lines and still no matching angle bracket.
return False
def CheckSpacing(filename, clean_lines, linenum, nesting_state, error):
"""Checks for the correctness of various spacing issues in the code.
Things we check for: spaces around operators, spaces after
if/for/while/switch, no spaces around parens in function calls, two
spaces between code and comment, don't start a block with a blank
line, don't end a function with a blank line, don't add a blank line
after public/protected/private, don't have too many blank lines in a row.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Don't use "elided" lines here, otherwise we can't check commented lines.
# Don't want to use "raw" either, because we don't want to check inside C++11
# raw strings,
raw = clean_lines.lines_without_raw_strings
line = raw[linenum]
# Before nixing comments, check if the line is blank for no good
# reason. This includes the first line after a block is opened, and
# blank lines at the end of a function (ie, right before a line like '}'
#
# Skip all the blank line checks if we are immediately inside a
# namespace body. In other words, don't issue blank line warnings
# for this block:
# namespace {
#
# }
#
# A warning about missing end of namespace comments will be issued instead.
if IsBlankLine(line) and not nesting_state.InNamespaceBody():
elided = clean_lines.elided
prev_line = elided[linenum - 1]
prevbrace = prev_line.rfind('{')
# TODO(unknown): Don't complain if line before blank line, and line after,
# both start with alnums and are indented the same amount.
# This ignores whitespace at the start of a namespace block
# because those are not usually indented.
if prevbrace != -1 and prev_line[prevbrace:].find('}') == -1:
# OK, we have a blank line at the start of a code block. Before we
# complain, we check if it is an exception to the rule: The previous
# non-empty line has the parameters of a function header that are indented
# 4 spaces (because they did not fit in a 80 column line when placed on
# the same line as the function name). We also check for the case where
# the previous line is indented 6 spaces, which may happen when the
# initializers of a constructor do not fit into a 80 column line.
exception = False
if Match(r' {6}\w', prev_line): # Initializer list?
# We are looking for the opening column of initializer list, which
# should be indented 4 spaces to cause 6 space indentation afterwards.
search_position = linenum-2
while (search_position >= 0
and Match(r' {6}\w', elided[search_position])):
search_position -= 1
exception = (search_position >= 0
and elided[search_position][:5] == ' :')
else:
# Search for the function arguments or an initializer list. We use a
# simple heuristic here: If the line is indented 4 spaces; and we have a
# closing paren, without the opening paren, followed by an opening brace
# or colon (for initializer lists) we assume that it is the last line of
# a function header. If we have a colon indented 4 spaces, it is an
# initializer list.
exception = (Match(r' {4}\w[^\(]*\)\s*(const\s*)?(\{\s*$|:)',
prev_line)
or Match(r' {4}:', prev_line))
if not exception:
error(filename, linenum, 'whitespace/blank_line', 2,
'Redundant blank line at the start of a code block '
'should be deleted.')
# Ignore blank lines at the end of a block in a long if-else
# chain, like this:
# if (condition1) {
# // Something followed by a blank line
#
# } else if (condition2) {
# // Something else
# }
if linenum + 1 < clean_lines.NumLines():
next_line = raw[linenum + 1]
if (next_line
and Match(r'\s*}', next_line)
and next_line.find('} else ') == -1):
error(filename, linenum, 'whitespace/blank_line', 3,
'Redundant blank line at the end of a code block '
'should be deleted.')
matched = Match(r'\s*(public|protected|private):', prev_line)
if matched:
error(filename, linenum, 'whitespace/blank_line', 3,
'Do not leave a blank line after "%s:"' % matched.group(1))
# Next, we complain if there's a comment too near the text
commentpos = line.find('//')
if commentpos != -1:
# Check if the // may be in quotes. If so, ignore it
# Comparisons made explicit for clarity -- pylint: disable=g-explicit-bool-comparison
if (line.count('"', 0, commentpos) -
line.count('\\"', 0, commentpos)) % 2 == 0: # not in quotes
# Allow one space for new scopes, two spaces otherwise:
if (not Match(r'^\s*{ //', line) and
((commentpos >= 1 and
line[commentpos-1] not in string.whitespace) or
(commentpos >= 2 and
line[commentpos-2] not in string.whitespace))):
error(filename, linenum, 'whitespace/comments', 2,
'At least two spaces is best between code and comments')
# There should always be a space between the // and the comment
commentend = commentpos + 2
if commentend < len(line) and not line[commentend] == ' ':
# but some lines are exceptions -- e.g. if they're big
# comment delimiters like:
# //----------------------------------------------------------
# or are an empty C++ style Doxygen comment, like:
# ///
# or C++ style Doxygen comments placed after the variable:
# ///< Header comment
# //!< Header comment
# or they begin with multiple slashes followed by a space:
# //////// Header comment
match = (Search(r'[=/-]{4,}\s*$', line[commentend:]) or
Search(r'^/$', line[commentend:]) or
Search(r'^!< ', line[commentend:]) or
Search(r'^/< ', line[commentend:]) or
Search(r'^/+ ', line[commentend:]))
if not match:
error(filename, linenum, 'whitespace/comments', 4,
'Should have a space between // and comment')
CheckComment(line[commentpos:], filename, linenum, error)
line = clean_lines.elided[linenum] # get rid of comments and strings
# Don't try to do spacing checks for operator methods
line = re.sub(r'operator(==|!=|<|<<|<=|>=|>>|>)\(', 'operator\(', line)
# We allow no-spaces around = within an if: "if ( (a=Foo()) == 0 )".
# Otherwise not. Note we only check for non-spaces on *both* sides;
# sometimes people put non-spaces on one side when aligning ='s among
# many lines (not that this is behavior that I approve of...)
if Search(r'[\w.]=[\w.]', line) and not Search(r'\b(if|while) ', line):
error(filename, linenum, 'whitespace/operators', 4,
'Missing spaces around =')
# It's ok not to have spaces around binary operators like + - * /, but if
# there's too little whitespace, we get concerned. It's hard to tell,
# though, so we punt on this one for now. TODO.
# You should always have whitespace around binary operators.
#
# Check <= and >= first to avoid false positives with < and >, then
# check non-include lines for spacing around < and >.
match = Search(r'[^<>=!\s](==|!=|<=|>=)[^<>=!\s]', line)
if match:
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around %s' % match.group(1))
# We allow no-spaces around << when used like this: 10<<20, but
# not otherwise (particularly, not when used as streams)
# Also ignore using ns::operator<<;
match = Search(r'(operator|\S)(?:L|UL|ULL|l|ul|ull)?<<(\S)', line)
if (match and
not (match.group(1).isdigit() and match.group(2).isdigit()) and
not (match.group(1) == 'operator' and match.group(2) == ';')):
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around <<')
elif not Match(r'#.*include', line):
# Avoid false positives on ->
reduced_line = line.replace('->', '')
# Look for < that is not surrounded by spaces. This is only
# triggered if both sides are missing spaces, even though
# technically should should flag if at least one side is missing a
# space. This is done to avoid some false positives with shifts.
match = Search(r'[^\s<]<([^\s=<].*)', reduced_line)
if (match and
not FindNextMatchingAngleBracket(clean_lines, linenum, match.group(1))):
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around <')
# Look for > that is not surrounded by spaces. Similar to the
# above, we only trigger if both sides are missing spaces to avoid
# false positives with shifts.
match = Search(r'^(.*[^\s>])>[^\s=>]', reduced_line)
if (match and
not FindPreviousMatchingAngleBracket(clean_lines, linenum,
match.group(1))):
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around >')
# We allow no-spaces around >> for almost anything. This is because
# C++11 allows ">>" to close nested templates, which accounts for
# most cases when ">>" is not followed by a space.
#
# We still warn on ">>" followed by alpha character, because that is
# likely due to ">>" being used for right shifts, e.g.:
# value >> alpha
#
# When ">>" is used to close templates, the alphanumeric letter that
# follows would be part of an identifier, and there should still be
# a space separating the template type and the identifier.
# type<type<type>> alpha
match = Search(r'>>[a-zA-Z_]', line)
if match:
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around >>')
# There shouldn't be space around unary operators
match = Search(r'(!\s|~\s|[\s]--[\s;]|[\s]\+\+[\s;])', line)
if match:
error(filename, linenum, 'whitespace/operators', 4,
'Extra space for operator %s' % match.group(1))
# A pet peeve of mine: no spaces after an if, while, switch, or for
match = Search(r' (if\(|for\(|while\(|switch\()', line)
if match:
error(filename, linenum, 'whitespace/parens', 5,
'Missing space before ( in %s' % match.group(1))
# For if/for/while/switch, the left and right parens should be
# consistent about how many spaces are inside the parens, and
# there should either be zero or one spaces inside the parens.
# We don't want: "if ( foo)" or "if ( foo )".
# Exception: "for ( ; foo; bar)" and "for (foo; bar; )" are allowed.
match = Search(r'\b(if|for|while|switch)\s*'
r'\(([ ]*)(.).*[^ ]+([ ]*)\)\s*{\s*$',
line)
if match:
if len(match.group(2)) != len(match.group(4)):
if not (match.group(3) == ';' and
len(match.group(2)) == 1 + len(match.group(4)) or
not match.group(2) and Search(r'\bfor\s*\(.*; \)', line)):
error(filename, linenum, 'whitespace/parens', 5,
'Mismatching spaces inside () in %s' % match.group(1))
if len(match.group(2)) not in [0, 1]:
error(filename, linenum, 'whitespace/parens', 5,
'Should have zero or one spaces inside ( and ) in %s' %
match.group(1))
# You should always have a space after a comma (either as fn arg or operator)
#
# This does not apply when the non-space character following the
# comma is another comma, since the only time when that happens is
# for empty macro arguments.
#
# We run this check in two passes: first pass on elided lines to
# verify that lines contain missing whitespaces, second pass on raw
# lines to confirm that those missing whitespaces are not due to
# elided comments.
if Search(r',[^,\s]', line) and Search(r',[^,\s]', raw[linenum]):
error(filename, linenum, 'whitespace/comma', 3,
'Missing space after ,')
# You should always have a space after a semicolon
# except for few corner cases
# TODO(unknown): clarify if 'if (1) { return 1;}' is requires one more
# space after ;
if Search(r';[^\s};\\)/]', line):
error(filename, linenum, 'whitespace/semicolon', 3,
'Missing space after ;')
# Next we will look for issues with function calls.
CheckSpacingForFunctionCall(filename, line, linenum, error)
# Except after an opening paren, or after another opening brace (in case of
# an initializer list, for instance), you should have spaces before your
# braces. And since you should never have braces at the beginning of a line,
# this is an easy test.
match = Match(r'^(.*[^ ({]){', line)
if match:
# Try a bit harder to check for brace initialization. This
# happens in one of the following forms:
# Constructor() : initializer_list_{} { ... }
# Constructor{}.MemberFunction()
# Type variable{};
# FunctionCall(type{}, ...);
# LastArgument(..., type{});
# LOG(INFO) << type{} << " ...";
# map_of_type[{...}] = ...;
#
# We check for the character following the closing brace, and
# silence the warning if it's one of those listed above, i.e.
# "{.;,)<]".
#
# To account for nested initializer list, we allow any number of
# closing braces up to "{;,)<". We can't simply silence the
# warning on first sight of closing brace, because that would
# cause false negatives for things that are not initializer lists.
# Silence this: But not this:
# Outer{ if (...) {
# Inner{...} if (...){ // Missing space before {
# }; }
#
# There is a false negative with this approach if people inserted
# spurious semicolons, e.g. "if (cond){};", but we will catch the
# spurious semicolon with a separate check.
(endline, endlinenum, endpos) = CloseExpression(
clean_lines, linenum, len(match.group(1)))
trailing_text = ''
if endpos > -1:
trailing_text = endline[endpos:]
for offset in xrange(endlinenum + 1,
min(endlinenum + 3, clean_lines.NumLines() - 1)):
trailing_text += clean_lines.elided[offset]
if not Match(r'^[\s}]*[{.;,)<\]]', trailing_text):
error(filename, linenum, 'whitespace/braces', 5,
'Missing space before {')
# Make sure '} else {' has spaces.
if Search(r'}else', line):
error(filename, linenum, 'whitespace/braces', 5,
'Missing space before else')
# You shouldn't have spaces before your brackets, except maybe after
# 'delete []' or 'new char * []'.
if Search(r'\w\s+\[', line) and not Search(r'delete\s+\[', line):
error(filename, linenum, 'whitespace/braces', 5,
'Extra space before [')
# You shouldn't have a space before a semicolon at the end of the line.
# There's a special case for "for" since the style guide allows space before
# the semicolon there.
if Search(r':\s*;\s*$', line):
error(filename, linenum, 'whitespace/semicolon', 5,
'Semicolon defining empty statement. Use {} instead.')
elif Search(r'^\s*;\s*$', line):
error(filename, linenum, 'whitespace/semicolon', 5,
'Line contains only semicolon. If this should be an empty statement, '
'use {} instead.')
elif (Search(r'\s+;\s*$', line) and
not Search(r'\bfor\b', line)):
error(filename, linenum, 'whitespace/semicolon', 5,
'Extra space before last semicolon. If this should be an empty '
'statement, use {} instead.')
# In range-based for, we wanted spaces before and after the colon, but
# not around "::" tokens that might appear.
if (Search('for *\(.*[^:]:[^: ]', line) or
Search('for *\(.*[^: ]:[^:]', line)):
error(filename, linenum, 'whitespace/forcolon', 2,
'Missing space around colon in range-based for loop')
def CheckSectionSpacing(filename, clean_lines, class_info, linenum, error):
"""Checks for additional blank line issues related to sections.
Currently the only thing checked here is blank line before protected/private.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
class_info: A _ClassInfo objects.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Skip checks if the class is small, where small means 25 lines or less.
# 25 lines seems like a good cutoff since that's the usual height of
# terminals, and any class that can't fit in one screen can't really
# be considered "small".
#
# Also skip checks if we are on the first line. This accounts for
# classes that look like
# class Foo { public: ... };
#
# If we didn't find the end of the class, last_line would be zero,
# and the check will be skipped by the first condition.
if (class_info.last_line - class_info.starting_linenum <= 24 or
linenum <= class_info.starting_linenum):
return
matched = Match(r'\s*(public|protected|private):', clean_lines.lines[linenum])
if matched:
# Issue warning if the line before public/protected/private was
# not a blank line, but don't do this if the previous line contains
# "class" or "struct". This can happen two ways:
# - We are at the beginning of the class.
# - We are forward-declaring an inner class that is semantically
# private, but needed to be public for implementation reasons.
# Also ignores cases where the previous line ends with a backslash as can be
# common when defining classes in C macros.
prev_line = clean_lines.lines[linenum - 1]
if (not IsBlankLine(prev_line) and
not Search(r'\b(class|struct)\b', prev_line) and
not Search(r'\\$', prev_line)):
# Try a bit harder to find the beginning of the class. This is to
# account for multi-line base-specifier lists, e.g.:
# class Derived
# : public Base {
end_class_head = class_info.starting_linenum
for i in range(class_info.starting_linenum, linenum):
if Search(r'\{\s*$', clean_lines.lines[i]):
end_class_head = i
break
if end_class_head < linenum - 1:
error(filename, linenum, 'whitespace/blank_line', 3,
'"%s:" should be preceded by a blank line' % matched.group(1))
def GetPreviousNonBlankLine(clean_lines, linenum):
"""Return the most recent non-blank line and its line number.
Args:
clean_lines: A CleansedLines instance containing the file contents.
linenum: The number of the line to check.
Returns:
A tuple with two elements. The first element is the contents of the last
non-blank line before the current line, or the empty string if this is the
first non-blank line. The second is the line number of that line, or -1
if this is the first non-blank line.
"""
prevlinenum = linenum - 1
while prevlinenum >= 0:
prevline = clean_lines.elided[prevlinenum]
if not IsBlankLine(prevline): # if not a blank line...
return (prevline, prevlinenum)
prevlinenum -= 1
return ('', -1)
def CheckBraces(filename, clean_lines, linenum, error):
"""Looks for misplaced braces (e.g. at the end of line).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum] # get rid of comments and strings
if Match(r'\s*{\s*$', line):
# We allow an open brace to start a line in the case where someone is using
# braces in a block to explicitly create a new scope, which is commonly used
# to control the lifetime of stack-allocated variables. Braces are also
# used for brace initializers inside function calls. We don't detect this
# perfectly: we just don't complain if the last non-whitespace character on
# the previous non-blank line is ',', ';', ':', '(', '{', or '}', or if the
# previous line starts a preprocessor block.
prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
if (not Search(r'[,;:}{(]\s*$', prevline) and
not Match(r'\s*#', prevline)):
error(filename, linenum, 'whitespace/braces', 4,
'{ should almost always be at the end of the previous line')
# An else clause should be on the same line as the preceding closing brace.
if Match(r'\s*else\s*', line):
prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
if Match(r'\s*}\s*$', prevline):
error(filename, linenum, 'whitespace/newline', 4,
'An else should appear on the same line as the preceding }')
# If braces come on one side of an else, they should be on both.
# However, we have to worry about "else if" that spans multiple lines!
if Search(r'}\s*else[^{]*$', line) or Match(r'[^}]*else\s*{', line):
if Search(r'}\s*else if([^{]*)$', line): # could be multi-line if
# find the ( after the if
pos = line.find('else if')
pos = line.find('(', pos)
if pos > 0:
(endline, _, endpos) = CloseExpression(clean_lines, linenum, pos)
if endline[endpos:].find('{') == -1: # must be brace after if
error(filename, linenum, 'readability/braces', 5,
'If an else has a brace on one side, it should have it on both')
else: # common case: else not followed by a multi-line if
error(filename, linenum, 'readability/braces', 5,
'If an else has a brace on one side, it should have it on both')
# Likewise, an else should never have the else clause on the same line
if Search(r'\belse [^\s{]', line) and not Search(r'\belse if\b', line):
error(filename, linenum, 'whitespace/newline', 4,
'Else clause should never be on same line as else (use 2 lines)')
# In the same way, a do/while should never be on one line
if Match(r'\s*do [^\s{]', line):
error(filename, linenum, 'whitespace/newline', 4,
'do/while clauses should not be on a single line')
# Block bodies should not be followed by a semicolon. Due to C++11
# brace initialization, there are more places where semicolons are
# required than not, so we use a whitelist approach to check these
# rather than a blacklist. These are the places where "};" should
# be replaced by just "}":
# 1. Some flavor of block following closing parenthesis:
# for (;;) {};
# while (...) {};
# switch (...) {};
# Function(...) {};
# if (...) {};
# if (...) else if (...) {};
#
# 2. else block:
# if (...) else {};
#
# 3. const member function:
# Function(...) const {};
#
# 4. Block following some statement:
# x = 42;
# {};
#
# 5. Block at the beginning of a function:
# Function(...) {
# {};
# }
#
# Note that naively checking for the preceding "{" will also match
# braces inside multi-dimensional arrays, but this is fine since
# that expression will not contain semicolons.
#
# 6. Block following another block:
# while (true) {}
# {};
#
# 7. End of namespaces:
# namespace {};
#
# These semicolons seems far more common than other kinds of
# redundant semicolons, possibly due to people converting classes
# to namespaces. For now we do not warn for this case.
#
# Try matching case 1 first.
match = Match(r'^(.*\)\s*)\{', line)
if match:
# Matched closing parenthesis (case 1). Check the token before the
# matching opening parenthesis, and don't warn if it looks like a
# macro. This avoids these false positives:
# - macro that defines a base class
# - multi-line macro that defines a base class
# - macro that defines the whole class-head
#
# But we still issue warnings for macros that we know are safe to
# warn, specifically:
# - TEST, TEST_F, TEST_P, MATCHER, MATCHER_P
# - TYPED_TEST
# - INTERFACE_DEF
# - EXCLUSIVE_LOCKS_REQUIRED, SHARED_LOCKS_REQUIRED, LOCKS_EXCLUDED:
#
# We implement a whitelist of safe macros instead of a blacklist of
# unsafe macros, even though the latter appears less frequently in
# google code and would have been easier to implement. This is because
# the downside for getting the whitelist wrong means some extra
# semicolons, while the downside for getting the blacklist wrong
# would result in compile errors.
#
# In addition to macros, we also don't want to warn on compound
# literals.
closing_brace_pos = match.group(1).rfind(')')
opening_parenthesis = ReverseCloseExpression(
clean_lines, linenum, closing_brace_pos)
if opening_parenthesis[2] > -1:
line_prefix = opening_parenthesis[0][0:opening_parenthesis[2]]
macro = Search(r'\b([A-Z_]+)\s*$', line_prefix)
if ((macro and
macro.group(1) not in (
'TEST', 'TEST_F', 'MATCHER', 'MATCHER_P', 'TYPED_TEST',
'EXCLUSIVE_LOCKS_REQUIRED', 'SHARED_LOCKS_REQUIRED',
'LOCKS_EXCLUDED', 'INTERFACE_DEF')) or
Search(r'\s+=\s*$', line_prefix)):
match = None
else:
# Try matching cases 2-3.
match = Match(r'^(.*(?:else|\)\s*const)\s*)\{', line)
if not match:
# Try matching cases 4-6. These are always matched on separate lines.
#
# Note that we can't simply concatenate the previous line to the
# current line and do a single match, otherwise we may output
# duplicate warnings for the blank line case:
# if (cond) {
# // blank line
# }
prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
if prevline and Search(r'[;{}]\s*$', prevline):
match = Match(r'^(\s*)\{', line)
# Check matching closing brace
if match:
(endline, endlinenum, endpos) = CloseExpression(
clean_lines, linenum, len(match.group(1)))
if endpos > -1 and Match(r'^\s*;', endline[endpos:]):
# Current {} pair is eligible for semicolon check, and we have found
# the redundant semicolon, output warning here.
#
# Note: because we are scanning forward for opening braces, and
# outputting warnings for the matching closing brace, if there are
# nested blocks with trailing semicolons, we will get the error
# messages in reversed order.
error(filename, endlinenum, 'readability/braces', 4,
"You don't need a ; after a }")
def CheckEmptyBlockBody(filename, clean_lines, linenum, error):
"""Look for empty loop/conditional body with only a single semicolon.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Search for loop keywords at the beginning of the line. Because only
# whitespaces are allowed before the keywords, this will also ignore most
# do-while-loops, since those lines should start with closing brace.
#
# We also check "if" blocks here, since an empty conditional block
# is likely an error.
line = clean_lines.elided[linenum]
matched = Match(r'\s*(for|while|if)\s*\(', line)
if matched:
# Find the end of the conditional expression
(end_line, end_linenum, end_pos) = CloseExpression(
clean_lines, linenum, line.find('('))
# Output warning if what follows the condition expression is a semicolon.
# No warning for all other cases, including whitespace or newline, since we
# have a separate check for semicolons preceded by whitespace.
if end_pos >= 0 and Match(r';', end_line[end_pos:]):
if matched.group(1) == 'if':
error(filename, end_linenum, 'whitespace/empty_conditional_body', 5,
'Empty conditional bodies should use {}')
else:
error(filename, end_linenum, 'whitespace/empty_loop_body', 5,
'Empty loop bodies should use {} or continue')
def CheckCheck(filename, clean_lines, linenum, error):
"""Checks the use of CHECK and EXPECT macros.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Decide the set of replacement macros that should be suggested
lines = clean_lines.elided
check_macro = None
start_pos = -1
for macro in _CHECK_MACROS:
i = lines[linenum].find(macro)
if i >= 0:
check_macro = macro
# Find opening parenthesis. Do a regular expression match here
# to make sure that we are matching the expected CHECK macro, as
# opposed to some other macro that happens to contain the CHECK
# substring.
matched = Match(r'^(.*\b' + check_macro + r'\s*)\(', lines[linenum])
if not matched:
continue
start_pos = len(matched.group(1))
break
if not check_macro or start_pos < 0:
# Don't waste time here if line doesn't contain 'CHECK' or 'EXPECT'
return
# Find end of the boolean expression by matching parentheses
(last_line, end_line, end_pos) = CloseExpression(
clean_lines, linenum, start_pos)
if end_pos < 0:
return
if linenum == end_line:
expression = lines[linenum][start_pos + 1:end_pos - 1]
else:
expression = lines[linenum][start_pos + 1:]
for i in xrange(linenum + 1, end_line):
expression += lines[i]
expression += last_line[0:end_pos - 1]
# Parse expression so that we can take parentheses into account.
# This avoids false positives for inputs like "CHECK((a < 4) == b)",
# which is not replaceable by CHECK_LE.
lhs = ''
rhs = ''
operator = None
while expression:
matched = Match(r'^\s*(<<|<<=|>>|>>=|->\*|->|&&|\|\||'
r'==|!=|>=|>|<=|<|\()(.*)$', expression)
if matched:
token = matched.group(1)
if token == '(':
# Parenthesized operand
expression = matched.group(2)
(end, _) = FindEndOfExpressionInLine(expression, 0, 1, '(', ')')
if end < 0:
return # Unmatched parenthesis
lhs += '(' + expression[0:end]
expression = expression[end:]
elif token in ('&&', '||'):
# Logical and/or operators. This means the expression
# contains more than one term, for example:
# CHECK(42 < a && a < b);
#
# These are not replaceable with CHECK_LE, so bail out early.
return
elif token in ('<<', '<<=', '>>', '>>=', '->*', '->'):
# Non-relational operator
lhs += token
expression = matched.group(2)
else:
# Relational operator
operator = token
rhs = matched.group(2)
break
else:
# Unparenthesized operand. Instead of appending to lhs one character
# at a time, we do another regular expression match to consume several
# characters at once if possible. Trivial benchmark shows that this
# is more efficient when the operands are longer than a single
# character, which is generally the case.
matched = Match(r'^([^-=!<>()&|]+)(.*)$', expression)
if not matched:
matched = Match(r'^(\s*\S)(.*)$', expression)
if not matched:
break
lhs += matched.group(1)
expression = matched.group(2)
# Only apply checks if we got all parts of the boolean expression
if not (lhs and operator and rhs):
return
# Check that rhs do not contain logical operators. We already know
# that lhs is fine since the loop above parses out && and ||.
if rhs.find('&&') > -1 or rhs.find('||') > -1:
return
# At least one of the operands must be a constant literal. This is
# to avoid suggesting replacements for unprintable things like
# CHECK(variable != iterator)
#
# The following pattern matches decimal, hex integers, strings, and
# characters (in that order).
lhs = lhs.strip()
rhs = rhs.strip()
match_constant = r'^([-+]?(\d+|0[xX][0-9a-fA-F]+)[lLuU]{0,3}|".*"|\'.*\')$'
if Match(match_constant, lhs) or Match(match_constant, rhs):
# Note: since we know both lhs and rhs, we can provide a more
# descriptive error message like:
# Consider using CHECK_EQ(x, 42) instead of CHECK(x == 42)
# Instead of:
# Consider using CHECK_EQ instead of CHECK(a == b)
#
# We are still keeping the less descriptive message because if lhs
# or rhs gets long, the error message might become unreadable.
error(filename, linenum, 'readability/check', 2,
'Consider using %s instead of %s(a %s b)' % (
_CHECK_REPLACEMENT[check_macro][operator],
check_macro, operator))
def CheckAltTokens(filename, clean_lines, linenum, error):
"""Check alternative keywords being used in boolean expressions.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Avoid preprocessor lines
if Match(r'^\s*#', line):
return
# Last ditch effort to avoid multi-line comments. This will not help
# if the comment started before the current line or ended after the
# current line, but it catches most of the false positives. At least,
# it provides a way to workaround this warning for people who use
# multi-line comments in preprocessor macros.
#
# TODO(unknown): remove this once cpplint has better support for
# multi-line comments.
if line.find('/*') >= 0 or line.find('*/') >= 0:
return
for match in _ALT_TOKEN_REPLACEMENT_PATTERN.finditer(line):
error(filename, linenum, 'readability/alt_tokens', 2,
'Use operator %s instead of %s' % (
_ALT_TOKEN_REPLACEMENT[match.group(1)], match.group(1)))
def GetLineWidth(line):
"""Determines the width of the line in column positions.
Args:
line: A string, which may be a Unicode string.
Returns:
The width of the line in column positions, accounting for Unicode
combining characters and wide characters.
"""
if isinstance(line, unicode):
width = 0
for uc in unicodedata.normalize('NFC', line):
if unicodedata.east_asian_width(uc) in ('W', 'F'):
width += 2
elif not unicodedata.combining(uc):
width += 1
return width
else:
return len(line)
def CheckStyle(filename, clean_lines, linenum, file_extension, nesting_state,
error):
"""Checks rules from the 'C++ style rules' section of cppguide.html.
Most of these rules are hard to test (naming, comment style), but we
do what we can. In particular we check for 2-space indents, line lengths,
tab usage, spaces inside code, etc.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
file_extension: The extension (without the dot) of the filename.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Don't use "elided" lines here, otherwise we can't check commented lines.
# Don't want to use "raw" either, because we don't want to check inside C++11
# raw strings,
raw_lines = clean_lines.lines_without_raw_strings
line = raw_lines[linenum]
if line.find('\t') != -1:
error(filename, linenum, 'whitespace/tab', 1,
'Tab found; better to use spaces')
# One or three blank spaces at the beginning of the line is weird; it's
# hard to reconcile that with 2-space indents.
# NOTE: here are the conditions rob pike used for his tests. Mine aren't
# as sophisticated, but it may be worth becoming so: RLENGTH==initial_spaces
# if(RLENGTH > 20) complain = 0;
# if(match($0, " +(error|private|public|protected):")) complain = 0;
# if(match(prev, "&& *$")) complain = 0;
# if(match(prev, "\\|\\| *$")) complain = 0;
# if(match(prev, "[\",=><] *$")) complain = 0;
# if(match($0, " <<")) complain = 0;
# if(match(prev, " +for \\(")) complain = 0;
# if(prevodd && match(prevprev, " +for \\(")) complain = 0;
initial_spaces = 0
cleansed_line = clean_lines.elided[linenum]
while initial_spaces < len(line) and line[initial_spaces] == ' ':
initial_spaces += 1
if line and line[-1].isspace():
error(filename, linenum, 'whitespace/end_of_line', 4,
'Line ends in whitespace. Consider deleting these extra spaces.')
# There are certain situations we allow one space, notably for section labels
elif ((initial_spaces == 1 or initial_spaces == 3) and
not Match(r'\s*\w+\s*:\s*$', cleansed_line)):
error(filename, linenum, 'whitespace/indent', 3,
'Weird number of spaces at line-start. '
'Are you using a 2-space indent?')
# Check if the line is a header guard.
is_header_guard = False
if file_extension == 'h':
cppvar = GetHeaderGuardCPPVariable(filename)
if (line.startswith('#ifndef %s' % cppvar) or
line.startswith('#define %s' % cppvar) or
line.startswith('#endif // %s' % cppvar)):
is_header_guard = True
# #include lines and header guards can be long, since there's no clean way to
# split them.
#
# URLs can be long too. It's possible to split these, but it makes them
# harder to cut&paste.
#
# The "$Id:...$" comment may also get very long without it being the
# developers fault.
if (not line.startswith('#include') and not is_header_guard and
not Match(r'^\s*//.*http(s?)://\S*$', line) and
not Match(r'^// \$Id:.*#[0-9]+ \$$', line)):
line_width = GetLineWidth(line)
extended_length = int((_line_length * 1.25))
if line_width > extended_length:
error(filename, linenum, 'whitespace/line_length', 4,
'Lines should very rarely be longer than %i characters' %
extended_length)
elif line_width > _line_length:
error(filename, linenum, 'whitespace/line_length', 2,
'Lines should be <= %i characters long' % _line_length)
if (cleansed_line.count(';') > 1 and
# for loops are allowed two ;'s (and may run over two lines).
cleansed_line.find('for') == -1 and
(GetPreviousNonBlankLine(clean_lines, linenum)[0].find('for') == -1 or
GetPreviousNonBlankLine(clean_lines, linenum)[0].find(';') != -1) and
# It's ok to have many commands in a switch case that fits in 1 line
not ((cleansed_line.find('case ') != -1 or
cleansed_line.find('default:') != -1) and
cleansed_line.find('break;') != -1)):
error(filename, linenum, 'whitespace/newline', 0,
'More than one command on the same line')
# Some more style checks
CheckBraces(filename, clean_lines, linenum, error)
CheckEmptyBlockBody(filename, clean_lines, linenum, error)
CheckAccess(filename, clean_lines, linenum, nesting_state, error)
CheckSpacing(filename, clean_lines, linenum, nesting_state, error)
CheckCheck(filename, clean_lines, linenum, error)
CheckAltTokens(filename, clean_lines, linenum, error)
classinfo = nesting_state.InnermostClass()
if classinfo:
CheckSectionSpacing(filename, clean_lines, classinfo, linenum, error)
_RE_PATTERN_INCLUDE_NEW_STYLE = re.compile(r'#include +"[^/]+\.h"')
_RE_PATTERN_INCLUDE = re.compile(r'^\s*#\s*include\s*([<"])([^>"]*)[>"].*$')
# Matches the first component of a filename delimited by -s and _s. That is:
# _RE_FIRST_COMPONENT.match('foo').group(0) == 'foo'
# _RE_FIRST_COMPONENT.match('foo.cc').group(0) == 'foo'
# _RE_FIRST_COMPONENT.match('foo-bar_baz.cc').group(0) == 'foo'
# _RE_FIRST_COMPONENT.match('foo_bar-baz.cc').group(0) == 'foo'
_RE_FIRST_COMPONENT = re.compile(r'^[^-_.]+')
def _DropCommonSuffixes(filename):
"""Drops common suffixes like _test.cc or -inl.h from filename.
For example:
>>> _DropCommonSuffixes('foo/foo-inl.h')
'foo/foo'
>>> _DropCommonSuffixes('foo/bar/foo.cc')
'foo/bar/foo'
>>> _DropCommonSuffixes('foo/foo_internal.h')
'foo/foo'
>>> _DropCommonSuffixes('foo/foo_unusualinternal.h')
'foo/foo_unusualinternal'
Args:
filename: The input filename.
Returns:
The filename with the common suffix removed.
"""
for suffix in ('test.cc', 'regtest.cc', 'unittest.cc',
'inl.h', 'impl.h', 'internal.h'):
if (filename.endswith(suffix) and len(filename) > len(suffix) and
filename[-len(suffix) - 1] in ('-', '_')):
return filename[:-len(suffix) - 1]
return os.path.splitext(filename)[0]
def _IsTestFilename(filename):
"""Determines if the given filename has a suffix that identifies it as a test.
Args:
filename: The input filename.
Returns:
True if 'filename' looks like a test, False otherwise.
"""
if (filename.endswith('_test.cc') or
filename.endswith('_unittest.cc') or
filename.endswith('_regtest.cc')):
return True
else:
return False
def _ClassifyInclude(fileinfo, include, is_system):
"""Figures out what kind of header 'include' is.
Args:
fileinfo: The current file cpplint is running over. A FileInfo instance.
include: The path to a #included file.
is_system: True if the #include used <> rather than "".
Returns:
One of the _XXX_HEADER constants.
For example:
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True)
_C_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True)
_CPP_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
_LIKELY_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'),
... 'bar/foo_other_ext.h', False)
_POSSIBLE_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
_OTHER_HEADER
"""
# This is a list of all standard c++ header files, except
# those already checked for above.
is_cpp_h = include in _CPP_HEADERS
if is_system:
if is_cpp_h:
return _CPP_SYS_HEADER
else:
return _C_SYS_HEADER
# If the target file and the include we're checking share a
# basename when we drop common extensions, and the include
# lives in . , then it's likely to be owned by the target file.
target_dir, target_base = (
os.path.split(_DropCommonSuffixes(fileinfo.RepositoryName())))
include_dir, include_base = os.path.split(_DropCommonSuffixes(include))
if target_base == include_base and (
include_dir == target_dir or
include_dir == os.path.normpath(target_dir + '/../public')):
return _LIKELY_MY_HEADER
# If the target and include share some initial basename
# component, it's possible the target is implementing the
# include, so it's allowed to be first, but we'll never
# complain if it's not there.
target_first_component = _RE_FIRST_COMPONENT.match(target_base)
include_first_component = _RE_FIRST_COMPONENT.match(include_base)
if (target_first_component and include_first_component and
target_first_component.group(0) ==
include_first_component.group(0)):
return _POSSIBLE_MY_HEADER
return _OTHER_HEADER
def CheckIncludeLine(filename, clean_lines, linenum, include_state, error):
"""Check rules that are applicable to #include lines.
Strings on #include lines are NOT removed from elided line, to make
certain tasks easier. However, to prevent false positives, checks
applicable to #include lines in CheckLanguage must be put here.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
include_state: An _IncludeState instance in which the headers are inserted.
error: The function to call with any errors found.
"""
fileinfo = FileInfo(filename)
line = clean_lines.lines[linenum]
# "include" should use the new style "foo/bar.h" instead of just "bar.h"
if _RE_PATTERN_INCLUDE_NEW_STYLE.search(line):
error(filename, linenum, 'build/include_dir', 4,
'Include the directory when naming .h files')
# we shouldn't include a file more than once. actually, there are a
# handful of instances where doing so is okay, but in general it's
# not.
match = _RE_PATTERN_INCLUDE.search(line)
if match:
include = match.group(2)
is_system = (match.group(1) == '<')
if include in include_state:
error(filename, linenum, 'build/include', 4,
'"%s" already included at %s:%s' %
(include, filename, include_state[include]))
else:
include_state[include] = linenum
# We want to ensure that headers appear in the right order:
# 1) for foo.cc, foo.h (preferred location)
# 2) c system files
# 3) cpp system files
# 4) for foo.cc, foo.h (deprecated location)
# 5) other google headers
#
# We classify each include statement as one of those 5 types
# using a number of techniques. The include_state object keeps
# track of the highest type seen, and complains if we see a
# lower type after that.
error_message = include_state.CheckNextIncludeOrder(
_ClassifyInclude(fileinfo, include, is_system))
if error_message:
error(filename, linenum, 'build/include_order', 4,
'%s. Should be: %s.h, c system, c++ system, other.' %
(error_message, fileinfo.BaseName()))
canonical_include = include_state.CanonicalizeAlphabeticalOrder(include)
if not include_state.IsInAlphabeticalOrder(
clean_lines, linenum, canonical_include):
error(filename, linenum, 'build/include_alpha', 4,
'Include "%s" not in alphabetical order' % include)
include_state.SetLastHeader(canonical_include)
# Look for any of the stream classes that are part of standard C++.
match = _RE_PATTERN_INCLUDE.match(line)
if match:
include = match.group(2)
if Match(r'(f|ind|io|i|o|parse|pf|stdio|str|)?stream$', include):
# Many unit tests use cout, so we exempt them.
if not _IsTestFilename(filename):
error(filename, linenum, 'readability/streams', 3,
'Streams are highly discouraged.')
def _GetTextInside(text, start_pattern):
r"""Retrieves all the text between matching open and close parentheses.
Given a string of lines and a regular expression string, retrieve all the text
following the expression and between opening punctuation symbols like
(, [, or {, and the matching close-punctuation symbol. This properly nested
occurrences of the punctuations, so for the text like
printf(a(), b(c()));
a call to _GetTextInside(text, r'printf\(') will return 'a(), b(c())'.
start_pattern must match string having an open punctuation symbol at the end.
Args:
text: The lines to extract text. Its comments and strings must be elided.
It can be single line and can span multiple lines.
start_pattern: The regexp string indicating where to start extracting
the text.
Returns:
The extracted text.
None if either the opening string or ending punctuation could not be found.
"""
# TODO(sugawarayu): Audit cpplint.py to see what places could be profitably
# rewritten to use _GetTextInside (and use inferior regexp matching today).
# Give opening punctuations to get the matching close-punctuations.
matching_punctuation = {'(': ')', '{': '}', '[': ']'}
closing_punctuation = set(matching_punctuation.itervalues())
# Find the position to start extracting text.
match = re.search(start_pattern, text, re.M)
if not match: # start_pattern not found in text.
return None
start_position = match.end(0)
assert start_position > 0, (
'start_pattern must ends with an opening punctuation.')
assert text[start_position - 1] in matching_punctuation, (
'start_pattern must ends with an opening punctuation.')
# Stack of closing punctuations we expect to have in text after position.
punctuation_stack = [matching_punctuation[text[start_position - 1]]]
position = start_position
while punctuation_stack and position < len(text):
if text[position] == punctuation_stack[-1]:
punctuation_stack.pop()
elif text[position] in closing_punctuation:
# A closing punctuation without matching opening punctuations.
return None
elif text[position] in matching_punctuation:
punctuation_stack.append(matching_punctuation[text[position]])
position += 1
if punctuation_stack:
# Opening punctuations left without matching close-punctuations.
return None
# punctuations match.
return text[start_position:position - 1]
# Patterns for matching call-by-reference parameters.
#
# Supports nested templates up to 2 levels deep using this messy pattern:
# < (?: < (?: < [^<>]*
# >
# | [^<>] )*
# >
# | [^<>] )*
# >
_RE_PATTERN_IDENT = r'[_a-zA-Z]\w*' # =~ [[:alpha:]][[:alnum:]]*
_RE_PATTERN_TYPE = (
r'(?:const\s+)?(?:typename\s+|class\s+|struct\s+|union\s+|enum\s+)?'
r'(?:\w|'
r'\s*<(?:<(?:<[^<>]*>|[^<>])*>|[^<>])*>|'
r'::)+')
# A call-by-reference parameter ends with '& identifier'.
_RE_PATTERN_REF_PARAM = re.compile(
r'(' + _RE_PATTERN_TYPE + r'(?:\s*(?:\bconst\b|[*]))*\s*'
r'&\s*' + _RE_PATTERN_IDENT + r')\s*(?:=[^,()]+)?[,)]')
# A call-by-const-reference parameter either ends with 'const& identifier'
# or looks like 'const type& identifier' when 'type' is atomic.
_RE_PATTERN_CONST_REF_PARAM = (
r'(?:.*\s*\bconst\s*&\s*' + _RE_PATTERN_IDENT +
r'|const\s+' + _RE_PATTERN_TYPE + r'\s*&\s*' + _RE_PATTERN_IDENT + r')')
def CheckLanguage(filename, clean_lines, linenum, file_extension,
include_state, nesting_state, error):
"""Checks rules from the 'C++ language rules' section of cppguide.html.
Some of these rules are hard to test (function overloading, using
uint32 inappropriately), but we do the best we can.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
file_extension: The extension (without the dot) of the filename.
include_state: An _IncludeState instance in which the headers are inserted.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# If the line is empty or consists of entirely a comment, no need to
# check it.
line = clean_lines.elided[linenum]
if not line:
return
match = _RE_PATTERN_INCLUDE.search(line)
if match:
CheckIncludeLine(filename, clean_lines, linenum, include_state, error)
return
# Reset include state across preprocessor directives. This is meant
# to silence warnings for conditional includes.
if Match(r'^\s*#\s*(?:ifdef|elif|else|endif)\b', line):
include_state.ResetSection()
# Make Windows paths like Unix.
fullname = os.path.abspath(filename).replace('\\', '/')
# TODO(unknown): figure out if they're using default arguments in fn proto.
# Check to see if they're using an conversion function cast.
# I just try to capture the most common basic types, though there are more.
# Parameterless conversion functions, such as bool(), are allowed as they are
# probably a member operator declaration or default constructor.
match = Search(
r'(\bnew\s+)?\b' # Grab 'new' operator, if it's there
r'(int|float|double|bool|char|int32|uint32|int64|uint64)'
r'(\([^)].*)', line)
if match:
matched_new = match.group(1)
matched_type = match.group(2)
matched_funcptr = match.group(3)
# gMock methods are defined using some variant of MOCK_METHODx(name, type)
# where type may be float(), int(string), etc. Without context they are
# virtually indistinguishable from int(x) casts. Likewise, gMock's
# MockCallback takes a template parameter of the form return_type(arg_type),
# which looks much like the cast we're trying to detect.
#
# std::function<> wrapper has a similar problem.
#
# Return types for function pointers also look like casts if they
# don't have an extra space.
if (matched_new is None and # If new operator, then this isn't a cast
not (Match(r'^\s*MOCK_(CONST_)?METHOD\d+(_T)?\(', line) or
Search(r'\bMockCallback<.*>', line) or
Search(r'\bstd::function<.*>', line)) and
not (matched_funcptr and
Match(r'\((?:[^() ]+::\s*\*\s*)?[^() ]+\)\s*\(',
matched_funcptr))):
# Try a bit harder to catch gmock lines: the only place where
# something looks like an old-style cast is where we declare the
# return type of the mocked method, and the only time when we
# are missing context is if MOCK_METHOD was split across
# multiple lines. The missing MOCK_METHOD is usually one or two
# lines back, so scan back one or two lines.
#
# It's not possible for gmock macros to appear in the first 2
# lines, since the class head + section name takes up 2 lines.
if (linenum < 2 or
not (Match(r'^\s*MOCK_(?:CONST_)?METHOD\d+(?:_T)?\((?:\S+,)?\s*$',
clean_lines.elided[linenum - 1]) or
Match(r'^\s*MOCK_(?:CONST_)?METHOD\d+(?:_T)?\(\s*$',
clean_lines.elided[linenum - 2]))):
error(filename, linenum, 'readability/casting', 4,
'Using deprecated casting style. '
'Use static_cast<%s>(...) instead' %
matched_type)
CheckCStyleCast(filename, linenum, line, clean_lines.raw_lines[linenum],
'static_cast',
r'\((int|float|double|bool|char|u?int(16|32|64))\)', error)
# This doesn't catch all cases. Consider (const char * const)"hello".
#
# (char *) "foo" should always be a const_cast (reinterpret_cast won't
# compile).
if CheckCStyleCast(filename, linenum, line, clean_lines.raw_lines[linenum],
'const_cast', r'\((char\s?\*+\s?)\)\s*"', error):
pass
else:
# Check pointer casts for other than string constants
CheckCStyleCast(filename, linenum, line, clean_lines.raw_lines[linenum],
'reinterpret_cast', r'\((\w+\s?\*+\s?)\)', error)
# In addition, we look for people taking the address of a cast. This
# is dangerous -- casts can assign to temporaries, so the pointer doesn't
# point where you think.
match = Search(
r'(?:&\(([^)]+)\)[\w(])|'
r'(?:&(static|dynamic|down|reinterpret)_cast\b)', line)
if match and match.group(1) != '*':
error(filename, linenum, 'runtime/casting', 4,
('Are you taking an address of a cast? '
'This is dangerous: could be a temp var. '
'Take the address before doing the cast, rather than after'))
# Create an extended_line, which is the concatenation of the current and
# next lines, for more effective checking of code that may span more than one
# line.
if linenum + 1 < clean_lines.NumLines():
extended_line = line + clean_lines.elided[linenum + 1]
else:
extended_line = line
# Check for people declaring static/global STL strings at the top level.
# This is dangerous because the C++ language does not guarantee that
# globals with constructors are initialized before the first access.
match = Match(
r'((?:|static +)(?:|const +))string +([a-zA-Z0-9_:]+)\b(.*)',
line)
# Make sure it's not a function.
# Function template specialization looks like: "string foo<Type>(...".
# Class template definitions look like: "string Foo<Type>::Method(...".
#
# Also ignore things that look like operators. These are matched separately
# because operator names cross non-word boundaries. If we change the pattern
# above, we would decrease the accuracy of matching identifiers.
if (match and
not Search(r'\boperator\W', line) and
not Match(r'\s*(<.*>)?(::[a-zA-Z0-9_]+)?\s*\(([^"]|$)', match.group(3))):
error(filename, linenum, 'runtime/string', 4,
'For a static/global string constant, use a C style string instead: '
'"%schar %s[]".' %
(match.group(1), match.group(2)))
if Search(r'\b([A-Za-z0-9_]*_)\(\1\)', line):
error(filename, linenum, 'runtime/init', 4,
'You seem to be initializing a member variable with itself.')
if file_extension == 'h':
# TODO(unknown): check that 1-arg constructors are explicit.
# How to tell it's a constructor?
# (handled in CheckForNonStandardConstructs for now)
# TODO(unknown): check that classes have DISALLOW_EVIL_CONSTRUCTORS
# (level 1 error)
pass
# Check if people are using the verboten C basic types. The only exception
# we regularly allow is "unsigned short port" for port.
if Search(r'\bshort port\b', line):
if not Search(r'\bunsigned short port\b', line):
error(filename, linenum, 'runtime/int', 4,
'Use "unsigned short" for ports, not "short"')
else:
match = Search(r'\b(short|long(?! +double)|long long)\b', line)
if match:
error(filename, linenum, 'runtime/int', 4,
'Use int16/int64/etc, rather than the C type %s' % match.group(1))
# When snprintf is used, the second argument shouldn't be a literal.
match = Search(r'snprintf\s*\(([^,]*),\s*([0-9]*)\s*,', line)
if match and match.group(2) != '0':
# If 2nd arg is zero, snprintf is used to calculate size.
error(filename, linenum, 'runtime/printf', 3,
'If you can, use sizeof(%s) instead of %s as the 2nd arg '
'to snprintf.' % (match.group(1), match.group(2)))
# Check if some verboten C functions are being used.
if Search(r'\bsprintf\b', line):
error(filename, linenum, 'runtime/printf', 5,
'Never use sprintf. Use snprintf instead.')
match = Search(r'\b(strcpy|strcat)\b', line)
if match:
error(filename, linenum, 'runtime/printf', 4,
'Almost always, snprintf is better than %s' % match.group(1))
# Check if some verboten operator overloading is going on
# TODO(unknown): catch out-of-line unary operator&:
# class X {};
# int operator&(const X& x) { return 42; } // unary operator&
# The trick is it's hard to tell apart from binary operator&:
# class Y { int operator&(const Y& x) { return 23; } }; // binary operator&
if Search(r'\boperator\s*&\s*\(\s*\)', line):
error(filename, linenum, 'runtime/operator', 4,
'Unary operator& is dangerous. Do not use it.')
# Check for suspicious usage of "if" like
# } if (a == b) {
if Search(r'\}\s*if\s*\(', line):
error(filename, linenum, 'readability/braces', 4,
'Did you mean "else if"? If not, start a new line for "if".')
# Check for potential format string bugs like printf(foo).
# We constrain the pattern not to pick things like DocidForPrintf(foo).
# Not perfect but it can catch printf(foo.c_str()) and printf(foo->c_str())
# TODO(sugawarayu): Catch the following case. Need to change the calling
# convention of the whole function to process multiple line to handle it.
# printf(
# boy_this_is_a_really_long_variable_that_cannot_fit_on_the_prev_line);
printf_args = _GetTextInside(line, r'(?i)\b(string)?printf\s*\(')
if printf_args:
match = Match(r'([\w.\->()]+)$', printf_args)
if match and match.group(1) != '__VA_ARGS__':
function_name = re.search(r'\b((?:string)?printf)\s*\(',
line, re.I).group(1)
error(filename, linenum, 'runtime/printf', 4,
'Potential format string bug. Do %s("%%s", %s) instead.'
% (function_name, match.group(1)))
# Check for potential memset bugs like memset(buf, sizeof(buf), 0).
match = Search(r'memset\s*\(([^,]*),\s*([^,]*),\s*0\s*\)', line)
if match and not Match(r"^''|-?[0-9]+|0x[0-9A-Fa-f]$", match.group(2)):
error(filename, linenum, 'runtime/memset', 4,
'Did you mean "memset(%s, 0, %s)"?'
% (match.group(1), match.group(2)))
if Search(r'\busing namespace\b', line):
error(filename, linenum, 'build/namespaces', 5,
'Do not use namespace using-directives. '
'Use using-declarations instead.')
# Detect variable-length arrays.
match = Match(r'\s*(.+::)?(\w+) [a-z]\w*\[(.+)];', line)
if (match and match.group(2) != 'return' and match.group(2) != 'delete' and
match.group(3).find(']') == -1):
# Split the size using space and arithmetic operators as delimiters.
# If any of the resulting tokens are not compile time constants then
# report the error.
tokens = re.split(r'\s|\+|\-|\*|\/|<<|>>]', match.group(3))
is_const = True
skip_next = False
for tok in tokens:
if skip_next:
skip_next = False
continue
if Search(r'sizeof\(.+\)', tok): continue
if Search(r'arraysize\(\w+\)', tok): continue
tok = tok.lstrip('(')
tok = tok.rstrip(')')
if not tok: continue
if Match(r'\d+', tok): continue
if Match(r'0[xX][0-9a-fA-F]+', tok): continue
if Match(r'k[A-Z0-9]\w*', tok): continue
if Match(r'(.+::)?k[A-Z0-9]\w*', tok): continue
if Match(r'(.+::)?[A-Z][A-Z0-9_]*', tok): continue
# A catch all for tricky sizeof cases, including 'sizeof expression',
# 'sizeof(*type)', 'sizeof(const type)', 'sizeof(struct StructName)'
# requires skipping the next token because we split on ' ' and '*'.
if tok.startswith('sizeof'):
skip_next = True
continue
is_const = False
break
if not is_const:
error(filename, linenum, 'runtime/arrays', 1,
'Do not use variable-length arrays. Use an appropriately named '
"('k' followed by CamelCase) compile-time constant for the size.")
# If DISALLOW_EVIL_CONSTRUCTORS, DISALLOW_COPY_AND_ASSIGN, or
# DISALLOW_IMPLICIT_CONSTRUCTORS is present, then it should be the last thing
# in the class declaration.
match = Match(
(r'\s*'
r'(DISALLOW_(EVIL_CONSTRUCTORS|COPY_AND_ASSIGN|IMPLICIT_CONSTRUCTORS))'
r'\(.*\);$'),
line)
if match and linenum + 1 < clean_lines.NumLines():
next_line = clean_lines.elided[linenum + 1]
# We allow some, but not all, declarations of variables to be present
# in the statement that defines the class. The [\w\*,\s]* fragment of
# the regular expression below allows users to declare instances of
# the class or pointers to instances, but not less common types such
# as function pointers or arrays. It's a tradeoff between allowing
# reasonable code and avoiding trying to parse more C++ using regexps.
if not Search(r'^\s*}[\w\*,\s]*;', next_line):
error(filename, linenum, 'readability/constructors', 3,
match.group(1) + ' should be the last thing in the class')
# Check for use of unnamed namespaces in header files. Registration
# macros are typically OK, so we allow use of "namespace {" on lines
# that end with backslashes.
if (file_extension == 'h'
and Search(r'\bnamespace\s*{', line)
and line[-1] != '\\'):
error(filename, linenum, 'build/namespaces', 4,
'Do not use unnamed namespaces in header files. See '
'http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Namespaces'
' for more information.')
def CheckForNonConstReference(filename, clean_lines, linenum,
nesting_state, error):
"""Check for non-const references.
Separate from CheckLanguage since it scans backwards from current
line, instead of scanning forward.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Do nothing if there is no '&' on current line.
line = clean_lines.elided[linenum]
if '&' not in line:
return
# Long type names may be broken across multiple lines, usually in one
# of these forms:
# LongType
# ::LongTypeContinued &identifier
# LongType::
# LongTypeContinued &identifier
# LongType<
# ...>::LongTypeContinued &identifier
#
# If we detected a type split across two lines, join the previous
# line to current line so that we can match const references
# accordingly.
#
# Note that this only scans back one line, since scanning back
# arbitrary number of lines would be expensive. If you have a type
# that spans more than 2 lines, please use a typedef.
if linenum > 1:
previous = None
if Match(r'\s*::(?:[\w<>]|::)+\s*&\s*\S', line):
# previous_line\n + ::current_line
previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+[\w<>])\s*$',
clean_lines.elided[linenum - 1])
elif Match(r'\s*[a-zA-Z_]([\w<>]|::)+\s*&\s*\S', line):
# previous_line::\n + current_line
previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+::)\s*$',
clean_lines.elided[linenum - 1])
if previous:
line = previous.group(1) + line.lstrip()
else:
# Check for templated parameter that is split across multiple lines
endpos = line.rfind('>')
if endpos > -1:
(_, startline, startpos) = ReverseCloseExpression(
clean_lines, linenum, endpos)
if startpos > -1 and startline < linenum:
# Found the matching < on an earlier line, collect all
# pieces up to current line.
line = ''
for i in xrange(startline, linenum + 1):
line += clean_lines.elided[i].strip()
# Check for non-const references in function parameters. A single '&' may
# found in the following places:
# inside expression: binary & for bitwise AND
# inside expression: unary & for taking the address of something
# inside declarators: reference parameter
# We will exclude the first two cases by checking that we are not inside a
# function body, including one that was just introduced by a trailing '{'.
# TODO(unknwon): Doesn't account for preprocessor directives.
# TODO(unknown): Doesn't account for 'catch(Exception& e)' [rare].
check_params = False
if not nesting_state.stack:
check_params = True # top level
elif (isinstance(nesting_state.stack[-1], _ClassInfo) or
isinstance(nesting_state.stack[-1], _NamespaceInfo)):
check_params = True # within class or namespace
elif Match(r'.*{\s*$', line):
if (len(nesting_state.stack) == 1 or
isinstance(nesting_state.stack[-2], _ClassInfo) or
isinstance(nesting_state.stack[-2], _NamespaceInfo)):
check_params = True # just opened global/class/namespace block
# We allow non-const references in a few standard places, like functions
# called "swap()" or iostream operators like "<<" or ">>". Do not check
# those function parameters.
#
# We also accept & in static_assert, which looks like a function but
# it's actually a declaration expression.
whitelisted_functions = (r'(?:[sS]wap(?:<\w:+>)?|'
r'operator\s*[<>][<>]|'
r'static_assert|COMPILE_ASSERT'
r')\s*\(')
if Search(whitelisted_functions, line):
check_params = False
elif not Search(r'\S+\([^)]*$', line):
# Don't see a whitelisted function on this line. Actually we
# didn't see any function name on this line, so this is likely a
# multi-line parameter list. Try a bit harder to catch this case.
for i in xrange(2):
if (linenum > i and
Search(whitelisted_functions, clean_lines.elided[linenum - i - 1])):
check_params = False
break
if check_params:
decls = ReplaceAll(r'{[^}]*}', ' ', line) # exclude function body
for parameter in re.findall(_RE_PATTERN_REF_PARAM, decls):
if not Match(_RE_PATTERN_CONST_REF_PARAM, parameter):
error(filename, linenum, 'runtime/references', 2,
'Is this a non-const reference? '
'If so, make const or use a pointer: ' +
ReplaceAll(' *<', '<', parameter))
def CheckCStyleCast(filename, linenum, line, raw_line, cast_type, pattern,
error):
"""Checks for a C-style cast by looking for the pattern.
Args:
filename: The name of the current file.
linenum: The number of the line to check.
line: The line of code to check.
raw_line: The raw line of code to check, with comments.
cast_type: The string for the C++ cast to recommend. This is either
reinterpret_cast, static_cast, or const_cast, depending.
pattern: The regular expression used to find C-style casts.
error: The function to call with any errors found.
Returns:
True if an error was emitted.
False otherwise.
"""
match = Search(pattern, line)
if not match:
return False
# Exclude lines with sizeof, since sizeof looks like a cast.
sizeof_match = Match(r'.*sizeof\s*$', line[0:match.start(1) - 1])
if sizeof_match:
return False
# operator++(int) and operator--(int)
if (line[0:match.start(1) - 1].endswith(' operator++') or
line[0:match.start(1) - 1].endswith(' operator--')):
return False
# A single unnamed argument for a function tends to look like old
# style cast. If we see those, don't issue warnings for deprecated
# casts, instead issue warnings for unnamed arguments where
# appropriate.
#
# These are things that we want warnings for, since the style guide
# explicitly require all parameters to be named:
# Function(int);
# Function(int) {
# ConstMember(int) const;
# ConstMember(int) const {
# ExceptionMember(int) throw (...);
# ExceptionMember(int) throw (...) {
# PureVirtual(int) = 0;
#
# These are functions of some sort, where the compiler would be fine
# if they had named parameters, but people often omit those
# identifiers to reduce clutter:
# (FunctionPointer)(int);
# (FunctionPointer)(int) = value;
# Function((function_pointer_arg)(int))
# <TemplateArgument(int)>;
# <(FunctionPointerTemplateArgument)(int)>;
remainder = line[match.end(0):]
if Match(r'^\s*(?:;|const\b|throw\b|=|>|\{|\))', remainder):
# Looks like an unnamed parameter.
# Don't warn on any kind of template arguments.
if Match(r'^\s*>', remainder):
return False
# Don't warn on assignments to function pointers, but keep warnings for
# unnamed parameters to pure virtual functions. Note that this pattern
# will also pass on assignments of "0" to function pointers, but the
# preferred values for those would be "nullptr" or "NULL".
matched_zero = Match(r'^\s=\s*(\S+)\s*;', remainder)
if matched_zero and matched_zero.group(1) != '0':
return False
# Don't warn on function pointer declarations. For this we need
# to check what came before the "(type)" string.
if Match(r'.*\)\s*$', line[0:match.start(0)]):
return False
# Don't warn if the parameter is named with block comments, e.g.:
# Function(int /*unused_param*/);
if '/*' in raw_line:
return False
# Passed all filters, issue warning here.
error(filename, linenum, 'readability/function', 3,
'All parameters should be named in a function')
return True
# At this point, all that should be left is actual casts.
error(filename, linenum, 'readability/casting', 4,
'Using C-style cast. Use %s<%s>(...) instead' %
(cast_type, match.group(1)))
return True
_HEADERS_CONTAINING_TEMPLATES = (
('<deque>', ('deque',)),
('<functional>', ('unary_function', 'binary_function',
'plus', 'minus', 'multiplies', 'divides', 'modulus',
'negate',
'equal_to', 'not_equal_to', 'greater', 'less',
'greater_equal', 'less_equal',
'logical_and', 'logical_or', 'logical_not',
'unary_negate', 'not1', 'binary_negate', 'not2',
'bind1st', 'bind2nd',
'pointer_to_unary_function',
'pointer_to_binary_function',
'ptr_fun',
'mem_fun_t', 'mem_fun', 'mem_fun1_t', 'mem_fun1_ref_t',
'mem_fun_ref_t',
'const_mem_fun_t', 'const_mem_fun1_t',
'const_mem_fun_ref_t', 'const_mem_fun1_ref_t',
'mem_fun_ref',
)),
('<limits>', ('numeric_limits',)),
('<list>', ('list',)),
('<map>', ('map', 'multimap',)),
('<memory>', ('allocator',)),
('<queue>', ('queue', 'priority_queue',)),
('<set>', ('set', 'multiset',)),
('<stack>', ('stack',)),
('<string>', ('char_traits', 'basic_string',)),
('<utility>', ('pair',)),
('<vector>', ('vector',)),
# gcc extensions.
# Note: std::hash is their hash, ::hash is our hash
('<hash_map>', ('hash_map', 'hash_multimap',)),
('<hash_set>', ('hash_set', 'hash_multiset',)),
('<slist>', ('slist',)),
)
_RE_PATTERN_STRING = re.compile(r'\bstring\b')
_re_pattern_algorithm_header = []
for _template in ('copy', 'max', 'min', 'min_element', 'sort', 'swap',
'transform'):
# Match max<type>(..., ...), max(..., ...), but not foo->max, foo.max or
# type::max().
_re_pattern_algorithm_header.append(
(re.compile(r'[^>.]\b' + _template + r'(<.*?>)?\([^\)]'),
_template,
'<algorithm>'))
_re_pattern_templates = []
for _header, _templates in _HEADERS_CONTAINING_TEMPLATES:
for _template in _templates:
_re_pattern_templates.append(
(re.compile(r'(\<|\b)' + _template + r'\s*\<'),
_template + '<>',
_header))
def FilesBelongToSameModule(filename_cc, filename_h):
"""Check if these two filenames belong to the same module.
The concept of a 'module' here is a as follows:
foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
same 'module' if they are in the same directory.
some/path/public/xyzzy and some/path/internal/xyzzy are also considered
to belong to the same module here.
If the filename_cc contains a longer path than the filename_h, for example,
'/absolute/path/to/base/sysinfo.cc', and this file would include
'base/sysinfo.h', this function also produces the prefix needed to open the
header. This is used by the caller of this function to more robustly open the
header file. We don't have access to the real include paths in this context,
so we need this guesswork here.
Known bugs: tools/base/bar.cc and base/bar.h belong to the same module
according to this implementation. Because of this, this function gives
some false positives. This should be sufficiently rare in practice.
Args:
filename_cc: is the path for the .cc file
filename_h: is the path for the header path
Returns:
Tuple with a bool and a string:
bool: True if filename_cc and filename_h belong to the same module.
string: the additional prefix needed to open the header file.
"""
if not filename_cc.endswith('.cc'):
return (False, '')
filename_cc = filename_cc[:-len('.cc')]
if filename_cc.endswith('_unittest'):
filename_cc = filename_cc[:-len('_unittest')]
elif filename_cc.endswith('_test'):
filename_cc = filename_cc[:-len('_test')]
filename_cc = filename_cc.replace('/public/', '/')
filename_cc = filename_cc.replace('/internal/', '/')
if not filename_h.endswith('.h'):
return (False, '')
filename_h = filename_h[:-len('.h')]
if filename_h.endswith('-inl'):
filename_h = filename_h[:-len('-inl')]
filename_h = filename_h.replace('/public/', '/')
filename_h = filename_h.replace('/internal/', '/')
files_belong_to_same_module = filename_cc.endswith(filename_h)
common_path = ''
if files_belong_to_same_module:
common_path = filename_cc[:-len(filename_h)]
return files_belong_to_same_module, common_path
def UpdateIncludeState(filename, include_state, io=codecs):
"""Fill up the include_state with new includes found from the file.
Args:
filename: the name of the header to read.
include_state: an _IncludeState instance in which the headers are inserted.
io: The io factory to use to read the file. Provided for testability.
Returns:
True if a header was successfully added. False otherwise.
"""
headerfile = None
try:
headerfile = io.open(filename, 'r', 'utf8', 'replace')
except IOError:
return False
linenum = 0
for line in headerfile:
linenum += 1
clean_line = CleanseComments(line)
match = _RE_PATTERN_INCLUDE.search(clean_line)
if match:
include = match.group(2)
# The value formatting is cute, but not really used right now.
# What matters here is that the key is in include_state.
include_state.setdefault(include, '%s:%d' % (filename, linenum))
return True
def CheckForIncludeWhatYouUse(filename, clean_lines, include_state, error,
io=codecs):
"""Reports for missing stl includes.
This function will output warnings to make sure you are including the headers
necessary for the stl containers and functions that you use. We only give one
reason to include a header. For example, if you use both equal_to<> and
less<> in a .h file, only one (the latter in the file) of these will be
reported as a reason to include the <functional>.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
include_state: An _IncludeState instance.
error: The function to call with any errors found.
io: The IO factory to use to read the header file. Provided for unittest
injection.
"""
required = {} # A map of header name to linenumber and the template entity.
# Example of required: { '<functional>': (1219, 'less<>') }
for linenum in xrange(clean_lines.NumLines()):
line = clean_lines.elided[linenum]
if not line or line[0] == '#':
continue
# String is special -- it is a non-templatized type in STL.
matched = _RE_PATTERN_STRING.search(line)
if matched:
# Don't warn about strings in non-STL namespaces:
# (We check only the first match per line; good enough.)
prefix = line[:matched.start()]
if prefix.endswith('std::') or not prefix.endswith('::'):
required['<string>'] = (linenum, 'string')
for pattern, template, header in _re_pattern_algorithm_header:
if pattern.search(line):
required[header] = (linenum, template)
# The following function is just a speed up, no semantics are changed.
if not '<' in line: # Reduces the cpu time usage by skipping lines.
continue
for pattern, template, header in _re_pattern_templates:
if pattern.search(line):
required[header] = (linenum, template)
# The policy is that if you #include something in foo.h you don't need to
# include it again in foo.cc. Here, we will look at possible includes.
# Let's copy the include_state so it is only messed up within this function.
include_state = include_state.copy()
# Did we find the header for this file (if any) and successfully load it?
header_found = False
# Use the absolute path so that matching works properly.
abs_filename = FileInfo(filename).FullName()
# For Emacs's flymake.
# If cpplint is invoked from Emacs's flymake, a temporary file is generated
# by flymake and that file name might end with '_flymake.cc'. In that case,
# restore original file name here so that the corresponding header file can be
# found.
# e.g. If the file name is 'foo_flymake.cc', we should search for 'foo.h'
# instead of 'foo_flymake.h'
abs_filename = re.sub(r'_flymake\.cc$', '.cc', abs_filename)
# include_state is modified during iteration, so we iterate over a copy of
# the keys.
header_keys = include_state.keys()
for header in header_keys:
(same_module, common_path) = FilesBelongToSameModule(abs_filename, header)
fullpath = common_path + header
if same_module and UpdateIncludeState(fullpath, include_state, io):
header_found = True
# If we can't find the header file for a .cc, assume it's because we don't
# know where to look. In that case we'll give up as we're not sure they
# didn't include it in the .h file.
# TODO(unknown): Do a better job of finding .h files so we are confident that
# not having the .h file means there isn't one.
if filename.endswith('.cc') and not header_found:
return
# All the lines have been processed, report the errors found.
for required_header_unstripped in required:
template = required[required_header_unstripped][1]
if required_header_unstripped.strip('<>"') not in include_state:
error(filename, required[required_header_unstripped][0],
'build/include_what_you_use', 4,
'Add #include ' + required_header_unstripped + ' for ' + template)
_RE_PATTERN_EXPLICIT_MAKEPAIR = re.compile(r'\bmake_pair\s*<')
def CheckMakePairUsesDeduction(filename, clean_lines, linenum, error):
"""Check that make_pair's template arguments are deduced.
G++ 4.6 in C++0x mode fails badly if make_pair's template arguments are
specified explicitly, and such use isn't intended in any case.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
match = _RE_PATTERN_EXPLICIT_MAKEPAIR.search(line)
if match:
error(filename, linenum, 'build/explicit_make_pair',
4, # 4 = high confidence
'For C++11-compatibility, omit template arguments from make_pair'
' OR use pair directly OR if appropriate, construct a pair directly')
def ProcessLine(filename, file_extension, clean_lines, line,
include_state, function_state, nesting_state, error,
extra_check_functions=[]):
"""Processes a single line in the file.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
clean_lines: An array of strings, each representing a line of the file,
with comments stripped.
line: Number of line being processed.
include_state: An _IncludeState instance in which the headers are inserted.
function_state: A _FunctionState instance which counts function lines, etc.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error
"""
raw_lines = clean_lines.raw_lines
ParseNolintSuppressions(filename, raw_lines[line], line, error)
nesting_state.Update(filename, clean_lines, line, error)
if nesting_state.stack and nesting_state.stack[-1].inline_asm != _NO_ASM:
return
CheckForFunctionLengths(filename, clean_lines, line, function_state, error)
CheckForMultilineCommentsAndStrings(filename, clean_lines, line, error)
CheckStyle(filename, clean_lines, line, file_extension, nesting_state, error)
CheckLanguage(filename, clean_lines, line, file_extension, include_state,
nesting_state, error)
CheckForNonConstReference(filename, clean_lines, line, nesting_state, error)
CheckForNonStandardConstructs(filename, clean_lines, line,
nesting_state, error)
CheckVlogArguments(filename, clean_lines, line, error)
CheckCaffeAlternatives(filename, clean_lines, line, error)
CheckCaffeDataLayerSetUp(filename, clean_lines, line, error)
CheckCaffeRandom(filename, clean_lines, line, error)
CheckPosixThreading(filename, clean_lines, line, error)
CheckInvalidIncrement(filename, clean_lines, line, error)
CheckMakePairUsesDeduction(filename, clean_lines, line, error)
for check_fn in extra_check_functions:
check_fn(filename, clean_lines, line, error)
def ProcessFileData(filename, file_extension, lines, error,
extra_check_functions=[]):
"""Performs lint checks and reports any errors to the given error function.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
lines: An array of strings, each representing a line of the file, with the
last element being empty if the file is terminated with a newline.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error
"""
lines = (['// marker so line numbers and indices both start at 1'] + lines +
['// marker so line numbers end in a known way'])
include_state = _IncludeState()
function_state = _FunctionState()
nesting_state = _NestingState()
ResetNolintSuppressions()
CheckForCopyright(filename, lines, error)
if file_extension == 'h':
CheckForHeaderGuard(filename, lines, error)
RemoveMultiLineComments(filename, lines, error)
clean_lines = CleansedLines(lines)
for line in xrange(clean_lines.NumLines()):
ProcessLine(filename, file_extension, clean_lines, line,
include_state, function_state, nesting_state, error,
extra_check_functions)
nesting_state.CheckCompletedBlocks(filename, error)
CheckForIncludeWhatYouUse(filename, clean_lines, include_state, error)
# We check here rather than inside ProcessLine so that we see raw
# lines rather than "cleaned" lines.
CheckForBadCharacters(filename, lines, error)
CheckForNewlineAtEOF(filename, lines, error)
def ProcessFile(filename, vlevel, extra_check_functions=[]):
"""Does google-lint on a single file.
Args:
filename: The name of the file to parse.
vlevel: The level of errors to report. Every error of confidence
>= verbose_level will be reported. 0 is a good default.
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error
"""
_SetVerboseLevel(vlevel)
try:
# Support the UNIX convention of using "-" for stdin. Note that
# we are not opening the file with universal newline support
# (which codecs doesn't support anyway), so the resulting lines do
# contain trailing '\r' characters if we are reading a file that
# has CRLF endings.
# If after the split a trailing '\r' is present, it is removed
# below. If it is not expected to be present (i.e. os.linesep !=
# '\r\n' as in Windows), a warning is issued below if this file
# is processed.
if filename == '-':
lines = codecs.StreamReaderWriter(sys.stdin,
codecs.getreader('utf8'),
codecs.getwriter('utf8'),
'replace').read().split('\n')
else:
lines = codecs.open(filename, 'r', 'utf8', 'replace').read().split('\n')
carriage_return_found = False
# Remove trailing '\r'.
for linenum in range(len(lines)):
if lines[linenum].endswith('\r'):
lines[linenum] = lines[linenum].rstrip('\r')
carriage_return_found = True
except IOError:
sys.stderr.write(
"Skipping input '%s': Can't open for reading\n" % filename)
return
# Note, if no dot is found, this will give the entire filename as the ext.
file_extension = filename[filename.rfind('.') + 1:]
# When reading from stdin, the extension is unknown, so no cpplint tests
# should rely on the extension.
if filename != '-' and file_extension not in _valid_extensions:
sys.stderr.write('Ignoring %s; not a valid file name '
'(%s)\n' % (filename, ', '.join(_valid_extensions)))
else:
ProcessFileData(filename, file_extension, lines, Error,
extra_check_functions)
if carriage_return_found and os.linesep != '\r\n':
# Use 0 for linenum since outputting only one error for potentially
# several lines.
Error(filename, 0, 'whitespace/newline', 1,
'One or more unexpected \\r (^M) found;'
'better to use only a \\n')
sys.stderr.write('Done processing %s\n' % filename)
def PrintUsage(message):
"""Prints a brief usage string and exits, optionally with an error message.
Args:
message: The optional error message.
"""
sys.stderr.write(_USAGE)
if message:
sys.exit('\nFATAL ERROR: ' + message)
else:
sys.exit(1)
def PrintCategories():
"""Prints a list of all the error-categories used by error messages.
These are the categories used to filter messages via --filter.
"""
sys.stderr.write(''.join(' %s\n' % cat for cat in _ERROR_CATEGORIES))
sys.exit(0)
def ParseArguments(args):
"""Parses the command line arguments.
This may set the output format and verbosity level as side-effects.
Args:
args: The command line arguments:
Returns:
The list of filenames to lint.
"""
try:
(opts, filenames) = getopt.getopt(args, '', ['help', 'output=', 'verbose=',
'counting=',
'filter=',
'root=',
'linelength=',
'extensions='])
except getopt.GetoptError:
PrintUsage('Invalid arguments.')
verbosity = _VerboseLevel()
output_format = _OutputFormat()
filters = ''
counting_style = ''
for (opt, val) in opts:
if opt == '--help':
PrintUsage(None)
elif opt == '--output':
if val not in ('emacs', 'vs7', 'eclipse'):
PrintUsage('The only allowed output formats are emacs, vs7 and eclipse.')
output_format = val
elif opt == '--verbose':
verbosity = int(val)
elif opt == '--filter':
filters = val
if not filters:
PrintCategories()
elif opt == '--counting':
if val not in ('total', 'toplevel', 'detailed'):
PrintUsage('Valid counting options are total, toplevel, and detailed')
counting_style = val
elif opt == '--root':
global _root
_root = val
elif opt == '--linelength':
global _line_length
try:
_line_length = int(val)
except ValueError:
PrintUsage('Line length must be digits.')
elif opt == '--extensions':
global _valid_extensions
try:
_valid_extensions = set(val.split(','))
except ValueError:
PrintUsage('Extensions must be comma separated list.')
if not filenames:
PrintUsage('No files were specified.')
_SetOutputFormat(output_format)
_SetVerboseLevel(verbosity)
_SetFilters(filters)
_SetCountingStyle(counting_style)
return filenames
def main():
filenames = ParseArguments(sys.argv[1:])
# Change stderr to write with replacement characters so we don't die
# if we try to print something containing non-ASCII characters.
sys.stderr = codecs.StreamReaderWriter(sys.stderr,
codecs.getreader('utf8'),
codecs.getwriter('utf8'),
'replace')
_cpplint_state.ResetErrorCounts()
for filename in filenames:
ProcessFile(filename, _cpplint_state.verbose_level)
_cpplint_state.PrintErrorCounts()
sys.exit(_cpplint_state.error_count > 0)
if __name__ == '__main__':
main()
| 187,450 | 37.49887 | 93 | py |
bottom-up-attention | bottom-up-attention-master/caffe/scripts/split_caffe_proto.py | #!/usr/bin/env python
import mmap
import re
import os
import errno
script_path = os.path.dirname(os.path.realpath(__file__))
# a regex to match the parameter definitions in caffe.proto
r = re.compile(r'(?://.*\n)*message ([^ ]*) \{\n(?: .*\n|\n)*\}')
# create directory to put caffe.proto fragments
try:
os.mkdir(
os.path.join(script_path,
'../docs/_includes/'))
os.mkdir(
os.path.join(script_path,
'../docs/_includes/proto/'))
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
caffe_proto_fn = os.path.join(
script_path,
'../src/caffe/proto/caffe.proto')
with open(caffe_proto_fn, 'r') as fin:
for m in r.finditer(fin.read(), re.MULTILINE):
fn = os.path.join(
script_path,
'../docs/_includes/proto/%s.txt' % m.group(1))
with open(fn, 'w') as fout:
fout.write(m.group(0))
| 941 | 25.166667 | 65 | py |
bottom-up-attention | bottom-up-attention-master/caffe/scripts/download_model_binary.py | #!/usr/bin/env python
import os
import sys
import time
import yaml
import hashlib
import argparse
from six.moves import urllib
required_keys = ['caffemodel', 'caffemodel_url', 'sha1']
def reporthook(count, block_size, total_size):
"""
From http://blog.moleculea.com/2012/10/04/urlretrieve-progres-indicator/
"""
global start_time
if count == 0:
start_time = time.time()
return
duration = (time.time() - start_time) or 0.01
progress_size = int(count * block_size)
speed = int(progress_size / (1024 * duration))
percent = int(count * block_size * 100 / total_size)
sys.stdout.write("\r...%d%%, %d MB, %d KB/s, %d seconds passed" %
(percent, progress_size / (1024 * 1024), speed, duration))
sys.stdout.flush()
def parse_readme_frontmatter(dirname):
readme_filename = os.path.join(dirname, 'readme.md')
with open(readme_filename) as f:
lines = [line.strip() for line in f.readlines()]
top = lines.index('---')
bottom = lines.index('---', top + 1)
frontmatter = yaml.load('\n'.join(lines[top + 1:bottom]))
assert all(key in frontmatter for key in required_keys)
return dirname, frontmatter
def valid_dirname(dirname):
try:
return parse_readme_frontmatter(dirname)
except Exception as e:
print('ERROR: {}'.format(e))
raise argparse.ArgumentTypeError(
'Must be valid Caffe model directory with a correct readme.md')
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Download trained model binary.')
parser.add_argument('dirname', type=valid_dirname)
args = parser.parse_args()
# A tiny hack: the dirname validator also returns readme YAML frontmatter.
dirname = args.dirname[0]
frontmatter = args.dirname[1]
model_filename = os.path.join(dirname, frontmatter['caffemodel'])
# Closure-d function for checking SHA1.
def model_checks_out(filename=model_filename, sha1=frontmatter['sha1']):
with open(filename, 'rb') as f:
return hashlib.sha1(f.read()).hexdigest() == sha1
# Check if model exists.
if os.path.exists(model_filename) and model_checks_out():
print("Model already exists.")
sys.exit(0)
# Download and verify model.
urllib.request.urlretrieve(
frontmatter['caffemodel_url'], model_filename, reporthook)
if not model_checks_out():
print('ERROR: model did not download correctly! Run this again.')
sys.exit(1)
| 2,531 | 31.461538 | 78 | py |
bottom-up-attention | bottom-up-attention-master/caffe/scripts/copy_notebook.py | #!/usr/bin/env python
"""
Takes as arguments:
1. the path to a JSON file (such as an IPython notebook).
2. the path to output file
If 'metadata' dict in the JSON file contains 'include_in_docs': true,
then copies the file to output file, appending the 'metadata' property
as YAML front-matter, adding the field 'category' with value 'notebook'.
"""
import os
import sys
import json
filename = sys.argv[1]
output_filename = sys.argv[2]
content = json.load(open(filename))
if 'include_in_docs' in content['metadata'] and content['metadata']['include_in_docs']:
yaml_frontmatter = ['---']
for key, val in content['metadata'].iteritems():
if key == 'example_name':
key = 'title'
if val == '':
val = os.path.basename(filename)
yaml_frontmatter.append('{}: {}'.format(key, val))
yaml_frontmatter += ['category: notebook']
yaml_frontmatter += ['original_path: ' + filename]
with open(output_filename, 'w') as fo:
fo.write('\n'.join(yaml_frontmatter + ['---']) + '\n')
fo.write(open(filename).read())
| 1,089 | 32.030303 | 87 | py |
bottom-up-attention | bottom-up-attention-master/lib/setup.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import os
from os.path import join as pjoin
from setuptools import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
import subprocess
import numpy as np
def find_in_path(name, path):
"Find a file in a search path"
# Adapted fom
# http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/
for dir in path.split(os.pathsep):
binpath = pjoin(dir, name)
if os.path.exists(binpath):
return os.path.abspath(binpath)
return None
def locate_cuda():
"""Locate the CUDA environment on the system
Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'
and values giving the absolute path to each directory.
Starts by looking for the CUDAHOME env variable. If not found, everything
is based on finding 'nvcc' in the PATH.
"""
# first check if the CUDAHOME env variable is in use
if 'CUDAHOME' in os.environ:
home = os.environ['CUDAHOME']
nvcc = pjoin(home, 'bin', 'nvcc')
else:
# otherwise, search the PATH for NVCC
default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')
nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path)
if nvcc is None:
raise EnvironmentError('The nvcc binary could not be '
'located in your $PATH. Either add it to your path, or set $CUDAHOME')
home = os.path.dirname(os.path.dirname(nvcc))
cudaconfig = {'home':home, 'nvcc':nvcc,
'include': pjoin(home, 'include'),
'lib64': pjoin(home, 'lib64')}
for k, v in cudaconfig.iteritems():
if not os.path.exists(v):
raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v))
return cudaconfig
CUDA = locate_cuda()
# Obtain the numpy include directory. This logic works across numpy versions.
try:
numpy_include = np.get_include()
except AttributeError:
numpy_include = np.get_numpy_include()
def customize_compiler_for_nvcc(self):
"""inject deep into distutils to customize how the dispatch
to gcc/nvcc works.
If you subclass UnixCCompiler, it's not trivial to get your subclass
injected in, and still have the right customizations (i.e.
distutils.sysconfig.customize_compiler) run on it. So instead of going
the OO route, I have this. Note, it's kindof like a wierd functional
subclassing going on."""
# tell the compiler it can processes .cu
self.src_extensions.append('.cu')
# save references to the default compiler_so and _comple methods
default_compiler_so = self.compiler_so
super = self._compile
# now redefine the _compile method. This gets executed for each
# object but distutils doesn't have the ability to change compilers
# based on source extension: we add it.
def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts):
if os.path.splitext(src)[1] == '.cu':
# use the cuda for .cu files
self.set_executable('compiler_so', CUDA['nvcc'])
# use only a subset of the extra_postargs, which are 1-1 translated
# from the extra_compile_args in the Extension class
postargs = extra_postargs['nvcc']
else:
postargs = extra_postargs['gcc']
super(obj, src, ext, cc_args, postargs, pp_opts)
# reset the default compiler_so, which we might have changed for cuda
self.compiler_so = default_compiler_so
# inject our redefined _compile method into the class
self._compile = _compile
# run the customize_compiler
class custom_build_ext(build_ext):
def build_extensions(self):
customize_compiler_for_nvcc(self.compiler)
build_ext.build_extensions(self)
ext_modules = [
Extension(
"utils.cython_bbox",
["utils/bbox.pyx"],
extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]},
include_dirs = [numpy_include]
),
Extension(
"nms.cpu_nms",
["nms/cpu_nms.pyx"],
extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]},
include_dirs = [numpy_include]
),
Extension('nms.gpu_nms',
['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'],
library_dirs=[CUDA['lib64']],
libraries=['cudart'],
language='c++',
runtime_library_dirs=[CUDA['lib64']],
# this syntax is specific to this build system
# we're only going to use certain compiler args with nvcc and not with
# gcc the implementation of this trick is in customize_compiler() below
extra_compile_args={'gcc': ["-Wno-unused-function"],
'nvcc': ['-arch=sm_35',
'--ptxas-options=-v',
'-c',
'--compiler-options',
"'-fPIC'"]},
include_dirs = [numpy_include, CUDA['include']]
),
Extension(
'pycocotools._mask',
sources=['pycocotools/maskApi.c', 'pycocotools/_mask.pyx'],
include_dirs = [numpy_include, 'pycocotools'],
extra_compile_args={
'gcc': ['-Wno-cpp', '-Wno-unused-function', '-std=c99']},
),
]
setup(
name='fast_rcnn',
ext_modules=ext_modules,
# inject our custom trigger
cmdclass={'build_ext': custom_build_ext},
)
| 5,665 | 35.089172 | 90 | py |
bottom-up-attention | bottom-up-attention-master/lib/roi_data_layer/layer.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""The data layer used during training to train a Fast R-CNN network.
RoIDataLayer implements a Caffe Python layer.
"""
import caffe
from fast_rcnn.config import cfg
from roi_data_layer.minibatch import get_minibatch
import numpy as np
import yaml
from multiprocessing import Process, Queue
class RoIDataLayer(caffe.Layer):
"""Fast R-CNN data layer used for training."""
def _shuffle_roidb_inds(self, gpu_id=0):
self.gpu_id = gpu_id
"""Randomly permute the training roidb."""
if cfg.TRAIN.ASPECT_GROUPING:
widths = np.array([r['width'] for r in self._roidb])
heights = np.array([r['height'] for r in self._roidb])
horz = (widths >= heights)
vert = np.logical_not(horz)
horz_inds = np.where(horz)[0]
vert_inds = np.where(vert)[0]
inds = np.hstack((
np.random.permutation(horz_inds),
np.random.permutation(vert_inds)))
inds = np.reshape(inds, (-1, 2))
np.random.seed(gpu_id)
row_perm = np.random.permutation(np.arange(inds.shape[0]))
inds = np.reshape(inds[row_perm, :], (-1,))
self._perm = inds
else:
self._perm = np.random.permutation(np.arange(len(self._roidb)))
self._cur = 0
def _get_next_minibatch_inds(self):
"""Return the roidb indices for the next minibatch."""
if self._cur + cfg.TRAIN.IMS_PER_BATCH >= len(self._roidb):
self._shuffle_roidb_inds(self.gpu_id)
db_inds = self._perm[self._cur:self._cur + cfg.TRAIN.IMS_PER_BATCH]
self._cur += cfg.TRAIN.IMS_PER_BATCH
return db_inds
def _get_next_minibatch(self):
"""Return the blobs to be used for the next minibatch.
If cfg.TRAIN.USE_PREFETCH is True, then blobs will be computed in a
separate process and made available through self._blob_queue.
"""
if cfg.TRAIN.USE_PREFETCH:
return self._blob_queue.get()
else:
db_inds = self._get_next_minibatch_inds()
minibatch_db = [self._roidb[i] for i in db_inds]
return get_minibatch(minibatch_db, self._num_classes)
def set_roidb(self, roidb, gpu_id=0):
"""Set the roidb to be used by this layer during training."""
self._roidb = roidb
self._shuffle_roidb_inds(gpu_id)
if cfg.TRAIN.USE_PREFETCH:
self._blob_queue = Queue(10)
self._prefetch_process = BlobFetcher(self._blob_queue,
self._roidb,
self._num_classes, gpu_id)
self._prefetch_process.start()
# Terminate the child process when the parent exists
def cleanup():
print 'Terminating BlobFetcher'
self._prefetch_process.terminate()
self._prefetch_process.join()
import atexit
atexit.register(cleanup)
def setup(self, bottom, top):
"""Setup the RoIDataLayer."""
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str)
self._num_classes = layer_params['num_classes']
self._name_to_top_map = {}
# data blob: holds a batch of N images, each with 3 channels
idx = 0
top[idx].reshape(cfg.TRAIN.IMS_PER_BATCH, 3,
max(cfg.TRAIN.SCALES), cfg.TRAIN.MAX_SIZE)
self._name_to_top_map['data'] = idx
idx += 1
if cfg.TRAIN.HAS_RPN:
top[idx].reshape(1, 3)
self._name_to_top_map['im_info'] = idx
idx += 1
top[idx].reshape(1, 4)
self._name_to_top_map['gt_boxes'] = idx
idx += 1
else: # not using RPN
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[idx].reshape(1, 5, 1, 1)
self._name_to_top_map['rois'] = idx
idx += 1
# labels blob: R categorical labels in [0, ..., K] for K foreground
# classes plus background
top[idx].reshape(1, 1, 1, 1)
self._name_to_top_map['labels'] = idx
idx += 1
if cfg.TRAIN.BBOX_REG:
# bbox_targets blob: R bounding-box regression targets with 4
# targets per class
num_reg_class = 2 if cfg.TRAIN.AGNOSTIC else self._num_classes
top[idx].reshape(1, num_reg_class * 4, 1, 1)
self._name_to_top_map['bbox_targets'] = idx
idx += 1
# bbox_inside_weights blob: At most 4 targets per roi are active;
# thisbinary vector sepcifies the subset of active targets
top[idx].reshape(1, num_reg_class * 4, 1, 1)
self._name_to_top_map['bbox_inside_weights'] = idx
idx += 1
top[idx].reshape(1, num_reg_class * 4, 1, 1)
self._name_to_top_map['bbox_outside_weights'] = idx
idx += 1
print 'RoiDataLayer: name_to_top:', self._name_to_top_map
assert len(top) == len(self._name_to_top_map)
def forward(self, bottom, top):
"""Get blobs and copy them into this layer's top blob vector."""
blobs = self._get_next_minibatch()
for blob_name, blob in blobs.iteritems():
top_ind = self._name_to_top_map[blob_name]
shape = blob.shape
if len(shape) == 1:
blob = blob.reshape(blob.shape[0], 1, 1, 1)
if len(shape) == 2 and blob_name != 'im_info':
blob = blob.reshape(blob.shape[0], blob.shape[1], 1, 1)
top[top_ind].reshape(*(blob.shape))
# Copy data into net's input blobs
top[top_ind].data[...] = blob.astype(np.float32, copy=False)
def backward(self, top, propagate_down, bottom):
"""This layer does not propagate gradients."""
pass
def reshape(self, bottom, top):
"""Reshaping happens during the call to forward."""
pass
class BlobFetcher(Process):
"""Experimental class for prefetching blobs in a separate process."""
def __init__(self, queue, roidb, num_classes, gpu_id=0):
super(BlobFetcher, self).__init__()
self._queue = queue
self._roidb = roidb
self._num_classes = num_classes
self._perm = None
self._cur = 0
self.gpu_id = gpu_id
np.random.seed(gpu_id)
self._shuffle_roidb_inds()
def _shuffle_roidb_inds(self):
"""Randomly permute the training roidb."""
# TODO(rbg): remove duplicated code
self._perm = np.random.permutation(np.arange(len(self._roidb)))
self._cur = 0
def _get_next_minibatch_inds(self):
"""Return the roidb indices for the next minibatch."""
# TODO(rbg): remove duplicated code
if self._cur + cfg.TRAIN.IMS_PER_BATCH >= len(self._roidb):
self._shuffle_roidb_inds()
db_inds = self._perm[self._cur:self._cur + cfg.TRAIN.IMS_PER_BATCH]
self._cur += cfg.TRAIN.IMS_PER_BATCH
return db_inds
def run(self):
print 'BlobFetcher started'
while True:
db_inds = self._get_next_minibatch_inds()
minibatch_db = [self._roidb[i] for i in db_inds]
blobs = get_minibatch(minibatch_db, self._num_classes)
self._queue.put(blobs)
| 7,856 | 37.326829 | 81 | py |
bottom-up-attention | bottom-up-attention-master/lib/roi_data_layer/roidb.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Transform a roidb into a trainable roidb by adding a bunch of metadata."""
import numpy as np
from fast_rcnn.config import cfg
from fast_rcnn.bbox_transform import bbox_transform
from utils.cython_bbox import bbox_overlaps
import PIL
def prepare_roidb(imdb):
"""Enrich the imdb's roidb by adding some derived quantities that
are useful for training. This function precomputes the maximum
overlap, taken over ground-truth boxes, between each ROI and
each ground-truth box. The class with maximum overlap is also
recorded.
"""
sizes = [PIL.Image.open(imdb.image_path_at(i)).size
for i in xrange(imdb.num_images)]
roidb = imdb.roidb
for i in xrange(len(imdb.image_index)):
roidb[i]['image'] = imdb.image_path_at(i)
roidb[i]['width'] = sizes[i][0]
roidb[i]['height'] = sizes[i][1]
# need gt_overlaps as a dense array for argmax
gt_overlaps = roidb[i]['gt_overlaps'].toarray()
# max overlap with gt over classes (columns)
max_overlaps = gt_overlaps.max(axis=1)
# gt class that had the max overlap
max_classes = gt_overlaps.argmax(axis=1)
roidb[i]['max_classes'] = max_classes
roidb[i]['max_overlaps'] = max_overlaps
# sanity checks
# max overlap of 0 => class should be zero (background)
zero_inds = np.where(max_overlaps == 0)[0]
assert all(max_classes[zero_inds] == 0)
# max overlap > 0 => class should not be zero (must be a fg class)
nonzero_inds = np.where(max_overlaps > 0)[0]
assert all(max_classes[nonzero_inds] != 0)
def add_bbox_regression_targets(roidb):
"""Add information needed to train bounding-box regressors."""
assert len(roidb) > 0
assert 'max_classes' in roidb[0], 'Did you call prepare_roidb first?'
num_images = len(roidb)
# Infer number of classes from the number of columns in gt_overlaps
num_reg_classes = 2 if cfg.TRAIN.AGNOSTIC else roidb[0]['gt_overlaps'].shape[1]
for im_i in xrange(num_images):
rois = roidb[im_i]['boxes']
max_overlaps = roidb[im_i]['max_overlaps']
max_classes = roidb[im_i]['max_classes']
roidb[im_i]['bbox_targets'] = \
_compute_targets(rois, max_overlaps, max_classes)
if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
# Use fixed / precomputed "means" and "stds" instead of empirical values
means = np.tile(
np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS), (num_reg_classes, 1))
stds = np.tile(
np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS), (num_reg_classes, 1))
else:
# Compute values needed for means and stds
# var(x) = E(x^2) - E(x)^2
class_counts = np.zeros((num_reg_classes, 1)) + cfg.EPS
sums = np.zeros((num_reg_classes, 4))
squared_sums = np.zeros((num_reg_classes, 4))
for im_i in xrange(num_images):
targets = roidb[im_i]['bbox_targets']
for cls in xrange(1, num_reg_classes):
cls_inds = np.where(targets[:, 0] > 0)[0] if cfg.TRAIN.AGNOSTIC \
else np.where(targets[:, 0] == cls)[0]
if cls_inds.size > 0:
class_counts[cls] += cls_inds.size
sums[cls, :] += targets[cls_inds, 1:].sum(axis=0)
squared_sums[cls, :] += \
(targets[cls_inds, 1:] ** 2).sum(axis=0)
means = sums / class_counts
stds = np.sqrt(squared_sums / class_counts - means ** 2)
print 'bbox target means:'
print means
print means[1:, :].mean(axis=0) # ignore bg class
print 'bbox target stdevs:'
print stds
print stds[1:, :].mean(axis=0) # ignore bg class
# Normalize targets
if cfg.TRAIN.BBOX_NORMALIZE_TARGETS:
print "Normalizing targets"
for im_i in xrange(num_images):
targets = roidb[im_i]['bbox_targets']
for cls in xrange(1, num_reg_classes):
cls_inds = np.where(targets[:, 0] > 0) if cfg.TRAIN.AGNOSTIC \
else np.where(targets[:, 0] == cls)[0]
roidb[im_i]['bbox_targets'][cls_inds, 1:] -= means[cls, :]
roidb[im_i]['bbox_targets'][cls_inds, 1:] /= stds[cls, :]
else:
print "NOT normalizing targets"
# These values will be needed for making predictions
# (the predicts will need to be unnormalized and uncentered)
return means.ravel(), stds.ravel()
def _compute_targets(rois, overlaps, labels):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((rois.shape[0], 5), dtype=np.float32)
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
| 5,818 | 40.863309 | 83 | py |
bottom-up-attention | bottom-up-attention-master/lib/roi_data_layer/minibatch.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Compute minibatch blobs for training a Fast R-CNN network."""
import numpy as np
import numpy.random as npr
import scipy.sparse as sparse
import cv2
from fast_rcnn.config import cfg
from utils.blob import prep_im_for_blob, im_list_to_blob
def get_minibatch(roidb, num_classes):
"""Given a roidb, construct a minibatch sampled from it."""
num_images = len(roidb)
num_reg_class = 2 if cfg.TRAIN.AGNOSTIC else num_classes
# Sample random scales to use for each image in this batch
random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES),
size=num_images)
assert(cfg.TRAIN.BATCH_SIZE % num_images == 0) or (cfg.TRAIN.BATCH_SIZE == -1), \
'num_images ({}) must divide BATCH_SIZE ({})'. \
format(num_images, cfg.TRAIN.BATCH_SIZE)
rois_per_image = np.inf if cfg.TRAIN.BATCH_SIZE == -1 else cfg.TRAIN.BATCH_SIZE / num_images
fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image)
# Get the input image blob, formatted for caffe
im_blob, im_scales = _get_image_blob(roidb, random_scale_inds)
blobs = {'data': im_blob}
if cfg.TRAIN.HAS_RPN:
assert len(im_scales) == 1, "Single batch only"
assert len(roidb) == 1, "Single batch only"
# gt boxes: (x1, y1, x2, y2, cls)
gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
num_gt = len(gt_inds)
assert num_gt > 0, "gt must not be empty"
if cfg.TRAIN.HAS_ATTRIBUTES:
if cfg.TRAIN.HAS_RELATIONS:
gt_boxes = np.zeros((num_gt, 21 + num_gt), dtype=np.float32)
else:
gt_boxes = np.zeros((num_gt, 21), dtype=np.float32)
else:
gt_boxes = np.zeros((num_gt, 5), dtype=np.float32)
gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :] * im_scales[0]
gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
if cfg.TRAIN.HAS_ATTRIBUTES:
gt_boxes[:, 5:21] = roidb[0]['gt_attributes'][gt_inds].toarray()
if cfg.TRAIN.HAS_RELATIONS:
assert num_gt == roidb[0]['gt_classes'].shape[0], \
"Generation of gt_relations doesn't accomodate dropping objects"
coords = roidb[0]['gt_relations'] # i,relation,j
if coords.size > 0:
assert num_gt > coords.max(axis=0)[0], \
"gt_relations subject index exceeds number of objects"
assert num_gt > coords.max(axis=0)[2], \
"gt_relations object index exceeds number of objects"
np.random.shuffle(coords) # There may be multiple relations between same objects
rel_matrix = gt_boxes[:, 21:]
for r in range(coords.shape[0]):
rel_matrix[coords[r,0],coords[r,2]] = coords[r,1]
blobs['gt_boxes'] = gt_boxes
blobs['im_info'] = np.array(
[[im_blob.shape[2], im_blob.shape[3], im_scales[0]]],
dtype=np.float32)
else: # not using RPN
# Now, build the region of interest and label blobs
rois_blob = np.zeros((0, 5), dtype=np.float32)
labels_blob = np.zeros((0), dtype=np.float32)
bbox_targets_blob = np.zeros((0, 4 * num_reg_class), dtype=np.float32)
bbox_inside_blob = np.zeros(bbox_targets_blob.shape, dtype=np.float32)
# all_overlaps = []
for im_i in xrange(num_images):
labels, overlaps, im_rois, bbox_targets, bbox_inside_weights \
= _sample_rois(roidb[im_i], fg_rois_per_image, rois_per_image,
num_classes)
# Add to RoIs blob
rois = _project_im_rois(im_rois, im_scales[im_i])
batch_ind = im_i * np.ones((rois.shape[0], 1))
rois_blob_this_image = np.hstack((batch_ind, rois))
rois_blob = np.vstack((rois_blob, rois_blob_this_image))
# Add to labels, bbox targets, and bbox loss blobs
labels_blob = np.hstack((labels_blob, labels))
bbox_targets_blob = np.vstack((bbox_targets_blob, bbox_targets))
bbox_inside_blob = np.vstack((bbox_inside_blob, bbox_inside_weights))
# all_overlaps = np.hstack((all_overlaps, overlaps))
# For debug visualizations
# _vis_minibatch(im_blob, rois_blob, labels_blob, all_overlaps)
blobs['rois'] = rois_blob
blobs['labels'] = labels_blob
if cfg.TRAIN.BBOX_REG:
blobs['bbox_targets'] = bbox_targets_blob
blobs['bbox_inside_weights'] = bbox_inside_blob
blobs['bbox_outside_weights'] = \
np.array(bbox_inside_blob > 0).astype(np.float32)
return blobs
def _sample_rois(roidb, fg_rois_per_image, rois_per_image, num_classes):
"""Generate a random sample of RoIs comprising foreground and background
examples.
"""
# label = class RoI has max overlap with
labels = roidb['max_classes']
overlaps = roidb['max_overlaps']
rois = roidb['boxes']
# Select foreground RoIs as those with >= FG_THRESH overlap
fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
# Guard against the case when an image has fewer than fg_rois_per_image
# foreground RoIs
fg_rois_per_this_image = np.minimum(fg_rois_per_image, fg_inds.size)
# Sample foreground regions without replacement
if fg_inds.size > 0:
fg_inds = npr.choice(
fg_inds, size=fg_rois_per_this_image, replace=False)
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &
(overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
# Compute number of background RoIs to take from this image (guarding
# against there being fewer than desired)
bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image
bg_rois_per_this_image = np.minimum(bg_rois_per_this_image,
bg_inds.size)
# Sample foreground regions without replacement
if bg_inds.size > 0:
bg_inds = npr.choice(
bg_inds, size=bg_rois_per_this_image, replace=False)
# The indices that we're selecting (both fg and bg)
keep_inds = np.append(fg_inds, bg_inds)
# Select sampled values from various arrays:
labels = labels[keep_inds]
# Clamp labels for the background RoIs to 0
labels[fg_rois_per_this_image:] = 0
overlaps = overlaps[keep_inds]
rois = rois[keep_inds]
bbox_targets, bbox_inside_weights = _get_bbox_regression_labels(
roidb['bbox_targets'][keep_inds, :], num_classes)
return labels, overlaps, rois, bbox_targets, bbox_inside_weights
def _get_image_blob(roidb, scale_inds):
"""Builds an input blob from the images in the roidb at the specified
scales.
"""
num_images = len(roidb)
processed_ims = []
im_scales = []
for i in xrange(num_images):
im = cv2.imread(roidb[i]['image'])
if roidb[i]['flipped']:
im = im[:, ::-1, :]
target_size = cfg.TRAIN.SCALES[scale_inds[i]]
im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,
cfg.TRAIN.MAX_SIZE)
im_scales.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_scales
def _project_im_rois(im_rois, im_scale_factor):
"""Project image RoIs into the rescaled training image."""
rois = im_rois * im_scale_factor
return rois
def _get_bbox_regression_labels(bbox_target_data, num_classes):
"""Bounding-box regression targets are stored in a compact form in the
roidb.
This function expands those targets into the 4-of-4*K representation used
by the network (i.e. only one class has non-zero targets). The loss weights
are similarly expanded.
Returns:
bbox_target_data (ndarray): N x 4K blob of regression targets
bbox_inside_weights (ndarray): N x 4K blob of loss weights
"""
clss = bbox_target_data[:, 0]
num_reg_class = 2 if cfg.TRAIN.AGNOSTIC else num_classes
bbox_targets = np.zeros((clss.size, 4 * num_reg_class), dtype=np.float32)
bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
inds = np.where(clss > 0)[0]
if cfg.TRAIN.AGNOSTIC:
for ind in inds:
cls = clss[ind]
start = 4 * (1 if cls > 0 else 0)
end = start + 4
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
else:
for ind in inds:
cls = clss[ind]
start = 4 * cls
end = start + 4
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
return bbox_targets, bbox_inside_weights
def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):
"""Visualize a mini-batch for debugging."""
import matplotlib.pyplot as plt
for i in xrange(rois_blob.shape[0]):
rois = rois_blob[i, :]
im_ind = rois[0]
roi = rois[1:]
im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
im += cfg.PIXEL_MEANS
im = im[:, :, (2, 1, 0)]
im = im.astype(np.uint8)
cls = labels_blob[i]
plt.imshow(im)
print 'class: ', cls, ' overlap: ', overlaps[i]
plt.gca().add_patch(
plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
roi[3] - roi[1], fill=False,
edgecolor='r', linewidth=3)
)
plt.show()
| 10,006 | 41.046218 | 96 | py |
bottom-up-attention | bottom-up-attention-master/lib/roi_data_layer/__init__.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
| 248 | 34.571429 | 58 | py |
bottom-up-attention | bottom-up-attention-master/lib/fast_rcnn/test.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Test a Fast R-CNN network on an imdb (image database)."""
from fast_rcnn.config import cfg, get_output_dir
from fast_rcnn.bbox_transform import clip_boxes, bbox_transform_inv
import argparse
from utils.timer import Timer
import numpy as np
import cv2
import caffe
from fast_rcnn.nms_wrapper import nms, soft_nms
import cPickle
from utils.blob import im_list_to_blob
import os
from utils.cython_bbox import bbox_overlaps
def _get_image_blob(im):
"""Converts an image into a network input.
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
processed_ims = []
im_scale_factors = []
for target_size in cfg.TEST.SCALES:
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
im_scale_factors.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, np.array(im_scale_factors)
def _get_rois_blob(im_rois, im_scale_factors):
"""Converts RoIs into network inputs.
Arguments:
im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates
im_scale_factors (list): scale factors as returned by _get_image_blob
Returns:
blob (ndarray): R x 5 matrix of RoIs in the image pyramid
"""
rois, levels = _project_im_rois(im_rois, im_scale_factors)
rois_blob = np.hstack((levels, rois))
return rois_blob.astype(np.float32, copy=False)
def _project_im_rois(im_rois, scales):
"""Project image RoIs into the image pyramid built by _get_image_blob.
Arguments:
im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates
scales (list): scale factors as returned by _get_image_blob
Returns:
rois (ndarray): R x 4 matrix of projected RoI coordinates
levels (list): image pyramid levels used by each projected RoI
"""
im_rois = im_rois.astype(np.float, copy=False)
if len(scales) > 1:
widths = im_rois[:, 2] - im_rois[:, 0] + 1
heights = im_rois[:, 3] - im_rois[:, 1] + 1
areas = widths * heights
scaled_areas = areas[:, np.newaxis] * (scales[np.newaxis, :] ** 2)
diff_areas = np.abs(scaled_areas - 224 * 224)
levels = diff_areas.argmin(axis=1)[:, np.newaxis]
else:
levels = np.zeros((im_rois.shape[0], 1), dtype=np.int)
rois = im_rois * scales[levels]
return rois, levels
def _get_blobs(im, rois):
"""Convert an image and RoIs within that image into network inputs."""
blobs = {'data' : None, 'rois' : None}
blobs['data'], im_scale_factors = _get_image_blob(im)
if not cfg.TEST.HAS_RPN:
blobs['rois'] = _get_rois_blob(rois, im_scale_factors)
return blobs, im_scale_factors
def im_detect(net, im, boxes=None, force_boxes=False):
"""Detect object classes in an image given object proposals.
Arguments:
net (caffe.Net): Fast R-CNN network to use
im (ndarray): color image to test (in BGR order)
boxes (ndarray): R x 4 array of object proposals or None (for RPN)
Returns:
scores (ndarray): R x K array of object class scores (K includes
background as object category 0)
boxes (ndarray): R x (4*K) array of predicted bounding boxes
attr_scores (ndarray): R x M array of attribute class scores
"""
blobs, im_scales = _get_blobs(im, boxes)
if force_boxes:
blobs['rois'] = _get_rois_blob(boxes, im_scales)
# When mapping from image ROIs to feature map ROIs, there's some aliasing
# (some distinct image ROIs get mapped to the same feature ROI).
# Here, we identify duplicate feature ROIs, so we only compute features
# on the unique subset.
if cfg.DEDUP_BOXES > 0 and not cfg.TEST.HAS_RPN:
v = np.array([1, 1e3, 1e6, 1e9, 1e12])
hashes = np.round(blobs['rois'] * cfg.DEDUP_BOXES).dot(v)
_, index, inv_index = np.unique(hashes, return_index=True,
return_inverse=True)
blobs['rois'] = blobs['rois'][index, :]
boxes = boxes[index, :]
im_blob = blobs['data']
blobs['im_info'] = np.array(
[[im_blob.shape[2], im_blob.shape[3], im_scales[0]]],
dtype=np.float32)
# reshape network inputs
net.blobs['data'].reshape(*(blobs['data'].shape))
if 'im_info' in net.blobs:
net.blobs['im_info'].reshape(*(blobs['im_info'].shape))
if force_boxes or not cfg.TEST.HAS_RPN:
net.blobs['rois'].reshape(*(blobs['rois'].shape))
# do forward
forward_kwargs = {'data': blobs['data'].astype(np.float32, copy=False)}
if 'im_info' in net.blobs:
forward_kwargs['im_info'] = blobs['im_info'].astype(np.float32, copy=False)
if force_boxes or not cfg.TEST.HAS_RPN:
forward_kwargs['rois'] = blobs['rois'].astype(np.float32, copy=False)
blobs_out = net.forward(**forward_kwargs)
if cfg.TEST.HAS_RPN and not force_boxes:
assert len(im_scales) == 1, "Only single-image batch implemented"
rois = net.blobs['rois'].data.copy()
# unscale back to raw image space
boxes = rois[:, 1:5] / im_scales[0]
if cfg.TEST.SVM:
# use the raw scores before softmax under the assumption they
# were trained as linear SVMs
scores = net.blobs['cls_score'].data
else:
# use softmax estimated probabilities
scores = blobs_out['cls_prob']
if cfg.TEST.BBOX_REG:
# Apply bounding-box regression deltas
box_deltas = blobs_out['bbox_pred']
pred_boxes = bbox_transform_inv(boxes, box_deltas)
pred_boxes = clip_boxes(pred_boxes, im.shape)
else:
# Simply repeat the boxes, once for each class
pred_boxes = np.tile(boxes, (1, scores.shape[1]))
if cfg.DEDUP_BOXES > 0 and not cfg.TEST.HAS_RPN:
# Map scores and predictions back to the original set of boxes
scores = scores[inv_index, :]
pred_boxes = pred_boxes[inv_index, :]
if 'attr_prob' in net.blobs:
attr_scores = blobs_out['attr_prob']
else:
attr_scores = None
if 'rel_prob' in net.blobs:
rel_scores = blobs_out['rel_prob']
else:
rel_scores = None
return scores, pred_boxes, attr_scores, rel_scores
def vis_detections(im, class_name, dets, thresh=0.3, filename='vis.png'):
"""Visual debugging of detections."""
import matplotlib.pyplot as plt
im = im[:, :, (2, 1, 0)]
plt.cla()
plt.imshow(im)
for i in xrange(np.minimum(10, dets.shape[0])):
bbox = dets[i, :4]
score = dets[i, -1]
if score > thresh:
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='g', linewidth=3)
)
plt.title('{} {:.3f}'.format(class_name, score))
plt.show()
plt.savefig('./data/vis/%s' % filename)
def vis_multiple(im, class_names, all_boxes, filename='vis.png'):
"""Visual debugging of detections."""
print filename
import matplotlib.pyplot as plt
im = im[:, :, (2, 1, 0)]
plt.cla()
plt.imshow(im)
max_boxes = 10
image_scores = np.hstack([all_boxes[j][:, 4]
for j in xrange(1, len(class_names))])
if len(image_scores) > 10:
image_thresh = np.sort(image_scores)[-max_boxes]
else:
image_thresh = -np.inf
for j in xrange(1, len(class_names)):
keep = np.where(all_boxes[j][:, 4] >= image_thresh)[0]
dets = all_boxes[j][keep, :]
for i in range(dets.shape[0]):
bbox = dets[i, :4]
score = dets[i, -1]
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=1)
)
plt.gca().text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_names[j], score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=8, color='white')
plt.title('Best %d Attributes using gt boxes' % max_boxes)
plt.show()
plt.savefig('./data/vis/%s' % filename)
def vis_relations(im, class_names, box_proposals, scores, filename='vis.png'):
n = box_proposals.shape[0]
assert scores.shape[0] == n*n
print filename
import matplotlib.pyplot as plt
im = im[:, :, (2, 1, 0)]
plt.cla()
plt.imshow(im)
max_rels = 5
scores = scores[:, 1:]
image_scores = scores.flatten()
if len(image_scores) > 10:
image_thresh = np.sort(image_scores)[-max_rels]
else:
image_thresh = -np.inf
for i in xrange(n):
for j in xrange(n):
keep = np.where(scores[i*n+j] >= image_thresh)[0]
for ix in keep:
bbox = box_proposals[i]
score = scores[i*n+j, ix]
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=1)
)
plt.gca().text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_names[ix], score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=8, color='white')
bbox = box_proposals[j]
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=1)
)
plt.title('Best %d Relations using gt boxes' % max_rels)
plt.show()
plt.savefig('./data/vis/%s' % filename)
def apply_nms(all_boxes, thresh):
"""Apply non-maximum suppression to all predicted boxes output by the
test_net method.
"""
num_classes = len(all_boxes)
num_images = len(all_boxes[0])
nms_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(num_classes)]
for cls_ind in xrange(num_classes):
for im_ind in xrange(num_images):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# CPU NMS is much faster than GPU NMS when the number of boxes
# is relative small (e.g., < 10k)
# TODO(rbg): autotune NMS dispatch
keep = nms(dets, thresh, force_cpu=True)
if len(keep) == 0:
continue
nms_boxes[cls_ind][im_ind] = dets[keep, :].copy()
return nms_boxes
def test_net(net, imdb, max_per_image=400, thresh=-np.inf, vis=False, load_cache=False):
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)]
output_dir = get_output_dir(imdb, net)
det_file = os.path.join(output_dir, 'detections.pkl')
if load_cache and os.path.exists(det_file):
print 'Loading pickled detections from %s' % det_file
with open(det_file, 'rb') as f:
all_boxes = cPickle.load(f)
else:
# timers
_t = {'im_detect' : Timer(), 'misc' : Timer()}
if not cfg.TEST.HAS_RPN:
roidb = imdb.roidb
for i in xrange(num_images):
# filter out any ground truth boxes
if cfg.TEST.HAS_RPN:
box_proposals = None
else:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select those the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
scores, boxes, attr_scores, rel_scores = im_detect(net, im, box_proposals)
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in xrange(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
if cfg.TEST.AGNOSTIC:
cls_boxes = boxes[inds, 4:8]
else:
cls_boxes = boxes[inds, j*4:(j+1)*4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
#keep = soft_nms(cls_dets, method=cfg.TEST.SOFT_NMS)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
if vis:
vis_detections(im, imdb.classes[j], cls_dets)
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][i][:, 4]
for j in xrange(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in xrange(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, 4] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time)
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir)
def test_net_with_gt_boxes(net, imdb, max_per_image=400, thresh=-np.inf, vis=False, load_cache=False):
"""Test a Fast R-CNN network on an image database, evaluating attribute
and relation detections given ground truth boxes."""
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_attributes)]
rel_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_relations)]
output_dir = get_output_dir(imdb, net, attributes=True)
det_file = os.path.join(output_dir, 'attribute_detections.pkl')
rel_file = os.path.join(output_dir, 'relation_detections.pkl')
if load_cache and os.path.exists(det_file):
print 'Loading pickled detections from %s' % det_file
with open(det_file, 'rb') as f:
all_boxes = cPickle.load(f)
with open(rel_file, 'rb') as f:
rel_boxes = cPickle.load(f)
else:
# timers
_t = {'im_detect' : Timer(), 'misc' : Timer()}
roidb = imdb.gt_roidb()
for i in xrange(num_images):
box_proposals = roidb[i]['boxes']
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
scores, boxes, attr_scores, rel_scores = im_detect(net, im, box_proposals, force_boxes=True)
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the no attribute class
if attr_scores.shape[1] < imdb.num_attributes:
attr_scores = np.hstack((np.zeros((attr_scores.shape[0],1)),attr_scores))
if rel_scores and rel_scores.shape[1] < imdb.num_relations:
rel_scores = np.hstack((np.zeros((rel_scores.shape[0],1)),rel_scores))
for j in xrange(1, imdb.num_attributes):
inds = np.where(attr_scores[:, j] > thresh)[0]
cls_scores = attr_scores[inds, j]
cls_boxes = box_proposals[inds, :]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all attributes*
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][i][:, 4]
for j in xrange(1, imdb.num_attributes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in xrange(1, imdb.num_attributes):
keep = np.where(all_boxes[j][i][:, 4] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
if vis:
im_boxes = [all_boxes[j][i] for j in xrange(imdb.num_attributes)]
vis_multiple(im, imdb.attributes, im_boxes, filename='attr_%d.png' % i)
if rel_scores:
vis_relations(im, imdb.relations, box_proposals, rel_scores, filename='rel_%d.png' % i)
_t['misc'].toc()
print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time)
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'Evaluating attribute and / or relation detections'
imdb.evaluate_attributes(all_boxes, output_dir)
| 19,368 | 38.690574 | 123 | py |
bottom-up-attention | bottom-up-attention-master/lib/fast_rcnn/train_multi_gpu.py | # --------------------------------------------------------
# Written by Bharat Singh
# Modified version of py-R-FCN
# --------------------------------------------------------
"""Train a Fast R-CNN network."""
import caffe
from fast_rcnn.config import cfg
import roi_data_layer.roidb as rdl_roidb
from utils.timer import Timer
import numpy as np
import os
from caffe.proto import caffe_pb2
import google.protobuf as pb2
import google.protobuf.text_format
from multiprocessing import Process
class SolverWrapper(object):
"""A simple wrapper around Caffe's solver.
This wrapper gives us control over he snapshotting process, which we
use to unnormalize the learned bounding-box regression weights.
"""
def __init__(self, solver_prototxt, roidb, output_dir, gpu_id,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
self.gpu_id = gpu_id
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb, gpu_id)
def snapshot(self):
"""Take a snapshot of the network after unnormalizing the learned
bounding-box regression weights. This enables easy use at test-time.
"""
net = self.solver.net
scale_bbox_params_faster_rcnn = (cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS and
net.params.has_key('bbox_pred'))
scale_bbox_params_rfcn = (cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS and
net.params.has_key('rfcn_bbox'))
scale_bbox_params_rpn = (cfg.TRAIN.RPN_NORMALIZE_TARGETS and
net.params.has_key('rpn_bbox_pred'))
if scale_bbox_params_faster_rcnn:
# save original values
orig_0 = net.params['bbox_pred'][0].data.copy()
orig_1 = net.params['bbox_pred'][1].data.copy()
# scale and shift with bbox reg unnormalization; then save snapshot
net.params['bbox_pred'][0].data[...] = \
(net.params['bbox_pred'][0].data *
self.bbox_stds[:, np.newaxis])
net.params['bbox_pred'][1].data[...] = \
(net.params['bbox_pred'][1].data *
self.bbox_stds + self.bbox_means)
if scale_bbox_params_rpn:
rpn_orig_0 = net.params['rpn_bbox_pred'][0].data.copy()
rpn_orig_1 = net.params['rpn_bbox_pred'][1].data.copy()
num_anchor = rpn_orig_0.shape[0] / 4
# scale and shift with bbox reg unnormalization; then save snapshot
self.rpn_means = np.tile(np.asarray(cfg.TRAIN.RPN_NORMALIZE_MEANS),
num_anchor)
self.rpn_stds = np.tile(np.asarray(cfg.TRAIN.RPN_NORMALIZE_STDS),
num_anchor)
net.params['rpn_bbox_pred'][0].data[...] = \
(net.params['rpn_bbox_pred'][0].data *
self.rpn_stds[:, np.newaxis, np.newaxis, np.newaxis])
net.params['rpn_bbox_pred'][1].data[...] = \
(net.params['rpn_bbox_pred'][1].data *
self.rpn_stds + self.rpn_means)
if scale_bbox_params_rfcn:
# save original values
orig_0 = net.params['rfcn_bbox'][0].data.copy()
orig_1 = net.params['rfcn_bbox'][1].data.copy()
repeat = orig_1.shape[0] / self.bbox_means.shape[0]
# scale and shift with bbox reg unnormalization; then save snapshot
net.params['rfcn_bbox'][0].data[...] = \
(net.params['rfcn_bbox'][0].data *
np.repeat(self.bbox_stds, repeat).reshape((orig_1.shape[0], 1, 1, 1)))
net.params['rfcn_bbox'][1].data[...] = \
(net.params['rfcn_bbox'][1].data *
np.repeat(self.bbox_stds, repeat) + np.repeat(self.bbox_means, repeat))
infix = ('_' + cfg.TRAIN.SNAPSHOT_INFIX
if cfg.TRAIN.SNAPSHOT_INFIX != '' else '')
filename = (self.solver_param.snapshot_prefix + infix +
'_iter_{:d}'.format(self.solver.iter) + '.caffemodel')
filename = os.path.join(self.output_dir, filename)
if self.gpu_id == 0:
net.save(str(filename))
print 'Wrote snapshot to: {:s}'.format(filename)
if scale_bbox_params_faster_rcnn:
# restore net to original state
net.params['bbox_pred'][0].data[...] = orig_0
net.params['bbox_pred'][1].data[...] = orig_1
if scale_bbox_params_rfcn:
# restore net to original state
net.params['rfcn_bbox'][0].data[...] = orig_0
net.params['rfcn_bbox'][1].data[...] = orig_1
if scale_bbox_params_rpn:
# restore net to original state
net.params['rpn_bbox_pred'][0].data[...] = rpn_orig_0
net.params['rpn_bbox_pred'][1].data[...] = rpn_orig_1
return filename
def track_memory(self):
net = self.solver.net
print 'Memory Usage:'
total = 0.0
data = 0.0
params = 0.0
for k,v in net.blobs.iteritems():
gb = float(v.data.nbytes)/1024/1024/1024
print '%s : %.3f GB %s' % (k,gb,v.data.shape)
total += gb
data += gb
print 'Memory Usage: Data %.3f GB' % data
for k,v in net.params.iteritems():
for i,p in enumerate(v):
gb = float(p.data.nbytes)/1024/1024/1024
total += gb
params += gb
print '%s[%d] : %.3f GB %s' % (k,i,gb,p.data.shape)
print 'Memory Usage: Params %.3f GB' % params
print 'Memory Usage: Total %.3f GB' % total
def getSolver(self):
return self.solver
def solve(proto, roidb, pretrained_model, gpus, uid, rank, output_dir, max_iter):
caffe.set_mode_gpu()
caffe.set_device(gpus[rank])
caffe.set_solver_count(len(gpus))
caffe.set_solver_rank(rank)
caffe.set_multiprocess(True)
cfg.GPU_ID = gpus[rank]
solverW = SolverWrapper(proto, roidb, output_dir,rank,pretrained_model)
solver = solverW.getSolver()
nccl = caffe.NCCL(solver, uid)
nccl.bcast()
solver.add_callback(nccl)
if solver.param.layer_wise_reduce:
solver.net.after_backward(nccl)
count = 0
while count < max_iter:
print 'Solver step'
solver.step(cfg.TRAIN.SNAPSHOT_ITERS)
if rank == 0:
solverW.snapshot()
#solverW.track_memory()
count = count + cfg.TRAIN.SNAPSHOT_ITERS
def get_training_roidb(imdb):
"""Returns a roidb (Region of Interest database) for use in training."""
if cfg.TRAIN.USE_FLIPPED:
print 'Appending horizontally-flipped training examples...'
imdb.append_flipped_images()
print 'done'
print 'Preparing training data...'
rdl_roidb.prepare_roidb(imdb)
print 'done'
return imdb.roidb
def filter_roidb(roidb):
"""Remove roidb entries that have no usable RoIs."""
def is_valid(entry):
# Valid images have:
# (1) At least one foreground RoI OR
# (2) At least one background RoI
overlaps = entry['max_overlaps']
# find boxes with sufficient overlap
fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &
(overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
# image is only valid if such boxes exist
valid = len(fg_inds) > 0 or len(bg_inds) > 0
return valid
num = len(roidb)
filtered_roidb = [entry for entry in roidb if is_valid(entry)]
num_after = len(filtered_roidb)
print 'Filtered {} roidb entries: {} -> {}'.format(num - num_after,
num, num_after)
return filtered_roidb
def train_net_multi_gpu(solver_prototxt, roidb, output_dir, pretrained_model, max_iter, gpus):
"""Train a Fast R-CNN network."""
uid = caffe.NCCL.new_uid()
caffe.init_log()
caffe.log('Using devices %s' % str(gpus))
procs = []
for rank in range(len(gpus)):
p = Process(target=solve,
args=(solver_prototxt, roidb, pretrained_model, gpus, uid, rank, output_dir, max_iter))
p.daemon = False
p.start()
procs.append(p)
for p in procs:
p.join()
| 9,558 | 37.857724 | 107 | py |
bottom-up-attention | bottom-up-attention-master/lib/fast_rcnn/bbox_transform.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import numpy as np
def bbox_transform(ex_rois, gt_rois):
ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0
ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0
ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths
ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights
gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0
gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0
gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths
gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights
targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths
targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights
targets_dw = np.log(gt_widths / ex_widths)
targets_dh = np.log(gt_heights / ex_heights)
targets = np.vstack(
(targets_dx, targets_dy, targets_dw, targets_dh)).transpose()
return targets
def bbox_transform_inv(boxes, deltas):
if boxes.shape[0] == 0:
return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)
boxes = boxes.astype(deltas.dtype, copy=False)
widths = boxes[:, 2] - boxes[:, 0] + 1.0
heights = boxes[:, 3] - boxes[:, 1] + 1.0
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
dx = deltas[:, 0::4]
dy = deltas[:, 1::4]
dw = deltas[:, 2::4]
dh = deltas[:, 3::4]
pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
pred_w = np.exp(dw) * widths[:, np.newaxis]
pred_h = np.exp(dh) * heights[:, np.newaxis]
pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
# x1
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
# y1
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
# x2
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w
# y2
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h
return pred_boxes
def clip_boxes(boxes, im_shape):
"""
Clip boxes to image boundaries.
"""
# x1 >= 0
boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)
# y1 >= 0
boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0)
# x2 < im_shape[1]
boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0)
# y2 < im_shape[0]
boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0)
return boxes
| 2,539 | 32.421053 | 79 | py |
bottom-up-attention | bottom-up-attention-master/lib/fast_rcnn/nms_wrapper.py | # ----------------------------------------------------------
# Soft-NMS: Improving Object Detection With One Line of Code
# Copyright (c) University of Maryland, College Park
# Licensed under The MIT License [see LICENSE for details]
# Written by Navaneeth Bodla and Bharat Singh
# ----------------------------------------------------------
from fast_rcnn.config import cfg
from nms.gpu_nms import gpu_nms
from nms.cpu_nms import cpu_nms, cpu_soft_nms
import numpy as np
def soft_nms(dets, sigma=0.5, Nt=0.3, threshold=0.001, method=1):
keep = cpu_soft_nms(np.ascontiguousarray(dets, dtype=np.float32),
np.float32(sigma), np.float32(Nt),
np.float32(threshold),
np.uint8(method))
return keep
# Original NMS implementation
def nms(dets, thresh, force_cpu=False):
"""Dispatch to either CPU or GPU NMS implementations."""
if dets.shape[0] == 0:
return []
if cfg.USE_GPU_NMS and not force_cpu:
return gpu_nms(dets, thresh, device_id=cfg.GPU_ID)
else:
return cpu_nms(dets, thresh)
| 1,101 | 33.4375 | 69 | py |
bottom-up-attention | bottom-up-attention-master/lib/fast_rcnn/config.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Fast R-CNN config system.
This file specifies default config options for Fast R-CNN. You should not
change values in this file. Instead, you should write a config file (in yaml)
and use cfg_from_file(yaml_file) to load it and override the default options.
Most tools in $ROOT/tools take a --cfg option to specify an override file.
- See tools/{train,test}_net.py for example code that uses cfg_from_file()
- See experiments/cfgs/*.yml for example YAML config override files
"""
import os
import os.path as osp
import numpy as np
# `pip install easydict` if you don't have it
from easydict import EasyDict as edict
__C = edict()
# Consumers can get config by:
# from fast_rcnn_config import cfg
cfg = __C
#
# Training options
#
__C.TRAIN = edict()
# Scales to use during training (can list multiple scales)
# Each scale is the pixel size of an image's shortest side
__C.TRAIN.SCALES = (600,)
# Max pixel size of the longest side of a scaled input image
__C.TRAIN.MAX_SIZE = 1000
# Images to use per minibatch
__C.TRAIN.IMS_PER_BATCH = 2
# Minibatch size (number of regions of interest [ROIs])
__C.TRAIN.BATCH_SIZE = 128
# Fraction of minibatch that is labeled foreground (i.e. class > 0)
__C.TRAIN.FG_FRACTION = 0.25
# Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
__C.TRAIN.FG_THRESH = 0.5
# Overlap threshold for a ROI to be considered background (class = 0 if
# overlap in [LO, HI))
__C.TRAIN.BG_THRESH_HI = 0.5
__C.TRAIN.BG_THRESH_LO = 0.1
# Use horizontally-flipped images during training?
__C.TRAIN.USE_FLIPPED = True
# Train bounding-box regressors
__C.TRAIN.BBOX_REG = True
# Overlap required between a ROI and ground-truth box in order for that ROI to
# be used as a bounding-box regression training example
__C.TRAIN.BBOX_THRESH = 0.5
# Iterations between snapshots
__C.TRAIN.SNAPSHOT_ITERS = 10000
# solver.prototxt specifies the snapshot path prefix, this adds an optional
# infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel
__C.TRAIN.SNAPSHOT_INFIX = ''
# Use a prefetch thread in roi_data_layer.layer
# So far I haven't found this useful; likely more engineering work is required
__C.TRAIN.USE_PREFETCH = False
# Normalize the targets (subtract empirical mean, divide by empirical stddev)
__C.TRAIN.BBOX_NORMALIZE_TARGETS = True
# Deprecated (inside weights)
__C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
# Normalize the targets using "precomputed" (or made up) means and stdevs
# (BBOX_NORMALIZE_TARGETS must also be True)
__C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = False
__C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0)
__C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2)
__C.TRAIN.RPN_NORMALIZE_TARGETS = False
__C.TRAIN.RPN_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0)
__C.TRAIN.RPN_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2)
# Train using these proposals
__C.TRAIN.PROPOSAL_METHOD = 'selective_search'
# Make minibatches from images that have similar aspect ratios (i.e. both
# tall and thin or both short and wide) in order to avoid wasting computation
# on zero-padding.
__C.TRAIN.ASPECT_GROUPING = True
# Use RPN to detect objects
__C.TRAIN.HAS_RPN = False
# IOU >= thresh: positive example
__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7
# IOU < thresh: negative example
__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3
# If an anchor statisfied by positive and negative conditions set to negative
__C.TRAIN.RPN_CLOBBER_POSITIVES = False
# Max number of foreground examples
__C.TRAIN.RPN_FG_FRACTION = 0.5
# Total number of examples
__C.TRAIN.RPN_BATCHSIZE = 256
# NMS threshold used on RPN proposals
__C.TRAIN.RPN_NMS_THRESH = 0.7
# Number of top scoring boxes to keep before apply NMS to RPN proposals
__C.TRAIN.RPN_PRE_NMS_TOP_N = 12000
# Number of top scoring boxes to keep after applying NMS to RPN proposals
__C.TRAIN.RPN_POST_NMS_TOP_N = 2000
# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
__C.TRAIN.RPN_MIN_SIZE = 16
# Deprecated (outside weights)
__C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
# Give the positive RPN examples weight of p * 1 / {num positives}
# and give negatives a weight of (1 - p)
# Set to -1.0 to use uniform example weighting
__C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0
# whether use class aware box or not
__C.TRAIN.AGNOSTIC = False
# Detect attributes of objects
__C.TRAIN.HAS_ATTRIBUTES = False
# Detect relations between objects
__C.TRAIN.HAS_RELATIONS = False
# Fraction of relation minibatch that is labeled with a relation (i.e. class > 0)
__C.TRAIN.MIN_RELATION_FRACTION = 0.25
#
# Testing options
#
__C.TEST = edict()
# Scales to use during testing (can list multiple scales)
# Each scale is the pixel size of an image's shortest side
__C.TEST.SCALES = (600,)
# Max pixel size of the longest side of a scaled input image
__C.TEST.MAX_SIZE = 1000
# Overlap threshold used for non-maximum suppression (suppress boxes with
# IoU >= this threshold)
__C.TEST.NMS = 0.3
# Flag for soft-NMS method. 0 performs standard NMS, 1 performs soft-NMS with linear weighting and
# 2 performs soft-NMS with Gaussian weighting
__C.TEST.SOFT_NMS = 0
# Experimental: treat the (K+1) units in the cls_score layer as linear
# predictors (trained, eg, with one-vs-rest SVMs).
__C.TEST.SVM = False
# Test using bounding-box regressors
__C.TEST.BBOX_REG = True
# Propose boxes
__C.TEST.HAS_RPN = False
# Test using these proposals
__C.TEST.PROPOSAL_METHOD = 'selective_search'
## NMS threshold used on RPN proposals
__C.TEST.RPN_NMS_THRESH = 0.7
## Number of top scoring boxes to keep before apply NMS to RPN proposals
__C.TEST.RPN_PRE_NMS_TOP_N = 6000
## Number of top scoring boxes to keep after applying NMS to RPN proposals
__C.TEST.RPN_POST_NMS_TOP_N = 300
# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
__C.TEST.RPN_MIN_SIZE = 16
# whether use class aware box or not
__C.TEST.AGNOSTIC = False
# Detect attributes of objects
__C.TEST.HAS_ATTRIBUTES = False
# Detect relations between objects
__C.TEST.HAS_RELATIONS = False
#
# MISC
#
# The mapping from image coordinates to feature map coordinates might cause
# some boxes that are distinct in image space to become identical in feature
# coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor
# for identifying duplicate boxes.
# 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16
__C.DEDUP_BOXES = 1./16.
# Pixel mean values (BGR order) as a (1, 1, 3) array
# We use the same pixel mean for all networks even though it's not exactly what
# they were trained with
__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])
# For reproducibility
__C.RNG_SEED = 3
# A small number that's used many times
__C.EPS = 1e-14
# Root directory of project
__C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))
# Data directory
__C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data'))
# Model directory
__C.MODELS_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'models', 'pascal_voc'))
# Name (or path to) the matlab executable
__C.MATLAB = 'matlab'
# Place outputs under an experiments directory
__C.EXP_DIR = 'default'
# Use GPU implementation of non-maximum suppression
__C.USE_GPU_NMS = True
# Default GPU device id
__C.GPU_ID = 0
def get_output_dir(imdb, net=None, attributes=False):
"""Return the directory where experimental artifacts are placed.
If the directory does not exist, it is created.
A canonical path is built using the name from an imdb and a network
(if not None).
"""
outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))
if net is not None:
outdir = osp.join(outdir, net.name)
if attributes:
outdir = osp.join(outdir, "attr")
if not os.path.exists(outdir):
os.makedirs(outdir)
return outdir
def _merge_a_into_b(a, b):
"""Merge config dictionary a into config dictionary b, clobbering the
options in b whenever they are also specified in a.
"""
if type(a) is not edict:
return
for k, v in a.iteritems():
# a must specify keys that are in b
if not b.has_key(k):
raise KeyError('{} is not a valid config key'.format(k))
# the types must match, too
old_type = type(b[k])
if old_type is not type(v):
if isinstance(b[k], np.ndarray):
v = np.array(v, dtype=b[k].dtype)
else:
raise ValueError(('Type mismatch ({} vs. {}) '
'for config key: {}').format(type(b[k]),
type(v), k))
# recursively merge dicts
if type(v) is edict:
try:
_merge_a_into_b(a[k], b[k])
except:
print('Error under config key: {}'.format(k))
raise
else:
b[k] = v
def cfg_from_file(filename):
"""Load a config file and merge it into the default options."""
import yaml
with open(filename, 'r') as f:
yaml_cfg = edict(yaml.load(f))
_merge_a_into_b(yaml_cfg, __C)
def cfg_from_list(cfg_list):
"""Set config keys via list (e.g., from command line)."""
from ast import literal_eval
assert len(cfg_list) % 2 == 0
for k, v in zip(cfg_list[0::2], cfg_list[1::2]):
key_list = k.split('.')
d = __C
for subkey in key_list[:-1]:
assert d.has_key(subkey)
d = d[subkey]
subkey = key_list[-1]
assert d.has_key(subkey)
try:
value = literal_eval(v)
except:
# handle the case when v is a string literal
value = v
assert type(value) == type(d[subkey]), \
'type {} does not match original type {}'.format(
type(value), type(d[subkey]))
d[subkey] = value
| 10,118 | 31.329073 | 99 | py |
bottom-up-attention | bottom-up-attention-master/lib/fast_rcnn/__init__.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
| 248 | 34.571429 | 58 | py |
bottom-up-attention | bottom-up-attention-master/lib/fast_rcnn/train.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Train a Fast R-CNN network."""
import caffe
from fast_rcnn.config import cfg
import roi_data_layer.roidb as rdl_roidb
from utils.timer import Timer
import numpy as np
import os
from caffe.proto import caffe_pb2
import google.protobuf as pb2
import google.protobuf.text_format as text_format
class SolverWrapper(object):
"""A simple wrapper around Caffe's solver.
This wrapper gives us control over he snapshotting process, which we
use to unnormalize the learned bounding-box regression weights.
"""
def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb, cfg.GPU_ID)
def snapshot(self):
"""Take a snapshot of the network after unnormalizing the learned
bounding-box regression weights. This enables easy use at test-time.
"""
net = self.solver.net
scale_bbox_params_faster_rcnn = (cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS and
net.params.has_key('bbox_pred'))
scale_bbox_params_rfcn = (cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS and
net.params.has_key('rfcn_bbox'))
scale_bbox_params_rpn = (cfg.TRAIN.RPN_NORMALIZE_TARGETS and
net.params.has_key('rpn_bbox_pred'))
if scale_bbox_params_faster_rcnn:
# save original values
orig_0 = net.params['bbox_pred'][0].data.copy()
orig_1 = net.params['bbox_pred'][1].data.copy()
# scale and shift with bbox reg unnormalization; then save snapshot
net.params['bbox_pred'][0].data[...] = \
(net.params['bbox_pred'][0].data *
self.bbox_stds[:, np.newaxis])
net.params['bbox_pred'][1].data[...] = \
(net.params['bbox_pred'][1].data *
self.bbox_stds + self.bbox_means)
if scale_bbox_params_rpn:
rpn_orig_0 = net.params['rpn_bbox_pred'][0].data.copy()
rpn_orig_1 = net.params['rpn_bbox_pred'][1].data.copy()
num_anchor = rpn_orig_0.shape[0] / 4
# scale and shift with bbox reg unnormalization; then save snapshot
self.rpn_means = np.tile(np.asarray(cfg.TRAIN.RPN_NORMALIZE_MEANS),
num_anchor)
self.rpn_stds = np.tile(np.asarray(cfg.TRAIN.RPN_NORMALIZE_STDS),
num_anchor)
net.params['rpn_bbox_pred'][0].data[...] = \
(net.params['rpn_bbox_pred'][0].data *
self.rpn_stds[:, np.newaxis, np.newaxis, np.newaxis])
net.params['rpn_bbox_pred'][1].data[...] = \
(net.params['rpn_bbox_pred'][1].data *
self.rpn_stds + self.rpn_means)
if scale_bbox_params_rfcn:
# save original values
orig_0 = net.params['rfcn_bbox'][0].data.copy()
orig_1 = net.params['rfcn_bbox'][1].data.copy()
repeat = orig_1.shape[0] / self.bbox_means.shape[0]
# scale and shift with bbox reg unnormalization; then save snapshot
net.params['rfcn_bbox'][0].data[...] = \
(net.params['rfcn_bbox'][0].data *
np.repeat(self.bbox_stds, repeat).reshape((orig_1.shape[0], 1, 1, 1)))
net.params['rfcn_bbox'][1].data[...] = \
(net.params['rfcn_bbox'][1].data *
np.repeat(self.bbox_stds, repeat) + np.repeat(self.bbox_means, repeat))
infix = ('_' + cfg.TRAIN.SNAPSHOT_INFIX
if cfg.TRAIN.SNAPSHOT_INFIX != '' else '')
filename = (self.solver_param.snapshot_prefix + infix +
'_iter_{:d}'.format(self.solver.iter) + '.caffemodel')
filename = os.path.join(self.output_dir, filename)
net.save(str(filename))
print 'Wrote snapshot to: {:s}'.format(filename)
if scale_bbox_params_faster_rcnn:
# restore net to original state
net.params['bbox_pred'][0].data[...] = orig_0
net.params['bbox_pred'][1].data[...] = orig_1
if scale_bbox_params_rfcn:
# restore net to original state
net.params['rfcn_bbox'][0].data[...] = orig_0
net.params['rfcn_bbox'][1].data[...] = orig_1
if scale_bbox_params_rpn:
# restore net to original state
net.params['rpn_bbox_pred'][0].data[...] = rpn_orig_0
net.params['rpn_bbox_pred'][1].data[...] = rpn_orig_1
return filename
def train_model(self, max_iters):
"""Network training loop."""
last_snapshot_iter = -1
timer = Timer()
model_paths = []
while self.solver.iter < max_iters:
# Make one SGD update
timer.tic()
self.solver.step(1)
timer.toc()
if self.solver.iter % (10 * self.solver_param.display) == 0:
print 'speed: {:.3f}s / iter'.format(timer.average_time)
if self.solver.iter % cfg.TRAIN.SNAPSHOT_ITERS == 0:
last_snapshot_iter = self.solver.iter
model_paths.append(self.snapshot())
if last_snapshot_iter != self.solver.iter:
model_paths.append(self.snapshot())
return model_paths
def get_training_roidb(imdb):
"""Returns a roidb (Region of Interest database) for use in training."""
if cfg.TRAIN.USE_FLIPPED:
print 'Appending horizontally-flipped training examples...'
imdb.append_flipped_images()
print 'done'
print 'Preparing training data...'
rdl_roidb.prepare_roidb(imdb)
print 'done'
return imdb.roidb
def filter_roidb(roidb):
"""Remove roidb entries that have no usable RoIs."""
def is_valid(entry):
# Valid images have:
# (1) At least one foreground RoI OR
# (2) At least one background RoI
overlaps = entry['max_overlaps']
# find boxes with sufficient overlap
fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &
(overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
# image is only valid if such boxes exist
valid = len(fg_inds) > 0 or len(bg_inds) > 0
return valid
num = len(roidb)
filtered_roidb = [entry for entry in roidb if is_valid(entry)]
num_after = len(filtered_roidb)
print 'Filtered {} roidb entries: {} -> {}'.format(num - num_after,
num, num_after)
return filtered_roidb
def train_net(solver_prototxt, roidb, output_dir,
pretrained_model=None, max_iters=40000):
"""Train a Fast R-CNN network."""
roidb = filter_roidb(roidb)
sw = SolverWrapper(solver_prototxt, roidb, output_dir,
pretrained_model=pretrained_model)
print 'Solving...'
model_paths = sw.train_model(max_iters)
print 'done solving'
return model_paths
| 8,539 | 39.861244 | 92 | py |
bottom-up-attention | bottom-up-attention-master/lib/datasets/voc_eval.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
import xml.etree.ElementTree as ET
import os
import cPickle
import numpy as np
def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.find('truncated').text)
obj_struct['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_struct['bbox'] = [int(bbox.find('xmin').text),
int(bbox.find('ymin').text),
int(bbox.find('xmax').text),
int(bbox.find('ymax').text)]
objects.append(obj_struct)
return objects
def voc_ap(rec, prec, use_07_metric=False):
""" ap = voc_ap(rec, prec, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False).
"""
if use_07_metric:
# 11 point metric
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap = ap + p / 11.
else:
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def voc_eval(detpath,
annopath,
imagesetfile,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=False):
"""rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
detpath: Path to detections
detpath.format(classname) should produce the detection results file.
annopath: Path to annotations
annopath.format(imagename) should be the xml annotations file.
imagesetfile: Text file containing the list of images, one image per line.
classname: Category name (duh)
cachedir: Directory for caching the annotations
[ovthresh]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default False)
"""
# assumes detections are in detpath.format(classname)
# assumes annotations are in annopath.format(imagename)
# assumes imagesetfile is a text file with each line an image name
# cachedir caches the annotations in a pickle file
# first load gt
if not os.path.isdir(cachedir):
os.mkdir(cachedir)
cachefile = os.path.join(cachedir, 'annots.pkl')
# read list of images
with open(imagesetfile, 'r') as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
if not os.path.isfile(cachefile):
# load annots
recs = {}
for i, imagename in enumerate(imagenames):
recs[imagename] = parse_rec(annopath.format(imagename))
if i % 100 == 0:
print 'Reading annotation for {:d}/{:d}'.format(
i + 1, len(imagenames))
# save
print 'Saving cached annotations to {:s}'.format(cachefile)
with open(cachefile, 'w') as f:
cPickle.dump(recs, f)
else:
# load
with open(cachefile, 'r') as f:
recs = cPickle.load(f)
# extract gt objects for this class
class_recs = {}
npos = 0
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj['name'] == classname]
bbox = np.array([x['bbox'] for x in R])
difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
det = [False] * len(R)
npos = npos + sum(~difficult)
class_recs[imagename] = {'bbox': bbox,
'difficult': difficult,
'det': det}
# read dets
detfile = detpath.format(classname)
with open(detfile, 'r') as f:
lines = f.readlines()
splitlines = [x.strip().split(' ') for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
# go down dets and mark TPs and FPs
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
for d in range(nd):
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
if BBGT.size > 0:
# compute overlaps
# intersection
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(BBGT[:, 2] - BBGT[:, 0] + 1.) *
(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
if not R['difficult'][jmax]:
if not R['det'][jmax]:
tp[d] = 1.
R['det'][jmax] = 1
else:
fp[d] = 1.
else:
fp[d] = 1.
# compute precision recall
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap | 6,937 | 33.69 | 78 | py |
bottom-up-attention | bottom-up-attention-master/lib/datasets/vg.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import xml.etree.ElementTree as ET
import numpy as np
import scipy.sparse
import utils.cython_bbox
import cPickle
import gzip
import PIL
import json
from vg_eval import vg_eval
from fast_rcnn.config import cfg
class vg(imdb):
def __init__(self, version, image_set, ):
imdb.__init__(self, 'vg_' + version + '_' + image_set)
self._version = version
self._image_set = image_set
self._data_path = os.path.join(cfg.DATA_DIR, 'genome')
self._img_path = os.path.join(cfg.DATA_DIR, 'vg')
# VG specific config options
self.config = {'cleanup' : False}
# Load classes
self._classes = ['__background__']
self._class_to_ind = {}
self._class_to_ind[self._classes[0]] = 0
with open(os.path.join(self._data_path, self._version, 'objects_vocab.txt')) as f:
count = 1
for object in f.readlines():
names = [n.lower().strip() for n in object.split(',')]
self._classes.append(names[0])
for n in names:
self._class_to_ind[n] = count
count += 1
# Load attributes
self._attributes = ['__no_attribute__']
self._attribute_to_ind = {}
self._attribute_to_ind[self._attributes[0]] = 0
with open(os.path.join(self._data_path, self._version, 'attributes_vocab.txt')) as f:
count = 1
for att in f.readlines():
names = [n.lower().strip() for n in att.split(',')]
self._attributes.append(names[0])
for n in names:
self._attribute_to_ind[n] = count
count += 1
# Load relations
self._relations = ['__no_relation__']
self._relation_to_ind = {}
self._relation_to_ind[self._relations[0]] = 0
with open(os.path.join(self._data_path, self._version, 'relations_vocab.txt')) as f:
count = 1
for rel in f.readlines():
names = [n.lower().strip() for n in rel.split(',')]
self._relations.append(names[0])
for n in names:
self._relation_to_ind[n] = count
count += 1
self._image_ext = '.jpg'
self._image_index, self._id_to_dir = self._load_image_set_index()
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(self._image_index[i])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
folder = self._id_to_dir[index]
image_path = os.path.join(self._img_path, folder,
str(index) + self._image_ext)
assert os.path.exists(image_path), \
'Path does not exist: {}'.format(image_path)
return image_path
def _image_split_path(self):
if self._image_set == "minitrain":
return os.path.join(self._data_path, 'train.txt')
if self._image_set == "minival":
return os.path.join(self._data_path, 'val.txt')
else:
return os.path.join(self._data_path, self._image_set+'.txt')
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
training_split_file = self._image_split_path()
assert os.path.exists(training_split_file), \
'Path does not exist: {}'.format(training_split_file)
with open(training_split_file) as f:
metadata = f.readlines()
if self._image_set == "minitrain":
metadata = metadata[:1000]
elif self._image_set == "minival":
metadata = metadata[:100]
image_index = []
id_to_dir = {}
for line in metadata:
im_file,ann_file = line.split()
image_id = int(ann_file.split('/')[-1].split('.')[0])
filename = self._annotation_path(image_id)
if os.path.exists(filename):
# Some images have no bboxes after object filtering, so there
# is no xml annotation for these.
tree = ET.parse(filename)
for obj in tree.findall('object'):
obj_name = obj.find('name').text.lower().strip()
if obj_name in self._class_to_ind:
# We have to actually load and check these to make sure they have
# at least one object actually in vocab
image_index.append(image_id)
id_to_dir[image_id] = im_file.split('/')[0]
break
return image_index, id_to_dir
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
fid = gzip.open(cache_file,'rb')
roidb = cPickle.load(fid)
fid.close()
print '{} gt roidb loaded from {}'.format(self.name, cache_file)
return roidb
gt_roidb = [self._load_vg_annotation(index)
for index in self.image_index]
fid = gzip.open(cache_file,'wb')
cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
fid.close()
print 'wrote gt roidb to {}'.format(cache_file)
return gt_roidb
def _get_size(self, index):
return PIL.Image.open(self.image_path_from_index(index)).size
def _annotation_path(self, index):
return os.path.join(self._data_path, 'xml', str(index) + '.xml')
def _load_vg_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
width, height = self._get_size(index)
filename = self._annotation_path(index)
tree = ET.parse(filename)
objs = tree.findall('object')
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
# Max of 16 attributes are observed in the data
gt_attributes = np.zeros((num_objs, 16), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
obj_dict = {}
ix = 0
for obj in objs:
obj_name = obj.find('name').text.lower().strip()
if obj_name in self._class_to_ind:
bbox = obj.find('bndbox')
x1 = max(0,float(bbox.find('xmin').text))
y1 = max(0,float(bbox.find('ymin').text))
x2 = min(width-1,float(bbox.find('xmax').text))
y2 = min(height-1,float(bbox.find('ymax').text))
# If bboxes are not positive, just give whole image coords (there are a few examples)
if x2 < x1 or y2 < y1:
print 'Failed bbox in %s, object %s' % (filename, obj_name)
x1 = 0
y1 = 0
x2 = width-1
y2 = width-1
cls = self._class_to_ind[obj_name]
obj_dict[obj.find('object_id').text] = ix
atts = obj.findall('attribute')
n = 0
for att in atts:
att = att.text.lower().strip()
if att in self._attribute_to_ind:
gt_attributes[ix, n] = self._attribute_to_ind[att]
n += 1
if n >= 16:
break
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
ix += 1
overlaps = scipy.sparse.csr_matrix(overlaps)
gt_attributes = scipy.sparse.csr_matrix(gt_attributes)
rels = tree.findall('relation')
num_rels = len(rels)
gt_relations = set() # Avoid duplicates
for rel in rels:
pred = rel.find('predicate').text
if pred: # One is empty
pred = pred.lower().strip()
if pred in self._relation_to_ind:
try:
triple = []
triple.append(obj_dict[rel.find('subject_id').text])
triple.append(self._relation_to_ind[pred])
triple.append(obj_dict[rel.find('object_id').text])
gt_relations.add(tuple(triple))
except:
pass # Object not in dictionary
gt_relations = np.array(list(gt_relations), dtype=np.int32)
return {'boxes' : boxes,
'gt_classes': gt_classes,
'gt_attributes' : gt_attributes,
'gt_relations' : gt_relations,
'gt_overlaps' : overlaps,
'width' : width,
'height': height,
'flipped' : False,
'seg_areas' : seg_areas}
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(self.classes, all_boxes, output_dir)
self._do_python_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_vg_results_file_template(output_dir).format(cls)
os.remove(filename)
def evaluate_attributes(self, all_boxes, output_dir):
self._write_voc_results_file(self.attributes, all_boxes, output_dir)
self._do_python_eval(output_dir, eval_attributes = True)
if self.config['cleanup']:
for cls in self._attributes:
if cls == '__no_attribute__':
continue
filename = self._get_vg_results_file_template(output_dir).format(cls)
os.remove(filename)
def _get_vg_results_file_template(self, output_dir):
filename = 'detections_' + self._image_set + '_{:s}.txt'
path = os.path.join(output_dir, filename)
return path
def _write_voc_results_file(self, classes, all_boxes, output_dir):
for cls_ind, cls in enumerate(classes):
if cls == '__background__':
continue
print 'Writing "{}" vg results file'.format(cls)
filename = self._get_vg_results_file_template(output_dir).format(cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in xrange(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(str(index), dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def _do_python_eval(self, output_dir, pickle=True, eval_attributes = False):
# We re-use parts of the pascal voc python code for visual genome
aps = []
nposs = []
thresh = []
# The PASCAL VOC metric changed in 2010
use_07_metric = False
print 'VOC07 metric? ' + ('Yes' if use_07_metric else 'No')
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
# Load ground truth
gt_roidb = self.gt_roidb()
if eval_attributes:
classes = self._attributes
else:
classes = self._classes
for i, cls in enumerate(classes):
if cls == '__background__' or cls == '__no_attribute__':
continue
filename = self._get_vg_results_file_template(output_dir).format(cls)
rec, prec, ap, scores, npos = vg_eval(
filename, gt_roidb, self.image_index, i, ovthresh=0.5,
use_07_metric=use_07_metric, eval_attributes=eval_attributes)
# Determine per class detection thresholds that maximise f score
if npos > 1:
f = np.nan_to_num((prec*rec)/(prec+rec))
thresh += [scores[np.argmax(f)]]
else:
thresh += [0]
aps += [ap]
nposs += [float(npos)]
print('AP for {} = {:.4f} (npos={:,})'.format(cls, ap, npos))
if pickle:
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'w') as f:
cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap,
'scores': scores, 'npos':npos}, f)
# Set thresh to mean for classes with poor results
thresh = np.array(thresh)
avg_thresh = np.mean(thresh[thresh!=0])
thresh[thresh==0] = avg_thresh
if eval_attributes:
filename = 'attribute_thresholds_' + self._image_set + '.txt'
else:
filename = 'object_thresholds_' + self._image_set + '.txt'
path = os.path.join(output_dir, filename)
with open(path, 'wt') as f:
for i, cls in enumerate(classes[1:]):
f.write('{:s} {:.3f}\n'.format(cls, thresh[i]))
weights = np.array(nposs)
weights /= weights.sum()
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('Weighted Mean AP = {:.4f}'.format(np.average(aps, weights=weights)))
print('Mean Detection Threshold = {:.3f}'.format(avg_thresh))
print('~~~~~~~~')
print('Results:')
for ap,npos in zip(aps,nposs):
print('{:.3f}\t{:.3f}'.format(ap,npos))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** PASCAL VOC Python eval code.')
print('--------------------------------------------------------------')
if __name__ == '__main__':
d = datasets.vg('val')
res = d.roidb
from IPython import embed; embed()
| 15,143 | 40.719008 | 101 | py |
bottom-up-attention | bottom-up-attention-master/lib/datasets/pascal_voc.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import xml.etree.ElementTree as ET
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import cPickle
import subprocess
import uuid
from voc_eval import voc_eval
from fast_rcnn.config import cfg
class pascal_voc(imdb):
def __init__(self, image_set, year, devkit_path=None):
imdb.__init__(self, 'voc_' + year + '_' + image_set)
self._year = year
self._image_set = image_set
self._devkit_path = self._get_default_path() if devkit_path is None \
else devkit_path
self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)
self._classes = ('__background__', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
self._image_ext = '.jpg'
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.selective_search_roidb
self._salt = str(uuid.uuid4())
self._comp_id = 'comp4'
# PASCAL specific config options
self.config = {'cleanup' : True,
'use_salt' : True,
'use_diff' : False,
'matlab_eval' : False,
'rpn_file' : None,
'min_size' : 2}
assert os.path.exists(self._devkit_path), \
'VOCdevkit path does not exist: {}'.format(self._devkit_path)
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(self._image_index[i])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
image_path = os.path.join(self._data_path, 'JPEGImages',
index + self._image_ext)
assert os.path.exists(image_path), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main',
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year)
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = cPickle.load(fid)
print '{} gt roidb loaded from {}'.format(self.name, cache_file)
return roidb
gt_roidb = [self._load_pascal_annotation(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
print 'wrote gt roidb to {}'.format(cache_file)
return gt_roidb
def selective_search_roidb(self):
"""
Return the database of selective search regions of interest.
Ground-truth ROIs are also included.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path,
self.name + '_selective_search_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = cPickle.load(fid)
print '{} ss roidb loaded from {}'.format(self.name, cache_file)
return roidb
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
ss_roidb = self._load_selective_search_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, ss_roidb)
else:
roidb = self._load_selective_search_roidb(None)
with open(cache_file, 'wb') as fid:
cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
print 'wrote ss roidb to {}'.format(cache_file)
return roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print 'loading {}'.format(filename)
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = cPickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_selective_search_roidb(self, gt_roidb):
filename = os.path.abspath(os.path.join(cfg.DATA_DIR,
'selective_search_data',
self.name + '.mat'))
assert os.path.exists(filename), \
'Selective search data not found at: {}'.format(filename)
raw_data = sio.loadmat(filename)['boxes'].ravel()
box_list = []
for i in xrange(raw_data.shape[0]):
boxes = raw_data[i][:, (1, 0, 3, 2)] - 1
keep = ds_utils.unique_boxes(boxes)
boxes = boxes[keep, :]
keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
boxes = boxes[keep, :]
box_list.append(boxes)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes' : boxes,
'gt_classes': gt_classes,
'gt_overlaps' : overlaps,
'flipped' : False,
'seg_areas' : seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt'
path = os.path.join(
self._devkit_path,
'results',
'VOC' + self._year,
'Main',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print 'Writing {} VOC results file'.format(cls)
filename = self._get_voc_results_file_template().format(cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in xrange(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index, dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def _do_python_eval(self, output_dir = 'output'):
annopath = os.path.join(
self._devkit_path,
'VOC' + self._year,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._devkit_path,
'VOC' + self._year,
'ImageSets',
'Main',
self._image_set + '.txt')
cachedir = os.path.join(self._devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True if int(self._year) < 2010 else False
print 'VOC07 metric? ' + ('Yes' if use_07_metric else 'No')
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
rec, prec, ap = voc_eval(
filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'w') as f:
cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print '-----------------------------------------------------'
print 'Computing results with the official MATLAB eval code.'
print '-----------------------------------------------------'
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print('Running:\n{}'.format(cmd))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
if self.config['matlab_eval']:
self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.pascal_voc import pascal_voc
d = pascal_voc('trainval', '2007')
res = d.roidb
from IPython import embed; embed()
| 14,217 | 40.211594 | 80 | py |
bottom-up-attention | bottom-up-attention-master/lib/datasets/imdb.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import os
import os.path as osp
import PIL
from utils.cython_bbox import bbox_overlaps
import numpy as np
import scipy.sparse
from fast_rcnn.config import cfg
class imdb(object):
"""Image database."""
def __init__(self, name):
self._name = name
self._classes = []
self._attributes = []
self._relations = []
self._image_index = []
self._obj_proposer = 'selective_search'
self._roidb = None
self._roidb_handler = self.default_roidb
# Use this dict for storing dataset specific config options
self.config = {}
@property
def name(self):
return self._name
@property
def num_classes(self):
return len(self._classes)
@property
def num_attributes(self):
return len(self._attributes)
@property
def num_relations(self):
return len(self._relations)
@property
def classes(self):
return self._classes
@property
def attributes(self):
return self._attributes
@property
def relations(self):
return self._relations
@property
def image_index(self):
return self._image_index
@property
def roidb_handler(self):
return self._roidb_handler
@roidb_handler.setter
def roidb_handler(self, val):
self._roidb_handler = val
def set_proposal_method(self, method):
method = eval('self.' + method + '_roidb')
self.roidb_handler = method
@property
def roidb(self):
# A roidb is a list of dictionaries, each with the following keys (at minimum):
# boxes
# gt_overlaps
# gt_classes
# flipped
if self._roidb is not None:
return self._roidb
self._roidb = self.roidb_handler()
return self._roidb
@property
def cache_path(self):
cache_path = osp.abspath(osp.join(cfg.DATA_DIR, 'cache'))
if not os.path.exists(cache_path):
os.makedirs(cache_path)
return cache_path
@property
def num_images(self):
return len(self.image_index)
def image_path_at(self, i):
raise NotImplementedError
def default_roidb(self):
raise NotImplementedError
def evaluate_detections(self, all_boxes, output_dir=None):
"""
all_boxes is a list of length number-of-classes.
Each list element is a list of length number-of-images.
Each of those list elements is either an empty list []
or a numpy array of detection.
all_boxes[class][image] = [] or np.array of shape #dets x 5
"""
raise NotImplementedError
def evaluate_attributes(self, all_boxes, output_dir=None):
"""
all_boxes is a list of length number-of-classes.
Each list element is a list of length number-of-images.
Each of those list elements is either an empty list []
or a numpy array of detection.
all_boxes[class][image] = [] or np.array of shape #dets x 5
"""
raise NotImplementedError
def evaluate_relations(self, all_boxes, output_dir=None):
"""
all_boxes is a list of length number-of-classes.
Each list element is a list of length number-of-images.
Each of those list elements is either an empty list []
or a numpy array of detection.
all_boxes[class][image] = [] or np.array of shape #dets x 5
"""
raise NotImplementedError
def _get_widths(self):
return [PIL.Image.open(self.image_path_at(i)).size[0]
for i in xrange(self.num_images)]
def append_flipped_images(self):
num_images = self.num_images
widths = None
for i in xrange(num_images):
entry = self.roidb[i].copy()
if 'width' in entry:
width = entry['width']
else:
if not widths:
widths = self._get_widths()
width = widths[i]
boxes = self.roidb[i]['boxes'].copy()
oldx1 = boxes[:, 0].copy()
oldx2 = boxes[:, 2].copy()
boxes[:, 0] = width - oldx2 - 1
boxes[:, 2] = width - oldx1 - 1
assert (boxes[:, 2] >= boxes[:, 0]).all(), \
" image %d bounding boxes not positive, width %d:\n %s \n %s" \
% (i,width, entry['boxes'],boxes)
entry['boxes'] = boxes
entry['flipped'] = True
self.roidb.append(entry)
self._image_index = self._image_index * 2
def evaluate_recall(self, candidate_boxes=None, thresholds=None,
area='all', limit=None):
"""Evaluate detection proposal recall metrics.
Returns:
results: dictionary of results with keys
'ar': average recall
'recalls': vector recalls at each IoU overlap threshold
'thresholds': vector of IoU overlap thresholds
'gt_overlaps': vector of all ground-truth overlaps
"""
# Record max overlap value for each gt box
# Return vector of overlap values
areas = { 'all': 0, 'small': 1, 'medium': 2, 'large': 3,
'96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7}
area_ranges = [ [0**2, 1e5**2], # all
[0**2, 32**2], # small
[32**2, 96**2], # medium
[96**2, 1e5**2], # large
[96**2, 128**2], # 96-128
[128**2, 256**2], # 128-256
[256**2, 512**2], # 256-512
[512**2, 1e5**2], # 512-inf
]
assert areas.has_key(area), 'unknown area range: {}'.format(area)
area_range = area_ranges[areas[area]]
gt_overlaps = np.zeros(0)
num_pos = 0
for i in xrange(self.num_images):
# Checking for max_overlaps == 1 avoids including crowd annotations
# (...pretty hacking :/)
max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1)
gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) &
(max_gt_overlaps == 1))[0]
gt_boxes = self.roidb[i]['boxes'][gt_inds, :]
gt_areas = self.roidb[i]['seg_areas'][gt_inds]
valid_gt_inds = np.where((gt_areas >= area_range[0]) &
(gt_areas <= area_range[1]))[0]
gt_boxes = gt_boxes[valid_gt_inds, :]
num_pos += len(valid_gt_inds)
if candidate_boxes is None:
# If candidate_boxes is not supplied, the default is to use the
# non-ground-truth boxes from this roidb
non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0]
boxes = self.roidb[i]['boxes'][non_gt_inds, :]
else:
boxes = candidate_boxes[i]
if boxes.shape[0] == 0:
continue
if limit is not None and boxes.shape[0] > limit:
boxes = boxes[:limit, :]
overlaps = bbox_overlaps(boxes.astype(np.float),
gt_boxes.astype(np.float))
_gt_overlaps = np.zeros((gt_boxes.shape[0]))
for j in xrange(gt_boxes.shape[0]):
# find which proposal box maximally covers each gt box
argmax_overlaps = overlaps.argmax(axis=0)
# and get the iou amount of coverage for each gt box
max_overlaps = overlaps.max(axis=0)
# find which gt box is 'best' covered (i.e. 'best' = most iou)
gt_ind = max_overlaps.argmax()
gt_ovr = max_overlaps.max()
assert(gt_ovr >= 0)
# find the proposal box that covers the best covered gt box
box_ind = argmax_overlaps[gt_ind]
# record the iou coverage of this gt box
_gt_overlaps[j] = overlaps[box_ind, gt_ind]
assert(_gt_overlaps[j] == gt_ovr)
# mark the proposal box and the gt box as used
overlaps[box_ind, :] = -1
overlaps[:, gt_ind] = -1
# append recorded iou coverage level
gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))
gt_overlaps = np.sort(gt_overlaps)
if thresholds is None:
step = 0.05
thresholds = np.arange(0.5, 0.95 + 1e-5, step)
recalls = np.zeros_like(thresholds)
# compute recall for each iou threshold
for i, t in enumerate(thresholds):
recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
# ar = 2 * np.trapz(recalls, thresholds)
ar = recalls.mean()
return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds,
'gt_overlaps': gt_overlaps}
def create_roidb_from_box_list(self, box_list, gt_roidb):
assert len(box_list) == self.num_images, \
'Number of boxes must match number of ground-truth images'
roidb = []
for i in xrange(self.num_images):
boxes = box_list[i]
num_boxes = boxes.shape[0]
overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32)
if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0:
gt_boxes = gt_roidb[i]['boxes']
gt_classes = gt_roidb[i]['gt_classes']
gt_overlaps = bbox_overlaps(boxes.astype(np.float),
gt_boxes.astype(np.float))
argmaxes = gt_overlaps.argmax(axis=1)
maxes = gt_overlaps.max(axis=1)
I = np.where(maxes > 0)[0]
overlaps[I, gt_classes[argmaxes[I]]] = maxes[I]
overlaps = scipy.sparse.csr_matrix(overlaps)
roidb.append({
'boxes' : boxes,
'gt_classes' : np.zeros((num_boxes,), dtype=np.int32),
'gt_overlaps' : overlaps,
'flipped' : False,
'seg_areas' : np.zeros((num_boxes,), dtype=np.float32),
})
return roidb
@staticmethod
def merge_roidbs(a, b):
assert len(a) == len(b)
for i in xrange(len(a)):
a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes']))
a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'],
b[i]['gt_classes']))
a[i]['gt_overlaps'] = scipy.sparse.vstack([a[i]['gt_overlaps'],
b[i]['gt_overlaps']])
a[i]['seg_areas'] = np.hstack((a[i]['seg_areas'],
b[i]['seg_areas']))
if 'gt_attributes' in a[i]:
a[i]['gt_attributes'] = scipy.sparse.vstack((a[i]['gt_attributes'],
b[i]['gt_attributes']))
if 'gt_relations' in a[i]:
a[i]['gt_relations'] = np.vstack((a[i]['gt_relations'],
b[i]['gt_relations']))
return a
def competition_mode(self, on):
"""Turn competition mode on or off."""
pass
| 11,606 | 36.931373 | 87 | py |
bottom-up-attention | bottom-up-attention-master/lib/datasets/factory.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Factory method for easily getting imdbs by name."""
__sets = {}
from datasets.pascal_voc import pascal_voc
from datasets.coco import coco
from datasets.imagenet import imagenet
from datasets.vg import vg
import numpy as np
# Set up voc_<year>_<split> using selective search "fast" mode
for year in ['2007', '2012', '0712']:
for split in ['train', 'val', 'trainval', 'test']:
name = 'voc_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: pascal_voc(split, year))
for split in ['train', 'val']:
name = 'imagenet_{}'.format(split)
devkit_path = '/scratch0/ILSVRC/devkit/'
data_path = '/scratch0/ILSVRC2015/'
__sets[name] = (lambda split=split, devkit_path=devkit_path, data_path=data_path: imagenet(split, devkit_path, data_path))
print name
print __sets[name]
# Set up coco_2014_<split>
for year in ['2014']:
for split in ['train', 'val', 'minival', 'valminusminival']:
name = 'coco_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: coco(split, year))
# Set up coco_2015_<split>
for year in ['2015']:
for split in ['test', 'test-dev']:
name = 'coco_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: coco(split, year))
# Set up vg_<split>
for version in ['1600-400-20']:
for split in ['minitrain', 'train', 'minival', 'val', 'test']:
name = 'vg_{}_{}'.format(version,split)
__sets[name] = (lambda split=split, version=version: vg(version, split))
def get_imdb(name):
"""Get an imdb (image database) by name."""
if not __sets.has_key(name):
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]()
def list_imdbs():
"""List all registered imdbs."""
return __sets.keys()
| 2,055 | 33.847458 | 126 | py |
bottom-up-attention | bottom-up-attention-master/lib/datasets/ds_utils.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import numpy as np
def unique_boxes(boxes, scale=1.0):
"""Return indices of unique boxes."""
v = np.array([1, 1e3, 1e6, 1e9])
hashes = np.round(boxes * scale).dot(v)
_, index = np.unique(hashes, return_index=True)
return np.sort(index)
def xywh_to_xyxy(boxes):
"""Convert [x y w h] box format to [x1 y1 x2 y2] format."""
return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1))
def xyxy_to_xywh(boxes):
"""Convert [x1 y1 x2 y2] box format to [x y w h] format."""
return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1))
def validate_boxes(boxes, width=0, height=0):
"""Check that a set of boxes are valid."""
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
assert (x1 >= 0).all()
assert (y1 >= 0).all()
assert (x2 >= x1).all()
assert (y2 >= y1).all()
assert (x2 < width).all()
assert (y2 < height).all()
def filter_small_boxes(boxes, min_size):
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
keep = np.where((w >= min_size) & (h > min_size))[0]
return keep
| 1,336 | 30.833333 | 72 | py |
bottom-up-attention | bottom-up-attention-master/lib/datasets/__init__.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
| 248 | 34.571429 | 58 | py |
bottom-up-attention | bottom-up-attention-master/lib/datasets/vg_eval.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
import xml.etree.ElementTree as ET
import os
import cPickle
import numpy as np
from voc_eval import voc_ap
def vg_eval( detpath,
gt_roidb,
image_index,
classindex,
ovthresh=0.5,
use_07_metric=False,
eval_attributes=False):
"""rec, prec, ap, sorted_scores, npos = voc_eval(
detpath,
gt_roidb,
image_index,
classindex,
[ovthresh],
[use_07_metric])
Top level function that does the Visual Genome evaluation.
detpath: Path to detections
gt_roidb: List of ground truth structs.
image_index: List of image ids.
classindex: Category index
[ovthresh]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default False)
"""
# extract gt objects for this class
class_recs = {}
npos = 0
for item,imagename in zip(gt_roidb,image_index):
if eval_attributes:
bbox = item['boxes'][np.where(np.any(item['gt_attributes'].toarray() == classindex, axis=1))[0], :]
else:
bbox = item['boxes'][np.where(item['gt_classes'] == classindex)[0], :]
difficult = np.zeros((bbox.shape[0],)).astype(np.bool)
det = [False] * bbox.shape[0]
npos = npos + sum(~difficult)
class_recs[str(imagename)] = {'bbox': bbox,
'difficult': difficult,
'det': det}
if npos == 0:
# No ground truth examples
return 0,0,0,0,npos
# read dets
with open(detpath, 'r') as f:
lines = f.readlines()
if len(lines) == 0:
# No detection examples
return 0,0,0,0,npos
splitlines = [x.strip().split(' ') for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = -np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
# go down dets and mark TPs and FPs
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
for d in range(nd):
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
if BBGT.size > 0:
# compute overlaps
# intersection
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(BBGT[:, 2] - BBGT[:, 0] + 1.) *
(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
if not R['difficult'][jmax]:
if not R['det'][jmax]:
tp[d] = 1.
R['det'][jmax] = 1
else:
fp[d] = 1.
else:
fp[d] = 1.
# compute precision recall
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap, sorted_scores, npos
| 4,153 | 31.968254 | 111 | py |
bottom-up-attention | bottom-up-attention-master/lib/datasets/imagenet.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import datasets
import datasets.imagenet
import os, sys
from datasets.imdb import imdb
import xml.dom.minidom as minidom
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import cPickle
import subprocess
class imagenet(imdb):
def __init__(self, image_set, devkit_path, data_path):
imdb.__init__(self, image_set)
self._image_set = image_set
self._devkit_path = devkit_path
self._data_path = data_path
synsets_image = sio.loadmat(os.path.join(self._devkit_path, 'data', 'meta_det.mat'))
synsets_video = sio.loadmat(os.path.join(self._devkit_path, 'data', 'meta_vid.mat'))
self._classes_image = ('__background__',)
self._wnid_image = (0,)
self._classes = ('__background__',)
self._wnid = (0,)
for i in xrange(200):
self._classes_image = self._classes_image + (synsets_image['synsets'][0][i][2][0],)
self._wnid_image = self._wnid_image + (synsets_image['synsets'][0][i][1][0],)
for i in xrange(30):
self._classes = self._classes + (synsets_video['synsets'][0][i][2][0],)
self._wnid = self._wnid + (synsets_video['synsets'][0][i][1][0],)
self._wnid_to_ind_image = dict(zip(self._wnid_image, xrange(201)))
self._class_to_ind_image = dict(zip(self._classes_image, xrange(201)))
self._wnid_to_ind = dict(zip(self._wnid, xrange(31)))
self._class_to_ind = dict(zip(self._classes, xrange(31)))
#check for valid intersection between video and image classes
self._valid_image_flag = [0]*201
for i in range(1,201):
if self._wnid_image[i] in self._wnid_to_ind:
self._valid_image_flag[i] = 1
self._image_ext = ['.JPEG']
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
# Specific config options
self.config = {'cleanup' : True,
'use_salt' : True,
'top_k' : 2000}
assert os.path.exists(self._devkit_path), 'Devkit path does not exist: {}'.format(self._devkit_path)
assert os.path.exists(self._data_path), 'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(self._image_index[i])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
image_path = os.path.join(self._data_path, 'Data', self._image_set, index + self._image_ext[0])
assert os.path.exists(image_path), 'path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._data_path + /ImageSets/val.txt
if self._image_set == 'train':
image_set_file = os.path.join(self._data_path, 'ImageSets', 'trainr.txt')
image_index = []
if os.path.exists(image_set_file):
f = open(image_set_file, 'r')
data = f.read().split()
for lines in data:
if lines != '':
image_index.append(lines)
f.close()
return image_index
for i in range(1,31):
print(i)
image_set_file = os.path.join(self._data_path, 'ImageSets', 'train_' + str(i) + '.txt')
with open(image_set_file) as f:
tmp_index = [x.strip() for x in f.readlines()]
vtmp_index = []
for line in tmp_index:
image_list = os.popen('ls ' + self._data_path + '/Data/train/' + line + '/*.JPEG').read().split()
tmp_list = []
for imgs in image_list:
tmp_list.append(imgs[:-5])
vtmp_index = vtmp_index + tmp_list
num_lines = len(vtmp_index)
ids = np.random.permutation(num_lines)
count = 0
while count < 2000:
image_index.append(vtmp_index[ids[count % num_lines]])
count = count + 1
for i in range(1,201):
if self._valid_image_flag[i] == 1:
image_set_file = os.path.join(self._data_path, 'ImageSets', 'train_pos_' + str(i) + '.txt')
with open(image_set_file) as f:
tmp_index = [x.strip() for x in f.readlines()]
num_lines = len(tmp_index)
ids = np.random.permutation(num_lines)
count = 0
while count < 2000:
image_index.append(tmp_index[ids[count % num_lines]])
count = count + 1
image_set_file = os.path.join(self._data_path, 'ImageSets', 'trainr.txt')
f = open(image_set_file, 'w')
for lines in image_index:
f.write(lines + '\n')
f.close()
else:
image_set_file = os.path.join(self._data_path, 'ImageSets', 'val.txt')
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
return image_index
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = cPickle.load(fid)
print '{} gt roidb loaded from {}'.format(self.name, cache_file)
return roidb
gt_roidb = [self._load_imagenet_annotation(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
print 'wrote gt roidb to {}'.format(cache_file)
return gt_roidb
def _load_imagenet_annotation(self, index):
"""
Load image and bounding boxes info from txt files of imagenet.
"""
filename = os.path.join(self._data_path, 'Annotations', self._image_set, index + '.xml')
# print 'Loading: {}'.format(filename)
def get_data_from_tag(node, tag):
return node.getElementsByTagName(tag)[0].childNodes[0].data
with open(filename) as f:
data = minidom.parseString(f.read())
objs = data.getElementsByTagName('object')
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
x1 = float(get_data_from_tag(obj, 'xmin'))
y1 = float(get_data_from_tag(obj, 'ymin'))
x2 = float(get_data_from_tag(obj, 'xmax'))
y2 = float(get_data_from_tag(obj, 'ymax'))
cls = self._wnid_to_ind[
str(get_data_from_tag(obj, "name")).lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes' : boxes,
'gt_classes': gt_classes,
'gt_overlaps' : overlaps,
'flipped' : False}
if __name__ == '__main__':
d = datasets.imagenet('val', '')
res = d.roidb
from IPython import embed; embed()
| 8,245 | 38.644231 | 121 | py |
bottom-up-attention | bottom-up-attention-master/lib/datasets/coco.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
from fast_rcnn.config import cfg
import os.path as osp
import sys
import os
import numpy as np
import scipy.sparse
import scipy.io as sio
import cPickle
import json
import uuid
# COCO API
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from pycocotools import mask as COCOmask
def _filter_crowd_proposals(roidb, crowd_thresh):
"""
Finds proposals that are inside crowd regions and marks them with
overlap = -1 (for all gt rois), which means they will be excluded from
training.
"""
for ix, entry in enumerate(roidb):
overlaps = entry['gt_overlaps'].toarray()
crowd_inds = np.where(overlaps.max(axis=1) == -1)[0]
non_gt_inds = np.where(entry['gt_classes'] == 0)[0]
if len(crowd_inds) == 0 or len(non_gt_inds) == 0:
continue
iscrowd = [int(True) for _ in xrange(len(crowd_inds))]
crowd_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][crowd_inds, :])
non_gt_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][non_gt_inds, :])
ious = COCOmask.iou(non_gt_boxes, crowd_boxes, iscrowd)
bad_inds = np.where(ious.max(axis=1) > crowd_thresh)[0]
overlaps[non_gt_inds[bad_inds], :] = -1
roidb[ix]['gt_overlaps'] = scipy.sparse.csr_matrix(overlaps)
return roidb
class coco(imdb):
def __init__(self, image_set, year):
imdb.__init__(self, 'coco_' + year + '_' + image_set)
# COCO specific config options
self.config = {'top_k' : 2000,
'use_salt' : True,
'cleanup' : True,
'crowd_thresh' : 0.7,
'rpn_file': None,
'min_size' : 2}
# name, paths
self._year = year
self._image_set = image_set
self._data_path = osp.join(cfg.DATA_DIR, 'coco')
# load COCO API, classes, class <-> id mappings
self._COCO = COCO(self._get_ann_file())
cats = self._COCO.loadCats(self._COCO.getCatIds())
self._classes = tuple(['__background__'] + [c['name'] for c in cats])
self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
self._class_to_coco_cat_id = dict(zip([c['name'] for c in cats],
self._COCO.getCatIds()))
self._image_index = self._load_image_set_index()
# Default to roidb handler
self.set_proposal_method('selective_search')
self.competition_mode(False)
# Some image sets are "views" (i.e. subsets) into others.
# For example, minival2014 is a random 5000 image subset of val2014.
# This mapping tells us where the view's images and proposals come from.
self._view_map = {
'minival2014' : 'val2014', # 5k val2014 subset
'valminusminival2014' : 'val2014', # val2014 \setminus minival2014
}
coco_name = image_set + year # e.g., "val2014"
self._data_name = (self._view_map[coco_name]
if self._view_map.has_key(coco_name)
else coco_name)
# Dataset splits that have ground-truth annotations (test splits
# do not have gt annotations)
self._gt_splits = ('train', 'val', 'minival')
def _get_ann_file(self):
prefix = 'instances' if self._image_set.find('test') == -1 \
else 'image_info'
return osp.join(self._data_path, 'annotations',
prefix + '_' + self._image_set + self._year + '.json')
def _load_image_set_index(self):
"""
Load image ids.
"""
image_ids = self._COCO.getImgIds()
return image_ids
def _get_widths(self):
anns = self._COCO.loadImgs(self._image_index)
widths = [ann['width'] for ann in anns]
return widths
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(self._image_index[i])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
# Example image path for index=119993:
# images/train2014/COCO_train2014_000000119993.jpg
file_name = ('COCO_' + self._data_name + '_' +
str(index).zfill(12) + '.jpg')
image_path = osp.join(self._data_path, 'images',
self._data_name, file_name)
assert osp.exists(image_path), \
'Path does not exist: {}'.format(image_path)
return image_path
def selective_search_roidb(self):
return self._roidb_from_proposals('selective_search')
def edge_boxes_roidb(self):
return self._roidb_from_proposals('edge_boxes_AR')
def mcg_roidb(self):
return self._roidb_from_proposals('MCG')
def rpn_roidb(self):
if (self._image_set != 'val') and ('test' not in self._image_set):
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print 'loading {}'.format(filename)
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = cPickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _roidb_from_proposals(self, method):
"""
Creates a roidb from pre-computed proposals of a particular methods.
"""
top_k = self.config['top_k']
cache_file = osp.join(self.cache_path, self.name +
'_{:s}_top{:d}'.format(method, top_k) +
'_roidb.pkl')
if osp.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = cPickle.load(fid)
print '{:s} {:s} roidb loaded from {:s}'.format(self.name, method,
cache_file)
return roidb
if self._image_set in self._gt_splits:
gt_roidb = self.gt_roidb()
method_roidb = self._load_proposals(method, gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, method_roidb)
# Make sure we don't use proposals that are contained in crowds
roidb = _filter_crowd_proposals(roidb, self.config['crowd_thresh'])
else:
roidb = self._load_proposals(method, None)
with open(cache_file, 'wb') as fid:
cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
print 'wrote {:s} roidb to {:s}'.format(method, cache_file)
return roidb
def _load_proposals(self, method, gt_roidb):
"""
Load pre-computed proposals in the format provided by Jan Hosang:
http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-
computing/research/object-recognition-and-scene-understanding/how-
good-are-detection-proposals-really/
For MCG, use boxes from http://www.eecs.berkeley.edu/Research/Projects/
CS/vision/grouping/mcg/ and convert the file layout using
lib/datasets/tools/mcg_munge.py.
"""
box_list = []
top_k = self.config['top_k']
valid_methods = [
'MCG',
'selective_search',
'edge_boxes_AR',
'edge_boxes_70']
assert method in valid_methods
print 'Loading {} boxes'.format(method)
for i, index in enumerate(self._image_index):
if i % 1000 == 0:
print '{:d} / {:d}'.format(i + 1, len(self._image_index))
box_file = osp.join(
cfg.DATA_DIR, 'coco_proposals', method, 'mat',
self._get_box_file(index))
raw_data = sio.loadmat(box_file)['boxes']
boxes = np.maximum(raw_data - 1, 0).astype(np.uint16)
if method == 'MCG':
# Boxes from the MCG website are in (y1, x1, y2, x2) order
boxes = boxes[:, (1, 0, 3, 2)]
# Remove duplicate boxes and very small boxes and then take top k
keep = ds_utils.unique_boxes(boxes)
boxes = boxes[keep, :]
keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
boxes = boxes[keep, :]
boxes = boxes[:top_k, :]
box_list.append(boxes)
# Sanity check
im_ann = self._COCO.loadImgs(index)[0]
width = im_ann['width']
height = im_ann['height']
ds_utils.validate_boxes(boxes, width=width, height=height)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = osp.join(self.cache_path, self.name + '_gt_roidb.pkl')
if osp.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = cPickle.load(fid)
print '{} gt roidb loaded from {}'.format(self.name, cache_file)
return roidb
gt_roidb = [self._load_coco_annotation(index)
for index in self._image_index]
with open(cache_file, 'wb') as fid:
cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
print 'wrote gt roidb to {}'.format(cache_file)
return gt_roidb
def _load_coco_annotation(self, index):
"""
Loads COCO bounding-box instance annotations. Crowd instances are
handled by marking their overlaps (with all categories) to -1. This
overlap value means that crowd "instances" are excluded from training.
"""
im_ann = self._COCO.loadImgs(index)[0]
width = im_ann['width']
height = im_ann['height']
annIds = self._COCO.getAnnIds(imgIds=index, iscrowd=None)
objs = self._COCO.loadAnns(annIds)
# Sanitize bboxes -- some are invalid
valid_objs = []
for obj in objs:
x1 = np.max((0, obj['bbox'][0]))
y1 = np.max((0, obj['bbox'][1]))
x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1))))
y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1))))
if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
obj['clean_bbox'] = [x1, y1, x2, y2]
valid_objs.append(obj)
objs = valid_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Lookup table to map from COCO category ids to our internal class
# indices
coco_cat_id_to_class_ind = dict([(self._class_to_coco_cat_id[cls],
self._class_to_ind[cls])
for cls in self._classes[1:]])
for ix, obj in enumerate(objs):
cls = coco_cat_id_to_class_ind[obj['category_id']]
boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
seg_areas[ix] = obj['area']
if obj['iscrowd']:
# Set overlap to -1 for all classes for crowd objects
# so they will be excluded during training
overlaps[ix, :] = -1.0
else:
overlaps[ix, cls] = 1.0
ds_utils.validate_boxes(boxes, width=width, height=height)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes' : boxes,
'gt_classes': gt_classes,
'gt_overlaps' : overlaps,
'flipped' : False,
'seg_areas' : seg_areas}
def _get_box_file(self, index):
# first 14 chars / first 22 chars / all chars + .mat
# COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat
file_name = ('COCO_' + self._data_name +
'_' + str(index).zfill(12) + '.mat')
return osp.join(file_name[:14], file_name[:22], file_name)
def _print_detection_eval_metrics(self, coco_eval):
IoU_lo_thresh = 0.5
IoU_hi_thresh = 0.95
def _get_thr_ind(coco_eval, thr):
ind = np.where((coco_eval.params.iouThrs > thr - 1e-5) &
(coco_eval.params.iouThrs < thr + 1e-5))[0][0]
iou_thr = coco_eval.params.iouThrs[ind]
assert np.isclose(iou_thr, thr)
return ind
ind_lo = _get_thr_ind(coco_eval, IoU_lo_thresh)
ind_hi = _get_thr_ind(coco_eval, IoU_hi_thresh)
# precision has dims (iou, recall, cls, area range, max dets)
# area range index 0: all area ranges
# max dets index 2: 100 per image
precision = \
coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, :, 0, 2]
ap_default = np.mean(precision[precision > -1])
print ('~~~~ Mean and per-category AP @ IoU=[{:.2f},{:.2f}] '
'~~~~').format(IoU_lo_thresh, IoU_hi_thresh)
print '{:.1f}'.format(100 * ap_default)
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
# minus 1 because of __background__
precision = coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, cls_ind - 1, 0, 2]
ap = np.mean(precision[precision > -1])
print '{:.1f}'.format(100 * ap)
print '~~~~ Summary metrics ~~~~'
coco_eval.summarize()
def _do_detection_eval(self, res_file, output_dir):
ann_type = 'bbox'
coco_dt = self._COCO.loadRes(res_file)
coco_eval = COCOeval(self._COCO, coco_dt)
coco_eval.params.useSegm = (ann_type == 'segm')
coco_eval.evaluate()
coco_eval.accumulate()
self._print_detection_eval_metrics(coco_eval)
eval_file = osp.join(output_dir, 'detection_results.pkl')
with open(eval_file, 'wb') as fid:
cPickle.dump(coco_eval, fid, cPickle.HIGHEST_PROTOCOL)
print 'Wrote COCO eval results to: {}'.format(eval_file)
def _coco_results_one_category(self, boxes, cat_id):
results = []
for im_ind, index in enumerate(self.image_index):
dets = boxes[im_ind].astype(np.float)
if dets == []:
continue
scores = dets[:, -1]
xs = dets[:, 0]
ys = dets[:, 1]
ws = dets[:, 2] - xs + 1
hs = dets[:, 3] - ys + 1
results.extend(
[{'image_id' : index,
'category_id' : cat_id,
'bbox' : [xs[k], ys[k], ws[k], hs[k]],
'score' : scores[k]} for k in xrange(dets.shape[0])])
return results
def _write_coco_results_file(self, all_boxes, res_file):
# [{"image_id": 42,
# "category_id": 18,
# "bbox": [258.15,41.29,348.26,243.78],
# "score": 0.236}, ...]
results = []
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print 'Collecting {} results ({:d}/{:d})'.format(cls, cls_ind,
self.num_classes - 1)
coco_cat_id = self._class_to_coco_cat_id[cls]
results.extend(self._coco_results_one_category(all_boxes[cls_ind],
coco_cat_id))
print 'Writing results json to {}'.format(res_file)
with open(res_file, 'w') as fid:
json.dump(results, fid)
def evaluate_detections(self, all_boxes, output_dir):
res_file = osp.join(output_dir, ('detections_' +
self._image_set +
self._year +
'_results'))
if self.config['use_salt']:
res_file += '_{}'.format(str(uuid.uuid4()))
res_file += '.json'
self._write_coco_results_file(all_boxes, res_file)
# Only do evaluation on non-test sets
if self._image_set.find('test') == -1:
self._do_detection_eval(res_file, output_dir)
# Optionally cleanup results json file
if self.config['cleanup']:
os.remove(res_file)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
| 17,316 | 40.727711 | 94 | py |
bottom-up-attention | bottom-up-attention-master/lib/datasets/tools/mcg_munge.py | import os
import sys
"""Hacky tool to convert file system layout of MCG boxes downloaded from
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/
so that it's consistent with those computed by Jan Hosang (see:
http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-
computing/research/object-recognition-and-scene-understanding/how-
good-are-detection-proposals-really/)
NB: Boxes from the MCG website are in (y1, x1, y2, x2) order.
Boxes from Hosang et al. are in (x1, y1, x2, y2) order.
"""
def munge(src_dir):
# stored as: ./MCG-COCO-val2014-boxes/COCO_val2014_000000193401.mat
# want: ./MCG/mat/COCO_val2014_0/COCO_val2014_000000141/COCO_val2014_000000141334.mat
files = os.listdir(src_dir)
for fn in files:
base, ext = os.path.splitext(fn)
# first 14 chars / first 22 chars / all chars + .mat
# COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat
first = base[:14]
second = base[:22]
dst_dir = os.path.join('MCG', 'mat', first, second)
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
src = os.path.join(src_dir, fn)
dst = os.path.join(dst_dir, fn)
print 'MV: {} -> {}'.format(src, dst)
os.rename(src, dst)
if __name__ == '__main__':
# src_dir should look something like:
# src_dir = 'MCG-COCO-val2014-boxes'
src_dir = sys.argv[1]
munge(src_dir)
| 1,451 | 36.230769 | 94 | py |
bottom-up-attention | bottom-up-attention-master/lib/rpn/proposal_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
import caffe
import numpy as np
import yaml
from fast_rcnn.config import cfg
from generate_anchors import generate_anchors
from fast_rcnn.bbox_transform import bbox_transform_inv, clip_boxes
from fast_rcnn.nms_wrapper import nms
DEBUG = False
DEBUG_SHAPE = False
class ProposalLayer(caffe.Layer):
"""
Outputs object detection proposals by applying estimated bounding-box
transformations to a set of regular boxes (called "anchors").
"""
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str)
self._feat_stride = layer_params['feat_stride']
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def forward(self, bottom, top):
# Algorithm:
#
# for each (H, W) location i
# generate A anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the A anchors
# clip predicted boxes to image
# remove predicted boxes with either height or width < threshold
# sort all (proposal, score) pairs by score from highest to lowest
# take top pre_nms_topN proposals before NMS
# apply NMS with threshold 0.7 to remaining proposals
# take after_nms_topN proposals after NMS
# return the top proposals (-> RoIs top, scores top)
assert bottom[0].data.shape[0] == 1, \
'Only single item batches are supported'
cfg_key = str('TRAIN' if self.phase == 0 else 'TEST') # either 'TRAIN' or 'TEST'
pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
min_size = cfg[cfg_key].RPN_MIN_SIZE
# the first set of _num_anchors channels are bg probs
# the second set are the fg probs, which we want
scores = bottom[0].data[:, self._num_anchors:, :, :]
bbox_deltas = bottom[1].data
im_info = bottom[2].data[0, :]
if DEBUG:
print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
print 'scale: {}'.format(im_info[2])
# 1. Generate proposals from bbox deltas and shifted anchors
height, width = scores.shape[-2:]
if DEBUG:
print 'score map size: {}'.format(scores.shape)
# Enumerate all shifts
shift_x = np.arange(0, width) * self._feat_stride
shift_y = np.arange(0, height) * self._feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
shift_x.ravel(), shift_y.ravel())).transpose()
# Enumerate all shifted anchors:
#
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = self._num_anchors
K = shifts.shape[0]
anchors = self._anchors.reshape((1, A, 4)) + \
shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4))
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
#
# bbox deltas will be (1, 4 * A, H, W) format
# transpose to (1, H, W, 4 * A)
# reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
# in slowest to fastest order
bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
if cfg_key == 'TRAIN' and cfg.TRAIN.RPN_NORMALIZE_TARGETS:
bbox_deltas *= cfg.TRAIN.RPN_NORMALIZE_STDS
bbox_deltas += cfg.TRAIN.RPN_NORMALIZE_MEANS
# Same story for the scores:
#
# scores are (1, A, H, W) format
# transpose to (1, H, W, A)
# reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
# Convert anchors into proposals via bbox transformations
proposals = bbox_transform_inv(anchors, bbox_deltas)
# 2. clip predicted boxes to image
proposals = clip_boxes(proposals, im_info[:2])
# 3. remove predicted boxes with either height or width < threshold
# (NOTE: convert min_size to input image scale stored in im_info[2])
keep = _filter_boxes(proposals, min_size * im_info[2])
proposals = proposals[keep, :]
scores = scores[keep]
# 4. sort all (proposal, score) pairs by score from highest to lowest
# 5. take top pre_nms_topN (e.g. 6000)
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
# 6. apply nms (e.g. threshold = 0.7)
# 7. take after_nms_topN (e.g. 300)
# 8. return the top proposals (-> RoIs top)
keep = nms(np.hstack((proposals, scores)), nms_thresh)
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
proposals = proposals[keep, :]
scores = scores[keep]
# Output rois blob
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
# print blob.shape
top[0].reshape(*(blob.shape))
top[0].data[...] = blob
if DEBUG_SHAPE:
print 'ProposalLayer top[0] size: {}'.format(top[0].data.shape)
# [Optional] output scores blob
if len(top) > 1:
top[1].reshape(*(scores.shape))
top[1].data[...] = scores
if DEBUG_SHAPE:
print 'ProposalLayer top[0] size: {}'.format(top[0].data.shape)
def backward(self, top, propagate_down, bottom):
"""This layer does not propagate gradients."""
pass
def reshape(self, bottom, top):
"""Reshaping happens during the call to forward."""
pass
def _filter_boxes(boxes, min_size):
"""Remove all boxes with any side smaller than min_size."""
ws = boxes[:, 2] - boxes[:, 0] + 1
hs = boxes[:, 3] - boxes[:, 1] + 1
keep = np.where((ws >= min_size) & (hs >= min_size))[0]
return keep
| 7,265 | 38.064516 | 88 | py |
bottom-up-attention | bottom-up-attention-master/lib/rpn/generate_anchors.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
import numpy as np
# Verify that we compute the same anchors as Shaoqing's matlab implementation:
#
# >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat
# >> anchors
#
# anchors =
#
# -83 -39 100 56
# -175 -87 192 104
# -359 -183 376 200
# -55 -55 72 72
# -119 -119 136 136
# -247 -247 264 264
# -35 -79 52 96
# -79 -167 96 184
# -167 -343 184 360
#array([[ -83., -39., 100., 56.],
# [-175., -87., 192., 104.],
# [-359., -183., 376., 200.],
# [ -55., -55., 72., 72.],
# [-119., -119., 136., 136.],
# [-247., -247., 264., 264.],
# [ -35., -79., 52., 96.],
# [ -79., -167., 96., 184.],
# [-167., -343., 184., 360.]])
def generate_anchors(base_size=16, ratios=[0.5, 1, 2],
scales=2**np.arange(3, 6)):
"""
Generate anchor (reference) windows by enumerating aspect ratios X
scales wrt a reference (0, 0, 15, 15) window.
"""
base_anchor = np.array([1, 1, base_size, base_size]) - 1
ratio_anchors = _ratio_enum(base_anchor, ratios)
anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales)
for i in xrange(ratio_anchors.shape[0])])
return anchors
def _whctrs(anchor):
"""
Return width, height, x center, and y center for an anchor (window).
"""
w = anchor[2] - anchor[0] + 1
h = anchor[3] - anchor[1] + 1
x_ctr = anchor[0] + 0.5 * (w - 1)
y_ctr = anchor[1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def _mkanchors(ws, hs, x_ctr, y_ctr):
"""
Given a vector of widths (ws) and heights (hs) around a center
(x_ctr, y_ctr), output a set of anchors (windows).
"""
ws = ws[:, np.newaxis]
hs = hs[:, np.newaxis]
anchors = np.hstack((x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1)))
return anchors
def _ratio_enum(anchor, ratios):
"""
Enumerate a set of anchors for each aspect ratio wrt an anchor.
"""
w, h, x_ctr, y_ctr = _whctrs(anchor)
size = w * h
size_ratios = size / ratios
ws = np.round(np.sqrt(size_ratios))
hs = np.round(ws * ratios)
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
def _scale_enum(anchor, scales):
"""
Enumerate a set of anchors for each scale wrt an anchor.
"""
w, h, x_ctr, y_ctr = _whctrs(anchor)
ws = w * scales
hs = h * scales
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
if __name__ == '__main__':
import time
t = time.time()
a = generate_anchors()
print time.time() - t
print a
from IPython import embed; embed()
| 3,110 | 28.349057 | 78 | py |
bottom-up-attention | bottom-up-attention-master/lib/rpn/generate.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
from fast_rcnn.config import cfg
from fast_rcnn.train import filter_roidb
from utils.blob import im_list_to_blob
from utils.timer import Timer
from generate_anchors import generate_anchors
from utils.cython_bbox import bbox_overlaps
from fast_rcnn.bbox_transform import bbox_transform
import numpy as np
import cv2
def _vis_proposals(im, dets, thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0]
if len(inds) == 0:
return
class_name = 'obj'
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=3.5)
)
ax.text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
ax.set_title(('{} detections with '
'p({} | box) >= {:.1f}').format(class_name, class_name,
thresh),
fontsize=14)
plt.axis('off')
plt.tight_layout()
plt.draw()
def _get_image_blob(im):
"""Converts an image into a network input.
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
processed_ims = []
assert len(cfg.TEST.SCALES) == 1
target_size = cfg.TEST.SCALES[0]
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
im_info = np.hstack((im.shape[:2], im_scale))[np.newaxis, :]
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_info
def im_proposals(net, im):
"""Generate RPN proposals on a single image."""
blobs = {}
blobs['data'], blobs['im_info'] = _get_image_blob(im)
net.blobs['data'].reshape(*(blobs['data'].shape))
net.blobs['im_info'].reshape(*(blobs['im_info'].shape))
blobs_out = net.forward(
data=blobs['data'].astype(np.float32, copy=False),
im_info=blobs['im_info'].astype(np.float32, copy=False))
scale = blobs['im_info'][0, 2]
boxes = blobs_out['rois'][:, 1:].copy() / scale
scores = blobs_out['scores'].copy()
return boxes, scores
def imdb_proposals(net, imdb):
"""Generate RPN proposals on all images in an imdb."""
_t = Timer()
imdb_boxes = [[] for _ in xrange(imdb.num_images)]
for i in xrange(imdb.num_images):
im = cv2.imread(imdb.image_path_at(i))
_t.tic()
imdb_boxes[i], scores = im_proposals(net, im)
_t.toc()
print 'im_proposals: {:d}/{:d} {:.3f}s' \
.format(i + 1, imdb.num_images, _t.average_time)
if 0:
dets = np.hstack((imdb_boxes[i], scores))
# from IPython import embed; embed()
_vis_proposals(im, dets[:3, :], thresh=0.9)
plt.show()
return imdb_boxes
def imdb_rpn_compute_stats(net, imdb, anchor_scales=(8,16,32),
feature_stride=16):
raw_anchors = generate_anchors(scales=np.array(anchor_scales))
print raw_anchors.shape
sums = 0
squred_sums = 0
counts = 0
roidb = filter_roidb(imdb.roidb)
# Compute a map of input image size and output feature map blob
map_w = {}
map_h = {}
for i in xrange(50, cfg.TRAIN.MAX_SIZE + 10):
blobs = {
'data': np.zeros((1, 3, i, i)),
'im_info': np.asarray([[i, i, 1.0]])
}
net.blobs['data'].reshape(*(blobs['data'].shape))
net.blobs['im_info'].reshape(*(blobs['im_info'].shape))
blobs_out = net.forward(
data=blobs['data'].astype(np.float32, copy=False),
im_info=blobs['im_info'].astype(np.float32, copy=False))
height, width = net.blobs['rpn/output'].data.shape[-2:]
map_w[i] = width
map_h[i] = height
for i in xrange(len(roidb)):
if not i % 5000:
print 'computing %d/%d' % (i, imdb.num_images)
im = cv2.imread(roidb[i]['image'])
im_data, im_info = _get_image_blob(im)
gt_boxes = roidb[i]['boxes']
gt_boxes = gt_boxes * im_info[0, 2]
height = map_h[im_data.shape[2]]
width = map_w[im_data.shape[3]]
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * feature_stride
shift_y = np.arange(0, height) * feature_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
shift_x.ravel(), shift_y.ravel())).transpose()
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = raw_anchors.shape[0]
K = shifts.shape[0]
all_anchors = (raw_anchors.reshape((1, A, 4)) +
shifts.reshape((1, K, 4)).transpose((1, 0, 2)))
all_anchors = all_anchors.reshape((K * A, 4))
# only keep anchors inside the image
inds_inside = np.where(
(all_anchors[:, 0] >= 0) &
(all_anchors[:, 1] >= 0) &
(all_anchors[:, 2] < im_info[0, 1]) & # width
(all_anchors[:, 3] < im_info[0, 0]) # height
)[0]
# keep only inside anchors
anchors = all_anchors[inds_inside, :]
overlaps = bbox_overlaps(
np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=np.float))
# There are 2 types of bbox targets
# 1. anchor whose overlaps with gt is greater than RPN_POSITIVE_OVERLAP
argmax_overlaps = overlaps.argmax(axis=1)
max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
fg_inds = np.where(max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP)[0]
# 2. anchors which best match certain gt
gt_argmax_overlaps = overlaps.argmax(axis=0)
gt_max_overlaps = overlaps[gt_argmax_overlaps,
np.arange(overlaps.shape[1])]
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
fg_inds = np.unique(np.hstack((fg_inds, gt_argmax_overlaps)))
gt_rois = gt_boxes[argmax_overlaps, :]
anchors = anchors[fg_inds, :]
gt_rois = gt_rois[fg_inds, :]
targets = bbox_transform(anchors, gt_rois[:, :4]).astype(np.float32, copy=False)
sums += targets.sum(axis=0)
squred_sums += (targets ** 2).sum(axis=0)
counts += targets.shape[0]
means = sums / counts
stds = np.sqrt(squred_sums / counts - means ** 2)
print means
print stds
return means, stds
| 7,868 | 35.771028 | 88 | py |
bottom-up-attention | bottom-up-attention-master/lib/rpn/proposal_target_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
import caffe
import yaml
import numpy as np
import numpy.random as npr
from fast_rcnn.config import cfg
from fast_rcnn.bbox_transform import bbox_transform
from utils.cython_bbox import bbox_overlaps
DEBUG = False
DEBUG_SHAPE = False
class ProposalTargetLayer(caffe.Layer):
"""
Assign object detection proposals to ground-truth targets. Produces proposal
classification labels and bounding-box regression targets.
"""
def setup(self, bottom, top):
self._count = 0
self._fg_num = 0
self._bg_num = 0
layer_params = yaml.load(self.param_str)
self._num_classes = layer_params['num_classes']
if 'num_attr_classes' in layer_params:
self._num_attr_classes = layer_params['num_attr_classes']
else:
self._num_attr_classes = 0
if 'num_rel_classes' in layer_params:
self._num_rel_classes = layer_params['num_rel_classes']
else:
self._num_rel_classes = 0
if 'ignore_label' in layer_params:
self._ignore_label = layer_params['ignore_label']
else:
self._ignore_label = -1
rois_per_image = 1 if cfg.TRAIN.BATCH_SIZE == -1 else cfg.TRAIN.BATCH_SIZE
# sampled rois (0, x1, y1, x2, y2)
top[0].reshape(rois_per_image, 5, 1, 1)
# labels
top[1].reshape(rois_per_image, 1, 1, 1)
# bbox_targets
top[2].reshape(rois_per_image, self._num_classes * 4, 1, 1)
# bbox_inside_weights
top[3].reshape(rois_per_image, self._num_classes * 4, 1, 1)
# bbox_outside_weights
top[4].reshape(rois_per_image, self._num_classes * 4, 1, 1)
ix = 5
fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image).astype(int)
if self._num_attr_classes > 0:
# attribute labels
top[ix].reshape(fg_rois_per_image, 16)
ix += 1
if self._num_rel_classes > 0:
# relation labels
top[ix].reshape(fg_rois_per_image*fg_rois_per_image, 1, 1, 1)
def forward(self, bottom, top):
# Proposal ROIs (0, x1, y1, x2, y2) coming from RPN
# (i.e., rpn.proposal_layer.ProposalLayer), or any other source
all_rois = bottom[0].data
# GT boxes (x1, y1, x2, y2, label, attributes[16], relations[num_objs])
# TODO(rbg): it's annoying that sometimes I have extra info before
# and other times after box coordinates -- normalize to one format
gt_boxes = bottom[1].data
gt_boxes = gt_boxes.reshape(gt_boxes.shape[0], gt_boxes.shape[1])
# Include ground-truth boxes in the set of candidate rois
zeros = np.zeros((gt_boxes.shape[0], 1), dtype=gt_boxes.dtype)
all_rois = np.vstack(
(all_rois, np.hstack((zeros, gt_boxes[:, :4])))
)
# Sanity check: single batch only
assert np.all(all_rois[:, 0] == 0), \
'Only single item batches are supported'
rois_per_image = np.inf if cfg.TRAIN.BATCH_SIZE == -1 else cfg.TRAIN.BATCH_SIZE
fg_rois_per_image = int(np.round(cfg.TRAIN.FG_FRACTION * rois_per_image))
# Sample rois with classification labels and bounding box regression
# targets
# print 'proposal_target_layer:', fg_rois_per_image
labels, rois, bbox_targets, bbox_inside_weights, attributes, relations = _sample_rois(
all_rois, gt_boxes, fg_rois_per_image,
rois_per_image, self._num_classes, self._num_attr_classes,
self._num_rel_classes, self._ignore_label)
if self._num_attr_classes > 0:
assert attributes is not None
if self._num_rel_classes > 0:
assert relations is not None
if DEBUG:
print 'num fg: {}'.format((labels > 0).sum())
print 'num bg: {}'.format((labels == 0).sum())
self._count += 1
self._fg_num += (labels > 0).sum()
self._bg_num += (labels == 0).sum()
print 'num fg avg: {}'.format(self._fg_num / self._count)
print 'num bg avg: {}'.format(self._bg_num / self._count)
print 'ratio: {:.3f}'.format(float(self._fg_num) / float(self._bg_num))
# sampled rois
# modified by ywxiong
rois = rois.reshape((rois.shape[0], rois.shape[1], 1, 1))
top[0].reshape(*rois.shape)
top[0].data[...] = rois
# classification labels
# modified by ywxiong
labels = labels.reshape((labels.shape[0], 1, 1, 1))
top[1].reshape(*labels.shape)
top[1].data[...] = labels
# bbox_targets
# modified by ywxiong
bbox_targets = bbox_targets.reshape((bbox_targets.shape[0], bbox_targets.shape[1], 1, 1))
top[2].reshape(*bbox_targets.shape)
top[2].data[...] = bbox_targets
# bbox_inside_weights
# modified by ywxiong
bbox_inside_weights = bbox_inside_weights.reshape((bbox_inside_weights.shape[0], bbox_inside_weights.shape[1], 1, 1))
top[3].reshape(*bbox_inside_weights.shape)
top[3].data[...] = bbox_inside_weights
# bbox_outside_weights
# modified by ywxiong
bbox_inside_weights = bbox_inside_weights.reshape((bbox_inside_weights.shape[0], bbox_inside_weights.shape[1], 1, 1))
top[4].reshape(*bbox_inside_weights.shape)
top[4].data[...] = np.array(bbox_inside_weights > 0).astype(np.float32)
#attribute labels
ix = 5
if self._num_attr_classes > 0:
attributes[:,1:][attributes[:,1:]==0] = self._ignore_label
top[ix].reshape(*attributes.shape)
top[ix].data[...] = attributes
ix += 1
# relation labels
if self._num_rel_classes > 0:
top[ix].reshape(*relations.shape)
top[ix].data[...] = relations
if DEBUG_SHAPE:
for i in range(len(top)):
print 'ProposalTargetLayer top[{}] size: {}'.format(i, top[i].data.shape)
def backward(self, top, propagate_down, bottom):
"""This layer does not propagate gradients."""
pass
def reshape(self, bottom, top):
"""Reshaping happens during the call to forward."""
pass
def _get_bbox_regression_labels(bbox_target_data, num_classes):
"""Bounding-box regression targets (bbox_target_data) are stored in a
compact form N x (class, tx, ty, tw, th)
This function expands those targets into the 4-of-4*K representation used
by the network (i.e. only one class has non-zero targets).
Returns:
bbox_target (ndarray): N x 4K blob of regression targets
bbox_inside_weights (ndarray): N x 4K blob of loss weights
"""
clss = bbox_target_data[:, 0]
bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32)
# print 'proposal_target_layer:', bbox_targets.shape
bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
inds = np.where(clss > 0)[0]
if cfg.TRAIN.AGNOSTIC:
for ind in inds:
cls = clss[ind]
start = 4 * (1 if cls > 0 else 0)
end = start + 4
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
else:
for ind in inds:
cls = clss[ind]
start = int(4 * cls)
end = int(start + 4)
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
return bbox_targets, bbox_inside_weights
def _compute_targets(ex_rois, gt_rois, labels):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 4
targets = bbox_transform(ex_rois, gt_rois)
if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
# Optionally normalize targets by a precomputed mean and stdev
targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS))
/ np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS))
return np.hstack(
(labels[:, np.newaxis], targets)).astype(np.float32, copy=False)
def _sample_rois(all_rois, gt_boxes, fg_rois_per_image, rois_per_image, num_classes,
num_attr_classes, num_rel_classes, ignore_label):
"""Generate a random sample of RoIs comprising foreground and background
examples.
"""
# overlaps: (rois x gt_boxes)
overlaps = bbox_overlaps(
np.ascontiguousarray(all_rois[:, 1:5], dtype=np.float),
np.ascontiguousarray(gt_boxes[:, :4], dtype=np.float))
# GT boxes (x1, y1, x2, y2, label, attributes[16], relations[num_objs])
has_attributes = num_attr_classes > 0
if has_attributes:
assert gt_boxes.shape[1] >= 21
has_relations = num_rel_classes > 0
if has_relations:
assert gt_boxes.shape[0] == gt_boxes.shape[1]-21, \
'relationships not found in gt_boxes, item length is only %d' % gt_boxes.shape[1]
gt_assignment = overlaps.argmax(axis=1)
max_overlaps = overlaps.max(axis=1)
labels = gt_boxes[gt_assignment, 4]
# Select foreground RoIs as those with >= FG_THRESH overlap
fg_inds = np.where(max_overlaps >= cfg.TRAIN.FG_THRESH)[0]
# Guard against the case when an image has fewer than fg_rois_per_image
# foreground RoIs
fg_rois_per_this_image = int(min(fg_rois_per_image, fg_inds.size))
# Sample foreground regions without replacement
if fg_inds.size > 0:
fg_inds = npr.choice(fg_inds, size=fg_rois_per_this_image, replace=False)
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = np.where((max_overlaps < cfg.TRAIN.BG_THRESH_HI) &
(max_overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
# Compute number of background RoIs to take from this image (guarding
# against there being fewer than desired)
bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image
bg_rois_per_this_image = int(min(bg_rois_per_this_image, bg_inds.size))
# Sample background regions without replacement
if bg_inds.size > 0:
bg_inds = npr.choice(bg_inds, size=bg_rois_per_this_image, replace=False)
# The indices that we're selecting (both fg and bg)
keep_inds = np.append(fg_inds, bg_inds)
# print 'proposal_target_layer:', keep_inds
# Select sampled values from various arrays:
labels = labels[keep_inds]
# Clamp labels for the background RoIs to 0 / ignore_label
labels[fg_rois_per_this_image:] = 0
fg_gt = np.array(gt_assignment[fg_inds])
if has_attributes:
attributes = np.ones((fg_rois_per_image,16))*ignore_label
attributes[:fg_rois_per_this_image,:] = gt_boxes[fg_gt, 5:21]
np.place(attributes[:,1:],attributes[:,1:] == 0, ignore_label)
else:
attributes = None
if has_relations:
expand_rels = gt_boxes[fg_gt, 21:].T[fg_gt].T
num_relations_per_this_image = np.count_nonzero(expand_rels)
# Keep an equal number of 'no relation' outputs, the rest can be ignore
expand_rels = expand_rels.flatten()
no_rel_inds = np.where(expand_rels==0)[0]
if len(no_rel_inds) > num_relations_per_this_image:
no_rel_inds = npr.choice(no_rel_inds, size=num_relations_per_this_image, replace=False)
np.place(expand_rels,expand_rels==0,ignore_label)
expand_rels[no_rel_inds] = 0
relations = np.ones((fg_rois_per_image,fg_rois_per_image),dtype=np.float)*ignore_label
relations[:fg_rois_per_this_image,:fg_rois_per_this_image] = expand_rels.reshape((fg_rois_per_this_image,fg_rois_per_this_image))
relations = relations.reshape((relations.size, 1, 1, 1))
else:
relations = None
rois = all_rois[keep_inds]
# print 'proposal_target_layer:', rois
bbox_target_data = _compute_targets(
rois[:, 1:5], gt_boxes[gt_assignment[keep_inds], :4], labels)
# print 'proposal_target_layer:', bbox_target_data
bbox_targets, bbox_inside_weights = \
_get_bbox_regression_labels(bbox_target_data, num_classes)
return labels, rois, bbox_targets, bbox_inside_weights, attributes, relations
| 12,616 | 41.625 | 137 | py |
bottom-up-attention | bottom-up-attention-master/lib/rpn/anchor_target_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
import os
import caffe
import yaml
from fast_rcnn.config import cfg
import numpy as np
import numpy.random as npr
from generate_anchors import generate_anchors
from utils.cython_bbox import bbox_overlaps
from fast_rcnn.bbox_transform import bbox_transform
DEBUG = False
class AnchorTargetLayer(caffe.Layer):
"""
Assign anchors to ground-truth targets. Produces anchor classification
labels and bounding-box regression targets.
"""
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str)
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
self._feat_stride = layer_params['feat_stride']
if DEBUG:
print 'anchors:'
print self._anchors
print 'anchor shapes:'
print np.hstack((
self._anchors[:, 2::4] - self._anchors[:, 0::4],
self._anchors[:, 3::4] - self._anchors[:, 1::4],
))
self._counts = cfg.EPS
self._sums = np.zeros((1, 4))
self._squared_sums = np.zeros((1, 4))
self._fg_sum = 0
self._bg_sum = 0
self._count = 0
# allow boxes to sit over the edge by a small amount
self._allowed_border = layer_params.get('allowed_border', 0)
height, width = bottom[0].data.shape[-2:]
if DEBUG:
print 'AnchorTargetLayer: height', height, 'width', width
A = self._num_anchors
# labels
top[0].reshape(1, 1, A * height, width)
# bbox_targets
top[1].reshape(1, A * 4, height, width)
# bbox_inside_weights
top[2].reshape(1, A * 4, height, width)
# bbox_outside_weights
top[3].reshape(1, A * 4, height, width)
def forward(self, bottom, top):
# Algorithm:
#
# for each (H, W) location i
# generate 9 anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the 9 anchors
# filter out-of-image anchors
# measure GT overlap
assert bottom[0].data.shape[0] == 1, \
'Only single item batches are supported'
# map of shape (..., H, W)
height, width = bottom[0].data.shape[-2:]
# GT boxes (x1, y1, x2, y2, label, ...)
gt_boxes = bottom[1].data[:,:5]
# im_info
im_info = bottom[2].data[0, :]
if DEBUG:
print ''
print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
print 'scale: {}'.format(im_info[2])
print 'height, width: ({}, {})'.format(height, width)
print 'rpn: gt_boxes.shape', gt_boxes.shape
print 'rpn: gt_boxes', gt_boxes
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * self._feat_stride
shift_y = np.arange(0, height) * self._feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
shift_x.ravel(), shift_y.ravel())).transpose()
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = self._num_anchors
K = shifts.shape[0]
all_anchors = (self._anchors.reshape((1, A, 4)) +
shifts.reshape((1, K, 4)).transpose((1, 0, 2)))
all_anchors = all_anchors.reshape((K * A, 4))
total_anchors = int(K * A)
# only keep anchors inside the image
inds_inside = np.where(
(all_anchors[:, 0] >= -self._allowed_border) &
(all_anchors[:, 1] >= -self._allowed_border) &
(all_anchors[:, 2] < im_info[1] + self._allowed_border) & # width
(all_anchors[:, 3] < im_info[0] + self._allowed_border) # height
)[0]
if DEBUG:
print 'total_anchors', total_anchors
print 'inds_inside', len(inds_inside)
# keep only inside anchors
anchors = all_anchors[inds_inside, :]
if DEBUG:
print 'anchors.shape', anchors.shape
# label: 1 is positive, 0 is negative, -1 is dont care
labels = np.empty((len(inds_inside), ), dtype=np.float32)
labels.fill(-1)
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt)
gt_boxes = gt_boxes.reshape(gt_boxes.shape[0], gt_boxes.shape[1])
overlaps = bbox_overlaps(
np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=np.float))
argmax_overlaps = overlaps.argmax(axis=1)
max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
gt_argmax_overlaps = overlaps.argmax(axis=0)
gt_max_overlaps = overlaps[gt_argmax_overlaps,
np.arange(overlaps.shape[1])]
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1
# fg label: above threshold IOU
labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1
if cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels last so that negative labels can clobber positives
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
# subsample positive labels if we have too many
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
fg_inds = np.where(labels == 1)[0]
if len(fg_inds) > num_fg:
disable_inds = npr.choice(
fg_inds, size=(len(fg_inds) - num_fg), replace=False)
labels[disable_inds] = -1
# subsample negative labels if we have too many
num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)
bg_inds = np.where(labels == 0)[0]
if len(bg_inds) > num_bg:
disable_inds = npr.choice(
bg_inds, size=(len(bg_inds) - num_bg), replace=False)
labels[disable_inds] = -1
#print "was %s inds, disabling %s, now %s inds" % (
#len(bg_inds), len(disable_inds), np.sum(labels == 0))
bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])
bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_inside_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS)
bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0:
# uniform weighting of examples (given non-uniform sampling)
num_examples = np.sum(labels >= 0)
positive_weights = np.ones((1, 4)) * 1.0 / num_examples
negative_weights = np.ones((1, 4)) * 1.0 / num_examples
else:
assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) &
(cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1))
positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT /
np.sum(labels == 1))
negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) /
np.sum(labels == 0))
bbox_outside_weights[labels == 1, :] = positive_weights
bbox_outside_weights[labels == 0, :] = negative_weights
if DEBUG:
self._sums += bbox_targets[labels == 1, :].sum(axis=0)
self._squared_sums += (bbox_targets[labels == 1, :] ** 2).sum(axis=0)
self._counts += np.sum(labels == 1)
means = self._sums / self._counts
stds = np.sqrt(self._squared_sums / self._counts - means ** 2)
print 'means:'
print means
print 'stdevs:'
print stds
# map up to original set of anchors
labels = _unmap(labels, total_anchors, inds_inside, fill=-1)
bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)
bbox_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0)
bbox_outside_weights = _unmap(bbox_outside_weights, total_anchors, inds_inside, fill=0)
if DEBUG:
print 'rpn: max max_overlap', np.max(max_overlaps)
print 'rpn: num_positive', np.sum(labels == 1)
print 'rpn: num_negative', np.sum(labels == 0)
self._fg_sum += np.sum(labels == 1)
self._bg_sum += np.sum(labels == 0)
self._count += 1
print 'rpn: num_positive avg', self._fg_sum / self._count
print 'rpn: num_negative avg', self._bg_sum / self._count
# labels
labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2)
labels = labels.reshape((1, 1, A * height, width))
top[0].reshape(*labels.shape)
top[0].data[...] = labels
# bbox_targets
bbox_targets = bbox_targets \
.reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2)
top[1].reshape(*bbox_targets.shape)
top[1].data[...] = bbox_targets
# bbox_inside_weights
bbox_inside_weights = bbox_inside_weights \
.reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2)
assert bbox_inside_weights.shape[2] == height
assert bbox_inside_weights.shape[3] == width
top[2].reshape(*bbox_inside_weights.shape)
top[2].data[...] = bbox_inside_weights
# bbox_outside_weights
bbox_outside_weights = bbox_outside_weights \
.reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2)
assert bbox_outside_weights.shape[2] == height
assert bbox_outside_weights.shape[3] == width
top[3].reshape(*bbox_outside_weights.shape)
top[3].data[...] = bbox_outside_weights
def backward(self, top, propagate_down, bottom):
"""This layer does not propagate gradients."""
pass
def reshape(self, bottom, top):
"""Reshaping happens during the call to forward."""
pass
def _unmap(data, count, inds, fill=0):
""" Unmap a subset of item (data) back to the original set of items (of
size count) """
if len(data.shape) == 1:
ret = np.empty((count, ), dtype=np.float32)
ret.fill(fill)
ret[inds] = data
else:
ret = np.empty((count, ) + data.shape[1:], dtype=np.float32)
ret.fill(fill)
ret[inds, :] = data
return ret
def _compute_targets(ex_rois, gt_rois):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 5
targets = bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
if cfg.TRAIN.RPN_NORMALIZE_TARGETS:
assert cfg.TRAIN.RPN_NORMALIZE_MEANS is not None
assert cfg.TRAIN.RPN_NORMALIZE_STDS is not None
targets -= cfg.TRAIN.RPN_NORMALIZE_MEANS
targets /= cfg.TRAIN.RPN_NORMALIZE_STDS
return targets
| 11,700 | 39.487889 | 95 | py |
bottom-up-attention | bottom-up-attention-master/lib/rpn/__init__.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
| 262 | 36.571429 | 58 | py |
bottom-up-attention | bottom-up-attention-master/lib/rpn/heatmap_layer.py |
import caffe
import yaml
import numpy as np
import numpy.random as npr
from fast_rcnn.config import cfg
from fast_rcnn.bbox_transform import bbox_transform
from utils.cython_bbox import bbox_overlaps
DEBUG = False
class HeatmapLayer(caffe.Layer):
"""
Takes regions of interest (rois) and outputs heatmaps.
"""
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str)
self._output_w = layer_params['output_w']
self._output_h = layer_params['output_h']
self._out_size = np.array([self._output_w, self._output_h,
self._output_w, self._output_h],dtype=float)
top[0].reshape(bottom[0].data.shape[0], 1, self._output_h, self._output_w)
def forward(self, bottom, top):
# im_info (height, width, scaling)
assert bottom[1].data.shape[0] == 1, 'Batch size == 1 only'
image_h = bottom[1].data[0][0]
image_w = bottom[1].data[0][1]
image_size = np.array([image_w, image_h, image_w, image_h],dtype=float)
# Proposal ROIs (0, x1, y1, x2, y2) coming from RPN
# (i.e., rpn.proposal_layer.ProposalLayer), or any other source
rois = bottom[0].data
rois = rois.reshape(rois.shape[0], rois.shape[1])
rois = rois[:,1:]*self._out_size/image_size
# This will fill occupied pixels in an approximate (dilated) fashion
rois_int = np.round(np.hstack((
np.floor(rois[:,[0]]),
np.floor(rois[:,[1]]),
np.minimum(self._output_w-1,np.ceil(rois[:,[2]])),
np.minimum(self._output_h-1,np.ceil(rois[:,[3]]))
))).astype(int)
top[0].reshape(bottom[0].data.shape[0], 1, self._output_h, self._output_w)
top[0].data[...] = -1
for i in range(rois.shape[0]):
top[0].data[i, 0, rois_int[i,1]:rois_int[i,3], rois_int[i,0]:rois_int[i,2]] = 1
def backward(self, top, propagate_down, bottom):
"""This layer does not propagate gradients."""
pass
def reshape(self, bottom, top):
"""Reshaping happens during the call to forward."""
pass
| 2,111 | 37.4 | 91 | py |
bottom-up-attention | bottom-up-attention-master/lib/pycocotools/cocoeval.py | __author__ = 'tsungyi'
import numpy as np
import datetime
import time
from collections import defaultdict
import mask
import copy
class COCOeval:
# Interface for evaluating detection on the Microsoft COCO dataset.
#
# The usage for CocoEval is as follows:
# cocoGt=..., cocoDt=... # load dataset and results
# E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object
# E.params.recThrs = ...; # set parameters as desired
# E.evaluate(); # run per image evaluation
# E.accumulate(); # accumulate per image results
# E.summarize(); # display summary metrics of results
# For example usage see evalDemo.m and http://mscoco.org/.
#
# The evaluation parameters are as follows (defaults in brackets):
# imgIds - [all] N img ids to use for evaluation
# catIds - [all] K cat ids to use for evaluation
# iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation
# recThrs - [0:.01:1] R=101 recall thresholds for evaluation
# areaRng - [...] A=4 object area ranges for evaluation
# maxDets - [1 10 100] M=3 thresholds on max detections per image
# useSegm - [1] if true evaluate against ground-truth segments
# useCats - [1] if true use category labels for evaluation # Note: if useSegm=0 the evaluation is run on bounding boxes.
# Note: if useCats=0 category labels are ignored as in proposal scoring.
# Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified.
#
# evaluate(): evaluates detections on every image and every category and
# concats the results into the "evalImgs" with fields:
# dtIds - [1xD] id for each of the D detections (dt)
# gtIds - [1xG] id for each of the G ground truths (gt)
# dtMatches - [TxD] matching gt id at each IoU or 0
# gtMatches - [TxG] matching dt id at each IoU or 0
# dtScores - [1xD] confidence of each dt
# gtIgnore - [1xG] ignore flag for each gt
# dtIgnore - [TxD] ignore flag for each dt at each IoU
#
# accumulate(): accumulates the per-image, per-category evaluation
# results in "evalImgs" into the dictionary "eval" with fields:
# params - parameters used for evaluation
# date - date evaluation was performed
# counts - [T,R,K,A,M] parameter dimensions (see above)
# precision - [TxRxKxAxM] precision for every evaluation setting
# recall - [TxKxAxM] max recall for every evaluation setting
# Note: precision and recall==-1 for settings with no gt objects.
#
# See also coco, mask, pycocoDemo, pycocoEvalDemo
#
# Microsoft COCO Toolbox. version 2.0
# Data, paper, and tutorials available at: http://mscoco.org/
# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
# Licensed under the Simplified BSD License [see coco/license.txt]
def __init__(self, cocoGt=None, cocoDt=None):
'''
Initialize CocoEval using coco APIs for gt and dt
:param cocoGt: coco object with ground truth annotations
:param cocoDt: coco object with detection results
:return: None
'''
self.cocoGt = cocoGt # ground truth COCO API
self.cocoDt = cocoDt # detections COCO API
self.params = {} # evaluation parameters
self.evalImgs = defaultdict(list) # per-image per-category evaluation results [KxAxI] elements
self.eval = {} # accumulated evaluation results
self._gts = defaultdict(list) # gt for evaluation
self._dts = defaultdict(list) # dt for evaluation
self.params = Params() # parameters
self._paramsEval = {} # parameters for evaluation
self.stats = [] # result summarization
self.ious = {} # ious between all gts and dts
if not cocoGt is None:
self.params.imgIds = sorted(cocoGt.getImgIds())
self.params.catIds = sorted(cocoGt.getCatIds())
def _prepare(self):
'''
Prepare ._gts and ._dts for evaluation based on params
:return: None
'''
#
def _toMask(objs, coco):
# modify segmentation by reference
for obj in objs:
t = coco.imgs[obj['image_id']]
if type(obj['segmentation']) == list:
if type(obj['segmentation'][0]) == dict:
print 'debug'
obj['segmentation'] = mask.frPyObjects(obj['segmentation'],t['height'],t['width'])
if len(obj['segmentation']) == 1:
obj['segmentation'] = obj['segmentation'][0]
else:
# an object can have multiple polygon regions
# merge them into one RLE mask
obj['segmentation'] = mask.merge(obj['segmentation'])
elif type(obj['segmentation']) == dict and type(obj['segmentation']['counts']) == list:
obj['segmentation'] = mask.frPyObjects([obj['segmentation']],t['height'],t['width'])[0]
elif type(obj['segmentation']) == dict and \
type(obj['segmentation']['counts'] == unicode or type(obj['segmentation']['counts']) == str):
pass
else:
raise Exception('segmentation format not supported.')
p = self.params
if p.useCats:
gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
else:
gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))
dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))
if p.useSegm:
_toMask(gts, self.cocoGt)
_toMask(dts, self.cocoDt)
self._gts = defaultdict(list) # gt for evaluation
self._dts = defaultdict(list) # dt for evaluation
for gt in gts:
self._gts[gt['image_id'], gt['category_id']].append(gt)
for dt in dts:
self._dts[dt['image_id'], dt['category_id']].append(dt)
self.evalImgs = defaultdict(list) # per-image per-category evaluation results
self.eval = {} # accumulated evaluation results
def evaluate(self):
'''
Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
:return: None
'''
tic = time.time()
print 'Running per image evaluation... '
p = self.params
p.imgIds = list(np.unique(p.imgIds))
if p.useCats:
p.catIds = list(np.unique(p.catIds))
p.maxDets = sorted(p.maxDets)
self.params=p
self._prepare()
# loop through images, area range, max detection number
catIds = p.catIds if p.useCats else [-1]
computeIoU = self.computeIoU
self.ious = {(imgId, catId): computeIoU(imgId, catId) \
for imgId in p.imgIds
for catId in catIds}
evaluateImg = self.evaluateImg
maxDet = p.maxDets[-1]
self.evalImgs = [evaluateImg(imgId, catId, areaRng, maxDet)
for catId in catIds
for areaRng in p.areaRng
for imgId in p.imgIds
]
self._paramsEval = copy.deepcopy(self.params)
toc = time.time()
print 'DONE (t=%0.2fs).'%(toc-tic)
def computeIoU(self, imgId, catId):
p = self.params
if p.useCats:
gt = self._gts[imgId,catId]
dt = self._dts[imgId,catId]
else:
gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]]
dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]]
if len(gt) == 0 and len(dt) ==0:
return []
dt = sorted(dt, key=lambda x: -x['score'])
if len(dt) > p.maxDets[-1]:
dt=dt[0:p.maxDets[-1]]
if p.useSegm:
g = [g['segmentation'] for g in gt]
d = [d['segmentation'] for d in dt]
else:
g = [g['bbox'] for g in gt]
d = [d['bbox'] for d in dt]
# compute iou between each dt and gt region
iscrowd = [int(o['iscrowd']) for o in gt]
ious = mask.iou(d,g,iscrowd)
return ious
def evaluateImg(self, imgId, catId, aRng, maxDet):
'''
perform evaluation for single category and image
:return: dict (single image results)
'''
#
p = self.params
if p.useCats:
gt = self._gts[imgId,catId]
dt = self._dts[imgId,catId]
else:
gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]]
dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]]
if len(gt) == 0 and len(dt) ==0:
return None
for g in gt:
if 'ignore' not in g:
g['ignore'] = 0
if g['iscrowd'] == 1 or g['ignore'] or (g['area']<aRng[0] or g['area']>aRng[1]):
g['_ignore'] = 1
else:
g['_ignore'] = 0
# sort dt highest score first, sort gt ignore last
# gt = sorted(gt, key=lambda x: x['_ignore'])
gtind = [ind for (ind, g) in sorted(enumerate(gt), key=lambda (ind, g): g['_ignore']) ]
gt = [gt[ind] for ind in gtind]
dt = sorted(dt, key=lambda x: -x['score'])[0:maxDet]
iscrowd = [int(o['iscrowd']) for o in gt]
# load computed ious
N_iou = len(self.ious[imgId, catId])
ious = self.ious[imgId, catId][0:maxDet, np.array(gtind)] if N_iou >0 else self.ious[imgId, catId]
T = len(p.iouThrs)
G = len(gt)
D = len(dt)
gtm = np.zeros((T,G))
dtm = np.zeros((T,D))
gtIg = np.array([g['_ignore'] for g in gt])
dtIg = np.zeros((T,D))
if not len(ious)==0:
for tind, t in enumerate(p.iouThrs):
for dind, d in enumerate(dt):
# information about best match so far (m=-1 -> unmatched)
iou = min([t,1-1e-10])
m = -1
for gind, g in enumerate(gt):
# if this gt already matched, and not a crowd, continue
if gtm[tind,gind]>0 and not iscrowd[gind]:
continue
# if dt matched to reg gt, and on ignore gt, stop
if m>-1 and gtIg[m]==0 and gtIg[gind]==1:
break
# continue to next gt unless better match made
if ious[dind,gind] < iou:
continue
# match successful and best so far, store appropriately
iou=ious[dind,gind]
m=gind
# if match made store id of match for both dt and gt
if m ==-1:
continue
dtIg[tind,dind] = gtIg[m]
dtm[tind,dind] = gt[m]['id']
gtm[tind,m] = d['id']
# set unmatched detections outside of area range to ignore
a = np.array([d['area']<aRng[0] or d['area']>aRng[1] for d in dt]).reshape((1, len(dt)))
dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0)))
# store results for given image and category
return {
'image_id': imgId,
'category_id': catId,
'aRng': aRng,
'maxDet': maxDet,
'dtIds': [d['id'] for d in dt],
'gtIds': [g['id'] for g in gt],
'dtMatches': dtm,
'gtMatches': gtm,
'dtScores': [d['score'] for d in dt],
'gtIgnore': gtIg,
'dtIgnore': dtIg,
}
def accumulate(self, p = None):
'''
Accumulate per image evaluation results and store the result in self.eval
:param p: input params for evaluation
:return: None
'''
print 'Accumulating evaluation results... '
tic = time.time()
if not self.evalImgs:
print 'Please run evaluate() first'
# allows input customized parameters
if p is None:
p = self.params
p.catIds = p.catIds if p.useCats == 1 else [-1]
T = len(p.iouThrs)
R = len(p.recThrs)
K = len(p.catIds) if p.useCats else 1
A = len(p.areaRng)
M = len(p.maxDets)
precision = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories
recall = -np.ones((T,K,A,M))
# create dictionary for future indexing
_pe = self._paramsEval
catIds = _pe.catIds if _pe.useCats else [-1]
setK = set(catIds)
setA = set(map(tuple, _pe.areaRng))
setM = set(_pe.maxDets)
setI = set(_pe.imgIds)
# get inds to evaluate
k_list = [n for n, k in enumerate(p.catIds) if k in setK]
m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA]
i_list = [n for n, i in enumerate(p.imgIds) if i in setI]
# K0 = len(_pe.catIds)
I0 = len(_pe.imgIds)
A0 = len(_pe.areaRng)
# retrieve E at each category, area range, and max number of detections
for k, k0 in enumerate(k_list):
Nk = k0*A0*I0
for a, a0 in enumerate(a_list):
Na = a0*I0
for m, maxDet in enumerate(m_list):
E = [self.evalImgs[Nk+Na+i] for i in i_list]
E = filter(None, E)
if len(E) == 0:
continue
dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E])
# different sorting method generates slightly different results.
# mergesort is used to be consistent as Matlab implementation.
inds = np.argsort(-dtScores, kind='mergesort')
dtm = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds]
dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet] for e in E], axis=1)[:,inds]
gtIg = np.concatenate([e['gtIgnore'] for e in E])
npig = len([ig for ig in gtIg if ig == 0])
if npig == 0:
continue
tps = np.logical_and( dtm, np.logical_not(dtIg) )
fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) )
tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
tp = np.array(tp)
fp = np.array(fp)
nd = len(tp)
rc = tp / npig
pr = tp / (fp+tp+np.spacing(1))
q = np.zeros((R,))
if nd:
recall[t,k,a,m] = rc[-1]
else:
recall[t,k,a,m] = 0
# numpy is slow without cython optimization for accessing elements
# use python array gets significant speed improvement
pr = pr.tolist(); q = q.tolist()
for i in range(nd-1, 0, -1):
if pr[i] > pr[i-1]:
pr[i-1] = pr[i]
inds = np.searchsorted(rc, p.recThrs)
try:
for ri, pi in enumerate(inds):
q[ri] = pr[pi]
except:
pass
precision[t,:,k,a,m] = np.array(q)
self.eval = {
'params': p,
'counts': [T, R, K, A, M],
'date': datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
'precision': precision,
'recall': recall,
}
toc = time.time()
print 'DONE (t=%0.2fs).'%( toc-tic )
def summarize(self):
'''
Compute and display summary metrics for evaluation results.
Note this functin can *only* be applied on the default parameter setting
'''
def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ):
p = self.params
iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6} | maxDets={:>3} ] = {}'
titleStr = 'Average Precision' if ap == 1 else 'Average Recall'
typeStr = '(AP)' if ap==1 else '(AR)'
iouStr = '%0.2f:%0.2f'%(p.iouThrs[0], p.iouThrs[-1]) if iouThr is None else '%0.2f'%(iouThr)
areaStr = areaRng
maxDetsStr = '%d'%(maxDets)
aind = [i for i, aRng in enumerate(['all', 'small', 'medium', 'large']) if aRng == areaRng]
mind = [i for i, mDet in enumerate([1, 10, 100]) if mDet == maxDets]
if ap == 1:
# dimension of precision: [TxRxKxAxM]
s = self.eval['precision']
# IoU
if iouThr is not None:
t = np.where(iouThr == p.iouThrs)[0]
s = s[t]
# areaRng
s = s[:,:,:,aind,mind]
else:
# dimension of recall: [TxKxAxM]
s = self.eval['recall']
s = s[:,:,aind,mind]
if len(s[s>-1])==0:
mean_s = -1
else:
mean_s = np.mean(s[s>-1])
print iStr.format(titleStr, typeStr, iouStr, areaStr, maxDetsStr, '%.3f'%(float(mean_s)))
return mean_s
if not self.eval:
raise Exception('Please run accumulate() first')
self.stats = np.zeros((12,))
self.stats[0] = _summarize(1)
self.stats[1] = _summarize(1,iouThr=.5)
self.stats[2] = _summarize(1,iouThr=.75)
self.stats[3] = _summarize(1,areaRng='small')
self.stats[4] = _summarize(1,areaRng='medium')
self.stats[5] = _summarize(1,areaRng='large')
self.stats[6] = _summarize(0,maxDets=1)
self.stats[7] = _summarize(0,maxDets=10)
self.stats[8] = _summarize(0,maxDets=100)
self.stats[9] = _summarize(0,areaRng='small')
self.stats[10] = _summarize(0,areaRng='medium')
self.stats[11] = _summarize(0,areaRng='large')
def __str__(self):
self.summarize()
class Params:
'''
Params for coco evaluation api
'''
def __init__(self):
self.imgIds = []
self.catIds = []
# np.arange causes trouble. the data point on arange is slightly larger than the true value
self.iouThrs = np.linspace(.5, 0.95, np.round((0.95-.5)/.05)+1, endpoint=True)
self.recThrs = np.linspace(.0, 1.00, np.round((1.00-.0)/.01)+1, endpoint=True)
self.maxDets = [1,10,100]
self.areaRng = [ [0**2,1e5**2], [0**2, 32**2], [32**2, 96**2], [96**2, 1e5**2] ]
self.useSegm = 0
self.useCats = 1 | 19,735 | 43.45045 | 131 | py |
bottom-up-attention | bottom-up-attention-master/lib/pycocotools/__init__.py | __author__ = 'tylin'
| 21 | 10 | 20 | py |
bottom-up-attention | bottom-up-attention-master/lib/pycocotools/coco.py | __author__ = 'tylin'
__version__ = '1.0.1'
# Interface for accessing the Microsoft COCO dataset.
# Microsoft COCO is a large image dataset designed for object detection,
# segmentation, and caption generation. pycocotools is a Python API that
# assists in loading, parsing and visualizing the annotations in COCO.
# Please visit http://mscoco.org/ for more information on COCO, including
# for the data, paper, and tutorials. The exact format of the annotations
# is also described on the COCO website. For example usage of the pycocotools
# please see pycocotools_demo.ipynb. In addition to this API, please download both
# the COCO images and annotations in order to run the demo.
# An alternative to using the API is to load the annotations directly
# into Python dictionary
# Using the API provides additional utility functions. Note that this API
# supports both *instance* and *caption* annotations. In the case of
# captions not all functions are defined (e.g. categories are undefined).
# The following API functions are defined:
# COCO - COCO api class that loads COCO annotation file and prepare data structures.
# decodeMask - Decode binary mask M encoded via run-length encoding.
# encodeMask - Encode binary mask M using run-length encoding.
# getAnnIds - Get ann ids that satisfy given filter conditions.
# getCatIds - Get cat ids that satisfy given filter conditions.
# getImgIds - Get img ids that satisfy given filter conditions.
# loadAnns - Load anns with the specified ids.
# loadCats - Load cats with the specified ids.
# loadImgs - Load imgs with the specified ids.
# segToMask - Convert polygon segmentation to binary mask.
# showAnns - Display the specified annotations.
# loadRes - Load algorithm results and create API for accessing them.
# download - Download COCO images from mscoco.org server.
# Throughout the API "ann"=annotation, "cat"=category, and "img"=image.
# Help on each functions can be accessed by: "help COCO>function".
# See also COCO>decodeMask,
# COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds,
# COCO>getImgIds, COCO>loadAnns, COCO>loadCats,
# COCO>loadImgs, COCO>segToMask, COCO>showAnns
# Microsoft COCO Toolbox. version 2.0
# Data, paper, and tutorials available at: http://mscoco.org/
# Code written by Piotr Dollar and Tsung-Yi Lin, 2014.
# Licensed under the Simplified BSD License [see bsd.txt]
import json
import datetime
import time
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon
import numpy as np
from skimage.draw import polygon
import urllib
import copy
import itertools
import mask
import os
class COCO:
def __init__(self, annotation_file=None):
"""
Constructor of Microsoft COCO helper class for reading and visualizing annotations.
:param annotation_file (str): location of annotation file
:param image_folder (str): location to the folder that hosts images.
:return:
"""
# load dataset
self.dataset = {}
self.anns = []
self.imgToAnns = {}
self.catToImgs = {}
self.imgs = {}
self.cats = {}
if not annotation_file == None:
print 'loading annotations into memory...'
tic = time.time()
dataset = json.load(open(annotation_file, 'r'))
print 'Done (t=%0.2fs)'%(time.time()- tic)
self.dataset = dataset
self.createIndex()
def createIndex(self):
# create index
print 'creating index...'
anns = {}
imgToAnns = {}
catToImgs = {}
cats = {}
imgs = {}
if 'annotations' in self.dataset:
imgToAnns = {ann['image_id']: [] for ann in self.dataset['annotations']}
anns = {ann['id']: [] for ann in self.dataset['annotations']}
for ann in self.dataset['annotations']:
imgToAnns[ann['image_id']] += [ann]
anns[ann['id']] = ann
if 'images' in self.dataset:
imgs = {im['id']: {} for im in self.dataset['images']}
for img in self.dataset['images']:
imgs[img['id']] = img
if 'categories' in self.dataset:
cats = {cat['id']: [] for cat in self.dataset['categories']}
for cat in self.dataset['categories']:
cats[cat['id']] = cat
catToImgs = {cat['id']: [] for cat in self.dataset['categories']}
if 'annotations' in self.dataset:
for ann in self.dataset['annotations']:
catToImgs[ann['category_id']] += [ann['image_id']]
print 'index created!'
# create class members
self.anns = anns
self.imgToAnns = imgToAnns
self.catToImgs = catToImgs
self.imgs = imgs
self.cats = cats
def info(self):
"""
Print information about the annotation file.
:return:
"""
for key, value in self.dataset['info'].items():
print '%s: %s'%(key, value)
def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
"""
Get ann ids that satisfy given filter conditions. default skips that filter
:param imgIds (int array) : get anns for given imgs
catIds (int array) : get anns for given cats
areaRng (float array) : get anns for given area range (e.g. [0 inf])
iscrowd (boolean) : get anns for given crowd label (False or True)
:return: ids (int array) : integer array of ann ids
"""
imgIds = imgIds if type(imgIds) == list else [imgIds]
catIds = catIds if type(catIds) == list else [catIds]
if len(imgIds) == len(catIds) == len(areaRng) == 0:
anns = self.dataset['annotations']
else:
if not len(imgIds) == 0:
# this can be changed by defaultdict
lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns]
anns = list(itertools.chain.from_iterable(lists))
else:
anns = self.dataset['annotations']
anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds]
anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]
if not iscrowd == None:
ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]
else:
ids = [ann['id'] for ann in anns]
return ids
def getCatIds(self, catNms=[], supNms=[], catIds=[]):
"""
filtering parameters. default skips that filter.
:param catNms (str array) : get cats for given cat names
:param supNms (str array) : get cats for given supercategory names
:param catIds (int array) : get cats for given cat ids
:return: ids (int array) : integer array of cat ids
"""
catNms = catNms if type(catNms) == list else [catNms]
supNms = supNms if type(supNms) == list else [supNms]
catIds = catIds if type(catIds) == list else [catIds]
if len(catNms) == len(supNms) == len(catIds) == 0:
cats = self.dataset['categories']
else:
cats = self.dataset['categories']
cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms]
cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms]
cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds]
ids = [cat['id'] for cat in cats]
return ids
def getImgIds(self, imgIds=[], catIds=[]):
'''
Get img ids that satisfy given filter conditions.
:param imgIds (int array) : get imgs for given ids
:param catIds (int array) : get imgs with all given cats
:return: ids (int array) : integer array of img ids
'''
imgIds = imgIds if type(imgIds) == list else [imgIds]
catIds = catIds if type(catIds) == list else [catIds]
if len(imgIds) == len(catIds) == 0:
ids = self.imgs.keys()
else:
ids = set(imgIds)
for i, catId in enumerate(catIds):
if i == 0 and len(ids) == 0:
ids = set(self.catToImgs[catId])
else:
ids &= set(self.catToImgs[catId])
return list(ids)
def loadAnns(self, ids=[]):
"""
Load anns with the specified ids.
:param ids (int array) : integer ids specifying anns
:return: anns (object array) : loaded ann objects
"""
if type(ids) == list:
return [self.anns[id] for id in ids]
elif type(ids) == int:
return [self.anns[ids]]
def loadCats(self, ids=[]):
"""
Load cats with the specified ids.
:param ids (int array) : integer ids specifying cats
:return: cats (object array) : loaded cat objects
"""
if type(ids) == list:
return [self.cats[id] for id in ids]
elif type(ids) == int:
return [self.cats[ids]]
def loadImgs(self, ids=[]):
"""
Load anns with the specified ids.
:param ids (int array) : integer ids specifying img
:return: imgs (object array) : loaded img objects
"""
if type(ids) == list:
return [self.imgs[id] for id in ids]
elif type(ids) == int:
return [self.imgs[ids]]
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
def loadRes(self, resFile):
"""
Load result file and return a result api object.
:param resFile (str) : file name of result file
:return: res (obj) : result api object
"""
res = COCO()
res.dataset['images'] = [img for img in self.dataset['images']]
# res.dataset['info'] = copy.deepcopy(self.dataset['info'])
# res.dataset['licenses'] = copy.deepcopy(self.dataset['licenses'])
print 'Loading and preparing results... '
tic = time.time()
anns = json.load(open(resFile))
assert type(anns) == list, 'results in not an array of objects'
annsImgIds = [ann['image_id'] for ann in anns]
assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
'Results do not correspond to current coco set'
if 'caption' in anns[0]:
imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
for id, ann in enumerate(anns):
ann['id'] = id+1
elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
for id, ann in enumerate(anns):
bb = ann['bbox']
x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
if not 'segmentation' in ann:
ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
ann['area'] = bb[2]*bb[3]
ann['id'] = id+1
ann['iscrowd'] = 0
elif 'segmentation' in anns[0]:
res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
for id, ann in enumerate(anns):
# now only support compressed RLE format as segmentation results
ann['area'] = mask.area([ann['segmentation']])[0]
if not 'bbox' in ann:
ann['bbox'] = mask.toBbox([ann['segmentation']])[0]
ann['id'] = id+1
ann['iscrowd'] = 0
print 'DONE (t=%0.2fs)'%(time.time()- tic)
res.dataset['annotations'] = anns
res.createIndex()
return res
def download( self, tarDir = None, imgIds = [] ):
'''
Download COCO images from mscoco.org server.
:param tarDir (str): COCO results directory name
imgIds (list): images to be downloaded
:return:
'''
if tarDir is None:
print 'Please specify target directory'
return -1
if len(imgIds) == 0:
imgs = self.imgs.values()
else:
imgs = self.loadImgs(imgIds)
N = len(imgs)
if not os.path.exists(tarDir):
os.makedirs(tarDir)
for i, img in enumerate(imgs):
tic = time.time()
fname = os.path.join(tarDir, img['file_name'])
if not os.path.exists(fname):
urllib.urlretrieve(img['coco_url'], fname)
print 'downloaded %d/%d images (t=%.1fs)'%(i, N, time.time()- tic)
| 14,881 | 41.278409 | 128 | py |
bottom-up-attention | bottom-up-attention-master/lib/pycocotools/mask.py | __author__ = 'tsungyi'
import pycocotools._mask as _mask
# Interface for manipulating masks stored in RLE format.
#
# RLE is a simple yet efficient format for storing binary masks. RLE
# first divides a vector (or vectorized image) into a series of piecewise
# constant regions and then for each piece simply stores the length of
# that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would
# be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1]
# (note that the odd counts are always the numbers of zeros). Instead of
# storing the counts directly, additional compression is achieved with a
# variable bitrate representation based on a common scheme called LEB128.
#
# Compression is greatest given large piecewise constant regions.
# Specifically, the size of the RLE is proportional to the number of
# *boundaries* in M (or for an image the number of boundaries in the y
# direction). Assuming fairly simple shapes, the RLE representation is
# O(sqrt(n)) where n is number of pixels in the object. Hence space usage
# is substantially lower, especially for large simple objects (large n).
#
# Many common operations on masks can be computed directly using the RLE
# (without need for decoding). This includes computations such as area,
# union, intersection, etc. All of these operations are linear in the
# size of the RLE, in other words they are O(sqrt(n)) where n is the area
# of the object. Computing these operations on the original mask is O(n).
# Thus, using the RLE can result in substantial computational savings.
#
# The following API functions are defined:
# encode - Encode binary masks using RLE.
# decode - Decode binary masks encoded via RLE.
# merge - Compute union or intersection of encoded masks.
# iou - Compute intersection over union between masks.
# area - Compute area of encoded masks.
# toBbox - Get bounding boxes surrounding encoded masks.
# frPyObjects - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask.
#
# Usage:
# Rs = encode( masks )
# masks = decode( Rs )
# R = merge( Rs, intersect=false )
# o = iou( dt, gt, iscrowd )
# a = area( Rs )
# bbs = toBbox( Rs )
# Rs = frPyObjects( [pyObjects], h, w )
#
# In the API the following formats are used:
# Rs - [dict] Run-length encoding of binary masks
# R - dict Run-length encoding of binary mask
# masks - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order)
# iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore
# bbs - [nx4] Bounding box(es) stored as [x y w h]
# poly - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list)
# dt,gt - May be either bounding boxes or encoded masks
# Both poly and bbs are 0-indexed (bbox=[0 0 1 1] encloses first pixel).
#
# Finally, a note about the intersection over union (iou) computation.
# The standard iou of a ground truth (gt) and detected (dt) object is
# iou(gt,dt) = area(intersect(gt,dt)) / area(union(gt,dt))
# For "crowd" regions, we use a modified criteria. If a gt object is
# marked as "iscrowd", we allow a dt to match any subregion of the gt.
# Choosing gt' in the crowd gt that best matches the dt can be done using
# gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing
# iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt)
# For crowd gt regions we use this modified criteria above for the iou.
#
# To compile run "python setup.py build_ext --inplace"
# Please do not contact us for help with compiling.
#
# Microsoft COCO Toolbox. version 2.0
# Data, paper, and tutorials available at: http://mscoco.org/
# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
# Licensed under the Simplified BSD License [see coco/license.txt]
encode = _mask.encode
decode = _mask.decode
iou = _mask.iou
merge = _mask.merge
area = _mask.area
toBbox = _mask.toBbox
frPyObjects = _mask.frPyObjects | 4,058 | 48.5 | 100 | py |
bottom-up-attention | bottom-up-attention-master/lib/utils/timer.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import time
class Timer(object):
"""A simple timer."""
def __init__(self):
self.total_time = 0.
self.calls = 0
self.start_time = 0.
self.diff = 0.
self.average_time = 0.
def tic(self):
# using time.time instead of time.clock because time time.clock
# does not normalize for multithreading
self.start_time = time.time()
def toc(self, average=True):
self.diff = time.time() - self.start_time
self.total_time += self.diff
self.calls += 1
self.average_time = self.total_time / self.calls
if average:
return self.average_time
else:
return self.diff
| 948 | 27.757576 | 71 | py |
bottom-up-attention | bottom-up-attention-master/lib/utils/blob.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Blob helper functions."""
import numpy as np
import cv2
def im_list_to_blob(ims):
"""Convert a list of images into a network input.
Assumes images are already prepared (means subtracted, BGR order, ...).
"""
max_shape = np.array([im.shape for im in ims]).max(axis=0)
num_images = len(ims)
blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),
dtype=np.float32)
for i in xrange(num_images):
im = ims[i]
blob[i, 0:im.shape[0], 0:im.shape[1], :] = im
# Move channels (axis 3) to axis 1
# Axis order will become: (batch elem, channel, height, width)
channel_swap = (0, 3, 1, 2)
blob = blob.transpose(channel_swap)
return blob
def prep_im_for_blob(im, pixel_means, target_size, max_size):
"""Mean subtract and scale an image for use in a blob."""
im = im.astype(np.float32, copy=False)
im -= pixel_means
im_shape = im.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > max_size:
im_scale = float(max_size) / float(im_size_max)
im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
return im, im_scale
| 1,625 | 34.347826 | 75 | py |
bottom-up-attention | bottom-up-attention-master/lib/utils/__init__.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
| 248 | 34.571429 | 58 | py |
bottom-up-attention | bottom-up-attention-master/lib/transform/__init__.py | 0 | 0 | 0 | py |
|
bottom-up-attention | bottom-up-attention-master/lib/transform/torch_image_transform_layer.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
""" Transform images for compatibility with models trained with
https://github.com/facebook/fb.resnet.torch.
Usage in model prototxt:
layer {
name: 'data_xform'
type: 'Python'
bottom: 'data_caffe'
top: 'data'
python_param {
module: 'transform.torch_image_transform_layer'
layer: 'TorchImageTransformLayer'
}
}
"""
import caffe
from fast_rcnn.config import cfg
import numpy as np
class TorchImageTransformLayer(caffe.Layer):
def setup(self, bottom, top):
# (1, 3, 1, 1) shaped arrays
self.PIXEL_MEANS = \
np.array([[[[0.48462227599918]],
[[0.45624044862054]],
[[0.40588363755159]]]])
self.PIXEL_STDS = \
np.array([[[[0.22889466674951]],
[[0.22446679341259]],
[[0.22495548344775]]]])
# The default ("old") pixel means that were already subtracted
channel_swap = (0, 3, 1, 2)
self.OLD_PIXEL_MEANS = \
cfg.PIXEL_MEANS[np.newaxis, :, :, :].transpose(channel_swap)
top[0].reshape(*(bottom[0].shape))
def forward(self, bottom, top):
ims = bottom[0].data
# Invert the channel means that were already subtracted
ims += self.OLD_PIXEL_MEANS
# 1. Permute BGR to RGB and normalize to [0, 1]
ims = ims[:, [2, 1, 0], :, :] / 255.0
# 2. Remove channel means
ims -= self.PIXEL_MEANS
# 3. Standardize channels
ims /= self.PIXEL_STDS
top[0].reshape(*(ims.shape))
top[0].data[...] = ims
def backward(self, top, propagate_down, bottom):
"""This layer does not propagate gradients."""
pass
def reshape(self, bottom, top):
"""Reshaping happens during the call to forward."""
pass
| 2,000 | 29.784615 | 72 | py |
bottom-up-attention | bottom-up-attention-master/lib/nms/py_cpu_nms.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import numpy as np
def py_cpu_nms(dets, thresh):
"""Pure Python NMS baseline."""
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
| 1,051 | 25.974359 | 59 | py |
bottom-up-attention | bottom-up-attention-master/lib/nms/__init__.py | 0 | 0 | 0 | py |
|
XDF-GAN | XDF-GAN-master/run-sgan-tessellate.py | """
Script to create very large tessellated GDF
Copyright 2019 Mike Smith
Please see COPYING for licence details
"""
import matplotlib as mpl
mpl.use("Agg")
# General imports
import numpy as np
import h5py
import os
from time import time
import argparse
import astropy.io.fits as pyfits
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import imageio
from skimage.util import view_as_windows
# ML specific imports
from keras.models import load_model
def un_min_max_norm(ar, ar_max, ar_min):
"""
Reverse min max normalising carried out on the original UDF data.
"""
return ar*(ar_max - ar_min) + ar_min
def find_nearest(ar, val):
"""
Get position in array of value nearest to 'val'.
"""
return np.argmin(np.abs(ar - val))
def get_sigma(hwhm):
"""
Given the half width at half maximum, find the standard deviation of a normal distribution.
"""
return (2*np.abs(hwhm))/(np.sqrt(8*np.log(2)))
def apply_noise_low_vals(ar):
"""
Apply noise to low values given an array.
"""
hist = np.histogram(ar, 100000)
maxpoint = np.max(hist[0])
negsx = hist[1][:-1][hist[1][:-1] <= 0]
negsy = hist[0][hist[1][:-1] <= 0]
hwhm = negsx[find_nearest(negsy, maxpoint/2)]
sigma = get_sigma(hwhm)
mu = 0
ar_replaced_noise = noise_replacement_low_vals(ar, sigma, mu)
return ar_replaced_noise.astype(np.float32)
def rescale(ar):
"""
Rescale so peak is at zero.
"""
hist = np.histogram(ar, 10000)
delta = hist[1][hist[0].argmax()]
return ar - delta
def shuffle_noise_given_array(ar):
"""
Shuffle noise values given an array.
"""
hist = np.histogram(ar, 100000)
maxpoint = np.max(hist[0])
negsx = hist[1][:-1][hist[1][:-1] <= 0]
negsy = hist[0][hist[1][:-1] <= 0]
hwhm = negsx[find_nearest(negsy, maxpoint/2)]
sigma = get_sigma(hwhm)
mu = 0
low_vals = np.random.permutation(ar[ar <= 2*sigma])
ar[np.where(ar <= 2*sigma)] = low_vals
return ar.astype(np.float32)
if __name__ == "__main__":
# Argument parsing
parser = argparse.ArgumentParser("Prorduce a fake xdf file.")
# Args
parser.add_argument("-m", "--model", help="Model file (h5).")
parser.add_argument("-l", "--logdir", nargs="?", default="../big_ims", help="Logdir, default ../big_ims")
parser.add_argument("-z", "--z_size", nargs="?", default=1024, type=int, help="Input noise array size (*16 for output size), default 1024. Must be a power of 2.")
parser.add_argument("-o", "--overlap", nargs="?", default=32, type=int, help="Overlap between tiles in z space.")
parser.add_argument("-f", "--fits", default=False, action="store_true", help="Output in FITS format.")
parser.add_argument("-p", "--png", default=False, action="store_true", help="Output greyscale PNG images + histogram.")
parser.add_argument("-n", "--numpy", default=False, action="store_true", help="Output numpy array.")
args = parser.parse_args()
dt = int(time())
model_file = args.model
logdir = "{}/{}/".format(args.logdir, dt)
os.mkdir(logdir)
z_size = args.z_size
overlap = args.overlap
chunks = z_size//64
maxes = [0.5262004, 0.44799575, 0.62030375]
mins = [-0.004748813, -0.0031752307, -0.011242471]
# Load generator
gen = load_model(model_file)
big_z = np.random.randn(z_size+overlap, z_size+overlap, 50).astype(np.float32)
mini_zs = np.squeeze(view_as_windows(big_z, ((z_size//chunks)+overlap, (z_size//chunks)+overlap, 50), step=(z_size//chunks, z_size//chunks, 1)))
print(mini_zs.shape)
z = np.reshape(mini_zs, (np.product(mini_zs.shape[0:2]), *mini_zs.shape[2:]))
print(z.shape)
print("Predicting imagery...")
print("Batch size 4")
ims = gen.predict(z, batch_size=4, verbose=1) # Batched for very large imagery
print(logdir, ims.shape)
ims = ims[:, (overlap*16)//2:-(overlap*16)//2, (overlap*16)//2:-(overlap*16)//2, :] # remove overlap
ims = np.reshape(ims, (*mini_zs.shape[0:2], 1024, 1024, 3))
im = np.concatenate(np.split(ims, len(ims), axis=0), axis=2) # Stitch image back together
im = np.squeeze(np.concatenate(np.split(im, len(ims), axis=1), axis=3)) # ditto...
# Output values
if args.numpy: # Output n-channel image in npy format
print("Outputting as npy")
np.save("{}array.npy".format(logdir), np.squeeze(im))
if args.png: # Output PNG images for each channel + a histogram for each (n-channel) image
print("Outputting as PNG")
hist = np.histogram(im, 10000)
plt.yscale("log")
plt.plot(hist[1][:-1], hist[0])
plt.savefig("{}hist.png".format(logdir))
plt.close()
for channel in np.arange(ims.shape[-1]):
plt.figure(figsize=(32, 32))
plt.tight_layout()
plt.imshow(np.squeeze(im[..., channel]))
plt.savefig("{}{}.png".format(logdir, channel))
plt.close()
if args.fits: # Output as a separate FITS image for each channel
print("Outputting as FITS")
#im = un_min_max_norm(im, ar_max=0.4142234, ar_min=-0.011242471) # Uncomment for image wise norming
for channel in np.arange(ims.shape[-1]):
print("Channel:", channel)
print("Before unnorming:", im[..., channel].max(), im[..., channel].min())
im[..., channel] = un_min_max_norm(im[..., channel], ar_max=maxes[channel], ar_min=mins[channel]) # For channel wise norming
im[..., channel] = rescale(im[..., channel])
print("After unnorming:", im[..., channel].max(), im[..., channel].min())
#pyfits.writeto("{}{}.fits".format(logdir, channel), np.squeeze(shuffle_noise_given_array(im[..., channel])), overwrite=True)
pyfits.writeto("{}{}.fits".format(logdir, channel), np.squeeze(im[..., channel]), overwrite=True)
| 5,897 | 35.8625 | 166 | py |
XDF-GAN | XDF-GAN-master/run-sgan.py | """
Script to run GDF generation
Copyright 2019 Mike Smith
Please see COPYING for licence details
"""
import matplotlib as mpl
mpl.use("Agg")
# General imports
import numpy as np
import h5py
import os
from time import time
import argparse
import astropy.io.fits as pyfits
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
# ML specific imports
from keras.models import load_model
def un_min_max_norm(ar, ar_max, ar_min):
"""
Reverse min max normalising carried out on the original UDF data.
"""
return ar*(ar_max - ar_min) + ar_min
def find_nearest(ar, val):
"""
Get position in array of value nearest to 'val'.
"""
return np.argmin(np.abs(ar - val))
def get_sigma(hwhm):
"""
Given the half width at half maximum, find the standard deviation of a normal distribution.
"""
return (2*np.abs(hwhm))/(np.sqrt(8*np.log(2)))
def noise_replacement_low_vals(x, sigma, mu):
"""
Replace low values with a random normal distribution
"""
return np.random.normal(mu, sigma) if np.abs(x) <= 2*sigma else x
def apply_noise_low_vals(ar):
"""
Apply noise to low values given an array.
"""
hist = np.histogram(ar, 100000)
maxpoint = np.max(hist[0])
negsx = hist[1][:-1][hist[1][:-1] <= 0]
negsy = hist[0][hist[1][:-1] <= 0]
hwhm = negsx[find_nearest(negsy, maxpoint/2)]
sigma = get_sigma(hwhm)
mu = 0
ar_replaced_noise = noise_replacement_low_vals(ar, sigma, mu)
return ar_replaced_noise.astype(np.float32)
def noise_replacement_all_vals(x, sigma, mu):
"""
Add a noise sampled from a gaussian to all values
"""
return x + np.random.normal(mu, sigma)
def apply_noise_all_vals(ar):
"""
Apply additive noise to all values given an array.
"""
hist = np.histogram(ar, 100000)
maxpoint = np.max(hist[0])
negsx = hist[1][:-1][hist[1][:-1] <= 0]
negsy = hist[0][hist[1][:-1] <= 0]
hwhm = negsx[find_nearest(negsy, maxpoint/2)]
sigma = get_sigma(hwhm)
mu = 0
ar_replaced_noise = noise_replacement_all_vals(ar, sigma, mu)
return ar_replaced_noise.astype(np.float32)
def rescale(ar):
"""
Rescale so peak is at zero.
"""
hist = np.histogram(ar, 10000)
delta = hist[1][hist[0].argmax()]
return ar - delta
def shuffle_noise_given_array(ar):
"""
Shuffle noise values given an array.
"""
hist = np.histogram(ar, 100000)
maxpoint = np.max(hist[0])
negsx = hist[1][:-1][hist[1][:-1] <= 0]
negsy = hist[0][hist[1][:-1] <= 0]
hwhm = negsx[find_nearest(negsy, maxpoint/2)]
sigma = get_sigma(hwhm)
mu = 0
low_vals = np.random.permutation(ar[ar <= 1*sigma])
ar[np.where(ar <= 1*sigma)] = low_vals
return ar.astype(np.float32)
if __name__ == "__main__":
# Argument parsing
parser = argparse.ArgumentParser("Produce a fake xdf file.")
# Args
parser.add_argument("-m", "--model", help="Model file (h5).")
parser.add_argument("-l", "--logdir", nargs="?", default="../logs/outs", help="Logdir, default ../logs/outs/$UNIXTIME")
parser.add_argument("-z", "--z_size", nargs="?", default=64, type=int, help="Input noise array size (*16 for output size), default 64.")
parser.add_argument("-n", "--images", nargs="?", default=10, type=int, help="Number of images to generate.")
parser.add_argument("-f", "--fits", default=False, action="store_true", help="Output in FITS format.")
parser.add_argument("-p", "--png", default=False, action="store_true", help="Output greyscale PNG images + histogram.")
parser.add_argument("--numpy", default=False, action="store_true", help="Output numpy array.")
parser.add_argument("-s", "--shuffle", default=False, action="store_true", help="Shuffle output to mitigate noise waffling in FITS output.")
parser.add_argument("--seed", nargs="?", default=42, type=int, help="A seed for np.random.seed")
args = parser.parse_args()
np.random.seed(args.seed)
dt = int(time())
model_file = args.model
n_images = args.images
logdir = "{}/{}/".format(args.logdir, dt)
os.mkdir(logdir)
z_size = args.z_size
test_batch_size = 100
# These are the original image maxima and minima for each channel
maxes = [0.5262004, 0.44799575, 0.62030375]
mins = [-0.004748813, -0.0031752307, -0.011242471]
noise_replacement_low_vals = np.vectorize(noise_replacement_low_vals)
noise_replacement_all_vals = np.vectorize(noise_replacement_all_vals)
# Load generator
gen = load_model(model_file)
z = np.random.randn(n_images, z_size, z_size, 50).astype(np.float32)
ims = gen.predict(z, batch_size=1, verbose=1) # added dtype still needs testing
print(logdir, ims.shape, ims.dtype)
# Output values
for i, im in enumerate(ims):
if args.numpy: # Output n-channel image in npy format
print("Outputting as npy")
np.save("{}{}.npy".format(logdir, i), np.squeeze(im))
if args.png: # Output PNG images for each channel + a histogram for each (n-channel) image
print("Outputting as PNG")
hist = np.histogram(im, 10000)
plt.yscale("log")
plt.plot(hist[1][:-1], hist[0])
plt.savefig("{}{}-hist.png".format(logdir, i))
plt.close()
for channel in np.arange(ims.shape[-1]):
plt.figure(figsize=(16, 16))
plt.imshow(np.squeeze(im[..., channel]), norm=LogNorm())
plt.savefig("{}{}-{}.png".format(logdir, i, channel))
plt.close()
if args.fits: # Output as a separate FITS image for each channel
print("Outputting as FITS")
#im = un_min_max_norm(im, ar_max=0.4142234, ar_min=-0.011242471) # Uncomment for image wide norming
for channel in np.arange(ims.shape[-1]):
print("Channel:", channel)
print("Before unnorming:", im[..., channel].max(), im[..., channel].min())
im[..., channel] = un_min_max_norm(im[..., channel], ar_max=maxes[channel], ar_min=mins[channel]) # For channel wise norming
im[..., channel] = rescale(im[..., channel])
print("After unnorming:", im[..., channel].max(), im[..., channel].min())
if args.shuffle:
pyfits.writeto("{}{}-{}.fits".format(logdir, i, channel), np.squeeze(shuffle_noise_given_array(im[..., channel])), overwrite=True)
else:
pyfits.writeto("{}{}-{}.fits".format(logdir, i, channel), np.squeeze(im[..., channel]), overwrite=True)
| 6,620 | 35.379121 | 150 | py |
XDF-GAN | XDF-GAN-master/sgan.py | """
Script to train GDF-SGAN
Copyright 2019 Mike Smith
Please see COPYING for licence details
"""
import matplotlib as mpl
mpl.use("Agg")
# General imports
import numpy as np
import h5py
import os
from time import time
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import argparse
# ML specific imports
import tensorflow as tf
import keras.backend as K
from keras.models import Model
from keras.layers import Input, Dense, Lambda, Conv2D, Conv2DTranspose, LeakyReLU, ELU, GlobalAveragePooling2D, Concatenate
from keras.optimizers import Adam
from keras.utils.generic_utils import Progbar
from keras.preprocessing.image import ImageDataGenerator
def get_images(file):
"""
Get XDF fits (np) file.
"""
im = np.load(file)
print(im.shape)
return im
def random_crop(img, crop_size=128):
"""
Random crop big xdf image.
"""
height, width = img.shape[0], img.shape[1]
x = np.random.randint(0, width - crop_size + 1)
y = np.random.randint(0, height - crop_size + 1)
return img[y:(y+crop_size), x:(x+crop_size), :]
def gen(z_shape=(None, None, 50), num_layers=4):
"""
Model a spatial GAN generator with `num_layers` hidden layers.
"""
fs = [32*2**f for f in np.arange(num_layers)][::-1] # define filter sizes
z = Input(shape=z_shape) # z
ct = Conv2DTranspose(filters=fs[0], kernel_size=4, strides=2, padding="same")(z)
ct = ELU()(ct)
for f in fs[1:]:
ct = Conv2DTranspose(filters=f, kernel_size=4, strides=2, padding="same")(ct)
ct = ELU()(ct)
ct = Conv2DTranspose(filters=f, kernel_size=4, strides=1, padding="same")(ct)
ct = ELU()(ct)
ct = Conv2DTranspose(filters=f, kernel_size=4, strides=1, padding="same")(ct)
ct = ELU()(ct)
G_z = Conv2DTranspose(filters=3, kernel_size=3, strides=1, padding="same", activation="sigmoid")(ct)
model = Model(z, G_z, name="Generator")
model.summary()
return model
def disc(x_shape=(None, None, 6), num_layers=4):
"""
Model a spatial GAN discriminator.
"""
fs = [32*2**f for f in np.arange(num_layers)] # define filter sizes
x = Input(shape=x_shape)
c = Conv2D(filters=fs[0], kernel_size=4, strides=2, padding="same")(x)
c = LeakyReLU(0.1)(c)
for f in fs[1:]:
c = Conv2D(filters=f, kernel_size=4, strides=2, padding="same")(c)
c = LeakyReLU(0.1)(c)
gap = GlobalAveragePooling2D()(c)
y = Dense(1)(gap)
model = Model(x, y, name="Discriminator")
model.summary()
return model
if __name__ == "__main__":
# Argument parsing
parser = argparse.ArgumentParser("Run a spatial GAN on XDF FITS data.")
# Args
parser.add_argument("-f", "--im_file", nargs="?", default="./data/mc_channelwise_clipping.npy", help="Numpy file containing image data.")
parser.add_argument("-b", "--batch_size", type=int, default=32, help="Batch size, default 32.")
parser.add_argument("-e", "--epochs", type=int, default=10001, help="Number of training epochs, default 301.")
parser.add_argument("-l", "--logdir", nargs="?", default="./logs", help="Logdir, default ./logs")
parser.add_argument("-r", "--learning_rate", nargs="?", type=float, default=0.0002, help="Learning rate for ADAM op")
parser.add_argument("-d", "--debug", dest="debug", default=False, action="store_true", help="Print example images/histograms at every epoch")
parser.add_argument("--gen_weights", nargs="?", help="File containing gen weights for continuation of training.")
parser.add_argument("--disc_weights", nargs="?", help="File containing disc weights for continuation of training.")
args = parser.parse_args()
batch_size = args.batch_size
epochs = args.epochs
debug = args.debug
disc_weights = args.disc_weights
gen_weights = args.gen_weights
dt = int(time())
logdir = "{}/{}/".format(args.logdir, dt)
print("logdir:", logdir)
os.mkdir(logdir)
sizes = [(4, 64), (8, 128), (16, 256)] # Possible input and output sizes
test_batch_size = (1, 32, 32, 50)
# might want to alter learning rate...
adam_op = Adam(lr=args.learning_rate, beta_1=0.5, beta_2=0.999)
xdf = get_images(args.im_file)[..., 1:4] # take F606W, F775W and F814W channels
og_histo = np.histogram(xdf, 10000)
# Define generator and discriminator models
gen = gen()
disc = disc()
if disc_weights is not None and gen_weights is not None:
gen.load_weights(gen_weights)
disc.load_weights(disc_weights)
# Define real and fake images
raw_reals = Input(shape=(None, None, 3))
reals = Lambda(lambda x: tf.split(x, num_or_size_splits=2, axis=0))(raw_reals)
reals = Concatenate(axis=-1)([reals[0], reals[1]])
z = Input(shape=(None, None, 50))
fakes = Lambda(lambda x: tf.split(x, num_or_size_splits=2, axis=0))(gen(z))
fakes = Concatenate(axis=-1)([fakes[0], fakes[1]])
disc_r = disc(reals) # C(x_r)
disc_f = disc(fakes) # C(x_f)
# Define generator and discriminator losses according to RaGAN described in Jolicoeur-Martineau (2018).
# Dummy predictions and trues are needed in Keras (see https://github.com/Smith42/keras-relativistic-gan).
def rel_disc_loss(y_true, y_pred):
epsilon = 1e-9
return K.abs(-(K.mean(K.log(K.sigmoid(disc_r - K.mean(disc_f, axis=0))+epsilon), axis=0)\
+K.mean(K.log(1-K.sigmoid(disc_f - K.mean(disc_r, axis=0))+epsilon), axis=0)))
def rel_gen_loss(y_true, y_pred):
epsilon = 1e-9
return K.abs(-(K.mean(K.log(K.sigmoid(disc_f - K.mean(disc_r, axis=0))+epsilon), axis=0)\
+K.mean(K.log(1-K.sigmoid(disc_r - K.mean(disc_f, axis=0))+epsilon), axis=0)))
# Define trainable generator and discriminator
gen_train = Model([z, raw_reals], [disc_r, disc_f])
disc.trainable = False
gen_train.compile(adam_op, loss=[rel_gen_loss, None])
gen_train.summary()
disc_train = Model([z, raw_reals], [disc_r, disc_f])
gen.trainable = False
disc.trainable = True
disc_train.compile(adam_op, loss=[rel_disc_loss, None])
disc_train.summary()
# Train RaGAN
gen_loss = []
disc_loss = []
dummy_y = np.zeros((batch_size, 1), dtype=np.float32)
test_z = np.random.randn(test_batch_size[0],\
test_batch_size[1],\
test_batch_size[2],\
test_batch_size[3]).astype(np.float32)
# Define batch flow
batchflow = ImageDataGenerator(rotation_range=0,\
width_shift_range=0.0,\
height_shift_range=0.0,\
shear_range=0.0,\
zoom_range=0.0,\
channel_shift_range=0.0,\
fill_mode='reflect',\
horizontal_flip=True,\
vertical_flip=True,\
rescale=None)
start_time = time()
for epoch in np.arange(epochs):
print(epoch, "/", epochs)
n_batches = 30 # int(len(ims) // batch_size)
prog_bar = Progbar(target=n_batches)
batch_start_time = time()
for index in np.arange(n_batches):
size = sizes[np.random.randint(len(sizes))]
prog_bar.update(index)
# Update G
image_batch = batchflow.flow(np.array([random_crop(xdf, size[1]) for i in np.arange(batch_size)]), batch_size=batch_size)[0]
z = np.random.randn(batch_size, size[0], size[0], 50).astype(np.float32)
disc.trainable = False
gen.trainable = True
gen_loss.append(gen_train.train_on_batch([z, image_batch], dummy_y))
# Update D
image_batch = batchflow.flow(np.array([random_crop(xdf, size[1]) for i in np.arange(batch_size)]), batch_size=batch_size)[0]
z = np.random.randn(batch_size, size[0], size[0], 50).astype(np.float32)
disc.trainable = True
gen.trainable = False
disc_loss.append(disc_train.train_on_batch([z, image_batch], dummy_y))
print("\nEpoch time", int(time() - batch_start_time))
print("Total elapsed time", int(time() - start_time))
print("Gen, Disc losses", gen_loss[-1], disc_loss[-1])
## Print out losses and pics of G(z) outputs ##
if debug or epoch % 5 == 0:
gen_image = gen.predict(test_z)
print("OG im: max, min, mean, std", xdf.max(), xdf.min(), xdf.mean(), xdf.std())
print("Gen im: max, min, mean, std", gen_image.max(), gen_image.min(), gen_image.mean(), gen_image.std())
# Plot generated/real histo comparison
gen_histo = np.histogram(gen_image, 10000)
fig, axs = plt.subplots(nrows=1, ncols=1, figsize=(16, 16))
axs.set_yscale("log")
axs.plot(og_histo[1][:-1], og_histo[0], label="Original")
axs.plot(gen_histo[1][:-1], gen_histo[0], label="Generated")
axs.legend()
plt.savefig("{}/{:05d}-histogram.png".format(logdir, epoch))
plt.close(fig)
# Plot generated image
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(30, 20))
axs[0, 0].imshow(gen_image[0, ..., 0], cmap="gray", norm=LogNorm())
axs[0, 1].imshow(gen_image[0, ..., 1], cmap="gray", norm=LogNorm())
axs[0, 2].imshow(gen_image[0, ..., 2], cmap="gray", norm=LogNorm())
#axs[1, 0].imshow(gen_image[0, ..., 3], cmap="gray", norm=LogNorm())
#axs[1, 1].imshow(gen_image[0, ..., 4], cmap="gray", norm=LogNorm())
axs[1, 0].imshow(gen_image[0], norm=LogNorm()) # was [1,2] and sliced [...,1:4]
plt.tight_layout()
plt.savefig("{}/{:05d}-example.png".format(logdir, epoch))
plt.close(fig)
## Save model ##
if epoch % 10 == 0:
gen.save("{}/{:05d}-gen-model.h5".format(logdir, epoch))
gen.save_weights("{}/{:05d}-gen-weights.h5".format(logdir, epoch))
disc.save_weights("{}/{:05d}-disc-weights.h5".format(logdir, epoch))
fig, axs = plt.subplots(nrows=1, ncols=1, figsize=(8, 4))
disc_loss_ar = np.array(disc_loss)[:, 0]
gen_loss_ar = np.array(gen_loss)[:, 0]
axs.set_title("Losses at epoch " + str(epoch))
axs.set_xlabel("Global step")
axs.set_ylabel("Loss")
axs.set_yscale("log")
axs.plot(disc_loss_ar, label="disc loss")
axs.plot(gen_loss_ar, label="gen loss")
axs.legend()
plt.savefig("{}/{:05d}-loss.png".format(logdir, epoch))
plt.close(fig)
| 10,761 | 39.920152 | 145 | py |
rlmeta | rlmeta-main/setup.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import multiprocessing
import os
import re
import subprocess
import sys
from distutils.version import LooseVersion
from setuptools import Extension, setup
from setuptools.command.build_ext import build_ext
# Reference:
# https://www.benjack.io/2017/06/12/python-cpp-tests.html
class CMakeExtension(Extension):
def __init__(self, name, src_dir=""):
Extension.__init__(self, name, sources=[])
self.src_dir = os.path.abspath(src_dir)
class CMakeBuild(build_ext):
def run(self):
try:
cmake_version = subprocess.check_output(["cmake", "--version"])
except OSError:
raise RuntimeError(
"CMake must be installed to build the following extensions: " +
", ".join(e.name for e in self.extensions))
cmake_version = LooseVersion(
re.search(r"version\s*([\d.]+)", cmake_version.decode()).group(1))
if cmake_version < "3.14":
raise RuntimeError("CMake >= 3.14 is required.")
for ext in self.extensions:
self.build_extension(ext)
def build_extension(self, ext):
ext_dir = os.path.abspath(
os.path.dirname(self.get_ext_fullpath(ext.name)))
cmake_args = [
"-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=" + ext_dir,
"-DPYTHON_EXECUTABLE=" + sys.executable
]
cfg = "Debug" if self.debug else "Release"
build_args = ["--config", cfg]
cmake_args += ["-DCMAKE_BUILD_TYPE=" + cfg]
build_args += ["--", f"-j{multiprocessing.cpu_count()}"]
env = os.environ.copy()
env["CXXFLAGS"] = f'{env.get("CXXFLAGS", "")} \
-DVERSION_INFO="{self.distribution.get_version()}"'
if not os.path.exists(self.build_temp):
os.makedirs(self.build_temp)
subprocess.check_call(["cmake", ext.src_dir] + cmake_args,
cwd=self.build_temp,
env=env)
subprocess.check_call(["cmake", "--build", "."] + build_args,
cwd=self.build_temp)
print() # Add an empty line for cleaner output
def main():
with open("./requirements.txt", "r") as f:
requires = f.read().splitlines()
setup(
name="rlmeta",
version="0.1",
description="A flexible and lightweight distributed RL framework",
long_description="",
license="MIT",
install_requires=requires,
ext_modules=[CMakeExtension("rlmeta", "./rlmeta")],
cmdclass=dict(build_ext=CMakeBuild),
zip_safe=False,
)
if __name__ == "__main__":
main()
| 2,829 | 28.789474 | 79 | py |
rlmeta | rlmeta-main/examples/plot.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
import re
from datetime import datetime
from typing import Any, Dict, Optional, Union
import matplotlib.pyplot as plt
import numpy as np
JSON_REGEX = re.compile("{.+}")
def parse_json(line: str) -> Optional[Dict[str, Any]]:
m = JSON_REGEX.search(line)
return None if m is None else json.loads(m.group())
def get_value(val: Union[float, Dict[str, float]]) -> float:
return val["mean"] if isinstance(val, dict) else val
def plot(log_file: str,
phase: str,
xkey: str,
ykey: str,
fig_file: Optional[str] = None) -> None:
x = []
y = []
with open(log_file, "r") as f:
line = f.readline()
cfg = parse_json(line)
for line in f:
stats = parse_json(line)
if stats is None:
continue
cur_phase = stats.get("phase", None)
if cur_phase == phase:
x.append(get_value(stats[xkey]))
y.append(get_value(stats[ykey]))
x = np.array(x)
y = np.array(y)
plt.plot(x, y)
plt.xlabel(xkey)
plt.ylabel(ykey)
if fig_file is not None:
plt.savefig(fig_file)
else:
plt.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--log_file", type=str, help="log file to plot")
parser.add_argument("--phase",
default="Eval",
type=str,
help="phase to plot.")
parser.add_argument("--xkey",
default="epoch",
type=str,
help="x values to plot.")
parser.add_argument("--ykey",
default="episode_return",
type=str,
help="y values to plot.")
parser.add_argument("--fig_file",
default=None,
type=str,
help="figure file to save.")
flags = parser.parse_intermixed_args()
plot(flags.log_file, flags.phase, flags.xkey, flags.ykey, flags.fig_file)
if __name__ == "__main__":
main()
| 2,320 | 26.305882 | 77 | py |
rlmeta | rlmeta-main/examples/__init__.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
| 179 | 35 | 65 | py |
rlmeta | rlmeta-main/examples/atari/__init__.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
| 179 | 35 | 65 | py |
rlmeta | rlmeta-main/examples/atari/ppo/atari_ppo_rnd_model.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import rlmeta.core.remote as remote
from rlmeta.agents.ppo import PPORNDModel
from rlmeta.core.types import NestedTensor
from rlmeta.models.actor_critic import DiscreteActorCriticRNDHead
from rlmeta.models.atari import NatureCNNBackbone, ImpalaCNNBackbone
class AtariPPORNDModel(PPORNDModel):
def __init__(self, num_actions: int, network: str = "nature") -> None:
super().__init__()
self._num_actions = num_actions
self._network = network.lower()
if self._network == "nature":
self._ppo_net = NatureCNNBackbone()
self._tgt_net = NatureCNNBackbone()
self._prd_net = NatureCNNBackbone()
self._head = DiscreteActorCriticRNDHead(self._ppo_net.output_size,
[512], num_actions)
elif self._network == "impala":
self._ppo_net = ImpalaCNNBackbone()
self._tgt_net = ImpalaCNNBackbone()
self._prd_net = ImpalaCNNBackbone()
self._head = DiscreteActorCriticRNDHead(self._ppo_net.output_size,
[256], num_actions)
else:
assert False, "Unsupported network."
def forward(
self, obs: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
x = obs.float() / 255.0
h = self._ppo_net(x)
logpi, ext_v, int_v = self._head(h)
return logpi, ext_v, int_v
@remote.remote_method(batch_size=128)
def act(
self, obs: torch.Tensor, deterministic_policy: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
with torch.no_grad():
logpi, ext_v, int_v = self.forward(obs)
greedy_action = logpi.argmax(-1, keepdim=True)
sample_action = logpi.exp().multinomial(1, replacement=True)
action = torch.where(deterministic_policy, greedy_action,
sample_action)
logpi = logpi.gather(dim=-1, index=action)
return action, logpi, ext_v, int_v
@remote.remote_method(batch_size=None)
def intrinsic_reward(self, obs: torch.Tensor) -> torch.Tensor:
return self._rnd_error(obs)
def rnd_loss(self, obs: torch.Tensor) -> torch.Tensor:
return self._rnd_error(obs).mean() * 0.5
def _rnd_error(self, obs: torch.Tensor) -> torch.Tensor:
x = obs.float() / 255.0
with torch.no_grad():
tgt = self._tgt_net(x)
prd = self._prd_net(x)
err = (prd - tgt).square().mean(-1, keepdim=True)
return err
| 2,889 | 35.125 | 78 | py |
rlmeta | rlmeta-main/examples/atari/ppo/atari_ppo_model.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Tuple
import torch
import torch.nn as nn
import rlmeta.core.remote as remote
from rlmeta.agents.ppo import PPOModel
from rlmeta.models.actor_critic import DiscreteActorCriticHead
from rlmeta.models.atari import NatureCNNBackbone, ImpalaCNNBackbone
class AtariPPOModel(PPOModel):
def __init__(self, num_actions: int, network: str = "nature") -> None:
super().__init__()
self._num_actions = num_actions
self._network = network.lower()
if self._network == "nature":
self._backbone = NatureCNNBackbone()
self._head = DiscreteActorCriticHead(self._backbone.output_size,
[512], num_actions)
elif self._network == "impala":
self._backbone = ImpalaCNNBackbone()
self._head = DiscreteActorCriticHead(self._backbone.output_size,
[256], num_actions)
else:
assert False, "Unsupported network."
def forward(self, obs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
x = obs.float() / 255.0
h = self._backbone(x)
logpi, v = self._head(h)
return logpi, v
@remote.remote_method(batch_size=128)
def act(
self, obs: torch.Tensor, deterministic_policy: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
with torch.no_grad():
logpi, v = self.forward(obs)
greedy_action = logpi.argmax(-1, keepdim=True)
sample_action = logpi.exp().multinomial(1, replacement=True)
action = torch.where(deterministic_policy, greedy_action,
sample_action)
logpi = logpi.gather(dim=-1, index=action)
return action, logpi, v
| 1,988 | 35.163636 | 78 | py |
rlmeta | rlmeta-main/examples/atari/ppo/atari_ppo.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import copy
import json
import logging
import time
import hydra
import torch
import torch.multiprocessing as mp
import rlmeta.envs.atari_wrapper as atari_wrapper
import rlmeta.utils.hydra_utils as hydra_utils
import rlmeta.utils.random_utils as random_utils
import rlmeta.utils.remote_utils as remote_utils
from examples.atari.ppo.atari_ppo_model import AtariPPOModel
from rlmeta.agents.agent import AgentFactory
from rlmeta.agents.ppo import PPOAgent
from rlmeta.core.controller import Phase, Controller
from rlmeta.core.loop import LoopList, ParallelLoop
from rlmeta.core.model import ModelVersion, RemotableModelPool
from rlmeta.core.model import make_remote_model, wrap_downstream_model
from rlmeta.core.replay_buffer import ReplayBuffer, make_remote_replay_buffer
from rlmeta.core.server import Server, ServerList
from rlmeta.samplers import UniformSampler
from rlmeta.storage import TensorCircularBuffer
from rlmeta.utils.optimizer_utils import make_optimizer
@hydra.main(config_path="./conf", config_name="conf_ppo")
def main(cfg):
if cfg.seed is not None:
random_utils.manual_seed(cfg.seed)
logging.info(hydra_utils.config_to_json(cfg))
env = atari_wrapper.make_atari_env(**cfg.env)
model = AtariPPOModel(env.action_space.n,
network=cfg.network).to(cfg.train_device)
model_pool = RemotableModelPool(copy.deepcopy(model).to(cfg.infer_device),
seed=cfg.seed)
optimizer = make_optimizer(model.parameters(), **cfg.optimizer)
replay_buffer = ReplayBuffer(TensorCircularBuffer(cfg.replay_buffer_size),
UniformSampler())
ctrl = Controller()
m_server = Server(cfg.m_server_name, cfg.m_server_addr)
r_server = Server(cfg.r_server_name, cfg.r_server_addr)
c_server = Server(cfg.c_server_name, cfg.c_server_addr)
m_server.add_service(model_pool)
r_server.add_service(replay_buffer)
c_server.add_service(ctrl)
servers = ServerList([m_server, r_server, c_server])
learner_model = wrap_downstream_model(model, m_server)
t_actor_model = make_remote_model(model,
m_server,
version=ModelVersion.LATEST)
# During blocking evaluation we have STABLE is LATEST
e_actor_model = make_remote_model(model,
m_server,
version=ModelVersion.LATEST)
learner_ctrl = remote_utils.make_remote(ctrl, c_server)
t_actor_ctrl = remote_utils.make_remote(ctrl, c_server)
e_actor_ctrl = remote_utils.make_remote(ctrl, c_server)
learner_replay_buffer = make_remote_replay_buffer(replay_buffer,
r_server,
prefetch=cfg.prefetch)
t_actor_replay_buffer = make_remote_replay_buffer(replay_buffer, r_server)
env_fac = atari_wrapper.AtariWrapperFactory(**cfg.env)
t_agent_fac = AgentFactory(PPOAgent,
t_actor_model,
replay_buffer=t_actor_replay_buffer,
gamma=cfg.gamma)
e_agent_fac = AgentFactory(
PPOAgent,
e_actor_model,
deterministic_policy=cfg.deterministic_evaluation)
t_loop = ParallelLoop(env_fac,
t_agent_fac,
t_actor_ctrl,
running_phase=Phase.TRAIN,
should_update=True,
num_rollouts=cfg.num_training_rollouts,
num_workers=cfg.num_training_workers,
seed=cfg.seed)
e_loop = ParallelLoop(env_fac,
e_agent_fac,
e_actor_ctrl,
running_phase=Phase.EVAL,
should_update=False,
num_rollouts=cfg.num_evaluation_rollouts,
num_workers=cfg.num_evaluation_workers,
seed=(None if cfg.seed is None else cfg.seed +
cfg.num_training_rollouts))
loops = LoopList([t_loop, e_loop])
learner = PPOAgent(learner_model,
replay_buffer=learner_replay_buffer,
controller=learner_ctrl,
optimizer=optimizer,
batch_size=cfg.batch_size,
gamma=cfg.gamma,
learning_starts=cfg.learning_starts,
model_push_period=cfg.model_push_period)
servers.start()
loops.start()
learner.connect()
start_time = time.perf_counter()
for epoch in range(cfg.num_epochs):
stats = learner.train(cfg.steps_per_epoch)
cur_time = time.perf_counter() - start_time
info = f"T Epoch {epoch}"
if cfg.table_view:
logging.info("\n\n" + stats.table(info, time=cur_time) + "\n")
else:
logging.info(
stats.json(info, phase="Train", epoch=epoch, time=cur_time))
time.sleep(1)
stats = learner.eval(cfg.num_evaluation_episodes,
keep_training_loops=True)
cur_time = time.perf_counter() - start_time
info = f"E Epoch {epoch}"
if cfg.table_view:
logging.info("\n\n" + stats.table(info, time=cur_time) + "\n")
else:
logging.info(
stats.json(info, phase="Eval", epoch=epoch, time=cur_time))
torch.save(model.state_dict(), f"ppo_agent-{epoch}.pth")
time.sleep(1)
loops.terminate()
servers.terminate()
if __name__ == "__main__":
mp.set_start_method("spawn")
main()
| 5,968 | 38.013072 | 78 | py |
rlmeta | rlmeta-main/examples/atari/ppo/atari_ppo_rnd.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import copy
import json
import logging
import time
import hydra
import torch
import torch.multiprocessing as mp
import rlmeta.envs.atari_wrapper as atari_wrapper
import rlmeta.utils.hydra_utils as hydra_utils
import rlmeta.utils.random_utils as random_utils
import rlmeta.utils.remote_utils as remote_utils
from examples.atari.ppo.atari_ppo_rnd_model import AtariPPORNDModel
from rlmeta.agents.agent import AgentFactory
from rlmeta.agents.ppo import PPORNDAgent
from rlmeta.core.controller import Phase, Controller
from rlmeta.core.loop import LoopList, ParallelLoop
from rlmeta.core.model import ModelVersion, RemotableModelPool
from rlmeta.core.model import make_remote_model, wrap_downstream_model
from rlmeta.core.replay_buffer import ReplayBuffer, make_remote_replay_buffer
from rlmeta.core.server import Server, ServerList
from rlmeta.samplers import UniformSampler
from rlmeta.storage import TensorCircularBuffer
from rlmeta.utils.optimizer_utils import make_optimizer
@hydra.main(config_path="./conf", config_name="conf_ppo")
def main(cfg):
if cfg.seed is not None:
random_utils.manual_seed(cfg.seed)
logging.info(hydra_utils.config_to_json(cfg))
env = atari_wrapper.make_atari_env(**cfg.env)
model = AtariPPORNDModel(env.action_space.n,
network=cfg.network).to(cfg.train_device)
model_pool = RemotableModelPool(copy.deepcopy(model).to(cfg.infer_device),
seed=cfg.seed)
optimizer = make_optimizer(model.parameters(), **cfg.optimizer)
ctrl = Controller()
replay_buffer = ReplayBuffer(TensorCircularBuffer(cfg.replay_buffer_size),
UniformSampler())
m_server = Server(cfg.m_server_name, cfg.m_server_addr)
r_server = Server(cfg.r_server_name, cfg.r_server_addr)
c_server = Server(cfg.c_server_name, cfg.c_server_addr)
m_server.add_service(model_pool)
r_server.add_service(replay_buffer)
c_server.add_service(ctrl)
servers = ServerList([m_server, r_server, c_server])
learner_model = wrap_downstream_model(model, m_server)
t_actor_model = make_remote_model(model,
m_server,
version=ModelVersion.LATEST)
# During blocking evaluation we have STABLE is LATEST
e_actor_model = make_remote_model(model,
m_server,
version=ModelVersion.LATEST)
a_ctrl = remote_utils.make_remote(ctrl, c_server)
t_ctrl = remote_utils.make_remote(ctrl, c_server)
e_ctrl = remote_utils.make_remote(ctrl, c_server)
learner_replay_buffer = make_remote_replay_buffer(replay_buffer,
r_server,
prefetch=cfg.prefetch)
t_actor_replay_buffer = make_remote_replay_buffer(replay_buffer, r_server)
env_fac = atari_wrapper.AtariWrapperFactory(**cfg.env)
t_agent_fac = AgentFactory(PPORNDAgent,
t_actor_model,
replay_buffer=t_actor_replay_buffer)
e_agent_fac = AgentFactory(
PPORNDAgent,
e_actor_model,
deterministic_policy=cfg.deterministic_evaluation)
t_loop = ParallelLoop(env_fac,
t_agent_fac,
t_ctrl,
running_phase=Phase.TRAIN,
should_update=True,
num_rollouts=cfg.num_training_rollouts,
num_workers=cfg.num_training_workers,
seed=cfg.seed)
e_loop = ParallelLoop(env_fac,
e_agent_fac,
e_ctrl,
running_phase=Phase.EVAL,
should_update=False,
num_rollouts=cfg.num_evaluation_rollouts,
num_workers=cfg.num_evaluation_workers,
seed=(None if cfg.seed is None else cfg.seed +
cfg.num_training_rollouts))
loops = LoopList([t_loop, e_loop])
learner = PPORNDAgent(learner_model,
replay_buffer=learner_replay_buffer,
controller=a_ctrl,
optimizer=optimizer,
batch_size=cfg.batch_size,
learning_starts=cfg.get("learning_starts", None),
model_push_period=cfg.model_push_period)
servers.start()
loops.start()
learner.connect()
start_time = time.perf_counter()
for epoch in range(cfg.num_epochs):
stats = learner.train(cfg.steps_per_epoch)
cur_time = time.perf_counter() - start_time
info = f"T Epoch {epoch}"
if cfg.table_view:
logging.info("\n\n" + stats.table(info, time=cur_time) + "\n")
else:
logging.info(
stats.json(info, phase="Train", epoch=epoch, time=cur_time))
time.sleep(1)
stats = learner.eval(cfg.num_evaluation_episodes,
keep_training_loops=True)
cur_time = time.perf_counter() - start_time
info = f"E Epoch {epoch}"
if cfg.table_view:
logging.info("\n\n" + stats.table(info, time=cur_time) + "\n")
else:
logging.info(
stats.json(info, phase="Eval", epoch=epoch, time=cur_time))
torch.save(model.state_dict(), f"ppo_rnd_agent-{epoch}.pth")
time.sleep(1)
loops.terminate()
servers.terminate()
if __name__ == "__main__":
mp.set_start_method("spawn")
main()
| 5,904 | 38.10596 | 78 | py |
rlmeta | rlmeta-main/examples/atari/dqn/atari_dqn_model.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import copy
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import rlmeta.core.remote as remote
import rlmeta.utils.nested_utils as nested_utils
from rlmeta.agents.dqn import DQNModel
from rlmeta.core.types import NestedTensor
from rlmeta.models.atari import NatureCNNBackbone, ImpalaCNNBackbone
from rlmeta.models.dqn import DQNHead, DuelingDQNHead
class AtariDQNNet(nn.Module):
def __init__(self,
num_actions: int,
network="nature",
dueling_dqn: bool = True,
spectral_norm: bool = True) -> None:
super().__init__()
self._num_actions = num_actions
self._network = network.lower()
self._dueling_dqn = dueling_dqn
self._spectral_norm = spectral_norm
head_cls = DuelingDQNHead if dueling_dqn else DQNHead
if self._network == "nature":
self._backbone = NatureCNNBackbone()
self._head = head_cls(self._backbone.output_size, [512],
num_actions)
elif self._network == "impala":
self._backbone = ImpalaCNNBackbone()
self._head = head_cls(self._backbone.output_size, [256],
num_actions)
else:
assert False, "Unsupported network."
def init_model(self) -> None:
if self._spectral_norm:
# Apply SN[-2] in https://arxiv.org/abs/2105.05246
if self._dueling_dqn:
nn.utils.parametrizations.spectral_norm(
self._head._mlp_a._layers[-3])
nn.utils.parametrizations.spectral_norm(
self._head._mlp_v._layers[-3])
else:
nn.utils.parametrizations.spectral_norm(
self._head._mlp._layers[-3])
def forward(self, observation: torch.Tensor) -> torch.Tensor:
x = observation.float() / 255.0
h = self._backbone(x)
a = self._head(h)
return a
class AtariDQNModel(DQNModel):
def __init__(self,
num_actions: int,
network: str = "nature",
dueling_dqn: bool = True,
spectral_norm: bool = True,
double_dqn: bool = False) -> None:
super().__init__()
self._num_actions = num_actions
self._network = network.lower()
self._dueling_dqn = dueling_dqn
self._spectral_norm = spectral_norm
self._double_dqn = double_dqn
# Bootstrapping with online network when double_dqn = False.
# https://arxiv.org/pdf/2209.07550.pdf
self._online_net = AtariDQNNet(num_actions,
network=network,
dueling_dqn=dueling_dqn,
spectral_norm=spectral_norm)
self._target_net = copy.deepcopy(
self._online_net) if double_dqn else None
def init_model(self) -> None:
self._online_net.init_model()
if self._target_net is not None:
self._target_net.init_model()
def forward(self, observation: torch.Tensor) -> torch.Tensor:
return self._online_net(observation)
def q(self, s: torch.Tensor, a: torch.Tensor) -> torch.Tensor:
q = self._online_net(s)
q = q.gather(dim=-1, index=a)
return q
@remote.remote_method(batch_size=256)
def act(self, observation: torch.Tensor,
eps: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
with torch.no_grad():
q = self._online_net(observation) # size = (batch_size, action_dim)
_, action_dim = q.size()
greedy_action = q.argmax(-1, keepdim=True)
pi = torch.ones_like(q) * (eps / action_dim)
pi.scatter_(dim=-1,
index=greedy_action,
src=1.0 - eps * (action_dim - 1) / action_dim)
action = pi.multinomial(1)
v = self._value(observation, q)
q = q.gather(dim=-1, index=action)
return action, q, v
def sync_target_net(self) -> None:
if self._target_net is not None:
self._target_net.load_state_dict(self._online_net.state_dict())
def _value(self,
observation: torch.Tensor,
q: Optional[torch.Tensor] = None) -> torch.Tensor:
if q is None:
q = self._online_net(observation)
if not self._double_dqn:
v = q.max(-1, keepdim=True)[0]
else:
a = q.argmax(-1, keepdim=True)
q = self._target_net(observation)
v = q.gather(dim=-1, index=a)
return v
| 4,918 | 34.388489 | 80 | py |
rlmeta | rlmeta-main/examples/atari/dqn/atari_apex_dqn.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import copy
import logging
import time
import hydra
import torch
import torch.multiprocessing as mp
import rlmeta.envs.atari_wrapper as atari_wrapper
import rlmeta.utils.hydra_utils as hydra_utils
import rlmeta.utils.random_utils as random_utils
import rlmeta.utils.remote_utils as remote_utils
from examples.atari.dqn.atari_dqn_model import AtariDQNModel
from rlmeta.agents.dqn import (ApexDQNAgent, ApexDQNAgentFactory,
ConstantEpsFunc, FlexibleEpsFunc)
from rlmeta.core.controller import Phase, Controller
from rlmeta.core.loop import LoopList, ParallelLoop
from rlmeta.core.model import ModelVersion, RemotableModelPool
from rlmeta.core.model import make_remote_model, wrap_downstream_model
from rlmeta.core.replay_buffer import ReplayBuffer, make_remote_replay_buffer
from rlmeta.core.server import Server, ServerList
from rlmeta.samplers import PrioritizedSampler
from rlmeta.storage import TensorCircularBuffer
from rlmeta.utils.optimizer_utils import make_optimizer
@hydra.main(config_path="./conf", config_name="conf_apex_dqn")
def main(cfg):
if cfg.seed is not None:
random_utils.manual_seed(cfg.seed)
logging.info(hydra_utils.config_to_json(cfg))
env = atari_wrapper.make_atari_env(**cfg.env)
model = AtariDQNModel(env.action_space.n,
network=cfg.network,
dueling_dqn=cfg.dueling_dqn,
spectral_norm=cfg.spectral_norm,
double_dqn=cfg.double_dqn).to(cfg.train_device)
infer_model = copy.deepcopy(model).to(cfg.infer_device)
infer_model.eval()
model_pool = RemotableModelPool(infer_model, seed=cfg.seed)
optimizer = make_optimizer(model.parameters(), **cfg.optimizer)
replay_buffer = ReplayBuffer(
TensorCircularBuffer(cfg.replay_buffer_size),
PrioritizedSampler(priority_exponent=cfg.priority_exponent))
ctrl = Controller()
m_server = Server(cfg.m_server_name, cfg.m_server_addr)
r_server = Server(cfg.r_server_name, cfg.r_server_addr)
c_server = Server(cfg.c_server_name, cfg.c_server_addr)
m_server.add_service(model_pool)
r_server.add_service(replay_buffer)
c_server.add_service(ctrl)
servers = ServerList([m_server, r_server, c_server])
learner_model = wrap_downstream_model(model, m_server)
t_actor_model = make_remote_model(model,
m_server,
version=ModelVersion.LATEST)
# During blocking evaluation we have STABLE is LATEST
e_actor_model = make_remote_model(model,
m_server,
version=ModelVersion.LATEST)
learner_ctrl = remote_utils.make_remote(ctrl, c_server)
t_actor_ctrl = remote_utils.make_remote(ctrl, c_server)
e_actor_ctrl = remote_utils.make_remote(ctrl, c_server)
learner_replay_buffer = make_remote_replay_buffer(replay_buffer,
r_server,
prefetch=cfg.prefetch)
t_actor_replay_buffer = make_remote_replay_buffer(replay_buffer, r_server)
env_fac = atari_wrapper.AtariWrapperFactory(**cfg.env)
t_agent_fac = ApexDQNAgentFactory(t_actor_model,
FlexibleEpsFunc(
cfg.eps, cfg.num_training_rollouts),
replay_buffer=t_actor_replay_buffer,
n_step=cfg.n_step,
gamma=cfg.gamma,
max_abs_reward=cfg.max_abs_reward,
rescale_value=cfg.rescale_value)
e_agent_fac = ApexDQNAgentFactory(e_actor_model,
ConstantEpsFunc(cfg.evaluation_eps))
t_loop = ParallelLoop(env_fac,
t_agent_fac,
t_actor_ctrl,
running_phase=Phase.TRAIN,
should_update=True,
num_rollouts=cfg.num_training_rollouts,
num_workers=cfg.num_training_workers,
seed=cfg.seed)
e_loop = ParallelLoop(env_fac,
e_agent_fac,
e_actor_ctrl,
running_phase=Phase.EVAL,
should_update=False,
num_rollouts=cfg.num_evaluation_rollouts,
num_workers=cfg.num_evaluation_workers,
seed=(None if cfg.seed is None else cfg.seed +
cfg.num_training_rollouts))
loops = LoopList([t_loop, e_loop])
learner = ApexDQNAgent(
learner_model,
replay_buffer=learner_replay_buffer,
controller=learner_ctrl,
optimizer=optimizer,
batch_size=cfg.batch_size,
max_grad_norm=cfg.max_grad_norm,
n_step=cfg.n_step,
gamma=cfg.gamma,
importance_sampling_exponent=cfg.importance_sampling_exponent,
value_clipping_eps=cfg.value_clipping_eps,
fr_kappa=cfg.fr_kappa,
target_sync_period=cfg.target_sync_period,
learning_starts=cfg.learning_starts,
model_push_period=cfg.model_push_period)
servers.start()
loops.start()
learner.connect()
learner_model.init_model()
learner_model.push()
start_time = time.perf_counter()
for epoch in range(cfg.num_epochs):
stats = learner.train(cfg.steps_per_epoch)
cur_time = time.perf_counter() - start_time
info = f"T Epoch {epoch}"
if cfg.table_view:
logging.info("\n\n" + stats.table(info, time=cur_time) + "\n")
else:
logging.info(
stats.json(info, phase="Train", epoch=epoch, time=cur_time))
time.sleep(1)
stats = learner.eval(cfg.num_evaluation_episodes,
keep_training_loops=True)
cur_time = time.perf_counter() - start_time
info = f"E Epoch {epoch}"
if cfg.table_view:
logging.info("\n\n" + stats.table(info, time=cur_time) + "\n")
else:
logging.info(
stats.json(info, phase="Eval", epoch=epoch, time=cur_time))
torch.save(model.state_dict(), f"dqn_agent-{epoch}.pth")
time.sleep(1)
loops.terminate()
servers.terminate()
if __name__ == "__main__":
mp.set_start_method("spawn")
main()
| 6,771 | 39.071006 | 78 | py |
rlmeta | rlmeta-main/examples/tutorials/loop_example.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import asyncio
import time
from typing import Optional
import numpy as np
import torch
import torch.multiprocessing as mp
import rlmeta.utils.remote_utils as remote_utils
from rlmeta.agents.agent import Agent
from rlmeta.core.controller import Controller, Phase
from rlmeta.core.loop import ParallelLoop
from rlmeta.core.server import Server
from rlmeta.core.types import Action, TimeStep
from rlmeta.envs.env import Env, EnvFactory
class MockEnv(Env):
def __init__(self,
index: int,
observation_space: int = 4,
action_space: int = 4,
episode_length: int = 10) -> None:
self.index = index
self.observation_space = observation_space
self.action_space = action_space
self.episode_length = episode_length
self.step_counter = 0
def reset(self, *args, **kwargs) -> TimeStep:
print(f"[Env {self.index}] reset")
print("")
self.step_counter = 0
obs = torch.randn(self.observation_space)
info = {"step_counter": 0}
return TimeStep(obs, done=False, info=info)
def step(self, action: Action) -> TimeStep:
self.step_counter += 1
time.sleep(1.0)
obs = torch.randn(self.observation_space)
reward = np.random.randn()
done = self.step_counter == self.episode_length
info = {"step_counter": self.step_counter}
print(
f"[Env {self.index}] step = {self.step_counter}, reward = {reward}")
print("")
return TimeStep(obs, reward, done, info)
def close(self) -> None:
pass
def seed(self, seed: Optional[int] = None) -> None:
pass
class MockAgent(Agent):
def __init__(self, index: int, action_space: int = 4) -> None:
self.index = index
self.action_space = action_space
async def async_act(self, timestep: TimeStep) -> Action:
_, reward, _, info = timestep
step_counter = info["step_counter"]
await asyncio.sleep(1.0)
act = np.random.randint(self.action_space)
print(f"[Agent {self.index}] step = {step_counter}, action = {act}")
return Action(act)
async def async_observe_init(self, timestep: TimeStep) -> None:
pass
async def async_observe(self, action: Action,
next_timestep: TimeStep) -> None:
pass
async def async_update(self) -> None:
pass
def env_factory(index: int) -> MockEnv:
return MockEnv(index)
def agent_factory(index: int) -> MockAgent:
return MockAgent(index)
def main() -> None:
server = Server("server", "127.0.0.1:4411")
ctrl = Controller()
server.add_service(ctrl)
loop_ctrl = remote_utils.make_remote(ctrl, server)
main_ctrl = remote_utils.make_remote(ctrl, server)
loop = ParallelLoop(env_factory,
agent_factory,
loop_ctrl,
running_phase=Phase.EVAL,
num_rollouts=2,
num_workers=1)
server.start()
loop.start()
main_ctrl.connect()
main_ctrl.set_phase(Phase.EVAL, reset=True)
time.sleep(30)
loop.terminate()
server.terminate()
if __name__ == "__main__":
mp.set_start_method("spawn")
main()
| 3,493 | 27.177419 | 80 | py |
rlmeta | rlmeta-main/examples/tutorials/remote_example.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import asyncio
import torch
import torch.multiprocessing as mp
import rlmeta.core.remote as remote
import rlmeta.utils.remote_utils as remote_utils
from rlmeta.core.server import Server
class Adder(remote.Remotable):
@remote.remote_method()
def add(self, a, b):
print(f"[Adder.add] a = {a}")
print(f"[Adder.add] b = {b}")
return a + b
@remote.remote_method(batch_size=10)
def batch_add(self, a, b):
print(f"[Adder.batch_add] a = {a}")
print(f"[Adder.batch_add] b = {b}")
if not isinstance(a, tuple) and not isinstance(b, tuple):
return a + b
else:
return tuple(sum(x) for x in zip(a, b))
async def run_batch(adder_client, send_tensor=False):
futs = []
for i in range(20):
if send_tensor:
a = torch.tensor([i])
b = torch.tensor([i + 1])
else:
a = i
b = i + 1
fut = adder_client.async_batch_add(a, b)
futs.append(fut)
await asyncio.sleep(1.0)
for i, fut in enumerate(futs):
if send_tensor:
a = torch.tensor([i])
b = torch.tensor([i + 1])
else:
a = i
b = i + 1
c = await fut
print(f"{a} + {b} = {c}")
def main():
adder = Adder()
adder_server = Server(name="adder_server", addr="127.0.0.1:4411")
adder_server.add_service(adder)
adder_client = remote_utils.make_remote(adder, adder_server)
adder_server.start()
adder_client.connect()
a = 1
b = 2
c = adder_client.add(a, b)
print(f"{a} + {b} = {c}")
print("")
asyncio.run(run_batch(adder_client, send_tensor=False))
print("")
asyncio.run(run_batch(adder_client, send_tensor=True))
adder_server.terminate()
if __name__ == "__main__":
mp.set_start_method("spawn")
main()
| 2,053 | 22.883721 | 69 | py |
rlmeta | rlmeta-main/examples/tutorials/__init__.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
| 179 | 35 | 65 | py |
rlmeta | rlmeta-main/tests/__init__.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
| 179 | 35 | 65 | py |
rlmeta | rlmeta-main/tests/test_utils.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import torch
import rlmeta.utils.data_utils as data_utils
class TestCaseBase(unittest.TestCase):
def assert_tensor_equal(self, x, y):
self.assertTrue(isinstance(x, type(y)))
x = data_utils.to_numpy(x)
y = data_utils.to_numpy(y)
np.testing.assert_array_equal(x, y)
def assert_tensor_close(self, x, y, rtol=1e-7, atol=0):
self.assertTrue(isinstance(x, type(y)))
x = data_utils.to_numpy(x)
y = data_utils.to_numpy(y)
np.testing.assert_allclose(x, y, rtol, atol)
| 752 | 26.888889 | 65 | py |
rlmeta | rlmeta-main/tests/core/replay_buffer_test.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
import rlmeta.utils.data_utils as data_utils
from rlmeta.core.replay_buffer import ReplayBuffer
from rlmeta.samplers import UniformSampler, PrioritizedSampler
from rlmeta.storage import CircularBuffer, TensorCircularBuffer
from tests.test_utils import TestCaseBase
class ReplayBufferTest(TestCaseBase):
def setUp(self) -> None:
self.size = 8
self.batch_size = 5
self.hidden_dim = 4
self.replay_buffer = ReplayBuffer(
CircularBuffer(self.size, collate_fn=torch.stack), UniformSampler())
self.flatten_data = dict(obs=torch.randn(self.batch_size,
self.hidden_dim),
rew=torch.randn(self.batch_size))
self.data = data_utils.unstack_fields(self.flatten_data,
self.batch_size)
def test_extend(self) -> None:
self.replay_buffer.reset()
keys = self.replay_buffer.extend(self.data)
expected_keys = torch.arange(self.batch_size)
self.assertEqual(len(self.replay_buffer), self.batch_size)
self.assert_tensor_equal(keys, expected_keys)
data = self.replay_buffer.get(keys)
self.assertEqual(data.keys(), self.flatten_data.keys())
for k, v in data.items():
self.assert_tensor_equal(v, self.flatten_data[k])
keys = self.replay_buffer.extend(self.data)
self.assertEqual(len(self.replay_buffer), self.size)
self.assert_tensor_equal(keys, expected_keys + self.batch_size)
data = self.replay_buffer.get(keys)
self.assertEqual(data.keys(), self.flatten_data.keys())
for k, v in data.items():
self.assert_tensor_equal(v, self.flatten_data[k])
def test_extend_stacked(self) -> None:
self.replay_buffer.reset()
keys = self.replay_buffer.extend(self.flatten_data, stacked=True)
expected_keys = torch.arange(self.batch_size)
self.assertEqual(len(self.replay_buffer), self.batch_size)
self.assert_tensor_equal(keys, expected_keys)
data = self.replay_buffer.get(keys)
self.assertEqual(data.keys(), self.flatten_data.keys())
for k, v in data.items():
self.assert_tensor_equal(v, self.flatten_data[k])
keys = self.replay_buffer.extend(self.flatten_data, stacked=True)
self.assertEqual(len(self.replay_buffer), self.size)
self.assert_tensor_equal(keys, expected_keys + self.batch_size)
data = self.replay_buffer.get(keys)
self.assertEqual(data.keys(), self.flatten_data.keys())
for k, v in data.items():
self.assert_tensor_equal(v, self.flatten_data[k])
def test_sample(self) -> None:
self.replay_buffer.reset()
self.replay_buffer.extend(self.data)
prob = 1.0 / self.batch_size
num_samples = self.batch_size
keys, _, probs = self.replay_buffer.sample(num_samples)
expected_probs = torch.full_like(probs, prob)
self.assert_tensor_equal(probs, expected_probs)
count = torch.bincount(keys)
self.assertEqual(count.max().item(), 1)
count = torch.zeros(self.batch_size, dtype=torch.int64)
for _ in range(20000):
keys, _, _ = self.replay_buffer.sample(3)
count[keys] += 1
actual_probs = count / count.sum()
expected_probs = torch.full_like(actual_probs, prob)
self.assert_tensor_close(actual_probs, expected_probs, atol=0.05)
# Test sample with replacement.
num_samples = 20000
keys, _, probs = self.replay_buffer.sample(num_samples,
replacement=True)
self.assert_tensor_equal(
probs, torch.full((num_samples,), prob, dtype=torch.float64))
actual_probs = torch.bincount(keys).float() / num_samples
expected_probs = torch.full_like(actual_probs, prob)
self.assert_tensor_close(actual_probs, expected_probs, atol=0.05)
def test_clear(self) -> None:
self.replay_buffer.reset()
self.replay_buffer.extend(self.data)
self.assertEqual(len(self.replay_buffer), len(self.data))
self.replay_buffer.clear()
self.assertEqual(len(self.replay_buffer), 0)
self.replay_buffer.extend(self.data)
self.assertEqual(len(self.replay_buffer), len(self.data))
class PrioritizedReplayBufferTest(TestCaseBase):
def setUp(self):
self.size = 8
self.batch_size = 5
self.hidden_dim = 4
self.flatten_data = dict(obs=torch.randn(self.batch_size,
self.hidden_dim),
rew=torch.randn(self.batch_size))
self.data = data_utils.unstack_fields(self.flatten_data,
self.batch_size)
def test_extend(self):
replay_buffer = ReplayBuffer(TensorCircularBuffer(self.size),
PrioritizedSampler(priority_exponent=0.6))
keys = replay_buffer.extend(self.data)
expected_keys = torch.arange(self.batch_size)
self.assertEqual(len(replay_buffer), self.batch_size)
self.assert_tensor_equal(keys, expected_keys)
data = replay_buffer.get(keys)
self.assertEqual(data.keys(), self.flatten_data.keys())
for k, v in data.items():
self.assert_tensor_equal(v, self.flatten_data[k])
keys = replay_buffer.extend(self.data)
self.assertEqual(len(replay_buffer), self.size)
self.assert_tensor_equal(keys, expected_keys + self.batch_size)
data = replay_buffer.get(keys)
self.assertEqual(data.keys(), self.flatten_data.keys())
for k, v in data.items():
self.assert_tensor_equal(v, self.flatten_data[k])
def test_extend_stacked(self):
replay_buffer = ReplayBuffer(TensorCircularBuffer(self.size),
PrioritizedSampler(priority_exponent=0.6))
keys = replay_buffer.extend(self.flatten_data, stacked=True)
expected_keys = torch.arange(self.batch_size)
self.assertEqual(len(replay_buffer), self.batch_size)
self.assert_tensor_equal(keys, expected_keys)
data = replay_buffer.get(keys)
self.assertEqual(data.keys(), self.flatten_data.keys())
for k, v in data.items():
self.assert_tensor_equal(v, self.flatten_data[k])
keys = replay_buffer.extend(self.flatten_data, stacked=True)
self.assertEqual(len(replay_buffer), self.size)
self.assert_tensor_equal(keys, expected_keys + self.batch_size)
data = replay_buffer.get(keys)
self.assertEqual(data.keys(), self.flatten_data.keys())
for k, v in data.items():
self.assert_tensor_equal(v, self.flatten_data[k])
def test_sample(self):
replay_buffer = ReplayBuffer(TensorCircularBuffer(self.size),
PrioritizedSampler(priority_exponent=1.0))
priorities = torch.rand((self.batch_size,)) * 10
expected_probs = priorities / priorities.sum()
replay_buffer.extend(self.data, priorities=priorities)
# Test sample without replacement.
# Disable this test because of stability.
# num_samples = self.batch_size
# keys, _, probs = replay_buffer.sample(num_samples)
# self.assert_tensor_close(probs,
# expected_probs[keys],
# rtol=1e-6,
# atol=1e-6)
# count = torch.bincount(keys)
# self.assertEqual(count.max().item(), 1)
# count = torch.zeros(self.batch_size, dtype=torch.int64)
# for _ in range(100000):
# keys, _, _ = replay_buffer.sample(3)
# count[keys] += 1
# actual_probs = count / count.sum()
# self.assert_tensor_close(actual_probs, expected_probs, atol=0.1)
# Test sample with replacement.
num_samples = 100000
keys, _, probs = replay_buffer.sample(num_samples, replacement=True)
actual_probs = torch.bincount(keys).float() / num_samples
self.assert_tensor_close(probs, expected_probs[keys], rtol=1e-6)
self.assert_tensor_close(actual_probs, expected_probs, atol=0.05)
def test_update(self):
alpha = 0.6
replay_buffer = ReplayBuffer(
TensorCircularBuffer(self.size),
PrioritizedSampler(priority_exponent=alpha))
priorities = torch.rand((self.batch_size,)) * 10
keys = replay_buffer.extend(self.data, priorities=priorities)
priorities = torch.rand((self.batch_size,)) * 10
expected_probs = priorities.pow(alpha)
expected_probs.div_(expected_probs.sum())
replay_buffer.update(keys, priorities)
num_samples = 100
keys, _, probs = replay_buffer.sample(num_samples, replacement=True)
self.assert_tensor_close(probs, expected_probs[keys], rtol=1e-6)
def test_reset(self) -> None:
replay_buffer = ReplayBuffer(TensorCircularBuffer(self.size),
PrioritizedSampler(priority_exponent=0.6))
replay_buffer.extend(self.data)
self.assertEqual(len(replay_buffer), len(self.data))
replay_buffer.reset()
self.assertEqual(len(replay_buffer), 0)
self.assertFalse(replay_buffer._storage._impl.initialized)
replay_buffer.extend(self.data)
self.assertEqual(len(replay_buffer), len(self.data))
def test_clear(self) -> None:
replay_buffer = ReplayBuffer(TensorCircularBuffer(self.size),
PrioritizedSampler(priority_exponent=0.6))
replay_buffer.extend(self.data)
self.assertEqual(len(replay_buffer), len(self.data))
replay_buffer.clear()
self.assertEqual(len(replay_buffer), 0)
self.assertTrue(replay_buffer._storage._impl.initialized)
replay_buffer.extend(self.data)
self.assertEqual(len(replay_buffer), len(self.data))
if __name__ == "__main__":
unittest.main()
| 10,383 | 42.087137 | 80 | py |
rlmeta | rlmeta-main/tests/core/remotable_test.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import torch
import rlmeta.core.remote as remote
import rlmeta.utils.remote_utils as remote_utils
from rlmeta.core.server import Server
class RemotableAdder(remote.Remotable):
@remote.remote_method()
def add(self, a, b):
return a + b
class ReplayBufferTest(unittest.TestCase):
def test_add_multiple(self):
server = Server(name="adder_server", addr="127.0.0.1:4412")
adder1 = RemotableAdder('a')
adder2 = RemotableAdder('b')
self.assertEqual(adder1.identifier, 'a')
self.assertEqual(adder2.identifier, 'b')
server.add_service([adder1, adder2])
adder_client1 = remote_utils.make_remote(adder1, server)
adder_client2 = remote_utils.make_remote(adder2, server)
server.start()
adder_client1.connect()
c = adder_client1.add(1, 1)
self.assertEqual(c, 2)
adder_client2.connect()
c = adder_client2.add(1, 1)
self.assertEqual(c, 2)
server.terminate()
if __name__ == "__main__":
unittest.main()
| 1,261 | 26.434783 | 67 | py |
rlmeta | rlmeta-main/tests/core/__init__.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
| 179 | 35 | 65 | py |
rlmeta | rlmeta-main/tests/core/rescalers_test.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import torch
from rlmeta.core.rescalers import MomentsRescaler, RMSRescaler, SqrtRescaler
from tests.test_utils import TestCaseBase
class RescalerTest(TestCaseBase):
def setUp(self) -> None:
self.size = (4, 5)
self.rtol = 1e-5
self.atol = 1e-5
def test_rms_rescaler(self) -> None:
rms_rescaler = RMSRescaler(self.size)
batch_size = np.random.randint(low=1, high=10)
data = torch.rand(batch_size, *self.size)
for x in torch.unbind(data):
rms_rescaler.update(x)
x = torch.rand(*self.size)
y = rms_rescaler.rescale(x)
y_expected = x / data.square().mean(dim=0).sqrt()
self.assert_tensor_close(y, y_expected, rtol=self.rtol, atol=self.atol)
self.assert_tensor_close(rms_rescaler.recover(y),
x,
rtol=self.rtol,
atol=self.atol)
def test_norm_rescaler(self) -> None:
norm_rescaler = MomentsRescaler(self.size)
batch_size = np.random.randint(low=1, high=10)
data = torch.rand(batch_size, *self.size)
for x in torch.unbind(data):
norm_rescaler.update(x)
x = torch.rand(*self.size)
y = norm_rescaler.rescale(x)
if batch_size == 1:
y_expected = x
else:
y_expected = (x - data.mean(dim=0)) / data.std(dim=0,
unbiased=False)
self.assert_tensor_close(y, y_expected, rtol=self.rtol, atol=self.atol)
self.assert_tensor_close(norm_rescaler.recover(y),
x,
rtol=self.rtol,
atol=self.atol)
def test_sqrt_rescaler(self) -> None:
eps = np.random.choice([0.0, 1e-5, 1e-3, 2e-2, 0.5])
sqrt_rescaler = SqrtRescaler(eps)
x = torch.randn(*self.size, dtype=torch.float64)
y = sqrt_rescaler.rescale(x)
y_expected = x.sign() * ((x.abs() + 1).sqrt() - 1) + eps * x
self.assert_tensor_close(y, y_expected, rtol=self.rtol, atol=self.atol)
self.assert_tensor_close(sqrt_rescaler.recover(y),
x,
rtol=self.rtol,
atol=self.atol)
if __name__ == "__main__":
unittest.main()
| 2,621 | 33.5 | 79 | py |
rlmeta | rlmeta-main/tests/utils/running_stats_test.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from rlmeta.utils.running_stats import RunningMoments, RunningRMS
from tests.test_utils import TestCaseBase
class RunningRMSTest(TestCaseBase):
def setUp(self) -> None:
self.outer_size = 10
self.inner_size = (4, 5)
self.running_rms = RunningRMS(self.inner_size)
self.rtol = 1e-6
self.atol = 1e-6
def test_single_update(self) -> None:
input = torch.rand(self.outer_size, *self.inner_size)
self.running_rms.reset()
for x in torch.unbind(input):
self.running_rms.update(x)
self._verify_running_rms(input)
def test_batch_update(self) -> None:
input = torch.rand(self.outer_size, *self.inner_size)
split_size = [1, 2, 3, 4]
self.running_rms.reset()
for x in torch.split(input, split_size):
self.running_rms.update(x)
self._verify_running_rms(input)
def _verify_running_rms(self, input: torch.Tensor) -> None:
self.assert_tensor_equal(self.running_rms.count(),
torch.tensor([self.outer_size]))
self.assert_tensor_close(self.running_rms.mean_square(),
input.square().mean(dim=0),
rtol=self.rtol,
atol=self.atol)
self.assert_tensor_close(self.running_rms.rms(),
input.square().mean(dim=0).sqrt(),
rtol=self.rtol,
atol=self.atol)
self.assert_tensor_close(self.running_rms.rrms(),
input.square().mean(dim=0).rsqrt(),
rtol=self.rtol,
atol=self.atol)
class RunningMomentsTest(TestCaseBase):
def setUp(self) -> None:
self.outer_size = 10
self.inner_size = (4, 5)
self.running_moments = RunningMoments(self.inner_size)
self.rtol = 1e-6
self.atol = 1e-6
def test_single_update(self) -> None:
input = torch.rand(self.outer_size, *self.inner_size)
self.running_moments.reset()
for x in torch.unbind(input):
self.running_moments.update(x)
self._verify_running_moments(input)
def test_batch_update(self) -> None:
input = torch.rand(self.outer_size, *self.inner_size)
split_size = [1, 2, 3, 4]
self.running_moments.reset()
for x in torch.split(input, split_size):
self.running_moments.update(x)
self._verify_running_moments(input)
def _verify_running_moments(self, input: torch.Tensor) -> None:
self.assert_tensor_equal(self.running_moments.count(),
torch.tensor([self.outer_size]))
self.assert_tensor_close(self.running_moments.mean(),
input.mean(dim=0),
rtol=self.rtol,
atol=self.atol)
self.assert_tensor_close(self.running_moments.var(),
input.var(dim=0, unbiased=False),
rtol=self.rtol,
atol=self.atol)
self.assert_tensor_close(self.running_moments.var(ddof=1),
input.var(dim=0, unbiased=True),
rtol=self.rtol,
atol=self.atol)
self.assert_tensor_close(self.running_moments.std(),
input.std(dim=0, unbiased=False),
rtol=self.rtol,
atol=self.atol)
self.assert_tensor_close(self.running_moments.std(ddof=1),
input.std(dim=0, unbiased=True),
rtol=self.rtol,
atol=self.atol)
self.assert_tensor_close(self.running_moments.rstd(),
input.std(dim=0, unbiased=False).reciprocal(),
rtol=self.rtol,
atol=self.atol)
self.assert_tensor_close(self.running_moments.rstd(ddof=1),
input.std(dim=0, unbiased=True).reciprocal(),
rtol=self.rtol,
atol=self.atol)
if __name__ == "__main__":
unittest.main()
| 4,653 | 39.824561 | 79 | py |
rlmeta | rlmeta-main/tests/utils/stats_dict_test.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
from rlmeta.utils.stats_dict import StatsDict
from tests.test_utils import TestCaseBase
class StatsDictTest(TestCaseBase):
def test_add(self) -> None:
n = 10
a = np.random.rand(n)
b = np.random.randn(n)
d = StatsDict()
for x, y in zip(a.tolist(), b.tolist()):
d.add("a", x)
d.add("b", y)
self.assertEqual(d["a"].count(), n)
self.assertEqual(d["a"].mean(), np.mean(a))
self.assertEqual(d["a"].var(ddof=0), np.var(a, ddof=0))
self.assertEqual(d["a"].std(ddof=0), np.std(a, ddof=0))
self.assertEqual(d["a"].min(), np.min(a))
self.assertEqual(d["a"].max(), np.max(a))
self.assertEqual(d["b"].count(), n)
self.assertEqual(d["b"].mean(), np.mean(b))
self.assertEqual(d["b"].var(ddof=1), np.var(b, ddof=1))
self.assertEqual(d["b"].std(ddof=1), np.std(b, ddof=1))
self.assertEqual(d["b"].min(), np.min(b))
self.assertEqual(d["b"].max(), np.max(b))
def test_extend(self) -> None:
n = 10
a = np.random.rand(n)
b = np.random.randn(n)
d = StatsDict()
for x, y in zip(a.tolist(), b.tolist()):
d.extend({"a": x, "b": y})
self.assertEqual(d["a"].count(), n)
self.assertEqual(d["a"].mean(), np.mean(a))
self.assertEqual(d["a"].var(ddof=0), np.var(a, ddof=0))
self.assertEqual(d["a"].std(ddof=0), np.std(a, ddof=0))
self.assertEqual(d["a"].min(), np.min(a))
self.assertEqual(d["a"].max(), np.max(a))
self.assertEqual(d["b"].count(), n)
self.assertEqual(d["b"].mean(), np.mean(b))
self.assertEqual(d["b"].var(ddof=1), np.var(b, ddof=1))
self.assertEqual(d["b"].std(ddof=1), np.std(b, ddof=1))
self.assertEqual(d["b"].min(), np.min(b))
self.assertEqual(d["b"].max(), np.max(b))
| 2,102 | 32.919355 | 65 | py |
rlmeta | rlmeta-main/tests/utils/__init__.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
| 179 | 35 | 65 | py |
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