import logging
import os
import time
import cv2
import numpy as np
import torch
import yaml
from matplotlib import colors
from matplotlib import pyplot as plt
from torch import Tensor, nn
from torch.utils.data import ConcatDataset
class CharsetMapper(object):
"""A simple class to map ids into strings.
It works only when the character set is 1:1 mapping between individual
characters and individual ids.
"""
def __init__(self,
filename='',
max_length=30,
null_char=u'\u2591'):
"""Creates a lookup table.
Args:
filename: Path to charset file which maps characters to ids.
max_sequence_length: The max length of ids and string.
null_char: A unicode character used to replace '<null>' character.
the default value is a light shade block '░'.
"""
self.null_char = null_char
self.max_length = max_length
self.label_to_char = self._read_charset(filename)
self.char_to_label = dict(map(reversed, self.label_to_char.items()))
self.num_classes = len(self.label_to_char)
def _read_charset(self, filename):
"""Reads a charset definition from a tab separated text file.
Args:
filename: a path to the charset file.
Returns:
a dictionary with keys equal to character codes and values - unicode
characters.
"""
import re
pattern = re.compile(r'(\d+)\t(.+)')
charset = {}
self.null_label = 0
charset[self.null_label] = self.null_char
with open(filename, 'r') as f:
for i, line in enumerate(f):
m = pattern.match(line)
assert m, f'Incorrect charset file. line #{i}: {line}'
label = int(m.group(1)) + 1
char = m.group(2)
charset[label] = char
return charset
def trim(self, text):
assert isinstance(text, str)
return text.replace(self.null_char, '')
def get_text(self, labels, length=None, padding=True, trim=False):
""" Returns a string corresponding to a sequence of character ids.
"""
length = length if length else self.max_length
labels = [l.item() if isinstance(l, Tensor) else int(l) for l in labels]
if padding:
labels = labels + [self.null_label] * (length-len(labels))
text = ''.join([self.label_to_char[label] for label in labels])
if trim: text = self.trim(text)
return text
def get_labels(self, text, length=None, padding=True, case_sensitive=False):
""" Returns the labels of the corresponding text.
"""
length = length if length else self.max_length
if padding:
text = text + self.null_char * (length - len(text))
if not case_sensitive:
text = text.lower()
labels = [self.char_to_label[char] for char in text]
return labels
def pad_labels(self, labels, length=None):
length = length if length else self.max_length
return labels + [self.null_label] * (length - len(labels))
@property
def digits(self):
return '0123456789'
@property
def digit_labels(self):
return self.get_labels(self.digits, padding=False)
@property
def alphabets(self):
all_chars = list(self.char_to_label.keys())
valid_chars = []
for c in all_chars:
if c in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ':
valid_chars.append(c)
return ''.join(valid_chars)
@property
def alphabet_labels(self):
return self.get_labels(self.alphabets, padding=False)
class Timer(object):
"""A simple timer."""
def __init__(self):
self.data_time = 0.
self.data_diff = 0.
self.data_total_time = 0.
self.data_call = 0
self.running_time = 0.
self.running_diff = 0.
self.running_total_time = 0.
self.running_call = 0
def tic(self):
self.start_time = time.time()
self.running_time = self.start_time
def toc_data(self):
self.data_time = time.time()
self.data_diff = self.data_time - self.running_time
self.data_total_time += self.data_diff
self.data_call += 1
def toc_running(self):
self.running_time = time.time()
self.running_diff = self.running_time - self.data_time
self.running_total_time += self.running_diff
self.running_call += 1
def total_time(self):
return self.data_total_time + self.running_total_time
def average_time(self):
return self.average_data_time() + self.average_running_time()
def average_data_time(self):
return self.data_total_time / (self.data_call or 1)
def average_running_time(self):
return self.running_total_time / (self.running_call or 1)
class Logger(object):
_handle = None
_root = None
@staticmethod
def init(output_dir, name, phase):
format = '[%(asctime)s %(filename)s:%(lineno)d %(levelname)s {}] ' \
'%(message)s'.format(name)
logging.basicConfig(level=logging.INFO, format=format)
try: os.makedirs(output_dir)
except: pass
config_path = os.path.join(output_dir, f'{phase}.txt')
Logger._handle = logging.FileHandler(config_path)
Logger._root = logging.getLogger()
@staticmethod
def enable_file():
if Logger._handle is None or Logger._root is None:
raise Exception('Invoke Logger.init() first!')
Logger._root.addHandler(Logger._handle)
@staticmethod
def disable_file():
if Logger._handle is None or Logger._root is None:
raise Exception('Invoke Logger.init() first!')
Logger._root.removeHandler(Logger._handle)
class Config(object):
def __init__(self, config_path, host=True):
def __dict2attr(d, prefix=''):
for k, v in d.items():
if isinstance(v, dict):
__dict2attr(v, f'{prefix}{k}_')
else:
if k == 'phase':
assert v in ['train', 'test']
if k == 'stage':
assert v in ['pretrain-vision', 'pretrain-language',
'train-semi-super', 'train-super']
self.__setattr__(f'{prefix}{k}', v)
assert os.path.exists(config_path), '%s does not exists!' % config_path
with open(config_path) as file:
config_dict = yaml.load(file, Loader=yaml.FullLoader)
with open('configs/template.yaml') as file:
default_config_dict = yaml.load(file, Loader=yaml.FullLoader)
__dict2attr(default_config_dict)
__dict2attr(config_dict)
def __getattr__(self, item):
attr = self.__dict__.get(item)
if attr is None:
attr = dict()
prefix = f'{item}_'
for k, v in self.__dict__.items():
if k.startswith(prefix):
n = k.replace(prefix, '')
attr[n] = v
return attr if len(attr) > 0 else None
else:
return attr
def __repr__(self):
str = 'ModelConfig(\n'
for i, (k, v) in enumerate(sorted(vars(self).items())):
str += f'\t({i}): {k} = {v}\n'
str += ')'
return str
def blend_mask(image, mask, alpha=0.5, cmap='jet', color='b', color_alpha=1.0):
# normalize mask
mask = (mask-mask.min()) / (mask.max() - mask.min() + np.finfo(float).eps)
if mask.shape != image.shape:
mask = cv2.resize(mask,(image.shape[1], image.shape[0]))
# get color map
color_map = plt.get_cmap(cmap)
mask = color_map(mask)[:,:,:3]
# convert float to uint8
mask = (mask * 255).astype(dtype=np.uint8)
# set the basic color
basic_color = np.array(colors.to_rgb(color)) * 255
basic_color = np.tile(basic_color, [image.shape[0], image.shape[1], 1])
basic_color = basic_color.astype(dtype=np.uint8)
# blend with basic color
blended_img = cv2.addWeighted(image, color_alpha, basic_color, 1-color_alpha, 0)
# blend with mask
blended_img = cv2.addWeighted(blended_img, alpha, mask, 1-alpha, 0)
return blended_img
def onehot(label, depth, device=None):
"""
Args:
label: shape (n1, n2, ..., )
depth: a scalar
Returns:
onehot: (n1, n2, ..., depth)
"""
if not isinstance(label, torch.Tensor):
label = torch.tensor(label, device=device)
onehot = torch.zeros(label.size() + torch.Size([depth]), device=device)
onehot = onehot.scatter_(-1, label.unsqueeze(-1), 1)
return onehot
class MyDataParallel(nn.DataParallel):
def gather(self, outputs, target_device):
r"""
Gathers tensors from different GPUs on a specified device
(-1 means the CPU).
"""
def gather_map(outputs):
out = outputs[0]
if isinstance(out, (str, int, float)):
return out
if isinstance(out, list) and isinstance(out[0], str):
return [o for out in outputs for o in out]
if isinstance(out, torch.Tensor):
return torch.nn.parallel._functions.Gather.apply(target_device, self.dim, *outputs)
if out is None:
return None
if isinstance(out, dict):
if not all((len(out) == len(d) for d in outputs)):
raise ValueError('All dicts must have the same number of keys')
return type(out)(((k, gather_map([d[k] for d in outputs]))
for k in out))
return type(out)(map(gather_map, zip(*outputs)))
# Recursive function calls like this create reference cycles.
# Setting the function to None clears the refcycle.
try:
res = gather_map(outputs)
finally:
gather_map = None
return res
class MyConcatDataset(ConcatDataset):
def __getattr__(self, k):
return getattr(self.datasets[0], k)