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# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Generaly Utilities.
"""
import numpy as np, cPickle, os, time
from six.moves import xrange
import src.file_utils as fu
import logging
class Timer():
def __init__(self):
self.calls = 0.
self.start_time = 0.
self.time_per_call = 0.
self.total_time = 0.
self.last_log_time = 0.
def tic(self):
self.start_time = time.time()
def toc(self, average=True, log_at=-1, log_str='', type='calls'):
if self.start_time == 0:
logging.error('Timer not started by calling tic().')
t = time.time()
diff = time.time() - self.start_time
self.total_time += diff
self.calls += 1.
self.time_per_call = self.total_time/self.calls
if type == 'calls' and log_at > 0 and np.mod(self.calls, log_at) == 0:
_ = []
logging.info('%s: %f seconds.', log_str, self.time_per_call)
elif type == 'time' and log_at > 0 and t - self.last_log_time >= log_at:
_ = []
logging.info('%s: %f seconds.', log_str, self.time_per_call)
self.last_log_time = t
if average:
return self.time_per_call
else:
return diff
class Foo(object):
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
def __str__(self):
str_ = ''
for v in vars(self).keys():
a = getattr(self, v)
if True: #isinstance(v, object):
str__ = str(a)
str__ = str__.replace('\n', '\n ')
else:
str__ = str(a)
str_ += '{:s}: {:s}'.format(v, str__)
str_ += '\n'
return str_
def dict_equal(dict1, dict2):
assert(set(dict1.keys()) == set(dict2.keys())), "Sets of keys between 2 dictionaries are different."
for k in dict1.keys():
assert(type(dict1[k]) == type(dict2[k])), "Type of key '{:s}' if different.".format(k)
if type(dict1[k]) == np.ndarray:
assert(dict1[k].dtype == dict2[k].dtype), "Numpy Type of key '{:s}' if different.".format(k)
assert(np.allclose(dict1[k], dict2[k])), "Value for key '{:s}' do not match.".format(k)
else:
assert(dict1[k] == dict2[k]), "Value for key '{:s}' do not match.".format(k)
return True
def subplot(plt, Y_X, sz_y_sz_x = (10, 10)):
Y,X = Y_X
sz_y, sz_x = sz_y_sz_x
plt.rcParams['figure.figsize'] = (X*sz_x, Y*sz_y)
fig, axes = plt.subplots(Y, X)
plt.subplots_adjust(wspace=0.1, hspace=0.1)
return fig, axes
def tic_toc_print(interval, string):
global tic_toc_print_time_old
if 'tic_toc_print_time_old' not in globals():
tic_toc_print_time_old = time.time()
print(string)
else:
new_time = time.time()
if new_time - tic_toc_print_time_old > interval:
tic_toc_print_time_old = new_time;
print(string)
def mkdir_if_missing(output_dir):
if not fu.exists(output_dir):
fu.makedirs(output_dir)
def save_variables(pickle_file_name, var, info, overwrite = False):
if fu.exists(pickle_file_name) and overwrite == False:
raise Exception('{:s} exists and over write is false.'.format(pickle_file_name))
# Construct the dictionary
assert(type(var) == list); assert(type(info) == list);
d = {}
for i in xrange(len(var)):
d[info[i]] = var[i]
with fu.fopen(pickle_file_name, 'w') as f:
cPickle.dump(d, f, cPickle.HIGHEST_PROTOCOL)
def load_variables(pickle_file_name):
if fu.exists(pickle_file_name):
with fu.fopen(pickle_file_name, 'r') as f:
d = cPickle.load(f)
return d
else:
raise Exception('{:s} does not exists.'.format(pickle_file_name))
def voc_ap(rec, prec):
rec = rec.reshape((-1,1))
prec = prec.reshape((-1,1))
z = np.zeros((1,1))
o = np.ones((1,1))
mrec = np.vstack((z, rec, o))
mpre = np.vstack((z, prec, z))
for i in range(len(mpre)-2, -1, -1):
mpre[i] = max(mpre[i], mpre[i+1])
I = np.where(mrec[1:] != mrec[0:-1])[0]+1;
ap = 0;
for i in I:
ap = ap + (mrec[i] - mrec[i-1])*mpre[i];
return ap
def tight_imshow_figure(plt, figsize=None):
fig = plt.figure(figsize=figsize)
ax = plt.Axes(fig, [0,0,1,1])
ax.set_axis_off()
fig.add_axes(ax)
return fig, ax
def calc_pr(gt, out, wt=None):
if wt is None:
wt = np.ones((gt.size,1))
gt = gt.astype(np.float64).reshape((-1,1))
wt = wt.astype(np.float64).reshape((-1,1))
out = out.astype(np.float64).reshape((-1,1))
gt = gt*wt
tog = np.concatenate([gt, wt, out], axis=1)*1.
ind = np.argsort(tog[:,2], axis=0)[::-1]
tog = tog[ind,:]
cumsumsortgt = np.cumsum(tog[:,0])
cumsumsortwt = np.cumsum(tog[:,1])
prec = cumsumsortgt / cumsumsortwt
rec = cumsumsortgt / np.sum(tog[:,0])
ap = voc_ap(rec, prec)
return ap, rec, prec
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