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{
"name": "Deeplearning",
"host": "192.168.40.83",
"protocol": "sftp",
"port": 22,
"username": "deeplearning",
"password": "cfca1234",
"remotePath": "/home/deeplearning/work/Deeplearning/TensorFlow/DeepWritingID/DeepHWS_ID/",
"uploadOnSave": true
}

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README.md
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# DeepHSV
source codes for paper: DeepHSV: User-independent Offline Signature Verification Using Two-Channel CNN

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dataset/dataset_paris.py Normal file
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"""
@file: dataset_paris.py
@time: 2018/7/31 15:03
@desc:Create the input data pipeline using `tf.data`
"""
import numpy as np
import tensorflow as tf
image_width = None
image_height = None
images_dir = None
channels = 1
def _read_image(filename, is_augment):
image_string = tf.read_file(tf.string_join([images_dir, filename]))
image_decoded = tf.image.decode_png(image_string, channels=channels)
true_constant = tf.constant(1, dtype=tf.int32, name="true_constant")
image_decoded = tf.cond(tf.equal(true_constant, is_augment),
lambda: tf.image.flip_left_right(image_decoded),
lambda: image_decoded)
image_resized = tf.image.resize_images(image_decoded, [image_width, image_height])
return image_resized
def _parse_function(item):
is_aug = tf.string_to_number(item[3], out_type=tf.int32)
image0 = _read_image(item[0], is_aug)
image1 = _read_image(item[1], is_aug)
image = tf.concat([image0, image1], 2)
return image, tf.string_to_number(item[2])
def _input_fn(params, is_training, is_augment=False, pos_repeating=1, only_label=None):
"""Train input function.
Args:
listfile_path: listfile has 3 item per line
params: contains hyperparameters of the model (ex: data_dir, image's width and height.)
"""
listfile_path = params.signature_train_list if is_training else params.signature_val_list
data = []
shuffle_neg = []
size_per_signer = params.positive_size + params.negative_size
file = open(listfile_path)
for i, line in enumerate(file.readlines()):
items = line.split(' ')
file0 = items[0]
file1 = items[1]
label = int(items[2])
if (only_label is not None and label != only_label) or label == 2:
continue
repeating = 1
if is_training and pos_repeating > 0 and i % size_per_signer == 0:
"""the number of positive/negative pairs is 276/996,
so we need to expand positive pairs, or reduce the negative pairs"""
shuffle_neg = np.arange(params.positive_size, size_per_signer)
np.random.shuffle(shuffle_neg)
shuffle_neg = shuffle_neg[:params.positive_size * pos_repeating]
if is_training and pos_repeating > 0:
"""expand positive pairs"""
if label == 2:
repeating = 1 if (i % params.negative_size) > params.positive_size * pos_repeating else 0
repeating = 0
elif label == 0:
"""reduce negative pairs """
repeating = 1 if i % size_per_signer in shuffle_neg else 0
elif label == 1:
repeating = pos_repeating
for j in range(repeating):
"""file0, file1, label, is_augment"""
data.append((file0, file1, label, 0))
if is_augment and is_training:
data.append((file0, file1, label, 1))
# data.append((file1, file0, label))
file.close()
np.random.shuffle(data)
print("examples of data: -> %d" % len(data))
dataset = tf.data.Dataset.from_tensor_slices(np.array(data))
dataset = dataset.map(_parse_function, num_parallel_calls=params.num_parallel_calls)
dataset = dataset.shuffle(10000)
dataset = dataset.repeat(params.num_epochs)
dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(
params.batch_size * params.num_gpus))
dataset = dataset.prefetch(10)
return dataset
def input_fn(params, is_training, repeating=1, is_augment=False, only_label=None):
global image_width, image_height, images_dir, channels
image_width = params.image_width
image_height = params.image_height
images_dir = params.images_dir
channels = params.channels
return _input_fn(params, is_training, pos_repeating=repeating, is_augment=is_augment, only_label=only_label)

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dataset/generate_list_bhsig260.py Normal file
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"""
@file: dataset_bhsig260.py
@time: 2018/6/20 15:03
@desc:Create the paris list of BHSig260 Database
"""
import copy
import os
import sys
import imageio
import numpy as np
num_genuine = 24
num_forged = 30
# 生成数组l的全部组合（长度k）
def combine(l, k):
answers = []
one = [0] * k
def next_c(li=0, ni=0):
if ni == k:
answers.append(copy.copy(one))
return
for lj in range(li, len(l)):
one[ni] = l[lj]
next_c(lj + 1, ni + 1)
next_c()
return answers
# 生成两个数组间的全部组合
def combine_2list(list1, list2):
answers = []
for i1 in list1:
for i2 in list2:
answers.append([i1, i2])
return answers
def generate_list(data_dir, train_size, filename_pre, listfile_name):
root_dir = os.path.basename(data_dir)
signers_list = os.listdir(data_dir)
list_file_train = open(listfile_name + '_train.txt', 'w')
list_file_test = open(listfile_name + '_val.txt', 'w')
train_indexs = np.arange(0, len(signers_list), 1)
np.random.shuffle(train_indexs)
train_indexs = train_indexs[:train_size]
for i, signer in enumerate(signers_list):
list_file = list_file_train if i in train_indexs else list_file_test
genuine_genuine_suf = combine(list(range(1, num_genuine + 1)), 2)
for item in genuine_genuine_suf:
genuine0 = "%s/%s/%s-%d-G-%02d%s" % (root_dir, signer, filename_pre, int(signer), item[0], '.jpg')
genuine1 = "%s/%s/%s-%d-G-%02d%s" % (root_dir, signer, filename_pre, int(signer), item[1], '.jpg')
line = genuine0 + ' ' + genuine1 + ' 1\n'
list_file.write(line)
genuine_forged_suf = combine_2list(list(range(1, num_genuine + 1)), list(range(1, num_forged + 1)))
for item in genuine_forged_suf:
genuine = "%s/%s/%s-%d-G-%02d%s" % (root_dir, signer, filename_pre, int(signer), item[0], '.jpg')
forged = "%s/%s/%s-%d-F-%02d%s" % (root_dir, signer, filename_pre, int(signer), item[1], '.jpg')
line = genuine + ' ' + forged + ' 0\n'
list_file.write(line)
list_file_train.close()
list_file_test.close()
def rename(dir_path):
for root, dirs, files in os.walk(dir_path):
for file in files:
if not file.endswith('.jpg'):
continue
new_filename = file.replace('-S-00', '-S-')
new_filename = new_filename.replace('-S-0', '-S-')
os.rename(os.path.join(root, file), os.path.join(root, new_filename))
def tif_to_jpg(tif_dir, jpg_dir):
for root, dirs, files in os.walk(tif_dir):
to_dir = root.replace(tif_dir, jpg_dir)
if not os.path.exists(to_dir):
os.mkdir(to_dir)
for file in files:
if not file.endswith('.tif'):
continue
image = imageio.imread(os.path.join(root, file))
jpg_file = file.replace('.tif', '.jpg')
imageio.imwrite(os.path.join(to_dir, jpg_file), image)
def main(argv=None):
if argv is None:
argv = sys.argv
rename('/home/deeplearning/work/Deeplearning/dataset/writingID/offline/BHSig260_jpgs/')
# generate_list('/home/deeplearning/work/Deeplearning/dataset/writingID/offline/BHSig260_jpgs/Hindi', 100, 'H-S',
# '../experiments/data_list/bhsig260_Hindi')
# generate_list('/home/deeplearning/work/Deeplearning/dataset/writingID/offline/BHSig260_jpgs/Bengali', 50,
# 'B-S',
# '../experiments/data_list/bhsig260_Bengali')
if __name__ == "__main__":
sys.exit(main())

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"""
@file: generate_list_cedar.py
@time: 2018/6/20 15:03
@desc:Create the paris list of CEDAR Database
"""
import copy
import os
import sys
import imageio
import numpy as np
num_genuine = 24
num_forged = 24
# 生成数组l的全部组合（长度k）
def combine(l, k):
answers = []
one = [0] * k
def next_c(li=0, ni=0):
if ni == k:
answers.append(copy.copy(one))
return
for lj in range(li, len(l)):
one[ni] = l[lj]
next_c(lj + 1, ni + 1)
next_c()
return answers
# 生成两个数组间的全部组合
def combine_2list(list1, list2):
answers = []
for i1 in list1:
for i2 in list2:
answers.append([i1, i2])
return answers
def generate_list(train_size, listfile_name):
signers_list = list(range(1, 56))
list_file_train = open(listfile_name + '_train.txt', 'w')
list_file_test = open(listfile_name + '_val.txt', 'w')
train_indexs = np.arange(0, len(signers_list), 1)
np.random.shuffle(train_indexs)
train_indexs = train_indexs[:train_size]
for i, signer in enumerate(signers_list):
list_file = list_file_train if i in train_indexs else list_file_test
genuine_genuine_suf = combine(list(range(1, num_genuine + 1)), 2)
for item in genuine_genuine_suf:
genuine0 = "%s%d_%d%s" % ('full_org/original_', int(signer), item[0], '.png')
genuine1 = "%s%d_%d%s" % ('full_org/original_', int(signer), item[1], '.png')
line = genuine0 + ' ' + genuine1 + ' 1\n'
list_file.write(line)
genuine_forged_suf = combine_2list(list(range(1, num_genuine + 1)), list(range(1, num_forged + 1)))
for item in genuine_forged_suf:
genuine = "%s%d_%d%s" % ('full_org/original_', int(signer), item[0], '.png')
forged = "%s%d_%d%s" % ('full_forg/forgeries_', int(signer), item[1], '.png')
line = genuine + ' ' + forged + ' 0\n'
list_file.write(line)
list_file_train.close()
list_file_test.close()
def tif_to_jpg(tif_dir, jpg_dir):
for root, dirs, files in os.walk(tif_dir):
to_dir = root.replace(tif_dir, jpg_dir)
if not os.path.exists(to_dir):
os.mkdir(to_dir)
for file in files:
if not file.endswith('.tif'):
continue
image = imageio.imread(os.path.join(root, file))
jpg_file = file.replace('.tif', '.jpg')
imageio.imwrite(os.path.join(to_dir, jpg_file), image)
def main(argv=None):
if argv is None:
argv = sys.argv
generate_list(50, '../experiments/data_list/cedar')
# generate_list(100, '../experiments/data_list/bhsig260_Hindi')
if __name__ == "__main__":
sys.exit(main())

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dataset/generate_list_firmas.py Normal file
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"""
@file: model.py
@time: 2018/4/17 15:03
@desc: Generate the list of data pairs
"""
import copy
import os
import sys
import numpy as np
image_dir = '/home/deeplearning/work/Deeplearning/dataset/writingID/offline/firmas/'
list_filename_train = '../experiments/data_list/firmas_pairs_c_train.txt'
list_filename_test = '../experiments/data_list/firmas_pairs_c_val.txt'
num_genuine = 24
num_forged = 30
# 生成数组l的全部组合（长度k）
def combine(l, k):
answers = []
one = [0] * k
def next_c(li=0, ni=0):
if ni == k:
answers.append(copy.copy(one))
return
for lj in range(li, len(l)):
one[ni] = l[lj]
next_c(lj + 1, ni + 1)
next_c()
return answers
# 生成两个数组间的全部组合
def combine_2list(list1, list2):
answers = []
for i1 in list1:
for i2 in list2:
answers.append([i1, i2])
return answers
def main(argv=None):
if argv is None:
argv = sys.argv
signers_list = os.listdir(image_dir)
list_file_train = open(list_filename_train, 'w')
list_file_test = open(list_filename_test, 'w')
for signer in signers_list:
list_file = list_file_train if int(signer) <= 3500 else list_file_test
genuine_genuine_suf = combine(list(range(1, num_genuine + 1)), 2)
for item in genuine_genuine_suf:
genuine0 = signer + '/c-' + signer + "-%02d" % (item[0]) + '.jpg'
genuine1 = signer + '/c-' + signer + "-%02d" % (item[1]) + '.jpg'
line = genuine0 + ' ' + genuine1 + ' 1\n'
list_file.write(line)
genuine_forged_suf = combine_2list(list(range(1, num_genuine + 1)), list(range(1, num_forged + 1)))
for item in genuine_forged_suf:
genuine = signer + '/c-' + signer + "-%02d" % (item[0]) + '.jpg'
forged = signer + '/cf-' + signer + "-%02d" % (item[1]) + '.jpg'
line = genuine + ' ' + forged + ' 0\n'
list_file.write(line)
"""随机伪造情况，每个writer 和其他writer组合"""
random_forged_nums = 2880000
# random_forged_val_nums = 2880000 * 0.15
writers = np.arange(1, 4001, 1)
writers = np.split(writers, 2)
writers_part1 = writers[0]
writers_part2 = writers[1]
genuine_forged_suf = combine_2list(writers_part1, writers_part2)
np.random.shuffle(genuine_forged_suf)
i = 0
for item in genuine_forged_suf:
if i > random_forged_nums:
break
i += 1
list_file = list_file_train if i % 6 != 0 else list_file_test
genuine = '%03d' % item[0] + '/c-' + '%03d' % item[0] + "-09" + '.jpg'
forged = '%03d' % item[1] + '/c-' + '%03d' % item[1] + "-09" + '.jpg'
line = genuine + ' ' + forged + ' 2\n'
list_file.write(line)
list_file_train.close()
list_file_test.close()
if __name__ == "__main__":
sys.exit(main())

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dataset/params.json Normal file
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{
"model": "Inception_2logits",
"signature_train_list": "./experiments/data_list/firmas_pairs_c_train.txt",
"signature_val_list": "./experiments/data_list/firmas_pairs_c_val.txt",
"images_dir": "/home/deeplearning/work/Deeplearning/dataset/writingID/offline/firmas_binarized/",
"is_augment": false,
"learning_rate": 1e-5,
"batch_size": 32,
"num_epochs": 1,
"use_batch_norm": true,
"bn_momentum": 0.9,
"margin": 5,
"embedding_size": 64,
"keep_prob": 0.4,
"squared": false,
"image_width": 220,
"image_height": 155,
"positive_size": 276,
"negative_size": 720,
"channels": 1,
"num_parallel_calls": 4,
"save_summary_steps": 100,
"save_checkpoints_steps": 1000,
"num_gpus": 3,
"keep_checkpoint_max": 25,
"eval_steps": 10
}

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dataset/preprosess_images.py Normal file
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# encoding: utf-8
"""
@author: lichuang
@license: (C) Copyright 2010, CFCA
@file: preprosess_images.py
@time: 2018/5/8 18:
@desc: regularize images, binaries, turn into black background
"""
import os
import sys
import imageio
import numpy as np
dir_to_process = '/home/deeplearning/work/Deeplearning/dataset/writingID/offline/firmas/'
dir_processed = '/home/deeplearning/work/Deeplearning/dataset/writingID/offline/firmas_binarized/'
def _normalize_images(images_dir, processed_dir, reverse):
"""binaries, turn into black background """
for root, dirs, files in os.walk(images_dir):
for name in files:
new_path = os.path.join(processed_dir, os.path.split(root)[-1])
if not os.path.exists(new_path):
os.mkdir(new_path)
if name.lower().endswith('.jpg'):
image = imageio.imread(os.path.join(root, name))
image[np.where(image < 230)] = 0
image[np.where(image >= 230)] = 255
if reverse:
image = 255 - image
imageio.imwrite(os.path.join(new_path, name), image)
print('all images processed!')
def main(argv=None):
if argv is None:
argv = sys.argv
_normalize_images(dir_to_process, dir_processed, False)
if __name__ == "__main__":
sys.exit(main())

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models.py Normal file
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# encoding: utf-8
"""
@file: models.py
@time: 2018/4/17 15:03
@desc: 4 models: Siamese, SiameseInception, 2ChannelsCNN, 2ChannelsSoftmax
"""
import tensorflow as tf
from tensorflow.contrib import layers
from tensorflow.contrib.framework.python.ops import arg_scope
from tensorflow.contrib.layers.python.layers import layers as layers_lib
import net.inception_v3 as inception_v3
import utils
def _embedding_alexnet(is_training, images, params):
with tf.variable_scope('Siamese', 'CFCASiamese', [images], reuse=tf.AUTO_REUSE):
with arg_scope(
[layers.conv2d], activation_fn=tf.nn.relu):
net = layers.conv2d(
images, 96, [11, 11], 4, padding='VALID', scope='conv1')
# net = layers.batch_norm(net, decay=0.9, epsilon=1e-06, is_training=is_training)
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1')
net = layers.conv2d(net, 256, [5, 5], scope='conv2')
# net = layers.batch_norm(net, decay=0.9, epsilon=1e-06, is_training=is_training)
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2')
net = layers_lib.dropout(
net, keep_prob=0.7, is_training=is_training)
net = layers.conv2d(net, 384, [3, 3], scope='conv3')
net = layers.conv2d(net, 256, [3, 3], scope='conv4')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5')
net = layers_lib.dropout(
net, keep_prob=0.7, is_training=is_training)
net = layers_lib.flatten(net, scope='flatten1')
net = layers_lib.fully_connected(net, 1024, scope='fc1',
weights_regularizer=layers.l2_regularizer(0.0005))
net = layers_lib.dropout(
net, keep_prob=0.5, is_training=is_training)
net = layers_lib.fully_connected(net, params.embedding_size, scope='fc2',
weights_regularizer=layers.l2_regularizer(0.0005))
return net
def _embedding_inception(is_training, images, params):
logits, endpoints = inception_v3.inception_v3(
images, num_classes=params.embedding_size, is_training=is_training,
dropout_keep_prob=params.keep_prob, reuse=tf.AUTO_REUSE, scope='InceptionV3')
return logits
def _embedding_2logits(is_training, embeddings, labels):
"""embeddings to 2 logits and losss"""
logits = layers_lib.fully_connected(
embeddings, 2, scope='fc3', reuse=tf.AUTO_REUSE)
logits_array = tf.split(logits, 2, 1)
logits_diff = tf.subtract(logits_array[0], logits_array[1])
if labels is not None:
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=tf.cast(labels, tf.int64)))
return loss, logits_diff
else:
return None, logits_diff
def _calculate_eucd2(embedding1, embedding2):
eucd2 = tf.pow(tf.subtract(embedding1, embedding2), 2)
eucd2 = tf.reduce_sum(eucd2, 1)
eucd = tf.sqrt(eucd2 + 1e-6, name="eucd")
return tf.reshape(eucd2, [-1, 1]), tf.reshape(eucd, [-1, 1])
def _loss_siamese(images, labels, params, is_training, embedding_func):
"""<SigNet: Convolutional Siamese Network for Writer
Independent Offline Signature Verification>"""
images = tf.split(images, 2, axis=3)
images0 = tf.reshape(
images[0], [-1, params.image_width, params.image_height, 1])
images1 = tf.reshape(
images[1], [-1, params.image_width, params.image_height, 1])
"""When using Siamese, The Complex network such as Inception will
cause overfitting even in first epoch"""
embeddings0 = embedding_func(is_training, images0, params)
embeddings1 = embedding_func(is_training, images1, params)
eucd2, eucd = _calculate_eucd2(embeddings0, embeddings1)
if labels is not None:
labels_t = tf.reshape(labels, [-1, 1])
labels_f = tf.reshape(tf.subtract(
1.0, labels, name="1-yi"), [-1, 1]) # labels_ = !labels;
c = tf.constant(int(params.margin), dtype=tf.float32, name="C")
pos = tf.multiply(labels_t, eucd2, name="yi_x_eucd2")
neg = tf.multiply(labels_f, tf.pow(tf.maximum(
tf.subtract(c, eucd), 0), 2), name="Nyi_x_C-eucd_xx_2")
losses = tf.add(pos, neg, name="losses")
loss = tf.reduce_mean(losses, name="loss")
return loss, eucd
else:
return None, eucd
def _loss_siamese_alexnet(images, labels, params, is_training):
return _loss_siamese(images, labels, params, is_training, _embedding_alexnet)
def _loss_siamese_inception(images, labels, params, is_training):
return _loss_siamese(images, labels, params, is_training, _embedding_inception)
def _loss_inception_2logits(images, labels, params, is_training):
images = tf.split(images, 2, axis=3)
images0 = tf.reshape(
images[0], [-1, params.image_width, params.image_height, 1])
images1 = tf.reshape(
images[1], [-1, params.image_width, params.image_height, 1])
embeddings0 = _embedding_inception(is_training, images0, params)
embeddings1 = _embedding_inception(is_training, images1, params)
embeddings = tf.concat([embeddings0, embeddings1], axis=1)
return _embedding_2logits(is_training, embeddings, labels)
def _loss_2channels_softmax_alex(images, labels, params, is_training):
# params.embedding_size = 2
embeddings = _embedding_alexnet(is_training, images, params)
logits = layers_lib.fully_connected(
embeddings, 2, scope='fc3', reuse=tf.AUTO_REUSE)
# logits = embeddings
logits_array = tf.split(logits, 2, 1)
logits_diff = tf.subtract(logits_array[0], logits_array[1])
if labels is not None:
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=tf.cast(labels, tf.int64)))
return loss, logits_diff
else:
return None, logits_diff
def _loss_2channels_softmax(images, labels, params, is_training):
logits, endpoints = inception_v3.inception_v3(
images, num_classes=2, is_training=is_training,
dropout_keep_prob=params.keep_prob, reuse=tf.AUTO_REUSE, scope='InceptionV3')
logits_array = tf.split(logits, 2, 1)
logits_diff = tf.subtract(logits_array[0], logits_array[1])
if labels is not None:
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=tf.cast(labels, tf.int64)))
return loss, logits_diff
else:
return None, logits_diff
def _loss_2channels(images, labels, params, is_training):
"""<Learning to Compare Image Patches via Convolutional Neural Networks>"""
logits, endpoints = inception_v3.inception_v3(
images, num_classes=1, is_training=is_training,
dropout_keep_prob=params.keep_prob, reuse=tf.AUTO_REUSE, scope='InceptionV3')
if labels is not None:
""" convert y from {0,1} to {-1,1}"""
labels = tf.multiply(labels, 2.0)
labels = tf.subtract(labels, 1.0)
labels = tf.reshape(labels, [-1, 1])
loss = tf.maximum(0.0, tf.subtract(1.0, tf.multiply(labels, logits)))
return tf.reduce_mean(loss), tf.subtract(1.0, logits)
else:
return None, tf.subtract(1.0, logits)
def _normlize_distance(distance):
"""normalization of distance"""
max_val = tf.reduce_max(distance)
min_val = tf.reduce_min(distance)
distance_norm = tf.div(tf.subtract(distance, min_val),
tf.subtract(max_val, min_val))
return distance_norm
models = {"Siamese": _loss_siamese_alexnet,
"SiameseInception": _loss_siamese_inception,
"Inception_2logits": _loss_inception_2logits,
"2ChannelsAlexnet": _loss_2channels_softmax_alex,
"2ChannelsCNN": _loss_2channels,
"2ChannelsSoftmax": _loss_2channels_softmax}
def model_fn_signature(features, labels, mode, params):
"""Model function for tf.estimator
Args:
features: input batch of images
labels:True or not
mode: can be one of tf.estimator.ModeKeys.{TRAIN, EVAL }
params: contains hyper parameters of the model (ex: `params.learning_rate`)
Returns:
model_spec: tf.estimator.EstimatorSpec object
"""
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
loss_function = models[params.model]
losses_all_tower = []
distance_all_tower = []
images_all_tower = tf.split(features, params.num_gpus, axis=0)
labels_all_tower = None
if labels is not None:
labels = tf.reshape(labels, [-1])
labels_all_tower = tf.split(labels, params.num_gpus, axis=0)
for i in range(params.num_gpus):
worker_device = '/{}:{}'.format('gpu', i)
images_tower = images_all_tower[i]
device_setter = utils.local_device_setter(
ps_device_type='gpu',
worker_device=worker_device,
ps_strategy=tf.contrib.training.GreedyLoadBalancingStrategy(
params.num_gpus, tf.contrib.training.byte_size_load_fn))
with tf.device(device_setter):
if labels_all_tower is not None:
loss, distance = loss_function(
images_tower, labels_all_tower[i], params, is_training)
losses_all_tower.append(loss)
else:
_, distance = loss_function(
images_tower, None, params, is_training)
distance_all_tower.append(distance)
consolidation_device = '/cpu:0'
with tf.device(consolidation_device):
distance = tf.concat(distance_all_tower, 0)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {'distance': distance}
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
loss = tf.reduce_mean(losses_all_tower, name='loss_mean')
labels = tf.reshape(labels, [-1, 1])
labels_reversal = tf.reshape(tf.subtract(
1.0, labels), [-1, 1]) # labels_ = !labels;
positive_distance = tf.reduce_mean(tf.multiply(labels, distance))
negative_distance = tf.reduce_mean(
tf.multiply(labels_reversal, distance))
tf.summary.scalar('loss', loss)
tf.summary.scalar('positive_distance', positive_distance)
tf.summary.scalar('negative_distance', negative_distance)
distance_norm = _normlize_distance(distance)
metric_ops = tf.metrics.auc(labels_reversal, distance_norm)
tf.summary.scalar('auc', metric_ops[1])
if mode == tf.estimator.ModeKeys.EVAL:
sec_at_spe_metric = tf.metrics.sensitivity_at_specificity(
labels_reversal, distance_norm, 0.90)
eval_metric_ops = {'evaluation_auc': metric_ops,
'sec_at_spe': sec_at_spe_metric}
return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=eval_metric_ops)
else:
logging_hook = tf.train.LoggingTensorHook({"positive_distance": positive_distance,
"negative_distance": negative_distance,
"auc": metric_ops[1]}, every_n_iter=100)
# optimizer = tf.train.RMSPropOptimizer(params.learning_rate)
optimizer = tf.train.AdamOptimizer(params.learning_rate)
global_step = tf.train.get_global_step()
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(
loss, global_step=global_step, colocate_gradients_with_ops=True)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op, training_hooks=[logging_hook])

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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.
# ==============================================================================
"""Contains the definition for inception v3 classification network."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib import layers
from tensorflow.contrib.framework.python.ops import arg_scope
from tensorflow.contrib.layers.python.layers import initializers
from tensorflow.contrib.layers.python.layers import layers as layers_lib
from tensorflow.contrib.layers.python.layers import regularizers
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import variable_scope
trunc_normal = lambda stddev: init_ops.truncated_normal_initializer(0.0, stddev)
def inception_v3_base(inputs,
final_endpoint='Mixed_7a',
min_depth=16,
depth_multiplier=1.0,
scope=None,
is_training=False):
"""Inception model from http://arxiv.org/abs/1512.00567.
Constructs an Inception v3 network from inputs to the given final endpoint.
This method can construct the network up to the final inception block
Mixed_7c.
Note that the names of the layers in the paper do not correspond to the names
of the endpoints registered by this function although they build the same
network.
Here is a mapping from the old_names to the new names:
Old name | New name
=======================================
conv0 | Conv2d_1a_3x3
conv1 | Conv2d_2a_3x3
conv2 | Conv2d_2b_3x3
pool1 | MaxPool_3a_3x3
conv3 | Conv2d_3b_1x1
conv4 | Conv2d_4a_3x3
pool2 | MaxPool_5a_3x3
mixed_35x35x256a | Mixed_5b
mixed_35x35x288a | Mixed_5c
mixed_35x35x288b | Mixed_5d
mixed_17x17x768a | Mixed_6a
mixed_17x17x768b | Mixed_6b
mixed_17x17x768c | Mixed_6c
mixed_17x17x768d | Mixed_6d
mixed_17x17x768e | Mixed_6e
mixed_8x8x1280a | Mixed_7a
mixed_8x8x2048a | Mixed_7b
mixed_8x8x2048b | Mixed_7c
Args:
inputs: a tensor of size [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c',
'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'].
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or depth_multiplier <= 0
:param is_training:
"""
# end_points will collect relevant activations for external use, for example
# summaries or losses.
end_points = {}
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with variable_scope.variable_scope(scope, 'InceptionV3', [inputs]):
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='VALID'):
# 299 x 299 x 3
end_point = 'Conv2d_1a_3x3'
net = layers.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 149 x 149 x 32
end_point = 'Conv2d_2a_3x3'
net = layers.conv2d(net, depth(32), [3, 3], scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 147 x 147 x 32
end_point = 'Conv2d_2b_3x3'
net = layers.conv2d(
net, depth(64), [3, 3], padding='SAME', scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 147 x 147 x 64
end_point = 'MaxPool_3a_3x3'
net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 73 x 73 x 64
end_point = 'Conv2d_3b_1x1'
net = layers.conv2d(net, depth(80), [1, 1], scope=end_point)
net = layers_lib.dropout(net, keep_prob=0.9, is_training=is_training)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 73 x 73 x 80.
end_point = 'Conv2d_4a_3x3'
net = layers.conv2d(net, depth(192), [3, 3], scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 71 x 71 x 192.
end_point = 'MaxPool_5a_3x3'
net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 35 x 35 x 192.
# Inception blocks
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='SAME'):
# mixed: 35 x 35 x 256.
end_point = 'Mixed_5b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, depth(64), [5, 5], scope='Conv2d_0b_5x5')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(
branch_2, depth(96), [3, 3], scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(
branch_2, depth(96), [3, 3], scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3, depth(32), [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_1: 35 x 35 x 288.
end_point = 'Mixed_5c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, depth(48), [1, 1], scope='Conv2d_0b_1x1')
branch_1 = layers.conv2d(
branch_1, depth(64), [5, 5], scope='Conv_1_0c_5x5')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(
branch_2, depth(96), [3, 3], scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(
branch_2, depth(96), [3, 3], scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3, depth(64), [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_2: 35 x 35 x 288.
end_point = 'Mixed_5d'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, depth(64), [5, 5], scope='Conv2d_0b_5x5')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(
branch_2, depth(96), [3, 3], scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(
branch_2, depth(96), [3, 3], scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3, depth(64), [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
net = layers_lib.dropout(net, keep_prob=0.8, is_training=is_training)
# mixed_3: 17 x 17 x 768.
end_point = 'Mixed_6a'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net,
depth(384), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3')
branch_1 = layers.conv2d(
branch_1,
depth(96), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(
net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed4: 17 x 17 x 768.
end_point = 'Mixed_6b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, depth(128), [1, 7], scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(
branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(
branch_2, depth(128), [7, 1], scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(
branch_2, depth(128), [1, 7], scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(
branch_2, depth(128), [7, 1], scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(
branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_5: 17 x 17 x 768.
end_point = 'Mixed_6c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, depth(160), [1, 7], scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(
branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(
branch_2, depth(160), [7, 1], scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(
branch_2, depth(160), [1, 7], scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(
branch_2, depth(160), [7, 1], scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(
branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_6: 17 x 17 x 768.
end_point = 'Mixed_6d'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, depth(160), [1, 7], scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(
branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(
branch_2, depth(160), [7, 1], scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(
branch_2, depth(160), [1, 7], scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(
branch_2, depth(160), [7, 1], scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(
branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_7: 17 x 17 x 768.
end_point = 'Mixed_6e'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, depth(192), [1, 7], scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(
branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(
branch_2, depth(192), [7, 1], scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(
branch_2, depth(192), [1, 7], scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(
branch_2, depth(192), [7, 1], scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(
branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
net = layers_lib.dropout(net, keep_prob=0.8, is_training=is_training)
# mixed_8: 8 x 8 x 1280.
end_point = 'Mixed_7a'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_0 = layers.conv2d(
branch_0,
depth(320), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_3x3')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, depth(192), [1, 7], scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(
branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1')
branch_1 = layers.conv2d(
branch_1,
depth(192), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(
net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_9: 8 x 8 x 2048.
end_point = 'Mixed_7b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = array_ops.concat(
[
layers.conv2d(
branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
layers.conv2d(
branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')
],
3)
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(
branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
branch_2 = array_ops.concat(
[
layers.conv2d(
branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
layers.conv2d(
branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')
],
3)
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_10: 8 x 8 x 2048.
end_point = 'Mixed_7c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = array_ops.concat(
[
layers.conv2d(
branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
layers.conv2d(
branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')
],
3)
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(
branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
branch_2 = array_ops.concat(
[
layers.conv2d(
branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
layers.conv2d(
branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')
],
3)
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
raise ValueError('Unknown final endpoint %s' % final_endpoint)
def inception_v3(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
min_depth=16,
depth_multiplier=1.0,
prediction_fn=layers_lib.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV3'):
"""Inception model from http://arxiv.org/abs/1512.00567.
"Rethinking the Inception Architecture for Computer Vision"
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,
Zbigniew Wojna.
With the default arguments this method constructs the exact model defined in
the paper. However, one can experiment with variations of the inception_v3
network by changing arguments dropout_keep_prob, min_depth and
depth_multiplier.
The default image size used to train this network is 299x299.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: the percentage of activation values that are retained.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
To use this parameter, the input images must be smaller
than 300x300 pixels, in which case the output logit layer
does not contain spatial information and can be removed.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: if 'depth_multiplier' is less than or equal to zero.
"""
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with variable_scope.variable_scope(
scope, 'InceptionV3', [inputs, num_classes], reuse=reuse) as scope:
with arg_scope(
[layers_lib.batch_norm, layers_lib.dropout], is_training=is_training):
net, end_points = inception_v3_base(
inputs,
scope=scope,
is_training=is_training,
min_depth=min_depth,
depth_multiplier=depth_multiplier)
# Auxiliary Head logits
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='SAME'):
aux_logits = end_points['Mixed_6e']
with variable_scope.variable_scope('AuxLogits'):
aux_logits = layers_lib.avg_pool2d(
aux_logits, [5, 5],
stride=3,
padding='VALID',
scope='AvgPool_1a_5x5')
aux_logits = layers.conv2d(
aux_logits, depth(128), [1, 1], scope='Conv2d_1b_1x1')
# Shape of feature map before the final layer.
kernel_size = _reduced_kernel_size_for_small_input(aux_logits, [5, 5])
aux_logits = layers.conv2d(
aux_logits,
depth(768),
kernel_size,
weights_initializer=trunc_normal(0.01),
padding='VALID',
scope='Conv2d_2a_{}x{}'.format(*kernel_size))
aux_logits = layers.conv2d(
aux_logits,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
weights_initializer=trunc_normal(0.001),
scope='Conv2d_2b_1x1')
if spatial_squeeze:
aux_logits = array_ops.squeeze(
aux_logits, [1, 2], name='SpatialSqueeze')
end_points['AuxLogits'] = aux_logits
# Final pooling and prediction
with variable_scope.variable_scope('Logits'):
kernel_size = _reduced_kernel_size_for_small_input(net, [8, 8])
net = layers_lib.avg_pool2d(
net,
kernel_size,
padding='VALID',
scope='AvgPool_1a_{}x{}'.format(*kernel_size))
# 1 x 1 x 2048
net = layers_lib.dropout(
net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
end_points['PreLogits'] = net
# 2048
logits = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_1c_1x1')
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2], name='SpatialSqueeze')
# 1000
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
inception_v3.default_image_size = 299
def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
"""Define kernel size which is automatically reduced for small input.
If the shape of the input images is unknown at graph construction time this
function assumes that the input images are is large enough.
Args:
input_tensor: input tensor of size [batch_size, height, width, channels].
kernel_size: desired kernel size of length 2: [kernel_height, kernel_width]
Returns:
a tensor with the kernel size.
TODO(jrru): Make this function work with unknown shapes. Theoretically, this
can be done with the code below. Problems are two-fold: (1) If the shape was
known, it will be lost. (2) inception.tf.contrib.slim.ops._two_element_tuple
cannot
handle tensors that define the kernel size.
shape = tf.shape(input_tensor)
return = tf.stack([tf.minimum(shape[1], kernel_size[0]),
tf.minimum(shape[2], kernel_size[1])])
"""
shape = input_tensor.get_shape().as_list()
if shape[1] is None or shape[2] is None:
kernel_size_out = kernel_size
else:
kernel_size_out = [
min(shape[1], kernel_size[0]), min(shape[2], kernel_size[1])
]
return kernel_size_out
def inception_v3_arg_scope(weight_decay=0.00004,
batch_norm_var_collection='moving_vars',
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001,
updates_collections=ops.GraphKeys.UPDATE_OPS,
use_fused_batchnorm=True):
"""Defines the default InceptionV3 arg scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
batch_norm_var_collection: The name of the collection for the batch norm
variables.
batch_norm_decay: Decay for batch norm moving average
batch_norm_epsilon: Small float added to variance to avoid division by zero
updates_collections: Collections for the update ops of the layer
use_fused_batchnorm: Enable fused batchnorm.
Returns:
An `arg_scope` to use for the inception v3 model.
"""
batch_norm_params = {
# Decay for the moving averages.
'decay': batch_norm_decay,
# epsilon to prevent 0s in variance.
'epsilon': batch_norm_epsilon,
# collection containing update_ops.
'updates_collections': updates_collections,
# Use fused batch norm if possible.
'fused': use_fused_batchnorm,
# collection containing the moving mean and moving variance.
'variables_collections': {
'beta': None,
'gamma': None,
'moving_mean': [batch_norm_var_collection],
'moving_variance': [batch_norm_var_collection],
}
}
# Set weight_decay for weights in Conv and FC layers.
with arg_scope(
[layers.conv2d, layers_lib.fully_connected],
weights_regularizer=regularizers.l2_regularizer(weight_decay)):
with arg_scope(
[layers.conv2d],
weights_initializer=initializers.variance_scaling_initializer(),
activation_fn=nn_ops.relu,
normalizer_fn=layers_lib.batch_norm,
normalizer_params=batch_norm_params) as sc:
return sc

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"""
@file: model.py
@time: 2018/4/17 15:03
@desc:Train and evaluate the model
"""
import argparse
import os
import tensorflow as tf
import utils
from dataset.dataset_paris import input_fn
from models import model_fn_signature as model_fn
from utils import Params
parser = argparse.ArgumentParser()
parser.add_argument('--model_dir', default='experiments/test')
parser.add_argument('--mode', default='evaluate')
if __name__ == '__main__':
tf.reset_default_graph()
tf.logging.set_verbosity(tf.logging.INFO)
# Load the parameters from json file
args = parser.parse_args()
json_path = 'dataset/params.json'
assert os.path.isfile(json_path), "No json configuration file found at {}".format(json_path)
params = Params(json_path)
config = tf.estimator.RunConfig(tf_random_seed=229,
model_dir=args.model_dir,
save_checkpoints_steps=params.save_checkpoints_steps,
save_summary_steps=params.save_summary_steps,
keep_checkpoint_max=params.keep_checkpoint_max)
estimator = tf.estimator.Estimator(model_fn, params=params, config=config)
if args.mode.lower() == 'train':
""" model:{"Siamese", "SiameseInception", "2ChannelsCNN", "2ChannelsSoftmax" """
tf.logging.info("Starting training model : {} ".format(params.model))
estimator.train(lambda: input_fn(params, is_training=True, repeating=1, is_augment=True))
# estimator.train(lambda: input_fn(params, is_training=True, repeating=1, is_augment=False))
res = estimator.evaluate(lambda: input_fn(params, is_training=False, is_augment=False))
elif args.mode.lower() == 'predict':
res = estimator.predict(lambda: input_fn(params, is_training=False, is_augment=False, only_label=0))
distance_negative = [x['distance'] for x in res]
res = estimator.predict(lambda: input_fn(params, is_training=False, is_augment=False, only_label=1))
distance_positive = [x['distance'] for x in res]
utils.compute_eer(distance_positive=distance_positive, distance_negative=distance_negative)
utils.visualize(distance_positive=distance_positive, distance_negative=distance_negative)
else:
tf.logging.info("Evaluation on test set.")
res = estimator.evaluate(lambda: input_fn(params, is_training=False, is_augment=False))
"""evaluate from first checkpoint to last"""
# checkpoint_file = open(args.model_dir + '/checkpoint', 'r')
# checkpoint_lines = list(checkpoint_file.readlines())
# checkpoint_file.close()
# for i in range(1, len(checkpoint_lines)):
# checkpoint = checkpoint_lines[i].split('\"')[-2]
#
# res = estimator.evaluate(
# lambda: input_fn(params, False, False),
# steps=100,
# checkpoint_path=args.model_dir + '/' + checkpoint)
# for key in res:
# print("{}: {}".format(key, res[key]))

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"""
@file: model.py
@time: 2018/4/17 15:03
@desc:General utility functions
"""
import json
import logging
import sys
import matplotlib.pyplot as plt
import numpy as np
import six
from tensorflow.core.framework import node_def_pb2
from tensorflow.python.framework import device as pydev
from tensorflow.python.training import device_setter
class Params:
"""Class that loads hyperparameters from a json file.
Example:
```
params = Params(json_path)
print(params.learning_rate)
params.learning_rate = 0.5 # change the value of learning_rate in params
```
"""
def __init__(self, json_path):
self.update(json_path)
def save(self, json_path):
"""Saves parameters to json file"""
with open(json_path, 'w') as f:
json.dump(self.__dict__, f, indent=4)
def update(self, json_path):
"""Loads parameters from json file"""
with open(json_path) as f:
params = json.load(f)
self.__dict__.update(params)
@property
def dict(self):
"""Gives dict-like access to Params instance by `params.dict['learning_rate']`"""
return self.__dict__
def visualize(distance_positive, distance_negative):
kwargs = dict(histtype='stepfilled', alpha=0.5, normed=True, bins=40)
plt.hist(distance_positive, **kwargs)
plt.hist(distance_negative, **kwargs)
plt.title('visualize distance')
plt.show()
def compute_eer(distance_positive, distance_negative):
all_true = len(distance_negative)
all_false = len(distance_positive)
distance_positive = np.column_stack((np.array(distance_positive), np.zeros(len(distance_positive))))
distance_negative = np.column_stack((np.array(distance_negative), np.ones(len(distance_negative))))
distance = np.vstack((distance_positive, distance_negative))
distance = distance[distance[:, 0].argsort(), :] # sort by first column
# np.savetxt('distribution_siamese.txt', distance)
distance = np.matrix(distance)
min_dis = sys.maxsize
min_th = sys.maxsize
eer = sys.maxsize
fa = all_false
miss = 0
for i in range(0, all_true + all_false):
if distance[i, 1] == 1:
miss += 1
else:
fa -= 1
fa_rate = float(fa) / all_false
miss_rate = float(miss) / all_true
if abs(fa_rate - miss_rate) < min_dis:
min_dis = abs(fa_rate - miss_rate)
eer = max(fa_rate, miss_rate)
min_th = distance[i, 0]
print('eer:', eer, ' threshold:', min_th)
return [eer, min_th]
def set_logger(log_path):
"""Sets the logger to log info in terminal and file `log_path`.
In general, it is useful to have a logger so that every output to the terminal is saved
in a permanent file. Here we save it to `model_dir/train.log`.
Example:
```
logging.info("Starting training...")
```
Args:
log_path: (string) where to log
"""
logger = logging.getLogger()
logger.setLevel(logging.INFO)
if not logger.handlers:
# Logging to a file
file_handler = logging.FileHandler(log_path)
file_handler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s: %(message)s'))
logger.addHandler(file_handler)
# Logging to console
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(logging.Formatter('%(message)s'))
logger.addHandler(stream_handler)
def save_dict_to_json(d, json_path):
"""Saves dict of floats in json file
Args:
d: (dict) of float-castable values (np.float, int, float, etc.)
json_path: (string) path to json file
"""
with open(json_path, 'w') as f:
# We need to convert the values to float for json (it doesn't accept np.array, np.float, )
d = {k: float(v) for k, v in d.items()}
json.dump(d, f, indent=4)
def local_device_setter(num_devices=1,
ps_device_type='cpu',
worker_device='/cpu:0',
ps_ops=None,
ps_strategy=None):
if ps_ops is None:
ps_ops = ['Variable', 'VariableV2', 'VarHandleOp']
if ps_strategy is None:
ps_strategy = device_setter._RoundRobinStrategy(num_devices)
if not six.callable(ps_strategy):
raise TypeError("ps_strategy must be callable")
def _local_device_chooser(op):
current_device = pydev.DeviceSpec.from_string(op.device or "")
node_def = op if isinstance(op, node_def_pb2.NodeDef) else op.node_def
if node_def.op in ps_ops:
ps_device_spec = pydev.DeviceSpec.from_string(
'/{}:{}'.format(ps_device_type, ps_strategy(op)))
ps_device_spec.merge_from(current_device)
return ps_device_spec.to_string()
else:
worker_device_spec = pydev.DeviceSpec.from_string(worker_device or "")
worker_device_spec.merge_from(current_device)
return worker_device_spec.to_string()
return _local_device_chooser
def reduced_kernel_size_for_small_input(input_tensor, kernel_size):
"""Define kernel size which is automatically reduced for small input.
If the shape of the input images is unknown at graph construction time this
function assumes that the input images are is large enough.
Args:
input_tensor: input tensor of size [batch_size, height, width, channels].
kernel_size: desired kernel size of length 2: [kernel_height, kernel_width]
Returns:
a tensor with the kernel size.
TODO(jrru): Make this function work with unknown shapes. Theoretically, this
can be done with the code below. Problems are two-fold: (1) If the shape was
known, it will be lost. (2) inception.tf.contrib.slim.ops._two_element_tuple
cannot
handle tensors that define the kernel size.
shape = tf.shape(input_tensor)
return = tf.stack([tf.minimum(shape[1], kernel_size[0]),
tf.minimum(shape[2], kernel_size[1])])
"""
shape = input_tensor.get_shape().as_list()
if shape[1] is None or shape[2] is None:
kernel_size_out = kernel_size
else:
kernel_size_out = [
min(shape[1], kernel_size[0]), min(shape[2], kernel_size[1])
]
return kernel_size_out

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"""
@file: model.py
@time: 2018/6/17 15:03
@desc:
"""
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve
def compute_er(y_true, y_prob):
fpr, tpr, thresholds = roc_curve(y_true, y_prob, pos_label=True)
sum_sensitivity_specificity_train = tpr + (1 - fpr)
best_threshold_id = np.argmax(sum_sensitivity_specificity_train)
best_threshold = thresholds[best_threshold_id]
y = y_prob > best_threshold
cm_test = confusion_matrix(y_true, y)
acc_test = accuracy_score(y_true, y)
auc_test = roc_auc_score(y_true, y)
print('Test Accuracy: %s ' % acc_test)
print('Test AUC: %s ' % auc_test)
print('Test Confusion Matrix:')
print(cm_test)
tpr_score = float(cm_test[1][1]) / (cm_test[1][1] + cm_test[1][0])
fpr_score = float(cm_test[0][1]) / (cm_test[0][0] + cm_test[0][1])
return fpr, tpr
def read_y_prob(filename):
TwoChannel2logit = np.loadtxt(filename)
siamese = np.split(TwoChannel2logit, 2, axis=1)
y_true = siamese[1]
y_prob = siamese[0]
return y_true, y_prob
def visualize_roc():
y_true_2logit, y_prob_2logit = read_y_prob('distribution_2Channel2logit_CEDAR.txt')
y_true_1logit, y_prob_1logit = read_y_prob('distribution_2ChannelsCNN_CEDAR.txt')
y_true_siamese, y_prob_siamese = read_y_prob('distribution_siamese_CEDAR.txt')
fpr_siamese, tpr_siamese = compute_er(y_true_siamese, y_prob_siamese)
fpr_1logit, tpr_1logit = compute_er(y_true_1logit, y_prob_1logit)
fpr_2logit, tpr_2logit = compute_er(y_true_2logit, y_prob_2logit)
fig = plt.figure(figsize=(5, 5))
ax2 = fig.add_subplot(111)
curve1 = ax2.plot(fpr_siamese, tpr_siamese)
curve2 = ax2.plot(fpr_1logit, tpr_1logit)
curve3 = ax2.plot(fpr_2logit, tpr_2logit)
curve4 = ax2.plot([0, 1], [0, 1], color='navy', linestyle='--')
# dot = ax2.plot(fpr_score, tpr_score, marker='o', color='black')
# ax2.text(fpr_score, tpr_score, s='(%.3f,%.3f)' % (fpr_score, tpr_score))
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
# plt.title('ROC curve (Test), AUC = %.4f' % auc_test)
params = {'legend.fontsize': 15,
'legend.handlelength': 2}
plt.rcParams.update(params)
plt.legend(['Siamese', '2ChannelCNN', '2Channel2logit'])
plt.savefig('ROC_CEDAR_with_backgroud', dpi=500)
plt.show()
visualize_roc()
def get_auc():
writer_val = tf.summary.FileWriter('C:\work\Projects\HWS_ID\\test\\2Channels\\val')
writer_train = tf.summary.FileWriter('C:\work\Projects\HWS_ID\\test\\2Channels\\train')
auc_var = tf.Variable(0.0)
tf.summary.scalar("auc", auc_var)
write_op = tf.summary.merge_all()
session = tf.InteractiveSession()
session.run(tf.global_variables_initializer())
for e in tf.train.summary_iterator(
"C:\work\Projects\HWS_ID\\test\\2Channels\\2channelscnn.Deep-Ubantu"):
for v in e.summary.value:
if 'auc' in v.tag:
summary = session.run(write_op, {auc_var: v.simple_value})
writer_train.add_summary(summary, e.step)
writer_train.flush()
for e in tf.train.summary_iterator(
"C:\work\Projects\HWS_ID\\test\\2Channels\\2channelsoftmax.Deep-Ubantu"):
for v in e.summary.value:
if 'auc' in v.tag:
summary = session.run(write_op, {auc_var: v.simple_value})
writer_val.add_summary(summary, e.step)
writer_val.flush()