diff --git a/.vscode/sftp.json b/.vscode/sftp.json
new file mode 100644
index 0000000..3192d7b
--- /dev/null
+++ b/.vscode/sftp.json
@@ -0,0 +1,10 @@
+{
+ "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
+}
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..1ce8758
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,661 @@
+ GNU AFFERO GENERAL PUBLIC LICENSE
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+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+
+ Copyright (C)
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published
+ by the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program. If not, see .
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source. For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code. There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+.
\ No newline at end of file
diff --git a/README.md b/README.md
deleted file mode 100644
index 10981e1..0000000
--- a/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-# DeepHSV
-source codes for paper: DeepHSV: User-independent Offline Signature Verification Using Two-Channel CNN
diff --git a/dataset/dataset_paris.py b/dataset/dataset_paris.py
new file mode 100644
index 0000000..805bd85
--- /dev/null
+++ b/dataset/dataset_paris.py
@@ -0,0 +1,104 @@
+"""
+@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)
+
diff --git a/dataset/generate_list_bhsig260.py b/dataset/generate_list_bhsig260.py
new file mode 100644
index 0000000..f71d9c8
--- /dev/null
+++ b/dataset/generate_list_bhsig260.py
@@ -0,0 +1,111 @@
+"""
+@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())
diff --git a/dataset/generate_list_cedar.py b/dataset/generate_list_cedar.py
new file mode 100644
index 0000000..dea4028
--- /dev/null
+++ b/dataset/generate_list_cedar.py
@@ -0,0 +1,96 @@
+"""
+@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())
diff --git a/dataset/generate_list_firmas.py b/dataset/generate_list_firmas.py
new file mode 100644
index 0000000..7467703
--- /dev/null
+++ b/dataset/generate_list_firmas.py
@@ -0,0 +1,97 @@
+"""
+@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())
diff --git a/dataset/params.json b/dataset/params.json
new file mode 100644
index 0000000..5f3144d
--- /dev/null
+++ b/dataset/params.json
@@ -0,0 +1,27 @@
+{
+ "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
+}
\ No newline at end of file
diff --git a/dataset/preprosess_images.py b/dataset/preprosess_images.py
new file mode 100644
index 0000000..d272ca4
--- /dev/null
+++ b/dataset/preprosess_images.py
@@ -0,0 +1,45 @@
+# 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())
diff --git a/models.py b/models.py
new file mode 100644
index 0000000..fde57bd
--- /dev/null
+++ b/models.py
@@ -0,0 +1,281 @@
+# 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):
+ """"""
+ 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):
+ """"""
+
+ 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])
diff --git a/net/inception_v3.py b/net/inception_v3.py
new file mode 100644
index 0000000..8aa6d12
--- /dev/null
+++ b/net/inception_v3.py
@@ -0,0 +1,736 @@
+# 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
+
diff --git a/run.py b/run.py
new file mode 100644
index 0000000..fc72614
--- /dev/null
+++ b/run.py
@@ -0,0 +1,70 @@
+"""
+@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]))
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..547f146
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,197 @@
+"""
+@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) <