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se_resnet.py
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se_resnet.py
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# Copyright 2018 Changan Wang. 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.
# ==============================================================================
import tensorflow as tf
USE_FUSED_BN = True
BN_EPSILON = 9.999999747378752e-06
BN_MOMENTUM = 0.99
# input image order: BGR, range [0-255]
# mean_value: 104, 117, 123
# only subtract mean is used
# for root block, use dummy input_filters, e.g. 128 rather than 64 for the first block
def se_bottleneck_block(inputs, input_filters, name_prefix, is_training, data_format='channels_last', need_reduce=True, is_root=False, reduced_scale=16):
bn_axis = -1 if data_format == 'channels_last' else 1
strides_to_use = 1
residuals = inputs
if need_reduce:
strides_to_use = 1 if is_root else 2
proj_mapping = tf.layers.conv2d(inputs, input_filters * 2, (1, 1), use_bias=False,
name=name_prefix + '_1x1_proj', strides=(strides_to_use, strides_to_use),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
residuals = tf.layers.batch_normalization(proj_mapping, momentum=BN_MOMENTUM,
name=name_prefix + '_1x1_proj/bn', axis=bn_axis,
epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN)
reduced_inputs = tf.layers.conv2d(inputs, input_filters / 2, (1, 1), use_bias=False,
name=name_prefix + '_1x1_reduce', strides=(strides_to_use, strides_to_use),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
reduced_inputs_bn = tf.layers.batch_normalization(reduced_inputs, momentum=BN_MOMENTUM,
name=name_prefix + '_1x1_reduce/bn', axis=bn_axis,
epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN)
reduced_inputs_relu = tf.nn.relu(reduced_inputs_bn, name=name_prefix + '_1x1_reduce/relu')
conv3_inputs = tf.layers.conv2d(reduced_inputs_relu, input_filters / 2, (3, 3), use_bias=False,
name=name_prefix + '_3x3', strides=(1, 1),
padding='same', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
conv3_inputs_bn = tf.layers.batch_normalization(conv3_inputs, momentum=BN_MOMENTUM, name=name_prefix + '_3x3/bn',
axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN)
conv3_inputs_relu = tf.nn.relu(conv3_inputs_bn, name=name_prefix + '_3x3/relu')
increase_inputs = tf.layers.conv2d(conv3_inputs_relu, input_filters * 2, (1, 1), use_bias=False,
name=name_prefix + '_1x1_increase', strides=(1, 1),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
increase_inputs_bn = tf.layers.batch_normalization(increase_inputs, momentum=BN_MOMENTUM,
name=name_prefix + '_1x1_increase/bn', axis=bn_axis,
epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN)
if data_format == 'channels_first':
pooled_inputs = tf.reduce_mean(increase_inputs_bn, [2, 3], name=name_prefix + '_global_pool', keep_dims=True)
else:
pooled_inputs = tf.reduce_mean(increase_inputs_bn, [1, 2], name=name_prefix + '_global_pool', keep_dims=True)
down_inputs = tf.layers.conv2d(pooled_inputs, (input_filters * 2) // reduced_scale, (1, 1), use_bias=True,
name=name_prefix + '_1x1_down', strides=(1, 1),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
down_inputs_relu = tf.nn.relu(down_inputs, name=name_prefix + '_1x1_down/relu')
up_inputs = tf.layers.conv2d(down_inputs_relu, input_filters * 2, (1, 1), use_bias=True,
name=name_prefix + '_1x1_up', strides=(1, 1),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
prob_outputs = tf.nn.sigmoid(up_inputs, name=name_prefix + '_prob')
rescaled_feat = tf.multiply(prob_outputs, increase_inputs_bn, name=name_prefix + '_mul')
pre_act = tf.add(residuals, rescaled_feat, name=name_prefix + '_add')
return tf.nn.relu(pre_act, name=name_prefix + '/relu')
#return tf.nn.relu(residuals + prob_outputs * increase_inputs_bn, name=name_prefix + '/relu')
def SE_ResNet(input_image, num_classes, is_training=False, data_format='channels_last', net_depth=50):
bn_axis = -1 if data_format == 'channels_last' else 1
# the input image should in BGR order, note that this is not the common case in Tensorflow
# convert from RGB to BGR
if data_format == 'channels_last':
image_channels = tf.unstack(input_image, axis=-1)
swaped_input_image = tf.stack([image_channels[2], image_channels[1], image_channels[0]], axis=-1)
else:
image_channels = tf.unstack(input_image, axis=1)
swaped_input_image = tf.stack([image_channels[2], image_channels[1], image_channels[0]], axis=1)
if net_depth not in [50, 101]:
raise TypeError('Only ResNet50 or ResNet101 is supprted now.')
input_depth = [128, 256, 512, 1024] # the input depth of the the first block is dummy input
num_units = [3, 4, 6, 3] if net_depth==50 else [3, 4, 23, 3]
block_name_prefix = ['conv2_{}', 'conv3_{}', 'conv4_{}', 'conv5_{}']
if data_format == 'channels_first':
swaped_input_image = tf.pad(swaped_input_image, paddings = [[0, 0], [0, 0], [3, 3], [3, 3]])
else:
swaped_input_image = tf.pad(swaped_input_image, paddings = [[0, 0], [3, 3], [3, 3], [0, 0]])
inputs_features = tf.layers.conv2d(swaped_input_image, input_depth[0]//2, (7, 7), use_bias=False,
name='conv1/7x7_s2', strides=(2, 2),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
inputs_features = tf.layers.batch_normalization(inputs_features, momentum=BN_MOMENTUM,
name='conv1/7x7_s2/bn', axis=bn_axis,
epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN)
inputs_features = tf.nn.relu(inputs_features, name='conv1/relu_7x7_s2')
inputs_features = tf.layers.max_pooling2d(inputs_features, [3, 3], [2, 2], padding='same', data_format=data_format, name='pool1/3x3_s2')
is_root = True
for ind, num_unit in enumerate(num_units):
need_reduce = True
for unit_index in range(1, num_unit+1):
inputs_features = se_bottleneck_block(inputs_features, input_depth[ind], block_name_prefix[ind].format(unit_index), is_training=is_training, data_format=data_format, need_reduce=need_reduce, is_root=is_root)
need_reduce = False
is_root = False
if data_format == 'channels_first':
pooled_inputs = tf.reduce_mean(inputs_features, [2, 3], name='pool5/7x7_s1', keep_dims=True)
else:
pooled_inputs = tf.reduce_mean(inputs_features, [1, 2], name='pool5/7x7_s1', keep_dims=True)
pooled_inputs = tf.layers.flatten(pooled_inputs)
logits_output = tf.layers.dense(pooled_inputs, num_classes,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer(), use_bias=True)
return logits_output, tf.nn.softmax(logits_output, name='prob')
'''run test for the chcekpoint again
'''
import numpy as np
tf.reset_default_graph()
input_image = tf.placeholder(tf.float32, shape = (None, 3, 224, 224), name = 'input_placeholder')
outputs = SE_ResNet(input_image, 1000, is_training = False, data_format='channels_first')
saver = tf.train.Saver()
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, "./model/seresnet50/se_resnet50.ckpt")
predict = sess.run(outputs, feed_dict = {input_image : np.expand_dims(np.concatenate([np.ones((1, 224,224))*0.2, np.ones((1, 224,224))*0.4, np.ones((1, 224,224))*0.6], axis=0), axis=0)})
#predict = sess.run(outputs, feed_dict = {input_image : np.expand_dims(np.concatenate([np.ones((224,224,1))*0.2, np.ones((224,224,1))*0.4, np.ones((224,224,1))*0.6], axis=-1), axis=0)})
print(predict)
print(np.argmax(predict))