def forward_network(self, input_img, reuse=False):
with tf.compat.v1.variable_scope(self.name, reuse=reuse):
tf_utils.print_activations(input_img, logger=None)
inputs = self.conv2d_fixed_padding(inputs=input_img, filters=64, kernel_size=7, strides=2, name='conv1')
inputs = tf_utils.max_pool(inputs, name='3x3_maxpool', ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
logger=None)
inputs = self.block_layer(inputs=inputs, filters=64, block_fn=self.bottleneck_block, blocks=self.layers[0],
strides=1, train_mode=False, name='block_layer1')
inputs = self.block_layer(inputs=inputs, filters=128, block_fn=self.bottleneck_block, blocks=self.layers[1],
strides=2, train_mode=False, name='block_layer2')
inputs = self.block_layer(inputs=inputs, filters=256, block_fn=self.bottleneck_block, blocks=self.layers[2],
strides=2, train_mode=False, name='block_layer3')
inputs = self.block_layer(inputs=inputs, filters=512, block_fn=self.bottleneck_block, blocks=self.layers[3],
strides=2, train_mode=False, name='block_layer4')
inputs = tf_utils.relu(inputs, name='before_flatten_relu', logger=None)
# _, h, w, _ = inputs.get_shape().as_list()
# inputs = tf_utils.avg_pool(inputs, name='gap', ksize=[1, h, w, 1], strides=[1, 1, 1, 1], logger=self.logger)
# Flatten & FC1
inputs = tf_utils.flatten(inputs, name='flatten', logger=None)
inputs = tf_utils.linear(inputs, 512, name='FC1')
inputs = tf_utils.relu(inputs, name='FC1_relu', logger=None)
inputs = tf_utils.linear(inputs, 256, name='FC2')
inputs = tf_utils.relu(inputs, name='FC2_relu', logger=None)
logits = tf_utils.linear(inputs, self.num_attribute, name='Out')
return logits
def forward_network(self, img, padding='SAME', reuse=False):
with tf.compat.v1.variable_scope(self.name, reuse=reuse):
# Stage 0
s0_conv1 = tf_utils.conv2d(x=img,
output_dim=self.conv_dims[0],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s0_conv1')
s0_conv1 = tf_utils.relu(s0_conv1, name='relu_s0_conv1')
s0_conv2 = tf_utils.conv2d(x=s0_conv1,
output_dim=self.conv_dims[0],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s0_conv2')
s0_conv2 = tf_utils.norm(s0_conv2,
name='s0_norm1',
_type='batch',
_ops=self._ops,
is_train=False)
s0_conv2 = tf_utils.relu(s0_conv2, name='relu_s0_conv2')
# Stage 1
s1_maxpool = tf_utils.max_pool(x=s0_conv2, name='s1_maxpool2d')
s1_conv1 = tf_utils.conv2d(x=s1_maxpool,
output_dim=self.conv_dims[0],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s1_conv1')
s1_conv1 = tf_utils.norm(s1_conv1,
name='s1_norm0',
_type='batch',
_ops=self._ops,
is_train=False)
s1_conv1 = tf_utils.relu(s1_conv1, name='relu_s1_conv1')
s1_conv2 = tf_utils.conv2d(x=s1_conv1,
output_dim=self.conv_dims[1],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s1_conv2')
s1_conv2 = tf_utils.norm(s1_conv2,
name='s1_norm1',
_type='batch',
_ops=self._ops,
is_train=False)
s1_conv2 = tf_utils.relu(s1_conv2, name='relu_s1_conv2')
# Stage 2
s2_maxpool = tf_utils.max_pool(x=s1_conv2, name='s2_maxpool2d')
s2_conv1 = tf_utils.conv2d(x=s2_maxpool,
output_dim=self.conv_dims[2],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s2_conv1')
s2_conv1 = tf_utils.norm(s2_conv1,
name='s2_norm0',
_type='batch',
_ops=self._ops,
is_train=False)
s2_conv1 = tf_utils.relu(s2_conv1, name='relu_s2_conv1')
s2_conv2 = tf_utils.conv2d(x=s2_conv1,
output_dim=self.conv_dims[3],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s2_conv2')
s2_conv2 = tf_utils.norm(s2_conv2,
name='s2_norm1',
_type='batch',
_ops=self._ops,
is_train=False)
s2_conv2 = tf_utils.relu(s2_conv2, name='relu_s2_conv2')
# Stage 3
s3_maxpool = tf_utils.max_pool(x=s2_conv2, name='s3_maxpool2d')
s3_conv1 = tf_utils.conv2d(x=s3_maxpool,
output_dim=self.conv_dims[4],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s3_conv1')
s3_conv1 = tf_utils.norm(s3_conv1,
name='s3_norm0',
_type='batch',
_ops=self._ops,
is_train=False)
s3_conv1 = tf_utils.relu(s3_conv1, name='relu_s3_conv1')
s3_conv2 = tf_utils.conv2d(x=s3_conv1,
output_dim=self.conv_dims[5],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s3_conv2')
s3_conv2 = tf_utils.norm(s3_conv2,
name='s3_norm1',
_type='batch',
_ops=self._ops,
is_train=False)
s3_conv2 = tf_utils.relu(s3_conv2, name='relu_s3_conv2')
# Stage 4
s4_maxpool = tf_utils.max_pool(x=s3_conv2, name='s4_maxpool2d')
s4_conv1 = tf_utils.conv2d(x=s4_maxpool,
output_dim=self.conv_dims[6],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s4_conv1')
s4_conv1 = tf_utils.norm(s4_conv1,
name='s4_norm0',
_type='batch',
_ops=self._ops,
is_train=False)
s4_conv1 = tf_utils.relu(s4_conv1, name='relu_s4_conv1')
s4_conv2 = tf_utils.conv2d(x=s4_conv1,
output_dim=self.conv_dims[7],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s4_conv2')
s4_conv2 = tf_utils.norm(s4_conv2,
name='s4_norm1',
_type='batch',
_ops=self._ops,
is_train=False)
s4_conv2 = tf_utils.relu(s4_conv2, name='relu_s4_conv2')
s4_conv2_drop = tf_utils.dropout(x=s4_conv2,
keep_prob=0.,
name='s4_dropout')
# Stage 5
s5_maxpool = tf_utils.max_pool(x=s4_conv2_drop,
name='s5_maxpool2d')
s5_conv1 = tf_utils.conv2d(x=s5_maxpool,
output_dim=self.conv_dims[8],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s5_conv1')
s5_conv1 = tf_utils.norm(s5_conv1,
name='s5_norm0',
_type='batch',
_ops=self._ops,
is_train=False)
s5_conv1 = tf_utils.relu(s5_conv1, name='relu_s5_conv1')
s5_conv2 = tf_utils.conv2d(x=s5_conv1,
output_dim=self.conv_dims[9],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s5_conv2')
s5_conv2 = tf_utils.norm(s5_conv2,
name='s5_norm1',
_type='batch',
_ops=self._ops,
is_train=False)
s5_conv2 = tf_utils.relu(s5_conv2, name='relu_s5_conv2')
s5_conv2_drop = tf_utils.dropout(x=s5_conv2,
keep_prob=0.,
name='s5_dropout')
# Stage 6
s6_deconv1 = tf_utils.deconv2d(x=s5_conv2_drop,
output_dim=self.conv_dims[10],
k_h=2,
k_w=2,
initializer='He',
name='s6_deconv1')
s6_deconv1 = tf_utils.norm(s6_deconv1,
name='s6_norm0',
_type='batch',
_ops=self._ops,
is_train=False)
s6_deconv1 = tf_utils.relu(s6_deconv1, name='relu_s6_deconv1')
# Cropping
w1 = s4_conv2_drop.get_shape().as_list()[2]
w2 = s6_deconv1.get_shape().as_list()[2] - s4_conv2_drop.get_shape(
).as_list()[2]
s6_deconv1_split, _ = tf.split(s6_deconv1,
num_or_size_splits=[w1, w2],
axis=2,
name='axis2_split')
tf_utils.print_activations(s6_deconv1_split)
# Concat
s6_concat = tf_utils.concat(
values=[s6_deconv1_split, s4_conv2_drop],
axis=3,
name='s6_axis3_concat')
s6_conv2 = tf_utils.conv2d(x=s6_concat,
output_dim=self.conv_dims[11],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s6_conv2')
s6_conv2 = tf_utils.norm(s6_conv2,
name='s6_norm1',
_type='batch',
_ops=self._ops,
is_train=False)
s6_conv2 = tf_utils.relu(s6_conv2, name='relu_s6_conv2')
s6_conv3 = tf_utils.conv2d(x=s6_conv2,
output_dim=self.conv_dims[12],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s6_conv3')
s6_conv3 = tf_utils.norm(s6_conv3,
name='s6_norm2',
_type='batch',
_ops=self._ops,
is_train=False)
s6_conv3 = tf_utils.relu(s6_conv3, name='relu_s6_conv3')
# Stage 7
s7_deconv1 = tf_utils.deconv2d(x=s6_conv3,
output_dim=self.conv_dims[13],
k_h=2,
k_w=2,
initializer='He',
name='s7_deconv1')
s7_deconv1 = tf_utils.norm(s7_deconv1,
name='s7_norm0',
_type='batch',
_ops=self._ops,
is_train=False)
s7_deconv1 = tf_utils.relu(s7_deconv1, name='relu_s7_deconv1')
# Concat
s7_concat = tf_utils.concat(values=[s7_deconv1, s3_conv2],
axis=3,
name='s7_axis3_concat')
s7_conv2 = tf_utils.conv2d(x=s7_concat,
output_dim=self.conv_dims[14],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s7_conv2')
s7_conv2 = tf_utils.norm(s7_conv2,
name='s7_norm1',
_type='batch',
_ops=self._ops,
is_train=False)
s7_conv2 = tf_utils.relu(s7_conv2, name='relu_s7_conv2')
s7_conv3 = tf_utils.conv2d(x=s7_conv2,
output_dim=self.conv_dims[15],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s7_conv3')
s7_conv3 = tf_utils.norm(s7_conv3,
name='s7_norm2',
_type='batch',
_ops=self._ops,
is_train=False)
s7_conv3 = tf_utils.relu(s7_conv3, name='relu_s7_conv3')
# Stage 8
s8_deconv1 = tf_utils.deconv2d(x=s7_conv3,
output_dim=self.conv_dims[16],
k_h=2,
k_w=2,
initializer='He',
name='s8_deconv1')
s8_deconv1 = tf_utils.norm(s8_deconv1,
name='s8_norm0',
_type='batch',
_ops=self._ops,
is_train=False)
s8_deconv1 = tf_utils.relu(s8_deconv1, name='relu_s8_deconv1')
# Concat
s8_concat = tf_utils.concat(values=[s8_deconv1, s2_conv2],
axis=3,
name='s8_axis3_concat')
s8_conv2 = tf_utils.conv2d(x=s8_concat,
output_dim=self.conv_dims[17],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s8_conv2')
s8_conv2 = tf_utils.norm(s8_conv2,
name='s8_norm1',
_type='batch',
_ops=self._ops,
is_train=False)
s8_conv2 = tf_utils.relu(s8_conv2, name='relu_s8_conv2')
s8_conv3 = tf_utils.conv2d(x=s8_conv2,
output_dim=self.conv_dims[18],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s8_conv3')
s8_conv3 = tf_utils.norm(s8_conv3,
name='s8_norm2',
_type='batch',
_ops=self._ops,
is_train=False)
s8_conv3 = tf_utils.relu(s8_conv3, name='relu_conv3')
# Stage 9
s9_deconv1 = tf_utils.deconv2d(x=s8_conv3,
output_dim=self.conv_dims[19],
k_h=2,
k_w=2,
initializer='He',
name='s9_deconv1')
s9_deconv1 = tf_utils.norm(s9_deconv1,
name='s9_norm0',
_type='batch',
_ops=self._ops,
is_train=False)
s9_deconv1 = tf_utils.relu(s9_deconv1, name='relu_s9_deconv1')
# Concat
s9_concat = tf_utils.concat(values=[s9_deconv1, s1_conv2],
axis=3,
name='s9_axis3_concat')
s9_conv2 = tf_utils.conv2d(x=s9_concat,
output_dim=self.conv_dims[20],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s9_conv2')
s9_conv2 = tf_utils.norm(s9_conv2,
name='s9_norm1',
_type='batch',
_ops=self._ops,
is_train=False)
s9_conv2 = tf_utils.relu(s9_conv2, name='relu_s9_conv2')
s9_conv3 = tf_utils.conv2d(x=s9_conv2,
output_dim=self.conv_dims[21],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s9_conv3')
s9_conv3 = tf_utils.norm(s9_conv3,
name='s9_norm2',
_type='batch',
_ops=self._ops,
is_train=False)
s9_conv3 = tf_utils.relu(s9_conv3, name='relu_s9_conv3')
s10_deconv1 = tf_utils.deconv2d(x=s9_conv3,
output_dim=self.conv_dims[-1],
k_h=2,
k_w=2,
initializer='He',
name='s10_deconv1')
s10_deconv1 = tf_utils.norm(s10_deconv1,
name='s10_norm0',
_type='batch',
_ops=self._ops,
is_train=False)
s10_deconv1 = tf_utils.relu(s10_deconv1, name='relu_s10_deconv1')
# Concat
s10_concat = tf_utils.concat(values=[s10_deconv1, s0_conv2],
axis=3,
name='s10_axis3_concat')
s10_conv2 = tf_utils.conv2d(s10_concat,
output_dim=self.conv_dims[-1],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s10_conv2')
s10_conv2 = tf_utils.norm(s10_conv2,
name='s10_norm1',
_type='batch',
_ops=self._ops,
is_train=False)
s10_conv2 = tf_utils.relu(s10_conv2, name='relu_s10_conv2')
s10_conv3 = tf_utils.conv2d(x=s10_conv2,
output_dim=self.conv_dims[-1],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='s10_conv3')
s10_conv3 = tf_utils.norm(s10_conv3,
name='s10_norm2',
_type='batch',
_ops=self._ops,
is_train=False)
s10_conv3 = tf_utils.relu(s10_conv3, name='relu_s10_conv3')
output = tf_utils.conv2d(s10_conv3,
output_dim=self.num_classes,
k_h=1,
k_w=1,
d_h=1,
d_w=1,
padding=padding,
initializer='He',
name='output')
return output
def forward_network(self, inputImg, padding='SAME', reuse=False):
with tf.compat.v1.variable_scope(self.name, reuse=reuse):
# This part is for compatible between input size [640, 400] and [320, 200]
if self.resize_factor == 1.0:
# Stage 0
tf_utils.print_activations(inputImg, logger=self.logger)
s0_conv1 = tf_utils.conv2d(x=inputImg, output_dim=self.conv_dims[0], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s0_conv1', logger=self.logger)
s0_conv1 = tf_utils.relu(s0_conv1, name='relu_s0_conv1', logger=self.logger)
s0_conv2 = tf_utils.conv2d(x=s0_conv1, output_dim=self.conv_dims[0], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s0_conv2', logger=self.logger)
if self.use_batch_norm:
s0_conv2 = tf_utils.norm(s0_conv2, name='s0_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s0_conv2 = tf_utils.relu(s0_conv2, name='relu_s0_conv2', logger=self.logger)
# Stage 1
s1_maxpool = tf_utils.max_pool(x=s0_conv2, name='s1_maxpool2d', logger=self.logger)
s1_conv1 = tf_utils.conv2d(x=s1_maxpool, output_dim=self.conv_dims[0], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s1_conv1', logger=self.logger)
if self.use_batch_norm:
s1_conv1 = tf_utils.norm(s1_conv1, name='s1_norm0', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s1_conv1 = tf_utils.relu(s1_conv1, name='relu_s1_conv1', logger=self.logger)
s1_conv2 = tf_utils.conv2d(x=s1_conv1, output_dim=self.conv_dims[1], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s1_conv2', logger=self.logger)
if self.use_batch_norm:
s1_conv2 = tf_utils.norm(s1_conv2, name='s1_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s1_conv2 = tf_utils.relu(s1_conv2, name='relu_s1_conv2', logger=self.logger)
else:
# Stage 1
tf_utils.print_activations(inputImg, logger=self.logger)
s1_conv1 = tf_utils.conv2d(x=inputImg, output_dim=self.conv_dims[0], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s1_conv1', logger=self.logger)
s1_conv1 = tf_utils.relu(s1_conv1, name='relu_s1_conv1', logger=self.logger)
s1_conv2 = tf_utils.conv2d(x=s1_conv1, output_dim=self.conv_dims[1], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s1_conv2', logger=self.logger)
if self.use_batch_norm:
s1_conv2 = tf_utils.norm(s1_conv2, name='s1_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s1_conv2 = tf_utils.relu(s1_conv2, name='relu_s1_conv2', logger=self.logger)
# Stage 2
s2_maxpool = tf_utils.max_pool(x=s1_conv2, name='s2_maxpool2d', logger=self.logger)
s2_conv1 = tf_utils.conv2d(x=s2_maxpool, output_dim=self.conv_dims[2], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s2_conv1', logger=self.logger)
if self.use_batch_norm:
s2_conv1 = tf_utils.norm(s2_conv1, name='s2_norm0', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s2_conv1 = tf_utils.relu(s2_conv1, name='relu_s2_conv1', logger=self.logger)
s2_conv2 = tf_utils.conv2d(x=s2_conv1, output_dim=self.conv_dims[3], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s2_conv2', logger=self.logger)
if self.use_batch_norm:
s2_conv2 = tf_utils.norm(s2_conv2, name='s2_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s2_conv2 = tf_utils.relu(s2_conv2, name='relu_s2_conv2', logger=self.logger)
# Stage 3
s3_maxpool = tf_utils.max_pool(x=s2_conv2, name='s3_maxpool2d', logger=self.logger)
s3_conv1 = tf_utils.conv2d(x=s3_maxpool, output_dim=self.conv_dims[4], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s3_conv1', logger=self.logger)
if self.use_batch_norm:
s3_conv1 = tf_utils.norm(s3_conv1, name='s3_norm0', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s3_conv1 = tf_utils.relu(s3_conv1, name='relu_s3_conv1', logger=self.logger)
s3_conv2 = tf_utils.conv2d(x=s3_conv1, output_dim=self.conv_dims[5], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s3_conv2', logger=self.logger)
if self.use_batch_norm:
s3_conv2 = tf_utils.norm(s3_conv2, name='s3_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s3_conv2 = tf_utils.relu(s3_conv2, name='relu_s3_conv2', logger=self.logger)
# Stage 4
s4_maxpool = tf_utils.max_pool(x=s3_conv2, name='s4_maxpool2d', logger=self.logger)
s4_conv1 = tf_utils.conv2d(x=s4_maxpool, output_dim=self.conv_dims[6], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s4_conv1', logger=self.logger)
if self.use_batch_norm:
s4_conv1 = tf_utils.norm(s4_conv1, name='s4_norm0', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s4_conv1 = tf_utils.relu(s4_conv1, name='relu_s4_conv1', logger=self.logger)
s4_conv2 = tf_utils.conv2d(x=s4_conv1, output_dim=self.conv_dims[7], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s4_conv2', logger=self.logger)
if self.use_batch_norm:
s4_conv2 = tf_utils.norm(s4_conv2, name='s4_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s4_conv2 = tf_utils.relu(s4_conv2, name='relu_s4_conv2', logger=self.logger)
s4_conv2_drop = tf_utils.dropout(x=s4_conv2, keep_prob=self.ratePh, name='s4_dropout',
logger=self.logger)
# Stage 5
s5_maxpool = tf_utils.max_pool(x=s4_conv2_drop, name='s5_maxpool2d', logger=self.logger)
s5_conv1 = tf_utils.conv2d(x=s5_maxpool, output_dim=self.conv_dims[8], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s5_conv1', logger=self.logger)
if self.use_batch_norm:
s5_conv1 = tf_utils.norm(s5_conv1, name='s5_norm0', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s5_conv1 = tf_utils.relu(s5_conv1, name='relu_s5_conv1', logger=self.logger)
s5_conv2 = tf_utils.conv2d(x=s5_conv1, output_dim=self.conv_dims[9], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s5_conv2', logger=self.logger)
if self.use_batch_norm:
s5_conv2 = tf_utils.norm(s5_conv2, name='s5_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s5_conv2 = tf_utils.relu(s5_conv2, name='relu_s5_conv2', logger=self.logger)
s5_conv2_drop = tf_utils.dropout(x=s5_conv2, keep_prob=self.ratePh, name='s5_dropout',
logger=self.logger)
# Stage 6
s6_deconv1 = tf_utils.deconv2d(x=s5_conv2_drop, output_dim=self.conv_dims[10], k_h=2, k_w=2,
initializer='He', name='s6_deconv1', logger=self.logger)
if self.use_batch_norm:
s6_deconv1 = tf_utils.norm(s6_deconv1, name='s6_norm0', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s6_deconv1 = tf_utils.relu(s6_deconv1, name='relu_s6_deconv1', logger=self.logger)
# Cropping
w1 = s4_conv2_drop.get_shape().as_list()[2]
w2 = s6_deconv1.get_shape().as_list()[2] - s4_conv2_drop.get_shape().as_list()[2]
s6_deconv1_split, _ = tf.split(s6_deconv1, num_or_size_splits=[w1, w2], axis=2, name='axis2_split')
tf_utils.print_activations(s6_deconv1_split, logger=self.logger)
# Concat
s6_concat = tf_utils.concat(values=[s6_deconv1_split, s4_conv2_drop], axis=3, name='s6_axis3_concat',
logger=self.logger)
s6_conv2 = tf_utils.conv2d(x=s6_concat, output_dim=self.conv_dims[11], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s6_conv2', logger=self.logger)
if self.use_batch_norm:
s6_conv2 = tf_utils.norm(s6_conv2, name='s6_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s6_conv2 = tf_utils.relu(s6_conv2, name='relu_s6_conv2', logger=self.logger)
s6_conv3 = tf_utils.conv2d(x=s6_conv2, output_dim=self.conv_dims[12], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s6_conv3', logger=self.logger)
if self.use_batch_norm:
s6_conv3 = tf_utils.norm(s6_conv3, name='s6_norm2', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s6_conv3 = tf_utils.relu(s6_conv3, name='relu_s6_conv3', logger=self.logger)
# Stage 7
s7_deconv1 = tf_utils.deconv2d(x=s6_conv3, output_dim=self.conv_dims[13], k_h=2, k_w=2, initializer='He',
name='s7_deconv1', logger=self.logger)
if self.use_batch_norm:
s7_deconv1 = tf_utils.norm(s7_deconv1, name='s7_norm0', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s7_deconv1 = tf_utils.relu(s7_deconv1, name='relu_s7_deconv1', logger=self.logger)
# Concat
s7_concat = tf_utils.concat(values=[s7_deconv1, s3_conv2], axis=3, name='s7_axis3_concat',
logger=self.logger)
s7_conv2 = tf_utils.conv2d(x=s7_concat, output_dim=self.conv_dims[14], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s7_conv2', logger=self.logger)
if self.use_batch_norm:
s7_conv2 = tf_utils.norm(s7_conv2, name='s7_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s7_conv2 = tf_utils.relu(s7_conv2, name='relu_s7_conv2', logger=self.logger)
s7_conv3 = tf_utils.conv2d(x=s7_conv2, output_dim=self.conv_dims[15], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s7_conv3', logger=self.logger)
if self.use_batch_norm:
s7_conv3 = tf_utils.norm(s7_conv3, name='s7_norm2', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s7_conv3 = tf_utils.relu(s7_conv3, name='relu_s7_conv3', logger=self.logger)
# Stage 8
s8_deconv1 = tf_utils.deconv2d(x=s7_conv3, output_dim=self.conv_dims[16], k_h=2, k_w=2, initializer='He',
name='s8_deconv1', logger=self.logger)
if self.use_batch_norm:
s8_deconv1 = tf_utils.norm(s8_deconv1, name='s8_norm0', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s8_deconv1 = tf_utils.relu(s8_deconv1, name='relu_s8_deconv1', logger=self.logger)
# Concat
s8_concat = tf_utils.concat(values=[s8_deconv1,s2_conv2], axis=3, name='s8_axis3_concat',
logger=self.logger)
s8_conv2 = tf_utils.conv2d(x=s8_concat, output_dim=self.conv_dims[17], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s8_conv2', logger=self.logger)
if self.use_batch_norm:
s8_conv2 = tf_utils.norm(s8_conv2, name='s8_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s8_conv2 = tf_utils.relu(s8_conv2, name='relu_s8_conv2', logger=self.logger)
s8_conv3 = tf_utils.conv2d(x=s8_conv2, output_dim=self.conv_dims[18], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s8_conv3', logger=self.logger)
if self.use_batch_norm:
s8_conv3 = tf_utils.norm(s8_conv3, name='s8_norm2', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s8_conv3 = tf_utils.relu(s8_conv3, name='relu_conv3', logger=self.logger)
# Stage 9
s9_deconv1 = tf_utils.deconv2d(x=s8_conv3, output_dim=self.conv_dims[19], k_h=2, k_w=2,
initializer='He', name='s9_deconv1', logger=self.logger)
if self.use_batch_norm:
s9_deconv1 = tf_utils.norm(s9_deconv1, name='s9_norm0', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s9_deconv1 = tf_utils.relu(s9_deconv1, name='relu_s9_deconv1', logger=self.logger)
# Concat
s9_concat = tf_utils.concat(values=[s9_deconv1, s1_conv2], axis=3, name='s9_axis3_concat',
logger=self.logger)
s9_conv2 = tf_utils.conv2d(x=s9_concat, output_dim=self.conv_dims[20], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s9_conv2', logger=self.logger)
if self.use_batch_norm:
s9_conv2 = tf_utils.norm(s9_conv2, name='s9_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s9_conv2 = tf_utils.relu(s9_conv2, name='relu_s9_conv2', logger=self.logger)
s9_conv3 = tf_utils.conv2d(x=s9_conv2, output_dim=self.conv_dims[21], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s9_conv3', logger=self.logger)
if self.use_batch_norm:
s9_conv3 = tf_utils.norm(s9_conv3, name='s9_norm2', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s9_conv3 = tf_utils.relu(s9_conv3, name='relu_s9_conv3', logger=self.logger)
if self.resize_factor == 1.0:
s10_deconv1 = tf_utils.deconv2d(x=s9_conv3, output_dim=self.conv_dims[-1], k_h=2, k_w=2,
initializer='He', name='s10_deconv1', logger=self.logger)
if self.use_batch_norm:
s10_deconv1 = tf_utils.norm(s10_deconv1, name='s10_norm0', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s10_deconv1 = tf_utils.relu(s10_deconv1, name='relu_s10_deconv1', logger=self.logger)
# Concat
s10_concat = tf_utils.concat(values=[s10_deconv1, s0_conv2], axis=3, name='s10_axis3_concat',
logger=self.logger)
s10_conv2 = tf_utils.conv2d(s10_concat, output_dim=self.conv_dims[-1], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s10_conv2', logger=self.logger)
if self.use_batch_norm:
s10_conv2 = tf_utils.norm(s10_conv2, name='s10_norm1', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s10_conv2 = tf_utils.relu(s10_conv2, name='relu_s10_conv2', logger=self.logger)
s10_conv3 = tf_utils.conv2d(x=s10_conv2, output_dim=self.conv_dims[-1], k_h=3, k_w=3, d_h=1, d_w=1,
padding=padding, initializer='He', name='s10_conv3', logger=self.logger)
if self.use_batch_norm:
s10_conv3 = tf_utils.norm(s10_conv3, name='s10_norm2', _type='batch', _ops=self._ops,
is_train=self.trainMode, logger=self.logger)
s10_conv3 = tf_utils.relu(s10_conv3, name='relu_s10_conv3', logger=self.logger)
output = tf_utils.conv2d(s10_conv3, output_dim=self.numClasses, k_h=1, k_w=1, d_h=1, d_w=1,
padding=padding, initializer='He', name='output', logger=self.logger)
else:
output = tf_utils.conv2d(s9_conv3, output_dim=self.numClasses, k_h=1, k_w=1, d_h=1, d_w=1,
padding=padding, initializer='He', name='output', logger=self.logger)
return output
def u_net(self):
# Stage 1
tf_utils.print_activations(self.inp_img, logger=self.logger)
s1_conv1 = tf_utils.conv2d(x=self.inp_img,
output_dim=self.conv_dims[0],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s1_conv1',
logger=self.logger)
s1_conv1 = tf_utils.relu(s1_conv1,
name='relu_s1_conv1',
logger=self.logger)
s1_conv2 = tf_utils.conv2d(x=s1_conv1,
output_dim=self.conv_dims[1],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s1_conv2',
logger=self.logger)
s1_conv2 = tf_utils.relu(s1_conv2,
name='relu_s1_conv2',
logger=self.logger)
# Stage 2
s2_maxpool = tf_utils.max_pool(x=s1_conv2,
name='s2_maxpool',
logger=self.logger)
s2_conv1 = tf_utils.conv2d(x=s2_maxpool,
output_dim=self.conv_dims[2],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s2_conv1',
logger=self.logger)
s2_conv1 = tf_utils.relu(s2_conv1,
name='relu_s2_conv1',
logger=self.logger)
s2_conv2 = tf_utils.conv2d(x=s2_conv1,
output_dim=self.conv_dims[3],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s2_conv2',
logger=self.logger)
s2_conv2 = tf_utils.relu(s2_conv2,
name='relu_s2_conv2',
logger=self.logger)
# Stage 3
s3_maxpool = tf_utils.max_pool(x=s2_conv2,
name='s3_maxpool',
logger=self.logger)
s3_conv1 = tf_utils.conv2d(x=s3_maxpool,
output_dim=self.conv_dims[4],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s3_conv1',
logger=self.logger)
s3_conv1 = tf_utils.relu(s3_conv1,
name='relu_s3_conv1',
logger=self.logger)
s3_conv2 = tf_utils.conv2d(x=s3_conv1,
output_dim=self.conv_dims[5],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s3_conv2',
logger=self.logger)
s3_conv2 = tf_utils.relu(s3_conv2,
name='relu_s3_conv2',
logger=self.logger)
# Stage 4
s4_maxpool = tf_utils.max_pool(x=s3_conv2,
name='s4_maxpool',
logger=self.logger)
s4_conv1 = tf_utils.conv2d(x=s4_maxpool,
output_dim=self.conv_dims[6],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s4_conv1',
logger=self.logger)
s4_conv1 = tf_utils.relu(s4_conv1,
name='relu_s4_conv1',
logger=self.logger)
s4_conv2 = tf_utils.conv2d(x=s4_conv1,
output_dim=self.conv_dims[7],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s4_conv2',
logger=self.logger)
s4_conv2 = tf_utils.relu(s4_conv2,
name='relu_s4_conv2',
logger=self.logger)
s4_conv2_drop = tf_utils.dropout(x=s4_conv2,
keep_prob=self.keep_prob,
name='s4_conv2_dropout',
logger=self.logger)
# Stage 5
s5_maxpool = tf_utils.max_pool(x=s4_conv2_drop,
name='s5_maxpool',
logger=self.logger)
s5_conv1 = tf_utils.conv2d(x=s5_maxpool,
output_dim=self.conv_dims[8],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s5_conv1',
logger=self.logger)
s5_conv1 = tf_utils.relu(s5_conv1,
name='relu_s5_conv1',
logger=self.logger)
s5_conv2 = tf_utils.conv2d(x=s5_conv1,
output_dim=self.conv_dims[9],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s5_conv2',
logger=self.logger)
s5_conv2 = tf_utils.relu(s5_conv2,
name='relu_s5_conv2',
logger=self.logger)
s5_conv2_drop = tf_utils.dropout(x=s5_conv2,
keep_prob=self.keep_prob,
name='s5_conv2_dropout',
logger=self.logger)
# Stage 6
s6_deconv1 = tf_utils.deconv2d(x=s5_conv2_drop,
output_dim=self.conv_dims[10],
k_h=2,
k_w=2,
initializer='He',
name='s6_deconv1',
logger=self.logger)
s6_deconv1 = tf_utils.relu(s6_deconv1,
name='relu_s6_deconv1',
logger=self.logger)
# Cropping
h1, w1 = s4_conv2_drop.get_shape().as_list()[1:3]
h2, w2 = s6_deconv1.get_shape().as_list()[1:3]
s4_conv2_crop = tf.image.crop_to_bounding_box(
image=s4_conv2_drop,
offset_height=int(0.5 * (h1 - h2)),
offset_width=int(0.5 * (w1 - w2)),
target_height=h2,
target_width=w2)
tf_utils.print_activations(s4_conv2_crop, logger=self.logger)
s6_concat = tf_utils.concat(values=[s4_conv2_crop, s6_deconv1],
axis=3,
name='s6_concat',
logger=self.logger)
s6_conv2 = tf_utils.conv2d(x=s6_concat,
output_dim=self.conv_dims[11],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s6_conv2',
logger=self.logger)
s6_conv2 = tf_utils.relu(s6_conv2,
name='relu_s6_conv2',
logger=self.logger)
s6_conv3 = tf_utils.conv2d(x=s6_conv2,
output_dim=self.conv_dims[12],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s6_conv3',
logger=self.logger)
s6_conv3 = tf_utils.relu(s6_conv3,
name='relu_s6_conv3',
logger=self.logger)
# Stage 7
s7_deconv1 = tf_utils.deconv2d(x=s6_conv3,
output_dim=self.conv_dims[13],
k_h=2,
k_w=2,
initializer='He',
name='s7_deconv1',
logger=self.logger)
s7_deconv1 = tf_utils.relu(s7_deconv1,
name='relu_s7_deconv1',
logger=self.logger)
# Cropping
h1, w1 = s3_conv2.get_shape().as_list()[1:3]
h2, w2 = s7_deconv1.get_shape().as_list()[1:3]
s3_conv2_crop = tf.image.crop_to_bounding_box(
image=s3_conv2,
offset_height=int(0.5 * (h1 - h2)),
offset_width=int(0.5 * (w1 - w2)),
target_height=h2,
target_width=w2)
tf_utils.print_activations(s3_conv2_crop, logger=self.logger)
s7_concat = tf_utils.concat(values=[s3_conv2_crop, s7_deconv1],
axis=3,
name='s7_concat',
logger=self.logger)
s7_conv2 = tf_utils.conv2d(x=s7_concat,
output_dim=self.conv_dims[14],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s7_conv2',
logger=self.logger)
s7_conv2 = tf_utils.relu(s7_conv2,
name='relu_s7_conv2',
logger=self.logger)
s7_conv3 = tf_utils.conv2d(x=s7_conv2,
output_dim=self.conv_dims[15],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s7_conv3',
logger=self.logger)
s7_conv3 = tf_utils.relu(s7_conv3,
name='relu_s7_conv3',
logger=self.logger)
# Stage 8
s8_deconv1 = tf_utils.deconv2d(x=s7_conv3,
output_dim=self.conv_dims[16],
k_h=2,
k_w=2,
initializer='He',
name='s8_deconv1',
logger=self.logger)
s8_deconv1 = tf_utils.relu(s8_deconv1,
name='relu_s8_deconv1',
logger=self.logger)
# Cropping
h1, w1 = s2_conv2.get_shape().as_list()[1:3]
h2, w2 = s8_deconv1.get_shape().as_list()[1:3]
s2_conv2_crop = tf.image.crop_to_bounding_box(
image=s2_conv2,
offset_height=int(0.5 * (h1 - h2)),
offset_width=int(0.5 * (w1 - w2)),
target_height=h2,
target_width=w2)
tf_utils.print_activations(s2_conv2_crop, logger=self.logger)
s8_concat = tf_utils.concat(values=[s2_conv2_crop, s8_deconv1],
axis=3,
name='s8_concat',
logger=self.logger)
s8_conv2 = tf_utils.conv2d(x=s8_concat,
output_dim=self.conv_dims[17],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s8_conv2',
logger=self.logger)
s8_conv2 = tf_utils.relu(s8_conv2,
name='relu_s8_conv2',
logger=self.logger)
s8_conv3 = tf_utils.conv2d(x=s8_conv2,
output_dim=self.conv_dims[18],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s8_conv3',
logger=self.logger)
s8_conv3 = tf_utils.relu(s8_conv3,
name='relu_conv3',
logger=self.logger)
# Stage 9
s9_deconv1 = tf_utils.deconv2d(x=s8_conv3,
output_dim=self.conv_dims[19],
k_h=2,
k_w=2,
initializer='He',
name='s9_deconv1',
logger=self.logger)
s9_deconv1 = tf_utils.relu(s9_deconv1,
name='relu_s9_deconv1',
logger=self.logger)
# Cropping
h1, w1 = s1_conv2.get_shape().as_list()[1:3]
h2, w2 = s9_deconv1.get_shape().as_list()[1:3]
s1_conv2_crop = tf.image.crop_to_bounding_box(
image=s1_conv2,
offset_height=int(0.5 * (h1 - h2)),
offset_width=int(0.5 * (w1 - w2)),
target_height=h2,
target_width=w2)
tf_utils.print_activations(s1_conv2_crop, logger=self.logger)
s9_concat = tf_utils.concat(values=[s1_conv2_crop, s9_deconv1],
axis=3,
name='s9_concat',
logger=self.logger)
s9_conv2 = tf_utils.conv2d(x=s9_concat,
output_dim=self.conv_dims[20],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s9_conv2',
logger=self.logger)
s9_conv2 = tf_utils.relu(s9_conv2,
name='relu_s9_conv2',
logger=self.logger)
s9_conv3 = tf_utils.conv2d(x=s9_conv2,
output_dim=self.conv_dims[21],
k_h=3,
k_w=3,
d_h=1,
d_w=1,
padding='VALID',
initializer='He',
name='s9_conv3',
logger=self.logger)
s9_conv3 = tf_utils.relu(s9_conv3,
name='relu_s9_conv3',
logger=self.logger)
self.pred = tf_utils.conv2d(x=s9_conv3,
output_dim=self.conv_dims[22],
k_h=1,
k_w=1,
d_h=1,
d_w=1,
padding='SAME',
initializer='He',
name='output',
logger=self.logger)
def __init__(self, input_dim=[32, 32, 3], output_dim=[128, 256, 512, 1000, 10], optimizer=None, use_dropout=True,
lr=0.001, weight_decay=1e-4, random_seed=123, is_train=True, log_dir=None, name=None):
self.name = name
self.is_train = is_train
self.log_dir = log_dir
self.cur_lr = None
self.logger, self.file_handler, self.stream_handler = utils.init_logger(log_dir=self.log_dir,
name=self.name,
is_train=self.is_train)
with tf.variable_scope(self.name):
# Placeholders for inputs
self.X = tf.placeholder(dtype=tf.float32, shape=[None, *input_dim], name='X')
self.y = tf.placeholder(dtype=tf.float32, shape=[None, output_dim[-1]], name='y')
self.y_cls = tf.math.argmax(input=self.y, axis=1)
self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')
tf_utils.print_activations(self.X, logger=self.logger if self.is_train else None)
# Placeholders for TensorBoard
self.train_acc = tf.placeholder(tf.float32, name='train_acc')
self.val_acc = tf.placeholder(tf.float32, name='val_acc')
# Convolutional layers
net = self.X
if use_dropout:
net = tf_utils.dropout(x=net,
keep_prob=self.keep_prob,
seed=random_seed,
name='dropout_input',
logger=self.logger if self.is_train else None)
for idx in range(3):
net = tf_utils.conv2d(x=net,
output_dim=output_dim[idx],
k_h=5,
k_w=5,
d_h=1,
d_w=1,
name='conv2d'+str(idx),
logger=self.logger if self.is_train else None)
net = tf_utils.max_pool(x=net,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
name='maxpool'+str(idx),
logger=self.logger if self.is_train else None)
net = tf_utils.relu(x=net,
name='relu'+str(idx),
is_print=True,
logger=self.logger if self.is_train else None)
# Fully conneted layers, flatten first
net = tf_utils.flatten(x=net,
name='fc2_flatten',
logger=self.logger if self.is_train else None)
net = tf_utils.linear(x=net,
output_size=output_dim[-2],
name='fc3',
logger=self.logger if self.is_train else None)
if use_dropout:
net = tf_utils.dropout(x=net,
keep_prob=self.keep_prob,
seed=random_seed,
name='dropout3',
logger=self.logger if self.is_train else None)
net = tf_utils.relu(x=net,
name='relu3',
logger=self.logger if self.is_train else None)
# Last predict layer
self.y_pred = tf_utils.linear(x=net,
output_size=output_dim[-1],
name='last_fc',
logger=self.logger if self.is_train else None)
# Loss = data loss + regularization term
self.data_loss = tf.math.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=self.y_pred, labels=self.y))
self.reg_term = weight_decay * tf.reduce_sum(
[tf.nn.l2_loss(weight) for weight in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)])
self.loss = self.data_loss + self.reg_term
# Optimizer
self.train_op, self.cur_lr = optimizer_fn(optimizer, lr=lr, loss=self.loss, name=self.name)
# Accuracy etc
self.y_pred_cls = tf.math.argmax(input=self.y_pred, axis=1)
correct_prediction = tf.math.equal(self.y_pred_cls, self.y_cls)
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, dtype=tf.float32)) * 100.
self._tensorboard()
tf_utils.show_all_variables(logger=self.logger if self.is_train else None)
def forward_network(self, input_img, padding='SAME', reuse=False):
with tf.compat.v1.variable_scope(self.name, reuse=reuse):
tf_utils.print_activations(input_img, logger=self.logger)
inputs = self.conv2d_fixed_padding(inputs=input_img,
filters=64,
kernel_size=7,
strides=2,
name='conv1')
inputs = tf_utils.max_pool(inputs,
name='3x3_maxpool',
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
logger=self.logger)
inputs = self.block_layer(inputs=inputs,
filters=64,
block_fn=self.bottleneck_block,
blocks=self.layers[0],
strides=1,
train_mode=self.train_mode,
name='block_layer1')
inputs = self.block_layer(inputs=inputs,
filters=128,
block_fn=self.bottleneck_block,
blocks=self.layers[1],
strides=2,
train_mode=self.train_mode,
name='block_layer2')
inputs = self.block_layer(inputs=inputs,
filters=256,
block_fn=self.bottleneck_block,
blocks=self.layers[2],
strides=2,
train_mode=self.train_mode,
name='block_layer3')
inputs = self.block_layer(inputs=inputs,
filters=512,
block_fn=self.bottleneck_block,
blocks=self.layers[3],
strides=2,
train_mode=self.train_mode,
name='block_layer4')
inputs = tf_utils.norm(inputs,
name='before_gap_batch_norm',
_type='batch',
_ops=self._ops,
is_train=self.train_mode,
logger=self.logger)
inputs = tf_utils.relu(inputs,
name='before_gap_relu',
logger=self.logger)
_, h, w, _ = inputs.get_shape().as_list()
inputs = tf_utils.avg_pool(inputs,
name='gap',
ksize=[1, h, w, 1],
strides=[1, 1, 1, 1],
logger=self.logger)
inputs = tf_utils.flatten(inputs,
name='flatten',
logger=self.logger)
logits = tf_utils.linear(inputs, self.num_classes, name='logits')
return logits