def _conv_layers(self,x):
conv_layers = Layers(x)
# Convolutional layers
res_blocks = [1,3,4,23,3]
output_channels = [64,256,512,1024,2048]
with tf.variable_scope('scale0'):
conv_layers.conv2d(filter_size=7,output_channels=output_channels[0],stride=2,padding='SAME',b_value=None)
conv_layers.maxpool(k=3)
with tf.variable_scope('scale1'):
conv_layers.res_layer(filter_size=3, output_channels=output_channels[1], stride=2)
for block in range(res_blocks[1]-1):
conv_layers.conv_layers.res_layer(filter_size=3, output_channels=output_channels[1], stride=1)
with tf.variable_scope('scale2'):
conv_layers.res_layer(filter_size=3, output_channels=output_channels[2], stride=2)
for block in range(res_blocks[2]-1):
conv_layers.conv_layers.res_layer(filter_size=3, output_channels=output_channels[2], stride=1)
with tf.variable_scope('scale3'):
conv_layers.res_layer(filter_size=3, output_channels=output_channels[3], stride=2)
for block in range(res_blocks[3]-1):
conv_layers.conv_layers.res_layer(filter_size=3, output_channels=output_channels[3], stride=1)
with tf.variable_scope('scale4'):
conv_layers.res_layer(filter_size=3, output_channels=output_channels[4], stride=2)
for block in range(res_blocks[4]-1):
conv_layers.conv_layers.res_layer(filter_size=3, output_channels=output_channels[4], stride=1)
conv_layers.avgpool(globe=True)
# Fully Connected Layer
conv_layers.fc(output_nodes=10)
return conv_layers.get_output()
def _conv_layers(self, x):
conv_layers = Layers(x)
# Convolutional layers
res_blocks = [1, 3, 4, 23, 3]
output_channels = [64, 256, 512, 1024, 2048]
with tf.variable_scope('scale0'):
conv_layers.conv2d(filter_size=7,
output_channels=output_channels[0],
stride=2,
padding='SAME',
b_value=None)
conv_layers.maxpool(k=3)
with tf.variable_scope('scale1'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[1],
stride=2)
for block in range(res_blocks[1] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[1],
stride=1)
with tf.variable_scope('scale2'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[2],
stride=2)
for block in range(res_blocks[2] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[2],
stride=1)
with tf.variable_scope('scale3'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[3],
stride=2)
for block in range(res_blocks[3] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[3],
stride=1)
with tf.variable_scope('scale4'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[4],
stride=2)
for block in range(res_blocks[4] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[4],
stride=1)
conv_layers.avgpool(globe=True)
# Fully Connected Layer
conv_layers.fc(output_nodes=10)
return conv_layers.get_output()
def _network(self, x):
conv_layers = Layers(x)
# Convolutional layers
with tf.variable_scope('resnet101'):
res_blocks = [1, 3, 4, 23, 3]
output_channels = [64, 256, 512, 1024, 2048]
with tf.variable_scope('scale0'):
conv_layers.conv2d(filter_size=7,
output_channels=output_channels[0],
stride=2,
padding='SAME',
b_value=None)
conv_layers.maxpool(k=3)
with tf.variable_scope('scale1'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[1],
stride=2)
for block in range(res_blocks[1] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[1],
stride=1)
with tf.variable_scope('scale2'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[2],
stride=2)
for block in range(res_blocks[2] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[2],
stride=1)
with tf.variable_scope('scale3'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[3],
stride=2)
for block in range(res_blocks[3] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[3],
stride=1)
with tf.variable_scope('scale4'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[4],
stride=2)
for block in range(res_blocks[4] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[4],
stride=1)
return conv_layers
def _network(self, x):
conv_layers = Layers(x)
# Convolutional layers
scope = 'resnet' + str(self.depth)
with tf.variable_scope(scope):
res_blocks = self.architectures[self.depth]
output_channels = [64, 256, 512, 1024, 2048]
with tf.variable_scope('scale0'):
conv_layers.conv2d(filter_size=7,
output_channels=output_channels[0],
stride=2,
padding='SAME',
b_value=None) # Downsample
conv_layers.maxpool(k=3, s=2) # Downsample
with tf.variable_scope('scale1'):
for block in range(res_blocks[1]):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[1],
stride=1)
with tf.variable_scope('scale2'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[2],
stride=2) # Downsample
for block in range(res_blocks[2] - 1):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[2],
stride=1)
with tf.variable_scope('scale3'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[3],
stride=2) # Downsample
for block in range(res_blocks[3] - 1):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[3],
stride=1)
with tf.variable_scope('scale4'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[4],
stride=2) # Downsample
for block in range(res_blocks[4] - 1):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[4],
stride=1)
return conv_layers
