def conv_block(self, inputs, out_filters, ksize):
"""
Pre-activated conv block (BN-ReLU-Conv)
Parameters
----------
inputs: Input tensor
out_filters: Number of output filters
ksize: Kernel size. One integer of tuple of two integers
"""
use_bias = not self.use_bn
outputs = inputs
if self.use_bn:
outputs = batch_normalization(outputs, training=self.training)
outputs = relu(outputs)
outputs = conv(outputs, out_filters, ksize=ksize, use_bias=use_bias)
return outputs
def forward(self, inputs):
"""
Forward process
Parameters
----------
inputs: Input tensor
"""
# encoder process
outputs = inputs
encoders = []
for i, filters in enumerate(self.block_filters):
first_blocks = i == 0
outputs = self.build_res_blocks(outputs, filters,
self.block_res_nums[i],
first_blocks)
if i != len(self.block_filters) - 1:
encoders.append(outputs)
outputs = maxpool(outputs, pool_size=2)
# decoder process
encoder_num = len(encoders)
for k, encoder in enumerate(encoders[::-1]):
i = encoder_num - k - 1
first_blocks = i == 0
filters = int(encoder.get_shape()[-1])
outputs = conv_bn_relu(outputs,
filters,
3,
1,
1,
use_bn=self.use_bn,
training=self.training)
outputs = upsample(outputs, 2, self.interpolation_type)
outputs = concat([encoder, outputs])
outputs = self.build_res_blocks(outputs, filters,
self.block_res_nums[i],
first_blocks)
outputs = conv(outputs, self.config.get('output_channel'))
outputs = softmax(outputs)
return outputs