def _residual_block_pre_activation(x,
n_channel,
filter_=(3, 3),
name='residual_block_pre_activation'):
with tf.variable_scope(name):
stack = Stacker(x)
stack.add_layer(batchActivation)
stack.layers_conv2d(n_channel, filter_, (1, 1), 'SAME')
stack.add_layer(batchActivation)
stack.layers_conv2d(n_channel, filter_, (1, 1), 'SAME')
stack.residual_add(x)
return stack.last_layer
def build(self):
self.DynamicDropoutRate = DynamicDropoutRate(self.dropout_rate)
self.drop_out_tensor = self.DynamicDropoutRate.tensor
with tf.variable_scope(self.name, reuse=self.reuse):
self.encoder = InceptionV2UNetEncoderModule(self.x,
None,
resize_shape=(201,
201),
capacity=self.capacity)
self.encoder.build()
encode = self.encoder.last_layer
skip_tensors = self.encoder.skip_tensors[::-1]
bottom_layer = self.bottom_layer(encode, self.capacity * 128,
self.depth, self.drop_out_tensor)
pprint(skip_tensors)
stacker = Stacker(bottom_layer)
stacker.layers_conv2d_transpose(self.n_channel * 16 * 2, (3, 3),
(2, 2), 'SAME')
stacker.bn()
stacker.relu()
stacker.concat(
[stacker.last_layer[:, :13, :13, :], skip_tensors[0]], 3)
stacker.layers_dropout(self.drop_out_tensor)
stacker.layers_conv2d(self.n_channel * 16 * 2, (3, 3), (1, 1),
'SAME')
stacker.bn()
stacker.relu()
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 16 * 2)
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 16 * 2)
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 16 * 2)
stacker.bn()
stacker.relu()
# 12 to 25
stacker.layers_conv2d_transpose(self.n_channel * 8 * 2, (3, 3),
(2, 2), 'SAME')
stacker.bn()
stacker.relu()
stacker.concat([stacker.last_layer, skip_tensors[1]], 3)
stacker.layers_dropout(self.drop_out_tensor)
stacker.layers_conv2d(self.n_channel * 8 * 2, (3, 3), (1, 1),
'SAME')
stacker.bn()
stacker.relu()
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 8 * 2)
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 8 * 2)
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 8 * 2)
stacker.bn()
stacker.relu()
# 25 to 50
stacker.layers_conv2d_transpose(self.n_channel * 4 * 2, (3, 3),
(2, 2), 'SAME')
stacker.bn()
stacker.relu()
stacker.concat(
[stacker.last_layer[:, :51, :51, :], skip_tensors[2]], 3)
stacker.layers_dropout(self.drop_out_tensor)
stacker.layers_conv2d(self.n_channel * 4 * 2, (3, 3), (1, 1),
'SAME')
stacker.bn()
stacker.relu()
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 4 * 2)
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 4 * 2)
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 4 * 2)
stacker.bn()
stacker.relu()
# 50 to 101
stacker.layers_conv2d_transpose(self.n_channel * 2 * 2, (3, 3),
(2, 2), 'SAME')
stacker.bn()
stacker.relu()
stacker.concat(
[stacker.last_layer[:, :101, :101, :], skip_tensors[3]], 3)
stacker.layers_dropout(self.drop_out_tensor)
stacker.layers_conv2d(self.n_channel * 2 * 2, (3, 3), (1, 1),
'SAME')
stacker.bn()
stacker.relu()
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 2 * 2)
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 2 * 2)
stacker.add_layer(_residual_block_pre_activation,
self.n_channel * 2 * 2)
stacker.bn()
stacker.relu()
decode = stacker.last_layer
stacker = Stacker(decode, name='to_match')
stacker.conv2d(self.n_classes, CONV_FILTER_3311)
self.logit = stacker.last_layer
self.proba = stacker.sigmoid()
