def Dense3(inputs, growth_rate, training):
# [b, w, h, d, c] = inputs.get_shape().as_list()
bn_relu1 = BN_ReLU(inputs, training)
conv1 = Conv3D(bn_relu1, growth_rate, 3, 1)
concat1 = tf.concat((inputs, conv1), axis=4)
bn_relu2 = BN_ReLU(concat1, training)
conv2 = Conv3D(bn_relu2, growth_rate, kernel_size=3, strides=1)
concat2 = tf.concat((concat1, conv2), axis=4)
bn_relu3 = BN_ReLU(concat2, training)
conv3 = Conv3D(bn_relu3, growth_rate, kernel_size=3, strides=1)
concat3 = tf.concat((concat2, conv3), axis=4)
bn_relu4 = BN_ReLU(concat3, training)
conv4 = Conv3D(bn_relu4, c[3] + 3 * growth_rate, kernel_size=1, strides=1)
return conv4
def _attention_block(self, inputs, filters, training, projection_shortcut, strides): """Attentional building block for residual networks with BN before convolutions. Args: inputs: A tensor of size [batch, depth_in, height_in, width_in, channels]. filters: The number of filters for the convolutions. training: A Boolean for whether the model is in training or inference mode. Needed for batch normalization. projection_shortcut: The function to use for projection shortcuts (typically a 1x1 convolution when downsampling the input). strides: The block's stride. If greater than 1, this block will ultimately downsample the input. Returns: The output tensor of the block. """ shortcut = inputs inputs = BN_ReLU(inputs, training) # The projection shortcut should come after the first batch norm and ReLU # since it performs a 1x1 convolution. if projection_shortcut is not None: shortcut = projection_shortcut(inputs) if strides != 1: layer_type = 'UP' else: layer_type = 'SAME' inputs = multihead_attention_3d( inputs, filters, filters, filters, 1, training, layer_type) return inputs + shortcut
def _build_network(self, inputs, training):
"""Build the network.
"""
inputs = Conv3D(
inputs=inputs,
filters=self.num_filters,
kernel_size=3,
strides=1)
inputs = tf.identity(inputs, 'initial_conv')
skip_inputs = []
for i, num_blocks in enumerate(self.block_sizes):
# print(i, num_blocks)
num_filters = self.num_filters * (2**i)
inputs = self._encoding_block_layer(
inputs=inputs, filters=num_filters,
block_fn=self._residual_block, blocks=num_blocks,
strides=self.block_strides[i], training=training,
name='encode_block_layer{}'.format(i+1))
skip_inputs.append(inputs)
# print(inputs.shape)
# print(len(skip_inputs))
inputs = BN_ReLU(inputs, training)
num_filters = self.num_filters * (2**(len(self.block_sizes)-1))
# print(num_filters)
inputs = multihead_attention_3d(
inputs, num_filters, num_filters, num_filters, 2, training, layer_type='SAME')
inputs += skip_inputs[-1]
for i, num_blocks in reversed(list(enumerate(self.block_sizes[1:]))):
# print(i, num_blocks)
num_filters = self.num_filters * (2**i)
if i == len(self.block_sizes) - 2:
inputs = self._att_decoding_block_layer(
inputs=inputs, skip_inputs=skip_inputs[i],
filters=num_filters, block_fn=self._residual_block,
blocks=1, strides=self.block_strides[i+1],
training=training,
name='decode_block_layer{}'.format(len(self.block_sizes)-i-1))
else:
inputs = self._decoding_block_layer(
inputs=inputs, skip_inputs=skip_inputs[i],
filters=num_filters, block_fn=self._residual_block,
blocks=1, strides=self.block_strides[i+1],
training=training,
name='decode_block_layer{}'.format(len(self.block_sizes)-i-1))
# print(inputs.shape)
inputs = self._output_block_layer(inputs=inputs, training=training)
# print(inputs.shape)
return inputs
def _output_block_layer(self, inputs, training):
inputs = BN_ReLU(inputs, training)
inputs = tf.layers.dropout(inputs, rate=0.5, training=training)
inputs = Conv3D(inputs=inputs,
filters=self.num_classes,
kernel_size=1,
strides=1,
use_bias=True)
return tf.identity(inputs, 'output')
def res_inc_deconv(inputs, training):
# [b, w, h, d, c]= inputs.get_shape().as_list()
deconv1_1_1 = Deconv3D(inputs,
32,
kernel_size=3,
strides=1,
use_bias=False) #44
deconv1_1 = BN_ReLU(deconv1_1_1, training)
deconv2_1_1 = Deconv3D(inputs,
32,
kernel_size=3,
strides=1,
use_bias=False) #44
deconv2_1 = BN_ReLU(deconv2_1_1, training)
deconv2_3 = Deconv3D(deconv2_1,
64,
kernel_size=3,
strides=1,
use_bias=False) #88
deconv2_2 = BN_ReLU(deconv2_3, training)
deconv3_1_1 = Deconv3D(inputs,
32,
kernel_size=3,
strides=1,
use_bias=False) #44
deconv3_1 = BN_ReLU(deconv3_1_1, training)
deconv3_2_1 = Dilated_Conv3D(deconv3_1,
32,
kernel_size=3,
dilation_rate=2,
use_bias=False) #44
deconv3_2 = BN_ReLU(deconv3_2_1, training)
concat = tf.concat((deconv1_1, deconv2_2, deconv3_2), axis=4)
deconv1 = Deconv3D(concat, 128, kernel_size=3, strides=1,
use_bias=False) #176
deconv = BN_ReLU(deconv1, training)
fuse = tf.add(inputs, deconv)
return fuse
def unpool(inputs, training):
# [b, w, h, d, c] = inputs.get_shape().as_list()
conv31 = Conv3D(inputs, 176, kernel_size=3, strides=1)
deconv31 = BN_ReLU(conv31, training)
deconv1_1 = Deconv3D(deconv31,
176,
kernel_size=3,
strides=1,
use_bias=False)
deconv1 = BN_ReLU(deconv1_1, training)
deconv1_2 = Deconv3D(deconv1, 88, kernel_size=3, strides=2, use_bias=False)
deconv2 = BN_ReLU(deconv1_2, training)
deconv2_1 = Deconv3D(inputs, 176, kernel_size=3, strides=1, use_bias=False)
deconv3 = BN_ReLU(deconv2_1, training)
deconv2_2 = Dilated_Conv3D(deconv3,
176,
kernel_size=3,
dilation_rate=2,
use_bias=False)
deconv4 = BN_ReLU(deconv2_2, training)
deconv2_3 = Deconv3D(deconv4, 88, kernel_size=3, strides=2, use_bias=False)
deconv5 = BN_ReLU(deconv2_3, training)
concat = tf.concat((deconv2, deconv5), axis=4)
return concat