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import logging, os
logging.disable(logging.WARNING)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import tensorflow as tf
from basic_ops import *
"""This script defines non-attention same-, up-, down- modules.
Note that pre-activation is used for residual-like blocks.
Note that the residual block could be used for downsampling.
"""
def res_block(inputs, output_filters, training, dimension, name):
"""Standard residual block with pre-activation.
Args:
inputs: a Tensor with shape [batch, (d,) h, w, channels]
output_filters: an integer
training: a boolean for batch normalization and dropout
dimension: a string, dimension of inputs/outputs -- 2D, 3D
name: a string
Returns:
A Tensor of shape [batch, (_d,) _h, _w, output_filters]
"""
if dimension == '2D':
convolution = convolution_2D
kernel_size = 3
elif dimension == '3D':
convolution = convolution_3D
kernel_size = 3
else:
raise ValueError("Dimension (%s) must be 2D or 3D." % (dimension))
with tf.variable_scope(name):
if inputs.shape[-1] == output_filters:
shortcut = inputs
inputs = batch_norm(inputs, training, 'batch_norm_1')
inputs = relu(inputs, 'relu_1')
else:
inputs = batch_norm(inputs, training, 'batch_norm_1')
inputs = relu(inputs, 'relu_1')
shortcut = convolution(inputs, output_filters, 1, 1, False, 'projection_shortcut')
inputs = convolution(inputs, output_filters, kernel_size, 1, False, 'convolution_1')
inputs = batch_norm(inputs, training, 'batch_norm_2')
inputs = relu(inputs, 'relu_2')
inputs = convolution(inputs, output_filters, kernel_size, 1, False, 'convolution_2')
return tf.add(shortcut, inputs)
def down_res_block(inputs, output_filters, training, dimension, name):
"""Standard residual block with pre-activation for downsampling."""
if dimension == '2D':
convolution = convolution_2D
projection_shortcut = convolution_2D
elif dimension == '3D':
convolution = convolution_3D
projection_shortcut = convolution_3D
else:
raise ValueError("Dimension (%s) must be 2D or 3D." % (dimension))
with tf.variable_scope(name):
# The projection_shortcut should come after the first batch norm and ReLU.
inputs = batch_norm(inputs, training, 'batch_norm_1')
inputs = relu(inputs, 'relu_1')
shortcut = projection_shortcut(inputs, output_filters, 1, 2, False, 'projection_shortcut')
inputs = convolution(inputs, output_filters, 2, 2, False, 'convolution_1')
inputs = batch_norm(inputs, training, 'batch_norm_2')
inputs = relu(inputs, 'relu_2')
inputs = convolution(inputs, output_filters, 3, 1, False, 'convolution_2')
return tf.add(shortcut, inputs)
def down_convolution(inputs, output_filters, training, dimension, name):
"""Use a single stride 2 convolution for downsampling."""
if dimension == '2D':
convolution = convolution_2D
pool = tf.layers.max_pooling2d
elif dimension == '3D':
convolution = convolution_3D
pool = tf.layers.max_pooling3d
else:
raise ValueError("Dimension (%s) must be 2D or 3D." % (dimension))
with tf.variable_scope(name):
inputs = convolution(inputs, output_filters, 2, 2, True, 'convolution')
return inputs
def up_transposed_convolution(inputs, output_filters, training, dimension, name):
"""Use a single stride 2 transposed convolution for upsampling."""
if dimension == '2D':
transposed_convolution = transposed_convolution_2D
elif dimension == '3D':
transposed_convolution = transposed_convolution_3D
else:
raise ValueError("Dimension (%s) must be 2D or 3D." % (dimension))
with tf.variable_scope(name):
inputs = transposed_convolution(inputs, output_filters, 2, 2, True, 'transposed_convolution')
return inputs
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