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import logging, os
logging.disable(logging.WARNING)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import tensorflow as tf
from network_configure import conf_basic_ops
"""This script defines basic operaters.
"""
def convolution_2D(inputs, filters, kernel_size, strides, use_bias, name=None):
"""Performs 2D convolution without activation function.
If followed by batch normalization, set use_bias=False.
"""
return tf.layers.conv2d(
inputs=inputs,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
use_bias=use_bias,
kernel_initializer=conf_basic_ops['kernel_initializer'],
name=name,
)
def convolution_3D(inputs, filters, kernel_size, strides, use_bias, name=None):
"""Performs 3D convolution without activation function.
If followed by batch normalization, set use_bias=False.
"""
return tf.layers.conv3d(
inputs=inputs,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
use_bias=use_bias,
kernel_initializer=conf_basic_ops['kernel_initializer'],
name=name,
)
def transposed_convolution_2D(inputs, filters, kernel_size, strides, use_bias, name=None):
"""Performs 2D transposed convolution without activation function.
If followed by batch normalization, set use_bias=False.
"""
return tf.layers.conv2d_transpose(
inputs=inputs,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
use_bias=use_bias,
kernel_initializer=conf_basic_ops['kernel_initializer'],
name=name,
)
def transposed_convolution_3D(inputs, filters, kernel_size, strides, use_bias, name=None):
"""Performs 3D transposed convolution without activation function.
If followed by batch normalization, set use_bias=False.
"""
return tf.layers.conv3d_transpose(
inputs=inputs,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
use_bias=use_bias,
kernel_initializer=conf_basic_ops['kernel_initializer'],
name=name,
)
def batch_norm(inputs, training, name=None):
"""Performs a batch normalization.
We set fused=True for a significant performance boost.
See https://www.tensorflow.org/performance/performance_guide#common_fused_ops
"""
return tf.layers.batch_normalization(
inputs=inputs,
momentum=conf_basic_ops['momentum'],
epsilon=conf_basic_ops['epsilon'],
center=True,
scale=True,
training=training,
fused=True,
name=name,
)
def relu(inputs, name=None):
return tf.nn.relu(inputs, name=name) if conf_basic_ops['relu_type'] == 'relu' \
else tf.nn.relu6(inputs, name=name)
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