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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) | |