Source code for dltk.networks.regression_classification.resnet

from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import

import tensorflow as tf

from dltk.core.residual_unit import vanilla_residual_unit_3d


[docs]def resnet_3d(inputs, num_classes, num_res_units=1, filters=(16, 32, 64, 128), strides=((1, 1, 1), (2, 2, 2), (2, 2, 2), (2, 2, 2)), mode=tf.estimator.ModeKeys.EVAL, use_bias=False, activation=tf.nn.relu6, kernel_initializer=tf.initializers.variance_scaling(distribution='uniform'), bias_initializer=tf.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None): """ Regression/classification network based on a flexible resnet architecture [1] using residual units proposed in [2]. The downsampling of features is done via strided convolutions. On each resolution scale s are num_convolutions with filter size = filters[s]. strides[s] determine the downsampling factor at each resolution scale. [1] K. He et al. Deep residual learning for image recognition. CVPR 2016. [2] K. He et al. Identity Mappings in Deep Residual Networks. ECCV 2016. Args: inputs (tf.Tensor): Input feature tensor to the network (rank 5 required). num_classes (int): Number of output channels or classes. num_res_units (int, optional): Number of residual units per resolution scale. filters (tuple, optional): Number of filters for all residual units at each resolution scale. strides (tuple, optional): Stride of the first unit on a resolution scale. mode (TYPE, optional): One of the tf.estimator.ModeKeys strings: TRAIN, EVAL or PREDICT use_bias (bool, optional): Boolean, whether the layer uses a bias. activation (optional): A function to use as activation function. kernel_initializer (TYPE, optional): An initializer for the convolution kernel. bias_initializer (TYPE, optional): An initializer for the bias vector. If None, no bias will be applied. kernel_regularizer (None, optional): Optional regularizer for the convolution kernel. bias_regularizer (None, optional): Optional regularizer for the bias vector. Returns: dict: dictionary of output tensors """ outputs = {} assert len(strides) == len(filters) assert len(inputs.get_shape().as_list()) == 5, \ 'inputs are required to have a rank of 5.' relu_op = tf.nn.relu6 conv_params = {'padding': 'same', 'use_bias': use_bias, 'kernel_initializer': kernel_initializer, 'bias_initializer': bias_initializer, 'kernel_regularizer': kernel_regularizer, 'bias_regularizer': bias_regularizer} x = inputs # Inital convolution with filters[0] k = [s * 2 if s > 1 else 3 for s in strides[0]] x = tf.layers.conv3d(x, filters[0], k, strides[0], **conv_params) tf.logging.info('Init conv tensor shape {}'.format(x.get_shape())) # Residual feature encoding blocks with num_res_units at different # resolution scales res_scales res_scales = [x] saved_strides = [] for res_scale in range(1, len(filters)): # Features are downsampled via strided convolutions. These are defined # in `strides` and subsequently saved with tf.variable_scope('unit_{}_0'.format(res_scale)): x = vanilla_residual_unit_3d( inputs=x, out_filters=filters[res_scale], strides=strides[res_scale], activation=activation, mode=mode) saved_strides.append(strides[res_scale]) for i in range(1, num_res_units): with tf.variable_scope('unit_{}_{}'.format(res_scale, i)): x = vanilla_residual_unit_3d( inputs=x, out_filters=filters[res_scale], strides=(1, 1, 1), activation=activation, mode=mode) res_scales.append(x) tf.logging.info('Encoder at res_scale {} tensor shape: {}'.format( res_scale, x.get_shape())) # Global pool and last unit with tf.variable_scope('pool'): x = tf.layers.batch_normalization( x, training=mode == tf.estimator.ModeKeys.TRAIN) x = relu_op(x) axis = tuple(range(len(x.get_shape().as_list())))[1:-1] x = tf.reduce_mean(x, axis=axis, name='global_avg_pool') tf.logging.info('Global pool shape {}'.format(x.get_shape())) with tf.variable_scope('last'): x = tf.layers.dense(inputs=x, units=num_classes, activation=None, use_bias=conv_params['use_bias'], kernel_initializer=conv_params['kernel_initializer'], bias_initializer=conv_params['bias_initializer'], kernel_regularizer=conv_params['kernel_regularizer'], bias_regularizer=conv_params['bias_regularizer'], name='hidden_units') tf.logging.info('Output tensor shape {}'.format(x.get_shape())) # Define the outputs outputs['logits'] = x with tf.variable_scope('pred'): y_prob = tf.nn.softmax(x) outputs['y_prob'] = y_prob y_ = tf.argmax(x, axis=-1) \ if num_classes > 1 \ else tf.cast(tf.greater_equal(x[..., 0], 0.5), tf.int32) outputs['y_'] = y_ return outputs