我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用tensorflow.contrib.slim.separable_conv2d()。
def create_architecture(self, mode, tag=None): training = mode == 'TRAIN' testing = mode == 'TEST' assert tag != None # handle most of the regularizers here weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY) biases_regularizer = weights_regularizer # list as many types of layers as possible, even if they are not used now with arg_scope([slim.conv2d, slim.conv2d_in_plane, slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], weights_regularizer=weights_regularizer, biases_regularizer=biases_regularizer, biases_initializer=tf.constant_initializer(0.0)): self.build_network() elbo = self.add_losses() self._summary_op = tf.summary.merge_all() return elbo
def separable_conv2d_same(inputs, kernel_size, stride, rate=1, scope=None): """Strided 2-D separable convolution with 'SAME' padding. Args: inputs: A 4-D tensor of size [batch, height_in, width_in, channels]. kernel_size: An int with the kernel_size of the filters. stride: An integer, the output stride. rate: An integer, rate for atrous convolution. scope: Scope. Returns: output: A 4-D tensor of size [batch, height_out, width_out, channels] with the convolution output. """ # By passing filters=None # separable_conv2d produces only a depth-wise convolution layer if stride == 1: return slim.separable_conv2d(inputs, None, kernel_size, depth_multiplier=1, stride=1, rate=rate, padding='SAME', scope=scope) else: kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1) pad_total = kernel_size_effective - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]]) return slim.separable_conv2d(inputs, None, kernel_size, depth_multiplier=1, stride=stride, rate=rate, padding='VALID', scope=scope) # The following is adapted from: # https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.py # Conv and DepthSepConv named tuple define layers of the MobileNet architecture # Conv defines 3x3 convolution layers # DepthSepConv defines 3x3 depthwise convolution followed by 1x1 convolution. # stride is the stride of the convolution # depth is the number of channels or filters in a layer
def mobilenet_v1_arg_scope(is_training=True, stddev=0.09): batch_norm_params = { 'is_training': False, 'center': True, 'scale': True, 'decay': 0.9997, 'epsilon': 0.001, 'trainable': False, } # Set weight_decay for weights in Conv and DepthSepConv layers. weights_init = tf.truncated_normal_initializer(stddev=stddev) regularizer = tf.contrib.layers.l2_regularizer(cfg.MOBILENET.WEIGHT_DECAY) if cfg.MOBILENET.REGU_DEPTH: depthwise_regularizer = regularizer else: depthwise_regularizer = None with slim.arg_scope([slim.conv2d, slim.separable_conv2d], trainable=is_training, weights_initializer=weights_init, activation_fn=tf.nn.relu6, normalizer_fn=slim.batch_norm, padding='SAME'): with slim.arg_scope([slim.batch_norm], **batch_norm_params): with slim.arg_scope([slim.conv2d], weights_regularizer=regularizer): with slim.arg_scope([slim.separable_conv2d], weights_regularizer=depthwise_regularizer) as sc: return sc
def mobilenet_v1_arg_scope(is_training=True, batch_norm_decay=0.9997, batch_norm_epsilon=0.001, batch_norm_scale=True, weight_decay=0.00004, stddev=0.09, regularize_depthwise=False): """Defines the default MobilenetV1 arg scope. Args: is_training: Whether or not we're training the model. batch_norm_decay: The moving average decay when estimating layer activation statistics in batch normalization. batch_norm_epsilon: Small constant to prevent division by zero when normalizing activations by their variance in batch normalization. batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the activations in the batch normalization layer. weight_decay: The weight decay to use for regularizing the model. stddev: The standard deviation of the trunctated normal weight initializer. regularize_depthwise: Whether or not apply regularization on depthwise. Returns: An `arg_scope` to use for the mobilenet v1 model. """ batch_norm_params = { 'is_training': is_training, 'center': True, 'scale': batch_norm_scale, 'decay': batch_norm_decay, 'epsilon': batch_norm_epsilon, } # Set weight_decay for weights in Conv and DepthSepConv layers. weights_init = tf.truncated_normal_initializer(stddev=stddev) regularizer = tf.contrib.layers.l2_regularizer(weight_decay) if regularize_depthwise: depthwise_regularizer = regularizer else: depthwise_regularizer = None with slim.arg_scope([slim.conv2d, slim.separable_conv2d], weights_initializer=weights_init, activation_fn=tf.nn.relu6, normalizer_fn=slim.batch_norm): with slim.arg_scope([slim.batch_norm], **batch_norm_params): with slim.arg_scope([slim.conv2d], weights_regularizer=regularizer): with slim.arg_scope([slim.separable_conv2d], weights_regularizer=depthwise_regularizer) as sc: return sc
