hourglass_tiny.py 文件源码

def _conv_bn_relu(self, inputs, filters, kernel_size = 1, strides = 1, pad = 'VALID', name = 'conv_bn_relu'):
        """ Spatial Convolution (CONV2D) + BatchNormalization + ReLU Activation
        Args:
            inputs          : Input Tensor (Data Type : NHWC)
            filters     : Number of filters (channels)
            kernel_size : Size of kernel
            strides     : Stride
            pad             : Padding Type (VALID/SAME) # DO NOT USE 'SAME' NETWORK BUILT FOR VALID
            name            : Name of the block
        Returns:
            norm            : Output Tensor
        """
        with tf.name_scope(name):
            kernel = tf.Variable(tf.contrib.layers.xavier_initializer(uniform=False)([kernel_size,kernel_size, inputs.get_shape().as_list()[3], filters]), name= 'weights')
            conv = tf.nn.conv2d(inputs, kernel, [1,strides,strides,1], padding='VALID', data_format='NHWC')
            norm = tf.contrib.layers.batch_norm(conv, 0.9, epsilon=1e-5, activation_fn = tf.nn.relu, is_training = self.training)
            if self.w_summary:
                with tf.device('/cpu:0'):
                    tf.summary.histogram('weights_summary', kernel, collections = ['weight'])
            return norm