human_pose_nn.py 文件源码

def get_network(self, input_tensor, is_training):
        # Load pre-trained inception-resnet model
        with slim.arg_scope(inception_resnet_v2_arg_scope(batch_norm_decay = 0.999, weight_decay = 0.0001)):
            net, end_points = inception_resnet_v2(input_tensor, is_training = is_training)

        # Adding some modification to original InceptionResnetV2 - changing scoring of AUXILIARY TOWER
        weight_decay = 0.0005
        with tf.variable_scope('NewInceptionResnetV2'):
            with tf.variable_scope('AuxiliaryScoring'):
                with slim.arg_scope([layers.convolution2d, layers.convolution2d_transpose],
                                    weights_regularizer = slim.l2_regularizer(weight_decay),
                                    biases_regularizer = slim.l2_regularizer(weight_decay),
                                    activation_fn = None):
                    tf.summary.histogram('Last_layer/activations', net, [KEY_SUMMARIES])

                    # Scoring
                    net = slim.dropout(net, 0.7, is_training = is_training, scope = 'Dropout')
                    net = layers.convolution2d(net, num_outputs = self.FEATURES, kernel_size = 1, stride = 1,
                                               scope = 'Scoring_layer')
                    feature = net
                    tf.summary.histogram('Scoring_layer/activations', net, [KEY_SUMMARIES])

                    # Upsampling
                    net = layers.convolution2d_transpose(net, num_outputs = 16, kernel_size = 17, stride = 17,
                                                         padding = 'VALID', scope = 'Upsampling_layer')

                    tf.summary.histogram('Upsampling_layer/activations', net, [KEY_SUMMARIES])

            # Smoothing layer - separable gaussian filters
            net = super()._get_gauss_smoothing_net(net, size = self.SMOOTH_SIZE, std = 1.0, kernel_sum = 0.2)

            return net, feature