cpm_hand_slim.py 文件源码

def build_model(self, input_image, center_map, batch_size):
        self.batch_size = batch_size
        self.input_image = input_image
        self.center_map = center_map
        with tf.variable_scope('pooled_center_map'):
            # center map is a gaussion template which gather the respose
            self.center_map = slim.avg_pool2d(self.center_map,
                                              [9, 9], stride=8,
                                              padding='SAME',
                                              scope='center_map')

        with slim.arg_scope([slim.conv2d],
                            padding='SAME',
                            activation_fn=tf.nn.relu,
                            weights_initializer=tf.contrib.layers.xavier_initializer()):
            with tf.variable_scope('sub_stages'):
                net = slim.conv2d(input_image, 64, [3, 3], scope='sub_conv1')
                net = slim.conv2d(net, 64, [3, 3], scope='sub_conv2')
                net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='sub_pool1')
                net = slim.conv2d(net, 128, [3, 3], scope='sub_conv3')
                net = slim.conv2d(net, 128, [3, 3], scope='sub_conv4')
                net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='sub_pool2')
                net = slim.conv2d(net, 256, [3, 3], scope='sub_conv5')
                net = slim.conv2d(net, 256, [3, 3], scope='sub_conv6')
                net = slim.conv2d(net, 256, [3, 3], scope='sub_conv7')
                net = slim.conv2d(net, 256, [3, 3], scope='sub_conv8')
                net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='sub_pool3')
                net = slim.conv2d(net, 512, [3, 3], scope='sub_conv9')
                net = slim.conv2d(net, 512, [3, 3], scope='sub_conv10')
                net = slim.conv2d(net, 512, [3, 3], scope='sub_conv11')
                net = slim.conv2d(net, 512, [3, 3], scope='sub_conv12')
                net = slim.conv2d(net, 512, [3, 3], scope='sub_conv13')
                net = slim.conv2d(net, 512, [3, 3], scope='sub_conv14')

                self.sub_stage_img_feature = slim.conv2d(net, 128, [3, 3],
                                                         scope='sub_stage_img_feature')

            with tf.variable_scope('stage_1'):
                conv1 = slim.conv2d(self.sub_stage_img_feature, 512, [1, 1],
                                    scope='conv1')
                self.stage_heatmap.append(slim.conv2d(conv1, self.joints, [1, 1],
                                                      scope='stage_heatmap'))

            for stage in range(2, self.stages + 1):
                self._middle_conv(stage)