resnet.py 文件源码

def create_trunk(self, images):
        red, green, blue = tf.split(images*255, 3, axis=3)
        images = tf.concat([blue, green, red], 3) - MEAN_COLOR

        with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training=self.training,
                                                       weight_decay=self.weight_decay,
                                                       batch_norm_decay=args.bn_decay)):
            blocks = [
                resnet_utils.Block(
                    'block1', bottleneck, [(256, 64, 1)] * 3),
                resnet_utils.Block(
                    'block2', bottleneck, [(512, 128, 2)] + [(512, 128, 1)] * 3),
                resnet_utils.Block(
                    'block3', bottleneck, [(1024, 256, 2)] + [(1024, 256, 1)] * self.num_block3),
                resnet_utils.Block(
                    'block4', bottleneck, [(2048, 512, 2)] + [(2048, 512, 1)] * 2)
            ]

            net, endpoints = resnet_v1.resnet_v1(images, blocks,
                                                 global_pool=False,
                                                 reuse=self.reuse,
                                                 scope=self.scope)
            self.outputs = endpoints
        self.add_extra_layers(net)