１、outputs_collections用法，方便獲取各層的end_points

    with tf.variable_scope(scope, 'resnet_v1', [inputs], reuse=reuse) as sc:
        end_points_collection = sc.name + '_end_points'
        with slim.arg_scope([slim.conv2d, bottleneck,
                             resnet_utils.stack_blocks_dense],
                            outputs_collections=end_points_collection):
            with
 
 slim.arg_scope([slim.batch_norm], is_training=is_training):
                net = inputs
                if include_root_block:
                    if output_stride is not None:
                        if output_stride % 4 != 0:
                            raise ValueError('The output_stride needs to be a multiple of 4.'
 
)
                        output_stride /= 4
                    net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
                    net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')

                    net = slim.utils.collect_named_outputs(end_points_collection, 'pool2'
 
, net)

                net = resnet_utils.stack_blocks_dense(net, blocks, output_stride)

                end_points = slim.utils.convert_collection_to_dict(end_points_collection)

                # end_points['pool2'] = end_points['resnet_v1_50/pool1/MaxPool:0']
                try:
                    end_points['pool3'] = end_points['resnet_v1_50/block1']
                    end_points['pool4'] = end_points['resnet_v1_50/block2']
                except:
                    end_points['pool3'] = end_points['Detection/resnet_v1_50/block1']
                    end_points['pool4'] = end_points['Detection/resnet_v1_50/block2']
                end_points['pool5'] = net