diff --git a/slim/prune/README.md b/slim/prune/README.md
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+>运行该示例前请安装Paddle1.6或更高版本
+
+# 检测模型卷积通道剪裁示例
+
+## 概述
+
+该示例使用PaddleSlim提供的[卷积通道剪裁压缩策略](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)对检测库中的模型进行压缩。
+在阅读该示例前，建议您先了解以下内容：
+
+- 检测库的常规训练方法
+- [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md)
+
+
+## 配置文件说明
+
+关于配置文件如何编写您可以参考：
+
+- [PaddleSlim配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#122-%E9%85%8D%E7%BD%AE%E6%96%87%E4%BB%B6%E7%9A%84%E4%BD%BF%E7%94%A8)
+- [裁剪策略配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#22-%E6%A8%A1%E5%9E%8B%E9%80%9A%E9%81%93%E5%89%AA%E8%A3%81)
+
+其中，配置文件中的`pruned_params`需要根据当前模型的网络结构特点设置，它用来指定要裁剪的parameters.
+
+这里以MobileNetV1-YoloV3模型为例，其卷积可以三种：主干网络中的普通卷积，主干网络中的`depthwise convolution`和`yolo block`里的普通卷积。PaddleSlim暂时无法对`depthwise convolution`直接进行剪裁， 因为`depthwise convolution`的`channel`的变化会同时影响到前后的卷积层。我们这里只对主干网络中的普通卷积和`yolo block`里的普通卷积做裁剪。
+
+通过以下方式可视化模型结构：
+
+```
+from paddle.fluid.framework import IrGraph
+from paddle.fluid import core
+
+graph = IrGraph(core.Graph(train_prog.desc), for_test=True)
+marked_nodes = set()
+for op in graph.all_op_nodes():
+ print(op.name())
+ if op.name().find('conv') > -1:
+ marked_nodes.add(op)
+graph.draw('.', 'forward', marked_nodes)
+```
+
+该示例中MobileNetV1-YoloV3模型结构的可视化结果：MobileNetV1-YoloV3.pdf
+
+同时通过以下命令观察目标卷积层的参数（parameters）的名称和shape:
+
+```
+for param in fluid.default_main_program().global_block().all_parameters():
+ if 'weights' in param.name:
+ print param.name, param.shape
+```
+
+
+从可视化结果，我们可以排除后续会做concat的卷积层，最终得到如下要裁剪的参数名称：
+
+```
+conv2_1_sep_weights
+conv2_2_sep_weights
+conv3_1_sep_weights
+conv4_1_sep_weights
+conv5_1_sep_weights
+conv5_2_sep_weights
+conv5_3_sep_weights
+conv5_4_sep_weights
+conv5_5_sep_weights
+conv5_6_sep_weights
+yolo_block.0.0.0.conv.weights
+yolo_block.0.0.1.conv.weights
+yolo_block.0.1.0.conv.weights
+yolo_block.0.1.1.conv.weights
+yolo_block.1.0.0.conv.weights
+yolo_block.1.0.1.conv.weights
+yolo_block.1.1.0.conv.weights
+yolo_block.1.1.1.conv.weights
+yolo_block.1.2.conv.weights
+yolo_block.2.0.0.conv.weights
+yolo_block.2.0.1.conv.weights
+yolo_block.2.1.1.conv.weights
+yolo_block.2.2.conv.weights
+yolo_block.2.tip.conv.weights
+```
+
+```
+(conv2_1_sep_weights)|(conv2_2_sep_weights)|(conv3_1_sep_weights)|(conv4_1_sep_weights)|(conv5_1_sep_weights)|(conv5_2_sep_weights)|(conv5_3_sep_weights)|(conv5_4_sep_weights)|(conv5_5_sep_weights)|(conv5_6_sep_weights)|(yolo_block.0.0.0.conv.weights)|(yolo_block.0.0.1.conv.weights)|(yolo_block.0.1.0.conv.weights)|(yolo_block.0.1.1.conv.weights)|(yolo_block.1.0.0.conv.weights)|(yolo_block.1.0.1.conv.weights)|(yolo_block.1.1.0.conv.weights)|(yolo_block.1.1.1.conv.weights)|(yolo_block.1.2.conv.weights)|(yolo_block.2.0.0.conv.weights)|(yolo_block.2.0.1.conv.weights)|(yolo_block.2.1.1.conv.weights)|(yolo_block.2.2.conv.weights)|(yolo_block.2.tip.conv.weights)
+```
+
+综上，我们将MobileNetV2配置文件中的`pruned_params`设置为以下正则表达式：
+
+```
+(conv2_1_sep_weights)|(conv2_2_sep_weights)|(conv3_1_sep_weights)|(conv4_1_sep_weights)|(conv5_1_sep_weights)|(conv5_2_sep_weights)|(conv5_3_sep_weights)|(conv5_4_sep_weights)|(conv5_5_sep_weights)|(conv5_6_sep_weights)|(yolo_block.0.0.0.conv.weights)|(yolo_block.0.0.1.conv.weights)|(yolo_block.0.1.0.conv.weights)|(yolo_block.0.1.1.conv.weights)|(yolo_block.1.0.0.conv.weights)|(yolo_block.1.0.1.conv.weights)|(yolo_block.1.1.0.conv.weights)|(yolo_block.1.1.1.conv.weights)|(yolo_block.1.2.conv.weights)|(yolo_block.2.0.0.conv.weights)|(yolo_block.2.0.1.conv.weights)|(yolo_block.2.1.1.conv.weights)|(yolo_block.2.2.conv.weights)|(yolo_block.2.tip.conv.weights)
+```
+
+我们可以用上述操作观察其它检测模型的参数名称规律，然后设置合适的正则表达式来剪裁合适的参数。
+
+## 训练
+
+根据PaddleDetection/tools/train.py编写压缩脚本compress.py。
+在该脚本中定义了Compressor对象，用于执行压缩任务。
+
+### 执行示例
+
+step1: 设置gpu卡
+```
+export CUDA_VISIBLE_DEVICES=0
+```
+step2: 开始训练
+
+使用PaddleDetection提供的配置文件在用8卡进行训练：
+
+```
+python compress.py \
+ -s yolov3_mobilenet_v1_slim.yaml \
+ -c ../../configs/yolov3_mobilenet_v1_voc.yml \
+ -o max_iters=258 \
+ -d "../../dataset/voc"
+```
+
+>通过命令行覆盖设置max_iters选项，因为PaddleDetection中训练是以`batch`为单位迭代的，并没有涉及`epoch`的概念，但是PaddleSlim需要知道当前训练进行到第几个`epoch`, 所以需要将`max_iters`设置为一个`epoch`内的`batch`的数量。
+
+如果要调整训练卡数，需要调整配置文件`yolov3_mobilenet_v1_voc.yml`中的以下参数：
+
+- **max_iters:** 一个`epoch`中batch的数量，需要设置为`total_num / batch_size`, 其中`total_num`为训练样本总数量，`batch_size`为多卡上总的batch size.
+- **YoloTrainFeed.batch_size:** 单张卡上的batch size, 受限于显存大小。
+- **LeaningRate.base_lr:** 根据多卡的总`batch_size`调整`base_lr`，两者大小正相关，可以简单的按比例进行调整。
+- **LearningRate.schedulers.PiecewiseDecay.milestones：**请根据batch size的变化对其调整。
+- **LearningRate.schedulers.PiecewiseDecay.LinearWarmup.steps：** 请根据batch size的变化对其进行调整。
+
+
+以下为4卡训练示例，通过命令行覆盖`yolov3_mobilenet_v1_voc.yml`中的参数：
+
+```
+python compress.py \
+ -s yolov3_mobilenet_v1_slim.yaml \
+ -c ../../configs/yolov3_mobilenet_v1_voc.yml \
+ -o max_iters=258 \
+ -o YoloTrainFeed.batch_size = 16 \
+ -d "../../dataset/voc"
+```
+
+以下为2卡训练示例，受显存所制，单卡`batch_size`不变，总`batch_size`减小，`base_lr`减小，一个epoch内batch数量增加，同时需要调整学习率相关参数，如下：
+```
+python compress.py \
+ -s yolov3_mobilenet_v1_slim.yaml \
+ -c ../../configs/yolov3_mobilenet_v1_voc.yml \
+ -o max_iters=516 \
+ -o LeaningRate.base_lr=0.005 \ # 0.001 /2
+ -o YoloTrainFeed.batch_size = 16 \
+ -o LearningRate.schedulers='[!PiecewiseDecay {gamma: 0.1, milestones: [110000, 124000]}, !LinearWarmup {start_factor: 0., steps: 2000}]' \
+ -d "../../dataset/voc"
+```
+
+通过`python compress.py --help`查看可配置参数。
+通过`python ../../tools/configure.py ${option_name} help`查看如何通过命令行覆盖配置文件`yolov3_mobilenet_v1_voc.yml`中的参数。
+
+### 保存断点（checkpoint）
+
+如果在配置文件中设置了`checkpoint_path`, 则在压缩任务执行过程中会自动保存断点，当任务异常中断时，
+重启任务会自动从`checkpoint_path`路径下按数字顺序加载最新的checkpoint文件。如果不想让重启的任务从断点恢复，
+需要修改配置文件中的`checkpoint_path`，或者将`checkpoint_path`路径下文件清空。
+
+>注意：配置文件中的信息不会保存在断点中，重启前对配置文件的修改将会生效。
+
+
+## 评估
+
+如果在配置文件中设置了`checkpoint_path`，则每个epoch会保存一个压缩后的用于评估的模型，
+该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下，包含`__model__`和`__params__`两个文件。
+其中，`__model__`用于保存模型结构信息，`__params__`用于保存参数（parameters）信息。
+
+如果不需要保存评估模型，可以在定义Compressor对象时，将`save_eval_model`选项设置为False（默认为True）。
+
+## 预测
+
+如果在配置文件中设置了`checkpoint_path`，并且在定义Compressor对象时指定了`prune_infer_model`选项，则每个epoch都会
+保存一个`inference model`。该模型是通过删除eval_program中多余的operators而得到的。
+
+该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下，包含`__model__.infer`和`__params__`两个文件。
+其中，`__model__.infer`用于保存模型结构信息，`__params__`用于保存参数（parameters）信息。
+
+更多关于`prune_infer_model`选项的介绍，请参考：[Compressor介绍](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#121-%E5%A6%82%E4%BD%95%E6%94%B9%E5%86%99%E6%99%AE%E9%80%9A%E8%AE%AD%E7%BB%83%E8%84%9A%E6%9C%AC)
+
+### python预测
+
+在脚本PaddleDetection/tools/infer.py中展示了如何使用fluid python API加载使用预测模型进行预测。
+
+### PaddleLite
+
+该示例中产出的预测（inference）模型可以直接用PaddleLite进行加载使用。
+关于PaddleLite如何使用，请参考：[PaddleLite使用文档](https://github.com/PaddlePaddle/Paddle-Lite/wiki#%E4%BD%BF%E7%94%A8)
+
+## 示例结果
+
+### MobileNetV1-YOLO-V3
+
+| FLOPS |top1_acc/top5_acc| model_size |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)|
+|---|---|---|---|---|
+|baseline|- |- |- |-|
+|-10%|- |- |- |-|
+|-30%|- |- |- |-|
+|-50%|- |- |- |-|
+
+## FAQ
diff --git a/slim/prune/compress.py b/slim/prune/compress.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d012e46f1dc624697006c06d75cceafccd1c3a1
--- /dev/null
+++ b/slim/prune/compress.py
@@ -0,0 +1,258 @@
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import multiprocessing
+import numpy as np
+import sys
+sys.path.append("../../")
+from paddle.fluid.contrib.slim import Compressor
+
+def set_paddle_flags(**kwargs):
+ for key, value in kwargs.items():
+ if os.environ.get(key, None) is None:
+ os.environ[key] = str(value)
+
+# NOTE(paddle-dev): All of these flags should be set before
+# `import paddle`. Otherwise, it would not take any effect.
+set_paddle_flags(
+ FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
+)
+
+from paddle import fluid
+from ppdet.core.workspace import load_config, merge_config, create
+from ppdet.data.data_feed import create_reader
+from ppdet.utils.eval_utils import parse_fetches, eval_results
+from ppdet.utils.cli import ArgsParser
+from ppdet.utils.check import check_gpu
+import ppdet.utils.checkpoint as checkpoint
+from ppdet.modeling.model_input import create_feed
+
+import logging
+FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+logger = logging.getLogger(__name__)
+def eval_run(exe, compile_program, reader, keys, values, cls, test_feed):
+ """
+ Run evaluation program, return program outputs.
+ """
+ iter_id = 0
+ results = []
+ if len(cls) != 0:
+ values = []
+ for i in range(len(cls)):
+ _, accum_map = cls[i].get_map_var()
+ cls[i].reset(exe)
+ values.append(accum_map)
+
+ images_num = 0
+ start_time = time.time()
+ has_bbox = 'bbox' in keys
+ for data in reader():
+ data = test_feed.feed(data)
+ feed_data = {'image': data['image'],
+ 'im_size': data['im_size']}
+ outs = exe.run(compile_program,
+ feed=feed_data,
+ fetch_list=[values[0]],
+ return_numpy=False)
+ outs.append(data['gt_box'])
+ outs.append(data['gt_label'])
+ outs.append(data['is_difficult'])
+ res = {
+ k: (np.array(v), v.recursive_sequence_lengths())
+ for k, v in zip(keys, outs)
+ }
+ results.append(res)
+ if iter_id % 100 == 0:
+ logger.info('Test iter {}'.format(iter_id))
+ iter_id += 1
+ images_num += len(res['bbox'][1][0]) if has_bbox else 1
+ logger.info('Test finish iter {}'.format(iter_id))
+
+ end_time = time.time()
+ fps = images_num / (end_time - start_time)
+ if has_bbox:
+ logger.info('Total number of images: {}, inference time: {} fps.'.
+ format(images_num, fps))
+ else:
+ logger.info('Total iteration: {}, inference time: {} batch/s.'.format(
+ images_num, fps))
+
+ return results
+
+
+def main():
+ cfg = load_config(FLAGS.config)
+ if 'architecture' in cfg:
+ main_arch = cfg.architecture
+ else:
+ raise ValueError("'architecture' not specified in config file.")
+
+ merge_config(FLAGS.opt)
+ if 'log_iter' not in cfg:
+ cfg.log_iter = 20
+
+ # check if set use_gpu=True in paddlepaddle cpu version
+ check_gpu(cfg.use_gpu)
+
+ if cfg.use_gpu:
+ devices_num = fluid.core.get_cuda_device_count()
+ else:
+ devices_num = int(
+ os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+
+ if 'train_feed' not in cfg:
+ train_feed = create(main_arch + 'TrainFeed')
+ else:
+ train_feed = create(cfg.train_feed)
+
+ if 'eval_feed' not in cfg:
+ eval_feed = create(main_arch + 'EvalFeed')
+ else:
+ eval_feed = create(cfg.eval_feed)
+
+ place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
+ exe = fluid.Executor(place)
+
+ lr_builder = create('LearningRate')
+ optim_builder = create('OptimizerBuilder')
+
+ # build program
+ startup_prog = fluid.Program()
+ train_prog = fluid.Program()
+ with fluid.program_guard(train_prog, startup_prog):
+ with fluid.unique_name.guard():
+ model = create(main_arch)
+ train_loader, feed_vars = create_feed(train_feed, iterable=True)
+ train_fetches = model.train(feed_vars)
+ loss = train_fetches['loss']
+ lr = lr_builder()
+ optimizer = optim_builder(lr)
+ optimizer.minimize(loss)
+
+
+ train_reader = create_reader(train_feed, cfg.max_iters * devices_num,
+ FLAGS.dataset_dir)
+ train_loader.set_sample_list_generator(train_reader, place)
+
+ # parse train fetches
+ train_keys, train_values, _ = parse_fetches(train_fetches)
+ train_keys.append("lr")
+ train_values.append(lr.name)
+
+ train_fetch_list=[]
+ for k, v in zip(train_keys, train_values):
+ train_fetch_list.append((k, v))
+
+ eval_prog = fluid.Program()
+ with fluid.program_guard(eval_prog, startup_prog):
+ with fluid.unique_name.guard():
+ model = create(main_arch)
+ _, test_feed_vars = create_feed(eval_feed, iterable=True)
+ fetches = model.eval(test_feed_vars)
+
+ eval_prog = eval_prog.clone(True)
+
+ eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
+ #eval_pyreader.decorate_sample_list_generator(eval_reader, place)
+ test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
+
+ # parse eval fetches
+ extra_keys = []
+ if cfg.metric == 'COCO':
+ extra_keys = ['im_info', 'im_id', 'im_shape']
+ if cfg.metric == 'VOC':
+ extra_keys = ['gt_box', 'gt_label', 'is_difficult']
+ eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
+ extra_keys)
+ eval_fetch_list=[]
+ for k, v in zip(eval_keys, eval_values):
+ eval_fetch_list.append((k, v))
+
+ exe.run(startup_prog)
+ checkpoint.load_params(exe, train_prog, cfg.pretrain_weights)
+
+ best_box_ap_list = []
+
+ def eval_func(program, scope):
+
+ #place = fluid.CPUPlace()
+ #exe = fluid.Executor(place)
+ results = eval_run(exe, program, eval_reader,
+ eval_keys, eval_values, eval_cls, test_data_feed)
+
+ resolution = None
+ if 'mask' in results[0]:
+ resolution = model.mask_head.resolution
+ box_ap_stats = eval_results(results, eval_feed, cfg.metric, cfg.num_classes,
+ resolution, False, FLAGS.output_eval)
+ if len(best_box_ap_list) == 0:
+ best_box_ap_list.append(box_ap_stats[0])
+ elif box_ap_stats[0] > best_box_ap_list[0]:
+ best_box_ap_list[0] = box_ap_stats[0]
+ checkpoint.save(exe, train_prog, os.path.join(save_dir,"best_model"))
+ logger.info("Best test box ap: {}".format(
+ best_box_ap_list[0]))
+ return best_box_ap_list[0]
+
+ test_feed = [('image', test_feed_vars['image'].name),
+ ('im_size', test_feed_vars['im_size'].name)]
+
+ com = Compressor(
+ place,
+ fluid.global_scope(),
+ train_prog,
+ train_reader=train_reader,
+ train_feed_list=[(key, value.name) for key, value in feed_vars.items()],
+ train_fetch_list=train_fetch_list,
+ eval_program=eval_prog,
+ eval_reader=eval_reader,
+ eval_feed_list=test_feed,
+ eval_func={'map': eval_func},
+ eval_fetch_list=[eval_fetch_list[0]],
+ save_eval_model=True,
+ prune_infer_model=[["image", "im_size"],["multiclass_nms_0.tmp_0"]],
+ train_optimizer=None)
+ com.config(FLAGS.slim_file)
+ com.run()
+
+
+
+if __name__ == '__main__':
+ parser = ArgsParser()
+ parser.add_argument(
+ "-s",
+ "--slim_file",
+ default=None,
+ type=str,
+ help="Config file of PaddleSlim.")
+ parser.add_argument(
+ "--output_eval",
+ default=None,
+ type=str,
+ help="Evaluation directory, default is current directory.")
+ parser.add_argument(
+ "-d",
+ "--dataset_dir",
+ default=None,
+ type=str,
+ help="Dataset path, same as DataFeed.dataset.dataset_dir")
+ FLAGS = parser.parse_args()
+ main()
diff --git a/slim/prune/images/MobileNetV1-YoloV3.pdf b/slim/prune/images/MobileNetV1-YoloV3.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..f5d3a22db5030ffc6beb1e7f8c92bddd61e366e6
Binary files /dev/null and b/slim/prune/images/MobileNetV1-YoloV3.pdf differ
diff --git a/slim/prune/yolov3_mobilenet_v1_slim.yaml b/slim/prune/yolov3_mobilenet_v1_slim.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ff9859eafe5872dee13f33a3703502e334d1c85e
--- /dev/null
+++ b/slim/prune/yolov3_mobilenet_v1_slim.yaml
@@ -0,0 +1,23 @@
+version: 1.0
+pruners:
+ pruner_1:
+ class: 'StructurePruner'
+ pruning_axis:
+ '*': 0
+ criterions:
+ '*': 'l1_norm'
+strategies:
+ uniform_pruning_strategy:
+ class: 'UniformPruneStrategy'
+ pruner: 'pruner_1'
+ start_epoch: 0
+ target_ratio: 0.5
+ pruned_params: '(conv2_1_sep_weights)|(conv2_2_sep_weights)|(conv3_1_sep_weights)|(conv4_1_sep_weights)|(conv5_1_sep_weights)|(conv5_2_sep_weights)|(conv5_3_sep_weights)|(conv5_4_sep_weights)|(conv5_5_sep_weights)|(conv5_6_sep_weights)|(yolo_block.0.0.0.conv.weights)|(yolo_block.0.0.1.conv.weights)|(yolo_block.0.1.0.conv.weights)|(yolo_block.0.1.1.conv.weights)|(yolo_block.1.0.0.conv.weights)|(yolo_block.1.0.1.conv.weights)|(yolo_block.1.1.0.conv.weights)|(yolo_block.1.1.1.conv.weights)|(yolo_block.1.2.conv.weights)|(yolo_block.2.0.0.conv.weights)|(yolo_block.2.0.1.conv.weights)|(yolo_block.2.1.1.conv.weights)|(yolo_block.2.2.conv.weights)|(yolo_block.2.tip.conv.weights)'
+ metric_name: 'acc_top1'
+compressor:
+ epoch: 271
+ eval_epoch: 10
+ #init_model: './checkpoints/0' # Please enable this option for loading checkpoint.
+ checkpoint_path: './checkpoints/'
+ strategies:
+ - uniform_pruning_strategy