add PP-YOLOE auto commpression (#1090)

demo/auto-compression/detection/README.md

-# 目标检测模型自动压缩
-
-预测模型保存接口：
-动态图使用``paddle.jit.save``保存；
-静态图使用``paddle.static.save_inference_model``保存。
-
-本示例将介绍如何使用PaddleDetection中预测模型进行蒸馏量化训练。
-
-## 模型量化蒸馏训练流程
-
-### 1. 准备COCO格式数据
-
-参考[COCO数据准备文档](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/PrepareDataSet.md#coco%E6%95%B0%E6%8D%AE)
-
-### 2. 准备需要量化的环境
-
-- PaddlePaddle >= 2.2
-- PaddleDet >= 2.3
-
-```shell
-pip install paddledet
-```
-
-#### 3 准备待量化模型
-- 下载代码
-```
-git clone https://github.com/PaddlePaddle/PaddleDetection.git
-```
-- 导出预测模型
-```shell
-python tools/export_model.py -c configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams
-```
-或直接下载：
-```shell
-wget https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_270e_coco.tar
-tar -xf yolov3_mobilenet_v1_270e_coco.tar
-```
-
-#### 2.4 测试模型精度
-拷贝``yolov3_mobilenet_v1_270e_coco``文件夹到``PaddleSlim/demo/auto-compression/``文件夹。
-```
-cd PaddleSlim/demo/auto-compression/
-```
-使用[run_main.py](run_main.py)脚本得到模型的mAP：
-```
-python3.7 run_main.py --config_path='./configs/yolov3_mbv1_qat_dis.yaml --eval=True
-```
-
-### 3. 进行多策略融合压缩
-
-每一个小章节代表一种多策略融合压缩，不代表需要串行执行。
-
-### 3.1 进行量化蒸馏压缩
-蒸馏量化训练示例脚本为[run_main.py](run_main.py)，使用接口``paddleslim.auto_compression.AutoCompression``对模型进行量化训练。运行命令为：
-```
-python run_main.py --config_path='./configs/yolov3_mbv1_qat_dis.yaml --save_dir='./output/' --devices='gpu'
-```

demo/auto-compression/README.md → demo/auto_compression/README.md

@@ -50,9 +50,9 @@ python tools/export_model.py \
     -o Global.save_inference_dir=infermodel_mobilenetv2
 ```
 #### 2.4 测试模型精度
-拷贝``infermodel_mobilenetv2``文件夹到``PaddleSlim/demo/auto-compression/``文件夹。
+拷贝``infermodel_mobilenetv2``文件夹到``PaddleSlim/demo/auto_compression/``文件夹。
 ```
-cd PaddleSlim/demo/auto-compression/
+cd PaddleSlim/demo/auto_compression/
 ```
 使用[eval.py](../quant/quant_post/eval.py)脚本得到模型的分类精度，压缩后的模型也可以使用同一个脚本测试精度：
 ```

demo/auto_compression/detection/README.md

+# 目标检测模型自动压缩
+
+本示例将介绍如何使用PaddleDetection中Inference部署模型进行自动压缩。
+
+## Benchmark
+
+- PP-YOLOE模型：
+
+| 模型  |  策略  | 输入尺寸 | mAP<sup>val<br>0.5:0.95 | 预测时延<sup><small>FP32</small><sup><br><sup>(ms) |预测时延<sup><small>FP32</small><sup><br><sup>(ms) | 预测时延<sup><small>INT8</small><sup><br><sup>(ms) |  配置文件 | Inference模型  |
+| :-------- |:-------- |:--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: |
+| PP-YOLOE-l |  Base模型 | 640*640  |  50.9   |   11.2  |   7.7ms   |  -  |  [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/detection/ppyoloe_crn_l_300e_coco.tar) |
+| PP-YOLOE-l |  量化+蒸馏 | 640*640  |  49.5   |   - |   -   |  6.7ms  |  [config](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/auto_compression/detection/configs/ppyoloe_l_qat_dist.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/detection/ppyoloe_crn_l_300e_coco_quant.tar) |
+
+- mAP的指标均在COCO val2017数据集中评测得到。
+- PP-YOLOE模型在Tesla V100的GPU环境下测试，测试脚本是[benchmark demo](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/deploy/python)
+
+## 环境准备
+
+### 1. 准备数据
+
+本案例默认以COCO数据进行自动压缩实验，如果自定义COCO数据，或者其他格式数据，请参考[PaddleDetection数据准备文档](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/PrepareDataSet.md) 来准备数据。
+
+如果数据集为非COCO格式数据，请修改[configs](./configs)中reader配置文件中的Dataset字段。
+
+### 2. 准备需要量化的环境
+
+- PaddlePaddle >= 2.2
+- PaddleDet >= 2.4
+
+```shell
+pip install paddledet
+```
+
+注：安装PaddleDet的目的是为了直接使用PaddleDetection中的Dataloader组件。
+
+### 3. 准备待量化的部署模型
+
+如果已经准备好部署的`model.pdmodel`和`model.pdiparams`部署模型，跳过此步。
+
+根据[PaddleDetection文档](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/GETTING_STARTED_cn.md#8-%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA) 导出Inference模型，具体可参考下方PP-YOLOE模型的导出示例：
+- 下载代码
+```
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
+```
+- 导出预测模型
+
+```shell
+python tools/export_model.py \
+        -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml \
+        -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams \
+        trt=True \
+```
+
+**注意**：PP-YOLOE导出时设置`trt=True`旨在优化在TensorRT上的性能，其他模型不需要设置`trt=True`。
+
+或直接下载：
+```shell
+wget https://bj.bcebos.com/v1/paddle-slim-models/detection/ppyoloe_crn_l_300e_coco.tar
+tar -xf ppyoloe_crn_l_300e_coco.tar
+```
+
+### 4. 测试模型精度
+
+使用[run_main.py](run_main.py)脚本得到模型的mAP：
+```
+python3.7 run_main.py --config_path=./configs/ppyoloe_l_qat_dist.yaml --eval=True
+```
+
+**注意**：TinyPose模型暂不支持精度测试。
+
+## 开始自动压缩
+
+### 进行量化蒸馏自动压缩
+蒸馏量化自动压缩示例通过[run_main.py](run_main.py)脚本启动，会使用接口``paddleslim.auto_compression.AutoCompression``对模型进行量化训练。具体运行命令为：
+```
+python run_main.py --config_path=./configs/ppyoloe_l_qat_dist.yaml --save_dir='./output/' --devices='gpu'
+```
+
+
+## 部署
+
+可以参考[PaddleDetection部署教程](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/deploy)：
+- GPU上量化模型开启TensorRT并设置trt_int8模式进行部署；
+- CPU上可参考[X86 CPU部署量化模型教程](https://github.com/PaddlePaddle/Paddle-Inference-Demo/blob/master/docs/optimize/paddle_x86_cpu_int8.md)；
+- 移动端请直接使用[Paddle Lite Demo](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/deploy/lite)部署。

demo/auto_compression/detection/configs/ppyoloe_l_qat_dis.yaml

+
+Global:
+  reader_config: configs/yolo_reader.yml
+  input_list: ['image', 'scale_factor']
+  Evaluation: True
+  model_dir: ./ppyoloe_crn_l_300e_coco/
+  model_filename: model.pdmodel
+  params_filename: model.pdiparams
+
+Distillation:
+  distill_lambda: 1.0
+  distill_loss: l2_loss
+  distill_node_pair:
+  - teacher_concat_15.tmp_0
+  - concat_15.tmp_0
+  - teacher_concat_14.tmp_0
+  - concat_14.tmp_0
+  merge_feed: true
+  teacher_model_dir: ./ppyoloe_crn_l_300e_coco/
+  teacher_model_filename: model.pdmodel
+  teacher_params_filename: model.pdiparams
+
+Quantization:
+  use_pact: true
+  activation_bits: 8
+  weight_bits: 8
+  activation_quantize_type: 'range_abs_max'
+  weight_quantize_type: 'channel_wise_abs_max'
+  is_full_quantize: false
+  not_quant_pattern:
+  - skip_quant
+  quantize_op_types:
+  - conv2d
+  - depthwise_conv2d
+
+TrainConfig:
+  epochs: 1
+  eval_iter: 1000
+  learning_rate: 0.00001
+  optimizer: SGD
+  optim_args:
+    weight_decay: 4.0e-05
+

demo/auto-compression/detection/configs/yolo_reader.yml → demo/auto_compression/detection/configs/yolo_reader.yml

@@ -14,7 +14,7 @@ EvalDataset:
     anno_path: annotations/instances_val2017.json
     dataset_dir: dataset/coco/
 
-worker_num: 8
+worker_num: 4
 
 # preprocess reader in test
 EvalReader:

demo/auto-compression/detection/configs/yolov3_mbv1_qat_dis.yaml → demo/auto_compression/detection/configs/yolov3_mbv1_qat_dis.yaml

@@ -2,7 +2,7 @@ Global:
   reader_config: configs/yolo_reader.yml
   input_list: ['image', 'im_shape', 'scale_factor']
   Evaluation: True
-  model_dir: ./yolov3_mobilenet_v1_270e_coco/
+  model_dir: ./yolov3_mobilenet_v1_270e_coco/  # Model Link: https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_270e_coco.tar
   model_filename: model.pdmodel
   params_filename: model.pdiparams
 
@@ -23,6 +23,7 @@ Distillation:
 
 Quantization:
   activation_bits: 8
+  weight_bits: 8
   activation_quantize_type: 'range_abs_max'
   weight_quantize_type: 'channel_wise_abs_max'
   is_full_quantize: false
@@ -31,7 +32,6 @@ Quantization:
   quantize_op_types:
   - conv2d
   - depthwise_conv2d
-  weight_bits: 8
 
 TrainConfig:
   epochs: 1

demo/auto-compression/detection/run_main.py → demo/auto_compression/detection/run_main.py

+# Copyright (c) 2022 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.
+
 import os
 import sys
-sys.path[0] = os.path.join(os.path.dirname("__file__"), os.path.pardir)
-import argparse
-import functools
-from functools import partial
 import numpy as np
+import argparse
 import paddle
 from ppdet.core.workspace import load_config, merge_config
 from ppdet.core.workspace import create
@@ -12,20 +23,36 @@ from ppdet.metrics import COCOMetric
 from paddleslim.auto_compression.config_helpers import load_config as load_slim_config
 from paddleslim.auto_compression import AutoCompression
 
-paddle.enable_static()
-
-from utility import add_arguments, print_arguments
 
-parser = argparse.ArgumentParser(description=__doc__)
-add_arg = functools.partial(add_arguments, argparser=parser)
-
-# yapf: disable
-add_arg('save_dir',                    str,    'output',         "directory to save compressed model.")
-add_arg('devices',                     str,    'gpu',        "which device used to compress.")
-add_arg('batch_size',                  int,    1,            "train batch size.")
-add_arg('config_path',                 str,    None,         "path of compression strategy config.")
-add_arg('eval',                  bool,    False,            "whether to run evaluation.")
-# yapf: enable
+def argsparser():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        '--config_path',
+        type=str,
+        default=None,
+        help="path of compression strategy config.",
+        required=True)
+    parser.add_argument(
+        '--save_dir',
+        type=str,
+        default='output',
+        help="directory to save compressed model.")
+    parser.add_argument(
+        '--devices',
+        type=str,
+        default='gpu',
+        help="which device used to compress.")
+    parser.add_argument(
+        '--eval', type=bool, default=False, help="whether to run evaluation.")
+
+    return parser
+
+
+def print_arguments(args):
+    print('-----------  Running Arguments -----------')
+    for arg, value in sorted(vars(args).items()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------')
 
 
 def reader_wrapper(reader, input_list):
@@ -39,9 +66,9 @@ def reader_wrapper(reader, input_list):
     return gen
 
 
-def eval(args, compress_config):
+def eval(compress_config):
 
-    place = paddle.CUDAPlace(0) if args.devices == 'gpu' else paddle.CPUPlace()
+    place = paddle.CUDAPlace(0) if FLAGS.devices == 'gpu' else paddle.CPUPlace()
     exe = paddle.static.Executor(place)
 
     val_program, feed_target_names, fetch_targets = paddle.fluid.io.load_inference_model(
@@ -114,8 +141,8 @@ def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
     return map_res['bbox'][0]
 
 
-def main(args):
-    compress_config, train_config = load_slim_config(args.config_path)
+def main():
+    compress_config, train_config = load_slim_config(FLAGS.config_path)
     reader_cfg = load_config(compress_config['reader_config'])
 
     train_loader = create('EvalReader')(reader_cfg['TrainDataset'],
@@ -130,8 +157,8 @@ def main(args):
                                       reader_cfg['worker_num'],
                                       return_list=True)
 
-    if args.eval:
-        eval(args, compress_config)
+    if FLAGS.eval:
+        eval(compress_config)
         sys.exit(0)
 
     if 'Evaluation' in compress_config.keys() and compress_config['Evaluation']:
@@ -143,7 +170,7 @@ def main(args):
         model_dir=compress_config["model_dir"],
         model_filename=compress_config["model_filename"],
         params_filename=compress_config["params_filename"],
-        save_dir=args.save_dir,
+        save_dir=FLAGS.save_dir,
         strategy_config=compress_config,
         train_config=train_config,
         train_dataloader=train_loader,
@@ -153,7 +180,12 @@ def main(args):
 
 
 if __name__ == '__main__':
-    args = parser.parse_args()
-    print_arguments(args)
     paddle.enable_static()
-    main(args)
+    parser = argsparser()
+    FLAGS = parser.parse_args()
+    print_arguments(FLAGS)
+
+    assert FLAGS.devices in ['cpu', 'gpu', 'xpu', 'npu']
+    paddle.set_device(FLAGS.devices)
+
+    main()

paddleslim/auto_compression/compressor.py

@@ -490,4 +490,6 @@ class AutoCompression:
             feeded_var_names=test_program_info.feed_target_names,
             target_vars=test_program_info.fetch_targets,
             executor=self._exe,
-            main_program=test_program)
+            main_program=test_program,
+            model_filename='model.pdmodel',
+            params_filename='model.pdiparams')