optimize segmentic readme (#1273)

example/auto_compression/semantic_segmentation/README.md

@@ -41,16 +41,6 @@
   - 软件：CUDA 11.0, cuDNN 8.0, TensorRT 8.0
   - 测试配置：batch_size: 40, max_seq_len: 128
 
-- PP-HumanSeg-Lite数据集
-
-  - 数据集：AISegment + PP-HumanSeg14K + 内部自建数据集。其中 AISegment 是开源数据集，可从[链接](https://github.com/aisegmentcn/matting_human_datasets)处获取；PP-HumanSeg14K 是 PaddleSeg 自建数据集，可从[官方渠道](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.5/contrib/PP-HumanSeg/paper.md#pp-humanseg14k-a-large-scale-teleconferencing-video-dataset)获取；内部数据集不对外公开。
-  - 示例数据集: 用于快速跑通人像分割的压缩和推理流程, 不能用该数据集复现 benckmark 表中的压缩效果。 [下载链接](https://paddleseg.bj.bcebos.com/humanseg/data/mini_supervisely.zip)
-
-- PP-Liteseg，HRNet，UNet，Deeplabv3-ResNet50数据集
-
-  - cityscapes: 请从[cityscapes官网](https://www.cityscapes-dataset.com/login/)下载完整数据
-  - 示例数据集: cityscapes数据集的一个子集，用于快速跑通压缩和推理流程，不能用该数据集复现 benchmark 表中的压缩效果。[下载链接](https://bj.bcebos.com/v1/paddle-slim-models/data/mini_cityscapes/mini_cityscapes.tar)
-
 下面将以开源数据集为例介绍如何对PP-HumanSeg-Lite进行自动压缩。
 
 ## 3. 自动压缩流程
@@ -86,14 +76,25 @@ pip install paddleseg
 
 开发者可下载开源数据集 (如[AISegment](https://github.com/aisegmentcn/matting_human_datasets)) 或自定义语义分割数据集。请参考[PaddleSeg数据准备文档](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.5/docs/data/marker/marker_cn.md)来检查、对齐数据格式即可。
 
-可以通过以下命令下载人像分割示例数据:
+本示例使用示例开源数据集 AISegment 数据集为例介绍如何对PP-HumanSeg-Lite进行自动压缩。示例中的数据集仅用于快速跑通自动压缩流程，并不能复现出 benckmark 表中的压缩效果。
 
+可以通过以下命令下载人像分割示例数据:
 ```shell
-cd ./data
-python download_data.py mini_humanseg
-
+python ./data/download_data.py mini_humanseg
+### 下载后的数据位置为 ./data/humanseg/
 ```
 
+** 提示: **
+- PP-HumanSeg-Lite压缩过程使用的数据集
+
+  - 数据集：AISegment + PP-HumanSeg14K + 内部自建数据集。其中 AISegment 是开源数据集，可从[链接](https://github.com/aisegmentcn/matting_human_datasets)处获取；PP-HumanSeg14K 是 PaddleSeg 自建数据集，可从[官方渠道](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.5/contrib/PP-HumanSeg/paper.md#pp-humanseg14k-a-large-scale-teleconferencing-video-dataset)获取；内部数据集不对外公开。
+  - 示例数据集: 用于快速跑通人像分割的压缩和推理流程, 不能用该数据集复现 benckmark 表中的压缩效果。 [下载链接](https://paddleseg.bj.bcebos.com/humanseg/data/mini_supervisely.zip)
+
+- PP-Liteseg，HRNet，UNet，Deeplabv3-ResNet50数据集
+
+  - cityscapes: 请从[cityscapes官网](https://www.cityscapes-dataset.com/login/)下载完整数据
+  - 示例数据集: cityscapes数据集的一个子集，用于快速跑通压缩和推理流程，不能用该数据集复现 benchmark 表中的压缩效果。[下载链接](https://bj.bcebos.com/v1/paddle-slim-models/data/mini_cityscapes/mini_cityscapes.tar)
+
 #### 3.3 准备预测模型
 
 预测模型的格式为：`model.pdmodel` 和 `model.pdiparams`两个，带`pdmodel`的是模型文件，带`pdiparams`后缀的是权重文件。
@@ -111,77 +112,40 @@ tar -xzf ppseg_lite_portrait_398x224_with_softmax.tar.gz
 
 #### 3.4 自动压缩并产出模型
 
-自动压缩示例通过run.py脚本启动，会使用接口```paddleslim.auto_compression.AutoCompression```对模型进行自动压缩。首先要配置config文件中模型路径、数据集路径、蒸馏、量化、稀疏化和训练等部分的参数，配置完成后便可对模型进行非结构化稀疏、蒸馏和量化、蒸馏。
+自动压缩示例通过run.py脚本启动，会使用接口 ```paddleslim.auto_compression.AutoCompression``` 对模型进行自动压缩。首先要配置config文件中模型路径、数据集路径、蒸馏、量化、稀疏化和训练等部分的参数，配置完成后便可对模型进行非结构化稀疏、蒸馏和量化、蒸馏。
 
-当只设置训练参数，并传入``deploy_hardware``字段时，将自动搜索压缩策略进行压缩。以骁龙710（SD710）为部署硬件，进行自动压缩的运行命令如下：
+当只设置训练参数，并在config文件中 ```Global``` 配置中传入 ```deploy_hardware``` 字段时，将自动搜索压缩策略进行压缩。以骁龙710（SD710）为部署硬件，进行自动压缩的运行命令如下：
 
 ```shell
 # 单卡启动
 export CUDA_VISIBLE_DEVICES=0
-python run.py \
-    --model_dir='./ppseg_lite_portrait_398x224_with_softmax' \
-    --model_filename='model.pdmodel' \
-    --params_filename='model.pdiparams' \
-    --save_dir='./save_model' \
-    --strategy_config='configs/pp_humanseg/pp_humanseg_auto.yaml' \
-    --dataset_config='configs/dataset/humanseg_dataset.yaml' \
-    --deploy_hardware='SD710'
+python run.py --config_path='./configs/pp_humanseg/pp_humanseg_auto.yaml' --save_dir='./save_compressed_model'
 
 # 多卡启动
 export CUDA_VISIBLE_DEVICES=0,1
-python -m paddle.distributed.launch run.py \
-    --model_dir='./ppseg_lite_portrait_398x224_with_softmax' \
-    --model_filename='model.pdmodel' \
-    --params_filename='model.pdiparams' \
-    --save_dir='./save_model' \
-    --strategy_config='configs/pp_humanseg/pp_humanseg_auto.yaml' \
-    --dataset_config='configs/dataset/humanseg_dataset.yaml' \
-    --deploy_hardware='SD710'
+python -m paddle.distributed.launch run.py --config_path='./configs/pp_humanseg/pp_humanseg_auto.yaml' --save_dir='./save_compressed_model'
 ```
+
 - 自行配置稀疏参数进行非结构化稀疏和蒸馏训练，配置参数含义详见[自动压缩超参文档](https://github.com/PaddlePaddle/PaddleSlim/blob/27dafe1c722476f1b16879f7045e9215b6f37559/demo/auto_compression/hyperparameter_tutorial.md)。具体命令如下所示：
 ```shell
 # 单卡启动
 export CUDA_VISIBLE_DEVICES=0
-python run.py \
-    --model_dir='./ppseg_lite_portrait_398x224_with_softmax' \
-    --model_filename='model.pdmodel' \
-    --params_filename='model.pdiparams' \
-    --save_dir='./save_model' \
-    --strategy_config='configs/pp_humanseg/pp_humanseg_sparse.yaml' \
-    --dataset_config='configs/dataset/humanseg_dataset.yaml'
+python run.py --config_path='./configs/pp_humanseg/pp_humanseg_sparse.yaml' --save_dir='./save_sparse_model'
 
 # 多卡启动
 export CUDA_VISIBLE_DEVICES=0,1
-python -m paddle.distributed.launch run.py \
-    --model_dir='./ppseg_lite_portrait_398x224_with_softmax' \
-    --model_filename='model.pdmodel' \
-    --params_filename='model.pdiparams' \
-    --save_dir='./save_model' \
-    --strategy_config='configs/pp_humanseg/pp_humanseg_sparse.yaml' \
-    --dataset_config='configs/dataset/humanseg_dataset.yaml'
+python -m paddle.distributed.launch run.py --config_path='./configs/pp_humanseg/pp_humanseg_sparse.yaml' --save_dir='./save_sparse_model'
 ```
 
 - 自行配置量化参数进行量化和蒸馏训练，配置参数含义详见[自动压缩超参文档](https://github.com/PaddlePaddle/PaddleSlim/blob/27dafe1c722476f1b16879f7045e9215b6f37559/demo/auto_compression/hyperparameter_tutorial.md)。具体命令如下所示：
 ```shell
 # 单卡启动
 export CUDA_VISIBLE_DEVICES=0
-python run.py \
-    --model_dir='./ppseg_lite_portrait_398x224_with_softmax' \
-    --model_filename='model.pdmodel' \
-    --params_filename='model.pdiparams' \
-    --save_dir='./save_model' \
-    --strategy_config='configs/pp_humanseg/pp_humanseg_qat.yaml' \
-    --dataset_config='configs/dataset/humanseg_dataset.yaml'
+python run.py --config_path='./configs/pp_humanseg/pp_humanseg_qat.yaml' --save_dir='./save_quant_model'
 
 # 多卡启动
 export CUDA_VISIBLE_DEVICES=0,1
-python -m paddle.distributed.launch run.py \
-    --model_dir='./ppseg_lite_portrait_398x224_with_softmax' \
-    --model_filename='model.pdmodel' \
-    --params_filename='model.pdiparams' \
-    --save_dir='./save_model' \
-    --strategy_config='configs/pp_humanseg/pp_humanseg_qat.yaml' \
-    --dataset_config='configs/dataset/humanseg_dataset.yaml'
+python -m paddle.distributed.launch run.py --config_path='./configs/pp_humanseg/pp_humanseg_qat.yaml' --save_dir='./save_quant_model'
 ```
 
 压缩完成后会在`save_dir`中产出压缩好的预测模型，可直接预测部署。

example/auto_compression/semantic_segmentation/configs/deeplabv3/deeplabv3_qat.yaml

 Global:
   reader_config: configs/dataset/cityscapes_1024x512_scale1.0.yml
+  model_dir: ./RES-paddle2-Deeplabv3-ResNet50
+  model_filename: model
+  params_filename: params
 
 Distillation:
   alpha: 1.0

example/auto_compression/semantic_segmentation/configs/hrnet/hrnet_qat.yaml

 Global:
   reader_config: configs/dataset/cityscapes_1024x512_scale1.0.yml
+  model_dir: ./RES-paddle2-HRNetW18-Seg
+  model_filename: model
+  params_filename: params
 
 Distillation:
   alpha: 1.0

example/auto_compression/semantic_segmentation/configs/hrnet/hrnet_sparse.yaml

 Global:
   reader_config: configs/dataset/cityscapes_1024x512_scale1.0.yml
+  model_dir: ./RES-paddle2-HRNetW18-Seg
+  model_filename: model
+  params_filename: params
 
 Distillation:
   alpha: 1.0

example/auto_compression/semantic_segmentation/configs/pp_humanseg/pp_humanseg_auto.yaml

+Global:
+  reader_config: configs/dataset/humanseg_dataset.yaml
+  model_dir: ./ppseg_lite_portrait_398x224_with_softmax
+  model_filename: model.pdmodel
+  params_filename: model.pdiparams
+  deploy_hardware: SD710
+
 TrainConfig:
   epochs: 14
   eval_iter: 400

example/auto_compression/semantic_segmentation/configs/pp_humanseg/pp_humanseg_qat.yaml

+Global:
+  reader_config: configs/dataset/humanseg_dataset.yaml
+  model_dir: ./ppseg_lite_portrait_398x224_with_softmax
+  model_filename: model.pdmodel
+  params_filename: model.pdiparams
+
 Distillation:
   alpha: 1.0
   loss: l2

example/auto_compression/semantic_segmentation/configs/pp_humanseg/pp_humanseg_sparse.yaml

+Global:
+  reader_config: configs/dataset/humanseg_dataset.yaml
+  model_dir: ./ppseg_lite_portrait_398x224_with_softmax
+  model_filename: model.pdmodel
+  params_filename: model.pdiparams
+
 Distillation:
   alpha: 1.0
   loss: l2

example/auto_compression/semantic_segmentation/configs/pp_liteseg/pp_liteseg_auto.yaml

+Global:
+  reader_config: configs/dataset/cityscapes_1024x512_scale1.0.yml
+  model_dir: ./RES-paddle2-PPLIteSegSTDC1
+  model_filename: model
+  params_filename: params
+
 TrainConfig:
  epochs: 14
  eval_iter: 90

example/auto_compression/semantic_segmentation/configs/pp_liteseg/pp_liteseg_qat.yaml

+Global:
+  reader_config: configs/dataset/cityscapes_1024x512_scale1.0.yml
+  model_dir: ./RES-paddle2-PPLIteSegSTDC1
+  model_filename: model
+  params_filename: params
+
 Distillation:
   alpha: 1.0
   loss: l2

example/auto_compression/semantic_segmentation/configs/pp_liteseg/pp_liteseg_sparse.yaml

+Global:
+  reader_config: configs/dataset/cityscapes_1024x512_scale1.0.yml
+  model_dir: ./RES-paddle2-PPLIteSegSTDC1
+  model_filename: model
+  params_filename: params
+
 Distillation:
   alpha: 1.0
   loss: l2

example/auto_compression/semantic_segmentation/configs/unet/unet_channel_prune.yaml

 Global:
   reader_config: configs/dataset/cityscapes_1024x512_scale1.0.yml
+  model_dir: ./RES-paddle2-UNet
+  model_filename: model
+  params_filename: params
 
 Distillation:
   alpha: 1.0

example/auto_compression/semantic_segmentation/configs/unet/unet_qat.yaml

 Global:
   reader_config: configs/dataset/cityscapes_1024x512_scale1.0.yml
+  model_dir: ./RES-paddle2-UNet
+  model_filename: model
+  params_filename: params
 
 Distillation:
   alpha: 1.0

example/auto_compression/semantic_segmentation/run.py

@@ -21,48 +21,24 @@ from paddleseg.cvlibs import Config as PaddleSegDataConfig
 from paddleseg.utils import worker_init_fn
 
 from paddleslim.auto_compression import AutoCompression
+from paddleslim.auto_compression.config_helpers import load_config as load_slim_config
 from paddleseg.core.infer import reverse_transform
 from paddleseg.utils import metrics
 
 
-def parse_args():
-    parser = argparse.ArgumentParser(description='Model training')
+def argsparser():
+    parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument(
-        '--model_dir',
+        '--config_path',
         type=str,
         default=None,
-        help="inference model directory.")
-    parser.add_argument(
-        '--model_filename',
-        type=str,
-        default=None,
-        help="inference model filename.")
-    parser.add_argument(
-        '--params_filename',
-        type=str,
-        default=None,
-        help="inference params filename.")
+        help="path of compression strategy config.")
     parser.add_argument(
         '--save_dir',
         type=str,
         default=None,
         help="directory to save compressed model.")
-    parser.add_argument(
-        '--strategy_config',
-        type=str,
-        default=None,
-        help="path of compression strategy config.")
-    parser.add_argument(
-        '--dataset_config',
-        type=str,
-        default=None,
-        help="path of dataset config.")
-    parser.add_argument(
-        '--deploy_hardware',
-        type=str,
-        default=None,
-        help="The hardware you want to deploy.")
-    return parser.parse_args()
+    return parser
 
 
 def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
@@ -141,13 +117,15 @@ def reader_wrapper(reader):
     return gen
 
 
-if __name__ == '__main__':
+def main(args):
+    all_config = load_slim_config(args.config_path)
+    assert "Global" in all_config, f"Key 'Global' not found in config file. \n{all_config}"
+    config = all_config["Global"]
+
     rank_id = paddle.distributed.get_rank()
     place = paddle.CUDAPlace(rank_id)
-    args = parse_args()
-    paddle.enable_static()
     # step1: load dataset config and create dataloader
-    data_cfg = PaddleSegDataConfig(args.dataset_config)
+    data_cfg = PaddleSegDataConfig(config['reader_config'])
     train_dataset = data_cfg.train_dataset
     eval_dataset = data_cfg.val_dataset
     batch_sampler = paddle.io.DistributedBatchSampler(
@@ -166,19 +144,24 @@ if __name__ == '__main__':
 
     nranks = paddle.distributed.get_world_size()
     rank_id = paddle.distributed.get_rank()
-    if nranks > 1 and rank_id != 0:
-        eval_function = None
 
     # step2: create and instance of AutoCompression
     ac = AutoCompression(
-        model_dir=args.model_dir,
-        model_filename=args.model_filename,
-        params_filename=args.params_filename,
+        model_dir=config['model_dir'],
+        model_filename=config['model_filename'],
+        params_filename=config['params_filename'],
         save_dir=args.save_dir,
-        config=args.strategy_config,
+        config=all_config,
         train_dataloader=train_dataloader,
-        eval_callback=eval_function,
-        deploy_hardware=args.deploy_hardware)
+        eval_callback=eval_function if nranks > 1 and rank_id != 0 else None,
+        deploy_hardware=config.get('deploy_hardware') or None)
 
     # step3: start the compression job
     ac.compress()
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    parser = argsparser()
+    args = parser.parse_args()
+    main(args)

paddleslim/auto_compression/compressor.py

@@ -465,10 +465,10 @@ class AutoCompression:
                     'train_config must has `epochs` or `train_iter` field.')
             config_dict['gmp_config'] = {
                 'stable_iterations': 0,
-                'pruning_iterations': 0.45 * total_iters,
-                'tunning_iterations': 0.45 * total_iters,
+                'pruning_iterations': max(0.45 * total_iters, 30),
+                'tunning_iterations': max(0.45 * total_iters, 30),
                 'resume_iteration': -1,
-                'pruning_steps': 100,
+                'pruning_steps': 100 if (0.45 * total_iters) > 1000 else 1,
                 'initial_ratio': 0.15,
             }
         ### add prune program