adjust seg's demo. (PaddlePaddle#1105)

Author: zzjjay

demo/auto_compression/semantic_segmentation/README.md

@@ -82,15 +82,37 @@ 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。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：
+
+```shell
+python run.py \
+    --model_dir='./ppseg_lite_portrait_398x224_with_softmax' \
+    --model_filename='model.pdmodel' \
+    --params_filename='model.pdiparams' \
+    --save_dir='./save_model' \
+    --config_path='configs/pp_humanseg_auto.yaml'
+    --deploy_hardware='SD710'
+```
+- 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
+python run.py \
+    --model_dir='./ppseg_lite_portrait_398x224_with_softmax' \
+    --model_filename='model.pdmodel' \
+    --params_filename='model.pdiparams' \
+    --save_dir='./save_model' \
+    --config_path='configs/pp_humanseg_sparse_dis.yaml'
+```
+
+- 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
 python run.py \
-    --model_dir='inference_model' \
-    --model_filename='inference.pdmodel' \
-    --params_filename='./inference.pdiparams' \
+    --model_dir='./ppseg_lite_portrait_398x224_with_softmax' \
+    --model_filename='model.pdmodel' \
+    --params_filename='model.pdiparams' \
     --save_dir='./save_model' \
-    --config_path='configs/humanseg_sparse_dis.yaml'
+    --config_path='configs/pp_humanseg_quant_dis.yaml'
 ```
 
 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。

demo/auto_compression/semantic_segmentation/configs/pp_humanseg_auto.yaml

@@ -0,0 +1,11 @@
+Global:
+  reader_config: configs/pp_humanseg_lite.yml
+
+TrainConfig:
+  epochs: 14
+  eval_iter: 400
+  learning_rate: 5.0e-03
+  optim_args:
+    weight_decay: 0.0005
+  optimizer: SGD
+  

demo/auto_compression/semantic_segmentation/configs/pp_humanseg_lite.yaml

@@ -0,0 +1,36 @@
+batch_size: 128
+
+train_dataset:
+  type: Dataset
+  dataset_root: data/humanseg
+  train_path: data/humanseg/train.txt
+  num_classes: 2
+  transforms:
+  - type: PaddingByAspectRatio
+    aspect_ratio: 1.77777778
+  - type: Resize
+    target_size: [398, 224]
+  - type: ResizeStepScaling
+    scale_step_size: 0
+  - type: RandomRotation
+  - type: RandomPaddingCrop
+    crop_size: [398, 224]
+  - type: RandomHorizontalFlip
+  - type: RandomDistort
+  - type: RandomBlur
+    prob: 0.3
+  - type: Normalize
+  mode: train
+
+val_dataset:
+  type: Dataset
+  dataset_root: data/humanseg
+  train_path: data/humanseg/val.txt
+  num_classes: 2
+  transforms:
+  - type: PaddingByAspectRatio
+    aspect_ratio: 1.77777778
+  - type: Resize
+    target_size: [398, 224]
+  - type: Normalize
+  mode: val

demo/auto_compression/semantic_segmentation/configs/pp_humanseg_quant_dis.yaml

@@ -1,43 +1,16 @@
+Global:
+  reader_config: configs/pp_humanseg_lite.yml
+
 Distillation:
   distill_lambda: 1.0
   distill_loss: l2_loss
   distill_node_pair:
-  - teacher_reshape2_1.tmp_0
-  - reshape2_1.tmp_0
-  - teacher_reshape2_3.tmp_0
-  - reshape2_3.tmp_0
-  - teacher_reshape2_5.tmp_0
-  - reshape2_5.tmp_0
-  - teacher_reshape2_7.tmp_0 #block1
-  - reshape2_7.tmp_0
-  - teacher_reshape2_9.tmp_0
-  - reshape2_9.tmp_0
-  - teacher_reshape2_11.tmp_0
-  - reshape2_11.tmp_0
-  - teacher_reshape2_13.tmp_0
-  - reshape2_13.tmp_0
-  - teacher_reshape2_15.tmp_0
-  - reshape2_15.tmp_0
-  - teacher_reshape2_17.tmp_0
-  - reshape2_17.tmp_0
-  - teacher_reshape2_19.tmp_0
-  - reshape2_19.tmp_0
-  - teacher_reshape2_21.tmp_0
-  - reshape2_21.tmp_0
-  - teacher_depthwise_conv2d_14.tmp_0 # block2
-  - depthwise_conv2d_14.tmp_0
-  - teacher_depthwise_conv2d_15.tmp_0
-  - depthwise_conv2d_15.tmp_0
-  - teacher_reshape2_23.tmp_0 #block3
-  - reshape2_23.tmp_0
-  - teacher_relu_30.tmp_0 # final_conv
-  - relu_30.tmp_0 
-  - teacher_bilinear_interp_v2_1.tmp_0
-  - bilinear_interp_v2_1.tmp_0
+  - teacher_batch_norm_47.tmp_2
+  - batch_norm_47.tmp_2
   merge_feed: true
-  teacher_model_dir: ./inference_model
-  teacher_model_filename: inference.pdmodel
-  teacher_params_filename: inference.pdiparams
+  teacher_model_dir: ./ppseg_lite_portrait_398x224_with_softmax
+  teacher_model_filename: model.pdmodel
+  teacher_params_filename: model.pdiparams
 Quantization:
   activation_bits: 8
   is_full_quantize: false

demo/auto_compression/semantic_segmentation/configs/pp_humanseg_sparse_dis.yaml

@@ -1,43 +1,16 @@
+Global:
+  reader_config: configs/pp_humanseg_lite.yml
+
 Distillation:
   distill_lambda: 1.0
   distill_loss: l2_loss
   distill_node_pair: 
-  - teacher_reshape2_1.tmp_0 
-  - reshape2_1.tmp_0
-  - teacher_reshape2_3.tmp_0 
-  - reshape2_3.tmp_0
-  - teacher_reshape2_5.tmp_0 
-  - reshape2_5.tmp_0
-  - teacher_reshape2_7.tmp_0 #block1
-  - reshape2_7.tmp_0
-  - teacher_reshape2_9.tmp_0 
-  - reshape2_9.tmp_0
-  - teacher_reshape2_11.tmp_0 
-  - reshape2_11.tmp_0
-  - teacher_reshape2_13.tmp_0 
-  - reshape2_13.tmp_0
-  - teacher_reshape2_15.tmp_0 
-  - reshape2_15.tmp_0
-  - teacher_reshape2_17.tmp_0 
-  - reshape2_17.tmp_0
-  - teacher_reshape2_19.tmp_0 
-  - reshape2_19.tmp_0
-  - teacher_reshape2_21.tmp_0 
-  - reshape2_21.tmp_0
-  - teacher_depthwise_conv2d_14.tmp_0 # block2
-  - depthwise_conv2d_14.tmp_0
-  - teacher_depthwise_conv2d_15.tmp_0
-  - depthwise_conv2d_15.tmp_0
-  - teacher_reshape2_23.tmp_0 #block1
-  - reshape2_23.tmp_0
-  - teacher_relu_30.tmp_0 # final_conv
-  - relu_30.tmp_0 
-  - teacher_bilinear_interp_v2_1.tmp_0
-  - bilinear_interp_v2_1.tmp_0
+  - teacher_batch_norm_47.tmp_2
+  - batch_norm_47.tmp_2
   merge_feed: true
-  teacher_model_dir: ./inference_model
-  teacher_model_filename: inference.pdmodel
-  teacher_params_filename: inference.pdiparams
+  teacher_model_dir: ./ppseg_lite_portrait_398x224_with_softmax
+  teacher_model_filename: model.pdmodel
+  teacher_params_filename: model.pdiparams
 UnstructurePrune:
   prune_strategy: gmp
   prune_mode: ratio

demo/auto_compression/semantic_segmentation/run.py

@@ -3,17 +3,12 @@
 import random
 import paddle
 import numpy as np
-import paddleseg.transforms as T
-from paddleseg.datasets import Dataset
+from paddleseg.cvlibs import Config
 from paddleseg.utils import worker_init_fn
 from paddleslim.auto_compression.config_helpers import load_config
 from paddleslim.auto_compression import AutoCompression
-
 from paddleseg.core.infer import reverse_transform
 from paddleseg.utils import metrics
-import paddle.nn.functional as F
-import cv2
-import paddle.fluid as fluid
 
 
 def parse_args():
@@ -43,6 +38,11 @@ def parse_args():
         type=str,
         default=None,
         help="path of compression strategy config.")
+    parser.add_argument(
+        '--deploy_hardware',
+        type=str,
+        default=None,
+        help="The hardware you want to deploy.")
     return parser.parse_args()
 
 
@@ -65,7 +65,6 @@ def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
 
     print("Start evaluating (total_samples: {}, total_iters: {})...".format(
         len(eval_dataset), total_iters))
-    print("nranks:", nranks)
 
     for iter, (image, label) in enumerate(loader):
         paddle.enable_static()
@@ -149,34 +148,22 @@ def gen():
 
     args = parse_args()
 
-    transforms = [T.RandomPaddingCrop(crop_size=(512, 512)), T.Normalize()]
-    train_dataset = Dataset(
-        transforms=transforms,
-        dataset_root='dataset_root',  # Need to fill in
-        num_classes=2,
-        train_path='train_path',  # Need to fill in
-        mode='train')
-    eval_dataset = Dataset(
-        transforms=transforms,
-        dataset_root='dataset_root',  # Need to fill in
-        num_classes=2,
-        train_path='val_path',  # Need to fill in
-        mode='val')
+    compress_config, train_config = load_config(args.config_path)
+    cfg = Config(compress_config['reader_config'])
 
+    train_dataset = cfg.train_dataset
+    eval_dataset = cfg.val_dataset
     batch_sampler = paddle.io.DistributedBatchSampler(
-        train_dataset, batch_size=128, shuffle=True, drop_last=True)
-
+        train_dataset, batch_size=cfg.batch_size, shuffle=True, drop_last=True)
     train_loader = paddle.io.DataLoader(
         train_dataset,
         batch_sampler=batch_sampler,
         num_workers=2,
         return_list=True,
-        worker_init_fn=worker_init_fn, )
+        worker_init_fn=worker_init_fn)
     train_dataloader = reader_wrapper(train_loader)
 
     # set auto_compression
-    compress_config, train_config = load_config(args.config_path)
-
     ac = AutoCompression(
         model_dir=args.model_dir,
         model_filename=args.model_filename,
@@ -185,6 +172,7 @@ def gen():
         strategy_config=compress_config,
         train_config=train_config,
         train_dataloader=train_dataloader,
-        eval_callback=eval_function)
+        eval_callback=eval_function,
+        deploy_hardware=args.deploy_hardware)
 
     ac.compress()