Adjusted demo for segmentation and classification on auto compress (#1365)

example/auto_compression/image_classification/README.md

@@ -24,27 +24,27 @@
 | 模型 | 策略 | Top-1 Acc | GPU 耗时(ms) | ARM CPU 耗时(ms) | 配置文件 | Inference模型 |
 |:------:|:------:|:------:|:------:|:------:|:------:|:------:|
 | MobileNetV1 | Baseline | 70.90 | - | 33.15 | - | [Model](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_infer.tar) |
-| MobileNetV1 | 量化+蒸馏 | 70.57 | - | 13.64 | [Config](./config/MobileNetV1/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/MobileNetV1_QAT.tar) |
+| MobileNetV1 | 量化+蒸馏 | 70.57 | - | 13.64 | [Config](./configs/MobileNetV1/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/MobileNetV1_QAT.tar) |
 | ResNet50_vd | Baseline | 79.12 | 3.19 | - | - | [Model](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar) |
-| ResNet50_vd | 量化+蒸馏 | 78.74 | 0.92 | - | [Config](./config/ResNet50_vd/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/ResNet50_vd_QAT.tar) |
+| ResNet50_vd | 量化+蒸馏 | 78.74 | 0.92 | - | [Config](./configs/ResNet50_vd/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/ResNet50_vd_QAT.tar) |
 | ShuffleNetV2_x1_0 | Baseline | 68.65 | - | 10.43 | - | [Model](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x1_0_infer.tar) |
-| ShuffleNetV2_x1_0 | 量化+蒸馏 | 68.32 | - | 5.51 | [Config](./config/ShuffleNetV2_x1_0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/ShuffleNetV2_x1_0_QAT.tar) |
+| ShuffleNetV2_x1_0 | 量化+蒸馏 | 68.32 | - | 5.51 | [Config](./configs/ShuffleNetV2_x1_0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/ShuffleNetV2_x1_0_QAT.tar) |
 | SqueezeNet1_0 | Baseline | 59.60 | - | 35.98 | - | [Model](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SqueezeNet1_0_infer.tar) |
-| SqueezeNet1_0 | 量化+蒸馏 | 59.45 | - | 16.96 | [Config](./config/SqueezeNet1_0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/SqueezeNet1_0_QAT.tar) |
+| SqueezeNet1_0 | 量化+蒸馏 | 59.45 | - | 16.96 | [Config](./configs/SqueezeNet1_0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/SqueezeNet1_0_QAT.tar) |
 | PPLCNetV2_base | Baseline | 76.86 | - | 36.50 | - | [Model](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar) |
-| PPLCNetV2_base | 量化+蒸馏 | 76.43 | - | 15.79 | [Config](./config/PPLCNetV2_base/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/PPLCNetV2_base_QAT.tar) |
+| PPLCNetV2_base | 量化+蒸馏 | 76.43 | - | 15.79 | [Config](./configs/PPLCNetV2_base/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/PPLCNetV2_base_QAT.tar) |
 | PPHGNet_tiny | Baseline | 79.59 | 2.82 | - | - |[Model](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar) |
-| PPHGNet_tiny | 量化+蒸馏 | 79.20 | 0.98 | - | [Config](./config/PPHGNet_tiny/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/PPHGNet_tiny_QAT.tar) |
+| PPHGNet_tiny | 量化+蒸馏 | 79.20 | 0.98 | - | [Config](./configs/PPHGNet_tiny/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/PPHGNet_tiny_QAT.tar) |
 | InceptionV3 | Baseline | 79.14 | 4.79 | - | - | [Model](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/InceptionV3_infer.tar) |
-| InceptionV3 | 量化+蒸馏 | 78.32 | 1.47 | - | [Config](./config/InceptionV3/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/InceptionV3_QAT.tar) |
+| InceptionV3 | 量化+蒸馏 | 78.32 | 1.47 | - | [Config](./configs/InceptionV3/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/InceptionV3_QAT.tar) |
 | EfficientNetB0 | Baseline | 77.02 | 1.95 | - | - | [Model](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB0_infer.tar) |
-| EfficientNetB0 | 量化+蒸馏 | 75.39 | 1.44 | - | [Config](./config/EfficientNetB0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/EfficientNetB0_QAT.tar) |
+| EfficientNetB0 | 量化+蒸馏 | 75.39 | 1.44 | - | [Config](./configs/EfficientNetB0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/EfficientNetB0_QAT.tar) |
 | GhostNet_x1_0 | Baseline | 74.02 | 2.93 | - | - | [Model](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_0_infer.tar) |
-| GhostNet_x1_0 | 量化+蒸馏 | 72.62 | 1.03 | - | [Config](./config/GhostNet_x1_0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/GhostNet_x1_0_QAT.tar) |
+| GhostNet_x1_0 | 量化+蒸馏 | 72.62 | 1.03 | - | [Config](./configs/GhostNet_x1_0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/GhostNet_x1_0_QAT.tar) |
 | MobileNetV3_large_x1_0 | Baseline | 75.32 | - | 16.62 | - | [Model](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar) |
-| MobileNetV3_large_x1_0 | 量化+蒸馏 | 74.41 | - | 9.85 | [Config](./config/MobileNetV3_large_x1_0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/MobileNetV3_large_x1_0_QAT.tar) |
+| MobileNetV3_large_x1_0 | 量化+蒸馏 | 74.41 | - | 9.85 | [Config](./configs/MobileNetV3_large_x1_0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/MobileNetV3_large_x1_0_QAT.tar) |
 | MobileNetV3_large_x1_0_ssld | Baseline | 78.96 | - | 16.62 | - | [Model](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_ssld_infer.tar) |
-| MobileNetV3_large_x1_0_ssld | 量化+蒸馏 | 77.17 | - | 9.85 | [Config](./config/MobileNetV3_large_x1_0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/MobileNetV3_large_x1_0_ssld_QAT.tar) |
+| MobileNetV3_large_x1_0_ssld | 量化+蒸馏 | 77.17 | - | 9.85 | [Config](./configs/MobileNetV3_large_x1_0/qat_dis.yaml) | [Model](https://paddle-slim-models.bj.bcebos.com/act/MobileNetV3_large_x1_0_ssld_QAT.tar) |
 
 - ARM CPU 测试环境：`SDM865(4xA77+4xA55)`
 - Nvidia GPU 测试环境：

example/auto_compression/image_classification/run.py

@@ -46,6 +46,11 @@ def argsparser():
         type=int,
         default=1281167,
         help="the number of total training images.")
+    parser.add_argument(
+        '--devices',
+        type=str,
+        default='gpu',
+        help="which device used to compress.")
     return parser
 
 
@@ -122,7 +127,12 @@ def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
 
 def main():
     rank_id = paddle.distributed.get_rank()
-    place = paddle.CUDAPlace(rank_id)
+    if args.devices == 'gpu':
+        place = paddle.CUDAPlace(rank_id)
+        paddle.set_device('gpu')
+    else:
+        place = paddle.CPUPlace()
+        paddle.set_device('cpu')
     global global_config
     all_config = load_slim_config(args.config_path)
 

example/auto_compression/semantic_segmentation/run.py

@@ -38,6 +38,11 @@ def argsparser():
         type=str,
         default=None,
         help="directory to save compressed model.")
+    parser.add_argument(
+        '--devices',
+        type=str,
+        default='gpu',
+        help="which device used to compress.")
     return parser
 
 
@@ -123,7 +128,12 @@ def main(args):
     config = all_config["Global"]
 
     rank_id = paddle.distributed.get_rank()
-    place = paddle.CUDAPlace(rank_id)
+    if args.devices == 'gpu':
+        place = paddle.CUDAPlace(rank_id)
+        paddle.set_device('gpu')
+    else:
+        place = paddle.CPUPlace()
+        paddle.set_device('cpu')
     # step1: load dataset config and create dataloader
     data_cfg = PaddleSegDataConfig(config['reader_config'])
     train_dataset = data_cfg.train_dataset