Merge pull request #13 from WuHaobo/master

resolve code 

.gitignore

 *.pyc
 *.sw*
-*log*
-/dataset
+*/workerlog*
+dataset/
 checkpoints/
+output/
 pretrained/
 *.ipynb*
 build/

configs/AlexNet/AlexNet.yaml

 mode: 'train'
 architecture: "AlexNet"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1
@@ -48,8 +48,6 @@ TRAIN:
             order: ''
         - ToCHWImage:
 
-
-
 VALID:
     batch_size: 64
     num_workers: 4
@@ -72,4 +70,3 @@ VALID:
             order: ''
         - ToCHWImage:
 
-

configs/DPN/DPN107.yaml

 mode: 'train'
 architecture: 'DPN107'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/DPN/DPN131.yaml

 mode: 'train'
 architecture: 'DPN131'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/DPN/DPN68.yaml

 mode: 'train'
 architecture: 'DPN68'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/DPN/DPN92.yaml

 mode: 'train'
 architecture: 'DPN92'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/DPN/DPN98.yaml

 mode: 'train'
 architecture: 'DPN98'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/DarkNet/DarkNet53.yaml

 mode: 'train'
 architecture: "DarkNet53"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/DenseNet/DenseNet121.yaml

 mode: 'train'
 architecture: 'DenseNet121'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/DenseNet/DenseNet161.yaml

 mode: 'train'
 architecture: 'DenseNet161'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/DenseNet/DenseNet169.yaml

 mode: 'train'
 architecture: 'DenseNet169'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/DenseNet/DenseNet201.yaml

 mode: 'train'
 architecture: 'DenseNet201'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/DenseNet/DenseNet264.yaml

 mode: 'train'
 architecture: 'DenseNet264'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/HRNet/HRNet_W18_C.yaml

 mode: 'train'
 architecture: 'HRNet_W18_C'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/HRNet/HRNet_W30_C.yaml

 mode: 'train'
 architecture: 'HRNet_W30_C'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/HRNet/HRNet_W32_C.yaml

 mode: 'train'
 architecture: 'HRNet_W32_C'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/HRNet/HRNet_W40_C.yaml

 mode: 'train'
 architecture: 'HRNet_W40_C'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/HRNet/HRNet_W44_C.yaml

 mode: 'train'
 architecture: 'HRNet_W44_C'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/HRNet/HRNet_W48_C.yaml

 mode: 'train'
 architecture: 'HRNet_W48_C'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/HRNet/HRNet_W64_C.yaml

 mode: 'train'
 architecture: 'HRNet_W64_C'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/Inception/GoogLeNet.yaml

 mode: 'train'
 architecture: "GoogLeNet"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/Inception/InceptionV4.yaml

 mode: 'train'
 architecture: 'InceptionV4'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV1/MobileNetV1.yaml

 mode: 'train'
 architecture: "MobileNetV1"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV1/MobileNetV1_x0_25.yaml

 mode: 'train'
 architecture: "MobileNetV1_x0_25"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV1/MobileNetV1_x0_5.yaml

 mode: 'train'
 architecture: "MobileNetV1_x0_5"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV1/MobileNetV1_x0_75.yaml

 mode: 'train'
 architecture: "MobileNetV1_x0_75"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV2/MobileNetV2.yaml

 mode: 'train'
 architecture: "MobileNetV2"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV2/MobileNetV2_x0_25.yaml

 mode: 'train'
 architecture: "MobileNetV2_x0_25"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV2/MobileNetV2_x0_5.yaml

 mode: 'train'
 architecture: "MobileNetV2_x0_5"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV2/MobileNetV2_x0_75.yaml

 mode: 'train'
 architecture: "MobileNetV2_x0_75"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV2/MobileNetV2_x1_5.yaml

 mode: 'train'
 architecture: "MobileNetV2_x1_5"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV2/MobileNetV2_x2_0.yaml

 mode: 'train'
 architecture: "MobileNetV2_x2_0"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV3/MobileNetV3_large_x0_35.yaml

 mode: 'train'
 architecture: "MobileNetV3_large_x0_35"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV3/MobileNetV3_large_x0_5.yaml

 mode: 'train'
 architecture: "MobileNetV3_large_x0_5"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV3/MobileNetV3_large_x0_75.yaml

 mode: 'train'
 architecture: "MobileNetV3_large_x0_75"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV3/MobileNetV3_large_x1_0.yaml

 mode: 'train'
 architecture: "MobileNetV3_large_x1_0"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV3/MobileNetV3_large_x1_25.yaml

 mode: 'train'
 architecture: "MobileNetV3_large_x1_25"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV3/MobileNetV3_small_x0_35.yaml

 mode: 'train'
 architecture: "MobileNetV3_small_x0_35"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV3/MobileNetV3_small_x0_5.yaml

 mode: 'train'
 architecture: "MobileNetV3_small_x0_5"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV3/MobileNetV3_small_x0_75.yaml

 mode: 'train'
 architecture: "MobileNetV3_small_x0_75"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV3/MobileNetV3_small_x1_0.yaml

 mode: 'train'
 architecture: "MobileNetV3_small_x1_0"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/MobileNetV3/MobileNetV3_small_x1_25.yaml

 mode: 'train'
 architecture: "MobileNetV3_small_x1_25"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/Res2Net/Res2Net101_vd_26w_4s.yaml

 mode: 'train'
 architecture: 'Res2Net101_vd_26w_4s'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/Res2Net/Res2Net200_vd_26w_4s.yaml

 mode: 'train'
 architecture: 'Res2Net200_vd_26w_4s'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/Res2Net/Res2Net50_14w_8s.yaml

 mode: 'train'
 architecture: 'Res2Net50_14w_8s'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/Res2Net/Res2Net50_26w_4s.yaml

 mode: 'train'
 architecture: 'Res2Net50_26w_4s'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/Res2Net/Res2Net50_vd_26w_4s.yaml

 mode: 'train'
 architecture: 'Res2Net50_vd_26w_4s'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt101_32x4d.yaml

 mode: 'train'
 architecture: 'ResNeXt101_32x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt101_64x4d.yaml

 mode: 'train'
 architecture: 'ResNeXt101_64x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt101_vd_32x4d.yaml

 mode: 'train'
 architecture: 'ResNeXt101_vd_32x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt101_vd_64x4d.yaml

 mode: 'train'
 architecture: 'ResNeXt101_vd_64x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt152_32x4d.yaml

 mode: 'train'
 architecture: 'ResNeXt152_32x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt152_64x4d.yaml

 mode: 'train'
 architecture: 'ResNeXt152_64x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt152_vd_32x4d.yaml

 mode: 'train'
 architecture: 'ResNeXt152_vd_32x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt152_vd_64x4d.yaml

 mode: 'train'
 architecture: 'ResNeXt152_vd_64x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt50_32x4d.yaml

 mode: 'train'
 architecture: 'ResNeXt50_32x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt50_64x4d.yaml

 mode: 'train'
 architecture: "ResNeXt50_64x4d"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt50_vd_32x4d.yaml

 mode: 'train'
 architecture: "ResNeXt50_vd_32x4d"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNeXt/ResNeXt50_vd_64x4d.yaml

 mode: 'train'
 architecture: 'ResNeXt50_vd_64x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet101.yaml

 mode: 'train'
 architecture: 'ResNet101'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet101_vd.yaml

 mode: 'train'
 architecture: 'ResNet101_vd'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet152.yaml

 mode: 'train'
 architecture: 'ResNet152'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet152_vd.yaml

 mode: 'train'
 architecture: 'ResNet152_vd'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet18.yaml

 mode: 'train'
 architecture: 'ResNet18'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet18_vd.yaml

 mode: 'train'
 architecture: 'ResNet18_vd'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet200_vd.yaml

 mode: 'train'
 architecture: 'ResNet200_vd'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet34.yaml

 mode: 'train'
 architecture: 'ResNet34'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet34_vd.yaml

 mode: 'train'
 architecture: 'ResNet34_vd'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet50.yaml

 mode: 'train'
 architecture: 'ResNet50'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet50_vc.yaml

 mode: 'train'
 architecture: 'ResNet50_vc'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet/ResNet50_vd.yaml

 mode: 'train'
 architecture: 'ResNet50_vd'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ResNet_ACNet/ResNet_ACNet.yaml

 mode: 'train'
 architecture: "ResNet_ACNet"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/SENet/SENet154_vd.yaml

 mode: 'train'
 architecture: 'SENet154_vd'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/SENet/SE_ResNeXt101_32x4d.yaml

 mode: 'train'
 architecture: 'SE_ResNeXt101_32x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/SENet/SE_ResNeXt50_32x4d.yaml

 mode: 'train'
 architecture: 'SE_ResNeXt50_32x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/SENet/SE_ResNeXt50_vd_32x4d.yaml

 mode: 'train'
 architecture: 'SE_ResNeXt50_vd_32x4d'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/SENet/SE_ResNet18_vd.yaml

 mode: 'train'
 architecture: 'SE_ResNet18_vd'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/SENet/SE_ResNet34_vd.yaml

 mode: 'train'
 architecture: 'SE_ResNet34_vd'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/SENet/SE_ResNet50_vd.yaml

 mode: 'train'
 architecture: 'SE_ResNet50_vd'
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ShuffleNet/ShuffleNetV2.yaml

 mode: 'train'
 architecture: "ShuffleNetV2"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ShuffleNet/ShuffleNetV2_swish.yaml

 mode: 'train'
 architecture: "ShuffleNetV2_swish"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ShuffleNet/ShuffleNetV2_x0_25.yaml

 mode: 'train'
 architecture: "ShuffleNetV2_x0_25"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ShuffleNet/ShuffleNetV2_x0_33.yaml

 mode: 'train'
 architecture: "ShuffleNetV2_x0_33"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ShuffleNet/ShuffleNetV2_x0_5.yaml

 mode: 'train'
 architecture: "ShuffleNetV2_x0_5"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ShuffleNet/ShuffleNetV2_x1_5.yaml

 mode: 'train'
 architecture: "ShuffleNetV2_x1_5"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/ShuffleNet/ShuffleNetV2_x2_0.yaml

 mode: 'train'
 architecture: "ShuffleNetV2_x2_0"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/SqueezeNet/SqueezeNet1_0.yaml

 mode: 'train'
 architecture: "SqueezeNet1_0"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/SqueezeNet/SqueezeNet1_1.yaml

 mode: 'train'
 architecture: "SqueezeNet1_1"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/VGG/VGG11.yaml

 mode: 'train'
 architecture: "VGG11"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/VGG/VGG13.yaml

 mode: 'train'
 architecture: "VGG13"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/VGG/VGG16.yaml

 mode: 'train'
 architecture: "VGG16"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/VGG/VGG19.yaml

 mode: 'train'
 architecture: "VGG19"
 pretrained_model: ""
-model_save_dir: "./checkpoints/"
+model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167
 save_interval: 1

configs/eval.yaml

@@ -6,7 +6,6 @@ total_images: 1281167
 topk: 5
 image_shape: [3, 224, 224]
 
-
 VALID:
     batch_size: 16
     num_workers: 4

dataset/README.md

+#mannual

dataset/download_imagenet2012.sh

+set -e
+if [ "x${IMAGENET_USERNAME}" == x -o "x${IMAGENET_ACCESS_KEY}" == x ];then
+  echo "Please create an account on image-net.org."
+  echo "It will provide you a pair of username and accesskey to download imagenet data."
+  read -p "Username: " IMAGENET_USERNAME
+  read -p "Accesskey: " IMAGENET_ACCESS_KEY
+fi
+
+root_url=http://www.image-net.org/challenges/LSVRC/2012/nnoupb
+valid_tar=ILSVRC2012_img_val.tar
+train_tar=ILSVRC2012_img_train.tar
+train_folder=train/
+valid_folder=val/
+
+echo "Download imagenet training data..."
+mkdir -p ${train_folder}
+wget -nd -c ${root_url}/${train_tar}
+tar xf ${train_tar} -C ${train_folder}
+
+cd ${train_folder}
+for x in `ls *.tar`
+do
+  filename=`basename $x .tar`
+  mkdir -p $filename
+  tar -xf $x -C $filename
+  rm -rf $x
+done
+cd -
+
+echo "Download imagenet validation data..."
+mkdir -p ${valid_folder}
+wget -nd -c ${root_url}/${valid_tar}
+tar xf ${valid_tar} -C ${valid_folder}
+
+echo "Download imagenet label file: val_list.txt & train_list.txt"
+label_file=ImageNet_label.tgz
+label_url=http://paddle-imagenet-models.bj.bcebos.com/${label_file}
+wget -nd -c ${label_url}
+tar zxf ${label_file}
+

docs/zh_cn/tutorials/getting_started.md

@@ -22,8 +22,8 @@ PaddleClas 提供模型训练与评估脚本：tools/train.py和tools/eval.py
 python -m paddle.distributed.launch \
     --selected_gpus="0,1,2,3" \
     --log_dir=log_ResNet50 \
-    train.py \
-        -c ./configs/ResNet/ResNet50.yaml \
+    tools/train.py \
+        -c ./configs/ResNet/ResNet50.yaml 
 ```
 
 - 输出日志示例如下：
@@ -38,9 +38,9 @@ epoch:0    train    step:13    loss:7.9561    top1:0.0156    top5:0.1094    lr:0
 python -m paddle.distributed.launch \
     --selected_gpus="0,1,2,3" \
     --log_dir=log_ResNet50_vd \
-    train.py \
+    tools/train.py \
         -c ./configs/ResNet/ResNet50_vd.yaml \
-    -o use_mix=1 \
+        -o use_mix=1 
 
 ```
 
@@ -56,7 +56,7 @@ epoch:0    train    step:522    loss:1.6330    lr:0.100000    elapse:0.210
 ### 2.2 模型评估
 
 ```bash
-python eval.py \
+python tools/eval.py \
     -c ./configs/eval.yaml \
     -o architecture="ResNet50_vd" \
     -o pretrained_model=path_to_pretrained_models
@@ -76,7 +76,7 @@ python tools/export_model.py \
 ```
 之后，通过预测引擎进行推理
 ```bash
-python tools/predict.py \
+python tools/infer/predict.py \
     -m model文件路径 \
     -p params文件路径 \
     -i 图片路径 \

ppcls/utils/logger.py

+#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#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 logging
+import random
+
+DEBUG = logging.DEBUG  #10
+INFO = logging.INFO  #20
+WARN = logging.WARN  #30
+ERROR = logging.ERROR  #40
+
+
+class Logger(object):
+    """
+    Logger
+    """
+
+    def __init__(self, level=DEBUG):
+        self.init(level)
+
+    def init(self, level=DEBUG):
+        """
+        init
+        """
+        self._logger = logging.getLogger()
+        self._logger.setLevel(level)
+
+    def info(self, fmt, *args):
+        """info"""
+        self._logger.info(fmt, *args)
+
+    def warning(self, fmt, *args):
+        """warning"""
+        self._logger.warning(fmt, *args)
+
+    def error(self, fmt, *args):
+        """error"""
+        self._logger.error(fmt, *args)
+
+
+_logger = Logger()
+
+
+def init(level=DEBUG):
+    """init for external"""
+    _logger.init(level)
+
+
+def info(fmt, *args):
+    """info"""
+    _logger.info(fmt, *args)
+
+
+def warning(fmt, *args):
+    """warn"""
+    _logger.warning(fmt, *args)
+
+
+def error(fmt, *args):
+    """error"""
+    _logger.error(fmt, *args)

ppcls/utils/save_load.py

@@ -16,9 +16,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import errno
 import os
-import tempfile
 import shutil
+import tempfile
 
 import paddle
 import paddle.fluid as fluid
@@ -30,10 +31,18 @@ __all__ = ['init_model', 'save_model']
 
 def _mkdir_if_not_exist(path):
     """
-    mkdir if not exists
+    mkdir if not exists, ignore the exception when multiprocess mkdir together
     """
-    if not os.path.exists(os.path.join(path)):
-        os.makedirs(os.path.join(path))
+    if not os.path.exists(path):
+        try:
+            os.makedirs(path)
+        except OSError as e:
+            if e.errno == errno.EEXIST and os.path.isdir(path):
+                logger.warning(
+                    'be happy if some process has already created {}'.format(
+                        path))
+            else:
+                raise OSError('Failed to mkdir {}'.format(path))
 
 
 def _load_state(path):

tools/run.sh

 #!/usr/bin/env bash
 
-export PYTHONPATH=$(dirname "$PWD"):$PWD:$PYTHONPATH
-
-#python download.py -a ResNet181 -p ./pretrained/ -d 1
-
-#python download.py -a ResNet18 -p ./pretrained/ -d 1
-
-#python download.py -a ResNet34 -p ./pretrained/ -d 0
-
-#python -m paddle.distributed.launch --selected_gpus="0,1,2,3" --log_dir=mylog tools/train.py
-
-#python -m paddle.distributed.launch --selected_gpus="0,1,2,3" --log_dir=mylog ./eval.py
+export PYTHONPATH=$PWD:$PYTHONPATH
 
 python -m paddle.distributed.launch \
     --selected_gpus="0,1,2,3" \
-    --log_dir=mylog \
+    --log_dir=log_ResNet50 \
     tools/train.py \
-        -c configs/ResNet/ResNet50_vd.yaml \
-        -o use_mix=0 \
-        -o TRAIN.batch_size=128 \
-        -o TRAIN.transforms.3.NormalizeImage.mean.2=0.4
+        -c ./configs/ResNet/ResNet50.yaml