mirror of
https://github.com/PaddlePaddle/PaddleClas.git
synced 2025-06-03 21:55:06 +08:00
commit
019936a1ac
5
.gitignore
vendored
5
.gitignore
vendored
@ -1,8 +1,9 @@
|
||||
*.pyc
|
||||
*.sw*
|
||||
*log*
|
||||
/dataset
|
||||
*/workerlog*
|
||||
dataset/
|
||||
checkpoints/
|
||||
output/
|
||||
pretrained/
|
||||
*.ipynb*
|
||||
build/
|
||||
|
@ -1,7 +1,7 @@
|
||||
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:
|
||||
|
||||
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'DPN107'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'DPN131'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'DPN68'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'DPN92'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'DPN98'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "DarkNet53"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'DenseNet121'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'DenseNet161'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'DenseNet169'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'DenseNet201'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'DenseNet264'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "GoogLeNet"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'InceptionV4'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "MobileNetV1"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "MobileNetV2"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNeXt101_32x4d'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNeXt101_64x4d'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNeXt152_32x4d'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNeXt152_64x4d'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNeXt50_32x4d'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "ResNeXt50_64x4d"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet101'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet101_vd'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet152'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet152_vd'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet18'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet18_vd'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet200_vd'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet34'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet34_vd'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet50'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet50_vc'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'ResNet50_vd'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "ResNet_ACNet"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: 'SENet154_vd'
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "ShuffleNetV2"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "ShuffleNetV2_swish"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
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
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "SqueezeNet1_0"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "SqueezeNet1_1"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "VGG11"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "VGG13"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "VGG16"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -1,7 +1,7 @@
|
||||
mode: 'train'
|
||||
architecture: "VGG19"
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./checkpoints/"
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
|
@ -6,7 +6,6 @@ total_images: 1281167
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 16
|
||||
num_workers: 4
|
||||
|
1
dataset/README.md
Normal file
1
dataset/README.md
Normal file
@ -0,0 +1 @@
|
||||
#mannual
|
40
dataset/download_imagenet2012.sh
Normal file
40
dataset/download_imagenet2012.sh
Normal file
@ -0,0 +1,40 @@
|
||||
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}
|
||||
|
@ -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 \
|
||||
|
73
ppcls/utils/logger.py
Normal file
73
ppcls/utils/logger.py
Normal file
@ -0,0 +1,73 @@
|
||||
#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)
|
@ -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):
|
||||
|
19
tools/run.sh
19
tools/run.sh
@ -1,22 +1,9 @@
|
||||
#!/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
|
||||
|
Loading…
x
Reference in New Issue
Block a user