pre-commit check

mmpretrain/configs/_base_/datasets/imagenet_bs32_simclr.py

@@ -4,7 +4,7 @@ from mmcv.transforms import (LoadImageFromFile, RandomApply, RandomFlip,
                              RandomGrayscale)
 from mmengine.dataset import DefaultSampler, default_collate
 
-from mmpretrain.datasets import (ImageNet, ColorJitter, GaussianBlur, ImageNet,
+from mmpretrain.datasets import (ColorJitter, GaussianBlur, ImageNet,
                                  MultiView, PackInputs, RandomResizedCrop)
 from mmpretrain.models import SelfSupDataPreprocessor
 

mmpretrain/configs/_base_/datasets/imagenet_bs64_pil_resize.py

@@ -1,13 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
+from mmcv.transforms import LoadImageFromFile, RandomFlip
 from mmengine.dataset import DefaultSampler
 
-from mmcv.transforms import (LoadImageFromFile, RandomFlip)
-
-from mmpretrain.datasets import (ImageNet, CenterCrop, LoadImageFromFile,
+from mmpretrain.datasets import (CenterCrop, ImageNet, LoadImageFromFile,
                                  PackInputs, RandomFlip, RandomResizedCrop,
                                  ResizeEdge)
-
 from mmpretrain.evaluation import Accuracy
 
 # dataset settings
@@ -60,4 +58,4 @@ val_evaluator = dict(type=Accuracy, topk=(1, 5))
 
 # If you want standard test, please manually configure the test dataset
 test_dataloader = val_dataloader
-test_evaluator = val_evaluator
+test_evaluator = val_evaluator

mmpretrain/configs/_base_/datasets/imagenet_bs64_pil_resize_autoaug.py

@@ -1,17 +1,14 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
+from mmcv.transforms import LoadImageFromFile
 from mmengine.dataset import DefaultSampler
 
-from mmcv.transforms import LoadImageFromFile
-
-from mmpretrain.datasets import (ImageNet, CenterCrop, LoadImageFromFile,
+from mmpretrain.datasets import (CenterCrop, ImageNet, LoadImageFromFile,
                                  PackInputs, RandomFlip, RandomResizedCrop,
                                  ResizeEdge)
-
 from mmpretrain.datasets.transforms import AutoAugment
 from mmpretrain.evaluation import Accuracy
 
-
 # dataset settings
 dataset_type = ImageNet
 data_preprocessor = dict(
@@ -79,4 +76,4 @@ val_evaluator = dict(type=Accuracy, topk=(1, 5))
 
 # If you want standard test, please manually configure the test dataset
 test_dataloader = val_dataloader
-test_evaluator = val_evaluator
+test_evaluator = val_evaluator

mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_224.py

@@ -1,9 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
 from mmengine.dataset import DefaultSampler
-from mmpretrain.datasets import (ImageNet, LoadImageFromFile, PackInputs,
-                                 RandomFlip, RandomResizedCrop, CenterCrop, ResizeEdge,
-                                 RandAugment, RandomErasing)
+
+from mmpretrain.datasets import (CenterCrop, ImageNet, LoadImageFromFile,
+                                 PackInputs, RandAugment, RandomErasing,
+                                 RandomFlip, RandomResizedCrop, ResizeEdge)
 from mmpretrain.evaluation import Accuracy
 
 # dataset settings
@@ -85,4 +86,4 @@ val_evaluator = dict(type=Accuracy, topk=(1, 5))
 
 # If you want standard test, please manually configure the test dataset
 test_dataloader = val_dataloader
-test_evaluator = val_evaluator
+test_evaluator = val_evaluator

mmpretrain/configs/_base_/models/vit_base_p16.py

@@ -1,9 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
-from mmpretrain.models import (VisionTransformer, ImageClassifier, VisionTransformerClsHead, CrossEntropyLoss)
-
 from mmengine.model.weight_init import KaimingInit
 
+from mmpretrain.models import (CrossEntropyLoss, ImageClassifier,
+                               VisionTransformer, VisionTransformerClsHead)
+
 # model settings
 model = dict(
     type=ImageClassifier,
@@ -27,4 +28,4 @@ model = dict(
         in_channels=1024,
         loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
         topk=(1, 5),
-    ))
+    ))

mmpretrain/configs/_base_/models/vit_base_p32.py

@@ -1,8 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
-from mmpretrain.models import (VisionTransformer, ImageClassifier, VisionTransformerClsHead, CrossEntropyLoss)
 from mmengine.model.weight_init import KaimingInit
 
+from mmpretrain.models import (CrossEntropyLoss, ImageClassifier,
+                               VisionTransformer, VisionTransformerClsHead)
+
 # model settings
 model = dict(
     type=ImageClassifier,
@@ -26,4 +28,4 @@ model = dict(
         in_channels=768,
         loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
         topk=(1, 5),
-    ))
+    ))

mmpretrain/configs/_base_/models/vit_large_p16.py

@@ -1,8 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
-from mmpretrain.models import (VisionTransformer, ImageClassifier, VisionTransformerClsHead, CrossEntropyLoss)
 from mmengine.model.weight_init import KaimingInit
 
+from mmpretrain.models import (CrossEntropyLoss, ImageClassifier,
+                               VisionTransformer, VisionTransformerClsHead)
+
 # model settings
 model = dict(
     type=ImageClassifier,
@@ -26,4 +28,4 @@ model = dict(
         in_channels=1024,
         loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
         topk=(1, 5),
-    ))
+    ))

mmpretrain/configs/_base_/models/vit_large_p32.py

@@ -1,8 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
-from mmpretrain.models import (VisionTransformer, ImageClassifier, VisionTransformerClsHead, CrossEntropyLoss)
 from mmengine.model.weight_init import KaimingInit
 
+from mmpretrain.models import (CrossEntropyLoss, ImageClassifier,
+                               VisionTransformer, VisionTransformerClsHead)
+
 # model settings
 model = dict(
     type=ImageClassifier,
@@ -26,4 +28,4 @@ model = dict(
         in_channels=1024,
         loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
         topk=(1, 5),
-    ))
+    ))

mmpretrain/configs/_base_/schedules/imagenet_bs4096_adamw.py

@@ -1,7 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
 from mmengine.optim import CosineAnnealingLR, LinearLR
-
 from torch.optim import AdamW
 
 # optimizer
@@ -42,4 +41,4 @@ test_cfg = dict()
 
 # NOTE: `auto_scale_lr` is for automatically scaling LR,
 # based on the actual training batch size.
-auto_scale_lr = dict(base_batch_size=4096)
+auto_scale_lr = dict(base_batch_size=4096)

mmpretrain/configs/vision_transformer/vit_base_p16_32xb128_mae_in1k.py

@@ -1,17 +1,17 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
 from mmengine.config import read_base
-from mmpretrain.models import Mixup, CutMix
-from mmpretrain.models import (VisionTransformer, ImageClassifier, VisionTransformerClsHead, LabelSmoothLoss,
-                               TruncNormalInit, ConstantInit)
-from mmpretrain.engine import EMAHook
 from torch.optim import AdamW
 
+from mmpretrain.engine import EMAHook
+from mmpretrain.models import (ConstantInit, CutMix, ImageClassifier,
+                               LabelSmoothLoss, Mixup, TruncNormalInit,
+                               VisionTransformer, VisionTransformerClsHead)
 
 with read_base():
     from .._base_.datasets.imagenet_bs64_swin_224 import *
-    from .._base_.schedules.imagenet_bs1024_adamw_swin import *
     from .._base_.default_runtime import *
+    from .._base_.schedules.imagenet_bs1024_adamw_swin import *
 
 # model settings
 model = dict(
@@ -27,17 +27,15 @@ model = dict(
         type=VisionTransformerClsHead,
         num_classes=1000,
         in_channels=768,
-        loss=dict(
-            type=LabelSmoothLoss, label_smooth_val=0.1, mode='original'),
+        loss=dict(type=LabelSmoothLoss, label_smooth_val=0.1, mode='original'),
     ),
     init_cfg=[
         dict(type=TruncNormalInit, layer='Linear', std=.02),
         dict(type=ConstantInit, layer='LayerNorm', val=1., bias=0.),
     ],
-    train_cfg=dict(augments=[
-        dict(type=Mixup, alpha=0.8),
-        dict(type=CutMix, alpha=1.0)
-    ]))
+    train_cfg=dict(
+        augments=[dict(type=Mixup, alpha=0.8),
+                  dict(type=CutMix, alpha=1.0)]))
 
 # dataset settings
 train_dataloader.update(batch_size=128)
@@ -64,4 +62,4 @@ custom_hooks = [dict(type=EMAHook, momentum=1e-4)]
 # NOTE: `auto_scale_lr` is for automatically scaling LR
 # based on the actual training batch size.
 # base_batch_size = (32 GPUs) x (128 samples per GPU)
-auto_scale_lr.update(base_batch_size=4096)
+auto_scale_lr.update(base_batch_size=4096)

mmpretrain/configs/vision_transformer/vit_base_p16_4xb544_ipu_in1k.py

@@ -1,18 +1,19 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
+from mmcv.transforms import (CenterCrop, ImageToTensor, Normalize, Resize,
+                             ToTensor)
 from mmengine.config import read_base
 from mmengine.model import PretrainedInit
 from mmengine.optim import CosineAnnealingLR, LinearLR
-from mmcv.transforms import (Normalize, ImageToTensor, ToTensor, Resize, CenterCrop)
-from mmengine.runner import IterBasedRunner, CheckpointHook
+from mmengine.runner import CheckpointHook, IterBasedRunner
+from torch.optim import SGD
 
 from mmpretrain.datasets import Collect
-from torch.optim import SGD
 
 with read_base():
     from .._base_.datasets.imagenet_bs64_pil_resize_autoaug import *
-    from .._base_.models.vit_base_p16 import *
     from .._base_.default_runtime import *
+    from .._base_.models.vit_base_p16 import *
 
 # specific to vit pretrain
 paramwise_cfg = dict(custom_keys={
@@ -121,4 +122,4 @@ runner = dict(
 
 default_hooks = dict(checkpoint=dict(type=CheckpointHook, interval=1000))
 
-fp16 = dict(loss_scale=256.0, velocity_accum_type='half', accum_type='half')
+fp16 = dict(loss_scale=256.0, velocity_accum_type='half', accum_type='half')

mmpretrain/configs/vision_transformer/vit_base_p16_64xb64_in1k.py

@@ -5,10 +5,10 @@ from mmengine.config import read_base
 from mmpretrain.models import Mixup
 
 with read_base():
-    from .._base_.models.vit_base_p16 import *
     from .._base_.datasets.imagenet_bs64_pil_resize_autoaug import *
-    from .._base_.schedules.imagenet_bs4096_adamw import *
     from .._base_.default_runtime import *
+    from .._base_.models.vit_base_p16 import *
+    from .._base_.schedules.imagenet_bs4096_adamw import *
 
 # model setting
 model.update(
@@ -17,4 +17,4 @@ model.update(
 )
 
 # schedule setting
-optim_wrapper.update(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_base_p16_64xb64_in1k_384px.py

@@ -1,19 +1,16 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
+from mmcv.transforms import LoadImageFromFile, RandomFlip
 from mmengine.config import read_base
 
-from mmcv.transforms import (LoadImageFromFile, RandomFlip)
-
-from mmpretrain.datasets import (CenterCrop, LoadImageFromFile,
-                                 PackInputs, RandomFlip, RandomResizedCrop,
-                                 ResizeEdge)
+from mmpretrain.datasets import (CenterCrop, LoadImageFromFile, PackInputs,
+                                 RandomFlip, RandomResizedCrop, ResizeEdge)
 
 with read_base():
-    from .._base_.models.vit_base_p16 import *
     from .._base_.datasets.imagenet_bs64_pil_resize import *
-    from .._base_.schedules.imagenet_bs4096_adamw import *
     from .._base_.default_runtime import *
-
+    from .._base_.models.vit_base_p16 import *
+    from .._base_.schedules.imagenet_bs4096_adamw import *
 
 # model setting
 model.update(backbone=dict(img_size=384))
@@ -45,4 +42,4 @@ val_dataloader.update(dataset=dict(pipeline=test_pipeline))
 test_dataloader.update(dataset=dict(pipeline=test_pipeline))
 
 # schedule setting
-optim_wrapper.update(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_base_p32_64xb64_in1k.py

@@ -5,11 +5,10 @@ from mmengine.config import read_base
 from mmpretrain.models import Mixup
 
 with read_base():
-    from .._base_.models.vit_base_p32 import *
     from .._base_.datasets.imagenet_bs64_pil_resize_autoaug import *
-    from .._base_.schedules.imagenet_bs4096_adamw import *
     from .._base_.default_runtime import *
-
+    from .._base_.models.vit_base_p32 import *
+    from .._base_.schedules.imagenet_bs4096_adamw import *
 
 # model setting
 model.update(
@@ -18,4 +17,4 @@ model.update(
 )
 
 # schedule setting
-optim_wrapper.update(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_base_p32_64xb64_in1k_384px.py

@@ -1,18 +1,16 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
+from mmcv.transforms import LoadImageFromFile, RandomFlip
 from mmengine.config import read_base
 
-from mmcv.transforms import (LoadImageFromFile, RandomFlip)
-
-from mmpretrain.datasets import (CenterCrop, LoadImageFromFile,
-                                 PackInputs, RandomFlip, RandomResizedCrop,
-                                 ResizeEdge)
+from mmpretrain.datasets import (CenterCrop, LoadImageFromFile, PackInputs,
+                                 RandomFlip, RandomResizedCrop, ResizeEdge)
 
 with read_base():
-    from .._base_.models.vit_base_p32 import *
     from .._base_.datasets.imagenet_bs64_pil_resize import *
-    from .._base_.schedules.imagenet_bs4096_adamw import *
     from .._base_.default_runtime import *
+    from .._base_.models.vit_base_p32 import *
+    from .._base_.schedules.imagenet_bs4096_adamw import *
 
 # model setting
 model.update(backbone=dict(img_size=384))
@@ -44,4 +42,4 @@ val_dataloader.update(dataset=dict(pipeline=test_pipeline))
 test_dataloader.update(dataset=dict(pipeline=test_pipeline))
 
 # schedule setting
-optim_wrapper.update(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_large_p16_64xb64_in1k.py

@@ -1,14 +1,14 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
 from mmengine.config import read_base
+
 from mmpretrain.models import Mixup
 
 with read_base():
-    from .._base_.models.vit_large_p16 import *
     from .._base_.datasets.imagenet_bs64_pil_resize_autoaug import *
-    from .._base_.schedules.imagenet_bs4096_adamw import *
     from .._base_.default_runtime import *
-
+    from .._base_.models.vit_large_p16 import *
+    from .._base_.schedules.imagenet_bs4096_adamw import *
 
 # model setting
 model.update(
@@ -17,4 +17,4 @@ model.update(
 )
 
 # schedule setting
-optim_wrapper.update(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_large_p16_64xb64_in1k_384px.py

@@ -1,19 +1,16 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
+from mmcv.transforms import LoadImageFromFile, RandomFlip
 from mmengine.config import read_base
 
-from mmcv.transforms import (LoadImageFromFile, RandomFlip)
-
-from mmpretrain.datasets import (CenterCrop, LoadImageFromFile,
-                                 PackInputs, RandomFlip, RandomResizedCrop,
-                                 ResizeEdge)
+from mmpretrain.datasets import (CenterCrop, LoadImageFromFile, PackInputs,
+                                 RandomFlip, RandomResizedCrop, ResizeEdge)
 
 with read_base():
-    from .._base_.models.vit_large_p16 import *
     from .._base_.datasets.imagenet_bs64_pil_resize import *
-    from .._base_.schedules.imagenet_bs4096_adamw import *
     from .._base_.default_runtime import *
-
+    from .._base_.models.vit_large_p16 import *
+    from .._base_.schedules.imagenet_bs4096_adamw import *
 
 # model setting
 model.update(backbone=dict(img_size=384))
@@ -45,4 +42,4 @@ val_dataloader.update(dataset=dict(pipeline=test_pipeline))
 test_dataloader.update(dataset=dict(pipeline=test_pipeline))
 
 # schedule setting
-optim_wrapper.update(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_large_p32_64xb64_in1k.py

@@ -1,13 +1,14 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
 from mmengine.config import read_base
+
 from mmpretrain.models import Mixup
 
 with read_base():
-    from .._base_.models.vit_large_p32 import *
     from .._base_.datasets.imagenet_bs64_pil_resize_autoaug import *
-    from .._base_.schedules.imagenet_bs4096_adamw import *
     from .._base_.default_runtime import *
+    from .._base_.models.vit_large_p32 import *
+    from .._base_.schedules.imagenet_bs4096_adamw import *
 
 # model setting
 model.update(
@@ -16,4 +17,4 @@ model.update(
 )
 
 # schedule setting
-optim_wrapper.update(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_large_p32_64xb64_in1k_384px.py

@@ -1,16 +1,16 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # This is a BETA new format config file, and the usage may change recently.
+from mmcv.transforms import LoadImageFromFile, RandomFlip
 from mmengine.config import read_base
-from mmcv.transforms import (LoadImageFromFile, RandomFlip)
-from mmpretrain.datasets import (CenterCrop, LoadImageFromFile,
-                                 PackInputs, RandomFlip, RandomResizedCrop,
-                                 ResizeEdge)
+
+from mmpretrain.datasets import (CenterCrop, LoadImageFromFile, PackInputs,
+                                 RandomFlip, RandomResizedCrop, ResizeEdge)
 
 with read_base():
-    from .._base_.models.vit_large_p32 import *
     from .._base_.datasets.imagenet_bs64_pil_resize import *
-    from .._base_.schedules.imagenet_bs4096_adamw import *
     from .._base_.default_runtime import *
+    from .._base_.models.vit_large_p32 import *
+    from .._base_.schedules.imagenet_bs4096_adamw import *
 
 # model setting
 model.update(backbone=dict(img_size=384))
@@ -42,4 +42,4 @@ val_dataloader.update(dataset=dict(pipeline=test_pipeline))
 test_dataloader.update(dataset=dict(pipeline=test_pipeline))
 
 # schedule setting
-optim_wrapper.update(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))