finish 4 vit_large*.py

mmpretrain/configs/_base_/models/vit_base_p16.py

@@ -1,6 +1,6 @@
 # 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)
+from mmpretrain.models import (VisionTransformer, ImageClassifier, VisionTransformerClsHead, CrossEntropyLoss)
 
 from mmengine.model.weight_init import KaimingInit
 
@@ -25,6 +25,6 @@ model = dict(
         type=VisionTransformerClsHead,
         num_classes=1000,
         in_channels=1024,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
         topk=(1, 5),
     ))

mmpretrain/configs/_base_/models/vit_base_p32.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 mmpretrain.models import (VisionTransformer, ImageClassifier, VisionTransformerClsHead)
-
+from mmpretrain.models import (VisionTransformer, ImageClassifier, VisionTransformerClsHead, CrossEntropyLoss)
 from mmengine.model.weight_init import KaimingInit
 
 # model settings
@@ -25,6 +24,6 @@ model = dict(
         type=VisionTransformerClsHead,
         num_classes=1000,
         in_channels=768,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
         topk=(1, 5),
     ))

mmpretrain/configs/_base_/models/vit_large_p16.py

@@ -0,0 +1,29 @@
+# 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
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=VisionTransformer,
+        arch='l',
+        img_size=224,
+        patch_size=16,
+        drop_rate=0.1,
+        init_cfg=[
+            dict(
+                type=KaimingInit,
+                layer='Conv2d',
+                mode='fan_in',
+                nonlinearity='linear')
+        ]),
+    neck=None,
+    head=dict(
+        type=VisionTransformerClsHead,
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+        topk=(1, 5),
+    ))

mmpretrain/configs/_base_/models/vit_large_p32.py

@@ -0,0 +1,29 @@
+# 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
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=VisionTransformer,
+        arch='l',
+        img_size=224,
+        patch_size=32,
+        drop_rate=0.1,
+        init_cfg=[
+            dict(
+                type=KaimingInit,
+                layer='Conv2d',
+                mode='fan_in',
+                nonlinearity='linear')
+        ]),
+    neck=None,
+    head=dict(
+        type=VisionTransformerClsHead,
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+        topk=(1, 5),
+    ))

mmpretrain/configs/vision_transformer/vit_base_p32_64xb64_in1k_384px.py

@@ -14,7 +14,6 @@ with read_base():
     from .._base_.schedules.imagenet_bs4096_AdamW import *
     from .._base_.default_runtime import *
 
-
 # model setting
 model = dict(backbone=dict(img_size=384))
 

mmpretrain/configs/vision_transformer/vit_large_p16_64xb64_in1k.py

@@ -0,0 +1,20 @@
+# 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 *
+
+
+# model setting
+model = dict(
+    head=dict(hidden_dim=3072),
+    train_cfg=dict(augments=dict(type=Mixup, alpha=0.2)),
+)
+
+# schedule setting
+optim_wrapper = dict(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_large_p16_64xb64_in1k_384px.py

@@ -0,0 +1,48 @@
+# 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 mmcv.transforms import (LoadImageFromFile, RandomFlip)
+
+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 *
+
+
+# model setting
+model = dict(backbone=dict(img_size=384))
+
+# dataset setting
+data_preprocessor = dict(
+    mean=[127.5, 127.5, 127.5],
+    std=[127.5, 127.5, 127.5],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=RandomResizedCrop, scale=384, backend='pillow'),
+    dict(type=RandomFlip, prob=0.5, direction='horizontal'),
+    dict(type=PackInputs),
+]
+
+test_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=ResizeEdge, scale=384, edge='short', backend='pillow'),
+    dict(type=CenterCrop, crop_size=384),
+    dict(type=PackInputs),
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+
+# schedule setting
+optim_wrapper = dict(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_large_p32_64xb64_in1k.py

@@ -0,0 +1,19 @@
+# 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 *
+
+# model setting
+model = dict(
+    head=dict(hidden_dim=3072),
+    train_cfg=dict(augments=dict(type=Mixup, alpha=0.2)),
+)
+
+# schedule setting
+optim_wrapper = dict(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_large_p32_64xb64_in1k_384px.py

@@ -0,0 +1,45 @@
+# 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 mmcv.transforms import (LoadImageFromFile, RandomFlip)
+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 *
+
+# model setting
+model = dict(backbone=dict(img_size=384))
+
+# dataset setting
+data_preprocessor = dict(
+    mean=[127.5, 127.5, 127.5],
+    std=[127.5, 127.5, 127.5],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=RandomResizedCrop, scale=384, backend='pillow'),
+    dict(type=RandomFlip, prob=0.5, direction='horizontal'),
+    dict(type=PackInputs),
+]
+
+test_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=ResizeEdge, scale=384, edge='short', backend='pillow'),
+    dict(type=CenterCrop, crop_size=384),
+    dict(type=PackInputs),
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+
+# schedule setting
+optim_wrapper = dict(clip_grad=dict(max_norm=1.0))