[CodeCamp2023-338] New Version of config Adapting Swin Transformer Algorithm

mmpretrain/configs/_base_/datasets/cub_bs8_384.py

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+# 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 (CUB, CenterCrop, LoadImageFromFile,
+                                 PackInputs, RandomCrop, RandomFlip, Resize)
+from mmpretrain.evaluation import Accuracy
+
+# dataset settings
+dataset_type = CUB
+data_preprocessor = dict(
+    num_classes=200,
+    # RGB format normalization parameters
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=Resize, scale=510),
+    dict(type=RandomCrop, crop_size=384),
+    dict(type=RandomFlip, prob=0.5, direction='horizontal'),
+    dict(type=PackInputs),
+]
+
+test_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=Resize, scale=510),
+    dict(type=CenterCrop, crop_size=384),
+    dict(type=PackInputs),
+]
+
+train_dataloader = dict(
+    batch_size=8,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/CUB_200_2011',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=True),
+)
+
+val_dataloader = dict(
+    batch_size=8,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/CUB_200_2011',
+        split='test',
+        pipeline=test_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=False),
+)
+val_evaluator = dict(type=Accuracy, topk=(1, ))
+
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_256.py

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+# 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 (CenterCrop, ImageNet, LoadImageFromFile,
+                                 PackInputs, RandAugment, RandomErasing,
+                                 RandomFlip, RandomResizedCrop, ResizeEdge)
+from mmpretrain.evaluation import Accuracy
+
+# dataset settings
+dataset_type = ImageNet
+data_preprocessor = dict(
+    num_classes=1000,
+    # RGB format normalization parameters
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+bgr_mean = data_preprocessor['mean'][::-1]
+bgr_std = data_preprocessor['std'][::-1]
+
+train_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(
+        type=RandomResizedCrop,
+        scale=256,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type=RandomFlip, prob=0.5, direction='horizontal'),
+    dict(
+        type=RandAugment,
+        policies='timm_increasing',
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
+    dict(
+        type=RandomErasing,
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=bgr_mean,
+        fill_std=bgr_std),
+    dict(type=PackInputs),
+]
+
+test_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(
+        type=ResizeEdge,
+        scale=292,  # ( 256 / 224 * 256 )
+        edge='short',
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type=CenterCrop, crop_size=256),
+    dict(type=PackInputs),
+]
+
+train_dataloader = dict(
+    batch_size=64,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=True),
+)
+
+val_dataloader = dict(
+    batch_size=64,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        split='val',
+        pipeline=test_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=False),
+)
+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

mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_384 copy.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# dataset settings
+dataset_type = 'ImageNet'
+data_preprocessor = dict(
+    num_classes=1000,
+    # RGB format normalization parameters
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='PackInputs'),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=384, backend='pillow', interpolation='bicubic'),
+    dict(type='PackInputs'),
+]
+
+train_dataloader = dict(
+    batch_size=64,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+)
+
+val_dataloader = dict(
+    batch_size=64,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        split='val',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+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

mmpretrain/configs/_base_/models/swin_transformer/base_224.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmengine.model import ConstantInit, TruncNormalInit
+
+from mmpretrain.models import (CutMix, GlobalAveragePooling, ImageClassifier,
+                               LabelSmoothLoss, LinearClsHead, Mixup,
+                               SwinTransformer)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=SwinTransformer, arch='base', img_size=224, drop_path_rate=0.5),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=1024,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(type=LabelSmoothLoss, label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type=TruncNormalInit, layer='Linear', std=0.02, bias=0.),
+        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)]),
+)

mmpretrain/configs/_base_/models/swin_transformer/base_384.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, SwinTransformer)
+
+# model settings
+# Only for evaluation
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=SwinTransformer,
+        arch='base',
+        img_size=384,
+        stage_cfgs=dict(block_cfgs=dict(window_size=12))),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+        topk=(1, 5)))

mmpretrain/configs/_base_/models/swin_transformer/large_224.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, SwinTransformer)
+
+# model settings
+# Only for evaluation
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(type=SwinTransformer, arch='large', img_size=224),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=1536,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+        topk=(1, 5)))

mmpretrain/configs/_base_/models/swin_transformer/large_384.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, SwinTransformer)
+
+# model settings
+# Only for evaluation
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=SwinTransformer,
+        arch='large',
+        img_size=384,
+        stage_cfgs=dict(block_cfgs=dict(window_size=12))),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=1536,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+        topk=(1, 5)))

mmpretrain/configs/_base_/models/swin_transformer/small_224.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmengine.model import ConstantInit, TruncNormalInit
+
+from mmpretrain.models import (CutMix, GlobalAveragePooling, ImageClassifier,
+                               LabelSmoothLoss, LinearClsHead, Mixup,
+                               SwinTransformer)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=SwinTransformer, arch='small', img_size=224, drop_path_rate=0.3),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=768,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(type=LabelSmoothLoss, label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type=TruncNormalInit, layer='Linear', std=0.02, bias=0.),
+        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)]),
+)

mmpretrain/configs/_base_/models/swin_transformer/tiny_224.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmengine.model import ConstantInit, TruncNormalInit
+
+from mmpretrain.models import (CutMix, GlobalAveragePooling, ImageClassifier,
+                               LabelSmoothLoss, LinearClsHead, Mixup,
+                               SwinTransformer)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=SwinTransformer, arch='tiny', img_size=224, drop_path_rate=0.2),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=768,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(type=LabelSmoothLoss, label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type=TruncNormalInit, layer='Linear', std=0.02, bias=0.),
+        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)]),
+)

mmpretrain/configs/_base_/models/swin_transformer_v2/base_256.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmengine.model import ConstantInit, TruncNormalInit
+
+from mmpretrain.models import (CutMix, GlobalAveragePooling, ImageClassifier,
+                               LabelSmoothLoss, LinearClsHead, Mixup,
+                               SwinTransformerV2)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=SwinTransformerV2, arch='base', img_size=256, drop_path_rate=0.5),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=1024,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(type=LabelSmoothLoss, label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type=TruncNormalInit, layer='Linear', std=0.02, bias=0.),
+        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)]),
+)