Merge pull request open-mmlab#1780 from timerring/dev

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

mmpretrain/configs/_base_/datasets/cub_bs8_384.py

@@ -0,0 +1,59 @@
+# 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

@@ -0,0 +1,89 @@
+# 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_/models/swin_transformer_base.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 mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, SwinTransformer)
+
+# model settings
+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_v2_base.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 mmpretrain.models import (GlobalAveragePooling, ImageClassifier,
+                               LabelSmoothLoss, LinearClsHead,
+                               SwinTransformerV2)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=SwinTransformerV2, arch='base', img_size=384, drop_path_rate=0.2),
+    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))

mmpretrain/configs/_base_/schedules/cub_bs64.py

@@ -0,0 +1,39 @@
+# 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 SGD
+
+# optimizer
+optim_wrapper = dict(
+    optimizer=dict(
+        type=SGD, lr=0.01, momentum=0.9, weight_decay=0.0005, nesterov=True))
+
+# learning policy
+param_scheduler = [
+    # warm up learning rate scheduler
+    dict(
+        type=LinearLR,
+        start_factor=0.01,
+        by_epoch=True,
+        begin=0,
+        end=5,
+        # update by iter
+        convert_to_iter_based=True),
+    # main learning rate scheduler
+    dict(
+        type=CosineAnnealingLR,
+        T_max=95,
+        by_epoch=True,
+        begin=5,
+        end=100,
+    )
+]
+
+# train, val, test setting
+train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1)
+val_cfg = dict()
+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=64)

mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k.py

@@ -0,0 +1,35 @@
+# 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 mmengine.model import ConstantInit, TruncNormalInit
+
+from mmpretrain.models import CutMix, LabelSmoothLoss, Mixup
+
+with read_base():
+    from .._base_.datasets.imagenet_bs64_swin_224 import *
+    from .._base_.default_runtime import *
+    from .._base_.models.swin_transformer_base import *
+    from .._base_.schedules.imagenet_bs1024_adamw_swin import *
+
+# model settings
+model.update(
+    backbone=dict(img_size=224, drop_path_rate=0.5, stage_cfgs=None),
+    head=dict(
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type=LabelSmoothLoss,
+            label_smooth_val=0.1,
+            mode='original',
+            loss_weight=0),
+        topk=None,
+        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)]))
+
+# schedule settings
+optim_wrapper = dict(clip_grad=dict(max_norm=5.0))

mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k_384px.py

@@ -0,0 +1,12 @@
+# 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
+
+with read_base():
+    from .._base_.datasets.imagenet_bs64_swin_384 import *
+    from .._base_.default_runtime import *
+    from .._base_.models.swin_transformer_base import *
+    from .._base_.schedules.imagenet_bs1024_adamw_swin import *
+
+# schedule settings
+optim_wrapper = dict(clip_grad=dict(max_norm=5.0))

mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k.py

@@ -0,0 +1,18 @@
+# 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
+
+with read_base():
+    from .._base_.datasets.imagenet_bs64_swin_224 import *
+    from .._base_.default_runtime import *
+    from .._base_.models.swin_transformer_base import *
+    from .._base_.schedules.imagenet_bs1024_adamw_swin import *
+
+# model settings
+model.update(
+    backbone=dict(arch='large', img_size=224, stage_cfgs=None),
+    head=dict(in_channels=1536),
+)
+
+# schedule settings
+optim_wrapper = dict(clip_grad=dict(max_norm=5.0))

mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k_384px.py

@@ -0,0 +1,18 @@
+# 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
+
+with read_base():
+    from .._base_.datasets.imagenet_bs64_swin_384 import *
+    from .._base_.default_runtime import *
+    from .._base_.models.swin_transformer_base import *
+    from .._base_.schedules.imagenet_bs1024_adamw_swin import *
+
+# model settings
+model.update(
+    backbone=dict(arch='large'),
+    head=dict(in_channels=1536),
+)
+
+# schedule settings
+optim_wrapper = dict(clip_grad=dict(max_norm=5.0))

mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py

@@ -0,0 +1,49 @@
+# 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 mmengine.hooks import CheckpointHook, LoggerHook
+from mmengine.model import PretrainedInit
+from torch.optim.adamw import AdamW
+
+from mmpretrain.models import ImageClassifier
+
+with read_base():
+    from .._base_.datasets.cub_bs8_384 import *
+    from .._base_.default_runtime import *
+    from .._base_.models.swin_transformer_base import *
+    from .._base_.schedules.cub_bs64 import *
+
+# model settings
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-large_3rdparty_in21k-384px.pth'  # noqa
+
+model.update(
+    backbone=dict(
+        arch='large',
+        init_cfg=dict(
+            type=PretrainedInit, checkpoint=checkpoint, prefix='backbone')),
+    head=dict(num_classes=200, in_channels=1536))
+
+# schedule settings
+optim_wrapper = dict(
+    optimizer=dict(
+        _delete_=True,
+        type=AdamW,
+        lr=5e-6,
+        weight_decay=0.0005,
+        eps=1e-8,
+        betas=(0.9, 0.999)),
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        bias_decay_mult=0.0,
+        custom_keys={
+            '.absolute_pos_embed': dict(decay_mult=0.0),
+            '.relative_position_bias_table': dict(decay_mult=0.0)
+        }),
+    clip_grad=dict(max_norm=5.0),
+)
+
+default_hooks = dict(
+    # log every 20 intervals
+    logger=dict(type=LoggerHook, interval=20),
+    # save last three checkpoints
+    checkpoint=dict(type=CheckpointHook, interval=1, max_keep_ckpts=3))