Merge 7840fb5 into 5c71eba

mmpretrain/configs/_base_/datasets/imagenet_bs32_simclr.py

@@ -9,7 +9,7 @@
 from mmpretrain.models import SelfSupDataPreprocessor
 
 # dataset settings
-dataset_type = 'ImageNet'
+dataset_type = ImageNet
 data_root = 'data/imagenet/'
 data_preprocessor = dict(
     type=SelfSupDataPreprocessor,

mmpretrain/configs/_base_/datasets/imagenet_bs64_pil_resize.py

@@ -0,0 +1,60 @@
+# 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, 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,
+)
+
+train_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=RandomResizedCrop, scale=224, backend='pillow'),
+    dict(type=RandomFlip, prob=0.5, direction='horizontal'),
+    dict(type=PackInputs),
+]
+
+test_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=ResizeEdge, scale=256, edge='short', backend='pillow'),
+    dict(type=CenterCrop, crop_size=224),
+    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_pil_resize_autoaug.py

@@ -0,0 +1,78 @@
+# 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, RandomFlip, RandomResizedCrop,
+                                 ResizeEdge)
+from mmpretrain.datasets.transforms import AutoAugment
+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=224,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type=RandomFlip, prob=0.5, direction='horizontal'),
+    dict(
+        type=AutoAugment,
+        policies='imagenet',
+        hparams=dict(
+            pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
+    dict(type=PackInputs),
+]
+
+test_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(
+        type=ResizeEdge,
+        scale=256,
+        edge='short',
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type=CenterCrop, crop_size=224),
+    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_224.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=224,
+        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=256,
+        edge='short',
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type=CenterCrop, crop_size=224),
+    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/vit_base_p16.py

@@ -0,0 +1,31 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmengine.model.weight_init import KaimingInit
+
+from mmpretrain.models import (ImageClassifier, LabelSmoothLoss,
+                               VisionTransformer, VisionTransformerClsHead)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=VisionTransformer,
+        arch='b',
+        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=768,
+        loss=dict(
+            type=LabelSmoothLoss, label_smooth_val=0.1, mode='classy_vision'),
+    ))

mmpretrain/configs/_base_/schedules/imagenet_bs4096_adamw.py

@@ -0,0 +1,44 @@
+# 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
+optim_wrapper = dict(
+    optimizer=dict(type=AdamW, lr=0.003, weight_decay=0.3),
+    # specific to vit pretrain
+    paramwise_cfg=dict(custom_keys={
+        '.cls_token': dict(decay_mult=0.0),
+        '.pos_embed': dict(decay_mult=0.0)
+    }),
+)
+
+# learning policy
+param_scheduler = [
+    # warm up learning rate scheduler
+    dict(
+        type=LinearLR,
+        start_factor=1e-4,
+        by_epoch=True,
+        begin=0,
+        end=30,
+        # update by iter
+        convert_to_iter_based=True),
+    # main learning rate scheduler
+    dict(
+        type=CosineAnnealingLR,
+        T_max=270,
+        by_epoch=True,
+        begin=30,
+        end=300,
+    )
+]
+
+# train, val, test setting
+train_cfg = dict(by_epoch=True, max_epochs=300, 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=4096)

mmpretrain/configs/vision_transformer/vit_base_p16_32xb128_mae_in1k.py

@@ -0,0 +1,52 @@
+# 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 torch.optim import AdamW
+
+from mmpretrain.engine import EMAHook
+from mmpretrain.models import CutMix, Mixup
+
+with read_base():
+    from .._base_.datasets.imagenet_bs64_swin_224 import *
+    from .._base_.default_runtime import *
+    from .._base_.models.vit_base_p16 import *
+    from .._base_.schedules.imagenet_bs1024_adamw_swin import *
+
+model.update(
+    backbone=dict(drop_rate=0, drop_path_rate=0.1, init_cfg=None),
+    head=dict(loss=dict(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)]))
+
+# dataset settings
+train_dataloader.update(batch_size=128)
+
+# schedule settings
+optim_wrapper.update(
+    optimizer=dict(
+        type=AdamW,
+        lr=1e-4 * 4096 / 256,
+        weight_decay=0.3,
+        eps=1e-8,
+        betas=(0.9, 0.95)),
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        bias_decay_mult=0.0,
+        custom_keys={
+            '.cls_token': dict(decay_mult=0.0),
+            '.pos_embed': dict(decay_mult=0.0)
+        }))
+
+# runtime settings
+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)

mmpretrain/configs/vision_transformer/vit_base_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_.datasets.imagenet_bs64_pil_resize_autoaug import *
+    from .._base_.default_runtime import *
+    from .._base_.models.vit_base_p16 import *
+    from .._base_.schedules.imagenet_bs4096_adamw import *
+
+# model setting
+model.update(
+    head=dict(hidden_dim=3072),
+    train_cfg=dict(augments=dict(type=Mixup, alpha=0.2)),
+)
+
+# schedule setting
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))