From 853f0c6bcab6d5d34c5843bd6bf580f5c6be2314 Mon Sep 17 00:00:00 2001 From: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com> Date: Tue, 22 Aug 2023 11:29:42 +0800 Subject: [PATCH 01/15] [DOC] Update datset download score from opendatalab to openXlab (#1765) * update opendatalab to openXlab * update dataset-index --------- Co-authored-by: fangyixiao18 --- dataset-index.yml | 4 ++-- docs/en/user_guides/dataset_prepare.md | 14 +++++++------- docs/zh_CN/user_guides/dataset_prepare.md | 14 +++++++------- 3 files changed, 16 insertions(+), 16 deletions(-) diff --git a/dataset-index.yml b/dataset-index.yml index ecf7f5b5..40ca6206 100644 --- a/dataset-index.yml +++ b/dataset-index.yml @@ -1,11 +1,11 @@ imagenet1k: - dataset: ImageNet-1K + dataset: OpenDataLab/ImageNet-1K download_root: data data_root: data/imagenet script: tools/dataset_converters/odl_imagenet1k_preprocess.sh cub: - dataset: CUB-200-2011 + dataset: OpenDataLab/CUB-200-2011 download_root: data data_root: data/CUB_200_2011 script: tools/dataset_converters/odl_cub_preprocess.sh diff --git a/docs/en/user_guides/dataset_prepare.md b/docs/en/user_guides/dataset_prepare.md index 7421be22..17ec229b 100644 --- a/docs/en/user_guides/dataset_prepare.md +++ b/docs/en/user_guides/dataset_prepare.md @@ -144,15 +144,15 @@ ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012]( ````{group-tab} Download by MIM -MIM supports downloading from [OpenDataLab](https://opendatalab.com/) and preprocessing ImageNet dataset with one command line. +MIM supports downloading from [OpenXlab](https://openxlab.org.cn/datasets) and preprocessing ImageNet dataset with one command line. -_You need to register an account at [OpenDataLab official website](https://opendatalab.com/) and login by CLI._ +_You need to register an account at [OpenXlab official website](https://openxlab.org.cn/datasets) and login by CLI._ ```Bash -# install OpenDataLab CLI tools -pip install -U opendatalab -# log in OpenDataLab, register if you don't have an account. -odl login +# install OpenXlab CLI tools +pip install -U openxlab +# log in OpenXLab +openxlab login # download and preprocess by MIM, better to execute in $MMPreTrain directory. mim download mmpretrain --dataset imagenet1k ``` @@ -278,7 +278,7 @@ test_dataloader = val_dataloader | [`SUN397`](mmpretrain.datasets.SUN397)(data_root[, split, pipeline, ...]) | ["train", "test"] | [SUN397](https://vision.princeton.edu/projects/2010/SUN/) Dataset. | | [`VOC`](mmpretrain.datasets.VOC)(data_root[, image_set_path, pipeline, ...]) | ["train", "val", "tranval", "test"] | [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) Dataset. | -Some dataset homepage links may be unavailable, and you can download datasets through [OpenDataLab](https://opendatalab.com/), such as [Stanford Cars](https://opendatalab.com/Stanford_Cars/download). +Some dataset homepage links may be unavailable, and you can download datasets through [OpenXLab](https://openxlab.org.cn/datasets), such as [Stanford Cars](https://openxlab.org.cn/datasets/OpenDataLab/Stanford_Cars). ## Supported Multi-modality Datasets diff --git a/docs/zh_CN/user_guides/dataset_prepare.md b/docs/zh_CN/user_guides/dataset_prepare.md index 59a0d0af..aa1e1fde 100644 --- a/docs/zh_CN/user_guides/dataset_prepare.md +++ b/docs/zh_CN/user_guides/dataset_prepare.md @@ -142,15 +142,15 @@ ImageNet 有多个版本，但最常用的一个是 [ILSVRC 2012](http://www.ima ````{group-tab} MIM 下载 -MIM支持使用一条命令行从 [OpenDataLab](https://opendatalab.com/) 下载并预处理 ImageNet 数据集。 +MIM支持使用一条命令行从 [OpenXLab](https://openxlab.org.cn/datasets?lang=zh-CN) 下载并预处理 ImageNet 数据集。 -_需要在 [OpenDataLab 官网](https://opendatalab.com/) 注册账号并命令行登录_。 +_需要在 [OpenXLab 官网](https://openxlab.org.cn/datasets?lang=zh-CN) 注册账号并命令行登录_。 ```Bash -# 安装opendatalab库 -pip install -U opendatalab -# 登录到 OpenDataLab, 如果还没有注册，请到官网注册一个 -odl login +# 安装 OpenXLab CLI 工具 +pip install -U openxlab +# 登录 OpenXLab +openxlab login # 使用 MIM 下载数据集, 最好在 $MMPreTrain 目录执行 mim download mmpretrain --dataset imagenet1k ``` @@ -276,7 +276,7 @@ test_dataloader = val_dataloader | [`SUN397`](mmpretrain.datasets.SUN397)(data_root[, split, pipeline, ...]) | ["train", "test"] | [SUN397](https://vision.princeton.edu/projects/2010/SUN/) 数据集 | | [`VOC`](mmpretrain.datasets.VOC)(data_root[, image_set_path, pipeline, ...]) | ["train", "val", "tranval", "test"] | [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) 数据集 | -有些数据集主页链接可能已经失效，您可以通过[OpenDataLab](https://opendatalab.com/)下载数据集，例如 [Stanford Cars](https://opendatalab.com/Stanford_Cars/download)数据集。 +有些数据集主页链接可能已经失效，您可以通过[OpenXLab](https://openxlab.org.cn/datasets?lang=zh-CN)下载数据集，例如 [Stanford Cars](https://openxlab.org.cn/datasets/OpenDataLab/Stanford_Cars)数据集。 ## OpenMMLab 2.0 标准数据集 From e1675e893e4720629ef995c620bc2c63f1d52b65 Mon Sep 17 00:00:00 2001 From: "zhengjie.xu" Date: Wed, 30 Aug 2023 06:47:21 -0500 Subject: [PATCH 02/15] [Docs] Update QRcode (#1778) * Add miaomiao_qrcode.jpg * Update qrcode --- README_zh-CN.md | 4 ++-- resources/miaomiao_qrcode.jpg | Bin 0 -> 225737 bytes 2 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 resources/miaomiao_qrcode.jpg diff --git a/README_zh-CN.md b/README_zh-CN.md index 6820dd64..801d3183 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -333,10 +333,10 @@ MMPreTrain 是一款由不同学校和公司共同贡献的开源项目。我们 ## 欢迎加入 OpenMMLab 社区 -扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3) 或联络 OpenMMLab 官方微信小助手 +扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，扫描下方微信二维码添加喵喵好友，进入 MMPretrain 微信交流社群。【加好友申请格式：研究方向+地区+学校/公司+姓名】
- +
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.../configs/_base_/datasets/cub_bs8_384.py | 59 ++++++++++++ .../_base_/datasets/imagenet_bs64_swin_256.py | 89 +++++++++++++++++++ .../datasets/imagenet_bs64_swin_384 copy.py | 55 ++++++++++++ .../models/swin_transformer/base_224.py | 29 ++++++ .../models/swin_transformer/base_384.py | 21 +++++ .../models/swin_transformer/large_224.py | 17 ++++ .../models/swin_transformer/large_384.py | 21 +++++ .../models/swin_transformer/small_224.py | 29 ++++++ .../models/swin_transformer/tiny_224.py | 29 ++++++ .../models/swin_transformer_v2/base_256.py | 29 ++++++ .../models/swin_transformer_v2/base_384.py | 19 ++++ .../models/swin_transformer_v2/large_256.py | 20 +++++ .../models/swin_transformer_v2/large_384.py | 20 +++++ .../models/swin_transformer_v2/small_256.py | 30 +++++++ .../models/swin_transformer_v2/tiny_256.py | 29 ++++++ .../configs/_base_/schedules/cub_bs64.py | 39 ++++++++ .../swin-base_16xb64_in1k-384px.py | 12 +++ .../swin_transformer/swin-base_16xb64_in1k.py | 12 +++ .../swin-large_16xb64_in1k-384px.py | 12 +++ .../swin-large_16xb64_in1k.py | 12 +++ .../swin-large_8xb8_cub-384px.py | 48 ++++++++++ .../swin-small_16xb64_in1k.py | 12 +++ .../swin_transformer/swin-tiny_16xb64_in1k.py | 12 +++ .../swinv2-base-w12_8xb128_in21k-192px.py | 22 +++++ .../swinv2-base-w16_16xb64_in1k-256px.py | 11 +++ ...v2-base-w16_in21k-pre_16xb64_in1k-256px.py | 18 ++++ ...v2-base-w24_in21k-pre_16xb64_in1k-384px.py | 19 ++++ .../swinv2-base-w8_16xb64_in1k-256px.py | 9 ++ .../swinv2-large-w12_8xb128_in21k-192px.py | 22 +++++ ...2-large-w16_in21k-pre_16xb64_in1k-256px.py | 18 ++++ ...2-large-w24_in21k-pre_16xb64_in1k-384px.py | 20 +++++ .../swinv2-small-w16_16xb64_in1k-256px.py | 11 +++ .../swinv2-small-w8_16xb64_in1k-256px.py | 9 ++ .../swinv2-tiny-w16_16xb64_in1k-256px.py | 11 +++ .../swinv2-tiny-w8_16xb64_in1k-256px.py | 9 ++ 35 files changed, 834 insertions(+) create mode 100644 mmpretrain/configs/_base_/datasets/cub_bs8_384.py create mode 100644 mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_256.py create mode 100644 mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_384 copy.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer/base_224.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer/base_384.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer/large_224.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer/large_384.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer/small_224.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer/tiny_224.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer_v2/base_256.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer_v2/base_384.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer_v2/large_256.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer_v2/large_384.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer_v2/small_256.py create mode 100644 mmpretrain/configs/_base_/models/swin_transformer_v2/tiny_256.py create mode 100644 mmpretrain/configs/_base_/schedules/cub_bs64.py create mode 100644 mmpretrain/configs/swin_transformer/swin-base_16xb64_in1k-384px.py create mode 100644 mmpretrain/configs/swin_transformer/swin-base_16xb64_in1k.py create mode 100644 mmpretrain/configs/swin_transformer/swin-large_16xb64_in1k-384px.py create mode 100644 mmpretrain/configs/swin_transformer/swin-large_16xb64_in1k.py create mode 100644 mmpretrain/configs/swin_transformer/swin-large_8xb8_cub-384px.py create mode 100644 mmpretrain/configs/swin_transformer/swin-small_16xb64_in1k.py create mode 100644 mmpretrain/configs/swin_transformer/swin-tiny_16xb64_in1k.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-base-w12_8xb128_in21k-192px.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-large-w12_8xb128_in21k-192px.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py create mode 100644 mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py diff --git a/mmpretrain/configs/_base_/datasets/cub_bs8_384.py b/mmpretrain/configs/_base_/datasets/cub_bs8_384.py new file mode 100644 index 00000000..b193bf83 --- /dev/null +++ b/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 diff --git a/mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_256.py b/mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_256.py new file mode 100644 index 00000000..9690ff84 --- /dev/null +++ b/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 diff --git a/mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_384 copy.py b/mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_384 copy.py new file mode 100644 index 00000000..fb1102b3 --- /dev/null +++ b/mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_384 copy.py @@ -0,0 +1,55 @@ +# 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 diff --git a/mmpretrain/configs/_base_/models/swin_transformer/base_224.py b/mmpretrain/configs/_base_/models/swin_transformer/base_224.py new file mode 100644 index 00000000..5ba4adac --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer/base_224.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 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)]), +) diff --git a/mmpretrain/configs/_base_/models/swin_transformer/base_384.py b/mmpretrain/configs/_base_/models/swin_transformer/base_384.py new file mode 100644 index 00000000..d747fa08 --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer/base_384.py @@ -0,0 +1,21 @@ +# 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))) diff --git a/mmpretrain/configs/_base_/models/swin_transformer/large_224.py b/mmpretrain/configs/_base_/models/swin_transformer/large_224.py new file mode 100644 index 00000000..758600e7 --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer/large_224.py @@ -0,0 +1,17 @@ +# 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))) diff --git a/mmpretrain/configs/_base_/models/swin_transformer/large_384.py b/mmpretrain/configs/_base_/models/swin_transformer/large_384.py new file mode 100644 index 00000000..9cb01033 --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer/large_384.py @@ -0,0 +1,21 @@ +# 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))) diff --git a/mmpretrain/configs/_base_/models/swin_transformer/small_224.py b/mmpretrain/configs/_base_/models/swin_transformer/small_224.py new file mode 100644 index 00000000..f6de6ac0 --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer/small_224.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 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)]), +) diff --git a/mmpretrain/configs/_base_/models/swin_transformer/tiny_224.py b/mmpretrain/configs/_base_/models/swin_transformer/tiny_224.py new file mode 100644 index 00000000..fc976cc0 --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer/tiny_224.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 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)]), +) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/base_256.py b/mmpretrain/configs/_base_/models/swin_transformer_v2/base_256.py new file mode 100644 index 00000000..9fcfffeb --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer_v2/base_256.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 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)]), +) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/base_384.py b/mmpretrain/configs/_base_/models/swin_transformer_v2/base_384.py new file mode 100644 index 00000000..c7566b5e --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer_v2/base_384.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)) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/large_256.py b/mmpretrain/configs/_base_/models/swin_transformer_v2/large_256.py new file mode 100644 index 00000000..da36e679 --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer_v2/large_256.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, + SwinTransformerV2) + +# model settings +# Only for evaluation +model = dict( + type=ImageClassifier, + backbone=dict( + type=SwinTransformerV2, arch='large', img_size=256, + drop_path_rate=0.2), + 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))) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/large_384.py b/mmpretrain/configs/_base_/models/swin_transformer_v2/large_384.py new file mode 100644 index 00000000..5e1323d5 --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer_v2/large_384.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, + SwinTransformerV2) + +# model settings +# Only for evaluation +model = dict( + type=ImageClassifier, + backbone=dict( + type=SwinTransformerV2, arch='large', img_size=384, + drop_path_rate=0.2), + 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))) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/small_256.py b/mmpretrain/configs/_base_/models/swin_transformer_v2/small_256.py new file mode 100644 index 00000000..e747fd6a --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer_v2/small_256.py @@ -0,0 +1,30 @@ +# 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='small', img_size=256, + 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)]), +) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/tiny_256.py b/mmpretrain/configs/_base_/models/swin_transformer_v2/tiny_256.py new file mode 100644 index 00000000..8d8bfacf --- /dev/null +++ b/mmpretrain/configs/_base_/models/swin_transformer_v2/tiny_256.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 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='tiny', img_size=256, 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)]), +) diff --git a/mmpretrain/configs/_base_/schedules/cub_bs64.py b/mmpretrain/configs/_base_/schedules/cub_bs64.py new file mode 100644 index 00000000..2ca40bfe --- /dev/null +++ b/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) diff --git a/mmpretrain/configs/swin_transformer/swin-base_16xb64_in1k-384px.py b/mmpretrain/configs/swin_transformer/swin-base_16xb64_in1k-384px.py new file mode 100644 index 00000000..76548d93 --- /dev/null +++ b/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_384 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +# schedule settings +optim_wrapper = dict(clip_grad=dict(max_norm=5.0)) diff --git a/mmpretrain/configs/swin_transformer/swin-base_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin-base_16xb64_in1k.py new file mode 100644 index 00000000..12ec65ea --- /dev/null +++ b/mmpretrain/configs/swin_transformer/swin-base_16xb64_in1k.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_224 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer.base_224 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +# schedule settings +optim_wrapper = dict(clip_grad=dict(max_norm=5.0)) diff --git a/mmpretrain/configs/swin_transformer/swin-large_16xb64_in1k-384px.py b/mmpretrain/configs/swin_transformer/swin-large_16xb64_in1k-384px.py new file mode 100644 index 00000000..f4a6143b --- /dev/null +++ b/mmpretrain/configs/swin_transformer/swin-large_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.large_384 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +# schedule settings +optim_wrapper = dict(clip_grad=dict(max_norm=5.0)) diff --git a/mmpretrain/configs/swin_transformer/swin-large_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin-large_16xb64_in1k.py new file mode 100644 index 00000000..4b22f5ae --- /dev/null +++ b/mmpretrain/configs/swin_transformer/swin-large_16xb64_in1k.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_224 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer.large_224 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +# schedule settings +optim_wrapper = dict(clip_grad=dict(max_norm=5.0)) diff --git a/mmpretrain/configs/swin_transformer/swin-large_8xb8_cub-384px.py b/mmpretrain/configs/swin_transformer/swin-large_8xb8_cub-384px.py new file mode 100644 index 00000000..6156e306 --- /dev/null +++ b/mmpretrain/configs/swin_transformer/swin-large_8xb8_cub-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 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.large_384 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 = dict( + type=ImageClassifier, + backbone=dict( + init_cfg=dict( + type=PretrainedInit, checkpoint=checkpoint, prefix='backbone')), + head=dict(num_classes=200, )) + +# 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)) diff --git a/mmpretrain/configs/swin_transformer/swin-small_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin-small_16xb64_in1k.py new file mode 100644 index 00000000..969edee7 --- /dev/null +++ b/mmpretrain/configs/swin_transformer/swin-small_16xb64_in1k.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_224 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer.small_224 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +# schedule settings +optim_wrapper = dict(clip_grad=dict(max_norm=5.0)) diff --git a/mmpretrain/configs/swin_transformer/swin-tiny_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin-tiny_16xb64_in1k.py new file mode 100644 index 00000000..ded80639 --- /dev/null +++ b/mmpretrain/configs/swin_transformer/swin-tiny_16xb64_in1k.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_224 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer.tiny_224 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +# schedule settings +optim_wrapper = dict(clip_grad=dict(max_norm=5.0)) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-base-w12_8xb128_in21k-192px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-base-w12_8xb128_in21k-192px.py new file mode 100644 index 00000000..7ca933f8 --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-base-w12_8xb128_in21k-192px.py @@ -0,0 +1,22 @@ +# 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.imagenet21k_bs128 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.base_256 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +# model settings +model = dict( + backbone=dict(img_size=192, window_size=[12, 12, 12, 6]), + head=dict(num_classes=21841), +) + +# dataset settings +data_preprocessor = dict(num_classes=21841) + +_base_['train_pipeline'][1]['scale'] = 192 # RandomResizedCrop +_base_['test_pipeline'][1]['scale'] = 219 # ResizeEdge +_base_['test_pipeline'][2]['crop_size'] = 192 # CenterCrop diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py new file mode 100644 index 00000000..6df69c48 --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py @@ -0,0 +1,11 @@ +# 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_256 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.base_256 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py new file mode 100644 index 00000000..f9f05216 --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.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 + +from mmpretrain.models import ImageClassifier + +with read_base(): + from .._base_.datasets.imagenet_bs64_swin_256 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.base_256 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +model = dict( + type=ImageClassifier, + backbone=dict( + window_size=[16, 16, 16, 8], + drop_path_rate=0.2, + pretrained_window_sizes=[12, 12, 12, 6])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py new file mode 100644 index 00000000..6538144f --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.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 ImageClassifier + +with read_base(): + from .._base_.datasets.imagenet_bs64_swin_384 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.base_384 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +model = dict( + type=ImageClassifier, + backbone=dict( + img_size=384, + window_size=[24, 24, 24, 12], + drop_path_rate=0.2, + pretrained_window_sizes=[12, 12, 12, 6])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py new file mode 100644 index 00000000..34298ff6 --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py @@ -0,0 +1,9 @@ +# 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_256 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.base_256 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-large-w12_8xb128_in21k-192px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-large-w12_8xb128_in21k-192px.py new file mode 100644 index 00000000..7ca933f8 --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-large-w12_8xb128_in21k-192px.py @@ -0,0 +1,22 @@ +# 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.imagenet21k_bs128 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.base_256 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +# model settings +model = dict( + backbone=dict(img_size=192, window_size=[12, 12, 12, 6]), + head=dict(num_classes=21841), +) + +# dataset settings +data_preprocessor = dict(num_classes=21841) + +_base_['train_pipeline'][1]['scale'] = 192 # RandomResizedCrop +_base_['test_pipeline'][1]['scale'] = 219 # ResizeEdge +_base_['test_pipeline'][2]['crop_size'] = 192 # CenterCrop diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py new file mode 100644 index 00000000..bbfe9283 --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py @@ -0,0 +1,18 @@ +# Only for evaluation +# 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 ImageClassifier + +with read_base(): + from .._base_.datasets.imagenet_bs64_swin_256 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.large_256 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +model = dict( + type=ImageClassifier, + backbone=dict( + window_size=[16, 16, 16, 8], pretrained_window_sizes=[12, 12, 12, 6]), +) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py new file mode 100644 index 00000000..a481c79d --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py @@ -0,0 +1,20 @@ +# Only for evaluation +# 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 ImageClassifier + +with read_base(): + from .._base_.datasets.imagenet_bs64_swin_384 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.large_384 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +model = dict( + type=ImageClassifier, + backbone=dict( + img_size=384, + window_size=[24, 24, 24, 12], + pretrained_window_sizes=[12, 12, 12, 6]), +) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py new file mode 100644 index 00000000..8051f050 --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py @@ -0,0 +1,11 @@ +# 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_256 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.small_256 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py new file mode 100644 index 00000000..d28ffd06 --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py @@ -0,0 +1,9 @@ +# 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_256 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.small_256 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py new file mode 100644 index 00000000..a95485da --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py @@ -0,0 +1,11 @@ +# 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_256 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.tiny_256 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py new file mode 100644 index 00000000..59ba55c3 --- /dev/null +++ b/mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py @@ -0,0 +1,9 @@ +# 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_256 import * + from .._base_.default_runtime import * + from .._base_.models.swin_transformer_v2.tiny_256 import * + from .._base_.schedules.imagenet_bs1024_adamw_swin import * From da1da48eb6bb8285b28277f1dd06ca30ffbe3dfe Mon Sep 17 00:00:00 2001 From: ZhangYiqin <312065559@qq.com> Date: Mon, 4 Sep 2023 13:11:16 +0800 Subject: [PATCH 04/15] [Enhance] Add iTPN Supports for Non-three channel image (#1735) * Add channel argments to mae_head When trying iTPN pretrain, it only supports images with 3 channels. One of the restrictions is from MAEHead. * Transfer other argments from iTPNHiViT to HiViT The HiViT supports specifying channels, but the iTPNHiViT class can't pass channel argments to it. This is one of the reasons that iTPNHiViT implementation only support images with 3 channels. * Update itpn.py Fix hint problem --- mmpretrain/models/heads/mae_head.py | 23 +++++++++++++---------- mmpretrain/models/selfsup/itpn.py | 5 ++++- 2 files changed, 17 insertions(+), 11 deletions(-) diff --git a/mmpretrain/models/heads/mae_head.py b/mmpretrain/models/heads/mae_head.py index 1a5366d1..b76ecedd 100644 --- a/mmpretrain/models/heads/mae_head.py +++ b/mmpretrain/models/heads/mae_head.py @@ -14,15 +14,18 @@ class MAEPretrainHead(BaseModule): norm_pix_loss (bool): Whether or not normalize target. Defaults to False. patch_size (int): Patch size. Defaults to 16. + in_channels (int): Number of input channels. Defaults to 3. """ def __init__(self, loss: dict, norm_pix: bool = False, - patch_size: int = 16) -> None: + patch_size: int = 16, + in_channels: int = 3) -> None: super().__init__() self.norm_pix = norm_pix self.patch_size = patch_size + self.in_channels = in_channels self.loss_module = MODELS.build(loss) def patchify(self, imgs: torch.Tensor) -> torch.Tensor: @@ -30,19 +33,19 @@ class MAEPretrainHead(BaseModule): Args: imgs (torch.Tensor): A batch of images. The shape should - be :math:`(B, 3, H, W)`. + be :math:`(B, C, H, W)`. Returns: torch.Tensor: Patchified images. The shape is - :math:`(B, L, \text{patch_size}^2 \times 3)`. + :math:`(B, L, \text{patch_size}^2 \times C)`. """ p = self.patch_size assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0 h = w = imgs.shape[2] // p - x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p)) + x = imgs.reshape(shape=(imgs.shape[0], self.in_channels, h, p, w, p)) x = torch.einsum('nchpwq->nhwpqc', x) - x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * 3)) + x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * self.in_channels)) return x def unpatchify(self, x: torch.Tensor) -> torch.Tensor: @@ -50,18 +53,18 @@ class MAEPretrainHead(BaseModule): Args: x (torch.Tensor): The shape is - :math:`(B, L, \text{patch_size}^2 \times 3)`. + :math:`(B, L, \text{patch_size}^2 \times C)`. Returns: - torch.Tensor: The shape is :math:`(B, 3, H, W)`. + torch.Tensor: The shape is :math:`(B, C, H, W)`. """ p = self.patch_size h = w = int(x.shape[1]**.5) assert h * w == x.shape[1] - x = x.reshape(shape=(x.shape[0], h, w, p, p, 3)) + x = x.reshape(shape=(x.shape[0], h, w, p, p, self.in_channels)) x = torch.einsum('nhwpqc->nchpwq', x) - imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p)) + imgs = x.reshape(shape=(x.shape[0], self.in_channels, h * p, h * p)) return imgs def construct_target(self, target: torch.Tensor) -> torch.Tensor: @@ -71,7 +74,7 @@ class MAEPretrainHead(BaseModule): normalize the image according to ``norm_pix``. Args: - target (torch.Tensor): Image with the shape of B x 3 x H x W + target (torch.Tensor): Image with the shape of B x C x H x W Returns: torch.Tensor: Tokenized images with the shape of B x L x C diff --git a/mmpretrain/models/selfsup/itpn.py b/mmpretrain/models/selfsup/itpn.py index 85efd254..488a9963 100644 --- a/mmpretrain/models/selfsup/itpn.py +++ b/mmpretrain/models/selfsup/itpn.py @@ -64,6 +64,7 @@ class iTPNHiViT(HiViT): layer_scale_init_value: float = 0.0, mask_ratio: float = 0.75, reconstruction_type: str = 'pixel', + **kwargs, ): super().__init__( arch=arch, @@ -80,7 +81,9 @@ class iTPNHiViT(HiViT): norm_cfg=norm_cfg, ape=ape, rpe=rpe, - layer_scale_init_value=layer_scale_init_value) + layer_scale_init_value=layer_scale_init_value, + **kwargs, + ) self.pos_embed.requires_grad = False self.mask_ratio = mask_ratio From ed3b7f8ae6d972ce0f84d8abd04495cf745276cf Mon Sep 17 00:00:00 2001 From: John Date: Tue, 5 Sep 2023 16:00:29 +0800 Subject: [PATCH 05/15] format all file names --- .../datasets/imagenet_bs64_swin_384 copy.py | 55 ------------------- ...6xb64_in1k.py => swin_base_16xb64_in1k.py} | 0 ...84px.py => swin_base_16xb64_in1k_384px.py} | 0 ...xb64_in1k.py => swin_large_16xb64_in1k.py} | 0 ...4px.py => swin_large_16xb64_in1k_384px.py} | 0 ...-384px.py => swin_large_8xb8_cub_384px.py} | 0 ...xb64_in1k.py => swin_small_16xb64_in1k.py} | 0 ...6xb64_in1k.py => swin_tiny_16xb64_in1k.py} | 0 ... => swinv2_base_w12_8xb128_in21k_192px.py} | 0 ...y => swinv2_base_w16_16xb64_in1k_256px.py} | 0 ...2_base_w16_in21k_pre_16xb64_in1k_256px.py} | 0 ...2_base_w24_in21k_pre_16xb64_in1k_384px.py} | 0 ...py => swinv2_base_w8_16xb64_in1k_256px.py} | 0 ...=> swinv2_large_w12_8xb128_in21k_192px.py} | 0 ..._large_w16_in21k_pre_16xb64_in1k_256px.py} | 0 ..._large_w24_in21k_pre_16xb64_in1k_384px.py} | 0 ... => swinv2_small_w16_16xb64_in1k_256px.py} | 0 ...y => swinv2_small_w8_16xb64_in1k_256px.py} | 0 ...y => swinv2_tiny_w16_16xb64_in1k_256px.py} | 0 ...py => swinv2_tiny_w8_16xb64_in1k_256px.py} | 0 20 files changed, 55 deletions(-) delete mode 100644 mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_384 copy.py rename mmpretrain/configs/swin_transformer/{swin-base_16xb64_in1k.py => swin_base_16xb64_in1k.py} (100%) rename mmpretrain/configs/swin_transformer/{swin-base_16xb64_in1k-384px.py => swin_base_16xb64_in1k_384px.py} (100%) rename mmpretrain/configs/swin_transformer/{swin-large_16xb64_in1k.py => swin_large_16xb64_in1k.py} (100%) rename mmpretrain/configs/swin_transformer/{swin-large_16xb64_in1k-384px.py => swin_large_16xb64_in1k_384px.py} (100%) rename mmpretrain/configs/swin_transformer/{swin-large_8xb8_cub-384px.py => swin_large_8xb8_cub_384px.py} (100%) rename mmpretrain/configs/swin_transformer/{swin-small_16xb64_in1k.py => swin_small_16xb64_in1k.py} (100%) rename mmpretrain/configs/swin_transformer/{swin-tiny_16xb64_in1k.py => swin_tiny_16xb64_in1k.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-base-w12_8xb128_in21k-192px.py => swinv2_base_w12_8xb128_in21k_192px.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-base-w16_16xb64_in1k-256px.py => swinv2_base_w16_16xb64_in1k_256px.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py => swinv2_base_w16_in21k_pre_16xb64_in1k_256px.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py => swinv2_base_w24_in21k_pre_16xb64_in1k_384px.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-base-w8_16xb64_in1k-256px.py => swinv2_base_w8_16xb64_in1k_256px.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-large-w12_8xb128_in21k-192px.py => swinv2_large_w12_8xb128_in21k_192px.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py => swinv2_large_w16_in21k_pre_16xb64_in1k_256px.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py => swinv2_large_w24_in21k_pre_16xb64_in1k_384px.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-small-w16_16xb64_in1k-256px.py => swinv2_small_w16_16xb64_in1k_256px.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-small-w8_16xb64_in1k-256px.py => swinv2_small_w8_16xb64_in1k_256px.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-tiny-w16_16xb64_in1k-256px.py => swinv2_tiny_w16_16xb64_in1k_256px.py} (100%) rename mmpretrain/configs/swin_transformer_v2/{swinv2-tiny-w8_16xb64_in1k-256px.py => swinv2_tiny_w8_16xb64_in1k_256px.py} (100%) diff --git a/mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_384 copy.py b/mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_384 copy.py deleted file mode 100644 index fb1102b3..00000000 --- a/mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_384 copy.py +++ /dev/null @@ -1,55 +0,0 @@ -# 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 diff --git a/mmpretrain/configs/swin_transformer/swin-base_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k.py similarity index 100% rename from mmpretrain/configs/swin_transformer/swin-base_16xb64_in1k.py rename to mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k.py diff --git a/mmpretrain/configs/swin_transformer/swin-base_16xb64_in1k-384px.py b/mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k_384px.py similarity index 100% rename from mmpretrain/configs/swin_transformer/swin-base_16xb64_in1k-384px.py rename to mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k_384px.py diff --git a/mmpretrain/configs/swin_transformer/swin-large_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k.py similarity index 100% rename from mmpretrain/configs/swin_transformer/swin-large_16xb64_in1k.py rename to mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k.py diff --git a/mmpretrain/configs/swin_transformer/swin-large_16xb64_in1k-384px.py b/mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k_384px.py similarity index 100% rename from mmpretrain/configs/swin_transformer/swin-large_16xb64_in1k-384px.py rename to mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k_384px.py diff --git a/mmpretrain/configs/swin_transformer/swin-large_8xb8_cub-384px.py b/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py similarity index 100% rename from mmpretrain/configs/swin_transformer/swin-large_8xb8_cub-384px.py rename to mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py diff --git a/mmpretrain/configs/swin_transformer/swin-small_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin_small_16xb64_in1k.py similarity index 100% rename from mmpretrain/configs/swin_transformer/swin-small_16xb64_in1k.py rename to mmpretrain/configs/swin_transformer/swin_small_16xb64_in1k.py diff --git a/mmpretrain/configs/swin_transformer/swin-tiny_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin_tiny_16xb64_in1k.py similarity index 100% rename from mmpretrain/configs/swin_transformer/swin-tiny_16xb64_in1k.py rename to mmpretrain/configs/swin_transformer/swin_tiny_16xb64_in1k.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-base-w12_8xb128_in21k-192px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w12_8xb128_in21k_192px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-base-w12_8xb128_in21k-192px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_base_w12_8xb128_in21k_192px.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_16xb64_in1k_256px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_16xb64_in1k_256px.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_in21k_pre_16xb64_in1k_256px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_in21k_pre_16xb64_in1k_256px.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w24_in21k_pre_16xb64_in1k_384px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_base_w24_in21k_pre_16xb64_in1k_384px.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w8_16xb64_in1k_256px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_base_w8_16xb64_in1k_256px.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-large-w12_8xb128_in21k-192px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w12_8xb128_in21k_192px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-large-w12_8xb128_in21k-192px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_large_w12_8xb128_in21k_192px.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w16_in21k_pre_16xb64_in1k_256px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_large_w16_in21k_pre_16xb64_in1k_256px.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w24_in21k_pre_16xb64_in1k_384px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_large_w24_in21k_pre_16xb64_in1k_384px.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_small_w16_16xb64_in1k_256px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_small_w16_16xb64_in1k_256px.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_small_w8_16xb64_in1k_256px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_small_w8_16xb64_in1k_256px.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w16_16xb64_in1k_256px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w16_16xb64_in1k_256px.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w8_16xb64_in1k_256px.py similarity index 100% rename from mmpretrain/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py rename to mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w8_16xb64_in1k_256px.py From f4d372ba7d9a1e10fc8422d01fd7fc0a875f8c7c Mon Sep 17 00:00:00 2001 From: John Date: Tue, 5 Sep 2023 21:26:43 +0800 Subject: [PATCH 06/15] only keep one file to set swin transformer model config --- .../models/swin_transformer/base_224.py | 29 ------------------- .../models/swin_transformer/large_224.py | 17 ----------- .../models/swin_transformer/large_384.py | 21 -------------- .../models/swin_transformer/small_224.py | 29 ------------------- .../models/swin_transformer/tiny_224.py | 29 ------------------- .../base_384.py => swin_transformer_base.py} | 0 .../swin_transformer/swin_base_16xb64_in1k.py | 25 +++++++++++++++- .../swin_base_16xb64_in1k_384px.py | 2 +- .../swin_large_16xb64_in1k.py | 8 ++++- .../swin_large_16xb64_in1k_384px.py | 8 ++++- .../swin_large_8xb8_cub_384px.py | 8 ++++- .../swin_small_16xb64_in1k.py | 27 ++++++++++++++++- .../swin_transformer/swin_tiny_16xb64_in1k.py | 27 ++++++++++++++++- 13 files changed, 98 insertions(+), 132 deletions(-) delete mode 100644 mmpretrain/configs/_base_/models/swin_transformer/base_224.py delete mode 100644 mmpretrain/configs/_base_/models/swin_transformer/large_224.py delete mode 100644 mmpretrain/configs/_base_/models/swin_transformer/large_384.py delete mode 100644 mmpretrain/configs/_base_/models/swin_transformer/small_224.py delete mode 100644 mmpretrain/configs/_base_/models/swin_transformer/tiny_224.py rename mmpretrain/configs/_base_/models/{swin_transformer/base_384.py => swin_transformer_base.py} (100%) diff --git a/mmpretrain/configs/_base_/models/swin_transformer/base_224.py b/mmpretrain/configs/_base_/models/swin_transformer/base_224.py deleted file mode 100644 index 5ba4adac..00000000 --- a/mmpretrain/configs/_base_/models/swin_transformer/base_224.py +++ /dev/null @@ -1,29 +0,0 @@ -# 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)]), -) diff --git a/mmpretrain/configs/_base_/models/swin_transformer/large_224.py b/mmpretrain/configs/_base_/models/swin_transformer/large_224.py deleted file mode 100644 index 758600e7..00000000 --- a/mmpretrain/configs/_base_/models/swin_transformer/large_224.py +++ /dev/null @@ -1,17 +0,0 @@ -# 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))) diff --git a/mmpretrain/configs/_base_/models/swin_transformer/large_384.py b/mmpretrain/configs/_base_/models/swin_transformer/large_384.py deleted file mode 100644 index 9cb01033..00000000 --- a/mmpretrain/configs/_base_/models/swin_transformer/large_384.py +++ /dev/null @@ -1,21 +0,0 @@ -# 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))) diff --git a/mmpretrain/configs/_base_/models/swin_transformer/small_224.py b/mmpretrain/configs/_base_/models/swin_transformer/small_224.py deleted file mode 100644 index f6de6ac0..00000000 --- a/mmpretrain/configs/_base_/models/swin_transformer/small_224.py +++ /dev/null @@ -1,29 +0,0 @@ -# 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)]), -) diff --git a/mmpretrain/configs/_base_/models/swin_transformer/tiny_224.py b/mmpretrain/configs/_base_/models/swin_transformer/tiny_224.py deleted file mode 100644 index fc976cc0..00000000 --- a/mmpretrain/configs/_base_/models/swin_transformer/tiny_224.py +++ /dev/null @@ -1,29 +0,0 @@ -# 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)]), -) diff --git a/mmpretrain/configs/_base_/models/swin_transformer/base_384.py b/mmpretrain/configs/_base_/models/swin_transformer_base.py similarity index 100% rename from mmpretrain/configs/_base_/models/swin_transformer/base_384.py rename to mmpretrain/configs/_base_/models/swin_transformer_base.py diff --git a/mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k.py index 12ec65ea..09af3d01 100644 --- a/mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k.py +++ b/mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k.py @@ -1,12 +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_224 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)) diff --git a/mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k_384px.py b/mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k_384px.py index 76548d93..aacdc327 100644 --- a/mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k_384px.py +++ b/mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k_384px.py @@ -5,7 +5,7 @@ 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_384 import * + from .._base_.models.swin_transformer_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * # schedule settings diff --git a/mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k.py index 4b22f5ae..b8fc2793 100644 --- a/mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k.py +++ b/mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k.py @@ -5,8 +5,14 @@ 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.large_224 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)) diff --git a/mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k_384px.py b/mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k_384px.py index f4a6143b..9a449aa6 100644 --- a/mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k_384px.py +++ b/mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k_384px.py @@ -5,8 +5,14 @@ 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.large_384 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)) diff --git a/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py b/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py index 6156e306..779daaa3 100644 --- a/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py +++ b/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py @@ -10,11 +10,17 @@ 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.large_384 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'), + head=dict(in_channels=1536), +) + model = dict( type=ImageClassifier, backbone=dict( diff --git a/mmpretrain/configs/swin_transformer/swin_small_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin_small_16xb64_in1k.py index 969edee7..59792528 100644 --- a/mmpretrain/configs/swin_transformer/swin_small_16xb64_in1k.py +++ b/mmpretrain/configs/swin_transformer/swin_small_16xb64_in1k.py @@ -1,12 +1,37 @@ # 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.small_224 import * + from .._base_.models.swin_transformer_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * +# model settings +model.update( + backbone=dict( + arch='small', img_size=224, drop_path_rate=0.3, stage_cfgs=None), + head=dict( + in_channels=768, + 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)) diff --git a/mmpretrain/configs/swin_transformer/swin_tiny_16xb64_in1k.py b/mmpretrain/configs/swin_transformer/swin_tiny_16xb64_in1k.py index ded80639..733e1ef0 100644 --- a/mmpretrain/configs/swin_transformer/swin_tiny_16xb64_in1k.py +++ b/mmpretrain/configs/swin_transformer/swin_tiny_16xb64_in1k.py @@ -1,12 +1,37 @@ # 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.tiny_224 import * + from .._base_.models.swin_transformer_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * +# model settings +model.update( + backbone=dict( + arch='tiny', img_size=224, drop_path_rate=0.2, stage_cfgs=None), + head=dict( + in_channels=768, + 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)) From 9b75ce0aa4dac6d2016cd5b7f975ef7e02bb20ec Mon Sep 17 00:00:00 2001 From: John Date: Tue, 5 Sep 2023 22:16:07 +0800 Subject: [PATCH 07/15] only keep one file to set swin transformer v2 model config --- .../_base_/models/swin_transformer_base.py | 1 - .../models/swin_transformer_v2/base_256.py | 29 ------------------ .../models/swin_transformer_v2/large_256.py | 20 ------------- .../models/swin_transformer_v2/large_384.py | 20 ------------- .../models/swin_transformer_v2/small_256.py | 30 ------------------- .../models/swin_transformer_v2/tiny_256.py | 29 ------------------ ...ase_384.py => swin_transformer_v2_base.py} | 0 .../swinv2_base_w12_8xb128_in21k_192px.py | 2 +- .../swinv2_base_w16_16xb64_in1k_256px.py | 2 +- ...v2_base_w16_in21k_pre_16xb64_in1k_256px.py | 2 +- ...v2_base_w24_in21k_pre_16xb64_in1k_384px.py | 2 +- .../swinv2_base_w8_16xb64_in1k_256px.py | 16 +++++++++- .../swinv2_large_w12_8xb128_in21k_192px.py | 2 +- ...2_large_w16_in21k_pre_16xb64_in1k_256px.py | 2 +- ...2_large_w24_in21k_pre_16xb64_in1k_384px.py | 2 +- .../swinv2_small_w16_16xb64_in1k_256px.py | 2 +- .../swinv2_small_w8_16xb64_in1k_256px.py | 17 ++++++++++- .../swinv2_tiny_w16_16xb64_in1k_256px.py | 2 +- .../swinv2_tiny_w8_16xb64_in1k_256px.py | 17 ++++++++++- 19 files changed, 56 insertions(+), 141 deletions(-) delete mode 100644 mmpretrain/configs/_base_/models/swin_transformer_v2/base_256.py delete mode 100644 mmpretrain/configs/_base_/models/swin_transformer_v2/large_256.py delete mode 100644 mmpretrain/configs/_base_/models/swin_transformer_v2/large_384.py delete mode 100644 mmpretrain/configs/_base_/models/swin_transformer_v2/small_256.py delete mode 100644 mmpretrain/configs/_base_/models/swin_transformer_v2/tiny_256.py rename mmpretrain/configs/_base_/models/{swin_transformer_v2/base_384.py => swin_transformer_v2_base.py} (100%) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_base.py b/mmpretrain/configs/_base_/models/swin_transformer_base.py index d747fa08..c73c254d 100644 --- a/mmpretrain/configs/_base_/models/swin_transformer_base.py +++ b/mmpretrain/configs/_base_/models/swin_transformer_base.py @@ -4,7 +4,6 @@ from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling, ImageClassifier, LinearClsHead, SwinTransformer) # model settings -# Only for evaluation model = dict( type=ImageClassifier, backbone=dict( diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/base_256.py b/mmpretrain/configs/_base_/models/swin_transformer_v2/base_256.py deleted file mode 100644 index 9fcfffeb..00000000 --- a/mmpretrain/configs/_base_/models/swin_transformer_v2/base_256.py +++ /dev/null @@ -1,29 +0,0 @@ -# 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)]), -) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/large_256.py b/mmpretrain/configs/_base_/models/swin_transformer_v2/large_256.py deleted file mode 100644 index da36e679..00000000 --- a/mmpretrain/configs/_base_/models/swin_transformer_v2/large_256.py +++ /dev/null @@ -1,20 +0,0 @@ -# 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, - SwinTransformerV2) - -# model settings -# Only for evaluation -model = dict( - type=ImageClassifier, - backbone=dict( - type=SwinTransformerV2, arch='large', img_size=256, - drop_path_rate=0.2), - 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))) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/large_384.py b/mmpretrain/configs/_base_/models/swin_transformer_v2/large_384.py deleted file mode 100644 index 5e1323d5..00000000 --- a/mmpretrain/configs/_base_/models/swin_transformer_v2/large_384.py +++ /dev/null @@ -1,20 +0,0 @@ -# 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, - SwinTransformerV2) - -# model settings -# Only for evaluation -model = dict( - type=ImageClassifier, - backbone=dict( - type=SwinTransformerV2, arch='large', img_size=384, - drop_path_rate=0.2), - 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))) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/small_256.py b/mmpretrain/configs/_base_/models/swin_transformer_v2/small_256.py deleted file mode 100644 index e747fd6a..00000000 --- a/mmpretrain/configs/_base_/models/swin_transformer_v2/small_256.py +++ /dev/null @@ -1,30 +0,0 @@ -# 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='small', img_size=256, - 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)]), -) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/tiny_256.py b/mmpretrain/configs/_base_/models/swin_transformer_v2/tiny_256.py deleted file mode 100644 index 8d8bfacf..00000000 --- a/mmpretrain/configs/_base_/models/swin_transformer_v2/tiny_256.py +++ /dev/null @@ -1,29 +0,0 @@ -# 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='tiny', img_size=256, 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)]), -) diff --git a/mmpretrain/configs/_base_/models/swin_transformer_v2/base_384.py b/mmpretrain/configs/_base_/models/swin_transformer_v2_base.py similarity index 100% rename from mmpretrain/configs/_base_/models/swin_transformer_v2/base_384.py rename to mmpretrain/configs/_base_/models/swin_transformer_v2_base.py diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w12_8xb128_in21k_192px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w12_8xb128_in21k_192px.py index 7ca933f8..79ad9f07 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w12_8xb128_in21k_192px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w12_8xb128_in21k_192px.py @@ -5,7 +5,7 @@ from mmengine.config import read_base with read_base(): from .._base_.datasets.imagenet21k_bs128 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.base_256 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * # model settings diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_16xb64_in1k_256px.py index 6df69c48..a10fc1e4 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_16xb64_in1k_256px.py @@ -5,7 +5,7 @@ from mmengine.config import read_base with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.base_256 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_in21k_pre_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_in21k_pre_16xb64_in1k_256px.py index f9f05216..d3dd0b35 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_in21k_pre_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_in21k_pre_16xb64_in1k_256px.py @@ -7,7 +7,7 @@ from mmpretrain.models import ImageClassifier with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.base_256 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * model = dict( diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w24_in21k_pre_16xb64_in1k_384px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w24_in21k_pre_16xb64_in1k_384px.py index 6538144f..e9ee34a4 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w24_in21k_pre_16xb64_in1k_384px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w24_in21k_pre_16xb64_in1k_384px.py @@ -7,7 +7,7 @@ from mmpretrain.models import ImageClassifier with read_base(): from .._base_.datasets.imagenet_bs64_swin_384 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.base_384 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * model = dict( diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w8_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w8_16xb64_in1k_256px.py index 34298ff6..d40144cb 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w8_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w8_16xb64_in1k_256px.py @@ -1,9 +1,23 @@ # 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, Mixup with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.base_256 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +# model settings +model.update( + backbone=dict(img_size=256, drop_path_rate=0.5), + 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)])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w12_8xb128_in21k_192px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w12_8xb128_in21k_192px.py index 7ca933f8..79ad9f07 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w12_8xb128_in21k_192px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w12_8xb128_in21k_192px.py @@ -5,7 +5,7 @@ from mmengine.config import read_base with read_base(): from .._base_.datasets.imagenet21k_bs128 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.base_256 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * # model settings diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w16_in21k_pre_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w16_in21k_pre_16xb64_in1k_256px.py index bbfe9283..8990b7fc 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w16_in21k_pre_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w16_in21k_pre_16xb64_in1k_256px.py @@ -8,7 +8,7 @@ from mmpretrain.models import ImageClassifier with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.large_256 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * model = dict( diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w24_in21k_pre_16xb64_in1k_384px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w24_in21k_pre_16xb64_in1k_384px.py index a481c79d..7cb8b7c0 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w24_in21k_pre_16xb64_in1k_384px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w24_in21k_pre_16xb64_in1k_384px.py @@ -8,7 +8,7 @@ from mmpretrain.models import ImageClassifier with read_base(): from .._base_.datasets.imagenet_bs64_swin_384 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.large_384 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * model = dict( diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_small_w16_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_small_w16_16xb64_in1k_256px.py index 8051f050..a10fc1e4 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_small_w16_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_small_w16_16xb64_in1k_256px.py @@ -5,7 +5,7 @@ from mmengine.config import read_base with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.small_256 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_small_w8_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_small_w8_16xb64_in1k_256px.py index d28ffd06..bfec3466 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_small_w8_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_small_w8_16xb64_in1k_256px.py @@ -1,9 +1,24 @@ # 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, Mixup with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.small_256 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +# model settings +model.update( + backbone=dict(arch='small', img_size=256, drop_path_rate=0.3), + head=dict(in_channels=768), + 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)])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w16_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w16_16xb64_in1k_256px.py index a95485da..a10fc1e4 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w16_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w16_16xb64_in1k_256px.py @@ -5,7 +5,7 @@ from mmengine.config import read_base with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.tiny_256 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w8_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w8_16xb64_in1k_256px.py index 59ba55c3..8cca2b38 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w8_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w8_16xb64_in1k_256px.py @@ -1,9 +1,24 @@ # 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, Mixup with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * from .._base_.default_runtime import * - from .._base_.models.swin_transformer_v2.tiny_256 import * + from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * + +# model settings +model.update( + backbone=dict(arch='tiny', img_size=256, drop_path_rate=0.2), + head=dict(in_channels=768), + 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)])) From b0b4422736d069c6eaeb3e0e584ea8d42b9d4138 Mon Sep 17 00:00:00 2001 From: John Date: Tue, 5 Sep 2023 22:22:43 +0800 Subject: [PATCH 08/15] fix a redundant --- .../configs/swin_transformer/swin_large_8xb8_cub_384px.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py b/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py index 779daaa3..ef2559a8 100644 --- a/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py +++ b/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py @@ -16,11 +16,6 @@ with read_base(): # 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'), - head=dict(in_channels=1536), -) - model = dict( type=ImageClassifier, backbone=dict( From 7734f073e42d6f284b7772aa1ac837970c1b6b49 Mon Sep 17 00:00:00 2001 From: John Date: Wed, 6 Sep 2023 23:56:03 +0800 Subject: [PATCH 09/15] set arch etc --- .../swin_large_8xb8_cub_384px.py | 6 +++--- .../swinv2_base_w12_8xb128_in21k_192px.py | 16 +++++++++++++--- .../swinv2_base_w16_16xb64_in1k_256px.py | 15 ++++++++++++++- ...v2_base_w16_in21k_pre_16xb64_in1k_256px.py | 18 +++++++++++++----- ...v2_base_w24_in21k_pre_16xb64_in1k_384px.py | 11 +++-------- .../swinv2_large_w12_8xb128_in21k_192px.py | 16 +++++++++++++--- ...2_large_w16_in21k_pre_16xb64_in1k_256px.py | 16 +++++++++++----- ...2_large_w24_in21k_pre_16xb64_in1k_384px.py | 12 ++++++++---- .../swinv2_small_w16_16xb64_in1k_256px.py | 19 ++++++++++++++++++- .../swinv2_tiny_w16_16xb64_in1k_256px.py | 19 ++++++++++++++++++- 10 files changed, 114 insertions(+), 34 deletions(-) diff --git a/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py b/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py index ef2559a8..2003cd3a 100644 --- a/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py +++ b/mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py @@ -16,12 +16,12 @@ with read_base(): # model settings checkpoint = 'https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-large_3rdparty_in21k-384px.pth' # noqa -model = dict( - type=ImageClassifier, +model.update( backbone=dict( + arch='large', init_cfg=dict( type=PretrainedInit, checkpoint=checkpoint, prefix='backbone')), - head=dict(num_classes=200, )) + head=dict(num_classes=200, in_channels=1536)) # schedule settings optim_wrapper = dict( diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w12_8xb128_in21k_192px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w12_8xb128_in21k_192px.py index 79ad9f07..1ecc4363 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w12_8xb128_in21k_192px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w12_8xb128_in21k_192px.py @@ -1,6 +1,9 @@ # 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, Mixup with read_base(): from .._base_.datasets.imagenet21k_bs128 import * @@ -9,10 +12,17 @@ with read_base(): from .._base_.schedules.imagenet_bs1024_adamw_swin import * # model settings -model = dict( - backbone=dict(img_size=192, window_size=[12, 12, 12, 6]), +model.update( + backbone=dict( + img_size=192, drop_path_rate=0.5, window_size=[12, 12, 12, 6]), head=dict(num_classes=21841), -) + 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)])) # dataset settings data_preprocessor = dict(num_classes=21841) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_16xb64_in1k_256px.py index a10fc1e4..103afb42 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_16xb64_in1k_256px.py @@ -1,6 +1,9 @@ # 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, Mixup with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * @@ -8,4 +11,14 @@ with read_base(): from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * -model = dict(backbone=dict(window_size=[16, 16, 16, 8])) +# model settings +model.update( + backbone=dict( + img_size=256, drop_path_rate=0.5, window_size=[16, 16, 16, 8]), + 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)])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_in21k_pre_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_in21k_pre_16xb64_in1k_256px.py index d3dd0b35..6588f50f 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_in21k_pre_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w16_in21k_pre_16xb64_in1k_256px.py @@ -1,8 +1,9 @@ # 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 ImageClassifier +from mmpretrain.models import CutMix, Mixup with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * @@ -10,9 +11,16 @@ with read_base(): from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * -model = dict( - type=ImageClassifier, +# model settings +model.update( backbone=dict( + img_size=256, window_size=[16, 16, 16, 8], - drop_path_rate=0.2, - pretrained_window_sizes=[12, 12, 12, 6])) + pretrained_window_sizes=[12, 12, 12, 6]), + 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)])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w24_in21k_pre_16xb64_in1k_384px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w24_in21k_pre_16xb64_in1k_384px.py index e9ee34a4..118c085e 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_base_w24_in21k_pre_16xb64_in1k_384px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_base_w24_in21k_pre_16xb64_in1k_384px.py @@ -2,18 +2,13 @@ # This is a BETA new format config file, and the usage may change recently. from mmengine.config import read_base -from mmpretrain.models import ImageClassifier - with read_base(): from .._base_.datasets.imagenet_bs64_swin_384 import * from .._base_.default_runtime import * from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * -model = dict( - type=ImageClassifier, +# model settings +model.update( backbone=dict( - img_size=384, - window_size=[24, 24, 24, 12], - drop_path_rate=0.2, - pretrained_window_sizes=[12, 12, 12, 6])) + window_size=[24, 24, 24, 12], pretrained_window_sizes=[12, 12, 12, 6])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w12_8xb128_in21k_192px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w12_8xb128_in21k_192px.py index 79ad9f07..1ecc4363 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w12_8xb128_in21k_192px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w12_8xb128_in21k_192px.py @@ -1,6 +1,9 @@ # 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, Mixup with read_base(): from .._base_.datasets.imagenet21k_bs128 import * @@ -9,10 +12,17 @@ with read_base(): from .._base_.schedules.imagenet_bs1024_adamw_swin import * # model settings -model = dict( - backbone=dict(img_size=192, window_size=[12, 12, 12, 6]), +model.update( + backbone=dict( + img_size=192, drop_path_rate=0.5, window_size=[12, 12, 12, 6]), head=dict(num_classes=21841), -) + 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)])) # dataset settings data_preprocessor = dict(num_classes=21841) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w16_in21k_pre_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w16_in21k_pre_16xb64_in1k_256px.py index 8990b7fc..0a1b59df 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w16_in21k_pre_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w16_in21k_pre_16xb64_in1k_256px.py @@ -3,7 +3,7 @@ # This is a BETA new format config file, and the usage may change recently. from mmengine.config import read_base -from mmpretrain.models import ImageClassifier +from mmpretrain.models import CrossEntropyLoss with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * @@ -11,8 +11,14 @@ with read_base(): from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * -model = dict( - type=ImageClassifier, +# model settings +model.update( backbone=dict( - window_size=[16, 16, 16, 8], pretrained_window_sizes=[12, 12, 12, 6]), -) + arch='large', + img_size=256, + window_size=[16, 16, 16, 8], + pretrained_window_sizes=[12, 12, 12, 6]), + head=dict( + in_channels=1536, + loss=dict(type=CrossEntropyLoss, loss_weight=1.0), + topk=(1, 5))) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w24_in21k_pre_16xb64_in1k_384px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w24_in21k_pre_16xb64_in1k_384px.py index 7cb8b7c0..b20bcead 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_large_w24_in21k_pre_16xb64_in1k_384px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_large_w24_in21k_pre_16xb64_in1k_384px.py @@ -3,7 +3,7 @@ # This is a BETA new format config file, and the usage may change recently. from mmengine.config import read_base -from mmpretrain.models import ImageClassifier +from mmpretrain.models import CrossEntropyLoss with read_base(): from .._base_.datasets.imagenet_bs64_swin_384 import * @@ -11,10 +11,14 @@ with read_base(): from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * -model = dict( - type=ImageClassifier, +# model settings +model.update( backbone=dict( + arch='large', img_size=384, window_size=[24, 24, 24, 12], pretrained_window_sizes=[12, 12, 12, 6]), -) + head=dict( + in_channels=1536, + loss=dict(type=CrossEntropyLoss, loss_weight=1.0), + topk=(1, 5))) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_small_w16_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_small_w16_16xb64_in1k_256px.py index a10fc1e4..dfd15c31 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_small_w16_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_small_w16_16xb64_in1k_256px.py @@ -1,6 +1,9 @@ # 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, Mixup with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * @@ -8,4 +11,18 @@ with read_base(): from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * -model = dict(backbone=dict(window_size=[16, 16, 16, 8])) +# model settings +model.update( + backbone=dict( + arch='small', + img_size=256, + drop_path_rate=0.3, + window_size=[16, 16, 16, 8]), + head=dict(in_channels=768), + 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)])) diff --git a/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w16_16xb64_in1k_256px.py b/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w16_16xb64_in1k_256px.py index a10fc1e4..f2fa1609 100644 --- a/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w16_16xb64_in1k_256px.py +++ b/mmpretrain/configs/swin_transformer_v2/swinv2_tiny_w16_16xb64_in1k_256px.py @@ -1,6 +1,9 @@ # 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, Mixup with read_base(): from .._base_.datasets.imagenet_bs64_swin_256 import * @@ -8,4 +11,18 @@ with read_base(): from .._base_.models.swin_transformer_v2_base import * from .._base_.schedules.imagenet_bs1024_adamw_swin import * -model = dict(backbone=dict(window_size=[16, 16, 16, 8])) +# model settings +model.update( + backbone=dict( + arch='tiny', + img_size=256, + drop_path_rate=0.2, + window_size=[16, 16, 16, 8]), + head=dict(in_channels=768), + 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)])) From 06bb586eb715626f19e97dfa8b632f104ba47d2b Mon Sep 17 00:00:00 2001 From: mzr1996 Date: Sun, 8 Oct 2023 15:44:37 +0800 Subject: [PATCH 10/15] [Fix] Fix pipeline bug in image retrieval inferencer --- mmpretrain/apis/image_retrieval.py | 1 + 1 file changed, 1 insertion(+) diff --git a/mmpretrain/apis/image_retrieval.py b/mmpretrain/apis/image_retrieval.py index deae1de7..27919b20 100644 --- a/mmpretrain/apis/image_retrieval.py +++ b/mmpretrain/apis/image_retrieval.py @@ -108,6 +108,7 @@ class ImageRetrievalInferencer(BaseInferencer): # A config of dataset from mmpretrain.registry import DATASETS test_pipeline = [dict(type='LoadImageFromFile'), self.pipeline] + prototype.setdefault('pipeline', test_pipeline) dataset = DATASETS.build(prototype) dataloader = build_dataloader(dataset) elif isinstance(prototype, DataLoader): From 3bcf7e2d6ed1d4c215dcf5e404dd6da52e8f0e3d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=A3=9E=E9=A3=9E?= <102729089+ASHORE1225@users.noreply.github.com> Date: Sun, 8 Oct 2023 15:46:47 +0800 Subject: [PATCH 11/15] =?UTF-8?q?[CodeCamp2023-341]=20=E5=A4=9A=E6=A8=A1?= =?UTF-8?q?=E6=80=81=E6=95=B0=E6=8D=AE=E9=9B=86=E6=96=87=E6=A1=A3=E8=A1=A5?= =?UTF-8?q?=E5=85=85-COCO=20Retrieval?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mmpretrain/datasets/coco_retrieval.py | 75 ++++++++++++++++++++++++++- 1 file changed, 73 insertions(+), 2 deletions(-) diff --git a/mmpretrain/datasets/coco_retrieval.py b/mmpretrain/datasets/coco_retrieval.py index 60d1586a..be8a0bcb 100644 --- a/mmpretrain/datasets/coco_retrieval.py +++ b/mmpretrain/datasets/coco_retrieval.py @@ -1,18 +1,45 @@ # Copyright (c) OpenMMLab. All rights reserved. import json +import os.path as osp from collections import OrderedDict -from typing import List +from os import PathLike +from typing import List, Sequence, Union from mmengine import get_file_backend -from mmpretrain.registry import DATASETS +from mmpretrain.registry import DATASETS, TRANSFORMS from .base_dataset import BaseDataset +def expanduser(data_prefix): + if isinstance(data_prefix, (str, PathLike)): + return osp.expanduser(data_prefix) + else: + return data_prefix + + @DATASETS.register_module() class COCORetrieval(BaseDataset): """COCO Retrieval dataset. + COCO (Common Objects in Context): The COCO dataset contains more than + 330K images,each of which has approximately 5 descriptive annotations. + This dataset was releasedin collaboration between Microsoft and Carnegie + Mellon University + + COCO_2014 dataset directory: :: + + COCO_2014 + ├── val2014 + ├── train2014 + ├── annotations + ├── instances_train2014.json + ├── instances_val2014.json + ├── person_keypoints_train2014.json + ├── person_keypoints_val2014.json + ├── captions_train2014.json + ├── captions_val2014.json + Args: ann_file (str): Annotation file path. test_mode (bool): Whether dataset is used for evaluation. This will @@ -23,8 +50,52 @@ class COCORetrieval(BaseDataset): data_prefix (str | dict): Prefix for training data. Defaults to ''. pipeline (Sequence): Processing pipeline. Defaults to an empty tuple. **kwargs: Other keyword arguments in :class:`BaseDataset`. + + Examples: + >>> from mmpretrain.datasets import COCORetrieval + >>> train_dataset=COCORetrieval(data_root='coco2014/') + >>> train_dataset + Dataset COCORetrieval + Number of samples: 414113 + Annotation file: /coco2014/annotations/captions_train2014.json + Prefix of images: /coco2014/ + >>> from mmpretrain.datasets import COCORetrieval + >>> val_dataset = COCORetrieval(data_root='coco2014/') + >>> val_dataset + Dataset COCORetrieval + Number of samples: 202654 + Annotation file: /coco2014/annotations/captions_val2014.json + Prefix of images: /coco2014/ """ + def __init__(self, + ann_file: str, + test_mode: bool = False, + data_prefix: Union[str, dict] = '', + data_root: str = '', + pipeline: Sequence = (), + **kwargs): + + if isinstance(data_prefix, str): + data_prefix = dict(img_path=expanduser(data_prefix)) + + ann_file = expanduser(ann_file) + transforms = [] + for transform in pipeline: + if isinstance(transform, dict): + transforms.append(TRANSFORMS.build(transform)) + else: + transforms.append(transform) + + super().__init__( + data_root=data_root, + data_prefix=data_prefix, + test_mode=test_mode, + pipeline=transforms, + ann_file=ann_file, + **kwargs, + ) + def load_data_list(self) -> List[dict]: """Load data list.""" # get file backend From b0a792eb08f7857e06977969b61354733d082d33 Mon Sep 17 00:00:00 2001 From: mzr1996 Date: Wed, 11 Oct 2023 11:11:59 +0800 Subject: [PATCH 12/15] Update OFA to compat with latest huggingface. --- mmpretrain/models/multimodal/ofa/ofa_modules.py | 1 + 1 file changed, 1 insertion(+) diff --git a/mmpretrain/models/multimodal/ofa/ofa_modules.py b/mmpretrain/models/multimodal/ofa/ofa_modules.py index 1c79049b..ef5c8533 100644 --- a/mmpretrain/models/multimodal/ofa/ofa_modules.py +++ b/mmpretrain/models/multimodal/ofa/ofa_modules.py @@ -1301,6 +1301,7 @@ class OFAEncoderDecoder(BaseModule, GenerationMixin): Defaults to an empty dict. init_cfg (dict, optional): The initialization config. Defaults to None. """ + base_model_prefix = '' def __init__( self, From 4849324629994aa719d09d27f6b851986fda7044 Mon Sep 17 00:00:00 2001 From: mzr1996 Date: Wed, 11 Oct 2023 11:12:32 +0800 Subject: [PATCH 13/15] Update train.py to compat with new config --- tools/train.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/tools/train.py b/tools/train.py index 84c1eec9..89c8548f 100644 --- a/tools/train.py +++ b/tools/train.py @@ -91,10 +91,6 @@ def merge_args(cfg, args): # enable automatic-mixed-precision training if args.amp is True: - optim_wrapper = cfg.optim_wrapper.get('type', 'OptimWrapper') - assert optim_wrapper in ['OptimWrapper', 'AmpOptimWrapper'], \ - '`--amp` is not supported custom optimizer wrapper type ' \ - f'`{optim_wrapper}.' cfg.optim_wrapper.type = 'AmpOptimWrapper' cfg.optim_wrapper.setdefault('loss_scale', 'dynamic') From c0766519b1094dc5c74ef661d41d0aa0db5639d7 Mon Sep 17 00:00:00 2001 From: hmtbgc <32740258+hmtbgc@users.noreply.github.com> Date: Thu, 12 Oct 2023 10:36:17 +0800 Subject: [PATCH 14/15] [Feature] Add minigpt4 gradio demo and training script. (#1758) * Add minigpt4 gradio demo * update minigpt4 demo * update minigpt4 demo (inference with float16) * update minigpt4 and some dependent files * add minigpt4 dataset for training * add training script for minigpt4 * restore files deleted by mistake * fix an error * remove useless modification * provide command line arguments for minigpt4 gradio demo and update some comments * update code * Update minigpt-4 readme --------- Co-authored-by: mzr1996 --- configs/minigpt4/README.md | 7 +- configs/minigpt4/metafile.yml | 13 +- .../minigpt4/minigpt-4_baichuan-7b_caption.py | 190 ++++++++++++++++++ .../minigpt4/minigpt-4_vicuna-7b_caption.py | 26 ++- mmpretrain/datasets/__init__.py | 4 +- mmpretrain/datasets/minigpt4_dataset.py | 79 ++++++++ .../models/multimodal/minigpt4/minigpt4.py | 101 ++++++---- projects/gradio_demo/conversation.py | 137 +++++++++++++ projects/gradio_demo/minigpt4_demo.py | 144 +++++++++++++ 9 files changed, 651 insertions(+), 50 deletions(-) create mode 100644 configs/minigpt4/minigpt-4_baichuan-7b_caption.py create mode 100644 mmpretrain/datasets/minigpt4_dataset.py create mode 100644 projects/gradio_demo/conversation.py create mode 100644 projects/gradio_demo/minigpt4_demo.py diff --git a/configs/minigpt4/README.md b/configs/minigpt4/README.md index 01e53954..23666fc9 100644 --- a/configs/minigpt4/README.md +++ b/configs/minigpt4/README.md @@ -34,9 +34,10 @@ For Vicuna model, please refer to [MiniGPT-4 page](https://github.com/Vision-CAI ### Pretrained models -| Model | Params (M) | Flops (G) | Config | Download | -| :------------------------------ | :--------: | :-------: | :--------------------------------------: | :------------------------------------------------------------------------------------------------------------: | -| `minigpt-4_vicuna-7b_caption`\* | 8121.32 | N/A | [config](minigpt-4_vicuna-7b_caption.py) | [model](https://download.openmmlab.com/mmpretrain/v1.0/minigpt4/minigpt-4_linear-projection_20230615-714b5f52.pth) | +| Model | Params (M) | Flops (G) | Config | Download | +| :------------------------------ | :--------: | :-------: | :----------------------------------------: | :----------------------------------------------------------------------------------------------------------: | +| `minigpt-4_baichuan-7b_caption` | 8094.77 | N/A | [config](minigpt-4_baichuan-7b_caption.py) | [model](https://download.openmmlab.com/mmclassification/v1/minigpt4/minigpt-4_linear_baichuan7b_20231011-5dca7ed6.pth) | +| `minigpt-4_vicuna-7b_caption`\* | 8121.32 | N/A | [config](minigpt-4_vicuna-7b_caption.py) | [model](https://download.openmmlab.com/mmclassification/v1/minigpt4/minigpt-4_linear_vicuna7b_20230615-714b5f52.pth) | *Models with * are converted from the [official repo](https://github.com/Vision-CAIR/MiniGPT-4/tree/main). The config files of these models are only for inference. We haven't reproduce the training results.* diff --git a/configs/minigpt4/metafile.yml b/configs/minigpt4/metafile.yml index a7879d98..f70cc9ba 100644 --- a/configs/minigpt4/metafile.yml +++ b/configs/minigpt4/metafile.yml @@ -19,8 +19,19 @@ Models: - Task: Image Caption Dataset: COCO Metrics: null - Weights: https://download.openmmlab.com/mmpretrain/v1.0/minigpt4/minigpt-4_linear-projection_20230615-714b5f52.pth + Weights: https://download.openmmlab.com/mmclassification/v1/minigpt4/minigpt-4_linear_vicuna7b_20230615-714b5f52.pth Config: configs/minigpt4/minigpt-4_vicuna-7b_caption.py Converted From: Weights: https://github.com/Vision-CAIR/MiniGPT-4/tree/main Code: https://github.com/Vision-CAIR/MiniGPT-4/tree/main + - Name: minigpt-4_baichuan-7b_caption + Metadata: + FLOPs: null + Parameters: 8094769024 + In Collection: MiniGPT4 + Results: + - Task: Image Caption + Dataset: COCO + Metrics: null + Weights: https://download.openmmlab.com/mmclassification/v1/minigpt4/minigpt-4_linear_baichuan7b_20231011-5dca7ed6.pth + Config: configs/minigpt4/minigpt-4_baichuan-7b_caption.py diff --git a/configs/minigpt4/minigpt-4_baichuan-7b_caption.py b/configs/minigpt4/minigpt-4_baichuan-7b_caption.py new file mode 100644 index 00000000..7e610a09 --- /dev/null +++ b/configs/minigpt4/minigpt-4_baichuan-7b_caption.py @@ -0,0 +1,190 @@ +_base_ = [ + '../_base_/default_runtime.py', +] + +data_preprocessor = dict( + type='MultiModalDataPreprocessor', + mean=[122.770938, 116.7460125, 104.09373615], + std=[68.5005327, 66.6321579, 70.32316305], + to_rgb=True, +) + +# dataset settings +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + scale=(224, 224), + interpolation='bicubic', + backend='pillow'), + dict(type='RandomFlip', prob=0.5, direction='horizontal'), + dict( + type='CleanCaption', + keys='chat_content', + remove_chars='', + lowercase=False), + dict( + type='PackInputs', + algorithm_keys=['chat_content', 'lang'], + meta_keys=['image_id']), +] + +train_dataloader = dict( + batch_size=2, + num_workers=4, + dataset=dict( + type='MiniGPT4Dataset', + data_root='YOUR_DATA_DIRECTORY', + ann_file='YOUR_DATA_FILE', + pipeline=train_pipeline), + sampler=dict(type='DefaultSampler', shuffle=True), + collate_fn=dict(type='default_collate'), + drop_last=False, +) + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + scale=(224, 224), + interpolation='bicubic', + backend='pillow'), + dict(type='PackInputs', meta_keys=['image_id']), +] + +test_evaluator = dict( + type='COCOCaption', + ann_file='data/coco/annotations/coco_karpathy_val_gt.json', +) + +test_dataloader = dict( + batch_size=1, + dataset=dict( + type='COCOCaption', + data_root='data/coco', + ann_file='annotations/coco_karpathy_val.json', + pipeline=test_pipeline)) + +# model settings +model = dict( + type='MiniGPT4', + vision_encoder=dict( + type='BEiTViT', + # eva-g without the final layer + arch=dict( + embed_dims=1408, + num_layers=39, + num_heads=16, + feedforward_channels=6144, + ), + img_size=224, + patch_size=14, + layer_scale_init_value=0.0, + frozen_stages=39, + use_abs_pos_emb=True, + use_rel_pos_bias=False, + final_norm=False, + use_shared_rel_pos_bias=False, + out_type='raw', + pretrained= # noqa + 'https://download.openmmlab.com/mmpretrain/v1.0/minigpt4/minigpt-4_eva-g-p14_20230615-e908c021.pth' # noqa + ), + q_former_model=dict( + type='Qformer', + model_style='bert-base-uncased', + vision_model_width=1408, + add_cross_attention=True, + cross_attention_freq=2, + num_query_token=32, + pretrained= # noqa + 'https://download.openmmlab.com/mmpretrain/v1.0/minigpt4/minigpt-4_qformer_20230615-1dfa889c.pth' # noqa + ), + lang_encoder=dict( + type='AutoModelForCausalLM', + name_or_path='baichuan-inc/baichuan-7B', + trust_remote_code=True), + tokenizer=dict( + type='AutoTokenizer', + name_or_path='baichuan-inc/baichuan-7B', + trust_remote_code=True), + task='caption', + prompt_template=dict([('en', '###Ask: {} ###Answer: '), + ('zh', '###问：{} ###答：')]), + raw_prompts=dict([ + ('en', [(' ' + 'Describe this image in detail.'), + (' ' + 'Take a look at this image and describe what you notice.'), + (' ' + 'Please provide a detailed description of the picture.'), + (' ' + 'Could you describe the contents of this image for me?')]), + ('zh', [(' ' + '详细描述这张图片。'), (' ' + '浏览这张图片并描述你注意到什么。'), + (' ' + '请对这张图片进行详细的描述。'), + (' ' + '你能为我描述这张图片的内容吗？')]) + ]), + max_txt_len=160, + end_sym='###') + +strategy = dict( + type='DeepSpeedStrategy', + fp16=dict( + enabled=True, + auto_cast=False, + fp16_master_weights_and_grads=False, + loss_scale=0, + loss_scale_window=1000, + hysteresis=1, + min_loss_scale=1, + initial_scale_power=16, + ), + inputs_to_half=[0], + zero_optimization=dict( + stage=2, + allgather_partitions=True, + allgather_bucket_size=2e8, + reduce_scatter=True, + reduce_bucket_size='auto', + overlap_comm=True, + contiguous_gradients=True, + ), +) + +# schedule settings +optim_wrapper = dict( + type='DeepSpeedOptimWrapper', + optimizer=dict(type='AdamW', lr=1e-3, weight_decay=0.05)) + +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1e-3 / 500, + by_epoch=False, + begin=0, + end=500, + ), + dict( + type='CosineAnnealingLR', + eta_min=2e-4, + by_epoch=False, + begin=500, + ), +] + +train_cfg = dict(by_epoch=True, max_epochs=6) +test_cfg = dict() + +runner_type = 'FlexibleRunner' + +default_hooks = dict( + checkpoint=dict( + type='CheckpointHook', + interval=1, + by_epoch=True, + save_last=True, + max_keep_ckpts=1, + )) diff --git a/configs/minigpt4/minigpt-4_vicuna-7b_caption.py b/configs/minigpt4/minigpt-4_vicuna-7b_caption.py index 704760af..f468e2d8 100644 --- a/configs/minigpt4/minigpt-4_vicuna-7b_caption.py +++ b/configs/minigpt4/minigpt-4_vicuna-7b_caption.py @@ -55,13 +55,25 @@ model = dict( type='AutoModelForCausalLM', name_or_path='YOUR_PATH_TO_VICUNA'), tokenizer=dict(type='LlamaTokenizer', name_or_path='YOUR_PATH_TO_VICUNA'), task='caption', - prompt_template='###Human: {} ###Assistant: ', - raw_prompts=[ - ' Describe this image in detail.', - ' Take a look at this image and describe what you notice.', # noqa - ' Please provide a detailed description of the picture.', # noqa - ' Could you describe the contents of this image for me?', # noqa - ], + prompt_template=dict([('en', '###Ask: {} ###Answer: '), + ('zh', '###问：{} ###答：')]), + raw_prompts=dict([ + ('en', [(' ' + 'Describe this image in detail.'), + (' ' + 'Take a look at this image and describe what you notice.'), + (' ' + 'Please provide a detailed description of the picture.'), + (' ' + 'Could you describe the contents of this image for me?')]), + ('zh', [(' ' + '详细描述这张图片。'), (' ' + '浏览这张图片并描述你注意到什么。'), + (' ' + '请对这张图片进行详细的描述。'), + (' ' + '你能为我描述这张图片的内容吗？')]) + ]), max_txt_len=160, end_sym='###') diff --git a/mmpretrain/datasets/__init__.py b/mmpretrain/datasets/__init__.py index 29753d70..e621e157 100644 --- a/mmpretrain/datasets/__init__.py +++ b/mmpretrain/datasets/__init__.py @@ -43,6 +43,7 @@ if WITH_MULTIMODAL: from .gqa_dataset import GQA from .iconqa import IconQA from .infographic_vqa import InfographicVQA + from .minigpt4_dataset import MiniGPT4Dataset from .nocaps import NoCaps from .ocr_vqa import OCRVQA from .refcoco import RefCOCO @@ -56,5 +57,6 @@ if WITH_MULTIMODAL: 'COCOCaption', 'COCORetrieval', 'COCOVQA', 'FlamingoEvalCOCOCaption', 'FlamingoEvalCOCOVQA', 'Flickr30kCaption', 'Flickr30kRetrieval', 'RefCOCO', 'VisualGenomeQA', 'ScienceQA', 'NoCaps', 'GQA', 'TextVQA', - 'VSR', 'VizWiz', 'OCRVQA', 'InfographicVQA', 'IconQA' + 'VSR', 'VizWiz', 'OCRVQA', 'InfographicVQA', 'IconQA', + 'MiniGPT4Dataset' ]) diff --git a/mmpretrain/datasets/minigpt4_dataset.py b/mmpretrain/datasets/minigpt4_dataset.py new file mode 100644 index 00000000..e14e5c35 --- /dev/null +++ b/mmpretrain/datasets/minigpt4_dataset.py @@ -0,0 +1,79 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import List + +import mmengine +from mmengine.dataset import BaseDataset +from mmengine.fileio import get_file_backend + +from mmpretrain.registry import DATASETS + + +@DATASETS.register_module() +class MiniGPT4Dataset(BaseDataset): + """Dataset for training MiniGPT4. + + MiniGPT4 dataset directory: + + minigpt4_dataset + ├── image + │ ├── id0.jpg + │ │── id1.jpg + │ │── id2.jpg + │ └── ... + └── conversation_data.json + + The structure of conversation_data.json: + + [ + // English data + { + "id": str(id0), + "conversation": "###Ask: [Ask content] + ###Answer: [Answer content]" + }, + + // Chinese data + { + "id": str(id1), + "conversation": "###问： [Ask content] + ###答：[Answer content]" + }, + + ... + ] + + Args: + data_root (str): The root directory for ``ann_file`` and ``image``. + ann_file (str): Conversation file path. + **kwargs: Other keyword arguments in :class:`BaseDataset`. + """ + + def load_data_list(self) -> List[dict]: + file_backend = get_file_backend(self.data_root) + conversation_path = file_backend.join_path(self.data_root, + self.ann_file) + conversation = mmengine.load(conversation_path) + img_ids = {} + n = 0 + for conv in conversation: + img_id = conv['id'] + if img_id not in img_ids.keys(): + img_ids[img_id] = n + n += 1 + + img_root = file_backend.join_path(self.data_root, 'image') + data_list = [] + for conv in conversation: + img_file = '{}.jpg'.format(conv['id']) + chat_content = conv['conversation'] + lang = 'en' if chat_content.startswith('###Ask: ') else 'zh' + data_info = { + 'image_id': img_ids[conv['id']], + 'img_path': file_backend.join_path(img_root, img_file), + 'chat_content': chat_content, + 'lang': lang, + } + + data_list.append(data_info) + + return data_list diff --git a/mmpretrain/models/multimodal/minigpt4/minigpt4.py b/mmpretrain/models/multimodal/minigpt4/minigpt4.py index eccbb27e..d25d0b6b 100644 --- a/mmpretrain/models/multimodal/minigpt4/minigpt4.py +++ b/mmpretrain/models/multimodal/minigpt4/minigpt4.py @@ -31,12 +31,12 @@ class MiniGPT4(BaseModel): True. num_query_token (int): Number of query tokens of Qformer. Defaults to 32. - prompt_template (str): Prompt template of the model. Defaults to - '###Human: {} ###Assistant: '. - raw_prompts (list): Prompts for training. Defaults to None. + prompt_template (dict): Multi-language prompt template of the model. Defaults to dict([ ('en', '###Ask: {} ###Answer: '), + ('zh', '###问：{} ###答：')]) + raw_prompts (dict): Prompts for training. Defaults to dict(). max_txt_len (int): Max token length while doing tokenization. Defaults to 32. - end_sym (str): Ended symbol of the sequence. Defaults to '\\n'. + end_sym (str): Ended symbol of the sequence. Defaults to '###'. generation_cfg (dict): The config of text generation. Defaults to dict(). data_preprocessor (:obj:`BaseDataPreprocessor`): Used for @@ -54,10 +54,12 @@ class MiniGPT4(BaseModel): freeze_vit: bool = True, freeze_q_former: bool = True, num_query_token: int = 32, - prompt_template: str = '###Human: {} ###Assistant: ', - raw_prompts: Optional[list] = None, + prompt_template: dict = dict([('en', + '###Ask: {} ###Answer: '), + ('zh', '###问：{} ###答：')]), + raw_prompts: dict = dict(), max_txt_len: int = 32, - end_sym: str = '\n', + end_sym: str = '###', generation_cfg: dict = dict(), data_preprocessor: Optional[dict] = None, init_cfg: Optional[dict] = None): @@ -135,16 +137,23 @@ class MiniGPT4(BaseModel): self.end_token_id = self.llama_tokenizer.encode(end_sym)[-1] # set prompts - if raw_prompts is not None: - filted_prompts = [ - raw_prompt for raw_prompt in raw_prompts + self.en_prompt_list, self.zh_prompt_list = [], [] + if raw_prompts.get('en') is not None: + en_filted_prompts = [ + raw_prompt for raw_prompt in raw_prompts['en'] if '' in raw_prompt ] - self.prompt_list = [ - prompt_template.format(p) for p in filted_prompts + self.en_prompt_list = [ + prompt_template['en'].format(p) for p in en_filted_prompts + ] + if raw_prompts.get('zh') is not None: + zh_filted_prompts = [ + raw_prompt for raw_prompt in raw_prompts['zh'] + if '' in raw_prompt + ] + self.zh_prompt_list = [ + prompt_template['zh'].format(p) for p in zh_filted_prompts ] - else: - self.prompt_list = [] # update generation configs self.generation_cfg = dict( @@ -153,7 +162,7 @@ class MiniGPT4(BaseModel): do_sample=True, min_length=1, top_p=0.9, - repetition_penalty=1.0, + repetition_penalty=1.1, length_penalty=1.0, temperature=1.0) self.generation_cfg.update(**generation_cfg) @@ -161,6 +170,10 @@ class MiniGPT4(BaseModel): if hasattr(self, 'register_load_state_dict_post_hook'): self.register_load_state_dict_post_hook(self._load_llama_proj_hook) + def half(self): + self.llama_model = self.llama_model.half() + return self + def encode_img(self, images: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """The function to encode the images.""" @@ -184,33 +197,39 @@ class MiniGPT4(BaseModel): return inputs_llama, atts_llama def prompt_wrap(self, img_embeds: torch.Tensor, atts_img: torch.Tensor, - prompt: str) -> Tuple[torch.Tensor, torch.Tensor]: + prompt: List[str]) -> Tuple[torch.Tensor, torch.Tensor]: """The function to wrap the image and prompt. - Currently, the function only supports applying one prompt to all input - images in the one batch. + Make sure that len(prompt) == img_embeds.shape[0]. Args: img_embeds (torch.Tensor): The embedding of the input images. atts_img (torch.Tensor): Attention map of the image embeddings. - prompt (str): The prompt of the batch data. + prompt (List[str]): The prompt of the batch data. Returns: Tuple[torch.Tensor, torch.Tensor]: The embedding and attention map. """ - if prompt: - batch_size = img_embeds.shape[0] - p_before, p_after = prompt.split('') + if len(prompt) > 0: + p_before_list, p_after_list = [], [] + for pro in prompt: + p_before, p_after = pro.split('') + p_before_list.append(p_before) + p_after_list.append(p_after) p_before_tokens = self.llama_tokenizer( - p_before, return_tensors='pt', + p_before_list, + return_tensors='pt', + padding='longest', add_special_tokens=False).to(img_embeds.device) p_after_tokens = self.llama_tokenizer( - p_after, return_tensors='pt', + p_after_list, + return_tensors='pt', + padding='longest', add_special_tokens=False).to(img_embeds.device) p_before_embeds = self.llama_model.model.embed_tokens( - p_before_tokens.input_ids).expand(batch_size, -1, -1) + p_before_tokens.input_ids) p_after_embeds = self.llama_model.model.embed_tokens( - p_after_tokens.input_ids).expand(batch_size, -1, -1) + p_after_tokens.input_ids) wrapped_img_embeds = torch.cat( [p_before_embeds, img_embeds, p_after_embeds], dim=1) wrapped_atts_img = atts_img[:, :1].expand( @@ -234,17 +253,22 @@ class MiniGPT4(BaseModel): """ img_embeds, atts_img = self.encode_img(images) - if self.task == 'caption' and self.prompt_list: - prompt = random.choice(self.prompt_list) - img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, - prompt) - self.llama_tokenizer.padding_side = 'right' - text = [t + self.end_sym for t in data_samples['text_input']] + prompts, texts = [], [] + for t in data_samples: + chat_content = t.chat_content + split_mark = '###Answer: ' if t.lang == 'en' else '###答：' + prompt, text = chat_content.split(split_mark) + prompt += split_mark + text += self.end_sym + prompts.append(prompt) + texts.append(text) + + img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, prompts) to_regress_tokens = self.llama_tokenizer( - text, + texts, return_tensors='pt', padding='longest', truncation=True, @@ -295,10 +319,12 @@ class MiniGPT4(BaseModel): with torch.no_grad(): img_embeds, atts_img = self.encode_img(images) - if self.task == 'caption' and self.prompt_list: - prompt = random.choice(self.prompt_list) - img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, - prompt) + prompts = [ + random.choice(self.zh_prompt_list) if hasattr(t, 'lang') + and t.lang == 'zh' else random.choice(self.en_prompt_list) + for t in data_samples + ] + img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, prompts) batch_size = img_embeds.shape[0] bos = torch.ones( @@ -336,7 +362,6 @@ class MiniGPT4(BaseModel): for output, data_sample in zip(outputs, data_samples): if self.task == 'caption': output = output.split('###')[0] - output = output.split('Assistant:')[-1].strip() data_sample.pred_caption = output else: # raw output diff --git a/projects/gradio_demo/conversation.py b/projects/gradio_demo/conversation.py new file mode 100644 index 00000000..3c594690 --- /dev/null +++ b/projects/gradio_demo/conversation.py @@ -0,0 +1,137 @@ +# Modified from +# https://github.com/Vision-CAIR/MiniGPT-4/blob/main/minigpt4/conversation/conversation.py +import dataclasses +from typing import List + +import torch + + +@dataclasses.dataclass +class Conversation: + system: str + roles: List[str] + messages: List[List[str]] + sep: str = '###' + + def get_prompt(self): + ret = self.system + self.sep + for role, message in self.messages: + if message: + ret += role + ': ' + message + self.sep + else: + ret += role + ':' + return ret + + def append_message(self, role, message): + self.messages.append([role, message]) + + def copy(self): + return Conversation( + system=self.system, + roles=[role for role in self.roles], + messages=[[y for y in x] for x in self.messages], + sep=self.sep, + ) + + def dict(self): + return { + 'system': self.system, + 'roles': self.roles, + 'messages': self.messages, + 'offset': self.offset, + 'sep': self.sep, + } + + +EN_CONV_VISION = Conversation( + system='Give the following image. ' + 'You will be able to see the image once I provide it to you. ' + 'Please answer my questions in detail.', + roles=['Ask', 'Answer'], + messages=[], + sep='###', +) + +ZH_CONV_VISION = Conversation( + system='给定一张图片，请仔细观察这张图片，并回答我的问题。', + roles=['问', '答'], + messages=[], + sep='###', +) + + +class Chat: + + def __init__(self, inferencer, device, is_half=False): + self.device = device + self.inferencer = inferencer + self.model = inferencer.model + self.is_half = is_half + if is_half: + self.model = self.model.half() + self.model = self.model.to(device) + self.max_length = 2000 + + def upload_img(self, image, conv, img_list): + img = next(self.inferencer.preprocess([image])) + img = self.model.data_preprocessor(img, False)['images'] + img = img.to(self.device) + image_emb, _ = self.model.encode_img(img) + img_list.append(image_emb) + conv.append_message(conv.roles[0], '') + + def get_context_emb(self, conv, img_list): + prompt = conv.get_prompt() + prompt_segs = prompt.split('') + seg_tokens = [ + self.model.llama_tokenizer( + seg, return_tensors='pt', + add_special_tokens=(i == 0)).to(self.device).input_ids + for i, seg in enumerate(prompt_segs) + ] + seg_embs = [ + self.model.llama_model.model.embed_tokens(seg_token) + for seg_token in seg_tokens + ] + mixed_embs = [ + emb for pair in zip(seg_embs[:-1], img_list) for emb in pair + ] + [seg_embs[-1]] + mixed_embs = torch.cat(mixed_embs, dim=1) + return mixed_embs + + def ask(self, text, conv): + if len(conv.messages) > 0 and conv.messages[-1][0] == conv.roles[ + 0] and conv.messages[-1][1][-6:] == '': + conv.messages[-1][1] = ' '.join([conv.messages[-1][1], text]) + else: + conv.append_message(conv.roles[0], text) + + def answer(self, conv, img_list, generation_cfg): + conv.append_message(conv.roles[1], None) + embs = self.get_context_emb(conv, img_list) + cur_max_len = generation_cfg['max_new_tokens'] + embs.shape[1] + if cur_max_len > self.max_length: + print('Warning: The number of tokens in current conversation' + 'exceeds the max length. ' + 'The model will not see the contexts outside the range.') + begin_idx = max(0, cur_max_len - self.max_length) + embs = embs[:, begin_idx:] + if self.is_half: + embs = embs.half() + outputs = self.model.llama_model.generate( + inputs_embeds=embs, + eos_token_id=self.model.end_token_id, + **generation_cfg) + + output_token = outputs[0] + if output_token[0] == 0: + output_token = output_token[1:] + elif output_token[0] == 1: + output_token = output_token[1:] + output_text = self.model.llama_tokenizer.decode( + output_token, + add_special_tokens=False, + skip_special_tokens=True) + output_text = output_text.split('###')[0] + conv.messages[-1][1] = output_text + return output_text diff --git a/projects/gradio_demo/minigpt4_demo.py b/projects/gradio_demo/minigpt4_demo.py new file mode 100644 index 00000000..e4d61426 --- /dev/null +++ b/projects/gradio_demo/minigpt4_demo.py @@ -0,0 +1,144 @@ +import argparse + +import gradio as gr +import numpy as np +import torch +from conversation import EN_CONV_VISION, ZH_CONV_VISION, Chat + +from mmpretrain import ImageCaptionInferencer + +parser = argparse.ArgumentParser(description='MiniGPT4 demo') +parser.add_argument( + 'cfg', type=str, help='config file for minigpt4 (absolute path)') +parser.add_argument( + 'ckpt', type=str, help='pretrained file for minigpt4 (absolute path)') +args = parser.parse_args() + +if torch.cuda.is_available(): + devices = [ + torch.device(f'cuda:{i}') for i in range(torch.cuda.device_count()) + ] +elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available(): + devices = [torch.device('mps')] +else: + devices = [torch.device('cpu')] + + +def get_free_device(): + if hasattr(torch.cuda, 'mem_get_info'): + free = [torch.cuda.mem_get_info(gpu)[0] for gpu in devices] + select = max(zip(free, range(len(free))))[1] + else: + import random + select = random.randint(0, len(devices) - 1) + return devices[select] + + +device = get_free_device() +inferencer = ImageCaptionInferencer(model=args.cfg, pretrained=args.ckpt) +model = inferencer.model +chat = Chat(inferencer, device=device, is_half=(device.type != 'cpu')) + + +def reset(chat_state, img_list): + if chat_state is not None: + chat_state.messages = [] + if img_list is not None: + img_list = [] + return (None, gr.update(value=None, interactive=True), + gr.update( + value=None, + placeholder='Please upload your image first', + interactive=False), + gr.update(value='Upload & Start Chat', + interactive=True), chat_state, img_list, + gr.update(value='Restart', interactive=False), + gr.update(value='English', interactive=True)) + + +def upload_img(gr_img, language, chat_state): + if gr_img is None: + return (None, + gr.update( + placeholder='Please upload your image first', + interactive=False), + gr.update(value='Upload & Start Chat', + interactive=True), chat_state, None, + gr.update(value='Restart', interactive=False), + gr.update(value='English', interactive=True)) + + if (language == 'English'): + chat_state = EN_CONV_VISION.copy() + else: + chat_state = ZH_CONV_VISION.copy() + img_list = [] + gr_img_array = np.asarray(gr_img) + chat.upload_img(gr_img_array, chat_state, img_list) + return (gr.update(interactive=False), + gr.update(placeholder='Type and press Enter', interactive=True), + gr.update(value='Start Chatting', + interactive=False), chat_state, img_list, + gr.update(value='Restart', + interactive=True), gr.update(interactive=False)) + + +def ask(user_message, chatbot, chat_state): + if (len(user_message) == 0): + return gr.update( + value=None, + placeholder='Input should not be empty!', + interactive=True), chatbot, chat_state + chat.ask(user_message, chat_state) + chatbot = chatbot + [[user_message, None]] + return '', chatbot, chat_state + + +def answer(chatbot, chat_state, img_list): + llm_message = chat.answer( + conv=chat_state, + img_list=img_list, + generation_cfg=model.generation_cfg) + chatbot[-1][1] = llm_message + return chatbot, chat_state, img_list + + +if __name__ == '__main__': + title = 'MMPretrain MiniGPT-4 Inference Demo' + with gr.Blocks(analytics_enabled=False, title=title) as demo: + gr.Markdown(f'# {title}') + with gr.Row(): + with gr.Column(): + image = gr.Image(type='pil') + language = gr.Dropdown(['English', 'Chinese'], + label='Language', + info='Select chatbot\'s language', + value='English', + interactive=True) + upload_button = gr.Button( + value='Upload & Start Chat', interactive=True) + clear = gr.Button(value='Restart', interactive=False) + + with gr.Column(): + chat_state = gr.State() + img_list = gr.State() + chatbot = gr.Chatbot( + label='MiniGPT-4', min_width=320, height=600) + text_input = gr.Textbox( + label='User', + placeholder='Please upload your image first', + interactive=False) + + upload_button.click(upload_img, [image, language, chat_state], [ + image, text_input, upload_button, chat_state, img_list, clear, + language + ]) + text_input.submit(ask, [text_input, chatbot, chat_state], + [text_input, chatbot, chat_state]).then( + answer, [chatbot, chat_state, img_list], + [chatbot, chat_state, img_list]) + clear.click(reset, [chat_state, img_list], [ + chatbot, image, text_input, upload_button, chat_state, img_list, + clear, language + ]) + + demo.launch(share=True) From a4c219e05d3ab78c20b9d22dedde7dded6fd206c Mon Sep 17 00:00:00 2001 From: mzr1996 Date: Thu, 12 Oct 2023 17:20:22 +0800 Subject: [PATCH 15/15] Bump version to v1.1.0 --- README.md | 9 +++------ README_zh-CN.md | 9 +++------ docker/serve/Dockerfile | 6 +++--- docs/en/notes/changelog.md | 22 ++++++++++++++++++++++ docs/en/notes/faq.md | 2 +- docs/zh_CN/notes/faq.md | 2 +- mmpretrain/__init__.py | 2 +- mmpretrain/version.py | 2 +- 8 files changed, 35 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index dc5c6cde..78d56fc1 100644 --- a/README.md +++ b/README.md @@ -86,13 +86,10 @@ https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351 ## What's new -🌟 v1.0.2 was released in 15/08/2023 +🌟 v1.1.0 was released in 12/10/2023 -Support [MFF](./configs/mff/) self-supervised algorithm and enhance the codebase. More details can be found in the [changelog](https://mmpretrain.readthedocs.io/en/latest/notes/changelog.html). - -🌟 v1.0.1 was released in 28/07/2023 - -Fix some bugs and enhance the codebase. Please refer to [changelog](https://mmpretrain.readthedocs.io/en/latest/notes/changelog.html) for more details. +- Support Mini-GPT4 training and provide a Chinese model (based on Baichuan-7B) +- Support zero-shot classification based on CLIP. 🌟 v1.0.0 was released in 04/07/2023 diff --git a/README_zh-CN.md b/README_zh-CN.md index 801d3183..06daeb1c 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -84,13 +84,10 @@ https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351 ## 更新日志 -🌟 2023/8/15 发布了 v1.0.2 版本 +🌟 2023/10/12 发布了 v1.1.0 版本 -支持了 [MFF](./configs/mff/) 自监督算法，增强算法库功能。细节请参考 [更新日志](https://mmpretrain.readthedocs.io/zh_CN/latest/notes/changelog.html)。 - -🌟 2023/7/28 发布了 v1.0.1 版本 - -修复部分 bug 和增强算法库功能。细节请参考 [更新日志](https://mmpretrain.readthedocs.io/zh_CN/latest/notes/changelog.html)。 +- 支持 Mini-GPT4 训练并提供一个基于 Baichuan-7B 的中文模型 +- 支持基于 CLIP 的零样本分类。 🌟 2023/7/4 发布了 v1.0.0 版本 diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile index bff871b7..86df2926 100644 --- a/docker/serve/Dockerfile +++ b/docker/serve/Dockerfile @@ -1,9 +1,9 @@ -ARG PYTORCH="1.12.1" -ARG CUDA="11.3" +ARG PYTORCH="2.0.1" +ARG CUDA="11.7" ARG CUDNN="8" FROM pytorch/torchserve:latest-gpu -ARG MMPRE="1.0.2" +ARG MMPRE="1.1.0" ENV PYTHONUNBUFFERED TRUE diff --git a/docs/en/notes/changelog.md b/docs/en/notes/changelog.md index f84d691a..7a8ab680 100644 --- a/docs/en/notes/changelog.md +++ b/docs/en/notes/changelog.md @@ -1,5 +1,27 @@ # Changelog (MMPreTrain) +## v1.1.0(12/10/2023) + +### New Features + +- [Feature] Implement of Zero-Shot CLIP Classifier ([#1737](https://github.com/open-mmlab/mmpretrain/pull/1737)) +- [Feature] Add minigpt4 gradio demo and training script. ([#1758](https://github.com/open-mmlab/mmpretrain/pull/1758)) + +### Improvements + +- [Config] New Version of config Adapting MobileNet Algorithm ([#1774](https://github.com/open-mmlab/mmpretrain/pull/1774)) +- [Config] Support DINO self-supervised learning in project ([#1756](https://github.com/open-mmlab/mmpretrain/pull/1756)) +- [Config] New Version of config Adapting Swin Transformer Algorithm ([#1780](https://github.com/open-mmlab/mmpretrain/pull/1780)) +- [Enhance] Add iTPN Supports for Non-three channel image ([#1735](https://github.com/open-mmlab/mmpretrain/pull/1735)) +- [Docs] Update dataset download script from opendatalab to openXlab ([#1765](https://github.com/open-mmlab/mmpretrain/pull/1765)) +- [Docs] Update COCO-Retrieval dataset docs. ([#1806](https://github.com/open-mmlab/mmpretrain/pull/1806)) + +### Bug Fix + +- Update `train.py` to compat with new config. +- Update OFA module to compat with the latest huggingface. +- Fix pipeline bug in ImageRetrievalInferencer. + ## v1.0.2(15/08/2023) ### New Features diff --git a/docs/en/notes/faq.md b/docs/en/notes/faq.md index 9f78a048..dd059114 100644 --- a/docs/en/notes/faq.md +++ b/docs/en/notes/faq.md @@ -16,7 +16,7 @@ and make sure you fill in all required information in the template. | MMPretrain version | MMEngine version | MMCV version | | :----------------: | :---------------: | :--------------: | - | 1.0.2 (main) | mmengine >= 0.8.3 | mmcv >= 2.0.0 | + | 1.1.0 (main) | mmengine >= 0.8.3 | mmcv >= 2.0.0 | | 1.0.0 | mmengine >= 0.8.0 | mmcv >= 2.0.0 | | 1.0.0rc8 | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 | | 1.0.0rc7 | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 | diff --git a/docs/zh_CN/notes/faq.md b/docs/zh_CN/notes/faq.md index efd2ff5e..23ec5f50 100644 --- a/docs/zh_CN/notes/faq.md +++ b/docs/zh_CN/notes/faq.md @@ -13,7 +13,7 @@ | MMPretrain 版本 | MMEngine 版本 | MMCV 版本 | | :-------------: | :---------------: | :--------------: | - | 1.0.2 (main) | mmengine >= 0.8.3 | mmcv >= 2.0.0 | + | 1.1.0 (main) | mmengine >= 0.8.3 | mmcv >= 2.0.0 | | 1.0.0 | mmengine >= 0.8.0 | mmcv >= 2.0.0 | | 1.0.0rc8 | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 | | 1.0.0rc7 | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 | diff --git a/mmpretrain/__init__.py b/mmpretrain/__init__.py index 0b0f573f..69c585bd 100644 --- a/mmpretrain/__init__.py +++ b/mmpretrain/__init__.py @@ -7,7 +7,7 @@ from .apis import * # noqa: F401, F403 from .version import __version__ mmcv_minimum_version = '2.0.0' -mmcv_maximum_version = '2.1.0' +mmcv_maximum_version = '2.2.0' mmcv_version = digit_version(mmcv.__version__) mmengine_minimum_version = '0.8.3' diff --git a/mmpretrain/version.py b/mmpretrain/version.py index 24b33124..32f800cd 100644 --- a/mmpretrain/version.py +++ b/mmpretrain/version.py @@ -1,6 +1,6 @@ # Copyright (c) OpenMMLab. All rights reserved -__version__ = '1.0.2' +__version__ = '1.1.0' def parse_version_info(version_str):