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CITATION.cff 0 → 100644
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cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: "OpenMMLab's Pre-training Toolbox and Benchmark"
authors:
- name: "MMPreTrain Contributors"
version: 0.15.0
date-released: 2023-04-06
repository-code: "https://github.com/open-mmlab/mmpretrain"
license: Apache-2.0
CONTRIBUTING.md 0 → 100644
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# Contributing to MMPreTrain
- [Contributing to MMPreTrain](#contributing-to-mmpretrain)
- [Workflow](#workflow)
- [Code style](#code-style)
- [Python](#python)
- [C++ and CUDA](#c-and-cuda)
- [Pre-commit Hook](#pre-commit-hook)
Thanks for your interest in contributing to MMPreTrain! All kinds of contributions are welcome, including but not limited to the following.
- Fix typo or bugs
- Add documentation or translate the documentation into other languages
- Add new features and components
## Workflow
We recommend the potential contributors follow this workflow for contribution.
1. Fork and pull the latest MMPreTrain repository, follow [get started](https://mmpretrain.readthedocs.io/en/latest/get_started.html) to setup the environment.
2. Checkout a new branch (**do not use the master or dev branch** for PRs)
```bash
git checkout -b xxxx # xxxx is the name of new branch
```
3. Edit the related files follow the code style mentioned below
4. Use [pre-commit hook](https://pre-commit.com/) to check and format your changes.
5. Commit your changes
6. Create a PR with related information
## Code style
### Python
We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
We use the following tools for linting and formatting:
- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
- [yapf](https://github.com/google/yapf): A formatter for Python files.
- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
Style configurations of yapf and isort can be found in [setup.cfg](https://github.com/open-mmlab/mmpretrain/blob/main/setup.cfg).
### C++ and CUDA
We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
## Pre-commit Hook
We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
The config for a pre-commit hook is stored in [.pre-commit-config](https://github.com/open-mmlab/mmpretrain/blob/main/.pre-commit-config.yaml).
After you clone the repository, you will need to install initialize pre-commit hook.
```shell
pip install -U pre-commit
```
From the repository folder
```shell
pre-commit install
```
After this on every commit check code linters and formatter will be enforced.
> Before you create a PR, make sure that your code lints and is formatted by yapf.
LICENSE 0 → 100644
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APPENDIX: How to apply the Apache License to your work.
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MANIFEST.in 0 → 100644
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include requirements/*.txt
include mmpretrain/.mim/model-index.yml
include mmpretrain/.mim/dataset-index.yml
recursive-include mmpretrain/.mim/configs *.py *.yml
recursive-include mmpretrain/.mim/tools *.py *.sh
configs/_base_/datasets/cifar100_bs16.py 0 → 100644
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# dataset settings
dataset_type = 'CIFAR100'
data_preprocessor = dict(
num_classes=100,
# RGB format normalization parameters
mean=[129.304, 124.070, 112.434],
std=[68.170, 65.392, 70.418],
# loaded images are already RGB format
to_rgb=False)
train_pipeline = [
dict(type='RandomCrop', crop_size=32, padding=4),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=16,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/cifar100',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
val_dataloader = dict(
batch_size=16,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/cifar100/',
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
configs/_base_/datasets/cifar10_bs16.py 0 → 100644
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# dataset settings
dataset_type = 'CIFAR10'
data_preprocessor = dict(
num_classes=10,
# RGB format normalization parameters
mean=[125.307, 122.961, 113.8575],
std=[51.5865, 50.847, 51.255],
# loaded images are already RGB format
to_rgb=False)
train_pipeline = [
dict(type='RandomCrop', crop_size=32, padding=4),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=16,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/cifar10',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
val_dataloader = dict(
batch_size=16,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/cifar10/',
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
configs/_base_/datasets/coco_caption.py 0 → 100644
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# data settings
# coco caption annotations can be grabbed from LAVIS repo
# https://github.com/salesforce/LAVIS/blob/main/lavis/configs/datasets/coco/defaults_cap.yaml
data_preprocessor = dict(
type='MultiModalDataPreprocessor',
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
interpolation='bicubic',
backend='pillow'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='CleanCaption', keys='gt_caption'),
dict(
type='PackInputs',
algorithm_keys=['gt_caption'],
meta_keys=['image_id'],
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(384, 384),
interpolation='bicubic',
backend='pillow'),
dict(type='PackInputs', meta_keys=['image_id']),
]
train_dataloader = dict(
batch_size=32,
num_workers=5,
dataset=dict(
type='COCOCaption',
data_root='data/coco',
ann_file='annotations/coco_karpathy_train.json',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=16,
num_workers=5,
dataset=dict(
type='COCOCaption',
data_root='data/coco',
ann_file='annotations/coco_karpathy_val.json',
pipeline=test_pipeline,
),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
val_evaluator = dict(
type='COCOCaption',
ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
)
# # If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
test_evaluator = val_evaluator
configs/_base_/datasets/coco_okvqa.py 0 → 100644
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# data settings
data_preprocessor = dict(
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
interpolation='bicubic',
backend='pillow'),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(480, 480),
interpolation='bicubic',
backend='pillow'),
dict(
type='CleanCaption',
keys=['question'],
),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
train_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='train2014',
question_file=
'annotations/okvqa_OpenEnded_mscoco_train2014_questions.json',
ann_file='annotations/okvqa_mscoco_train2014_annotations.json',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='val2014',
question_file=
'annotations/okvqa_OpenEnded_mscoco_val2014_questions.json',
ann_file='annotations/okvqa_mscoco_val2014_annotations.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
val_evaluator = dict(type='VQAAcc')
test_dataloader = val_dataloader
test_evaluator = val_evaluator
configs/_base_/datasets/coco_retrieval.py 0 → 100644
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# data settings
# Here are the links to download the annotations for coco retrieval for conveniency # noqa
# https://download.openmmlab.com/mmclassification/datasets/coco_retrieval/caption_karpathy_train2014.json
# https://download.openmmlab.com/mmclassification/datasets/coco_retrieval/caption_karpathy_val2014.json
# https://download.openmmlab.com/mmclassification/datasets/coco_retrieval/caption_karpathy_test2014.json
data_preprocessor = dict(
type='MultiModalDataPreprocessor',
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
rand_increasing_policies = [
dict(type='AutoContrast'),
dict(type='Equalize'),
dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
dict(
type='Brightness', magnitude_key='magnitude',
magnitude_range=(0, 0.0)),
dict(type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0)),
dict(
type='Shear',
magnitude_key='magnitude',
magnitude_range=(0, 0.3),
direction='horizontal'),
dict(
type='Shear',
magnitude_key='magnitude',
magnitude_range=(0, 0.3),
direction='vertical'),
]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
crop_ratio_range=(0.5, 1.0),
interpolation='bicubic'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies=rand_increasing_policies,
num_policies=2,
magnitude_level=5),
dict(type='CleanCaption', keys='text'),
dict(
type='PackInputs',
algorithm_keys=['text', 'is_matched'],
meta_keys=['image_id']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(384, 384),
interpolation='bicubic',
backend='pillow'),
dict(type='CleanCaption', keys='text'),
dict(
type='PackInputs',
algorithm_keys=['text', 'gt_text_id', 'gt_image_id'],
meta_keys=['image_id']),
]
train_dataloader = dict(
batch_size=32,
num_workers=16,
dataset=dict(
type='COCORetrieval',
data_root='data/coco',
ann_file='annotations/caption_karpathy_train2014.json',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=64,
num_workers=16,
dataset=dict(
type='COCORetrieval',
data_root='data/coco',
ann_file='annotations/caption_karpathy_val2014.json',
pipeline=test_pipeline,
# This is required for evaluation
test_mode=True,
),
sampler=dict(type='SequentialSampler', subsample_type='sequential'),
persistent_workers=True,
)
val_evaluator = dict(type='RetrievalRecall', topk=(1, 5, 10))
# If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
test_evaluator = val_evaluator
configs/_base_/datasets/coco_vg_vqa.py 0 → 100644
View file @ dff2c686
# data settings
data_preprocessor = dict(
type='MultiModalDataPreprocessor',
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=(480, 480),
crop_ratio_range=(0.5, 1.0),
interpolation='bicubic',
backend='pillow'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies='simple_increasing', # slightly different from LAVIS
num_policies=2,
magnitude_level=5),
dict(type='CleanCaption', keys=['question', 'gt_answer']),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(480, 480),
interpolation='bicubic',
backend='pillow'),
dict(type='CleanCaption', keys=['question']),
dict(
type='PackInputs',
algorithm_keys=['question'],
meta_keys=['question_id']),
]
train_dataloader = dict(
batch_size=32,
num_workers=8,
dataset=dict(
type='ConcatDataset',
datasets=[
# VQAv2 train
dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='train2014',
question_file=
'annotations/v2_OpenEnded_mscoco_train2014_questions.json',
ann_file='annotations/v2_mscoco_train2014_annotations.json',
pipeline=train_pipeline,
),
# VQAv2 val
dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='val2014',
question_file=
'annotations/v2_OpenEnded_mscoco_val2014_questions.json',
ann_file='annotations/v2_mscoco_val2014_annotations.json',
pipeline=train_pipeline,
),
# Visual Genome
dict(
type='VisualGenomeQA',
data_root='visual_genome',
data_prefix='image',
ann_file='question_answers.json',
pipeline=train_pipeline,
)
]),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
test_dataloader = dict(
batch_size=32,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='test2015',
question_file=
'annotations/v2_OpenEnded_mscoco_test2015_questions.json', # noqa: E501
pipeline=test_pipeline,
),
sampler=dict(type='DefaultSampler', shuffle=False),
)
test_evaluator = dict(type='ReportVQA', file_path='vqa_test.json')
configs/_base_/datasets/coco_vqa.py 0 → 100644
View file @ dff2c686
# data settings
data_preprocessor = dict(
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
interpolation='bicubic',
backend='pillow'),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(480, 480),
interpolation='bicubic',
backend='pillow'),
dict(
type='CleanCaption',
keys=['question'],
),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
train_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='train2014',
question_file=
'annotations/v2_OpenEnded_mscoco_train2014_questions.json',
ann_file='annotations/v2_mscoco_train2014_annotations.json',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='val2014',
question_file='annotations/v2_OpenEnded_mscoco_val2014_questions.json',
ann_file='annotations/v2_mscoco_val2014_annotations.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
val_evaluator = dict(type='VQAAcc')
test_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='test2015',
question_file= # noqa: E251
'annotations/v2_OpenEnded_mscoco_test2015_questions.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
)
test_evaluator = dict(type='ReportVQA', file_path='vqa_test.json')
configs/_base_/datasets/cub_bs8_384.py 0 → 100644
View file @ dff2c686
# 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
configs/_base_/datasets/cub_bs8_448.py 0 → 100644
View file @ dff2c686
# dataset settings
dataset_type = 'CUB'
data_preprocessor = dict(
num_classes=200,
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=600),
dict(type='RandomCrop', crop_size=448),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=600),
dict(type='CenterCrop', crop_size=448),
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
configs/_base_/datasets/flickr30k_caption.py 0 → 100644
View file @ dff2c686
# data settings
data_preprocessor = dict(
type='MultiModalDataPreprocessor',
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
interpolation='bicubic',
backend='pillow'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='CleanCaption', keys='gt_caption'),
dict(
type='PackInputs',
algorithm_keys=['gt_caption'],
meta_keys=['image_id'],
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(384, 384),
interpolation='bicubic',
backend='pillow'),
dict(type='PackInputs', meta_keys=['image_id']),
]
train_dataloader = dict(
batch_size=32,
num_workers=5,
dataset=dict(
type='Flickr30kCaption',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=16,
num_workers=5,
dataset=dict(
type='Flickr30kCaption',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='val',
pipeline=test_pipeline,
),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
# refer tools/dataset_converters/convert_flickr30k_ann.py
val_evaluator = dict(
type='COCOCaption',
ann_file='data/flickr30k_val_gt.json',
)
# # If you want standard test, please manually configure the test dataset
test_dataloader = dict(
batch_size=16,
num_workers=5,
dataset=dict(
type='Flickr30kCaption',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='test',
pipeline=test_pipeline,
),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
# refer tools/dataset_converters/convert_flickr30k_ann.py
test_evaluator = dict(
type='COCOCaption',
ann_file='data/flickr30k_test_gt.json',
)
configs/_base_/datasets/flickr30k_retrieval.py 0 → 100644
View file @ dff2c686
# data settings
data_preprocessor = dict(
type='MultiModalDataPreprocessor',
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
rand_increasing_policies = [
dict(type='AutoContrast'),
dict(type='Equalize'),
dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
dict(
type='Brightness', magnitude_key='magnitude',
magnitude_range=(0, 0.0)),
dict(type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0)),
dict(
type='Shear',
magnitude_key='magnitude',
magnitude_range=(0, 0.3),
direction='horizontal'),
dict(
type='Shear',
magnitude_key='magnitude',
magnitude_range=(0, 0.3),
direction='vertical'),
]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
crop_ratio_range=(0.5, 1.0),
interpolation='bicubic'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies=rand_increasing_policies,
num_policies=2,
magnitude_level=5),
dict(type='CleanCaption', keys='text'),
dict(
type='PackInputs',
algorithm_keys=['text', 'is_matched'],
meta_keys=['image_id']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(384, 384),
interpolation='bicubic',
backend='pillow'),
dict(type='CleanCaption', keys='text'),
dict(
type='PackInputs',
algorithm_keys=['text', 'gt_text_id', 'gt_image_id'],
meta_keys=['image_id']),
]
train_dataloader = dict(
batch_size=32,
num_workers=16,
dataset=dict(
type='Flickr30kRetrieval',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=64,
num_workers=16,
dataset=dict(
type='Flickr30kRetrieval',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='val',
pipeline=test_pipeline,
test_mode=True, # This is required for evaluation
),
sampler=dict(type='SequentialSampler', subsample_type='sequential'),
persistent_workers=True,
)
val_evaluator = dict(type='RetrievalRecall', topk=(1, 5, 10))
# If you want standard test, please manually configure the test dataset
test_dataloader = dict(
batch_size=64,
num_workers=16,
dataset=dict(
type='Flickr30kRetrieval',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='test',
pipeline=test_pipeline,
test_mode=True, # This is required for evaluation
),
sampler=dict(type='SequentialSampler', subsample_type='sequential'),
persistent_workers=True,
)
test_evaluator = val_evaluator
configs/_base_/datasets/gqa.py 0 → 100644
View file @ dff2c686
# data settings
data_preprocessor = dict(
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
interpolation='bicubic',
backend='pillow'),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(480, 480),
interpolation='bicubic',
backend='pillow'),
dict(
type='CleanCaption',
keys=['question'],
),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
train_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='GQA',
data_root='data/gqa',
data_prefix='images',
ann_file='annotations/train_balanced_questions.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='GQA',
data_root='data/gqa',
data_prefix='images',
ann_file='annotations/testdev_balanced_questions.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
val_evaluator = dict(type='GQAAcc')
test_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='GQA',
data_root='data/gqa',
data_prefix='images',
ann_file='annotations/testdev_balanced_questions.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
test_evaluator = val_evaluator
configs/_base_/datasets/imagenet21k_bs128.py 0 → 100644
View file @ dff2c686
# dataset settings
dataset_type = 'ImageNet21k'
data_preprocessor = dict(
num_classes=21842,
# RGB format normalization parameters
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', scale=224),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=128,
num_workers=5,
dataset=dict(
type=dataset_type,
data_root='data/imagenet21k',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
configs/_base_/datasets/imagenet_bs128_mbv3.py 0 → 100644
View file @ dff2c686
# dataset settings
dataset_type = 'ImageNet'
data_preprocessor = dict(
num_classes=1000,
# RGB format normalization parameters
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
to_rgb=True,
)
bgr_mean = data_preprocessor['mean'][::-1]
bgr_std = data_preprocessor['std'][::-1]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', scale=224, backend='pillow'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='AutoAugment',
policies='imagenet',
hparams=dict(pad_val=[round(x) for x in bgr_mean])),
dict(
type='RandomErasing',
erase_prob=0.2,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=1 / 3,
fill_color=bgr_mean,
fill_std=bgr_std),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=128,
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=128,
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
configs/_base_/datasets/imagenet_bs128_poolformer_medium_224.py 0 → 100644
View file @ dff2c686
# dataset settings
dataset_type = 'ImageNet'
data_preprocessor = dict(
num_classes=1000,
# RGB format normalization parameters
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
to_rgb=True,
)
bgr_mean = data_preprocessor['mean'][::-1]
bgr_std = data_preprocessor['std'][::-1]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=224,
backend='pillow',
interpolation='bicubic'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies='timm_increasing',
num_policies=2,
total_level=10,
magnitude_level=9,
magnitude_std=0.5,
hparams=dict(
pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
dict(
type='RandomErasing',
erase_prob=0.25,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=1 / 3,
fill_color=bgr_mean,
fill_std=bgr_std),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='ResizeEdge',
scale=236,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=128,
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=128,
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
configs/_base_/datasets/imagenet_bs128_poolformer_small_224.py 0 → 100644
View file @ dff2c686
# dataset settings
dataset_type = 'ImageNet'
data_preprocessor = dict(
num_classes=1000,
# RGB format normalization parameters
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
to_rgb=True,
)
bgr_mean = data_preprocessor['mean'][::-1]
bgr_std = data_preprocessor['std'][::-1]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=224,
backend='pillow',
interpolation='bicubic'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies='timm_increasing',
num_policies=2,
total_level=10,
magnitude_level=9,
magnitude_std=0.5,
hparams=dict(
pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
dict(
type='RandomErasing',
erase_prob=0.25,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=1 / 3,
fill_color=bgr_mean,
fill_std=bgr_std),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='ResizeEdge',
scale=248,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=128,
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=128,
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
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