Skip to content

GitLab

Commit 6a10c7bf authored by unknown's avatar unknown
Browse files

提交Swin-Transformer代码

parents
Expand all Hide whitespace changes
Inline Side-by-side
Showing with 860 additions and 0 deletions
.gitignore 0 → 100644
View file @ 6a10c7bf
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# launch bash
*.sh
# nsight system report files
*.nsys-rep
*.sqlite
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
CODE_OF_CONDUCT.md 0 → 100644
View file @ 6a10c7bf
# Microsoft Open Source Code of Conduct
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
Resources:
- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns
LICENSE 0 → 100644
View file @ 6a10c7bf
MIT License
Copyright (c) Microsoft Corporation.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE
MODELHUB.md 0 → 100644
View file @ 6a10c7bf
This diff is collapsed.
README.md 0 → 100644
View file @ 6a10c7bf
# Swin Transformer
## 模型介绍
Swin Transformer可以作为计算机视觉的通用支柱。将 Transformer 从语言转换为视觉的挑战来自于两个域之间的差异，例如视觉实体的尺度差异大，以及图像中像素相对于文本中单词的高分辨率。为了解决这些差异，提出了一个分层( hierarchical )Transformer，其表示是用移动窗口( Shifted windows )计算的。移动窗口方案通过将自注意力计算限制到非重叠的局部窗口，同时允许跨窗口连接，从而带来更高的效率。这种分层体系结构具有在各种尺度上建模的灵活性，并且具有与图像大小相关的线性计算复杂度。Swin Trans former 的这些特性使其可以兼容广泛的视觉任务，包括图像分类( ImageNet - 1K的top - 1准确率为87.3 %)和密集预测任务，如目标检测( 在COCO test-dev上实现了58.7 box AP和51.1 mask AP )和语义分割( 53.5 mIoU )。2021年，其性能在COCO上以 + 2.7 box AP 和 + 2.6 mask AP 的大幅优势超越了先前的先进水平，在ADE20K上以+ 3.2 mIoU的优势超越了先前的先进水平，显示了基于 Transformer 的模型作为视觉中枢的潜力。分层设计和移位窗口方法也被证明对 full-MLP 体系结构有利。
## 模型结构
Swin Transformer体系结构的概述如下图所示，其中说明了 tiny version ( Swin-T )。它首先通过 patch 分割模块(如ViT )将输入的RGB图像分割成不重叠的 patch 。每个 patch 被当作一个 "token" ( 相当于NLP中的词源 )处理，它的特征被设置为原始像素RGB值的 concatenation。在我们的实现中，我们使用了 4 × 4 的 patch 大小，因此每个 patch 的特征维度为 4 × 4 × 3 = 48。在这个原始值特征上应用一个线性嵌入层，将其投影到任意维度( 记为C )。Swin Transformer block将Transformer块中的标准多头自注意力( MSA )模块替换为基于移动窗口的模块，其他层保持不变。如图( b )所示，一个 SwinTransformer 模块由一个基于移动窗口的MSA模块组成，其后是一个2层的MLP，GELU非线性介于两者之间。在每个MSA模块和每个MLP之前施加一个 LayerNorm ( LN )层，在每个模块之后施加一个残差连接。
![img](https://img-blog.csdnimg.cn/cc163380115640d4a5d88ffb246bde44.png)
- ( a )Swin Transformer ( Swin-T )的结构；
- ( b )连续 2 个Swin Transformer 块。
## 数据集
在本测试中可以使用tiny-imagenet-200数据集。
数据集处理方法请参考imagenet官方介绍自行处理，也可通过下面链接下载使用。
链接：链接：https://pan.baidu.com/s/17dg8g5VhMfU5_9SUogMP7w?pwd=fy0p 提取码：fy0p
## Swin-Transformer训练
### 环境配置
提供[光源](https://www.sourcefind.cn/#/service-details)拉取的训练以及推理的docker镜像：
* 训练镜像：docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.10.0-centos7.6-dtk-22.10.1-py37-latest
### 训练
训练命令：
export HIP_VISIBLE_DEVICES=0
python3 -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.py --cfg configs/swin/swin_tiny_patch4_window7_224.yaml --data-path /code/Datasets/tiny-imagenet-200/ --batch-size 128 --disable_amp
## 性能和准确率数据
测试数据使用的是tiny-imagenet-200，使用的加速卡是DCU Z100L。
根据模型情况填写表格：
| 卡数 | 性能 | 精度 |
| :------: | :------: | :------: |
| 1 | 127.237 samples/s | Acc@1：63.416 Acc：@5 85.666 |
### 参考
https://github.com/microsoft/Swin-Transformer
\ No newline at end of file
SECURITY.md 0 → 100644
View file @ 6a10c7bf
<!-- BEGIN MICROSOFT SECURITY.MD V0.0.5 BLOCK -->
## Security
Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/).
If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)), please report it to us as described below.
## Reporting Security Issues
**Please do not report security vulnerabilities through public GitHub issues.**
Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report).
If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/en-us/msrc/pgp-key-msrc).
You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc).
Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
* Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
* Full paths of source file(s) related to the manifestation of the issue
* The location of the affected source code (tag/branch/commit or direct URL)
* Any special configuration required to reproduce the issue
* Step-by-step instructions to reproduce the issue
* Proof-of-concept or exploit code (if possible)
* Impact of the issue, including how an attacker might exploit the issue
This information will help us triage your report more quickly.
If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://microsoft.com/msrc/bounty) page for more details about our active programs.
## Preferred Languages
We prefer all communications to be in English.
## Policy
Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd).
<!-- END MICROSOFT SECURITY.MD BLOCK -->
\ No newline at end of file
SUPPORT.md 0 → 100644
View file @ 6a10c7bf
# TODO: The maintainer of this repo has not yet edited this file
**REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project?
- **No CSS support:** Fill out this template with information about how to file issues and get help.
- **Yes CSS support:** Fill out an intake form at [aka.ms/spot](https://aka.ms/spot). CSS will work with/help you to determine next steps. More details also available at [aka.ms/onboardsupport](https://aka.ms/onboardsupport).
- **Not sure?** Fill out a SPOT intake as though the answer were "Yes". CSS will help you decide.
*Then remove this first heading from this SUPPORT.MD file before publishing your repo.*
# Support
## How to file issues and get help
This project uses GitHub Issues to track bugs and feature requests. Please search the existing
issues before filing new issues to avoid duplicates. For new issues, file your bug or
feature request as a new Issue.
For help and questions about using this project, please **REPO MAINTAINER: INSERT INSTRUCTIONS HERE
FOR HOW TO ENGAGE REPO OWNERS OR COMMUNITY FOR HELP. COULD BE A STACK OVERFLOW TAG OR OTHER
CHANNEL. WHERE WILL YOU HELP PEOPLE?**.
## Microsoft Support Policy
Support for this **PROJECT or PRODUCT** is limited to the resources listed above.
SW_README.md 0 → 100644
View file @ 6a10c7bf
This diff is collapsed.
config.py 0 → 100644
View file @ 6a10c7bf
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------'
import os
import yaml
from yacs.config import CfgNode as CN
_C = CN()
# Base config files
_C.BASE = ['']
# -----------------------------------------------------------------------------
# Data settings
# -----------------------------------------------------------------------------
_C.DATA = CN()
# Batch size for a single GPU, could be overwritten by command line argument
_C.DATA.BATCH_SIZE = 128
# Path to dataset, could be overwritten by command line argument
_C.DATA.DATA_PATH = ''
# Dataset name
_C.DATA.DATASET = 'imagenet'
# Input image size
_C.DATA.IMG_SIZE = 224
# Interpolation to resize image (random, bilinear, bicubic)
_C.DATA.INTERPOLATION = 'bicubic'
# Use zipped dataset instead of folder dataset
# could be overwritten by command line argument
_C.DATA.ZIP_MODE = False
# Cache Data in Memory, could be overwritten by command line argument
_C.DATA.CACHE_MODE = 'part'
# Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.
_C.DATA.PIN_MEMORY = True
# Number of data loading threads
_C.DATA.NUM_WORKERS = 8
# [SimMIM] Mask patch size for MaskGenerator
_C.DATA.MASK_PATCH_SIZE = 32
# [SimMIM] Mask ratio for MaskGenerator
_C.DATA.MASK_RATIO = 0.6
# -----------------------------------------------------------------------------
# Model settings
# -----------------------------------------------------------------------------
_C.MODEL = CN()
# Model type
_C.MODEL.TYPE = 'swin'
# Model name
_C.MODEL.NAME = 'swin_tiny_patch4_window7_224'
# Pretrained weight from checkpoint, could be imagenet22k pretrained weight
# could be overwritten by command line argument
_C.MODEL.PRETRAINED = ''
# Checkpoint to resume, could be overwritten by command line argument
_C.MODEL.RESUME = ''
# Number of classes, overwritten in data preparation
_C.MODEL.NUM_CLASSES = 1000
# Dropout rate
_C.MODEL.DROP_RATE = 0.0
# Drop path rate
_C.MODEL.DROP_PATH_RATE = 0.1
# Label Smoothing
_C.MODEL.LABEL_SMOOTHING = 0.1
# Swin Transformer parameters
_C.MODEL.SWIN = CN()
_C.MODEL.SWIN.PATCH_SIZE = 4
_C.MODEL.SWIN.IN_CHANS = 3
_C.MODEL.SWIN.EMBED_DIM = 96
_C.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWIN.WINDOW_SIZE = 7
_C.MODEL.SWIN.MLP_RATIO = 4.
_C.MODEL.SWIN.QKV_BIAS = True
_C.MODEL.SWIN.QK_SCALE = None
_C.MODEL.SWIN.APE = False
_C.MODEL.SWIN.PATCH_NORM = True
# Swin Transformer V2 parameters
_C.MODEL.SWINV2 = CN()
_C.MODEL.SWINV2.PATCH_SIZE = 4
_C.MODEL.SWINV2.IN_CHANS = 3
_C.MODEL.SWINV2.EMBED_DIM = 96
_C.MODEL.SWINV2.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWINV2.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWINV2.WINDOW_SIZE = 7
_C.MODEL.SWINV2.MLP_RATIO = 4.
_C.MODEL.SWINV2.QKV_BIAS = True
_C.MODEL.SWINV2.APE = False
_C.MODEL.SWINV2.PATCH_NORM = True
_C.MODEL.SWINV2.PRETRAINED_WINDOW_SIZES = [0, 0, 0, 0]
# Swin Transformer MoE parameters
_C.MODEL.SWIN_MOE = CN()
_C.MODEL.SWIN_MOE.PATCH_SIZE = 4
_C.MODEL.SWIN_MOE.IN_CHANS = 3
_C.MODEL.SWIN_MOE.EMBED_DIM = 96
_C.MODEL.SWIN_MOE.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWIN_MOE.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWIN_MOE.WINDOW_SIZE = 7
_C.MODEL.SWIN_MOE.MLP_RATIO = 4.
_C.MODEL.SWIN_MOE.QKV_BIAS = True
_C.MODEL.SWIN_MOE.QK_SCALE = None
_C.MODEL.SWIN_MOE.APE = False
_C.MODEL.SWIN_MOE.PATCH_NORM = True
_C.MODEL.SWIN_MOE.MLP_FC2_BIAS = True
_C.MODEL.SWIN_MOE.INIT_STD = 0.02
_C.MODEL.SWIN_MOE.PRETRAINED_WINDOW_SIZES = [0, 0, 0, 0]
_C.MODEL.SWIN_MOE.MOE_BLOCKS = [[-1], [-1], [-1], [-1]]
_C.MODEL.SWIN_MOE.NUM_LOCAL_EXPERTS = 1
_C.MODEL.SWIN_MOE.TOP_VALUE = 1
_C.MODEL.SWIN_MOE.CAPACITY_FACTOR = 1.25
_C.MODEL.SWIN_MOE.COSINE_ROUTER = False
_C.MODEL.SWIN_MOE.NORMALIZE_GATE = False
_C.MODEL.SWIN_MOE.USE_BPR = True
_C.MODEL.SWIN_MOE.IS_GSHARD_LOSS = False
_C.MODEL.SWIN_MOE.GATE_NOISE = 1.0
_C.MODEL.SWIN_MOE.COSINE_ROUTER_DIM = 256
_C.MODEL.SWIN_MOE.COSINE_ROUTER_INIT_T = 0.5
_C.MODEL.SWIN_MOE.MOE_DROP = 0.0
_C.MODEL.SWIN_MOE.AUX_LOSS_WEIGHT = 0.01
# Swin MLP parameters
_C.MODEL.SWIN_MLP = CN()
_C.MODEL.SWIN_MLP.PATCH_SIZE = 4
_C.MODEL.SWIN_MLP.IN_CHANS = 3
_C.MODEL.SWIN_MLP.EMBED_DIM = 96
_C.MODEL.SWIN_MLP.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWIN_MLP.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWIN_MLP.WINDOW_SIZE = 7
_C.MODEL.SWIN_MLP.MLP_RATIO = 4.
_C.MODEL.SWIN_MLP.APE = False
_C.MODEL.SWIN_MLP.PATCH_NORM = True
# [SimMIM] Norm target during training
_C.MODEL.SIMMIM = CN()
_C.MODEL.SIMMIM.NORM_TARGET = CN()
_C.MODEL.SIMMIM.NORM_TARGET.ENABLE = False
_C.MODEL.SIMMIM.NORM_TARGET.PATCH_SIZE = 47
# -----------------------------------------------------------------------------
# Training settings
# -----------------------------------------------------------------------------
_C.TRAIN = CN()
_C.TRAIN.START_EPOCH = 0
_C.TRAIN.EPOCHS = 300
_C.TRAIN.WARMUP_EPOCHS = 20
_C.TRAIN.WEIGHT_DECAY = 0.05
_C.TRAIN.BASE_LR = 5e-4
_C.TRAIN.WARMUP_LR = 5e-7
_C.TRAIN.MIN_LR = 5e-6
# Clip gradient norm
_C.TRAIN.CLIP_GRAD = 5.0
# Auto resume from latest checkpoint
_C.TRAIN.AUTO_RESUME = True
# Gradient accumulation steps
# could be overwritten by command line argument
_C.TRAIN.ACCUMULATION_STEPS = 1
# Whether to use gradient checkpointing to save memory
# could be overwritten by command line argument
_C.TRAIN.USE_CHECKPOINT = False
# LR scheduler
_C.TRAIN.LR_SCHEDULER = CN()
_C.TRAIN.LR_SCHEDULER.NAME = 'cosine'
# Epoch interval to decay LR, used in StepLRScheduler
_C.TRAIN.LR_SCHEDULER.DECAY_EPOCHS = 30
# LR decay rate, used in StepLRScheduler
_C.TRAIN.LR_SCHEDULER.DECAY_RATE = 0.1
# warmup_prefix used in CosineLRScheduler
_C.TRAIN.LR_SCHEDULER.WARMUP_PREFIX = True
# [SimMIM] Gamma / Multi steps value, used in MultiStepLRScheduler
_C.TRAIN.LR_SCHEDULER.GAMMA = 0.1
_C.TRAIN.LR_SCHEDULER.MULTISTEPS = []
# Optimizer
_C.TRAIN.OPTIMIZER = CN()
_C.TRAIN.OPTIMIZER.NAME = 'adamw'
# Optimizer Epsilon
_C.TRAIN.OPTIMIZER.EPS = 1e-8
# Optimizer Betas
_C.TRAIN.OPTIMIZER.BETAS = (0.9, 0.999)
# SGD momentum
_C.TRAIN.OPTIMIZER.MOMENTUM = 0.9
# [SimMIM] Layer decay for fine-tuning
_C.TRAIN.LAYER_DECAY = 1.0
# MoE
_C.TRAIN.MOE = CN()
# Only save model on master device
_C.TRAIN.MOE.SAVE_MASTER = False
# -----------------------------------------------------------------------------
# Augmentation settings
# -----------------------------------------------------------------------------
_C.AUG = CN()
# Color jitter factor
_C.AUG.COLOR_JITTER = 0.4
# Use AutoAugment policy. "v0" or "original"
_C.AUG.AUTO_AUGMENT = 'rand-m9-mstd0.5-inc1'
# Random erase prob
_C.AUG.REPROB = 0.25
# Random erase mode
_C.AUG.REMODE = 'pixel'
# Random erase count
_C.AUG.RECOUNT = 1
# Mixup alpha, mixup enabled if > 0
_C.AUG.MIXUP = 0.8
# Cutmix alpha, cutmix enabled if > 0
_C.AUG.CUTMIX = 1.0
# Cutmix min/max ratio, overrides alpha and enables cutmix if set
_C.AUG.CUTMIX_MINMAX = None
# Probability of performing mixup or cutmix when either/both is enabled
_C.AUG.MIXUP_PROB = 1.0
# Probability of switching to cutmix when both mixup and cutmix enabled
_C.AUG.MIXUP_SWITCH_PROB = 0.5
# How to apply mixup/cutmix params. Per "batch", "pair", or "elem"
_C.AUG.MIXUP_MODE = 'batch'
# -----------------------------------------------------------------------------
# Testing settings
# -----------------------------------------------------------------------------
_C.TEST = CN()
# Whether to use center crop when testing
_C.TEST.CROP = True
# Whether to use SequentialSampler as validation sampler
_C.TEST.SEQUENTIAL = False
_C.TEST.SHUFFLE = False
# -----------------------------------------------------------------------------
# Misc
# -----------------------------------------------------------------------------
# [SimMIM] Whether to enable pytorch amp, overwritten by command line argument
_C.ENABLE_AMP = False
# Enable Pytorch automatic mixed precision (amp).
_C.AMP_ENABLE = True
# [Deprecated] Mixed precision opt level of apex, if O0, no apex amp is used ('O0', 'O1', 'O2')
_C.AMP_OPT_LEVEL = ''
# Path to output folder, overwritten by command line argument
_C.OUTPUT = ''
# Tag of experiment, overwritten by command line argument
_C.TAG = 'default'
# Frequency to save checkpoint
_C.SAVE_FREQ = 1
# Frequency to logging info
_C.PRINT_FREQ = 10
# Fixed random seed
_C.SEED = 0
# Perform evaluation only, overwritten by command line argument
_C.EVAL_MODE = False
# Test throughput only, overwritten by command line argument
_C.THROUGHPUT_MODE = False
# local rank for DistributedDataParallel, given by command line argument
_C.LOCAL_RANK = 0
# for acceleration
_C.FUSED_WINDOW_PROCESS = False
_C.FUSED_LAYERNORM = False
def _update_config_from_file(config, cfg_file):
config.defrost()
with open(cfg_file, 'r') as f:
yaml_cfg = yaml.load(f, Loader=yaml.FullLoader)
for cfg in yaml_cfg.setdefault('BASE', ['']):
if cfg:
_update_config_from_file(
config, os.path.join(os.path.dirname(cfg_file), cfg)
)
print('=> merge config from {}'.format(cfg_file))
config.merge_from_file(cfg_file)
config.freeze()
def update_config(config, args):
_update_config_from_file(config, args.cfg)
config.defrost()
if args.opts:
config.merge_from_list(args.opts)
def _check_args(name):
if hasattr(args, name) and eval(f'args.{name}'):
return True
return False
# merge from specific arguments
if _check_args('batch_size'):
config.DATA.BATCH_SIZE = args.batch_size
if _check_args('data_path'):
config.DATA.DATA_PATH = args.data_path
if _check_args('zip'):
config.DATA.ZIP_MODE = True
if _check_args('cache_mode'):
config.DATA.CACHE_MODE = args.cache_mode
if _check_args('pretrained'):
config.MODEL.PRETRAINED = args.pretrained
if _check_args('resume'):
config.MODEL.RESUME = args.resume
if _check_args('accumulation_steps'):
config.TRAIN.ACCUMULATION_STEPS = args.accumulation_steps
if _check_args('use_checkpoint'):
config.TRAIN.USE_CHECKPOINT = True
if _check_args('amp_opt_level'):
print("[warning] Apex amp has been deprecated, please use pytorch amp instead!")
if args.amp_opt_level == 'O0':
config.AMP_ENABLE = False
if _check_args('disable_amp'):
config.AMP_ENABLE = False
if _check_args('output'):
config.OUTPUT = args.output
if _check_args('tag'):
config.TAG = args.tag
if _check_args('eval'):
config.EVAL_MODE = True
if _check_args('throughput'):
config.THROUGHPUT_MODE = True
# [SimMIM]
if _check_args('enable_amp'):
config.ENABLE_AMP = args.enable_amp
# for acceleration
if _check_args('fused_window_process'):
config.FUSED_WINDOW_PROCESS = True
if _check_args('fused_layernorm'):
config.FUSED_LAYERNORM = True
## Overwrite optimizer if not None, currently we use it for [fused_adam, fused_lamb]
if _check_args('optim'):
config.TRAIN.OPTIMIZER.NAME = args.optim
# set local rank for distributed training
config.LOCAL_RANK = args.local_rank
# output folder
config.OUTPUT = os.path.join(config.OUTPUT, config.MODEL.NAME, config.TAG)
config.freeze()
def get_config(args):
"""Get a yacs CfgNode object with default values."""
# Return a clone so that the defaults will not be altered
# This is for the "local variable" use pattern
config = _C.clone()
update_config(config, args)
return config
configs/simmim/simmim_finetune__swin_base__img224_window7__800ep.yaml 0 → 100644
View file @ 6a10c7bf
MODEL:
TYPE: swin
NAME: simmim_finetune
DROP_PATH_RATE: 0.1
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 7
DATA:
IMG_SIZE: 224
TRAIN:
EPOCHS: 100
WARMUP_EPOCHS: 20
BASE_LR: 1.25e-3
WARMUP_LR: 2.5e-7
MIN_LR: 2.5e-7
WEIGHT_DECAY: 0.05
LAYER_DECAY: 0.8
PRINT_FREQ: 100
SAVE_FREQ: 5
TAG: simmim_finetune__swin_base__img224_window7__800ep
\ No newline at end of file
configs/simmim/simmim_finetune__swinv2_base__img224_window14__800ep.yaml 0 → 100644
View file @ 6a10c7bf
MODEL:
TYPE: swinv2
NAME: simmim_finetune
DROP_PATH_RATE: 0.1
SWINV2:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 14
PRETRAINED_WINDOW_SIZES: [ 12, 12, 12, 6 ]
DATA:
IMG_SIZE: 224
TRAIN:
EPOCHS: 100
WARMUP_EPOCHS: 20
BASE_LR: 1.25e-3
WARMUP_LR: 2.5e-7
MIN_LR: 2.5e-7
WEIGHT_DECAY: 0.05
LAYER_DECAY: 0.75
PRINT_FREQ: 100
SAVE_FREQ: 5
TAG: simmim_finetune__swinv2_base__img224_window14__800ep
\ No newline at end of file
configs/simmim/simmim_pretrain__swin_base__img192_window6__800ep.yaml 0 → 100644
View file @ 6a10c7bf
MODEL:
TYPE: swin
NAME: simmim_pretrain
DROP_PATH_RATE: 0.0
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 6
DATA:
IMG_SIZE: 192
MASK_PATCH_SIZE: 32
MASK_RATIO: 0.6
TRAIN:
EPOCHS: 800
WARMUP_EPOCHS: 10
BASE_LR: 1e-4
WARMUP_LR: 5e-7
WEIGHT_DECAY: 0.05
LR_SCHEDULER:
NAME: 'multistep'
GAMMA: 0.1
MULTISTEPS: [700,]
PRINT_FREQ: 100
SAVE_FREQ: 5
TAG: simmim_pretrain__swin_base__img192_window6__800ep
\ No newline at end of file
configs/simmim/simmim_pretrain__swinv2_base__img192_window12__800ep.yaml 0 → 100644
View file @ 6a10c7bf
MODEL:
TYPE: swinv2
NAME: simmim_pretrain
DROP_PATH_RATE: 0.1
SIMMIM:
NORM_TARGET:
ENABLE: True
PATCH_SIZE: 47
SWINV2:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
DATA:
IMG_SIZE: 192
MASK_PATCH_SIZE: 32
MASK_RATIO: 0.6
TRAIN:
EPOCHS: 800
WARMUP_EPOCHS: 10
BASE_LR: 1e-4
WARMUP_LR: 5e-7
WEIGHT_DECAY: 0.05
LR_SCHEDULER:
NAME: 'multistep'
GAMMA: 0.1
MULTISTEPS: [700,]
PRINT_FREQ: 100
SAVE_FREQ: 5
TAG: simmim_pretrain__swinv2_base__img192_window12__800ep
\ No newline at end of file
configs/swin/swin_base_patch4_window12_384_22kto1k_finetune.yaml 0 → 100644
View file @ 6a10c7bf
DATA:
IMG_SIZE: 384
MODEL:
TYPE: swin
NAME: swin_base_patch4_window12_384_22kto1k_finetune
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
TEST:
CROP: False
\ No newline at end of file
configs/swin/swin_base_patch4_window12_384_finetune.yaml 0 → 100644
View file @ 6a10c7bf
DATA:
IMG_SIZE: 384
MODEL:
TYPE: swin
NAME: swin_base_patch4_window12_384_finetune
DROP_PATH_RATE: 0.5
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
TEST:
CROP: False
\ No newline at end of file
configs/swin/swin_base_patch4_window7_224.yaml 0 → 100644
View file @ 6a10c7bf
MODEL:
TYPE: swin
NAME: swin_base_patch4_window7_224
DROP_PATH_RATE: 0.5
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 7
\ No newline at end of file
configs/swin/swin_base_patch4_window7_224_22k.yaml 0 → 100644
View file @ 6a10c7bf
DATA:
DATASET: imagenet22K
MODEL:
TYPE: swin
NAME: swin_base_patch4_window7_224_22k
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 7
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 0.05
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
\ No newline at end of file
configs/swin/swin_base_patch4_window7_224_22kto1k_finetune.yaml 0 → 100644
View file @ 6a10c7bf
MODEL:
TYPE: swin
NAME: swin_base_patch4_window7_224_22kto1k_finetune
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 7
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
\ No newline at end of file
configs/swin/swin_large_patch4_window12_384_22kto1k_finetune.yaml 0 → 100644
View file @ 6a10c7bf
DATA:
IMG_SIZE: 384
MODEL:
TYPE: swin
NAME: swin_large_patch4_window12_384_22kto1k_finetune
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 192
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 6, 12, 24, 48 ]
WINDOW_SIZE: 12
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
TEST:
CROP: False
\ No newline at end of file
configs/swin/swin_large_patch4_window7_224_22k.yaml 0 → 100644
View file @ 6a10c7bf
DATA:
DATASET: imagenet22K
MODEL:
TYPE: swin
NAME: swin_large_patch4_window7_224_22k
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 192
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 6, 12, 24, 48 ]
WINDOW_SIZE: 7
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 0.05
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
\ No newline at end of file
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment