-
Notifications
You must be signed in to change notification settings - Fork 1.1k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
[CodeCamp2023-338] New Version of config Adapting Swin Transformer Algorithm #1780
Merged
Merged
Changes from all commits
Commits
Show all changes
7 commits
Select commit
Hold shift + click to select a range
634852a
[CodeCamp2023-338] New Version of config Adapting Swin Transformer Al…
timerring ddc6d0b
Merge remote-tracking branch 'upstream/dev' into dev
timerring ed3b7f8
format all file names
timerring f4d372b
only keep one file to set swin transformer model config
timerring 9b75ce0
only keep one file to set swin transformer v2 model config
timerring b0b4422
fix a redundant
timerring 7734f07
set arch etc
timerring File filter
Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -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 |
89 changes: 89 additions & 0 deletions
89
mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_256.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -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 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,20 @@ | ||
# Copyright (c) OpenMMLab. All rights reserved. | ||
# This is a BETA new format config file, and the usage may change recently. | ||
from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling, | ||
ImageClassifier, LinearClsHead, SwinTransformer) | ||
|
||
# model settings | ||
model = dict( | ||
type=ImageClassifier, | ||
backbone=dict( | ||
type=SwinTransformer, | ||
arch='base', | ||
img_size=384, | ||
stage_cfgs=dict(block_cfgs=dict(window_size=12))), | ||
neck=dict(type=GlobalAveragePooling), | ||
head=dict( | ||
type=LinearClsHead, | ||
num_classes=1000, | ||
in_channels=1024, | ||
loss=dict(type=CrossEntropyLoss, loss_weight=1.0), | ||
topk=(1, 5))) |
19 changes: 19 additions & 0 deletions
19
mmpretrain/configs/_base_/models/swin_transformer_v2_base.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -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)) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -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) |
35 changes: 35 additions & 0 deletions
35
mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,35 @@ | ||
# Copyright (c) OpenMMLab. All rights reserved. | ||
# This is a BETA new format config file, and the usage may change recently. | ||
from mmengine.config import read_base | ||
from mmengine.model import ConstantInit, TruncNormalInit | ||
|
||
from mmpretrain.models import CutMix, LabelSmoothLoss, Mixup | ||
|
||
with read_base(): | ||
from .._base_.datasets.imagenet_bs64_swin_224 import * | ||
from .._base_.default_runtime import * | ||
from .._base_.models.swin_transformer_base import * | ||
from .._base_.schedules.imagenet_bs1024_adamw_swin import * | ||
|
||
# model settings | ||
model.update( | ||
backbone=dict(img_size=224, drop_path_rate=0.5, stage_cfgs=None), | ||
head=dict( | ||
init_cfg=None, # suppress the default init_cfg of LinearClsHead. | ||
loss=dict( | ||
type=LabelSmoothLoss, | ||
label_smooth_val=0.1, | ||
mode='original', | ||
loss_weight=0), | ||
topk=None, | ||
cal_acc=False), | ||
init_cfg=[ | ||
dict(type=TruncNormalInit, layer='Linear', std=0.02, bias=0.), | ||
dict(type=ConstantInit, layer='LayerNorm', val=1., bias=0.) | ||
], | ||
train_cfg=dict( | ||
augments=[dict(type=Mixup, alpha=0.8), | ||
dict(type=CutMix, alpha=1.0)])) | ||
|
||
# schedule settings | ||
optim_wrapper = dict(clip_grad=dict(max_norm=5.0)) |
12 changes: 12 additions & 0 deletions
12
mmpretrain/configs/swin_transformer/swin_base_16xb64_in1k_384px.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,12 @@ | ||
# Copyright (c) OpenMMLab. All rights reserved. | ||
# This is a BETA new format config file, and the usage may change recently. | ||
from mmengine.config import read_base | ||
|
||
with read_base(): | ||
from .._base_.datasets.imagenet_bs64_swin_384 import * | ||
from .._base_.default_runtime import * | ||
from .._base_.models.swin_transformer_base import * | ||
from .._base_.schedules.imagenet_bs1024_adamw_swin import * | ||
|
||
# schedule settings | ||
optim_wrapper = dict(clip_grad=dict(max_norm=5.0)) |
18 changes: 18 additions & 0 deletions
18
mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,18 @@ | ||
# Copyright (c) OpenMMLab. All rights reserved. | ||
# This is a BETA new format config file, and the usage may change recently. | ||
from mmengine.config import read_base | ||
|
||
with read_base(): | ||
from .._base_.datasets.imagenet_bs64_swin_224 import * | ||
from .._base_.default_runtime import * | ||
from .._base_.models.swin_transformer_base import * | ||
from .._base_.schedules.imagenet_bs1024_adamw_swin import * | ||
|
||
# model settings | ||
model.update( | ||
backbone=dict(arch='large', img_size=224, stage_cfgs=None), | ||
head=dict(in_channels=1536), | ||
) | ||
|
||
# schedule settings | ||
optim_wrapper = dict(clip_grad=dict(max_norm=5.0)) |
18 changes: 18 additions & 0 deletions
18
mmpretrain/configs/swin_transformer/swin_large_16xb64_in1k_384px.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,18 @@ | ||
# Copyright (c) OpenMMLab. All rights reserved. | ||
# This is a BETA new format config file, and the usage may change recently. | ||
from mmengine.config import read_base | ||
|
||
with read_base(): | ||
from .._base_.datasets.imagenet_bs64_swin_384 import * | ||
from .._base_.default_runtime import * | ||
from .._base_.models.swin_transformer_base import * | ||
from .._base_.schedules.imagenet_bs1024_adamw_swin import * | ||
|
||
# model settings | ||
model.update( | ||
backbone=dict(arch='large'), | ||
head=dict(in_channels=1536), | ||
) | ||
|
||
# schedule settings | ||
optim_wrapper = dict(clip_grad=dict(max_norm=5.0)) |
49 changes: 49 additions & 0 deletions
49
mmpretrain/configs/swin_transformer/swin_large_8xb8_cub_384px.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,49 @@ | ||
# Copyright (c) OpenMMLab. All rights reserved. | ||
# This is a BETA new format config file, and the usage may change recently. | ||
from mmengine.config import read_base | ||
from mmengine.hooks import CheckpointHook, LoggerHook | ||
from mmengine.model import PretrainedInit | ||
from torch.optim.adamw import AdamW | ||
|
||
from mmpretrain.models import ImageClassifier | ||
|
||
with read_base(): | ||
from .._base_.datasets.cub_bs8_384 import * | ||
from .._base_.default_runtime import * | ||
from .._base_.models.swin_transformer_base import * | ||
from .._base_.schedules.cub_bs64 import * | ||
|
||
# model settings | ||
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-large_3rdparty_in21k-384px.pth' # noqa | ||
|
||
model.update( | ||
backbone=dict( | ||
arch='large', | ||
init_cfg=dict( | ||
type=PretrainedInit, checkpoint=checkpoint, prefix='backbone')), | ||
head=dict(num_classes=200, in_channels=1536)) | ||
|
||
# schedule settings | ||
optim_wrapper = dict( | ||
optimizer=dict( | ||
_delete_=True, | ||
type=AdamW, | ||
lr=5e-6, | ||
weight_decay=0.0005, | ||
eps=1e-8, | ||
betas=(0.9, 0.999)), | ||
paramwise_cfg=dict( | ||
norm_decay_mult=0.0, | ||
bias_decay_mult=0.0, | ||
custom_keys={ | ||
'.absolute_pos_embed': dict(decay_mult=0.0), | ||
'.relative_position_bias_table': dict(decay_mult=0.0) | ||
}), | ||
clip_grad=dict(max_norm=5.0), | ||
) | ||
|
||
default_hooks = dict( | ||
# log every 20 intervals | ||
logger=dict(type=LoggerHook, interval=20), | ||
# save last three checkpoints | ||
checkpoint=dict(type=CheckpointHook, interval=1, max_keep_ckpts=3)) |
Oops, something went wrong.
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
set arch