[Doc] Add custom dataset tutorial (#522)
* [Doc] Add the tutorial for using custom dataset * [Doc] Add coco example * [Doc] Fix bug in command * [Doc] Fix bug in command * Update docs/en/user_guides/4_pretrain_custom_dataset.md Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> * [Doc] Update the doc according to the reviews Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com>pull/582/head
Songyang Zhang
Yixiao Fang
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f8a33ae543
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# Tutorial 4: Pretrain with Custom Dataset
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- [Tutorial 4: Pretrain with Custom Dataset](#tutorial-4-pretrain-with-custom-dataset)
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- [Train MAE on Custom Dataset](#train-mae-on-custom-dataset)
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- [Get the path of custom dataset](#get-the-path-of-custom-dataset)
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- [Choose one config as template](#choose-one-config-as-template)
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- [Edit the dataset related config](#edit-the-dataset-related-config)
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- [Train MAE on COCO Dataset](#train-mae-on-coco-dataset)
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- [Train SimCLR on Custom Dataset](#train-simclr-on-custom-dataset)
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- [Load Pre-trained Model to Speedup Convergence](#load-pre-trained-model-to-speedup-convergence)
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In this tutorial, we provide some tips on how to conduct self-supervised learning on your own dataset(without the need of label).
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## Train MAE on Custom Dataset
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In MMSelfSup, We support the `CustomDataset` from MMClassification(similar to the `ImageFolder` in `torchvision`), which is able to read the images within the specified folder directly. You only need to prepare the path information of the custom dataset and edit the config.
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### Get the path of custom dataset
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It should be like `data/custom_dataset/`
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### Choose one config as template
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Here, we would like to use `configs/selfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k.py` as the example. We first copy this config file and rename it as `mae_vit-base-p16_8xb512-coslr-400e_${custom_dataset}.py`.
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The content of this config is:
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```python
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_base_ = [
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'../_base_/models/mae_vit-base-p16.py',
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'../_base_/datasets/imagenet_mae.py',
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'../_base_/schedules/adamw_coslr-200e_in1k.py',
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'../_base_/default_runtime.py',
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]
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# dataset 8 x 512
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train_dataloader = dict(batch_size=512, num_workers=8)
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# optimizer wrapper
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optimizer = dict(
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type='AdamW', lr=1.5e-4 * 4096 / 256, betas=(0.9, 0.95), weight_decay=0.05)
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=optimizer,
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paramwise_cfg=dict(
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custom_keys={
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'ln': dict(decay_mult=0.0),
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'bias': dict(decay_mult=0.0),
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'pos_embed': dict(decay_mult=0.),
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'mask_token': dict(decay_mult=0.),
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'cls_token': dict(decay_mult=0.)
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}))
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# learning rate scheduler
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=1e-4,
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by_epoch=True,
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begin=0,
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end=40,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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T_max=360,
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by_epoch=True,
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begin=40,
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end=400,
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convert_to_iter_based=True)
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]
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# runtime settings
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# pre-train for 400 epochs
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train_cfg = dict(max_epochs=400)
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default_hooks = dict(
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logger=dict(type='LoggerHook', interval=100),
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# only keeps the latest 3 checkpoints
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
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# randomness
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randomness = dict(seed=0, diff_rank_seed=True)
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resume = True
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```
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### Edit the dataset related config
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The dataset related config is defined in `'../_base_/datasets/imagenet_mae.py'` in `_base_`. We then copy the content of dataset config file into our created file `mae_vit-base-p16_8xb512-coslr-400e_${custom_dataset}.py`.
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- Then we remove the `'../_base_/datasets/imagenet_mae.py'` in `_base_`.
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- Set the `dataset_type = 'mmcls.CustomDataset'`, and the path of the custom dataset ` data_root = /dataset/my_custom_dataset`.
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- Remove the `ann_file` in `train_dataloader`, and edit the `data_prefix` if needed.
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```{note}
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The `CustomDataset` is implemented in MMClassification, and we set the `dataset_type=mmcls.CustomDataset`.
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```
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And the edited config will be like this:
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```python
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# >>>>>>>>>>>>>>>>>>>>> Start of Changed >>>>>>>>>>>>>>>>>>>>>>>>>
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_base_ = [
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'../_base_/models/mae_vit-base-p16.py',
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# '../_base_/datasets/imagenet_mae.py',
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'../_base_/schedules/adamw_coslr-200e_in1k.py',
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'../_base_/default_runtime.py',
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]
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# custom dataset
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dataset_type = 'mmcls.CustomDataset'
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data_root = 'data/custom_dataset/'
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file_client_args = dict(backend='disk')
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train_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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dict(
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type='RandomResizedCrop',
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size=224,
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scale=(0.2, 1.0),
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backend='pillow',
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interpolation='bicubic'),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PackSelfSupInputs', meta_keys=['img_path'])
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]
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# dataset 8 x 512
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train_dataloader = dict(
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batch_size=512,
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num_workers=8,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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collate_fn=dict(type='default_collate'),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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# ann_file='meta/train.txt', # removed if you don't have the annotation file
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data_prefix=dict(img_path='./'),
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pipeline=train_pipeline))
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# <<<<<<<<<<<<<<<<<<<<<< End of Changed <<<<<<<<<<<<<<<<<<<<<<<<<<<
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# optimizer wrapper
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optimizer = dict(
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type='AdamW', lr=1.5e-4 * 4096 / 256, betas=(0.9, 0.95), weight_decay=0.05)
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=optimizer,
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paramwise_cfg=dict(
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custom_keys={
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'ln': dict(decay_mult=0.0),
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'bias': dict(decay_mult=0.0),
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'pos_embed': dict(decay_mult=0.),
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'mask_token': dict(decay_mult=0.),
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'cls_token': dict(decay_mult=0.)
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}))
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# learning rate scheduler
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=1e-4,
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by_epoch=True,
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begin=0,
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end=40,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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T_max=360,
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by_epoch=True,
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begin=40,
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end=400,
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convert_to_iter_based=True)
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]
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# runtime settings
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# pre-train for 400 epochs
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train_cfg = dict(max_epochs=400)
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default_hooks = dict(
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logger=dict(type='LoggerHook', interval=100),
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# only keeps the latest 3 checkpoints
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
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# randomness
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randomness = dict(seed=0, diff_rank_seed=True)
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resume = True
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```
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By using the edited config file, you are able to train a self-supervised model with MAE algorithm on the custom dataset.
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## Train MAE on COCO Dataset
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```{note}
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You need to install MMDetection to use the `mmdet.CocoDataset` follow this [documentation](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/get_started.md)
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```
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Follow the aforementioned idea, we also present an example of how to train MAE on COCO dataset. The edited file will be like this:
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```python
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# >>>>>>>>>>>>>>>>>>>>> Start of Changed >>>>>>>>>>>>>>>>>>>>>>>>>
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_base_ = [
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'../_base_/models/mae_vit-base-p16.py',
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# '../_base_/datasets/imagenet_mae.py',
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'../_base_/schedules/adamw_coslr-200e_in1k.py',
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'../_base_/default_runtime.py',
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]
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# custom dataset
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dataset_type = 'mmdet.CocoDataset'
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data_root = 'data/coco/'
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file_client_args = dict(backend='disk')
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train_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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dict(
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type='RandomResizedCrop',
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size=224,
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scale=(0.2, 1.0),
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backend='pillow',
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interpolation='bicubic'),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PackSelfSupInputs', meta_keys=['img_path'])
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]
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train_dataloader = dict(
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batch_size=128,
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num_workers=8,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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collate_fn=dict(type='default_collate'),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='annotations/instances_train2017.json',
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data_prefix=dict(img='train2017/'),
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pipeline=train_pipeline))
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# <<<<<<<<<<<<<<<<<<<<<< End of Changed <<<<<<<<<<<<<<<<<<<<<<<<<<<
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# optimizer wrapper
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optimizer = dict(
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type='AdamW', lr=1.5e-4 * 4096 / 256, betas=(0.9, 0.95), weight_decay=0.05)
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=optimizer,
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paramwise_cfg=dict(
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custom_keys={
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'ln': dict(decay_mult=0.0),
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'bias': dict(decay_mult=0.0),
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'pos_embed': dict(decay_mult=0.),
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'mask_token': dict(decay_mult=0.),
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'cls_token': dict(decay_mult=0.)
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}))
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# learning rate scheduler
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=1e-4,
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by_epoch=True,
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begin=0,
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end=40,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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T_max=360,
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by_epoch=True,
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begin=40,
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end=400,
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convert_to_iter_based=True)
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]
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# runtime settings
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# pre-train for 400 epochs
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train_cfg = dict(max_epochs=400)
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default_hooks = dict(
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logger=dict(type='LoggerHook', interval=100),
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# only keeps the latest 3 checkpoints
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
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# randomness
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randomness = dict(seed=0, diff_rank_seed=True)
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resume = True
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```
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## Train SimCLR on Custom Dataset
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We provide an example of using SimCLR on custom dataset, the main idea is similar to the [Train MAE on Custom Dataset
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](#train-mae-on-custom-dataset).
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The template config is `configs/selfsup/simclr/simclr_resnet50_8xb32-coslr-200e_in1k.py`. And the edited config is:
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```python
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# >>>>>>>>>>>>>>>>>>>>> Start of Changed >>>>>>>>>>>>>>>>>>>>>>>>>
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_base_ = [
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'../_base_/models/simclr.py',
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# '../_base_/datasets/imagenet_simclr.py',
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'../_base_/schedules/lars_coslr-200e_in1k.py',
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'../_base_/default_runtime.py',
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]
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# custom dataset
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dataset_type = 'mmcls.CustomDataset'
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data_root = 'data/custom_dataset/'
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file_client_args = dict(backend='disk')
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view_pipeline = [
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dict(type='RandomResizedCrop', size=224, backend='pillow'),
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dict(type='RandomFlip', prob=0.5),
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dict(
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type='RandomApply',
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transforms=[
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dict(
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type='ColorJitter',
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brightness=0.8,
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contrast=0.8,
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saturation=0.8,
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hue=0.2)
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],
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prob=0.8),
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dict(
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type='RandomGrayscale',
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prob=0.2,
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keep_channels=True,
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channel_weights=(0.114, 0.587, 0.2989)),
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dict(type='RandomGaussianBlur', sigma_min=0.1, sigma_max=2.0, prob=0.5),
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]
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train_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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dict(type='MultiView', num_views=2, transforms=[view_pipeline]),
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dict(type='PackSelfSupInputs', meta_keys=['img_path'])
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]
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train_dataloader = dict(
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batch_size=32,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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collate_fn=dict(type='default_collate'),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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# ann_file='meta/train.txt',
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data_prefix=dict(img_path='./'),
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pipeline=train_pipeline))
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# <<<<<<<<<<<<<<<<<<<<<< End of Changed <<<<<<<<<<<<<<<<<<<<<<<<<<<
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# optimizer
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optimizer = dict(type='LARS', lr=0.3, momentum=0.9, weight_decay=1e-6)
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=optimizer,
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paramwise_cfg=dict(
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custom_keys={
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'bn': dict(decay_mult=0, lars_exclude=True),
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'bias': dict(decay_mult=0, lars_exclude=True),
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# bn layer in ResNet block downsample module
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'downsample.1': dict(decay_mult=0, lars_exclude=True),
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}))
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# runtime settings
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default_hooks = dict(
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# only keeps the latest 3 checkpoints
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checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3))
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```
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## Load pre-trained model to speedup convergence
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To speedup the convergence of the model on your own dataset. You may use the pre-trained model as the initialization for the model's weight. You just need to specify the url of the pre-trained model via command. You can find our provide pre-trained checkpoint here: [Model Zoo](https://mmselfsup.readthedocs.io/en/1.x/model_zoo.html)
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```bash
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bash tools/dist_train.sh ${CONFIG} ${GPUS} --cfg-options model.pretrained=${PRETRAIN}
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```
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- `CONFIG`: the edited config path
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- `GPUS`: the number of GPU
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- `PRETRAIN`: the checkpoint url of pre-trained model provided by MMSelfSup
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@ -7,6 +7,7 @@ Pretrain
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1_config.md
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2_dataset_prepare.md
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3_pretrain.md
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4_pretrain_custom_dataset.md
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Downstream Tasks
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**************
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@ -0,0 +1,378 @@
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# Tutorial 4: Pretrain with Custom Dataset
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- [Tutorial 4: Pretrain with Custom Dataset](#tutorial-4-pretrain-with-custom-dataset)
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- [Train MAE on Custom Dataset](#train-mae-on-custom-dataset)
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- [Get the path of custom dataset](#get-the-path-of-custom-dataset)
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- [Choose one config as template](#choose-one-config-as-template)
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- [Edit the dataset related config](#edit-the-dataset-related-config)
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- [Train MAE on COCO Dataset](#train-mae-on-coco-dataset)
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- [Train SimCLR on Custom Dataset](#train-simclr-on-custom-dataset)
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- [Load Pre-trained Model to Speedup Convergence](#load-pre-trained-model-to-speedup-convergence)
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In this tutorial, we provide some tips on how to conduct self-supervised learning on your own dataset(without the need of label).
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|
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## Train MAE on Custom Dataset
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|
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In MMSelfSup, We support the `CustomDataset` from MMClassification(similar to the `ImageFolder` in `torchvision`), which is able to read the images within the specified folder directly. You only need to prepare the path information of the custom dataset and edit the config.
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|
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### Get the path of custom dataset
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It should be like `data/custom_dataset/`
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### Choose one config as template
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|
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Here, we would like to use `configs/selfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k.py` as the example. We first copy this config file and rename it as `mae_vit-base-p16_8xb512-coslr-400e_${custom_dataset}.py`.
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The content of this config is:
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```python
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_base_ = [
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'../_base_/models/mae_vit-base-p16.py',
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'../_base_/datasets/imagenet_mae.py',
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'../_base_/schedules/adamw_coslr-200e_in1k.py',
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'../_base_/default_runtime.py',
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]
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# dataset 8 x 512
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train_dataloader = dict(batch_size=512, num_workers=8)
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# optimizer wrapper
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optimizer = dict(
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type='AdamW', lr=1.5e-4 * 4096 / 256, betas=(0.9, 0.95), weight_decay=0.05)
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optim_wrapper = dict(
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type='OptimWrapper',
|
||||
optimizer=optimizer,
|
||||
paramwise_cfg=dict(
|
||||
custom_keys={
|
||||
'ln': dict(decay_mult=0.0),
|
||||
'bias': dict(decay_mult=0.0),
|
||||
'pos_embed': dict(decay_mult=0.),
|
||||
'mask_token': dict(decay_mult=0.),
|
||||
'cls_token': dict(decay_mult=0.)
|
||||
}))
|
||||
|
||||
# learning rate scheduler
|
||||
param_scheduler = [
|
||||
dict(
|
||||
type='LinearLR',
|
||||
start_factor=1e-4,
|
||||
by_epoch=True,
|
||||
begin=0,
|
||||
end=40,
|
||||
convert_to_iter_based=True),
|
||||
dict(
|
||||
type='CosineAnnealingLR',
|
||||
T_max=360,
|
||||
by_epoch=True,
|
||||
begin=40,
|
||||
end=400,
|
||||
convert_to_iter_based=True)
|
||||
]
|
||||
|
||||
# runtime settings
|
||||
# pre-train for 400 epochs
|
||||
train_cfg = dict(max_epochs=400)
|
||||
default_hooks = dict(
|
||||
logger=dict(type='LoggerHook', interval=100),
|
||||
# only keeps the latest 3 checkpoints
|
||||
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
|
||||
|
||||
# randomness
|
||||
randomness = dict(seed=0, diff_rank_seed=True)
|
||||
resume = True
|
||||
```
|
||||
|
||||
### Edit the dataset related config
|
||||
|
||||
The dataset related config is defined in `'../_base_/datasets/imagenet_mae.py'` in `_base_`. We then copy the content of dataset config file into our created file `mae_vit-base-p16_8xb512-coslr-400e_${custom_dataset}.py`.
|
||||
|
||||
- Then we remove the `'../_base_/datasets/imagenet_mae.py'` in `_base_`.
|
||||
- Set the `dataset_type = 'mmcls.CustomDataset'`, and the path of the custom dataset ` data_root = /dataset/my_custom_dataset`.
|
||||
- Remove the `ann_file` in `train_dataloader`, and edit the `data_prefix` if needed.
|
||||
|
||||
```{note}
|
||||
The `CustomDataset` is implemented in MMClassification, and we set the `dataset_type=mmcls.CustomDataset`.
|
||||
```
|
||||
|
||||
And the edited config will be like this:
|
||||
|
||||
```python
|
||||
# >>>>>>>>>>>>>>>>>>>>> Start of Changed >>>>>>>>>>>>>>>>>>>>>>>>>
|
||||
_base_ = [
|
||||
'../_base_/models/mae_vit-base-p16.py',
|
||||
# '../_base_/datasets/imagenet_mae.py',
|
||||
'../_base_/schedules/adamw_coslr-200e_in1k.py',
|
||||
'../_base_/default_runtime.py',
|
||||
]
|
||||
|
||||
# custom dataset
|
||||
dataset_type = 'mmcls.CustomDataset'
|
||||
|
||||
data_root = 'data/custom_dataset/'
|
||||
file_client_args = dict(backend='disk')
|
||||
train_pipeline = [
|
||||
dict(type='LoadImageFromFile', file_client_args=file_client_args),
|
||||
dict(
|
||||
type='RandomResizedCrop',
|
||||
size=224,
|
||||
scale=(0.2, 1.0),
|
||||
backend='pillow',
|
||||
interpolation='bicubic'),
|
||||
dict(type='RandomFlip', prob=0.5),
|
||||
dict(type='PackSelfSupInputs', meta_keys=['img_path'])
|
||||
]
|
||||
|
||||
# dataset 8 x 512
|
||||
train_dataloader = dict(
|
||||
batch_size=512,
|
||||
num_workers=8,
|
||||
persistent_workers=True,
|
||||
sampler=dict(type='DefaultSampler', shuffle=True),
|
||||
collate_fn=dict(type='default_collate'),
|
||||
dataset=dict(
|
||||
type=dataset_type,
|
||||
data_root=data_root,
|
||||
# ann_file='meta/train.txt', # removed if you don't have the annotation file
|
||||
data_prefix=dict(img_path='./'),
|
||||
pipeline=train_pipeline))
|
||||
# <<<<<<<<<<<<<<<<<<<<<< End of Changed <<<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
|
||||
# optimizer wrapper
|
||||
optimizer = dict(
|
||||
type='AdamW', lr=1.5e-4 * 4096 / 256, betas=(0.9, 0.95), weight_decay=0.05)
|
||||
optim_wrapper = dict(
|
||||
type='OptimWrapper',
|
||||
optimizer=optimizer,
|
||||
paramwise_cfg=dict(
|
||||
custom_keys={
|
||||
'ln': dict(decay_mult=0.0),
|
||||
'bias': dict(decay_mult=0.0),
|
||||
'pos_embed': dict(decay_mult=0.),
|
||||
'mask_token': dict(decay_mult=0.),
|
||||
'cls_token': dict(decay_mult=0.)
|
||||
}))
|
||||
|
||||
# learning rate scheduler
|
||||
param_scheduler = [
|
||||
dict(
|
||||
type='LinearLR',
|
||||
start_factor=1e-4,
|
||||
by_epoch=True,
|
||||
begin=0,
|
||||
end=40,
|
||||
convert_to_iter_based=True),
|
||||
dict(
|
||||
type='CosineAnnealingLR',
|
||||
T_max=360,
|
||||
by_epoch=True,
|
||||
begin=40,
|
||||
end=400,
|
||||
convert_to_iter_based=True)
|
||||
]
|
||||
|
||||
# runtime settings
|
||||
# pre-train for 400 epochs
|
||||
train_cfg = dict(max_epochs=400)
|
||||
default_hooks = dict(
|
||||
logger=dict(type='LoggerHook', interval=100),
|
||||
# only keeps the latest 3 checkpoints
|
||||
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
|
||||
|
||||
# randomness
|
||||
randomness = dict(seed=0, diff_rank_seed=True)
|
||||
resume = True
|
||||
```
|
||||
|
||||
By using the edited config file, you are able to train a self-supervised model with MAE algorithm on the custom dataset.
|
||||
|
||||
## Train MAE on COCO Dataset
|
||||
|
||||
```{note}
|
||||
You need to install MMDetection to use the `mmdet.CocoDataset` follow this [documentation](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/get_started.md)
|
||||
```
|
||||
|
||||
Follow the aforementioned idea, we also present an example of how to train MAE on COCO dataset. The edited file will be like this:
|
||||
|
||||
```python
|
||||
# >>>>>>>>>>>>>>>>>>>>> Start of Changed >>>>>>>>>>>>>>>>>>>>>>>>>
|
||||
_base_ = [
|
||||
'../_base_/models/mae_vit-base-p16.py',
|
||||
# '../_base_/datasets/imagenet_mae.py',
|
||||
'../_base_/schedules/adamw_coslr-200e_in1k.py',
|
||||
'../_base_/default_runtime.py',
|
||||
]
|
||||
|
||||
# custom dataset
|
||||
dataset_type = 'mmdet.CocoDataset'
|
||||
data_root = 'data/coco/'
|
||||
file_client_args = dict(backend='disk')
|
||||
|
||||
train_pipeline = [
|
||||
dict(type='LoadImageFromFile', file_client_args=file_client_args),
|
||||
dict(
|
||||
type='RandomResizedCrop',
|
||||
size=224,
|
||||
scale=(0.2, 1.0),
|
||||
backend='pillow',
|
||||
interpolation='bicubic'),
|
||||
dict(type='RandomFlip', prob=0.5),
|
||||
dict(type='PackSelfSupInputs', meta_keys=['img_path'])
|
||||
]
|
||||
|
||||
train_dataloader = dict(
|
||||
batch_size=128,
|
||||
num_workers=8,
|
||||
persistent_workers=True,
|
||||
sampler=dict(type='DefaultSampler', shuffle=True),
|
||||
collate_fn=dict(type='default_collate'),
|
||||
dataset=dict(
|
||||
type=dataset_type,
|
||||
data_root=data_root,
|
||||
ann_file='annotations/instances_train2017.json',
|
||||
data_prefix=dict(img='train2017/'),
|
||||
pipeline=train_pipeline))
|
||||
|
||||
# <<<<<<<<<<<<<<<<<<<<<< End of Changed <<<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
# optimizer wrapper
|
||||
optimizer = dict(
|
||||
type='AdamW', lr=1.5e-4 * 4096 / 256, betas=(0.9, 0.95), weight_decay=0.05)
|
||||
optim_wrapper = dict(
|
||||
type='OptimWrapper',
|
||||
optimizer=optimizer,
|
||||
paramwise_cfg=dict(
|
||||
custom_keys={
|
||||
'ln': dict(decay_mult=0.0),
|
||||
'bias': dict(decay_mult=0.0),
|
||||
'pos_embed': dict(decay_mult=0.),
|
||||
'mask_token': dict(decay_mult=0.),
|
||||
'cls_token': dict(decay_mult=0.)
|
||||
}))
|
||||
|
||||
# learning rate scheduler
|
||||
param_scheduler = [
|
||||
dict(
|
||||
type='LinearLR',
|
||||
start_factor=1e-4,
|
||||
by_epoch=True,
|
||||
begin=0,
|
||||
end=40,
|
||||
convert_to_iter_based=True),
|
||||
dict(
|
||||
type='CosineAnnealingLR',
|
||||
T_max=360,
|
||||
by_epoch=True,
|
||||
begin=40,
|
||||
end=400,
|
||||
convert_to_iter_based=True)
|
||||
]
|
||||
|
||||
# runtime settings
|
||||
# pre-train for 400 epochs
|
||||
train_cfg = dict(max_epochs=400)
|
||||
default_hooks = dict(
|
||||
logger=dict(type='LoggerHook', interval=100),
|
||||
# only keeps the latest 3 checkpoints
|
||||
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
|
||||
|
||||
# randomness
|
||||
randomness = dict(seed=0, diff_rank_seed=True)
|
||||
resume = True
|
||||
```
|
||||
|
||||
## Train SimCLR on Custom Dataset
|
||||
|
||||
We provide an example of using SimCLR on custom dataset, the main idea is similar to the [Train MAE on Custom Dataset
|
||||
](#train-mae-on-custom-dataset).
|
||||
|
||||
The template config is `configs/selfsup/simclr/simclr_resnet50_8xb32-coslr-200e_in1k.py`. And the edited config is:
|
||||
|
||||
```python
|
||||
# >>>>>>>>>>>>>>>>>>>>> Start of Changed >>>>>>>>>>>>>>>>>>>>>>>>>
|
||||
_base_ = [
|
||||
'../_base_/models/simclr.py',
|
||||
# '../_base_/datasets/imagenet_simclr.py',
|
||||
'../_base_/schedules/lars_coslr-200e_in1k.py',
|
||||
'../_base_/default_runtime.py',
|
||||
]
|
||||
|
||||
# custom dataset
|
||||
dataset_type = 'mmcls.CustomDataset'
|
||||
data_root = 'data/custom_dataset/'
|
||||
file_client_args = dict(backend='disk')
|
||||
|
||||
view_pipeline = [
|
||||
dict(type='RandomResizedCrop', size=224, backend='pillow'),
|
||||
dict(type='RandomFlip', prob=0.5),
|
||||
dict(
|
||||
type='RandomApply',
|
||||
transforms=[
|
||||
dict(
|
||||
type='ColorJitter',
|
||||
brightness=0.8,
|
||||
contrast=0.8,
|
||||
saturation=0.8,
|
||||
hue=0.2)
|
||||
],
|
||||
prob=0.8),
|
||||
dict(
|
||||
type='RandomGrayscale',
|
||||
prob=0.2,
|
||||
keep_channels=True,
|
||||
channel_weights=(0.114, 0.587, 0.2989)),
|
||||
dict(type='RandomGaussianBlur', sigma_min=0.1, sigma_max=2.0, prob=0.5),
|
||||
]
|
||||
|
||||
train_pipeline = [
|
||||
dict(type='LoadImageFromFile', file_client_args=file_client_args),
|
||||
dict(type='MultiView', num_views=2, transforms=[view_pipeline]),
|
||||
dict(type='PackSelfSupInputs', meta_keys=['img_path'])
|
||||
]
|
||||
|
||||
train_dataloader = dict(
|
||||
batch_size=32,
|
||||
num_workers=4,
|
||||
persistent_workers=True,
|
||||
sampler=dict(type='DefaultSampler', shuffle=True),
|
||||
collate_fn=dict(type='default_collate'),
|
||||
dataset=dict(
|
||||
type=dataset_type,
|
||||
data_root=data_root,
|
||||
# ann_file='meta/train.txt',
|
||||
data_prefix=dict(img_path='./'),
|
||||
pipeline=train_pipeline))
|
||||
# <<<<<<<<<<<<<<<<<<<<<< End of Changed <<<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
|
||||
# optimizer
|
||||
optimizer = dict(type='LARS', lr=0.3, momentum=0.9, weight_decay=1e-6)
|
||||
optim_wrapper = dict(
|
||||
type='OptimWrapper',
|
||||
optimizer=optimizer,
|
||||
paramwise_cfg=dict(
|
||||
custom_keys={
|
||||
'bn': dict(decay_mult=0, lars_exclude=True),
|
||||
'bias': dict(decay_mult=0, lars_exclude=True),
|
||||
# bn layer in ResNet block downsample module
|
||||
'downsample.1': dict(decay_mult=0, lars_exclude=True),
|
||||
}))
|
||||
|
||||
# runtime settings
|
||||
default_hooks = dict(
|
||||
# only keeps the latest 3 checkpoints
|
||||
checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3))
|
||||
|
||||
```
|
||||
|
||||
## Load pre-trained model to speedup convergence
|
||||
|
||||
To speedup the convergence of the model on your own dataset. You may use the pre-trained model as the initialization for the model's weight. You just need to specify the url of the pre-trained model via command. You can find our provide pre-trained checkpoint here: [Model Zoo](https://mmselfsup.readthedocs.io/en/1.x/model_zoo.html)
|
||||
|
||||
```bash
|
||||
bash tools/dist_train.sh ${CONFIG} ${GPUS} --cfg-options model.pretrained=${PRETRAIN}
|
||||
```
|
||||
|
||||
- `CONFIG`: the edited config path
|
||||
- `GPUS`: the number of GPU
|
||||
- `PRETRAIN`: the checkpoint url of pre-trained model provided by MMSelfSup
|
||||
|
|
@ -7,6 +7,7 @@ Pretrain
|
|||
1_config.md
|
||||
2_dataset_prepare.md
|
||||
3_pretrain.md
|
||||
4_pretrain_custom_dataset.md
|
||||
|
||||
Downstream Tasks
|
||||
**************
|
||||
|
|
|
|||
Loading…
Reference in New Issue