256 lines
7.4 KiB
Markdown
256 lines
7.4 KiB
Markdown
# How to Pretrain with Custom Dataset
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In this tutorial, we provide a practice example and some tips on how to train on your own dataset.
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In MMPretrain, We support the `CustomDataset` (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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## Step-1: Prepare your dataset
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Prepare your dataset following [Prepare Dataset](../user_guides/dataset_prepare.md).
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And the root folder of the dataset can be like `data/custom_dataset/`.
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Here, we assume you want to do unsupervised training, and use the sub-folder format `CustomDataset` to
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organize your dataset as:
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```text
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data/custom_dataset/
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├── sample1.png
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├── sample2.png
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├── sample3.png
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├── sample4.png
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└── ...
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```
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## Step-2: Choose one config as template
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Here, we would like to use `configs/mae/mae_vit-base-p16_8xb512-amp-coslr-300e_in1k.py` as the example. We
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first copy this config file to the same folder and rename it as
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`mae_vit-base-p16_8xb512-amp-coslr-300e_custom.py`.
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```{tip}
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As a convention, the last field of the config name is the dataset, e.g.,`in1k` for ImageNet dataset, `coco` for COCO dataset
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```
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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_bs512_mae.py',
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'../_base_/default_runtime.py',
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]
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# optimizer wrapper
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optim_wrapper = dict(
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type='AmpOptimWrapper',
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loss_scale='dynamic',
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optimizer=dict(
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type='AdamW',
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lr=1.5e-4 * 4096 / 256,
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betas=(0.9, 0.95),
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weight_decay=0.05),
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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=0.0001,
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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=260,
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by_epoch=True,
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begin=40,
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end=300,
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convert_to_iter_based=True)
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]
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# runtime settings
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300)
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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=1, max_keep_ckpts=3))
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randomness = dict(seed=0, diff_rank_seed=True)
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# auto resume
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resume = True
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# NOTE: `auto_scale_lr` is for automatically scaling LR
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# based on the actual training batch size.
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auto_scale_lr = dict(base_batch_size=4096)
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```
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## Step-3: Edit the dataset related config
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- Override the `type` of dataset settings as `'CustomDataset'`
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- Override the `data_root` of dataset settings as `data/custom_dataset`.
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- Override the `ann_file` of dataset settings as an empty string since we assume you are using the sub-folder
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format `CustomDataset`.
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- Override the `data_prefix` of dataset settings as an empty string since we are using the whole dataset under
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the `data_root`, and you don't need to split samples into different subset and set the `data_prefix`.
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The modified config will be like:
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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_bs512_mae.py',
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'../_base_/default_runtime.py',
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]
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# >>>>>>>>>>>>>>> Override dataset settings here >>>>>>>>>>>>>>>>>>>
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train_dataloader = dict(
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dataset=dict(
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type='CustomDataset',
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data_root='data/custom_dataset/',
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ann_file='', # We assume you are using the sub-folder format without ann_file
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data_prefix='', # The `data_root` is the data_prefix directly.
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with_label=False,
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)
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)
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# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
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# optimizer wrapper
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optim_wrapper = dict(
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type='AmpOptimWrapper',
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loss_scale='dynamic',
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optimizer=dict(
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type='AdamW',
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lr=1.5e-4 * 4096 / 256,
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betas=(0.9, 0.95),
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weight_decay=0.05),
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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=0.0001,
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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=260,
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by_epoch=True,
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begin=40,
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end=300,
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convert_to_iter_based=True)
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]
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# runtime settings
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300)
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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=1, max_keep_ckpts=3))
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randomness = dict(seed=0, diff_rank_seed=True)
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# auto resume
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resume = True
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# NOTE: `auto_scale_lr` is for automatically scaling LR
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# based on the actual training batch size.
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auto_scale_lr = dict(base_batch_size=4096)
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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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## Another example: 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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_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_/default_runtime.py',
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]
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# >>>>>>>>>>>>>>> Override dataset settings here >>>>>>>>>>>>>>>>>>>
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train_dataloader = dict(
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dataset=dict(
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type='mmdet.CocoDataset',
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data_root='data/coco/',
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ann_file='annotations/instances_train2017.json', # Only for loading images, and the labels won't be used.
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data_prefix=dict(img='train2017/'),
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)
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)
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# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
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# optimizer wrapper
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optim_wrapper = dict(
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type='AmpOptimWrapper',
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loss_scale='dynamic',
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optimizer=dict(
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type='AdamW',
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lr=1.5e-4 * 4096 / 256,
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betas=(0.9, 0.95),
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weight_decay=0.05),
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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=0.0001,
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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=260,
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by_epoch=True,
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begin=40,
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end=300,
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convert_to_iter_based=True)
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]
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# runtime settings
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300)
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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=1, max_keep_ckpts=3))
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randomness = dict(seed=0, diff_rank_seed=True)
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# auto resume
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resume = True
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# NOTE: `auto_scale_lr` is for automatically scaling LR
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# based on the actual training batch size.
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auto_scale_lr = dict(base_batch_size=4096)
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```
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