301 lines
14 KiB
Markdown
301 lines
14 KiB
Markdown
# Tutorial 1: Learn about Configs
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We incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments.
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If you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config.
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The classification part of mmfewshot is built upon the [mmcls](https://github.com/open-mmlab/mmclassification),
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thus it is highly recommended learning the basic of mmcls.
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## Modify config through script arguments
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When submitting jobs using "tools/classification/train.py" or "tools/classification/test.py", you may specify `--cfg-options` to in-place modify the config.
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- Update config keys of dict chains.
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The config options can be specified following the order of the dict keys in the original config.
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For example, `--cfg-options model.backbone.norm_eval=False` changes the all BN modules in model backbones to `train` mode.
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- Update keys inside a list of configs.
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Some config dicts are composed as a list in your config. For example, the training pipeline `data.train.pipeline` is normally a list
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e.g. `[dict(type='LoadImageFromFile'), ...]`. If you want to change `'LoadImageFromFile'` to `'LoadImageFromWebcam'` in the pipeline,
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you may specify `--cfg-options data.train.pipeline.0.type=LoadImageFromWebcam`.
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- Update values of list/tuples.
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If the value to be updated is a list or a tuple. For example, the config file normally sets `workflow=[('train', 1)]`. If you want to
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change this key, you may specify `--cfg-options workflow="[(train,1),(val,1)]"`. Note that the quotation mark \" is necessary to
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support list/tuple data types, and that **NO** white space is allowed inside the quotation marks in the specified value.
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## Config name style
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We follow the below style to name config files. Contributors are advised to follow the same style.
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```
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{algorithm}_[algorithm setting]_{backbone}_{gpu x batch_per_gpu}_[misc]_{dataset}_{meta test setting}.py
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```
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`{xxx}` is required field and `[yyy]` is optional.
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- `{algorithm}`: model type like `faster_rcnn`, `mask_rcnn`, etc.
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- `[algorithm setting]`: specific setting for some model, like `without_semantic` for `htc`, `moment` for `reppoints`, etc.
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- `{backbone}`: backbone type like `conv4`, `resnet12`.
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- `[norm_setting]`: `bn` (Batch Normalization) is used unless specified, other norm layer type could be `gn` (Group Normalization), `syncbn` (Synchronized Batch Normalization).
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`gn-head`/`gn-neck` indicates GN is applied in head/neck only, while `gn-all` means GN is applied in the entire model, e.g. backbone, neck, head.
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- `[gpu x batch_per_gpu]`: GPUs and samples per GPU. For episodic training methods we use the total number of images in one episode, i.e. n classes x (support images+query images).
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- `[misc]`: miscellaneous setting/plugins of model.
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- `{dataset}`: dataset like `cub`, `mini-imagenet` and `tiered-imagenet`.
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- `{meta test setting}`: n way k shot setting like `5way_1shot` or `5way_5shot`.
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## An example of Baseline
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To help the users have a basic idea of a complete config and the modules in a modern classification system,
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we make brief comments on the config of Baseline for MiniImageNet in 5 way 1 shot setting as the following.
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For more detailed usage and the corresponding alternative for each module, please refer to the API documentation.
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```python
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# config of model
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model = dict(
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# classifier name
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type='Baseline',
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# config of backbone
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backbone=dict(type='Conv4'),
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# config of classifier head
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head=dict(type='LinearHead', num_classes=64, in_channels=1600),
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# config of classifier head used in meta test
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meta_test_head=dict(type='LinearHead', num_classes=5, in_channels=1600))
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# data pipeline for training
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train_pipeline = [
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# first pipeline to load images from file path
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dict(type='LoadImageFromFile'),
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# random resize crop
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dict(type='RandomResizedCrop', size=84),
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# random flip
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dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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# color jitter
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dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
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dict(type='Normalize', # normalization
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# mean values used to normalization
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mean=[123.675, 116.28, 103.53],
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# standard variance used to normalization
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std=[58.395, 57.12, 57.375],
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# whether to invert the color channel, rgb2bgr or bgr2rgb
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to_rgb=True),
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# convert img into torch.Tensor
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dict(type='ImageToTensor', keys=['img']),
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# convert gt_label into torch.Tensor
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dict(type='ToTensor', keys=['gt_label']),
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# pipeline that decides which keys in the data should be passed to the runner
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dict(type='Collect', keys=['img', 'gt_label'])
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]
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# data pipeline for testing
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test_pipeline = [
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# first pipeline to load images from file path
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dict(type='LoadImageFromFile'),
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# resize image
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dict(type='Resize', size=(96, -1)),
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# center crop
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dict(type='CenterCrop', crop_size=84),
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dict(type='Normalize', # normalization
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# mean values used to normalization
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mean=[123.675, 116.28, 103.53],
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# standard variance used to normalization
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std=[58.395, 57.12, 57.375],
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# whether to invert the color channel, rgb2bgr or bgr2rgb
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to_rgb=True),
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# convert img into torch.Tensor
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dict(type='ImageToTensor', keys=['img']),
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# pipeline that decides which keys in the data should be passed to the runner
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dict(type='Collect', keys=['img', 'gt_label'])
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]
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# config of fine-tuning using support set in Meta Test
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meta_finetune_cfg = dict(
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# number of iterations in fine-tuning
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num_steps=150,
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# optimizer config in fine-tuning
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optimizer=dict(
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type='SGD', # optimizer name
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lr=0.01, # learning rate
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momentum=0.9, # momentum
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dampening=0.9, # dampening
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weight_decay=0.001)), # weight decay
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data = dict(
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# batch size of a single GPU
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samples_per_gpu=64,
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# worker to pre-fetch data for each single GPU
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workers_per_gpu=4,
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# config of training set
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train=dict(
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# name of dataset
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type='MiniImageNetDataset',
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# prefix of image
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data_prefix='data/mini_imagenet',
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# subset of dataset
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subset='train',
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# train pipeline
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pipeline=train_pipeline),
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# config of validation set
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val=dict(
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# dataset wrapper for Meta Test
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type='MetaTestDataset',
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# total number of test tasks
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num_episodes=100,
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num_ways=5, # number of class in each task
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num_shots=1, # number of support images in each task
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num_queries=15, # number of query images in each task
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dataset=dict( # config of dataset
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type='MiniImageNetDataset', # dataset name
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subset='val', # subset of dataset
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data_prefix='data/mini_imagenet', # prefix of images
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pipeline=test_pipeline),
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meta_test_cfg=dict( # config of Meta Test
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num_episodes=100, # total number of test tasks
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num_ways=5, # number of class in each task
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# whether to pre-compute features from backbone for acceleration
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fast_test=True,
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# dataloader setting for feature extraction of fast test
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test_set=dict(batch_size=16, num_workers=2),
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support=dict( # support set setting in meta test
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batch_size=4, # batch size for fine-tuning
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num_workers=0, # number of worker set 0 since the only 5 images
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drop_last=True, # drop last
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train=dict( # config of fine-tuning
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num_steps=150, # number of steps in fine-tuning
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optimizer=dict( # optimizer config in fine-tuning
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type='SGD', # optimizer name
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lr=0.01, # learning rate
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momentum=0.9, # momentum
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dampening=0.9, # dampening
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weight_decay=0.001))), # weight decay
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# query set setting predict 75 images
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query=dict(batch_size=75, num_workers=0))),
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test=dict( # used for model validation in Meta Test fashion
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type='MetaTestDataset', # dataset wrapper for Meta Test
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num_episodes=2000, # total number of test tasks
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num_ways=5, # number of class in each task
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num_shots=1, # number of support images in each task
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num_queries=15, # number of query images in each task
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dataset=dict( # config of dataset
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type='MiniImageNetDataset', # dataset name
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subset='test', # subset of dataset
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data_prefix='data/mini_imagenet', # prefix of images
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pipeline=test_pipeline),
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meta_test_cfg=dict( # config of Meta Test
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num_episodes=2000, # total number of test tasks
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num_ways=5, # number of class in each task
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# whether to pre-compute features from backbone for acceleration
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fast_test=True,
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# dataloader setting for feature extraction of fast test
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test_set=dict(batch_size=16, num_workers=2),
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support=dict( # support set setting in meta test
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batch_size=4, # batch size for fine-tuning
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num_workers=0, # number of worker set 0 since the only 5 images
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drop_last=True, # drop last
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train=dict( # config of fine-tuning
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num_steps=150, # number of steps in fine-tuning
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optimizer=dict( # optimizer config in fine-tuning
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type='SGD', # optimizer name
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lr=0.01, # learning rate
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momentum=0.9, # momentum
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dampening=0.9, # dampening
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weight_decay=0.001))), # weight decay
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# query set setting predict 75 images
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query=dict(batch_size=75, num_workers=0))))
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log_config = dict(
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interval=50, # interval to print the log
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hooks=[dict(type='TextLoggerHook')])
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checkpoint_config = dict(interval=20) # interval to save a checkpoint
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evaluation = dict(
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by_epoch=True, # eval model by epoch
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metric='accuracy', # Metrics used during evaluation
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interval=5) # interval to eval model
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# parameters to setup distributed training, the port can also be set.
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dist_params = dict(backend='nccl')
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log_level = 'INFO' # the output level of the log.
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load_from = None # load a pre-train checkpoints
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# resume checkpoints from a given path, the training will be resumed from
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# the epoch when the checkpoint's is saved.
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resume_from = None
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# workflow for runner. [('train', 1)] means there is only one workflow and
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# the workflow named 'train' is executed once.
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workflow = [('train', 1)]
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pin_memory = True # whether to use pin memory
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# whether to use infinite sampler; infinite sampler can accelerate training efficient
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use_infinite_sampler = True
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seed = 0 # random seed
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runner = dict(type='EpochBasedRunner', max_epochs=200) # runner type and epochs of training
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optimizer = dict( # the configuration file used to build the optimizer, support all optimizers in PyTorch.
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type='SGD', # optimizer type
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lr=0.05, # learning rat
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momentum=0.9, # momentum
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weight_decay=0.0001) # weight decay of SGD
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optimizer_config = dict(grad_clip=None) # most of the methods do not use gradient clip
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lr_config = dict(
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# the policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater
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# from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9.
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policy='step',
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warmup='linear', # warmup type
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warmup_iters=3000, # warmup iterations
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warmup_ratio=0.25, # warmup ratio
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step=[60, 120]) # Steps to decay the learning rate
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```
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## FAQ
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### Use intermediate variables in configs
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Some intermediate variables are used in the configuration file. The intermediate variables make the configuration file clearer and easier to modify.
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For example, `train_pipeline` / `test_pipeline` is the intermediate variable of the data pipeline. We first need to define `train_pipeline` / `test_pipeline`, and then pass them to `data`. If you want to modify the size of the input image during training and testing, you need to modify the intermediate variables of `train_pipeline` / `test_pipeline`.
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```python
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', size=384, backend='pillow',),
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dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_label']),
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dict(type='Collect', keys=['img', 'gt_label'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='Resize', size=384, backend='pillow'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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]
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data = dict(
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
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```
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### Ignore some fields in the base configs
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Sometimes, you need to set `_delete_=True` to ignore some domain content in the basic configuration file. You can refer to [mmcv](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html#inherit-from-base-config-with-ignored-fields) for more instructions.
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The following is an example. If you want to use cosine schedule, just using inheritance and directly modify it will report `get unexcepected keyword'step'` error, because the `'step'` field of the basic config in `lr_config` domain information is reserved, and you need to add `_delete_ =True` to ignore the content of `lr_config` related fields in the basic configuration file:
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```python
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lr_config = dict(
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_delete_=True,
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policy='CosineAnnealing',
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min_lr=0,
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warmup='linear',
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by_epoch=True,
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warmup_iters=5,
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warmup_ratio=0.1
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)
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```
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