thus it is highly recommended learning the basic of mmcls.
## Modify config through script arguments
When submitting jobs using "tools/classification/train.py" or "tools/classification/test.py", you may specify `--cfg-options` to in-place modify the config.
- Update config keys of dict chains.
The config options can be specified following the order of the dict keys in the original config.
For example, `--cfg-options model.backbone.norm_eval=False` changes the all BN modules in model backbones to `train` mode.
- Update keys inside a list of configs.
Some config dicts are composed as a list in your config. For example, the training pipeline `data.train.pipeline` is normally a list
e.g. `[dict(type='LoadImageFromFile'), ...]`. If you want to change `'LoadImageFromFile'` to `'LoadImageFromWebcam'` in the pipeline,
you may specify `--cfg-options data.train.pipeline.0.type=LoadImageFromWebcam`.
- Update values of list/tuples.
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
We follow the below style to name config files. Contributors are advised to follow the same style.
```
{algorithm}_[algorithm setting]_{backbone}_{gpu x batch_per_gpu}_[misc]_{dataset}_{meta test setting}.py
```
`{xxx}` is required field and `[yyy]` is optional.
-`{algorithm}`: model type like `faster_rcnn`, `mask_rcnn`, etc.
-`[algorithm setting]`: specific setting for some model, like `without_semantic` for `htc`, `moment` for `reppoints`, etc.
-`{backbone}`: backbone type like `conv4`, `resnet12`.
-`[norm_setting]`: `bn` (Batch Normalization) is used unless specified, other norm layer type could be `gn` (Group Normalization), `syncbn` (Synchronized Batch Normalization).
-`[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).
-`[misc]`: miscellaneous setting/plugins of model.
-`{dataset}`: dataset like `cub`, `mini-imagenet` and `tiered-imagenet`.
-`{meta test setting}`: n way k shot setting like `5way_1shot` or `5way_5shot`.
# whether to invert the color channel, rgb2bgr or bgr2rgb
to_rgb=True),
# convert img into torch.Tensor
dict(type='ImageToTensor', keys=['img']),
# convert gt_label into torch.Tensor
dict(type='ToTensor', keys=['gt_label']),
# pipeline that decides which keys in the data should be passed to the runner
dict(type='Collect', keys=['img', 'gt_label'])
]
# data pipeline for testing
test_pipeline = [
# first pipeline to load images from file path
dict(type='LoadImageFromFile'),
# resize image
dict(type='Resize', size=(96, -1)),
# center crop
dict(type='CenterCrop', crop_size=84),
dict(type='Normalize', # normalization
# mean values used to normalization
mean=[123.675, 116.28, 103.53],
# standard variance used to normalization
std=[58.395, 57.12, 57.375],
# whether to invert the color channel, rgb2bgr or bgr2rgb
to_rgb=True),
# convert img into torch.Tensor
dict(type='ImageToTensor', keys=['img']),
# pipeline that decides which keys in the data should be passed to the runner
dict(type='Collect', keys=['img', 'gt_label'])
]
# config of fine-tuning using support set in Meta Test
meta_finetune_cfg = dict(
# number of iterations in fine-tuning
num_steps=150,
# optimizer config in fine-tuning
optimizer=dict(
type='SGD', # optimizer name
lr=0.01, # learning rate
momentum=0.9, # momentum
dampening=0.9, # dampening
weight_decay=0.001)), # weight decay
data = dict(
# batch size of a single GPU
samples_per_gpu=64,
# worker to pre-fetch data for each single GPU
workers_per_gpu=4,
# config of training set
train=dict(
# name of dataset
type='MiniImageNetDataset',
# prefix of image
data_prefix='data/mini_imagenet',
# subset of dataset
subset='train',
# train pipeline
pipeline=train_pipeline),
# config of validation set
val=dict(
# dataset wrapper for Meta Test
type='MetaTestDataset',
# total number of test tasks
num_episodes=100,
num_ways=5, # number of class in each task
num_shots=1, # number of support images in each task
num_queries=15, # number of query images in each task
dataset=dict( # config of dataset
type='MiniImageNetDataset', # dataset name
subset='val', # subset of dataset
data_prefix='data/mini_imagenet', # prefix of images
pipeline=test_pipeline),
meta_test_cfg=dict( # config of Meta Test
num_episodes=100, # total number of test tasks
num_ways=5, # number of class in each task
# whether to pre-compute features from backbone for acceleration
fast_test=True,
# dataloader setting for feature extraction of fast test
test_set=dict(batch_size=16, num_workers=2),
support=dict( # support set setting in meta test
batch_size=4, # batch size for fine-tuning
num_workers=0, # number of worker set 0 since the only 5 images
drop_last=True, # drop last
train=dict( # config of fine-tuning
num_steps=150, # number of steps in fine-tuning
optimizer=dict( # optimizer config in fine-tuning
type='SGD', # optimizer name
lr=0.01, # learning rate
momentum=0.9, # momentum
dampening=0.9, # dampening
weight_decay=0.001))), # weight decay
# query set setting predict 75 images
query=dict(batch_size=75, num_workers=0))),
test=dict( # used for model validation in Meta Test fashion
type='MetaTestDataset', # dataset wrapper for Meta Test
num_episodes=2000, # total number of test tasks
num_ways=5, # number of class in each task
num_shots=1, # number of support images in each task
num_queries=15, # number of query images in each task
dataset=dict( # config of dataset
type='MiniImageNetDataset', # dataset name
subset='test', # subset of dataset
data_prefix='data/mini_imagenet', # prefix of images
pipeline=test_pipeline),
meta_test_cfg=dict( # config of Meta Test
num_episodes=2000, # total number of test tasks
num_ways=5, # number of class in each task
# whether to pre-compute features from backbone for acceleration
fast_test=True,
# dataloader setting for feature extraction of fast test
test_set=dict(batch_size=16, num_workers=2),
support=dict( # support set setting in meta test
batch_size=4, # batch size for fine-tuning
num_workers=0, # number of worker set 0 since the only 5 images
drop_last=True, # drop last
train=dict( # config of fine-tuning
num_steps=150, # number of steps in fine-tuning
optimizer=dict( # optimizer config in fine-tuning
type='SGD', # optimizer name
lr=0.01, # learning rate
momentum=0.9, # momentum
dampening=0.9, # dampening
weight_decay=0.001))), # weight decay
# query set setting predict 75 images
query=dict(batch_size=75, num_workers=0))))
log_config = dict(
interval=50, # interval to print the log
hooks=[dict(type='TextLoggerHook')])
checkpoint_config = dict(interval=20) # interval to save a checkpoint
evaluation = dict(
by_epoch=True, # eval model by epoch
metric='accuracy', # Metrics used during evaluation
interval=5) # interval to eval model
# parameters to setup distributed training, the port can also be set.
dist_params = dict(backend='nccl')
log_level = 'INFO' # the output level of the log.
load_from = None # load a pre-train checkpoints
# resume checkpoints from a given path, the training will be resumed from
# the epoch when the checkpoint's is saved.
resume_from = None
# workflow for runner. [('train', 1)] means there is only one workflow and
# the workflow named 'train' is executed once.
workflow = [('train', 1)]
pin_memory = True # whether to use pin memory
# whether to use infinite sampler; infinite sampler can accelerate training efficient
use_infinite_sampler = True
seed = 0 # random seed
runner = dict(type='EpochBasedRunner', max_epochs=200) # runner type and epochs of training
optimizer = dict( # the configuration file used to build the optimizer, support all optimizers in PyTorch.
type='SGD', # optimizer type
lr=0.05, # learning rat
momentum=0.9, # momentum
weight_decay=0.0001) # weight decay of SGD
optimizer_config = dict(grad_clip=None) # most of the methods do not use gradient clip
lr_config = dict(
# the policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater
# from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9.
policy='step',
warmup='linear', # warmup type
warmup_iters=3000, # warmup iterations
warmup_ratio=0.25, # warmup ratio
step=[60, 120]) # Steps to decay the learning rate
```
## FAQ
### Use intermediate variables in configs
Some intermediate variables are used in the configuration file. The intermediate variables make the configuration file clearer and easier to modify.
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`.
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.
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:
