OpenMixup mainly uses python files as configs. The design of our configuration file system integrates modularity and inheritance, facilitating users to conduct various experiments. All configuration files are placed in the configs folder, which mainly contains the primitive configuration folder of _base_ and many algorithm folders such as resnet, swin_transformer, vision_transformer, etc.
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.
- Tutorial 0: Learn about Configs
We follow the below convention to name config files. Contributors are advised to follow the same style. The config file names are divided into four parts: algorithm info, module information, training information and data information. Logically, different parts are concatenated by underscores '_', and words in the same part are concatenated by dashes '-'.
{algorithm info}_{module info}_{training info}_{data info}.py
algorithm info：algorithm information, model name and neural network architecture, such as resnet, etc.;module info： module information is used to represent some special neck, head and pretrain information;training info：Training information, some training schedule, including batch size, lr schedule, data augment and the like;data info：Data information, dataset name, input size and so on, such as imagenet, cifar, etc.;
For example, you can name a mixup classification algorithm config file that use puzzlemix based onresnet18 with CIFAR mixup classification training setting (CE) as follows:
r18_mixups_CE_none.py
The main algorithm name and the corresponding branch architecture information. E.g：
resnet50mobilenet-v3-largevit-small-patch32:patch32represents the size of the partition inViTalgorithm;seresnext101-32x4d:SeResNet101network structure,32x4dmeans thatgroupsandwidth_per_groupare 32 and 4 respectively inBottleneck;
Some special neck, head, pretrain and mixup methods information. In classification tasks, pretrain information is the most commonly used:
mixups: apply mixup augmentation methods;in21k-pre: pre-trained on ImageNet21k;in21k-pre-3rd-party: pre-trained on ImageNet21k and the checkpoint is converted from a third-party repository;
Training schedule, including training type, batch size, lr schedule, data augment, special loss functions and so on:
- format
{gpu x batch_per_gpu}, such as8xb32
Training type (mainly seen in the transformer network, such as the ViT algorithm, which is usually divided into two training type: pre-training and fine-tuning):
ft: configuration file for fine-tuningpt: configuration file for pretraining
Training recipe. Usually, only the part that is different from the original paper will be marked. These methods will be arranged in the order {pipeline aug}-{train aug}-{loss trick}-{scheduler}-{epochs}.
coslr-200e: use cosine scheduler to train 200 epochsautoaug-mixup-lbs-coslr-50e: useautoaug,mixup,label smooth,cosine schedulerto train 50 epochs
in1k:ImageNet1kdataset, default to use the input image size of 224x224;in21k:ImageNet21kdataset, also calledImageNet22kdataset, default to use the input image size of 224x224;in1k-384px: Indicates that the input image size is 384x384;cifar100
repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py
repvgg-D2se: Algorithm informationrepvgg: The main algorithm.D2se: The architecture.
deploy: Module information, means the backbone is in the deploy state.4xb64-autoaug-lbs-mixup-coslr-200e: Training information.4xb64: Use 4 GPUs and the size of batches per GPU is 64.autoaug: UseAutoAugmentin training pipeline.lbs: Use label smoothing loss.mixup: Usemixuptraining augment method.coslr: Use cosine learning rate scheduler.200e: Train the model for 200 epochs.
in1k: Dataset information. The config is forImageNet1kdataset and the input size is224x224.
Some configuration files currently do not follow this naming convention, and related files will be updated in the near future.
The naming of the weight mainly includes the configuration file name, date and hash value.
{config_name}_{date}-{hash}.pth
There are four kinds of basic component file in the configs/_base_ folders, namely：
You can easily build your own training config file by inherit some base config files. And the configs that are composed by components from _base_ are called primitive.
For easy understanding, we use ResNet50 primitive config as a example and comment the meaning of each line. For more details, please refer to the API documentation.
_base_ = [
'../_base_/models/resnet50.py', # model
'../_base_/datasets/imagenet_bs32.py', # data
'../_base_/schedules/imagenet_bs256.py', # training schedule
'../_base_/default_runtime.py' # runtime setting
]The four parts are explained separately below, and the above-mentioned ResNet50 primitive config are also used as an example.
The parameter "model" is a python dictionary in the configuration file, which mainly includes information such as network structure and loss function:
type： Classifier name, MMCls supportsImageClassifier, refer to API documentation.backbone： Backbone configs, refer to API documentation for available options.neck：Neck network name, MMCls supportsGlobalAveragePooling, please refer to API documentation.head: Head network name, MMCls supports single-label and multi-label classification head networks, available options refer to API documentation.loss: Loss function type, supportsCrossEntropyLoss,LabelSmoothLossetc., For available options, refer to API documentation.
train_cfg：Training augment config, MMCls supportsmixup,cutmixand other augments.
The 'type' in the configuration file is not a constructed parameter, but a class name.
model = dict(
type='ImageClassifier', # Classifier name
backbone=dict(
type='ResNet', # Backbones name
depth=50, # depth of backbone, ResNet has options of 18, 34, 50, 101, 152.
num_stages=4, # number of stages，The feature maps generated by these states are used as the input for the subsequent neck and head.
out_indices=(3, ), # The output index of the output feature maps.
frozen_stages=-1, # the stage to be frozen, '-1' means not be forzen
style='pytorch'), # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs.
neck=dict(type='GlobalAveragePooling'), # neck network name
head=dict(
type='LinearClsHead', # linear classification head，
num_classes=1000, # The number of output categories, consistent with the number of categories in the dataset
in_channels=2048, # The number of input channels, consistent with the output channel of the neck
loss=dict(type='CrossEntropyLoss', loss_weight=1.0), # Loss function configuration information
topk=(1, 5), # Evaluation index, Top-k accuracy rate, here is the accuracy rate of top1 and top5
))For mixup classification tasks, here is an example for the model parameter for CIFAR based on resnet18 backbone. As shown below, you can customize your own mixup classification strategies by designating different mixup mode, arguments and backbones.
# model settings
model = dict(
type='MixUpClassification',
pretrained=None,
alpha=1,
mix_mode="mixup",
mix_args=dict(
alignmix=dict(eps=0.1, max_iter=100),
attentivemix=dict(grid_size=32, top_k=None, beta=8), # AttentiveMix+ in this repo (use pre-trained)
automix=dict(mask_adjust=0, lam_margin=0), # require pre-trained mixblock
fmix=dict(decay_power=3, size=(32,32), max_soft=0., reformulate=False),
gridmix=dict(n_holes=(2, 6), hole_aspect_ratio=1.,
cut_area_ratio=(0.5, 1), cut_aspect_ratio=(0.5, 2)),
manifoldmix=dict(layer=(0, 3)),
puzzlemix=dict(transport=True, t_batch_size=None, t_size=4, # t_size for small-scale datasets
block_num=5, beta=1.2, gamma=0.5, eta=0.2, neigh_size=4, n_labels=3, t_eps=0.8),
resizemix=dict(scope=(0.1, 0.8), use_alpha=True),
samix=dict(mask_adjust=0, lam_margin=0.08), # require pre-trained mixblock
),
backbone=dict(
# type='ResNet_CIFAR', # CIFAR version
type='ResNet_Mix_CIFAR', # required by 'manifoldmix'
depth=18,
num_stages=4,
out_indices=(3,), # no conv-1, x-1: stage-x
style='pytorch'),
head=dict(
type='ClsHead', # normal CE loss (NOT SUPPORT PuzzleMix, use soft/sigm CE instead)
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
with_avg_pool=True, multi_label=False, in_channels=512, num_classes=100)
)The parameter "data" is a python dictionary in the configuration file, which mainly includes information to construct dataloader:
samples_per_gpu: the BatchSize of each GPU when building the dataloaderworkers_per_gpu: the number of threads per GPU when building dataloadertrain ｜ val ｜ test: config to construct datasettype: Dataset name, MMCls supportsImageNet,Cifaretc., refer to API documentationdata_prefix: Dataset root directorypipeline: Data processing pipeline, refer to related tutorial CUSTOM DATA PIPELINES
The parameter evaluation is also a dictionary, which is the configuration information of evaluation hook, mainly including evaluation interval, evaluation index, etc..
# dataset settings
dataset_type = 'ImageNet' # dataset name，
img_norm_cfg = dict( # Image normalization config to normalize the input images
mean=[123.675, 116.28, 103.53], # Mean values used to pre-training the pre-trained backbone models
std=[58.395, 57.12, 57.375], # Standard variance used to pre-training the pre-trained backbone models
to_rgb=True) # Whether to invert the color channel, rgb2bgr or bgr2rgb.
# train data pipeline
train_pipeline = [
dict(type='LoadImageFromFile'), # First pipeline to load images from file path
dict(type='RandomResizedCrop', size=224), # RandomResizedCrop
dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), # Randomly flip the picture horizontally with a probability of 0.5
dict(type='Normalize', **img_norm_cfg), # normalization
dict(type='ImageToTensor', keys=['img']), # convert image from numpy into torch.Tensor
dict(type='ToTensor', keys=['gt_label']), # convert gt_label into torch.Tensor
dict(type='Collect', keys=['img', 'gt_label']) # Pipeline that decides which keys in the data should be passed to the detector
]
# test data pipeline
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', size=(256, -1)),
dict(type='CenterCrop', crop_size=224),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']) # do not pass gt_label while testing
]
data = dict(
samples_per_gpu=32, # Batch size of a single GPU
workers_per_gpu=2, # Worker to pre-fetch data for each single GPU
train=dict( # Train dataset config
train=dict( # train data config
type=dataset_type, # dataset name
data_prefix='data/imagenet/train', # Dataset root, when ann_file does not exist, the category information is automatically obtained from the root folder
pipeline=train_pipeline), # train data pipeline
val=dict( # val data config
type=dataset_type,
data_prefix='data/imagenet/val',
ann_file='data/imagenet/meta/val.txt', # ann_file existes, the category information is obtained from file
pipeline=test_pipeline),
test=dict( # test data config
type=dataset_type,
data_prefix='data/imagenet/val',
ann_file='data/imagenet/meta/val.txt',
pipeline=test_pipeline))
evaluation = dict( # The config to build the evaluation hook, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details.
interval=1, # Evaluation interval
metric='accuracy') # Metrics used during evaluationMainly include optimizer settings, optimizer hook settings, learning rate schedule and runner settings:
optimizer: optimizer setting , support all optimizers inpytorch, refer to related mmcv documentation.optimizer_config:optimizer hookconfiguration file, such as setting gradient limit, refer to related mmcv code.lr_config: Learning rate scheduler, supports "CosineAnnealing", "Step", "Cyclic", etc. refer to related mmcv documentation for more options.runner: Forrunner, please refer tommcvforrunnerintroduction document.
# he configuration file used to build the optimizer, support all optimizers in PyTorch.
optimizer = dict(type='SGD', # Optimizer type
lr=0.1, # Learning rate of optimizers, see detail usages of the parameters in the documentation of PyTorch
momentum=0.9, # Momentum
weight_decay=0.0001) # Weight decay of SGD
# Config used to build the optimizer hook, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8 for implementation details.
optimizer_config = dict(grad_clip=None) # Most of the methods do not use gradient clip
# Learning rate scheduler config used to register LrUpdater hook
lr_config = dict(policy='step', # 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.
step=[30, 60, 90]) # Steps to decay the learning rate
runner = dict(type='EpochBasedRunner', # Type of runner to use (i.e. IterBasedRunner or EpochBasedRunner)
max_epochs=100) # Runner that runs the workflow in total max_epochs. For IterBasedRunner use `max_iters`This part mainly includes saving the checkpoint strategy, log configuration, training parameters, breakpoint weight path, working directory, etc..
# Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
checkpoint_config = dict(interval=1) # The save interval is 1
# config to register logger hook
log_config = dict(
interval=100, # Interval to print the log
hooks=[
dict(type='TextLoggerHook'), # The Tensorboard logger is also supported
# dict(type='TensorboardLoggerHook')
])
dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set.
log_level = 'INFO' # The output level of the log.
resume_from = None # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.
workflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once.
work_dir = 'work_dir' # Directory to save the model checkpoints and logs for the current experiments.For easy understanding, we recommend contributors to inherit from existing methods.
For all configs under the same folder, it is recommended to have only one primitive config. All other configs should inherit from the primitive config. In this way, the maximum of inheritance level is 3.
For example, if your config file is based on ResNet with some other modification, you can first inherit the basic ResNet structure, dataset and other training setting by specifying _base_ ='./resnet50_4xb64_step_ep100.py' (The path relative to your config file), and then modify the necessary parameters in the config file. A more specific example, now we want to use almost all configs in configs/resnet/resnet50_4xb64_step_ep100.py, but change the number of training epochs from 100 to 300, modify when to decay the learning rate, and modify the dataset path, you can create a new config file configs/resnet/resnet50_4xb64_step_ep300.py with content as below:
_base_ = './resnet50_4xb64_step_ep100.py'
runner = dict(max_epochs=300)
lr_config = dict(step=[150, 200, 250])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.
img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_pipeline = [
dict(type='RandomResizedCrop', size=384, interpolation=3), # bicubic
dict(type='RandomHorizontalFlip'),
]
test_pipeline = [
dict(type='Resize', size=448, interpolation=3),
dict(type='CenterCrop', size=384),
dict(type='ToTensor'),
dict(type='Normalize', **img_norm_cfg),
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(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 for more instructions.
The following is an example. If you wangt to use cosine schedule in the above ResNet50 case, 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:
_base_ = '../../configs/resnet/resnet50_4xb64_step_ep100.py'
lr_config = dict(
_delete_=True,
policy='CosineAnnealing',
min_lr=0,
warmup='linear',
by_epoch=True,
warmup_iters=5,
warmup_ratio=1e-6
)Sometimes, you may refer to some fields in the _base_ config, so as to avoid duplication of definitions. You can refer to mmcv for some more instructions.
The following is an example of using auto augment in the training data preprocessing pipeline， refer to configs/_base_/datasets/imagenet_bs64_autoaug.py. When defining train_pipeline, just add the definition file name of auto augment to _base_, and then use {{_base_.auto_increasing_policies}} to reference the variables:
_base_ = './_base_/datasets/imagenet/autoaug_sz224_4xbs64.py/autoaug_sz224_4xbs64.py'
# dataset settings
dataset_type = 'ClassificationDataset'
img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_pipeline = [
dict(type='RandomResizedCrop', size=224, interpolation=3), # bicubic
dict(type='RandomHorizontalFlip'),
dict(
type='AutoAugment',
policies='imagenet',
hparams=dict(
pad_val=[104, 116, 124], interpolation='bicubic')),
]
test_pipeline = [...]
data = dict(
samples_per_gpu=64,
workers_per_gpu=4,
train=dict(..., pipeline=train_pipeline),
val=dict(..., pipeline=test_pipeline))
# validation hook
evaluation = dict(
interval=1,
eval_param=dict(topk=(1, 5)))
# checkpoint hook
checkpoint_config = dict(interval=1, max_keep_ckpts=1)When users use the script "tools/train.py" or "tools/test.py" to submit tasks or use some other tools, they can directly modify the content of the configuration file used by specifying the --cfg-options parameter.
-
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=Falsechanges the all BN modules in model backbones totrainmode. -
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.pipelineis normally a list e.g.[dict(type='LoadImageFromFile'), dict(type='TopDownRandomFlip', flip_prob=0.5), ...]. If you want to change'flip_prob=0.5'to'flip_prob=0.0'in the pipeline, you may specify--cfg-options data.train.pipeline.1.flip_prob=0.0. -
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 change this key, you may specify--cfg-options workflow="[(train,1),(val,1)]". Note that the quotation mark " is necessary to support list/tuple data types, and that NO white space is allowed inside the quotation marks in the specified value.
This part may only be used when using OpenMixup as a third party library to build your own project, and beginners can skip it.
After studying the follow-up tutorials ADDING NEW DATASET, CUSTOM DATA PIPELINES, ADDING NEW MODULES. You may use MMClassification to complete your project and create new classes of datasets, models, data enhancements, etc. in the project. In order to streamline the code, you can use MMClassification as a third-party library, you just need to keep your own extra code and import your own custom module in the configuration files. For examples, you may refer to OpenMMLab Algorithm Competition Project .
Add the following code to your own configuration files:
custom_imports = dict(
imports=['your_dataset_class',
'your_transforme_class',
'your_model_class',
'your_module_class'],
allow_failed_imports=False)- None