Write a `.yaml` configuration file

Code for this section:

core/config/config.py
config/*

Composition of the configuration file in LibContinual

The configuration file of LibContinual uses a yaml format file and it also supports reading the global configuration changes from the command line. We have pre-defined a default configuration core/config/default.yaml. The users can put the custom configuration into the config/ directory, and save this file in the yaml format. At parsing, the sequencing relationship of defining the configuration of the method is default.yaml->config/->console. The latter definition overrides the same value in the former definition.

Although most of the basic configurations have been set in the default.yaml, you can not directly run a program just using the default.yaml. Before running the code, the users are required to define a configuration file of one method that has been implemented in LibContinual in the config/ directory.

Considering that CL menthods usually have some basic parameters, such as image_size or device id, which are often needed to be changed, LibContinual also supports making changes to some simple configurations on the command line without modifying the yaml file. Similarly, during training and test, because many parameters are the same of different methods, we wrap these same parameters together and put them into theconfig/headers for brevity. In this way, we can write the yaml files of the custom methods succinctly by importing them.

The following is the composition of the files in the config/headers directory.

data.yaml: The relevant configuration of the data is defined in this file.
device.yaml: The relevant configuration of GPU is defined in this file.
model.yaml: The relevant configuration of the model is defined in this file.
optimizer.yaml: The relevant configuration of the optimizer used for training is defined in this file.

The settings of the configuration file in LibContinual

The following details each part of the configuration file and explain how to write them. An example of how the bic method is configured is also presented.

The settings for data

data_root: The storage path of the dataset.
image_size: The size of the input image.
pin_memory: Whether to use memory acceleration for reading.
augment: Whether to use data augmentation.
init_cls_num: Initial number of classes.
inc_cls_num: Incremental number of classes.
task_num: Number of tasks.

works: Number of working threads for data loading and preprocessing.

data_root: /data/cifar100
image_size: 84
pin_memory: False
augment: True
init_cls_num: 20
inc_cls_num: 20
task_num: 5
works: 8

The settings for model

backbone: The backbone information used in the method.
- name: The name of the backbone, needs to match the case of the backbone implemented in LibContinual.
- kwargs: The parameters used in the backbone, must keep the name consistent with the name in the code.
  - num_classes: Number of classes.
  - args: Other parameters, for example, the dataset used.

  backbone:
      name: resnet18
      kwargs:
          num_classes: 100
            args: 
                dataset: cifar100

classifier: The classifier information used in the method.
- name: The name of the classifier, needs to match the case of the classifier implemented in LibContinual.
kwargs: The parameters used in the classifier initialization, must keep the name consistent with the name in the code.
- feat_dim: Dimension settings
```
classifier:
    name: bic
    kwargs:
        feat_dim: 512
```

The settings for training

epoch: The number of epoch during training.
test_epoch: The number of epoch during testing.
val_per_epoch: The number of epoch in each verification phase.
stage2_epoch: The number of epoch for strategy 2.

batch_size: The batch size for training.

epoch: 50
test_epoch: 5
val_per_epoch: 5
stage2_epoch: 100
batch_size: 128

The settings for optimizer

optimizer: Optimizer information used during training.
- name: The name of the Optimizer, only temporarily supports all Optimizers provided by PyTorch.
- kwargs: The parameters used in the optimizer, and the name needs to be the same as the parameter name required by the PyTorch optimizer.
- other: Currently, the framework only supports the learning rate used by each part of a separately specified method, and the name needs to be the same as the variable name used in the method.
```
optimizer:
    name: SGD
    kwargs:
        lr: 0.01
        weight_decay: 2e-4
        momentum: 0.9
```
lr_scheduler: The learning rate adjustment strategy used during training, only temporarily supports all the learning rate adjustment strategies provided by PyTorch.
- name: The name of the learning rate adjustment strategy.
- kwargs: Other parameters used in the learning rate adjustment strategy in PyTorch.
```
lr_scheduler:
  name: MultiStepLR
  kwargs:
    gamma: 0.1
    milestones: [25, 50]
```

The settings for Hardware

device_ids: The gpu number, which is the same as the nvidia-smi command.
n_gpu: The number of parallel gpu used during training, if 1, it can't apply to parallel training.
seed: Seed points used in numpy，torch，and cuda.
deterministic: Whether to turn on torch.backend.cudnn.benchmark and torch.backend.cudnn.deterministic and whether to determine random seeds during training.
```
device_ids: 0,1,2,3,4,5,6,7
n_gpu: 4
seed: 1993
deterministic: False
```

Write a .yaml configuration file