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Customize Training Schedule

In our codebase, default training schedules have been provided for common datasets such as CIFAR, ImageNet, etc. If we attempt to experiment on these datasets for higher accuracy or on different new methods and datasets, we might possibly need to modify the strategies.

In this tutorial, we will introduce how to modify configs to construct optimizers, use parameter-wise finely configuration, gradient clipping, gradient accumulation as well as customize learning rate and momentum schedules. Furthermore, introduce a template to customize self-implemented optimizationmethods for the project.

Customize optimization

We use the optim_wrapper field to configure the strategies of optimization, which includes choices of optimizer, choices of automatic mixed precision training, parameter-wise configurations, gradient clipping and accumulation. Details are seen below.

Use optimizers supported by PyTorch

We support all the optimizers implemented by PyTorch, and to use them, please change the optimizer field of config files.

For example, if you want to use SGD, the modification in config file could be as the following. Notice that optimization related settings should all wrapped inside the optim_wrapper.

optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='SGD', lr=0.0003, weight_decay=0.0001)
)

Note

type in optimizer is not a constructor but a optimizer name in PyTorch. Refers to List of optimizers supported by PyTorch for more choices.

To modify the learning rate of the model, just modify the lr in the config of optimizer. You can also directly set other arguments according to the API doc of PyTorch.

For example, if you want to use Adam with settings like torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False) in PyTorch. You could use the config below:

optim_wrapper = dict(
    type='OptimWrapper',
    optimizer = dict(
        type='Adam',
        lr=0.001,
        betas=(0.9, 0.999),
        eps=1e-08,
        weight_decay=0,
        amsgrad=False),
)

Note

The default type of optim_wrapper field is OptimWrapper, therefore, you can omit the type field usually, like:

optim_wrapper = dict(
    optimizer=dict(
        type='Adam',
        lr=0.001,
        betas=(0.9, 0.999),
        eps=1e-08,
        weight_decay=0,
        amsgrad=False))

Use AMP training

If we want to use the automatic mixed precision training, we can simply change the type of optim_wrapper to AmpOptimWrapper in config files.

optim_wrapper = dict(type='AmpOptimWrapper', optimizer=...)

Alternatively, for conveniency, we can set --amp parameter to turn on the AMP option directly in the tools/train.py script. Refers to Training and test tutorial for details of starting a training.

Parameter-wise finely configuration

Some models may have parameter-specific settings for optimization, for example, no weight decay to the BatchNorm layers or using different learning rates for different network layers. To finely configure them, we can use the paramwise_cfg argument in optim_wrapper.

  • Set different hyper-parameter multipliers for different types of parameters.

    For instance, we can set norm_decay_mult=0. in paramwise_cfg to change the weight decay of weight and bias of normalization layers to zero.

    optim_wrapper = dict(
        optimizer=dict(type='SGD', lr=0.8, weight_decay=1e-4),
        paramwise_cfg=dict(norm_decay_mult=0.))
    

    More types of parameters are supported to configured, list as follow:

    • lr_mult: Multiplier for learning rate of all parameters.

    • decay_mult: Multiplier for weight decay of all parameters.

    • bias_lr_mult: Multiplier for learning rate of bias (Not include normalization layers’ biases and deformable convolution layers’ offsets). Defaults to 1.

    • bias_decay_mult: Multiplier for weight decay of bias (Not include normalization layers’ biases and deformable convolution layers’ offsets). Defaults to 1.

    • norm_decay_mult: Multiplier for weight decay of weigh and bias of normalization layers. Defaults to 1.

    • dwconv_decay_mult: Multiplier for weight decay of depth-wise convolution layers. Defaults to 1.

    • bypass_duplicate: Whether to bypass duplicated parameters. Defaults to False.

    • dcn_offset_lr_mult: Multiplier for learning rate of deformable convolution layers. Defaults to 1.

  • Set different hyper-parameter multipliers for specific parameters.

    MMClassification can use custom_keys in paramwise_cfg to specify different parameters to use different learning rates or weight decay.

    For example, to set all learning rates and weight decays of backbone.layer0 to 0, the rest of backbone remains the same as optimizer and the learning rate of head to 0.001, use the configs below.

    optim_wrapper = dict(
        optimizer=dict(type='SGD', lr=0.01, weight_decay=0.0001),
        paramwise_cfg=dict(
            custom_keys={
                'backbone.layer0': dict(lr_mult=0, decay_mult=0),
                'backbone': dict(lr_mult=1),
                'head': dict(lr_mult=0.1)
            }))
    

Gradient clipping

During the training process, the loss function may get close to a cliffy region and cause gradient explosion. And gradient clipping is helpful to stabilize the training process. More introduction can be found in this page.

Currently we support clip_grad option in optim_wrapper for gradient clipping, refers to PyTorch Documentation.

Here is an example:

optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.01, weight_decay=0.0001),
    # norm_type: type of the used p-norm, here norm_type is 2.
    clip_grad=dict(max_norm=35, norm_type=2))

Gradient accumulation

When computing resources are lacking, the batch size can only be set to a small value, which may affect the performance of models. Gradient accumulation can be used to solve this problem. We support accumulative_counts option in optim_wrapper for gradient accumulation.

Here is an example:

train_dataloader = dict(batch_size=64)
optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.01, weight_decay=0.0001),
    accumulative_counts=4)

Indicates that during training, back-propagation is performed every 4 iters. And the above is equivalent to:

train_dataloader = dict(batch_size=256)
optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.01, weight_decay=0.0001))

Customize parameter schedules

In training, the optimzation parameters such as learing rate, momentum, are usually not fixed but changing through iterations or epochs. PyTorch supports several learning rate schedulers, which are not sufficient for complex strategies. In MMClassification, we provide param_scheduler for better controls of different parameter schedules.

Customize learning rate schedules

Learning rate schedulers are widely used to improve performance. We support most of the PyTorch schedulers, including ExponentialLR, LinearLR, StepLR, MultiStepLR, etc.

All available learning rate scheduler can be found here, and the names of learning rate schedulers end with LR.

  • Single learning rate schedule

    In most cases, we use only one learning rate schedule for simplicity. For instance, MultiStepLR is used as the default learning rate schedule for ResNet. Here, param_scheduler is a dictionary.

    param_scheduler = dict(
        type='MultiStepLR',
        by_epoch=True,
        milestones=[100, 150],
        gamma=0.1)
    

    Or, we want to use the CosineAnnealingLR scheduler to decay the learning rate:

    param_scheduler = dict(
        type='CosineAnnealingLR',
        by_epoch=True,
        T_max=num_epochs)
    
  • Multiple learning rate schedules

    In some of the training cases, multiple learning rate schedules are applied for higher accuracy. For example ,in the early stage, training is easy to be volatile, and warmup is a technique to reduce volatility. The learning rate will increase gradually from a minor value to the expected value by warmup and decay afterwards by other schedules.

    In MMClassification, simply combines desired schedules in param_scheduler as a list can achieve the warmup strategy.

    Here are some examples:

    1. linear warmup during the first 50 iters.

      param_scheduler = [
          # linear warm-up by iters
          dict(type='LinearLR',
              start_factor=0.001,
              by_epoch=False,  # by iters
              end=50),  # only warm up for first 50 iters
          # main learing rate schedule
          dict(type='MultiStepLR',
              by_epoch=True,
              milestones=[8, 11],
              gamma=0.1)
      ]
    
    1. linear warmup and update lr by iter during the first 10 epochs.

      param_scheduler = [
          # linear warm-up by epochs in [0, 10) epochs
          dict(type='LinearLR',
              start_factor=0.001,
              by_epoch=True,
              end=10,
              convert_to_iter_based=True,  # Update learning rate by iter.
          ),
          # use CosineAnnealing schedule after 10 epochs
          dict(type='CosineAnnealingLR', by_epoch=True, begin=10)
      ]
    

    Notice that, we use begin and end arguments here to assign the valid range, which is [begin, end) for this schedule. And the range unit is defined by by_epoch argument. If not specified, the begin is 0 and the end is the max epochs or iterations.

    If the ranges for all schedules are not continuous, the learning rate will stay constant in ignored range, otherwise all valid schedulers will be executed in order in a specific stage, which behaves the same as PyTorch ChainedScheduler.

    Tip

    To check that the learning rate curve is as expected, after completing your configuration file,you could use optimizer parameter visualization tool to draw the corresponding learning rate adjustment curve.

Customize momentum schedules

We support using momentum schedulers to modify the optimizer’s momentum according to learning rate, which could make the loss converge in a faster way. The usage is the same as learning rate schedulers.

All available learning rate scheduler can be found here, and the names of momentum rate schedulers end with Momentum.

Here is an example:

param_scheduler = [
    # the lr scheduler
    dict(type='LinearLR', ...),
    # the momentum scheduler
    dict(type='LinearMomentum',
         start_factor=0.001,
         by_epoch=False,
         begin=0,
         end=1000)
]

Add new optimizers or constructors

Note

This part will modify the MMClassification source code or add code to the MMClassification framework, beginners can skip it.

Add new optimizers

In academic research and industrial practice, it may be necessary to use optimization methods not implemented by MMClassification, and you can add them through the following methods.

1. Implement a new optimizer

Assume you want to add an optimizer named MyOptimizer, which has arguments a, b, and c. You need to create a new file under mmcls/engine/optimizers, and implement the new optimizer in the file, for example, in mmcls/engine/optimizers/my_optimizer.py:

from torch.optim import Optimizer
from mmcls.registry import OPTIMIZERS


@OPTIMIZERS.register_module()
class MyOptimizer(Optimizer):

    def __init__(self, a, b, c):
        ...

    def step(self, closure=None):
        ...

2. Import the optimizer

To find the above module defined above, this module should be imported during the running. There are two ways to achieve it.

  • Import it in the mmcls/engine/optimizers/__init__.py to add it into the mmcls.engine package.

    # In mmcls/engine/optimizers/__init__.py
    ...
    from .my_optimizer import MyOptimizer # MyOptimizer maybe other class name
    
    __all__ = [..., 'MyOptimizer']
    

    During running, we will automatically import the mmcls.engine package and register the MyOptimizer at the same time.

  • Use custom_imports in the config file to manually import it.

    custom_imports = dict(
        imports=['mmcls.engine.optimizers.my_optimizer'],
        allow_failed_imports=False,
    )
    

    The module mmcls.engine.optimizers.my_optimizer will be imported at the beginning of the program and the class MyOptimizer is then automatically registered. Note that only the package containing the class MyOptimizer should be imported. mmcls.engine.optimizers.my_optimizer.MyOptimizer cannot be imported directly.

3. Specify the optimizer in the config file

Then you can use MyOptimizer in the optim_wrapper.optimizer field of config files.

optim_wrapper = dict(
    optimizer=dict(type='MyOptimizer', a=a_value, b=b_value, c=c_value))

Add new optimizer constructors

Some models may have some parameter-specific settings for optimization, like different weight decay rate for all BatchNorm layers.

Although we already can use the optim_wrapper.paramwise_cfg field to configure various parameter-specific optimizer settings. It may still not cover your need.

Of course, you can modify it. By default, we use the DefaultOptimWrapperConstructor class to deal with the construction of optimizer. And during the construction, it fine-grainedly configures the optimizer settings of different parameters according to the paramwise_cfg,which could also serve as a template for new optimizer constructor.

You can overwrite these behaviors by add new optimizer constructors.

# In mmcls/engine/optimizers/my_optim_constructor.py
from mmengine.optim import DefaultOptimWrapperConstructor
from mmcls.registry import OPTIM_WRAPPER_CONSTRUCTORS


@OPTIM_WRAPPER_CONSTRUCTORS.register_module()
class MyOptimWrapperConstructor:

    def __init__(self, optim_wrapper_cfg, paramwise_cfg=None):
        ...

    def __call__(self, model):
        ...

And then, import it and use it almost like the optimizer tutorial.

  1. Import it in the mmcls/engine/optimizers/__init__.py to add it into the mmcls.engine package.

    # In mmcls/engine/optimizers/__init__.py
    ...
    from .my_optim_constructor import MyOptimWrapperConstructor
    
    __all__ = [..., 'MyOptimWrapperConstructor']
    
  2. Use MyOptimWrapperConstructor in the optim_wrapper.constructor field of config files.

    optim_wrapper = dict(
        constructor=dict(type='MyOptimWrapperConstructor'),
        optimizer=...,
        paramwise_cfg=...,
    )
    
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