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Tutorial 5: Adding New Modules

In this tutorial, we will introduce some methods about how to customize optimizer, develop new components and new a learning rate scheduler for this project.

Customize Optimizer

An example of customized optimizer is CopyOfSGD is defined in mmaction/core/optimizer/copy_of_sgd.py. More generally, a customized optimizer could be defined as following.

Assume you want to add an optimizer named as MyOptimizer, which has arguments a, b and c. You need to first implement the new optimizer in a file, e.g., in mmaction/core/optimizer/my_optimizer.py:

from .registry import OPTIMIZERS
from torch.optim import Optimizer

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

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

Then add this module in mmaction/core/optimizer/__init__.py, thus the registry will find the new module and add it:

from .my_optimizer import MyOptimizer

Then you can use MyOptimizer in optimizer field of config files. In the configs, the optimizers are defined by the field optimizer like the following:

optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)

To use your own optimizer, the field can be changed as

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

We already support to use all the optimizers implemented by PyTorch, and the only modification is to change the optimizer field of config files. For example, if you want to use ADAM, though the performance will drop a lot, the modification could be as the following.

optimizer = dict(type='Adam', lr=0.0003, weight_decay=0.0001)

The users can directly set arguments following the API doc of PyTorch.

Customize Optimizer Constructor

Some models may have some parameter-specific settings for optimization, e.g. weight decay for BatchNorm layers. The users can do those fine-grained parameter tuning through customizing optimizer constructor.

You can write a new optimizer constructor inherit from DefaultOptimizerConstructor and overwrite the add_params(self, params, module) method.

An example of customized optimizer constructor is TSMOptimizerConstructor. More generally, a customized optimizer constructor could be defined as following.

In mmaction/core/optimizer/my_optimizer_constructor.py:

from mmcv.runner import OPTIMIZER_BUILDERS, DefaultOptimizerConstructor

@OPTIMIZER_BUILDERS.register_module()
class MyOptimizerConstructor(DefaultOptimizerConstructor):

In mmaction/core/optimizer/__init__.py:

from .my_optimizer_constructor import MyOptimizerConstructor

Then you can use MyOptimizerConstructor in optimizer field of config files.

# optimizer
optimizer = dict(
    type='SGD',
    constructor='MyOptimizerConstructor',
    paramwise_cfg=dict(fc_lr5=True),
    lr=0.02,
    momentum=0.9,
    weight_decay=0.0001)

Develop New Components

We basically categorize model components into 4 types.

  • recognizer: the whole recognizer model pipeline, usually contains a backbone and cls_head.
  • backbone: usually an FCN network to extract feature maps, e.g., ResNet, BNInception.
  • cls_head: the component for classification task, usually contains an FC layer with some pooling layers.
  • localizer: the model for temporal localization task, currently available: BSN, BMN.

Add new backbones

Here we show how to develop new components with an example of TSN.

  1. Create a new file mmaction/models/backbones/resnet.py.

    import torch.nn as nn

from ..registry import BACKBONES

@BACKBONES.register_module()
class ResNet(nn.Module):

    def __init__(self, arg1, arg2):
        pass

    def forward(self, x):  # should return a tuple
        pass

    def init_weights(self, pretrained=None):
        pass

  2. Import the module in mmaction/models/backbones/__init__.py.

    from .resnet import ResNet

  3. Use it in your config file.

    model = dict(
    ...
    backbone=dict(
        type='ResNet',
        arg1=xxx,
        arg2=xxx),
)

Add new heads

Here we show how to develop a new head with the example of TSNHead as the following.

  1. Create a new file mmaction/models/heads/tsn_head.py.

    You can write a new classification head inheriting from BaseHead, and overwrite init_weights(self) and forward(self, x) method.

    from ..registry import HEADS
from .base import BaseHead


@HEADS.register_module()
class TSNHead(BaseHead):

    def __init__(self, arg1, arg2):
        pass

    def forward(self, x):
        pass

    def init_weights(self):
        pass

  2. Import the module in mmaction/models/heads/__init__.py

    from .tsn_head import TSNHead

  3. Use it in your config file

    model = dict(
    ...
    cls_head=dict(
        type='TSNHead',
        num_classes=400,
        in_channels=2048,
        arg1=xxx,
        arg2=xxx),

Add new loss

Assume you want to add a new loss as MyLoss. To add a new loss function, the users need implement it in mmaction/models/losses/my_loss.py.

import torch
import torch.nn as nn

from ..builder import LOSSES

def my_loss(pred, target):
    assert pred.size() == target.size() and target.numel() > 0
    loss = torch.abs(pred - target)
    return loss


@LOSSES.register_module()
class MyLoss(nn.Module):

    def forward(self, pred, target):
        loss = my_loss(pred, target)
        return loss

Then the users need to add it in the mmaction/models/losses/__init__.py

from .my_loss import MyLoss, my_loss

To use it, modify the loss_xxx field. Since MyLoss is for regression, we can use it for the bbox loss loss_bbox.

loss_bbox=dict(type='MyLoss'))

Add new learning rate scheduler (updater)

The default manner of constructing a lr updater(namely, 'scheduler' by pytorch convention), is to modify the config such as:

...
lr_config = dict(policy='step', step=[20, 40])
...

In the api for train.py, it will register the learning rate updater hook based on the config at:

...
    runner.register_training_hooks(
        cfg.lr_config,
        optimizer_config,
        cfg.checkpoint_config,
        cfg.log_config,
        cfg.get('momentum_config', None))
...

So far, the supported updaters can be find in mmcv, but if you want to customize a new learning rate updater, you may follow the steps below:

  1. First, write your own LrUpdaterHook in $MMAction2/mmaction/core/lr. The snippet followed is an example of cumtomized lr updater that uses learning rate based on a specific learning rate ratio: lrs, by which the learning rate decreases at each steps:
@HOOKS.register_module()
# Register it here
class RelativeStepLrUpdaterHook(LrUpdaterHook):
    # You should inheritate it from mmcv.LrUpdaterHook
    def __init__(self, runner, steps, lrs, **kwargs):
        super().__init__(**kwargs)
        assert len(steps) == (len(lrs))
        self.steps = steps
        self.lrs = lrs

    def get_lr(self, runner, base_lr):
        # Only this function is required to override
        # This function is called before each training epoch, return the specific learning rate here.
        progress = runner.epoch if self.by_epoch else runner.iter
        for i in range(len(self.steps)):
            if progress < self.steps[i]:
                return self.lrs[i]
  1. Modify your config:

In your config file, swap the original lr_config by:

lr_config = dict(policy='RelativeStep', steps=[20, 40, 60], lrs=[0.1, 0.01, 0.001])

More examples can be found in mmcv.