Tutorial 4: Adding New Modules

Develop new components

We basically categorize model components into 3 types.

  • backbone: usually an feature extraction network, e.g., ResNet, MobileNet.
  • neck: the component between backbones and heads, e.g., GlobalAveragePooling.
  • head: the component for specific tasks, e.g., classification or regression.

Add new backbones

Here we show how to develop new components with an example of ResNet_CIFAR. As the input size of CIFAR is 32x32, this backbone replaces the kernel_size=7, stride=2 to kernel_size=3, stride=1 and remove the MaxPooling after stem, to avoid forwarding small feature maps to residual blocks. It inherits from ResNet and only modifies the stem layers.

  1. Create a new file mmcls/models/backbones/resnet_cifar.py.
import torch.nn as nn

from ..builder import BACKBONES
from .resnet import ResNet

class ResNet_CIFAR(ResNet):

    """ResNet backbone for CIFAR.

    short description of the backbone

        depth(int): Network depth, from {18, 34, 50, 101, 152}.

    def __init__(self, depth, **kwargs):
        super(ResNet_CIFAR, self).__init__(depth, **kwargs)  # call ResNet init
        pass # other specific initialization

    def forward(self, x):  # should return a tuple
        # implementation is ignored

    def init_weights(self, pretrained=None):
        pass  # override ResNet init_weights if necessary

    def train(self, mode=True):
        pass   # override ResNet train if necessary
  1. Import the module in mmcls/models/backbones/__init__.py.
from .resnet_cifar import ResNet_CIFAR
  1. Use it in your config file.
model = dict(

Add new necks

Here we take GlobalAveragePooling as an example. It is a very simple neck without any arguments. To add a new neck, we mainly implement the forward function, which applies some operation on the output from backbone and forward the results to head.

  1. Create a new file in mmcls/models/necks/gap.py.

    import torch.nn as nn
    from ..builder import NECKS
    class GlobalAveragePooling(nn.Module):
        def __init__(self):
            self.gap = nn.AdaptiveAvgPool2d((1, 1))
        def forward(self, inputs):
            # we regard inputs as tensor for simplicity
            outs = self.gap(inputs)
            outs = outs.view(inputs.size(0), -1)
            return outs
  2. Import the module in mmcls/models/necks/__init__.py.

    from .gap import GlobalAveragePooling
  3. Modify the config file.

    model = dict(

Add new heads

Here we show how to develop a new head with the example of LinearClsHead as the following. To implement a new head, basically we need to implement forward_train, which takes the feature maps from necks or backbones as input and compute loss based on ground-truth labels.

  1. Create a new file in mmcls/models/heads/linear_head.py.

    from ..builder import HEADS
    from .cls_head import ClsHead
    class LinearClsHead(ClsHead):
        def __init__(self,
                  loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
                  topk=(1, )):
            super(LinearClsHead, self).__init__(loss=loss, topk=topk)
            self.in_channels = in_channels
            self.num_classes = num_classes
            if self.num_classes <= 0:
                raise ValueError(
                    f'num_classes={num_classes} must be a positive integer')
        def _init_layers(self):
            self.fc = nn.Linear(self.in_channels, self.num_classes)
        def init_weights(self):
            normal_init(self.fc, mean=0, std=0.01, bias=0)
        def forward_train(self, x, gt_label):
            cls_score = self.fc(x)
            losses = self.loss(cls_score, gt_label)
            return losses
  2. Import the module in mmcls/models/heads/__init__.py.

    from .linear_head import LinearClsHead
  3. Modify the config file.

Together with the added GlobalAveragePooling neck, an entire config for a model is as follows.

model = dict(
        out_indices=(3, ),
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
        topk=(1, 5),

Add new loss

To add a new loss function, we mainly implement the forward function in the loss module. In addition, it is helpful to leverage the decorator weighted_loss to weight the loss for each element. Assuming that we want to mimic a probablistic distribution generated from anther classification model, we implement a L1Loss to fulfil the purpose as below.

  1. Create a new file in mmcls/models/losses/l1_loss.py.

    import torch
    import torch.nn as nn
    from ..builder import LOSSES
    from .utils import weighted_loss
    def l1_loss(pred, target):
        assert pred.size() == target.size() and target.numel() > 0
        loss = torch.abs(pred - target)
        return loss
    class L1Loss(nn.Module):
        def __init__(self, reduction='mean', loss_weight=1.0):
            super(L1Loss, self).__init__()
            self.reduction = reduction
            self.loss_weight = loss_weight
        def forward(self,
            assert reduction_override in (None, 'none', 'mean', 'sum')
            reduction = (
                reduction_override if reduction_override else self.reduction)
            loss = self.loss_weight * l1_loss(
                pred, target, weight, reduction=reduction, avg_factor=avg_factor)
            return loss
  2. Import the module in mmcls/models/losses/__init__.py.

    from .l1_loss import L1Loss, l1_loss
  3. Modify loss field in the config.

    loss=dict(type='L1Loss', loss_weight=1.0))