Tutorial 4: Adding New Modules

Customize optimizer

A customized optimizer could be defined as following. Assume you want to add a 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 mmpose/core/optimizer/my_optimizer.py:

from mmcv.runner import OPTIMIZERS
from torch.optim import Optimizer

class MyOptimizer(Optimizer):

    def __init__(self, a, b, c)

Then add this module in mmpose/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.

from mmcv.utils import build_from_cfg

from mmcv.runner import OPTIMIZER_BUILDERS, OPTIMIZERS
from mmpose.utils import get_root_logger
from .cocktail_optimizer import CocktailOptimizer

class CocktailOptimizerConstructor:

    def __init__(self, optimizer_cfg, paramwise_cfg=None):

    def __call__(self, model):

        return my_optimizer

Develop new components

We basically categorize model components into 3 types.

  • detectors: the whole pose detector model pipeline, usually contains a backbone and keypoint_head.
  • backbone: usually an FCN network to extract feature maps, e.g., ResNet, HRNet.
  • keypoint_head: the component for pose estimation task, usually contains an deconv layers.
  1. Create a new file mmpose/models/backbones/my_model.py.
import torch.nn as nn

from ..registry import BACKBONES

class MyModel(nn.Module):

    def __init__(self, arg1, arg2):

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

    def init_weights(self, pretrained=None):
  1. Import the module in mmpose/models/backbones/__init__.py.
from .my_model import MyModel
  1. Create a new file mmpose/models/keypoint_heads/my_head.py.

You can write a new classification head inherit from nn.Module, and overwrite init_weights(self) and forward(self, x) method.

from ..registry import HEADS

class MyHead(nn.Module):

    def __init__(self, arg1, arg2):

    def forward(self, x):

    def init_weights(self):
  1. Import the module in mmpose/models/keypoint_heads/__init__.py
from .my_head import MyHead
  1. Use it in your config file.

For the top-down 2D pose estimation model, we set the module type as TopDown.

model = dict(

Add new loss

Assume you want to add a new loss as MyLoss, for bounding box regression. To add a new loss function, the users need implement it in mmpose/models/losses/my_loss.py. The decorator weighted_loss enable the loss to be weighted for each element.

import torch
import torch.nn as nn

from mmpose.models import LOSSES

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

class MyLoss(nn.Module):

    def __init__(self, use_target_weight=False):
        super(MyLoss, self).__init__()
        self.criterion = my_loss()
        self.use_target_weight = use_target_weight

    def forward(self, output, target, target_weight):
        batch_size = output.size(0)
        num_joints = output.size(1)

        heatmaps_pred = output.reshape(
            (batch_size, num_joints, -1)).split(1, 1)
        heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1)

        loss = 0.

        for idx in range(num_joints):
            heatmap_pred = heatmaps_pred[idx].squeeze(1)
            heatmap_gt = heatmaps_gt[idx].squeeze(1)
            if self.use_target_weight:
                loss += self.criterion(
                    heatmap_pred * target_weight[:, idx],
                    heatmap_gt * target_weight[:, idx])
                loss += self.criterion(heatmap_pred, heatmap_gt)

        return loss / num_joints

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

from .my_loss import MyLoss, my_loss

To use it, modify the loss_keypoint field in the model.

loss_keypoint=dict(type='MyLoss', use_target_weight=False)