TECHNICAL_DETAILS.md

Overview

In this section, we will introduce the main units of training a detector: data loading, model and iteration pipeline.

Data loading

Following typical conventions, we use Dataset and DataLoader for data loading with multiple workers. Dataset returns a dict of data items corresponding the arguments of models' forward method. Since the data in object detection may not be the same size (image size, gt bbox size, etc.), we introduce a new DataContainer type in mmcv to help collect and distribute data of different size. See here for more details.

Model

In mmdetection, model components are basically categorized as 4 types.

• backbone: usually a FCN network to extract feature maps, e.g., ResNet.
• neck: the part between backbones and heads, e.g., FPN, ASPP.
• head: the part for specific tasks, e.g., bbox prediction and mask prediction.
• roi extractor: the part for extracting features from feature maps, e.g., RoI Align.

We also write implement some general detection pipelines with the above components, such as SingleStageDetector and TwoStageDetector.

Build a model with basic components

Following some basic pipelines (e.g., two-stage detectors), the model structure can be customized through config files with no pains.

If we want to implement some new components, e.g, the path aggregation FPN structure in Path Aggregation Network for Instance Segmentation, there are two things to do.

1. create a new file in mmdet/models/necks/pafpn.py.

   class PAFPN(nn.Module):
   
       def __init__(self,
                   in_channels,
                   out_channels,
                   num_outs,
                   start_level=0,
                   end_level=-1,
                   add_extra_convs=False):
           pass
       
       def forward(self, inputs):
           # implementation is ignored
           pass

2. modify the config file from

   neck=dict(
       type='FPN',
       in_channels=[256, 512, 1024, 2048],
       out_channels=256,
       num_outs=5)

   to

   neck=dict(
       type='PAFPN',
       in_channels=[256, 512, 1024, 2048],
       out_channels=256,
       num_outs=5)

We will release more components (backbones, necks, heads) for research purpose.

Write a new model

To write a new detection pipeline, you need to inherit from BaseDetector, which defines the following abstract methods.

• extract_feat(): given an image batch of shape (n, c, h, w), extract the feature map(s).
• forward_train(): forward method of the training mode
• simple_test(): single scale testing without augmentation
• aug_test(): testing with augmentation (multi-scale, flip, etc.)

TwoStageDetector is a good example which shows how to do that.

Iteration pipeline

We adopt distributed training for both single machine and multiple machines. Supposing that the server has 8 GPUs, 8 processes will be started and each process runs on a single GPU.

Each process keeps an isolated model, data loader, and optimizer. Model parameters are only synchronized once at the begining. After a forward and backward pass, gradients will be allreduced among all GPUs, and the optimizer will update model parameters. Since the gradients are allreduced, the model parameter stays the same for all processes after the iteration.