We usually define a neural network in a deep learning task as a model, and this model is the core of an algorithm. MMEngine abstracts a unified model BaseModel to standardize the interfaces for training, testing and other processes. All models implemented by MMSegmentation inherit from BaseModel, and in MMSegmentation we implemented forward and added some functions for the semantic segmentation algorithm.

Common components


In MMSegmentation, we abstract the network architecture as a Segmentor, it is a model that contains all components of a network. We have already implemented EncoderDecoder and CascadeEncoderDecoder, which typically consist of Data preprocessor, Backbone, Decode head and Auxiliary head.

Data preprocessor

Data preprocessor is the part that copies data to the target device and preprocesses the data into the model input format.


Backbone is the part that transforms an image to feature maps, such as a ResNet-50 without the last fully connected layer.


Neck is the part that connects the backbone and heads. It performs some refinements or reconfigurations on the raw feature maps produced by the backbone. An example is Feature Pyramid Network (FPN).

Decode head

Decode head is the part that transforms the feature maps into a segmentation mask, such as PSPNet.

Auxiliary head

Auxiliary head is an optional component that transforms the feature maps into segmentation masks which only used for computing auxiliary losses.

Basic interfaces

MMSegmentation wraps BaseModel and implements the BaseSegmentor class, which mainly provides the interfaces forward, train_step, val_step and test_step. The following will introduce these interfaces in detail.


EncoderDecoder dataflow CascadeEncoderDecoder dataflow

The forward method returns losses or predictions of training, validation, testing, and a simple inference process.

The method should accept three modes: “tensor”, “predict” and “loss”:

  • “tensor”: Forward the whole network and return the tensor or tuple of tensor without any post-processing, same as a common nn.Module.

  • “predict”: Forward and return the predictions, which are fully processed to a list of SegDataSample.

  • “loss”: Forward and return a dict of losses according to the given inputs and data samples.

Note: SegDataSample is a data structure interface of MMSegmentation, it is used as an interface between different components. SegDataSample implements the abstract data element mmengine.structures.BaseDataElement, please refer to the SegDataSample documentation and data element documentation in MMEngine for more information.

Note that this method doesn’t handle either backpropagation or optimizer updating, which are done in the method train_step.


  • inputs (torch.Tensor) - The input tensor with shape (N, C, …) in general.

  • data_sample (list[SegDataSample]) - The seg data samples. It usually includes information such as metainfo and gt_sem_seg. Default to None.

  • mode (str) - Return what kind of value. Defaults to ‘tensor’.


  • dict or list:

    • If mode == "loss", return a dict of loss tensor used for backward and logging.

    • If mode == "predict", return a list of SegDataSample, the inference results will be incrementally added to the data_sample parameter passed to the forward method, each SegDataSample contains the following keys:

      • pred_sem_seg (PixelData): Prediction of semantic segmentation.

      • seg_logits (PixelData): Predicted logits of semantic segmentation before normalization.

    • If mode == "tensor", return a tensor or tuple of tensor or dict of tensor for custom use.

prediction modes

We briefly describe the fields of the model’s configuration in the config documentation, here we elaborate on the model.test_cfg field. model.test_cfg is used to control forward behavior, the forward method in "predict" mode can run in two modes:

  • whole_inference: If cfg.model.test_cfg.mode == 'whole', model will inference with full images.

    An whole_inference mode example config:

    model = dict(
  • slide_inference: If cfg.model.test_cfg.mode == 'slide', model will inference by sliding-window. Note: if you select the slide mode, cfg.model.test_cfg.stride and cfg.model.test_cfg.crop_size should also be specified.

    An slide_inference mode example config:

    model = dict(
      test_cfg=dict(mode='slide', crop_size=256, stride=170)


The train_step method calls the forward interface of the loss mode to get the loss dict. The BaseModel class implements the default model training process including preprocessing, model forward propagation, loss calculation, optimization, and back-propagation.


  • data (dict or tuple or list) - Data sampled from the dataset. In MMSegmentation, the data dict contains inputs and data_samples two fields.

  • optim_wrapper (OptimWrapper) - OptimWrapper instance used to update model parameters.

Note: OptimWrapper provides a common interface for updating parameters, please refer to optimizer wrapper documentation in MMEngine for more information.


  • Dict[str, torch.Tensor]: A dict of tensor for logging.

train_step dataflow


The val_step method calls the forward interface of the predict mode and returns the prediction result, which is further passed to the process interface of the evaluator and the after_val_iter interface of the Hook.


  • data (dict or tuple or list) - Data sampled from the dataset. In MMSegmentation, the data dict contains inputs and data_samples two fields.


  • list - The predictions of given data.

test_step/val_step dataflow


The BaseModel implements test_step the same as val_step.

Data Preprocessor

The SegDataPreProcessor implemented by MMSegmentation inherits from the BaseDataPreprocessor implemented by MMEngine and provides the functions of data preprocessing and copying data to the target device.

The runner carries the model to the specified device during the construction stage, while the data is carried to the specified device by the SegDataPreProcessor in train_step, val_step, and test_step, and the processed data is further passed to the model.

The parameters of the SegDataPreProcessor constructor:

  • mean (Sequence[Number], optional) - The pixel mean of R, G, B channels. Defaults to None.

  • std (Sequence[Number], optional) - The pixel standard deviation of R, G, B channels. Defaults to None.

  • size (tuple, optional) - Fixed padding size.

  • size_divisor (int, optional) - The divisor of padded size.

  • pad_val (float, optional) - Padding value. Default: 0.

  • seg_pad_val (float, optional) - Padding value of segmentation map. Default: 255.

  • bgr_to_rgb (bool) - whether to convert image from BGR to RGB. Defaults to False.

  • rgb_to_bgr (bool) - whether to convert image from RGB to BGR. Defaults to False.

  • batch_augments (list[dict], optional) - Batch-level augmentations. Default to None.

The data will be processed as follows:

  • Collate and move data to the target device.

  • Pad inputs to the input size with defined pad_val, and pad seg map with defined seg_pad_val.

  • Stack inputs to batch_inputs.

  • Convert inputs from bgr to rgb if the shape of input is (3, H, W).

  • Normalize image with defined std and mean.

  • Do batch augmentations like Mixup and Cutmix during training.

The parameters of the forward method:

  • data (dict) - data sampled from dataloader.

  • training (bool) - Whether to enable training time augmentation.

The returns of the forward method:

  • Dict: Data in the same format as the model input.

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