Class HRNet

segmentation/backbones/hrnet.py:218–642

HRNet backbone. This backbone is the implementation of `High-Resolution Representations for Labeling Pixels and Regions `_. Args: extra (dict): Detailed configuration for each stage of HRNet. There must be 4 stages, the configuratio

@BACKBONES.register_module()
class HRNet(BaseModule):
    """HRNet backbone.

    This backbone is the implementation of `High-Resolution Representations
    for Labeling Pixels and Regions <https://arxiv.org/abs/1904.04514>`_.

    Args:
        extra (dict): Detailed configuration for each stage of HRNet.
            There must be 4 stages, the configuration for each stage must have
            5 keys:

                - num_modules (int): The number of HRModule in this stage.
                - num_branches (int): The number of branches in the HRModule.
                - block (str): The type of convolution block.
                - num_blocks (tuple): The number of blocks in each branch.
                    The length must be equal to num_branches.
                - num_channels (tuple): The number of channels in each branch.
                    The length must be equal to num_branches.
        in_channels (int): Number of input image channels. Normally 3.
        conv_cfg (dict): Dictionary to construct and config conv layer.
            Default: None.
        norm_cfg (dict): Dictionary to construct and config norm layer.
            Use `BN` by default.
        norm_eval (bool): Whether to set norm layers to eval mode, namely,
            freeze running stats (mean and var). Note: Effect on Batch Norm
            and its variants only. Default: False.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters. Default: -1.
        zero_init_residual (bool): Whether to use zero init for last norm layer
            in resblocks to let them behave as identity. Default: False.
        multiscale_output (bool): Whether to output multi-level features
            produced by multiple branches. If False, only the first level
            feature will be output. Default: True.
        pretrained (str, optional): Model pretrained path. Default: None.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Default: None.

    Example:
        >>> from mmseg.models import HRNet
        >>> import torch
        >>> extra = dict(
        >>>     stage1=dict(
        >>>         num_modules=1,
        >>>         num_branches=1,
        >>>         block='BOTTLENECK',
        >>>         num_blocks=(4, ),
        >>>         num_channels=(64, )),
        >>>     stage2=dict(
        >>>         num_modules=1,
        >>>         num_branches=2,
        >>>         block='BASIC',
        >>>         num_blocks=(4, 4),
        >>>         num_channels=(32, 64)),
        >>>     stage3=dict(
        >>>         num_modules=4,
        >>>         num_branches=3,