Class ResNet

segmentation/backbones/resnet.py:311–685

ResNet backbone. This backbone is the improved implementation of `Deep Residual Learning for Image Recognition `_. Args: depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. in_channels (int): Number of input image channels. Defau

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

    This backbone is the improved implementation of `Deep Residual Learning
    for Image Recognition <https://arxiv.org/abs/1512.03385>`_.

    Args:
        depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
        in_channels (int): Number of input image channels. Default: 3.
        stem_channels (int): Number of stem channels. Default: 64.
        base_channels (int): Number of base channels of res layer. Default: 64.
        num_stages (int): Resnet stages, normally 4. Default: 4.
        strides (Sequence[int]): Strides of the first block of each stage.
            Default: (1, 2, 2, 2).
        dilations (Sequence[int]): Dilation of each stage.
            Default: (1, 1, 1, 1).
        out_indices (Sequence[int]): Output from which stages.
            Default: (0, 1, 2, 3).
        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
            layer is the 3x3 conv layer, otherwise the stride-two layer is
            the first 1x1 conv layer. Default: 'pytorch'.
        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
            Default: False.
        avg_down (bool): Use AvgPool instead of stride conv when
            downsampling in the bottleneck. Default: False.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters. Default: -1.
        conv_cfg (dict | None): Dictionary to construct and config conv layer.
            When conv_cfg is None, cfg will be set to dict(type='Conv2d').
            Default: None.
        norm_cfg (dict): Dictionary to construct and config norm layer.
            Default: dict(type='BN', requires_grad=True).
        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.
        dcn (dict | None): Dictionary to construct and config DCN conv layer.
            When dcn is not None, conv_cfg must be None. Default: None.
        stage_with_dcn (Sequence[bool]): Whether to set DCN conv for each
            stage. The length of stage_with_dcn is equal to num_stages.
            Default: (False, False, False, False).
        plugins (list[dict]): List of plugins for stages, each dict contains:

            - cfg (dict, required): Cfg dict to build plugin.

            - position (str, required): Position inside block to insert plugin,
            options: 'after_conv1', 'after_conv2', 'after_conv3'.

            - stages (tuple[bool], optional): Stages to apply plugin, length
            should be same as 'num_stages'.
            Default: None.
        multi_grid (Sequence[int]|None): Multi grid dilation rates of last
            stage. Default: None.
        contract_dilation (bool): Whether contract first dilation of each layer
            Default: False.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        zero_init_residual (bool): Whether to use zero init for last norm layer
            in resblocks to let them behave as identity. Default: True.