Note

You are reading the documentation for MMSelfSup 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMSelfSup 1.0.0rc versions to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMSelfSup 1.0.0rc for more details.

Source code for mmselfsup.models.backbones.resnext

# Copyright (c) OpenMMLab. All rights reserved.
from mmcls.models.backbones.resnet import ResLayer
from mmcls.models.backbones.resnext import Bottleneck

from ..builder import BACKBONES
from .resnet import ResNet


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

    Please refer to the `paper <https://arxiv.org/abs/1611.05431>`__ for
    details.

    As the behavior of forward function in MMSelfSup is different from
    MMCls, we register our own ResNeXt, inheriting from
    `mmselfsup.model.backbone.ResNet`.

    Args:
        depth (int): Network depth, from {50, 101, 152}.
        groups (int): Groups of conv2 in Bottleneck. Defaults to 32.
        width_per_group (int): Width per group of conv2 in Bottleneck.
            Defaults to 4.
        in_channels (int): Number of input image channels. Defaults to 3.
        stem_channels (int): Output channels of the stem layer. Defaults to 64.
        num_stages (int): Stages of the network. Defaults to 4.
        strides (Sequence[int]): Strides of the first block of each stage.
            Defaults to ``(1, 2, 2, 2)``.
        dilations (Sequence[int]): Dilation of each stage.
            Defaults to ``(1, 1, 1, 1)``.
        out_indices (Sequence[int]): Output from which stages. If only one
            stage is specified, a single tensor (feature map) is returned,
            otherwise multiple stages are specified, a tuple of tensors will
            be returned. Defaults to ``(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.
        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
            Defaults to False.
        avg_down (bool): Use AvgPool instead of stride conv when
            downsampling in the bottleneck. Defaults to False.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters. Defaults to -1.
        conv_cfg (dict | None): The config dict for conv layers.
            Defaults to None.
        norm_cfg (dict): The config dict for norm layers.
        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. Defaults to False.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Defaults to False.
        zero_init_residual (bool): Whether to use zero init for last norm layer
            in resblocks to let them behave as identity. Defaults to False.

    Example:
        >>> from mmselfsup.models import ResNeXt
        >>> import torch
        >>> self = ResNeXt(depth=50)
        >>> self.eval()
        >>> inputs = torch.rand(1, 3, 32, 32)
        >>> level_outputs = self.forward(inputs)
        >>> for level_out in level_outputs:
        ...     print(tuple(level_out.shape))
        (1, 256, 8, 8)
        (1, 512, 4, 4)
        (1, 1024, 2, 2)
        (1, 2048, 1, 1)
    """

    arch_settings = {
        50: (Bottleneck, (3, 4, 6, 3)),
        101: (Bottleneck, (3, 4, 23, 3)),
        152: (Bottleneck, (3, 8, 36, 3))
    }

    def __init__(self, depth, groups=32, width_per_group=4, **kwargs):
        self.groups = groups
        self.width_per_group = width_per_group
        super(ResNeXt, self).__init__(depth=depth, **kwargs)

    def make_res_layer(self, **kwargs):
        """Redefine the function for ResNeXt related args."""
        return ResLayer(
            groups=self.groups,
            width_per_group=self.width_per_group,
            base_channels=self.base_channels,
            **kwargs)