Shortcuts

Note

You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Source code for mmcls.models.backbones.resnext

# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import build_conv_layer, build_norm_layer

from ..builder import BACKBONES
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResLayer, ResNet


class Bottleneck(_Bottleneck):
    """Bottleneck block for ResNeXt.

    Args:
        in_channels (int): Input channels of this block.
        out_channels (int): Output channels of this block.
        groups (int): Groups of conv2.
        width_per_group (int): Width per group of conv2. 64x4d indicates
            ``groups=64, width_per_group=4`` and 32x8d indicates
            ``groups=32, width_per_group=8``.
        stride (int): stride of the block. Default: 1
        dilation (int): dilation of convolution. Default: 1
        downsample (nn.Module, optional): downsample operation on identity
            branch. Default: None
        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.
        conv_cfg (dict, optional): dictionary to construct and config conv
            layer. Default: None
        norm_cfg (dict): dictionary to construct and config norm layer.
            Default: dict(type='BN')
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 base_channels=64,
                 groups=32,
                 width_per_group=4,
                 **kwargs):
        super(Bottleneck, self).__init__(in_channels, out_channels, **kwargs)
        self.groups = groups
        self.width_per_group = width_per_group

        # For ResNet bottleneck, middle channels are determined by expansion
        # and out_channels, but for ResNeXt bottleneck, it is determined by
        # groups and width_per_group and the stage it is located in.
        if groups != 1:
            assert self.mid_channels % base_channels == 0
            self.mid_channels = (
                groups * width_per_group * self.mid_channels // base_channels)

        self.norm1_name, norm1 = build_norm_layer(
            self.norm_cfg, self.mid_channels, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(
            self.norm_cfg, self.mid_channels, postfix=2)
        self.norm3_name, norm3 = build_norm_layer(
            self.norm_cfg, self.out_channels, postfix=3)

        self.conv1 = build_conv_layer(
            self.conv_cfg,
            self.in_channels,
            self.mid_channels,
            kernel_size=1,
            stride=self.conv1_stride,
            bias=False)
        self.add_module(self.norm1_name, norm1)
        self.conv2 = build_conv_layer(
            self.conv_cfg,
            self.mid_channels,
            self.mid_channels,
            kernel_size=3,
            stride=self.conv2_stride,
            padding=self.dilation,
            dilation=self.dilation,
            groups=groups,
            bias=False)

        self.add_module(self.norm2_name, norm2)
        self.conv3 = build_conv_layer(
            self.conv_cfg,
            self.mid_channels,
            self.out_channels,
            kernel_size=1,
            bias=False)
        self.add_module(self.norm3_name, norm3)


[docs]@BACKBONES.register_module() class ResNeXt(ResNet): """ResNeXt backbone. Please refer to the `paper <https://arxiv.org/abs/1611.05431>`__ for details. Args: depth (int): Network depth, from {50, 101, 152}. groups (int): Groups of conv2 in Bottleneck. Default: 32. width_per_group (int): Width per group of conv2 in Bottleneck. Default: 4. in_channels (int): Number of input image channels. Default: 3. stem_channels (int): Output channels of the stem layer. Default: 64. num_stages (int): Stages of the network. 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. 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. Default: ``(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. 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): The config dict for conv layers. Default: 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. 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. """ 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, **kwargs) def make_res_layer(self, **kwargs): return ResLayer( groups=self.groups, width_per_group=self.width_per_group, base_channels=self.base_channels, **kwargs)
Read the Docs v: latest
Versions
master
latest
1.x
dev-1.x
Downloads
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.