mmdetection/mmdet/models/backbones/detectors_resnext.py

# Copyright (c) OpenMMLab. All rights reserved.
import math

from mmcv.cnn import build_conv_layer, build_norm_layer

from mmdet.registry import MODELS
from .detectors_resnet import Bottleneck as _Bottleneck
from .detectors_resnet import DetectoRS_ResNet


class Bottleneck(_Bottleneck):
    expansion = 4

    def __init__(self,
                 inplanes,
                 planes,
                 groups=1,
                 base_width=4,
                 base_channels=64,
                 **kwargs):
        """Bottleneck block for ResNeXt.

        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
        it is "caffe", the stride-two layer is the first 1x1 conv layer.
        """
        super(Bottleneck, self).__init__(inplanes, planes, **kwargs)

        if groups == 1:
            width = self.planes
        else:
            width = math.floor(self.planes *
                               (base_width / base_channels)) * groups

        self.norm1_name, norm1 = build_norm_layer(
            self.norm_cfg, width, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(
            self.norm_cfg, width, postfix=2)
        self.norm3_name, norm3 = build_norm_layer(
            self.norm_cfg, self.planes * self.expansion, postfix=3)

        self.conv1 = build_conv_layer(
            self.conv_cfg,
            self.inplanes,
            width,
            kernel_size=1,
            stride=self.conv1_stride,
            bias=False)
        self.add_module(self.norm1_name, norm1)
        fallback_on_stride = False
        self.with_modulated_dcn = False
        if self.with_dcn:
            fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
        if self.with_sac:
            self.conv2 = build_conv_layer(
                self.sac,
                width,
                width,
                kernel_size=3,
                stride=self.conv2_stride,
                padding=self.dilation,
                dilation=self.dilation,
                groups=groups,
                bias=False)
        elif not self.with_dcn or fallback_on_stride:
            self.conv2 = build_conv_layer(
                self.conv_cfg,
                width,
                width,
                kernel_size=3,
                stride=self.conv2_stride,
                padding=self.dilation,
                dilation=self.dilation,
                groups=groups,
                bias=False)
        else:
            assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
            self.conv2 = build_conv_layer(
                self.dcn,
                width,
                width,
                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,
            width,
            self.planes * self.expansion,
            kernel_size=1,
            bias=False)
        self.add_module(self.norm3_name, norm3)


@MODELS.register_module()
class DetectoRS_ResNeXt(DetectoRS_ResNet):
    """ResNeXt backbone for DetectoRS.

    Args:
        groups (int): The number of groups in ResNeXt.
        base_width (int): The base width of ResNeXt.
    """

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

    def __init__(self, groups=1, base_width=4, **kwargs):
        self.groups = groups
        self.base_width = base_width
        super(DetectoRS_ResNeXt, self).__init__(**kwargs)

    def make_res_layer(self, **kwargs):
        return super().make_res_layer(
            groups=self.groups,
            base_width=self.base_width,
            base_channels=self.base_channels,
            **kwargs)