yolov5dwconv替换conv

YoloV5中的Conv层使用的是传统的卷积操作，而YoloV5DWConv则使用深度可分离卷积来代替Conv层。深度可分离卷积可以将传统的卷积操作分解成两个部分：深度卷积和点卷积。其中，深度卷积只关注通道内的卷积操作，而点卷积则只关注通道间的卷积操作。这样一来，可以大幅减少计算量和参数量，提高模型的速度和精度。

如果要将Conv层替换为YoloV5DWConv，可以按照以下步骤进行：

1. 安装mmdetection和mmcv库

2. 修改yolov5.py文件中的Conv操作为YoloV5DWConv操作

from mmdet.models import ConvModule

class YOLOV5DWConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, groups=1, bias=False,):
        super(YOLOV5DWConv, self).__init__()
        self.dw_conv = ConvModule(
            in_channels,
            in_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=in_channels,
            bias=False,
            norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
            act_cfg=dict(type='SiLU', inplace=True))
        self.sep_conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=1,
            stride=1,
            padding=0,
            dilation=1,
            groups=1,
            bias=bias)

    def forward(self, x):
        x = self.dw_conv(x)
        x = self.sep_conv(x)
        return x

3. 在yolov5.py文件中使用YoloV5DWConv替换Conv操作

from .yolo_head import YOLOV5Head
from .yolo_neck import YOLOV5Neck
from .yolo_bbox_head import YOLOV5BBoxHead

class YOLOV5(nn.Module):
    def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None):
        super(YOLOV5, self).__init__()
        self.backbone = build_backbone(backbone)
        self.neck = build_neck(neck)
        self.bbox_head = build_head(bbox_head)
        self.train_cfg = train_cfg
        self.test_cfg = test_cfg
        self.init_weights(pretrained=pretrained)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                # 将Conv替换成YoloV5DWConv
                if m.kernel_size == (3, 3):
                    new_module = YOLOV5DWConv(
                        m.in_channels,
                        m.out_channels,
                        kernel_size=m.kernel_size,
                        stride=m.stride,
                        padding=m.padding,
                        dilation=m.dilation,
                        groups=m.groups,
                        bias=(m.bias is not None),
                    )
                    new_module.sep_conv.weight.data = m.weight.data
                    if m.bias is not None:
                        new_module.sep_conv.bias.data = m.bias.data
                    m = new_module
                else:
                    nn.init.normal_(m.weight, 0, 0.01)
                    if m.bias is not None:
                        nn.init.constant_(m.bias, 0)

    def forward(self, img, img_metas=None, return_loss=True, **kwargs):
        x = self.backbone(img)
        x = self.neck(x)
        outs = self.bbox_head(x)

        if return_loss:
            loss_inputs = outs + (img_metas,)
            losses = self.bbox_head.loss(*loss_inputs, **self.train_cfg)
            return losses
        else:
            bbox_list = self.bbox_head.get_bboxes(*outs, img_metas, **kwargs)
            return bbox_list

这样就可以使用YoloV5DWConv来替换Conv了。