class Focus(nn.Module): # Focus wh information into c-space def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super().__init__() self.conv = Conv(c1 * 4, c2, k, s, p, g, act) # self.contract = Contract(gain=2) def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) # return self.conv(self.contract(x))

这是一个定义了一个叫做Focus的PyTorch模块。它的作用是将输入的宽高信息通过卷积运算变换到一个新的通道空间，通道数量为c2。具体来说，它将输入x按照空间位置进行4等分，并将这四个位置的信息在通道维度上进行拼接，得到一个4c1通道的张量，然后通过一个卷积层将其变换为一个c2通道的输出。这个卷积层的卷积核大小为k，步长为s，填充为p，分组数为g。如果act=True，则在卷积层后面加上一个激活函数。

相关问题

class Focus(nn.Module): # Focus wh information into c-space def init(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super().init() self.conv = Conv(c1 * 4, c2, k, s, p, g, act) # self.contract = Contract(gain=2) def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) # return self.conv(self.contract(x)) 详细分析这个程序的结构和实现的功能

这是一个 PyTorch 中的神经网络模块。它被称为 Focus，作用是将输入张量中的宽和高信息集中到通道维度（c-space）中。下面是对这个程序的结构和功能的详细分析：

class Focus(nn.Module):
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
        super().__init__()
        self.conv = Conv(c1 * 4, c2, k, s, p, g, act) # Conv 是一个卷积层，这里定义了一个卷积层 self.conv
        # self.contract = Contract(gain=2) # 这行代码被注释掉了，不用理会

    def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
        # 输入的 x 是一个四维的张量，第一维是 batch_size，第二维是通道数，第三维和第四维是宽和高
        # 下面的代码将 x 中宽和高的信息集中到通道维度中，具体做法是将每个 2x2 的像素块中的四个像素在通道维度上拼接起来
        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
        # 这里用到了 PyTorch 中的 torch.cat 函数，将四个张量拼接在一起，第一个参数是要拼接的张量列表，第二个参数是指定在哪个维度上拼接

总之，这个 Focus 模块的作用是将输入张量的宽和高信息集中到通道维度中，以便后续的神经网络模型可以更好地利用这些信息进行特征提取和分类。

yolov7 common.py 源码

以下是 YOLOv7 的 common.py 源码：

import math
import torch.nn.functional as F
import torch.nn as nn
import torch

def make_divisible(x, divisor):
    # Returns x evenly divisible by divisor
    return math.ceil(x / divisor) * divisor


def autopad(k, p=None):  # kernel, padding
    # Pad to 'same'
    if p is None:
        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]
    return p


class Conv(nn.Module):
    # Standard convolution
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
        super(Conv, self).__init__()
        self.conv = nn.Conv2d(c1, c2, k, stride=s, padding=autopad(k, p), groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.Hardswish() if act else nn.Identity()

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))


class Bottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):
        super(Bottleneck, self).__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_, c2, 3, 1, g=g)
        self.add = shortcut and c1 == c2
        self.identity = nn.Identity() if self.add else None

    def forward(self, x):
        return self.identity(x) + self.cv2(self.cv1(x))


class SPP(nn.Module):
    # Spatial pyramid pooling layer used in YOLOv3-SPP
    def __init__(self, c1, c2, k=(5, 9, 13)):
        super(SPP, self).__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])

    def forward(self, x):
        x = self.cv1(x)
        return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))


class DWConv(nn.Module):
    # Depthwise convolution
    def __init__(self, c1, c2, k=1, s=1, p=None):
        super(DWConv, self).__init__()
        self.conv = nn.Conv2d(c1, c1, k, stride=s, padding=autopad(k, p), groups=c1, bias=False)
        self.bn = nn.BatchNorm2d(c1)
        self.act = nn.Hardswish()
        self.project = nn.Conv2d(c1, c2, 1, bias=False)
        self.bn2 = nn.BatchNorm2d(c2)
        self.act2 = nn.Hardswish()

    def forward(self, x):
        return self.act2(self.bn2(self.project(self.act(self.bn(self.conv(x))))))


class Focus(nn.Module):
    # Focus wh information into c-space
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
        super(Focus, self).__init__()
        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)

    def forward(self, x):
        # x(b,c,w,h) -> y(b,4c,w/2,h/2)
        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))


class Concat(nn.Module):
    # Concatenate a list of tensors along dimension
    def __init__(self, dimension=1):
        super(Concat, self).__init__()
        self.d = dimension

    def forward(self, x):
        return torch.cat(x, self.d)


class Detect(nn.Module):
    # Detect layer
    def __init__(self, nc, anchors):
        super(Detect, self).__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.na = len(anchors)  # number of anchors
        self.anchors = torch.tensor(anchors).float().view(self.na, -1)
        self.anchors /= self.anchors.sum(1).view(self.na, 1)  # normalized anchors
        self.register_buffer("anchor_grid", self.anchors.clone().view(1, -1, 1, 1))
        self.m = nn.Conv2d(self.no * self.na, self.no * self.na, 1)  # prediction conv

    def forward(self, x):
        # x(bs,255,h,w) -> p(bs,3,85,h,w)
        bs, _, ny, nx = x.shape
        device, dtype = x.device, x.dtype
        stride = self.anchor_grid.device / torch.tensor([nx, ny])[None, :, None, None].to(device)
        grid = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
        y = torch.stack(grid, 2).to(device).float()
        x = (x.sigmoid() * 2. - 0.5) * stride  # x(?,255,?,?) --sig--> x(?,255,?,?) --*2-0.5--> x(?,255,?,?) --*stride--> x(?,255,?,?)
        y = (y + 0.5) * stride  # y(?,2,?,?) --+0.5--> y(?,2,?,?) --*stride--> y(?,2,?,?)
        xy = torch.stack([x, y], 2).view(bs, 2, self.na * ny * nx).permute(0, 2, 1).contiguous().view(bs, self.na * ny * nx, 2)
        x = self.m(x.flatten(2).permute(0, 2, 1)).view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
        # x(bs,na,ny,nx,na) --view--> x(bs,na,ny,nx,no) --permute--> x(bs,na,ny,nx,no)
        if not self.training:
            x[..., 4:] = x[..., 4:].sigmoid()
            return x
        else:  # train
            return x, xy, self.anchor_grid.repeat(bs, 1, ny, nx)


class Model(nn.Module):
    # YOLOv7 model https://github.com/WongKinYiu/yolov7
    def __init__(self, nc=80, anchors=((10, 13), (16, 30), (33, 23), (30, 61), (62, 45), (59, 119), (116, 90), (156, 198), (373, 326)),
                 ch=[256, 512, 1024, 2048], depth=0.33):
        super(Model, self).__init__()
        assert depth in [0.33, 0.67, 1.0]
        self.depth = depth  # model depth multiplier
        self.grid = [torch.zeros(1)] * 5  # init grid
        self.stride = torch.tensor([8., 16., 32., 64., 128.])
        self.create_backbone(ch)
        self.create_neck()
        self.create_head(nc, anchors)

    def forward(self, x):
        z = []
        for i in range(5):
            x = self.backbone[i](x)
            z.append(x)
        x = self.neck(z)
        return self.head(x)

    def create_backbone(self, ch):
        # darknet backbone
        self.backbone = nn.ModuleList([Focus(3, ch[0], 3),
                                       Conv(ch[0], ch[1], 3, 2),
                                       Bottleneck(ch[1], ch[2]),
                                       Conv(ch[2], ch[3], 3, 2),
                                       Bottleneck(ch[3], ch[4]),
                                       Conv(ch[4], ch[5], 3, 2),
                                       SPP(ch[5], ch[5]),
                                       Bottleneck(ch[5], ch[6]),
                                       Conv(ch[6], ch[7], 1)])
        c2 = make_divisible(ch[7] * self.depth)  # ch_last
        self.backbone.append(Bottleneck(ch[7], c2, False))
        self.out_channels = [c2, ch[4], ch[2], ch[0]]

    def create_neck(self):
        # FPN-like attentional output
        self.neck = nn.Sequential(
            Concat(),
            Conv(self.out_channels[0], self.out_channels[0], 1),
            DWConv(self.out_channels[0], self.out_channels[1], 3, s=2),
            DWConv(self.out_channels[1], self.out_channels[2], 3, s=2),
            DWConv(self.out_channels[2], self.out_channels[3], 3, s=2),
            SPP(self.out_channels[3], self.out_channels[3]),
            DWConv(self.out_channels[3], self.out_channels[3], 3, dilation=3),
            DWConv(self.out_channels[3], self.out_channels[3], 3, dilation=3),
            DWConv(self.out_channels[3], self.out_channels[3], 3, dilation=3),
        )

    def create_head(self, nc, anchors):
        # detection head
        self.head = nn.Sequential(
            DWConv(self.out_channels[3], self.out_channels[3], 3, dilation=3),
            DWConv(self.out_channels[3], self.out_channels[3], 3, dilation=3),
            DWConv(self.out_channels[3], self.out_channels[3], 3, dilation=3),
            Concat(),
            Conv(self.out_channels[3] * 4, self.out_channels[3], 1),
            nn.Conv2d(self.out_channels[3], len(anchors) * (nc + 5), 1, bias=True),
            Detect(nc, anchors))


def attempt_load(weights, map_location=None, inplace=True):
    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
    if isinstance(weights, (list, tuple)):
        # Load a list of models (ensemble)
        ensemble = nn.ModuleList()
        for w in weights:
            model = Model()
            model.to(next(w.parameters()).device)
            try:
                ckpt = torch.load(w, map_location=map_location)  # load
                state_dict = ckpt['model'].float().state_dict()  # to FP32
                state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape}  # filter
                model.load_state_dict(state_dict, strict=False)  # load
                print(f"Transferred {len(state_dict)} from {w}")
            except:
                print(f"Error loading {w}")
            ensemble.append(model.eval())
        return ensemble

    else:
        # Load a single model
        model = Model()
        model.to(next(weights.parameters()).device)
        try:
            ckpt = torch.load(weights, map_location=map_location)  # load
            state_dict = ckpt['model'].float().state_dict()  # to FP32
            state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape}  # filter
            model.load_state_dict(state_dict, strict=False)  # load
            print(f"Transferred {len(state_dict)} from {weights}")
        except:
            print(f"Error loading {weights}")
        return model.eval()