torch.full((num_anchors,), -1, dtype=torch.long, device=device)

这行代码使用 PyTorch 创建一个形状为 (num_anchors,)，数据类型为 long，所有元素均为 -1 的张量，并将其存储在指定的设备上（如 GPU）。在目标检测中，通常会将这个张量用作目标检测算法输出的分类预测中的背景类别的索引。由于背景类别没有对应的物体，因此将其索引设置为 -1，以便在后续处理中将其忽略。

相关问题

anchors_bbox_map = torch.full((num_anchors,), -1, dtype=torch.long, device=device)

这行代码创建了一个形状为 `(num_anchors,)` 的张量 `anchors_bbox_map`，并将其用数值 -1 填充整个张量。具体来说，这个张量的每个元素都表示一个 anchor 与哪个 ground-truth box 匹配，如果该 anchor 没有匹配到任何一个 ground-truth box，则对应的元素值为 -1。

这里，`dtype=torch.long` 表示创建的张量的数据类型为 64 位整型，`device=device` 表示创建的张量将被放在指定的设备上（可能是 GPU 或 CPU）。

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()