1、.，*，+，?, \d，\w，\s，\b分别是什么? py

以上都是 Python 中的正则表达式元字符，具体含义如下：

- "." 代表任意单个字符。
- "*" 代表匹配前面的字符 0 次或多次。
- " " 代表空格字符。
- "?" 代表匹配前面的字符 0 次或 1 次。
- "\d" 代表任意一位数字。
- "\w" 代表任意一个字符或数字或下划线。
- "\s" 代表任意一个空白字符。
- "\b" 代表单词边界。

相关问题

Traceback (most recent call last): File "D:\anaconda3\envs\ml-lab\lib\site-packages\IPython\core\interactiveshell.py", line 3505, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-6c57772f5021>", line 1, in <module> runfile('D:/ml-lab/.idea/not1.py', wdir='D:/ml-lab/.idea') File "D:\PyCharm 2021.3.3\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "D:\PyCharm 2021.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "D:/ml-lab/.idea/not1.py", line 86, in <module> svm.fit(train_X, train_y) File "D:/ml-lab/.idea/not1.py", line 32, in fit self.E = self._E(self.X, self.y, self.b, self.alpha) File "D:/ml-lab/.idea/not1.py", line 83, in _E return np.dot(X, self.w) + b - y为啥报错

这是一个 Python 的 Traceback 错误信息，可以看到报错信息中包含了文件路径和行号。根据报错信息可以看出是在 `not1.py` 文件中的第 86 行出现了错误，具体是在 `svm.fit(train_X, train_y)` 这一行。在 `fit` 函数中调用了 `_E` 函数计算误差，而在 `_E` 函数中的第 83 行出现了错误。具体错误信息是 `return np.dot(X, self.w) + b - y`，这可能是由于某些变量的维度不匹配导致的。需要检查一下相关变量的维度是否正确以及是否有缺失值等问题。

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()