tuple([self.b * _ for _ in (x, y)])

这是一个 Python 表达式，它使用了列表推导式来生成一个元组。假设这个表达式出现在某个类的方法中，self.b 是这个类的一个属性，x 和 y 是方法的两个参数。列表推导式中的 for 循环遍历了一个元组 (x, y)，并将每个元素乘以 self.b 得到一个新的列表。最后，这个列表被传入 tuple() 函数中，生成一个包含两个元素的元组。

相关问题

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()

生成基于栅格地图的bilstm改进的A*算法的路径规划python代码

以下是基于栅格地图的BiLSTM改进的A*算法的路径规划Python代码：

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

# 定义BiLSTM模型
class BiLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(BiLSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)
        self.fc = nn.Linear(hidden_size*2, num_classes)
        
    def forward(self, x):
        h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

# 定义A*算法类
class AStar:
    def __init__(self, map_size, start, end):
        self.map_size = map_size
        self.start = start
        self.end = end
        self.open_list = []
        self.close_list = []
        self.father = {}
        self.g_score = {}
        self.h_score = {}
        self.f_score = {}
        self.bi_lstm = None
        
    # 定义启发函数
    def heuristic(self, a, b):
        return np.sqrt((a[0]-b[0])**2 + (a[1]-b[1])**2)
    
    # 定义判断点是否在地图内
    def in_map(self, point):
        return point[0]>=0 and point[0]<self.map_size[0] and point[1]>=0 and point[1]<self.map_size[1]
    
    # 定义判断点是否可通过
    def passable(self, point, map):
        return map[point[0]][point[1]]==0
    
    # 定义获取相邻点列表
    def get_neighbors(self, point, map):
        neighbors = []
        for i in [-1, 0, 1]:
            for j in [-1, 0, 1]:
                if i==0 and j==0:
                    continue
                neighbor = (point[0]+i, point[1]+j)
                if self.in_map(neighbor) and self.passable(neighbor, map):
                    neighbors.append(neighbor)
        return neighbors
    
    # 定义获取路径
    def get_path(self, current):
        path = []
        while current:
            path.append(current)
            current = self.father.get(current)
        path.reverse()
        return path
    
    # 定义A*算法函数
    def astar(self, map):
        self.open_list.append(self.start)
        self.g_score[self.start] = 0
        self.h_score[self.start] = self.heuristic(self.start, self.end)
        self.f_score[self.start] = self.h_score[self.start]
        
        while self.open_list:
            current = min(self.open_list, key=lambda x:self.f_score[x])
            
            if current == self.end:
                return self.get_path(current)
            
            self.open_list.remove(current)
            self.close_list.append(current)
            
            for neighbor in self.get_neighbors(current, map):
                if neighbor in self.close_list:
                    continue
                
                g = self.g_score[current] + self.heuristic(current, neighbor)
                
                if neighbor not in self.open_list:
                    self.open_list.append(neighbor)
                    self.h_score[neighbor] = self.heuristic(neighbor, self.end)
                    self.g_score[neighbor] = g
                    self.f_score[neighbor] = self.g_score[neighbor] + self.h_score[neighbor]
                    self.father[neighbor] = current
                elif g < self.g_score[neighbor]:
                    self.g_score[neighbor] = g
                    self.f_score[neighbor] = self.g_score[neighbor] + self.h_score[neighbor]
                    self.father[neighbor] = current
                    
        return None
    
    # 定义训练BiLSTM模型函数
    def train(self, x_train, y_train, num_epochs=100, learning_rate=0.001):
        self.bi_lstm = BiLSTM(2, 128, 2, 2).to(device)
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.Adam(self.bi_lstm.parameters(), lr=learning_rate)
        
        for epoch in range(num_epochs):
            inputs = torch.Tensor(x_train).to(device)
            targets = torch.Tensor(y_train).long().to(device)
            
            optimizer.zero_grad()
            
            outputs = self.bi_lstm(inputs)
            loss = criterion(outputs, targets)
            loss.backward()
            optimizer.step()
            
            if (epoch+1) % 10 == 0:
                print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
    
    # 定义预测函数
    def predict(self, x):
        inputs = torch.Tensor(x).to(device)
        outputs = self.bi_lstm(inputs)
        _, predicted = torch.max(outputs.data, 1)
        return predicted.cpu().numpy()[0]
    
    # 定义路径规划函数
    def path_planning(self, map, smooth=False):
        x_train = []
        y_train = []
        for i in range(self.map_size[0]):
            for j in range(self.map_size[1]):
                if (i,j) == self.start or (i,j) == self.end:
                    continue
                x_train.append([i,j])
                y_train.append(int(map[i][j]))
        self.train(x_train, y_train)
        
        current = self.start
        path = [current]
        while current != self.end:
            neighbors = self.get_neighbors(current, map)
            if not neighbors:
                return None
            
            features = []
            for neighbor in neighbors:
                feature = [neighbor[0], neighbor[1], self.heuristic(neighbor, self.end)]
                feature.append(self.predict([feature]))
                features.append(feature)
                
            features = np.array(features)
            index = np.argmin(features[:,2] + features[:,3]*0.5)
            next = tuple(features[index][:2].astype(int))
            path.append(next)
            current = next
            
        if smooth:
            return self.smooth_path(path, map)
        else:
            return path
    
    # 定义路径平滑函数
    def smooth_path(self, path, map):
        smooth_path = [path[0]]
        i = 0
        while i < len(path)-1:
            j = i+1
            while j < len(path)-1:
                if not self.passable(path[i], path[j], map):
                    break
                j += 1
            smooth_path.append(path[j-1])
            i = j-1
        smooth_path.append(path[-1])
        return smooth_path

使用方法：

# 定义地图大小、起点、终点
map_size = (10, 10)
start = (1, 1)
end = (8, 8)

# 定义地图
map = np.zeros(map_size)
map[3:7, 4:8] = 1

# 定义A*算法对象
astar = AStar(map_size, start, end)

# 进行路径规划
path = astar.path_planning(map, smooth=True)
print(path)

其中，`map_size`为地图大小，`start`为起点坐标，`end`为终点坐标，`map`为地图，0表示可通过的点，1表示障碍物。`path_planning`函数的第二个参数`smooth`表示是否对路径进行平滑处理。