import numpy as np outs=[] out.shape=[1632,64] outs.append(out) print(np.array(outs).shape) x=torch.cat(outs,dim=1)，经过上面这段代码，输出的outs的shape为(1,)这是为什么

在上面的代码中，`out.shape=[1632,64]`是一个二维数组，而`outs`是一个空列表。当我们执行`outs.append(out)`时，我们把`out`这个二维数组添加到了`outs`的末尾，此时`outs`中只有一个元素，即一个二维数组。

接下来，我们执行`np.array(outs)`，将`outs`转换为一个numpy数组。由于`outs`中只有一个元素，即一个二维数组，所以`np.array(outs)`的形状为`(1,)`。

最后，我们执行`x=torch.cat(outs,dim=1)`，将`outs`中的二维数组按照列拼接在一起，得到一个形状为`(1632, 64)`的二维数组`x`。

相关问题

import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable class Bottleneck(nn.Module): def init(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer): super(Bottleneck, self).init() self.out_planes = out_planes self.dense_depth = dense_depth self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(in_planes) self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False) self.bn2 = nn.BatchNorm2d(in_planes) self.conv3 = nn.Conv2d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(out_planes+dense_depth) self.shortcut = nn.Sequential() if first_layer: self.shortcut = nn.Sequential( nn.Conv2d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_planes+dense_depth) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) x = self.shortcut(x) d = self.out_planes out = torch.cat([x[:,:d,:,:]+out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1) out = F.relu(out) return out class DPN(nn.Module): def init(self, cfg): super(DPN, self).init() in_planes, out_planes = cfg['in_planes'], cfg['out_planes'] num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth'] self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.last_planes = 64 self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1) self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2) self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2) self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2) self.linear = nn.Linear(out_planes[3]+(num_blocks[3]+1)dense_depth[3], 10) def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride): strides = [stride] + 1 layers = [] for i,stride in (strides): layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0)) self.last_planes = out_planes + (i+2) * dense_depth return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = F.avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.linear(out) return out def DPN92(): cfg = { 'in_planes': (96,192,384,768), 'out_planes': (256,512,1024,2048), 'num_blocks': (3,4,20,3), 'dense_depth': (16,32,24,128) } return DPN(cfg)基于这个程序改成对摄像头采集的图像检测与分类输出坐标、大小和种类

首先，需要将输入的图像大小和通道数进行调整以适应模型输入。假设模型需要的输入大小为224x224，通道数为3，则可以使用OpenCV库中的resize函数将摄像头采集的图像缩放至指定大小。同时，需要将图像转换成PyTorch中的Tensor格式，并对像素值进行归一化操作。

代码如下：

import cv2
import numpy as np

# 调整图像大小和通道数
img_size = (224, 224)
img_channels = 3

# 初始化摄像头
cap = cv2.VideoCapture(0)

while True:
    # 读取摄像头图像
    ret, frame = cap.read()
    if not ret:
        break
    
    # 缩放图像
    frame = cv2.resize(frame, img_size)
    
    # 转换为Tensor格式，并进行归一化
    frame = np.transpose(frame, (2, 0, 1)).astype(np.float32) / 255.0
    frame = np.expand_dims(frame, axis=0)
    frame = torch.from_numpy(frame)
    
    # 调用模型进行预测
    output = model(frame)
    
    # TODO: 解析输出结果，得到分类、坐标和大小信息
    
    # 显示图像
    cv2.imshow('frame', frame)
    
    # 按下q键退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头
cap.release()
cv2.destroyAllWindows()

接下来，需要解析模型的输出结果，得到分类、坐标和大小信息。由于代码中没有给出模型输出的具体格式，这里需要根据模型输出结果进行相应的解析。

假设模型输出一个长度为4的向量，其中前两个元素表示物体的左上角坐标，后两个元素表示物体的宽度和高度，第三个元素表示物体的类别。则可以使用以下代码进行解析：

# 解析模型输出结果
x, y, w, h, cls = output[0]

# 计算物体的右下角坐标
x2 = x + w
y2 = y + h

# 显示分类、坐标和大小信息
class_names = ['class1', 'class2', 'class3', 'class4', 'class5']
print('Class:', class_names[cls])
print('Position: ({}, {})-({}, {})'.format(x, y, x2, y2))
print('Size: {}x{}'.format(w, h))

最后，将以上代码整合到一起，即可完成对摄像头采集的图像进行检测与分类输出坐标、大小和种类的任务。

完整代码如下：

import torch
import torch.nn as nn
import torch.nn.functional as F
import cv2
import numpy as np

class Bottleneck(nn.Module):
    def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
        super(Bottleneck, self).__init__()
        self.out_planes = out_planes
        self.dense_depth = dense_depth
        
        self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False)
        self.bn2 = nn.BatchNorm2d(in_planes)
        self.conv3 = nn.Conv2d(in_planes, out_planes * dense_depth, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_planes * dense_depth)
        
        self.shortcut = nn.Sequential()
        if first_layer:
            self.shortcut = nn.Sequential(
                nn.Conv2d(last_planes, out_planes * dense_depth, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_planes * dense_depth)
            )
    
    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        
        x = self.shortcut(x)
        d = self.out_planes * self.dense_depth
        out = torch.cat([x[:,:d,:,:], out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1)
        out = F.relu(out)
        
        return out


class DPN(nn.Module):
    def __init__(self, cfg):
        super(DPN, self).__init__()
        in_planes, out_planes = cfg['in_planes'], cfg['out_planes']
        num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth']
        
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.last_planes = 64
        
        self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1)
        self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2)
        self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2)
        self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2)
        
        self.linear = nn.Linear(out_planes[3] * (num_blocks[3] + 1) * dense_depth[3], 10)
    
    def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for i, stride in enumerate(strides):
            layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0))
            self.last_planes = out_planes * dense_depth
        return nn.Sequential(*layers)
    
    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def DPN92():
    cfg = {
        'in_planes': (96, 192, 384, 768),
        'out_planes': (256, 512, 1024, 2048),
        'num_blocks': (3, 4, 20, 3),
        'dense_depth': (16, 32, 24, 128)
    }
    return DPN(cfg)


# 调整图像大小和通道数
img_size = (224, 224)
img_channels = 3

# 初始化模型
model = DPN92()
model.load_state_dict(torch.load('dpn92.pth', map_location='cpu'))
model.eval()

# 初始化摄像头
cap = cv2.VideoCapture(0)

while True:
    # 读取摄像头图像
    ret, frame = cap.read()
    if not ret:
        break
    
    # 缩放图像
    frame = cv2.resize(frame, img_size)
    
    # 转换为Tensor格式，并进行归一化
    frame = np.transpose(frame, (2, 0, 1)).astype(np.float32) / 255.0
    frame = np.expand_dims(frame, axis=0)
    frame = torch.from_numpy(frame)
    
    # 调用模型进行预测
    output = model(frame)
    
    # 解析模型输出结果
    x, y, w, h, cls = output[0]
    x, y, w, h, cls = int(x), int(y), int(w), int(h), int(cls)
    
    # 计算物体的右下角坐标
    x2 = x + w
    y2 = y + h
    
    # 显示分类、坐标和大小信息
    class_names = ['class1', 'class2', 'class3', 'class4', 'class5']
    print('Class:', class_names[cls])
    print('Position: ({}, {})-({}, {})'.format(x, y, x2, y2))
    print('Size: {}x{}'.format(w, h))
    
    # 在图像上绘制矩形框
    cv2.rectangle(frame, (x, y), (x2, y2), (0, 255, 0), 2)
    
    # 显示图像
    cv2.imshow('frame', frame)
    
    # 按下q键退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头
cap.release()
cv2.destroyAllWindows()

import time, sys from datetime import datetime, timedelta from netCDF4 import Dataset, date2num, num2date import numpy as np day = 20170101 d = datetime.strptime(str(day), '%Y%m%d') f_in = 'tp_%d-%s.nc' % (day, (d + timedelta(days = 1)).strftime('%Y%m%d')) f_out = 'daily-tp_%d.nc' % day time_needed = [] for i in range(1, 25): time_needed.append(d + timedelta(hours = i)) with Dataset(f_in) as ds_src: var_time = ds_src.variables['time'] time_avail = num2date(var_time[:], var_time.units, calendar = var_time.calendar) indices = [] for tm in time_needed: a = np.where(time_avail == tm)[0] if len(a) == 0: sys.stderr.write('Error: precipitation data is missing/incomplete - %s!\n' % tm.strftime('%Y%m%d %H:%M:%S')) sys.exit(200) else: print('Found %s' % tm.strftime('%Y%m%d %H:%M:%S')) indices.append(a[0]) var_tp = ds_src.variables['tp'] tp_values_set = False for idx in indices: if not tp_values_set: data = var_tp[idx, :, :] tp_values_set = True else: data += var_tp[idx, :, :] with Dataset(f_out, mode = 'w', format = 'NETCDF3_64BIT_OFFSET') as ds_dest: # Dimensions for name in ['latitude', 'longitude']: dim_src = ds_src.dimensions[name] ds_dest.createDimension(name, dim_src.size) var_src = ds_src.variables[name] var_dest = ds_dest.createVariable(name, var_src.datatype, (name,)) var_dest[:] = var_src[:] var_dest.setncattr('units', var_src.units) var_dest.setncattr('long_name', var_src.long_name) ds_dest.createDimension('time', None) var = ds_dest.createVariable('time', np.int32, ('time',)) time_units = 'hours since 1900-01-01 00:00:00' time_cal = 'gregorian' var[:] = date2num([d], units = time_units, calendar = time_cal) var.setncattr('units', time_units) var.setncattr('long_name', 'time') var.setncattr('calendar', time_cal) # Variables var = ds_dest.createVariable(var_tp.name, np.double, var_tp.dimensions) var[0, :, :] = data var.setncattr('units', var_tp.units) var.setncattr('long_name', var_tp.long_name) # Attributes ds_dest.setncattr('Conventions', 'CF-1.6') ds_dest.setncattr('history', '%s %s' % (datetime.now().strftime('%Y-%m-%d %H:%M:%S'), ' '.join(time.tzname))) print('Done! Daily total precipitation saved in %s' % f_out)

null是一个表示空值或缺失值的特殊数据类型。在程序中，当一个变量的值为空时，通常会被赋予null值。null与undefined不同，undefined通常表示变量未被定义或未初始化，而null表示该变量的值为空。在许多编程语言中，null也可以被认为是一个对象，它没有任何属性。