我已经训练了一个分类器，现在正在尝试加载它并运行一些预测。我遇到了下面的错误：

....    return self._conv_forward(input, self.weight, self.bias)  File "/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 439, in _conv_forward    return F.conv2d(input, weight, bias, self.stride,TypeError: conv2d() received an invalid combination of arguments - got (list, Parameter, Parameter, tuple, tuple, tuple, int), but expected one of: * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, tuple of ints padding, tuple of ints dilation, int groups)      didn't match because some of the arguments have invalid types: (list, Parameter, Parameter, tuple, tuple, tuple, int) * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, str padding, tuple of ints dilation, int groups)      didn't match because some of the arguments have invalid types: (list, Parameter, Parameter, tuple, tuple, tuple, int)

这是我的代码：

变换器 – 用于编码图像

transformer = transforms.Compose([        transforms.RandomHorizontalFlip(0.5),        transforms.ToTensor(),    ])

获取文件并转换为张量

def get_file_as_tensor(file_path):    with np.load(file_path) as f:        melspec_image_array = f['arr_0']        image = Image.fromarray(melspec_image_array, mode='RGB')        image_tensor = transformer(image).div_(255).float()        return image_tensor.clone().detach()

预测函数，错误发生在运行model([tensor])时

def predict(tensor, model):    yhat = model([tensor])    yhat = yhat.clone().detach()    return yhatclass ConvBlock(nn.Module):    def __init__(self, in_channels, out_channels):        super().__init__()        self.conv1 = nn.Sequential(            nn.Conv2d(in_channels, out_channels, 3, 1, 1),            nn.BatchNorm2d(out_channels),            nn.ReLU(),        )        self.conv2 = nn.Sequential(            nn.Conv2d(out_channels, out_channels, 3, 1, 1),            nn.ReLU(),            nn.Dropout(0.5)        )        self._init_weights()    def _init_weights(self):        for m in self.modules():            if isinstance(m, nn.Conv2d):                nn.init.kaiming_normal_(m.weight)                if m.bias is not None:                    nn.init.zeros_(m.bias)            elif isinstance(m, nn.BatchNorm2d):                nn.init.constant_(m.weight, 1)                nn.init.zeros_(m.bias)    def forward(self, x):        x = self.conv1(x)        x = self.conv2(x)        x = F.avg_pool2d(x, 2)        return xclass Classifier(nn.Module):    def __init__(self, num_classes=10):        super().__init__()        self.conv = nn.Sequential(            ConvBlock(in_channels=3, out_channels=64),            ConvBlock(in_channels=64, out_channels=128),            ConvBlock(in_channels=128, out_channels=256),            ConvBlock(in_channels=256, out_channels=512),        )        self.fc = nn.Sequential(            nn.Dropout(0.4),            nn.Linear(512, 128),            nn.PReLU(),            #nn.BatchNorm1d(128),            nn.Dropout(0.2),            nn.Linear(128, num_classes),        )    def forward(self, x):        x = self.conv(x)        x = torch.mean(x, dim=3)        x, _ = torch.max(x, dim=2)        x = self.fc(x)        return xPATH = "models/model.pt"model = Classifier()model.load_state_dict(torch.load(PATH))model.eval()cry_file_path = "processed_np/car_file.npz"car_tensor = get_file_as_tensor(car_file_path)no_car_file_path = "raw_negative_processed/nocar-1041.npz"no_car_tensor = get_file_as_tensor(no_car_file_path)car_prediction = predict(car_tensor, model)no_cry_prediction = predict(no_car_tensor, model)print("car", car_prediction)print("no car", no_car_prediction)

代码本身已经很清楚了，但 Stack Overflow 要求更多文本。我是机器学习的新手，非常希望能得到一些帮助。

回答：

def predict(tensor, model):    yhat = model(tensor.unsqueeze(0))    yhat = yhat.clone().detach()    return yhat

你应该使用这个方法定义来替换你自己的定义。