输入类型（torch.FloatTensor）和权重类型（torch.cuda.FloatTensor）应该相同

Question

****我将我的模型和数据设置为同一个设备，但总是引发如下错误： RuntimeError：输入类型（torch.FloatTensor）和权重类型（torch.cuda.FloatTensor）应该相同** 以下是训练代码**

total_epoch = 1   
best_epoch = 0
training_losses = []
val_losses = []

for epoch in range(total_epoch):

    epoch_train_loss = 0

    for X, y in train_loader:

    X, y = X.cuda(), y.cuda()

    optimizer.zero_grad()
    result = model(X)
    loss = criterion(result, y).
    epoch_train_loss += loss.item()
    loss.backward()
    optimizer.step()

training_losses.append(epoch_train_loss)


epoch_val_loss = 0
correct = 0
total = 0

with torch.no_grad():
    for X, y in val_loader:

        X, y = X.cuda(), y.cuda()

        result = model(X)
        loss = criterion(result, y)
        epoch_val_loss += loss.item()
        _, maximum = torch.max(result.data, 1)
        total += y.size(0)
        correct += (maximum == y).sum().item()

val_losses.append(epoch_val_loss)
accuracy = correct/total
print("EPOCH:", epoch, ", Training Loss:", epoch_train_loss, ", Validation Loss:", epoch_val_loss, ", Accuracy: ", accuracy)


if min(val_losses) == val_losses[-1]:
    best_epoch = epoch
    checkpoint = {'model': model,
                        'state_dict': model.state_dict(),
                        'optimizer' : optimizer.state_dict()}

    torch.save(checkpoint, models_dir + '{}.pth'.format(epoch))
    print("Model saved")

当我使用cv2.capture(0) 运行以下代码进行检测时。

import cvlib as cv
from PIL import Image

cap = cv2.VideoCapture(0)

font_scale=1
thickness = 2
red = (0,0,255)
green = (0,255,0)
blue = (255,0,0)
font=cv2.FONT_HERSHEY_SIMPLEX

face_cascade = cv2.CascadeClassifier( cv2.data.haarcascades +'haarcascade_frontalface_default.xml')

while(cap.isOpened()):
    ret, frame = cap.read()
    if ret == True:

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.4, 4)

    for (x, y, w, h) in faces:

        cv2.rectangle(frame, (x, y), (x+w, y+h), blue, 2)

        croped_img = frame[y:y+h, x:x+w]
        pil_image = Image.fromarray(croped_img, mode = "RGB")
        pil_image = train_transforms(pil_image)
        image = pil_image.unsqueeze(0)


        result = loaded_model(image)
        _, maximum = torch.max(result.data, 1)
        prediction = maximum.item()


        if prediction == 0:
            cv2.putText(frame, "Masked", (x,y - 10), font, font_scale, green, thickness)
            cv2.rectangle(frame, (x, y), (x+w, y+h), green, 2)
        elif prediction == 1:
            cv2.putText(frame, "No Mask", (x,y - 10), font, font_scale, red, thickness)
            cv2.rectangle(frame, (x, y), (x+w, y+h), red, 2)

    cv2.imshow('frame',frame)

    if (cv2.waitKey(1) & 0xFF) == ord('q'):
        break
else:
    break

cap.release() cv2.destroyAllWindows()

关于函数loaded_model声明如下

def load_checkpoint(filepath):
checkpoint = torch.load(filepath)
model = checkpoint['model']
model.load_state_dict(checkpoint['state_dict'])
for parameter in model.parameters():
parameter.requires_grad = False



return model.eval()


filepath = models_dir + str(best_epoch) + ".pth"
loaded_model = load_checkpoint(filepath)

错误：

---------------------------------------------------------------------------

RuntimeError                              Traceback (most recent call last)

<ipython-input-29-b3a630684f44> in <module>()
     43 
     44 
---> 45             result = loaded_model(image)
     46             _, maximum = torch.max(result.data, 1)
     47             prediction = maximum.item()

5 frames

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
    344                             _pair(0), self.dilation, self.groups)
    345         return F.conv2d(input, weight, self.bias, self.stride,
--> 346                         self.padding, self.dilation, self.groups)
    347 
    348     def forward(self, input):

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same

**I hope you can answer it.Thanks!**

Answer 1

模型的权重在 GPU 上，而 image 在 CPU 上。你也需要把它放到 GPU 上。

image = pil_image.unsqueeze(0)
image = image.cuda()

result = loaded_model(image)

看起来您并没有手动将模型放到 GPU 上，而是您保存了模型的权重，这些权重最初是在 GPU 上的，PyTorch 在保存状态字典时会保留设备信息。如果要在 CPU 上运行模型，则应确保权重在 CPU 上。 torch.load 接受 map_location 参数，该参数强制加载的数据在指定设备上，而不是使用保存的设备。

# Load weights onto the CPU regardless of saved device.
checkpoint = torch.load(filepath, map_location="cpu")