RuntimeError: Type d'entrée (TORCH.FLOATTENSOR) ET TYPE DE POIDS (TORCH.CUDA.FLOATTENSOR) doit être le même

Question

J'essaie de former le CNN suivant comme suit, mais je continue à obtenir la même erreur concernant .Cuda () et je ne suis pas sûr de la réparer. Voici un morceau de mon code jusqu'à présent.

import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
import torchvision
from torchvision import datasets, transforms, models
from torch.utils.data.sampler import SubsetRandomSampler


data_dir = "/home/ubuntu/ML2/ExamII/train2/"
valid_size = .2

# Normalize the test and train sets with torchvision
train_transforms = transforms.Compose([transforms.Resize(224),
                                           transforms.ToTensor(),
                                           ])

test_transforms = transforms.Compose([transforms.Resize(224),
                                          transforms.ToTensor(),
                                          ])

# ImageFolder class to load the train and test images
train_data = datasets.ImageFolder(data_dir, transform=train_transforms)
test_data = datasets.ImageFolder(data_dir, transform=test_transforms)


# Number of train images
num_train = len(train_data)
indices = list(range(num_train))
# Split = 20% of train images
split = int(np.floor(valid_size * num_train))
# Shuffle indices of train images
np.random.shuffle(indices)
# Subset indices for test and train
train_idx, test_idx = indices[split:], indices[:split]
# Samples elements randomly from a given list of indices
train_sampler = SubsetRandomSampler(train_idx)
test_sampler = SubsetRandomSampler(test_idx)
# Batch and load the images
trainloader = torch.utils.data.DataLoader(train_data, sampler=train_sampler, batch_size=1)
testloader = torch.utils.data.DataLoader(test_data, sampler=test_sampler, batch_size=1)


#print(trainloader.dataset.classes)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = models.resnet50(pretrained=True)

model.fc = nn.Sequential(nn.Linear(2048, 512),
                                 nn.ReLU(),
                                 nn.Dropout(0.2),
                                 nn.Linear(512, 10),
                                 nn.LogSigmoid())
                                 # nn.LogSoftmax(dim=1))
# criterion = nn.NLLLoss()
criterion = nn.BCELoss()
optimizer = optim.Adam(model.fc.parameters(), lr=0.003)
model.to(device)

#Train the network
for Epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (Epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

Cependant, je continue à obtenir cette erreur dans la console:

RuntimeError: Type d'entrée (TORCH.FLOATTENSOR) ET TYPE DE POIDS (TORCH.CUDA.FLOATTENSOR) doit être le même "

Des pensées sur la façon de le réparer? J'ai lu que peut-être que le modèle n'a pas été poussé dans mon GPU, mais je ne sais pas comment le réparer. Merci!

Answer 1

   * when you get this error::RuntimeError: Input type 
   (torch.FloatTensor) and weight type (torch.cuda.FloatTensor should 
   be the same
   # Move tensors to GPU is CUDA is available
   # Check if CUDA is available

  train_on_gpu = torch.cuda.is_available()

  If train_on_gpu:
      print("CUDA is available! Training on GPU...")
  else:
      print("CUDA is not available. Training on CPU...")

 -------------------
 # Move tensors to GPU is CUDA is available
if train_on_gpu:

model.cuda()