Я намагаюся навчити наступну CNN так, але я все одно отримую ту ж помилку щодо .cuda (), і я не знаю, як її виправити. Ось шматок мого коду поки що.
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')
Однак я продовжую отримувати цю помилку в консолі:
RuntimeError: Тип вводу (факел.FloatTensor) та тип ваги (torch.cuda.FloatTensor) повинні бути однаковими "
Будь-які думки, як це виправити? Я читав, що, можливо, модель не була всунута в мій GPU, але не знаю, як це виправити. Дякую!