i am stuck here.anyone help me how to get rid of this error.
here is my code:
criterion = nn.CrossEntropyLoss()
trainset = torchvision.datasets.ImageFolder(root='./train', transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=1,
shuffle=True, num_workers=0)
singletestset = torchvision.datasets.ImageFolder(root='C:\\dataset\\FYP\\train1', transform=transform)
singletestloader = torch.utils.data.DataLoader(singletestset, batch_size=1,
shuffle=False, num_workers=0)
classes = (' meningioma', 'glioma', 'pituitary tumor')
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, 3)
self.fc1 = nn.Linear(64*14*14, 30)
self.fc2 = nn.Linear(30, 3)
# self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x))) # output size: [batch_size, 32, 255, 255]
x = self.pool(F.relu(self.conv2(x))) # output size: [batch_size, 64, 126, 126]
x = x.view(-1, 64*14*14) # output size: [batch_size, 64126126]
print(x.shape)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
# x=self.softmax(x)
return x
model = Net()
state_dict = torch.load('last_brain1.pth')['state_dict']
model.load_state_dict(state_dict)
# =============================================================================
# img = Image.open('920.png')
#
# transform = transforms.ToTensor()
# x = transform(img)
# x = x.unsqueeze(0)
# x = x.expand(-1, 3, -1, -1)
#
# =============================================================================
#output = model(x)
# Forward pass
#pred = torch.argmax(model, 1) # Get predicted class if multi-class classification
#print('Image predicted as ', pred)
loss=0.0
correct=0
total=0.0
itr=0
model.eval()
for images,labels in singletestloader:
images=Variable(images)
labels=Variable(labels)
#CUDA=torch.cuda.is_available()
#if CUDA:
#images=images.cuda()
#labels=labels.cuda()
outputs=model(images)
loss=criterion(outputs,labels)
loss+=loss.item()
_,predicted=torch.max(outputs,1)
total += labels.size(0)
correct+=(predicted==labels).sum()
itr+=1
#testloss.append(loss/itr)
#testaccuracy.append((100*correct/len(singletestset)))
#print('training loss:%f %%' %(itrloss/itr))
# print('training accuracy:%f %%'%(100*correct/len(trainset)))
#print('test loss:%f %%'%(loss/itr))
#print('test accuracy:%f %%'%((100*correct/len(singletestset))))
print('Accuracy of the network on the test images: %d %%' % (
100 * correct / total))
class_correct = list(0 for i in range(3))
class_total = list(0 for i in range(3))
with torch.no_grad():
for data in singletestloader:
images, labels = data
images = Variable(images)
labels = Variable(labels)
outputs = model(images)
_, predicted = torch.max(outputs, 1)
c = (predicted == labels).squeeze()
for i in range(labels.size(0)):
label = labels[0]
class_correct[label] += c[i].item()
class_total[label] += 1
for i in range(3):
print('Accuracy of %5s : %2f %%' % ( classes[i], 100 * class_correct[i] / class_total[i]))
and here is my error screenshot:
