我使用转移学习方法来训练模型，并保存了检测到的最佳权重。在另一个脚本中，我尝试使用保存的权重进行预测。但是我收到如下错误。我使用ResNet来微调网络，并且有4个类。
RuntimeError: Error(s) in loading state_dict for ResNet:
size mismatch for fc.bias: copying a param of torch.Size([1000]) from
checkpoint, where the shape is torch.Size([4]) in current model.
size mismatch for fc.weight: copying a param of torch.Size([1000,
512]) from checkpoint, where the shape is torch.Size([4, 512]) in
current model.
我正在使用以下代码来预测输出:
checkpoint = torch.load("./models/custom_model13.model")
model = resnet18(pretrained=True)
model.load_state_dict(checkpoint)
model.eval()
def predict_image(image_path):
transformation = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
image_tensor = transformation(image).float()
image_tensor = image_tensor.unsqueeze_(0)
if torch.cuda.is_available():
image_tensor.cuda()
input = Variable(image_tensor)
output = model(input)
index = output.data.numpy().argmax()
return index
if __name__ == "main":
imagefile = "image.png"
imagepath = os.path.join(os.getcwd(),imagefile)
prediction = predict_image(imagepath)
print("Predicted Class: ",prediction)
以及以下代码来训练和保存模型:
Data_dir = 'Dataset'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
shuffle=True, num_workers=4)
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print (device)
def save_models(epochs, model):
torch.save(model.state_dict(), "custom_model{}.model".format(epochs))
print("Checkpoint Saved")
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'train' and epoch_acc > best_acc:
save_models(epoch,model)
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
return model
def visualize_model(model, num_images=6):
was_training = model.training
model.eval()
images_so_far = 0
fig = plt.figure()
with torch.no_grad():
for i, (inputs, labels) in enumerate(dataloaders['val']):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(num_images//2, 2, images_so_far)
ax.axis('off')
ax.set_title('predicted: {}'.format(class_names[preds[j]]))
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
model.train(mode=was_training)
return
model.train(mode=was_training)
model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 4)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)
最佳答案
原因:
您通过以下方式训练了从resnet18派生的模型:
model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 4)
也就是说，您将最后一个
nn.Linear层的更改为输出4个暗淡预测，而不是默认值1000。当您尝试加载模型以进行预测时，您的代码为:
model = resnet18(pretrained=True)
model.load_state_dict(checkpoint)
您是而不是对
nn.Linear应用了最后一个model图层的相同更改，因此您尝试加载的checkpoint不适合。使固定:
(1)在加载
checkpoint之前应用相同的更改:model = resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 4) # make the change
model.load_state_dict(checkpoint) # load
(2)更好的是，使用
num_classes参数构造具有所需输出数量的resnet，其开头为:model = resnet18(pretrained=True, num_classes=4)
model.load_state_dict(checkpoint) # load