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python - PyTorch 中 fc.bias 和 fc.weight 的大小不匹配

转载 作者:太空宇宙 更新时间:2023-11-04 04:25:21 27 4
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我使用迁移学习方法训练模型并保存检测到的最佳权重。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 微调网络并有 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.

我使用以下代码预测输出:

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)

以及以下用于训练和保存模型的代码:

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:

您以这种方式训练了从 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,您更改最后一个 nn.Linear 层以输出 4 个暗淡预测而不是默认的 1000 个。
当您尝试加载模型进行预测时,您的代码是:

model = resnet18(pretrained=True)    
model.load_state_dict(checkpoint)

没有将最后一个nn.Linear 层的相同更改应用于模型,因此检查点您正在尝试加载不适合。

修复:

(1) 在加载检查点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 参数来构造 resnet,并以所需的输出数量开始:

model = resnet18(pretrained=True, num_classes=4)  
model.load_state_dict(checkpoint) # load

关于python - PyTorch 中 fc.bias 和 fc.weight 的大小不匹配,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/53612835/

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