Comments (2)
Hi @Peter-SungwooCho, The reason I think the model may not be showing the desired accuracy during inference is if different hyperparameters (such as hidden_dim
) were used during training and when loading the saved model." This is only a random guess. But it would be helpful if you could show me the code where you have tried to load the saved model. (keeping in mind The problem is only while loading the saved model and there is no overfitting).
from pytorch-cifar.
Hi @Peter-SungwooCho, The reason I think the model may not be showing the desired accuracy during inference is if different hyperparameters (such as
hidden_dim
) were used during training and when loading the saved model." This is only a random guess. But it would be helpful if you could show me the code where you have tried to load the saved model. (keeping in mind The problem is only while loading the saved model and there is no overfitting).
Hello, I face the same problem. This is my code:
device = torch.device("cuda")
full_model = ResNet18().to(device)
#print(full_model)
checkpoint = torch.load("./checkpoint/ckpt.pth")
print(checkpoint)
full_model.load_state_dict(checkpoint['net'],strict=False) #,strict=False
full_model.to(device)
full_model.eval()
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
test_dataset = torchvision.datasets.CIFAR10("./data", train=False, transform=transform_test, download=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=1)
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(test_loader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = full_model(inputs)
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
progress_bar(batch_idx, len(test_loader), 'Acc: %.3f%% (%d/%d)'
% (100.*correct/total, correct, total))
from pytorch-cifar.
Related Issues (20)
- pytorch-cifar复现问题 HOT 4
- MobileNetV2 training does not converge HOT 1
- The test set is being used as validation set HOT 6
- 对于精度有疑问 HOT 2
- pre-train weights
- request: epochs numbers to converge in readme.md HOT 1
- ResNet18 performs much better than expected! HOT 5
- how to train my own dataset and classes? HOT 1
- [Question] efficientnet performance HOT 1
- Error when loading the weights on CPU but trained on GPU HOT 1
- Errors when testing on CPU HOT 11
- checkpoint
- ValueError: not enough values to unpack (expected 2, got 0) HOT 3
- load state_dict HOT 1
- Overfitting on ResNet18 HOT 2
- A problem in ShuffleNet
- How long will it take you to train the cifar10 model? HOT 1
- Overfitting
- Adding more flags to main.py
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from pytorch-cifar.