I am working on an object detection task and I used a dataset where the boxes dimensions were provided as strings so I converted it to float using the following command.
@albanD I tried that , that is, using double tensor and it ran successfully on the train_set but giving the same error for the validation set which is being loaded from the same dataset class and I have also kept the model same for it.Actually I have written the train and validation set loss computation under the same function using the same model.
model = model.double()
def train(model, trainloader, validloader, optimizer, num_epochs=5):
i=0;
for epoch in tqdm(range(num_epochs)):
i+=1
train_loss_history = []
valid_loss_history = []
print("Epoch :", i)
for images, targets, image_ids in tqdm(trainloader):
images = list((torch.DoubleTensor(img.T)).to(device) for img in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
train_loss_dict = model(images, targets)
train_losses = sum(loss for loss in train_loss_dict.values())
train_loss_history.append(train_losses)
optimizer.zero_grad()
train_losses.backward()
optimizer.step()
plt.plot(np.arange(len(train_loss_history)), train_loss_history)
for images, targets, image_ids in tqdm(validloader):
images = list((torch.DoubleTensor(img.T)).to(device) for img in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
valid_loss_dict = model(images, targets)
valid_losses = sum(loss for loss in valid_loss_dict.values())
valid_loss_history.append(valid_losses)
plt.plot(np.arange(len(valid_loss_history)), valid_loss_history)
return model
If you want I can also share the my google colab link. Thanks in advance
But in your code, why do you use torch.DoubleTensor() on the result of the dataloader? Aren’t these Tensors already?
If they are numpy arrays, you should use torch.from_numpy() to get Tensors out of them.
