Hi, i am trying to run your efficient det on my custom dataset. In thee forward pass
TypeError: forward() missing 1 required positional argument: 'target' is thrown. I would kindly ask for your help with this issue.
This is how i call the forward pass from within the pytorch lightning framework:
def training_step(self, batch, batch_idx):
images, targets = batch
targets = [{k: v for k, v in t.items()} for t in targets]
# separate losses
images = torch.stack(images).float()
targets2 = {}
targets2["bbox"] = [
target["boxes"].float() for target in targets
] # variable number of instances, so the entire structure can be forced to tensor
targets2["cls"] = [target["labels"].float() for target in targets]
targets2["image_id"] = torch.tensor(
[target["image_id"] for target in targets]
).float()
targets2["img_scale"] = torch.tensor(
[target["img_scale"] for target in targets]
).float()
targets2["img_size"] = torch.tensor(
[(IMG_SIZE, IMG_SIZE) for target in targets]
).float()
print('targets2:')
print(targets2)
print('type of dict: ', type(targets2))
losses_dict = self.forward(images, targets2)
return {"loss": losses_dict["loss"], "log": losses_dict}
Hi, i am trying to run your efficient det on my custom dataset. In thee forward pass
TypeError: forward() missing 1 required positional argument: 'target'
is thrown. I would kindly ask for your help with this issue.This is how i instantiate the model:
This is how i call the forward pass from within the pytorch lightning framework:
Printed targets2:
{'bbox': [tensor([[139.1957, 137.6129, 210.4661, 204.0420]])], 'cls': [tensor([[0., 0., 0., 1., 0., 0., 0., 0.]])], 'image_id': tensor([47727.]), 'img_scale': tensor([1.]), 'img_size': tensor([[256., 256.]])}
The full callstack is:
Desktop (please complete the following information):