'NestedTensor' object has no attribute 'size'

[Aliweka2020]

I am trying to use efficientnet bacbone with DETR but get this error

File "C:\Users\Ali\AppData\Local\Continuum\anaconda3\lib\site-packages\timm\models\layers\padding.py", line 32, in pad_same
ih, iw = x.size()[-2:]
AttributeError: 'NestedTensor' object has no attribute 'size'

I searched for NestedTensor to unpack to get the dimension but useless.

Could anyone help?!

[arekmula]

I have a similar problem. This function requires a single tensor, but somehow I pass a batch of tensors to this. In my case list of tensors, so I have

    ih, iw = x.size()[-2:]
AttributeError: 'list' object has no attribute 'size'

[arekmula]

@Aliweka2020 So, I fixed this problem on my side.

I use train_one_epoch function from engine.py. I needed to adapt those lines:

images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]

loss_dict = model(images, targets)

to this:

        images = torch.stack(images)
        boxes = [target["boxes"].float() for target in targets]
        labels = [target["labels"].float() for target in targets]
        target = {
            "bbox": boxes,
            "cls": labels
        }

        loss_dict = model(images, target)

because as I mentioned, the efficientdet requires tensor (Batch x Channels x Height x Width) not list or NestedTensor.

There are more possible fixes to this problem.

• You can write your own collate_fn function that would do this if you are not using train_one_epoch.
• You can write a wrapper around model() that would transform the NestedTensor to Tensor and then forward the proper call.

[rwightman]

I'm going to move this to discussions since it isn't a bug.

The workound @arekmula provided if you're using a training setup that uses lists or tuples is correct, the 'bench' wrappers for training here expect tensors. Another approach would be to write an alternate Train/Predict bench wrapper (https://github.com/rwightman/efficientdet-pytorch/blob/master/effdet/bench.py#L79) that accept sequences. ie DetBenchTrainSeq / DetBenchPredictSeq ... and modify the factory create fn to accept 'train_seq' and 'predict_seq' as the bench type. I'd accept a PR for that.

For nested tensor, I haven't spent much time with it, but since it's out of PyTorch tree it's not a high priority for me to support but likely can be supported with a similar approach as above (specific bench wrappers).