Error in dimensions of model outputs while running for inference

[sujithvemi]

Hi,

Thanks for this repo, the work is really interesting. I tried to run the given code for the sample data provided and had run into the following problem.

Encode clothing features
Image shape: torch.Size([1, 3, 256, 256])
Label shape: torch.Size([1, 1, 256, 256])
/content/drive/My Drive/projects/FashionPlus/generation/models/pix2pixHD_model.py:407: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead.
image = Variable(image.cuda(), volatile=True)
Traceback (most recent call last):
File "./encode_clothing_features.py", line 58, in
feat = model.module.encode_features(data['image'], data['label'])
File "/content/drive/My Drive/projects/FashionPlus/generation/models/pix2pixHD_model.py", line 423, in encode_features
val[0, k] = feat_map[idx[0], idx[1] + k, idx[2], idx[3]].data[0]
IndexError: invalid index of a 0-dim tensor. Use tensor.item() in Python or tensor.item<T>() in C++ to convert a 0-dim tensor to a number

I have noticed that you are using "TrainOptions" and don't have an inference script which uses "TestOptions" instead. Is this error potentially due to some parameter problems because of those options? Also, can you please let me know if there are any plans for providing an updated codebase, I think it was mentioned in some other issue that you are planning to.

Thanks in advance.