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Hi Kaixin,
It seems that during both training and testing both the support and query images are resized to a fixed size (e.g. [417, 417]). However, in many few-shot segmentation works, the segmentation mask output is resized to the original image resolution for evaluation. How can I get the original query images and the corresponding ground-truth masks during testing phase?
I also output some unused key-value pairs in the dataloader dictionary, but they seem to be of the same fixed shape: sample_batched['support_images_t'][0][0].shape = torch.Size([1, 3, 417, 417]) sample_batched['query_images_t'][0].shape = torch.Size([1, 3, 417, 417]) sample_batched['query_masks'][0][0].shape = torch.Size([1, 1, 417, 417]) sample_batched['query_labels'][0].shape = torch.Size([1, 417, 417])
The text was updated successfully, but these errors were encountered:
Hi Kaixin,
It seems that during both training and testing both the support and query images are resized to a fixed size (e.g. [417, 417]). However, in many few-shot segmentation works, the segmentation mask output is resized to the original image resolution for evaluation. How can I get the original query images and the corresponding ground-truth masks during testing phase?
I also output some unused key-value pairs in the dataloader dictionary, but they seem to be of the same fixed shape:
sample_batched['support_images_t'][0][0].shape = torch.Size([1, 3, 417, 417]) sample_batched['query_images_t'][0].shape = torch.Size([1, 3, 417, 417]) sample_batched['query_masks'][0][0].shape = torch.Size([1, 1, 417, 417]) sample_batched['query_labels'][0].shape = torch.Size([1, 417, 417])
The text was updated successfully, but these errors were encountered: