Hello, I’m creating a data object for Segmentation problem. For that, I did this:
data = (SegmentationItemList.from_folder(path_img)
.split_none()
.label_from_func(get_y_fn, classes=codes)
.transform(size=380)
.databunch(bs=4,num_workers=1)
.normalize(imagenet_stats))
That creates this object:
ImageDataBunch;
Train: LabelList (700 items)
x: SegmentationItemList
Image (3, 380, 380),Image (3, 380, 380),Image (3, 380, 380),Image (3, 380, 380),Image (3, 380, 380)
y: SegmentationLabelList
ImageSegment (1, 380, 380),ImageSegment (1, 380, 380),ImageSegment (1, 380, 380),ImageSegment (1, 380, 380),ImageSegment (1, 380, 380)
Path: ../data2/test/061_HE_aligned/patches;
Valid: LabelList (0 items)
x: SegmentationItemList
y: SegmentationLabelList
Path: ../data2/test/061_HE_aligned/patches;
Test: None
I have my images that I want to do segmentation on (x), and the masks(y). However, when I do `learn.validate(data.train_dl), I get the following error:
AttributeError: valid_dl
Am I loading my dataset properly? What is the best approach to create a train/validation dataloader?
I would highly appreciate any suggestion