I am trying to load data for a segmentation task, using the very classic following function:
class MultiClassSegList(SegmentationLabelList):
def open(self, id_rles):
image_id, rles = id_rles[0], id_rles[1:]
shape = open_image(self.path/image_id).shape[-2:]
final_mask = torch.zeros((1, *shape)).long()
for k, rle in enumerate(rles):
if isinstance(rle, str):
mask = open_mask_rle(rle, shape).data.permute(0, 2, 1)
final_mask += (k+1)*mask
return ImageSegment(final_mask)
def load_data(path, csv, bs=32, size=(128, 800)):
train_list = (SegmentationItemList.
from_csv(path, csv).
split_by_rand_pct(valid_pct=0.2).
label_from_df(cols=list(range(5)), label_cls=MultiClassSegList, classes=[0, 1, 2, 3, 4]).
transform(get_transforms(), size=size, tfm_y=True).
databunch(bs=bs, num_workers=0).
normalize(imagenet_stats))
return train_list
I keep getting the following error:
RuntimeError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/fastai/data_block.py in _check_kwargs(ds, tfms, **kwargs)
590 x = ds[0]
--> 591 try: x.apply_tfms(tfms, **kwargs)
592 except Exception as e:
/opt/conda/lib/python3.6/site-packages/fastai/vision/image.py in apply_tfms(self, tfms, do_resolve, xtra, size, resize_method, mult, padding_mode, mode, remove_out)
121 if resize_method in (ResizeMethod.CROP,ResizeMethod.PAD):
--> 122 x = tfm(x, size=_get_crop_target(size,mult=mult), padding_mode=padding_mode)
123 else: x = tfm(x)
/opt/conda/lib/python3.6/site-packages/fastai/vision/image.py in __call__(self, x, *args, **kwargs)
517 "Randomly execute our tfm on `x`."
--> 518 return self.tfm(x, *args, **{**self.resolved, **kwargs}) if self.do_run else x
519
/opt/conda/lib/python3.6/site-packages/fastai/vision/image.py in __call__(self, p, is_random, use_on_y, *args, **kwargs)
463 "Calc now if `args` passed; else create a transform called prob `p` if `random`."
--> 464 if args: return self.calc(*args, **kwargs)
465 else: return RandTransform(self, kwargs=kwargs, is_random=is_random, use_on_y=use_on_y, p=p)
/opt/conda/lib/python3.6/site-packages/fastai/vision/image.py in calc(self, x, *args, **kwargs)
468 "Apply to image `x`, wrapping it if necessary."
--> 469 if self._wrap: return getattr(x, self._wrap)(self.func, *args, **kwargs)
470 else: return self.func(x, *args, **kwargs)
/opt/conda/lib/python3.6/site-packages/fastai/vision/image.py in pixel(self, func, *args, **kwargs)
171 "Equivalent to `image.px = func(image.px)`."
--> 172 self.px = func(self.px, *args, **kwargs)
173 return self
/opt/conda/lib/python3.6/site-packages/fastai/vision/image.py in px(self)
144 "Get the tensor pixel buffer."
--> 145 self.refresh()
146 return self._px
/opt/conda/lib/python3.6/site-packages/fastai/vision/image.py in refresh(self)
229 self.sample_kwargs['mode'] = 'nearest'
--> 230 return super().refresh()
231
/opt/conda/lib/python3.6/site-packages/fastai/vision/image.py in refresh(self)
131 if self._affine_mat is not None or self._flow is not None:
--> 132 self._px = _grid_sample(self._px, self.flow, **self.sample_kwargs)
133 self.sample_kwargs = {}
/opt/conda/lib/python3.6/site-packages/fastai/vision/image.py in _grid_sample(x, coords, mode, padding_mode, remove_out)
534 if d>1 and d>z: x = F.interpolate(x[None], scale_factor=1/d, mode='area')[0]
--> 535 return F.grid_sample(x[None], coords, mode=mode, padding_mode=padding_mode)[0]
536
/opt/conda/lib/python3.6/site-packages/torch/nn/functional.py in grid_sample(input, grid, mode, padding_mode)
2716
-> 2717 return torch.grid_sampler(input, grid, mode_enum, padding_mode_enum)
2718
RuntimeError: grid_sampler(): expected input and grid to have same dtype, but input has long and grid has float
During handling of the above exception, another exception occurred:
Exception Traceback (most recent call last)
<ipython-input-39-e7670957d52d> in <module>
----> 1 db = load_data(TRAIN_PATH, LABELS, bs=BATCH_SIZE, size=TRAIN_SIZE)
<ipython-input-36-15e1d1ba275a> in load_data(path, csv, bs, size)
4 split_by_rand_pct(valid_pct=0.2).
5 label_from_df(cols=list(range(5)), label_cls=MultiClassSegList, classes=[0, 1, 2, 3, 4]).
----> 6 transform(get_transforms(), size=size, tfm_y=True).
7 databunch(bs=bs, num_workers=0).
8 normalize(imagenet_stats))
/opt/conda/lib/python3.6/site-packages/fastai/data_block.py in transform(self, tfms, **kwargs)
500 if not tfms: tfms=(None,None)
501 assert is_listy(tfms) and len(tfms) == 2, "Please pass a list of two lists of transforms (train and valid)."
--> 502 self.train.transform(tfms[0], **kwargs)
503 self.valid.transform(tfms[1], **kwargs)
504 if self.test: self.test.transform(tfms[1], **kwargs)
/opt/conda/lib/python3.6/site-packages/fastai/data_block.py in transform(self, tfms, tfm_y, **kwargs)
722 if tfm_y is None: tfm_y = self.tfm_y
723 tfms_y = None if tfms is None else list(filter(lambda t: getattr(t, 'use_on_y', True), listify(tfms)))
--> 724 if tfm_y: _check_kwargs(self.y, tfms_y, **kwargs)
725 self.tfms,self.tfmargs = tfms,kwargs
726 self.tfm_y,self.tfms_y,self.tfmargs_y = tfm_y,tfms_y,kwargs
/opt/conda/lib/python3.6/site-packages/fastai/data_block.py in _check_kwargs(ds, tfms, **kwargs)
591 try: x.apply_tfms(tfms, **kwargs)
592 except Exception as e:
--> 593 raise Exception(f"It's not possible to apply those transforms to your dataset:\n {e}")
594
595 class LabelList(Dataset):
Exception: It's not possible to apply those transforms to your dataset:
grid_sampler(): expected input and grid to have same dtype, but input has long and grid has float
I understand my labels are of type long and my inputs of type float, but I don’t understand how it suddenly becomes a problem, while it works perfectly with the camvid notebook from the course for instance. There is probably a problem with my custom class but I can’t see where it comes from.