As a solution to this question I posed, I changed an import statement for transforms from
import transforms as T
to
from torchvision import transforms as T
I did this in order to fix this:
def get_transform(train):
transforms = []
# converts the image, a PIL image, into a PyTorch Tensor
transforms.append(T.Resize((400*5312/2988,400)))
transforms.append(T.ToTensor())
if train:
# during training, randomly flip the training images
# and ground-truth for data augmentation
transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)
The linked Q and A can supply more detail.
Now, with the torchvision import, I get a new error:
TypeError: call() takes 2 positional arguments but 3 were given
I dug around and there were similar questions. This new error has to do with transforms.Compose only able to take 2 positional arguments. A suggested solution was to write a custom Compose statement. I added that to my code. Now the transform section has this:
from torchvision import transforms as T
class MyCompose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img, tar):
for t in self.transforms:
img, tar = t(img, tar)
return img, tar
def get_transform(train):
transforms = []
# converts the image, a PIL image, into a PyTorch Tensor
transforms.append(T.Resize((400*5312/2988,400)))
transforms.append(T.ToTensor())
if train:
# during training, randomly flip the training images
# and ground-truth for data augmentation
transforms.append(T.RandomHorizontalFlip(0.5))
return MyCompose(transforms)
Now, I get this error:
TypeError Traceback (most recent call last)
in ()
5 collate_fn=utils.collate_fn)
6 # For Training
----> 7 images,targets = next(iter(data_loader))
8 images = list(image for image in images)
9 targets = [{k: v for k, v in t.items()} for t in targets]3 frames
/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in next(self)
515 if self._sampler_iter is None:
516 self._reset()
→ 517 data = self._next_data()
518 self._num_yielded += 1
519 if self._dataset_kind == _DatasetKind.Iterable and \/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
1197 else:
1198 del self._task_info[idx]
→ 1199 return self._process_data(data)
1200
1201 def _try_put_index(self):/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in _process_data(self, data)
1223 self._try_put_index()
1224 if isinstance(data, ExceptionWrapper):
→ 1225 data.reraise()
1226 return data
1227/usr/local/lib/python3.7/dist-packages/torch/_utils.py in reraise(self)
427 # have message field
428 raise self.exc_type(message=msg)
→ 429 raise self.exc_type(msg)
430
431TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File “/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py”, line 202, in _worker_loop
data = fetcher.fetch(index)
File “/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py”, line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File “/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py”, line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File “”, line 75, in getitem
img, target = self.transforms(img, target)
File “”, line 11, in call
img, tar = t(img, tar)
File “/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py”, line 889, in _call_impl
result = self.forward(*input, **kwargs)
TypeError: forward() takes 2 positional arguments but 3 were given
I don’t know where forward() is called exactly, but it appears to be part of the next(iter(dataset)) execution. More importantly, I don’t know what the positional arguments are. I mean, I expect them to be whatever part of the dataset are outputs in the objects images and targets. I don’t know what in the MyCompose would have changed to add another positional argument. How should I fix this?