I’m working on the Food-101 dataset and as you may know, the dataset comes with both train and test parts. Because the dataset could no longer be found on the ETH Zurich link, I had to divide them into partitions < 1GB each and clone them into Colab and reassemble. Its very tedious work but I got it working. I will omit the Python code but the file structure looks like this:
Food-101
images
train
...75750 train images
test
...25250 test images
meta
classes.txt
labes.txt
test.json
test.txt
train.json
train.txt
README.txt
license_agreement.txt
The following code is what’s throwing the runtime error
train_image_path = Path('images/train/')
test_image_path = Path('images/test/')
path = Path('../Food-101')
food_names = get_image_files(train_image_path)
file_parse = r'/([^/]+)_\d+\.(png|jpg|jpeg)'
data = ImageDataBunch.from_folder(train_image_path, test_image_path, valid_pct=0.2, ds_tfms=get_transforms(), size=224)
data.normalize(imagenet_stats)
My guess is that ImageDataBunch.from_folder() is what’s throwing the error but I don’t know why its getting caught up on the data types as (I don’t think) I’m supplying it with any data that has a specific type.
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
warnings.warn("The default behavior for interpolate/upsample with float scale_factor will change "
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
warnings.warn("The default behavior for interpolate/upsample with float scale_factor will change "
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
warnings.warn("The default behavior for interpolate/upsample with float scale_factor will change "
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
warnings.warn("The default behavior for interpolate/upsample with float scale_factor will change "
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
warnings.warn("The default behavior for interpolate/upsample with float scale_factor will change "
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
warnings.warn("The default behavior for interpolate/upsample with float scale_factor will change "
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
warnings.warn("The default behavior for interpolate/upsample with float scale_factor will change "
You can deactivate this warning by passing `no_check=True`.
/usr/local/lib/python3.6/dist-packages/fastai/basic_data.py:262: UserWarning: There seems to be something wrong with your dataset, for example, in the first batch can't access these elements in self.train_ds: 9600,37233,16116,38249,1826...
warn(warn_msg)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py in __call__(self, obj)
697 type_pprinters=self.type_printers,
698 deferred_pprinters=self.deferred_printers)
--> 699 printer.pretty(obj)
700 printer.flush()
701 return stream.getvalue()
11 frames
/usr/local/lib/python3.6/dist-packages/fastai/vision/image.py in affine(self, func, *args, **kwargs)
181 "Equivalent to `image.affine_mat = image.affine_mat @ func()`."
182 m = tensor(func(*args, **kwargs)).to(self.device)
--> 183 self.affine_mat = self.affine_mat @ m
184 return self
185
RuntimeError: Expected object of scalar type Float but got scalar type Double for argument #3 'mat2' in call to _th_addmm_out
I had also faced the same error and used no_check=True in your ImageDataBunch arguments.
Try using this before you create ImageDataBunch
import warnings
warnings.filterwarnings("ignore", category=UserWarning, module="torch.nn.functional")
Make sure that you downgrade your torch version to 1.0.0,