FastAI 在使用自定义训练和测试集时抛出运行时错误
FastAI throwing a runtime error when using custom train & test sets
我正在研究 Food-101 数据集，您可能知道，该数据集包含训练部分和测试部分。因为在苏黎世联邦理工学院 link 上找不到数据集，我不得不将它们分成每个 < 1GB 的分区，并将它们克隆到 Colab 中并重新组装。这是非常乏味的工作，但我让它工作了。我将省略 Python 代码，但文件结构如下所示：
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
以下代码是抛出运行时错误的原因
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)
我的猜测是 ImageDataBunch.from_folder() 是抛出错误的原因，但我不知道为什么它会陷入数据类型，因为（我不认为）我正在向它提供任何数据具有特定类型。
/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
我也遇到了同样的错误并在你的 ImageDataBunch 参数中使用了 no_check=True。
在创建 ImageDataBunch 之前尝试使用它
import warnings
warnings.filterwarnings("ignore", category=UserWarning, module="torch.nn.functional")
确保将 Torch 版本降级到 1.0.0，
我正在研究 Food-101 数据集，您可能知道，该数据集包含训练部分和测试部分。因为在苏黎世联邦理工学院 link 上找不到数据集，我不得不将它们分成每个 < 1GB 的分区，并将它们克隆到 Colab 中并重新组装。这是非常乏味的工作，但我让它工作了。我将省略 Python 代码，但文件结构如下所示：
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
以下代码是抛出运行时错误的原因
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)
我的猜测是 ImageDataBunch.from_folder() 是抛出错误的原因，但我不知道为什么它会陷入数据类型，因为（我不认为）我正在向它提供任何数据具有特定类型。
/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
我也遇到了同样的错误并在你的 ImageDataBunch 参数中使用了 no_check=True。
在创建 ImageDataBunch 之前尝试使用它
import warnings
warnings.filterwarnings("ignore", category=UserWarning, module="torch.nn.functional")
确保将 Torch 版本降级到 1.0.0，