Float PILImage not converted as writeable

🐛 Bug

When we have a float PIL-Image (e.g. mode='F'), e.g. for the purpose of applying transforms to it, and finally convert it with ToTensor then it will print the warning

/opt/conda/conda-bld/pytorch_1587428094786/work/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.

The writeability of the numpy array got lost somewhere when converting a numpy array to PILImage with Image.fromarray(numpy_array, mode='F') and then after some transforms to a tensor with ToTensor.
This does not happen with PIL Images other than float (e.g. mode='RGB').

This warning is especially annoying since it gets printed every epoch.

To Reproduce

Steps to reproduce the behavior:

    from torchvision.transforms import ToTensor
    import numpy as np
    from PIL import Image
    a = np.array([[1.0,0.5], [1.0,0.5]])
    print(a.flags.writeable)
    pilimg = Image.fromarray(a, mode='F')
    tensor = ToTensor()(pilimg)
    print(tensor.numpy().flags)

    b = np.asarray(pilimg)
    c = np.array(pilimg)
    print(b.flags)
    print(c.flags)

This code will print above warning.

Also note the following:

• the numpy array b is NOT writeable, the numpy array c is writeable.
• This suggests that the error is located in the conversion from numpy to PIL.
• But since I do not have the easy possibility to convert PIL to numpy and then to tensor within a transforms Compose and since numpy array c is writeable, I open this issue here.

As workaround I do the following:

class ToNumpy(object):
    def __call__(self, sample):
        return np.array(sample)

def fix_compose_transform(transform):
        if isinstance(transform.transforms[-1], torchvision.transforms.ToTensor):
            transform = torchvision.transforms.Compose([
                *transform.transforms[:-1],
                ToNumpy(),
                torchvision.transforms.ToTensor()
            ])
        return transform

Expected behavior

Warning is not printed and ToTensor method can deal with the misbehaviour of PIL image.

Environment

Collecting environment information...
PyTorch version: 1.5.0
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Ubuntu 18.04.4 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: version 3.10.2

Python version: 3.8
Is CUDA available: Yes
CUDA runtime version: 10.1.243
GPU models and configuration: GPU 0: GeForce GTX 970
Nvidia driver version: 418.87.01
cuDNN version: Could not collect

Versions of relevant libraries:
[pip3] numpy==1.18.3
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               10.1.243             h6bb024c_0  
[conda] mkl                       2020.0                      166  
[conda] mkl-service               2.3.0            py38he904b0f_0  
[conda] mkl_fft                   1.0.15           py38ha843d7b_0  
[conda] mkl_random                1.1.0            py38h962f231_0  
[conda] numpy                     1.18.1           py38h4f9e942_0  
[conda] numpy-base                1.18.1           py38hde5b4d6_1  
[conda] numpydoc                  0.9.2                      py_0  
[conda] pytorch                   1.5.0           py3.8_cuda10.1.243_cudnn7.6.3_0    pytorch
[conda] pytorch3d                 0.1.1                    pypi_0    pypi
[conda] torchvision               0.6.0                py38_cu101    pytorch

Start at torchvision.transforms.ToTensor and reproduce the reported conversion with a float PIL image in mode='F', comparing it with the RGB case and the writable copy from np.array. Done means the float-image conversion no longer emits the non-writeable NumPy warning while preserving the expected tensor result.