In transforms.Resize, tensor interpolate is not the same as PIL resize.

🐛 Bug

Resize supports tensors by F.interpolate, but the behavior is not the same as Pillow resize. https://github.com/pytorch/vision/blob/f95b0533243dfbc901b5ed5f5db28a5a46bdb699/torchvision/transforms/functional.py#L309-L312

To Reproduce

Steps to reproduce the behavior:

import urllib
from PIL import Image
from torchvision import transforms
from matplotlib import pyplot as plt

size = 112
img = Image.open(urllib.request.urlopen("https://pytorch.org/tutorials/_static/img/tv_tutorial/tv_image01.png"))

tensor_interpolate = transforms.Compose([transforms.ToTensor(), transforms.Resize(size), transforms.ToPILImage()])
pillow_resize = transforms.Compose([transforms.Resize(size)])

plt.subplot(311)
plt.imshow(img)
plt.title("original")
plt.subplot(312)
plt.imshow(tensor_interpolate(img))
plt.title("tensor interpolate")
plt.subplot(313)
plt.imshow(pillow_resize(img))
plt.title("pillow resize")
plt.show()

Expected behavior

Both should have the same or nearly identical output. Perhaps, it needs blur before interpolate.

Environment

I installed pytorch using the following command: conda install pytorch torchvision -c pytorch

python collect_env.py Collecting environment information… PyTorch version: 1.7.0 Is debug build: True CUDA used to build PyTorch: 11.0 ROCM used to build PyTorch: N/A

OS: Microsoft Windows 10 Home GCC version: (MinGW.org GCC-8.2.0-3) 8.2.0 Clang version: Could not collect CMake version: version 3.18.2

Python version: 3.8 (64-bit runtime) Is CUDA available: True CUDA runtime version: 10.0.130 GPU models and configuration: GPU 0: GeForce RTX 2060 Nvidia driver version: 456.38 cuDNN version: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin\cudnn64_7.dll HIP runtime version: N/A MIOpen runtime version: N/A

Versions of relevant libraries: [pip3] numpy==1.19.2 [pip3] torch==1.7.0 [pip3] torchvision==0.8.1 [conda] blas 1.0 mkl [conda] cudatoolkit 11.0.221 h74a9793_0 [conda] mkl 2020.2 256 [conda] mkl-service 2.3.0 py38hb782905_0 [conda] mkl_fft 1.2.0 py38h45dec08_0 [conda] mkl_random 1.1.1 py38h47e9c7a_0 [conda] numpy 1.19.2 py38hadc3359_0 [conda] numpy-base 1.19.2 py38ha3acd2a_0 [conda] pytorch 1.7.0 py3.8_cuda110_cudnn8_0 pytorch [conda] torchvision 0.8.1 py38_cu110 pytorch

cc @vfdev-5

[vfdev-5]

@iynaur since version 0.10.0 we added antialias option to produce similar results with tensors. Please, check out the following code:

import torch
import torchvision
print(torch.__version__, torchvision.__version__)
import matplotlib.pyplot as plt

import urllib
import torchvision.transforms.functional as f
from PIL import Image as Image


url = "https://user-images.githubusercontent.com/3275025/123925242-4c795b00-d9bd-11eb-9f0c-3c09a5204190.jpg"
img = Image.open(
    urllib.request.urlopen(url)
)

t_img = f.to_tensor(img)

img_small_pil = f.resize(img, 128, interpolation=Image.BILINEAR)
img_small_aa = f.to_pil_image(f.resize(t_img, 128, interpolation=Image.BILINEAR, antialias=True))
img_small = f.to_pil_image(f.resize(t_img, 128, interpolation=Image.BILINEAR, antialias=False))



plt.figure(figsize=(3 * 8, 8))
plt.subplot(131)
plt.title("PIL")
plt.imshow(img_small_pil)
plt.subplot(132)
plt.title("Tensor with antialias")
plt.imshow(img_small_aa)
plt.subplot(133)
plt.title("Tensor without antialias")
plt.imshow(img_small)

> 1.9.0 0.10.0

[tcapelle]

I wrote a summary of the issue here: https://tcapelle.github.io/pytorch/fastai/2021/02/26/image_resizing.html