Inconsistent API for tensor and PIL image for CenterCrop

🐛 Bug

CenterCrop transform crops image center. However, when crop size is smaller than image size(in any dimension), the results are different for PIL.Image input and tensor input.

For PIL.Image, it does padding, while for tensor, the results are completely wrong, due to incorrect crop location computation(negative position values computed).

To Reproduce

Steps to reproduce the behavior:

import torch
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF

pil_x = TF.to_pil_image(torch.randn(3, 24, 24))  
transforms.CenterCrop(50)(pil_x).size  # PIL image size is 50x50

tensor_x = torch.randn(3, 24, 24)
transforms.CenterCrop(50)(tensor_x).shape  # Tensor shape is torch.Size([3, 12, 12])

Expected behavior

Mainly, API should be consistent for both inputs or there should be two different methods for PIL & Tensor input(which is IMO less appealing). Side note - docs should be updated for what happens when crop_size is greater than image size. This condition, I believe is missing in documentation of other crop methods as well.

Environment

Collecting environment information...
PyTorch version: 1.7.0
Is debug build: True
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.10.2

Python version: 3.8 (64-bit runtime)
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: Quadro T2000
Nvidia driver version: 450.102.04
cuDNN version: Could not collect
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] torchaudio==0.7.0a0+ac17b64
[pip3] torchvision==0.8.1
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               10.2.89              hfd86e86_1  
[conda] mkl                       2020.2                      256  
[conda] mkl-service               2.3.0            py38he904b0f_0  
[conda] mkl_fft                   1.2.0            py38h23d657b_0  
[conda] mkl_random                1.1.1            py38h0573a6f_0  
[conda] numpy                     1.19.2           py38h54aff64_0  
[conda] numpy-base                1.19.2           py38hfa32c7d_0  
[conda] pytorch                   1.7.0           py3.8_cuda10.2.89_cudnn7.6.5_0    pytorch
[conda] torchaudio                0.7.0                      py38    pytorch
[conda] torchvision               0.8.1                py38_cu102    pytorch

cc @vfdev-5

[vfdev-5]

Sounds good! Please, take a look at our contributing guide which may help you with the dev setup etc. A draft PR is also good such that we can iterate over the implementation if needed and do not hesitate to ask questions here for details.

[saurabheights]

No worries and thank you for pad_symmetric, it would work fine here.

Tested on PIL version ‘6.2.1’(found this is too old, so retested on newer versions 😄) and ‘7.2.0’. And yep, there was padding in both.

Regarding ETA - I will hopefully make changes in 3/4 days(its only few hour work but working first time on vision lib, so might get delayed in setup by a little).