to_image does not handle numpy 2D arrays #8255
Description
🐛 Describe the bug
to_image should be able to handle numpy arrays with shape (H, W). This corresponds to the previous behavior of to_tensor. Running the following:
import numpy as np from torchvision.transforms.v2.functional import to_image img_npy = np.random.randint(0, 256, (224, 224), dtype=np.uint8) to_image(img_npy)results in error:
Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/mantasu/programs/anaconda/envs/glasses-detector-312/lib/python3.12/site-packages/torchvision/transforms/v2/functional/_type_conversion.py", line 14, in to_image output = torch.from_numpy(inpt).permute((2, 0, 1)).contiguous() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: permute(sparse_coo): number of dimensions in the tensor input does not match the length of the desired ordering of dimensions i.e. input.dim() = 2 is not equal to len(dims) = 3 PIL grayscale images are handled correctly:
from PIL import Image img_pil = Image.fromarray(img_npy) print(to_image(img_pil).shape) # (1, 224, 224)Versions
PyTorch version: 2.2.0 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35 Python version: 3.11.7 | packaged by conda-forge | (main, Dec 23 2023, 14:43:09) [GCC 12.3.0] (64-bit runtime) Python platform: Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080 Ti Laptop GPU Nvidia driver version: 546.33 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 39 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 24 On-line CPU(s) list: 0-23 Vendor ID: GenuineIntel Model name: 12th Gen Intel(R) Core(TM) i7-12800HX CPU family: 6 Model: 151 Thread(s) per core: 2 Core(s) per socket: 12 Socket(s): 1 Stepping: 2 BogoMIPS: 4607.99 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq vmx ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves avx_vnni umip waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm serialize flush_l1d arch_capabilities Virtualization: VT-x Hypervisor vendor: Microsoft Virtualization type: full L1d cache: 576 KiB (12 instances) L1i cache: 384 KiB (12 instances) L2 cache: 15 MiB (12 instances) L3 cache: 25 MiB (1 instance) Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==1.26.3 [pip3] torch==2.2.0 [pip3] torchaudio==2.2.0 [pip3] torchvision==0.17.0 [pip3] triton==2.2.0 [conda] blas 1.0 mkl conda-forge [conda] ffmpeg 4.3 hf484d3e_0 pytorch [conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch [conda] mkl 2023.1.0 h213fc3f_46344 [conda] numpy 1.26.3 py311h64a7726_0 conda-forge [conda] pytorch 2.2.0 py3.11_cuda12.1_cudnn8.9.2_0 pytorch [conda] pytorch-cuda 12.1 ha16c6d3_5 pytorch [conda] pytorch-mutex 1.0 cuda pytorch [conda] torchaudio 2.2.0 py311_cu121 pytorch [conda] torchtriton 2.2.0 py311 pytorch [conda] torchvision 0.17.0 py311_cu121 pytorch