Error in masking in the function multiclass_recall

[xiangn95]

🐛 Describe the bug

I think I found a bug at around this part of the code in the functional multiclass_recall. When one of the class is missing for both prediction and label, only the num_tp is masked and not the num_labels, which causes a mismatch between the shape of num_tp and num_labels. For example,

import torch
from torcheval.metrics.functional import multiclass_recall

pred = torch.tensor([0,1,2,5])
label = torch.tensor([0,2,1,3])

num_class = 6

multiclass_recall(pred, label, num_classes=num_class, average="macro")

will get an error of

Traceback (most recent call last):
  File "playground.py", line 1205, in <module>
    multiclass_recall(input1, label1, num_classes=num_class, average="macro")
  File "/opt/conda/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torcheval/metrics/functional/classification/recall.py", line 151, in multiclass_recall
    return _recall_compute(num_tp, num_labels, num_predictions, average)
  File "/opt/conda/lib/python3.7/site-packages/torcheval/metrics/functional/classification/recall.py", line 193, in _recall_compute
    recall = num_tp / num_labels
RuntimeError: The size of tensor a (5) must match the size of tensor b (6) at non-singleton dimension 0

Versions

Collecting environment information... PyTorch version: 1.12.1 Is debug build: False CUDA used to build PyTorch: 11.3 ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64) GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 Clang version: Could not collect CMake version: version 3.26.3 Libc version: glibc-2.17

Python version: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] (64-bit runtime) Python platform: Linux-4.19.91-26.al7.x86_64-x86_64-with-debian-buster-sid Is CUDA available: True CUDA runtime version: 11.3.109 CUDA_MODULE_LOADING set to: GPU models and configuration: GPU 0: Tesla T4 Nvidia driver version: 470.103.01 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.0 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 Byte Order: Little Endian CPU(s): 4 On-line CPU(s) list: 0-3 Thread(s) per core: 2 Core(s) per socket: 2 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz Stepping: 4 CPU MHz: 2499.998 BogoMIPS: 4999.99 Hypervisor vendor: KVM Virtualization type: full L1d cache: 32K L1i cache: 32K L2 cache: 1024K L3 cache: 33792K NUMA node0 CPU(s): 0-3 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 nonstop_tsc cpuid tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat

Versions of relevant libraries: [pip3] mypy==1.2.0 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.21.5 [pip3] torch==1.12.1 [pip3] torcheval==0.0.6 [pip3] torchtext==0.13.1 [pip3] torchtnt==0.0.7 [pip3] torchvision==0.13.1 [pip3] triton==2.0.0.post1 [conda] blas 1.0 mkl
[conda] cudatoolkit 11.3.1 ha36c431_9 nvidia [conda] ffmpeg 4.3 hf484d3e_0 pytorch [conda] mkl 2021.4.0 h06a4308_640
[conda] mkl-service 2.4.0 py37h7f8727e_0
[conda] mkl_fft 1.3.1 py37hd3c417c_0
[conda] mkl_random 1.2.2 py37h51133e4_0
[conda] numpy 1.21.5 py37he7a7128_2
[conda] numpy-base 1.21.5 py37hf524024_2
[conda] pytorch 1.12.1 py3.7_cuda11.3_cudnn8.3.2_0 pytorch [conda] pytorch-mutex 1.0 cuda pytorch [conda] torcheval 0.0.6 pypi_0 pypi [conda] torchtext 0.13.1 py37 pytorch [conda] torchtnt 0.0.7 pypi_0 pypi [conda] torchvision 0.13.1 py37_cu113 pytorch [conda] triton 2.0.0.post1 pypi_0 pypi

[ajyl]

Yeah I agree seems like this is a bug, I think num_label should be updated as num_label = num_label[mask].

[bobakfb]

@ananthsub closing since it seems like this has landed, please reopen if there is more to do.