Unify metrics output type · pytorch/ignite#1635

(12 comments) (0 reactions) (0 assignees)Python (4,313 stars) (602 forks)batch import

HacktoberfestPyDataGlobaldocsenhancementhelp wantedmodule: metricsneeds-discussion

Description

🚀 Feature

The idea is to verify the output type for all metrics (output of compute function) and update the docs accordingly.

In general, metric's output should be a float number. In some particular cases, like Recall/Precision with average=False, the output is a torch tensor. So, let's see and decide if the output of compute() method can be :

def compute() -> float
def compute() -> Union[float, torch.Tensor] and tensor is on CPU
def compute() -> torch.Tensor with tensor on CPU

To address this FR, we have to make sure for each metric what kind of type it supposes to return and update the docs accordingly.

Contributor guide

Tech stack: pythonpytorch
Domain: apibackend
Issue type: feature
Difficulty: 3
Estimated time: 3-5 days
Activity status: stale
Clarity: mostly clear
Prerequisites: PyTorch basicsPython type hints
Newbie friendliness: 35
Research direction: Start by listing all metrics in the repository under ignite/metrics/. For each metric, examine the compute method's return type and relevant tests. Then decide on the unified type based on the options provided. Update the documentation in docs/source/metrics.rst accordingly.