`MinMaxMetric` state not saved in the checkpoint

## 🐛 Bug `MinMaxMetric` stores `min_val` and `max_val` as plain tensor attributes rather than registered buffers (or `add_state` with `persistent=True`). This means they are not included in `state_dict()` and are therefore lost when saving and loading a checkpoint. After loading, `min_val` resets to `inf` and `max_val` resets to `-inf`, so the tracked minimum and maximum no longer reflect the full history of the experiment. ### To Reproduce <!-- Create a new Lightning Studio with code that reproduces the issue and share the link. Also include all the relevant files and data required to reproduce shared issue. In case the code does not crash, please add assert statements to show what is the real and expected output. A simple guide on how to create such a studio can be found [here]( https://www.youtube.com/watch?v=YcW-2Zt_bFg&ab_channel=LightningAI). --> <details> <summary>Code sample</summary> ```python import torch from torchmetrics.wrappers import MinMaxMetric from torchmetrics.classification import BinaryAccuracy # Create metric and simulate two epochs metric = MinMaxMetric(BinaryAccuracy()) # Epoch 1: perfect predictions metric.update(torch.tensor([1, 0, 1, 0]), torch.tensor([1, 0, 1, 0])) result = metric.compute() print(f"Epoch 1: {result}") # raw=1.0, min=1.0, max=1.0 metric.reset() # Epoch 2: worse predictions metric.update(torch.tensor([1, 1, 0, 0]), torch.tensor([1, 0, 1, 0])) result = metric.compute() print(f"Epoch 2: {result}") # raw=0.5, min=0.5, max=1.0 # Save and reload state dict (simulates checkpoint resume) state_dict = metric.state_dict() print(f"\nstate_dict keys: {list(state_dict.keys())}") # [] — empty! print(f"min_val in state_dict: {'min_val' in state_dict}") # False print(f"max_val in state_dict: {'max_val' in state_dict}") # False # Load into a fresh metric metric2 = MinMaxMetric(BinaryAccuracy()) metric2.load_state_dict(state_dict) # Epoch 3 after resume: min/max are lost metric2.reset() metric2.update(torch.tensor([1, 0, 0, 0]), torch.tensor([1, 0, 1, 0])) result = metric2.compute() print(f"\nEpoch 3 (after resume): {result}") # raw=0.75, min=0.75, max=0.75 # Expected: min=0.5 (from epoch 2), max=1.0 (from epoch 1) ``` </details> #### Expected behavior After loading a checkpoint, `min_val` and `max_val` should be restored to their saved values so that the min/max tracking reflects the entire experiment, not just the portion after the resume. #### Actual behavior `state_dict()` returns an empty dict. After loading, `min_val` and `max_val` are re-initialized to `inf` / `-inf`, so the historical min/max are lost. #### Root cause In `MinMaxMetric.__init__`, `min_val` and `max_val` are assigned as plain tensor attributes: ```python self.min_val = torch.tensor(float("inf")) self.max_val = torch.tensor(float("-inf")) ``` They are not registered via `register_buffer(`), so `nn.Module.state_dict()` does not include them. They also cannot use `add_state()` because `Metric.reset()` would then reset them to their defaults on every epoch, which would defeat their purpose. #### Suggested fix Register them as persistent buffers: ```python self.register_buffer("min_val", torch.tensor(float("inf"))) self.register_buffer("max_val", torch.tensor(float("-inf"))) ``` This would: - Include them in `state_dict()` / `load_state_dict()` for correct checkpoint behavior - Not affect `Metric.reset()`, which only resets states in `_defaults` (populated by `add_state`) - Automatically move them with the module on `.to(device)` / `.cuda()`, making the manual `.to(val.device)` calls in `compute()` redundant (but still correct as no-ops) <details> <summary>Environment</summary> - TorchMetrics version: 1.8.2 - Python: 3.11.14 - PyTorch: 2.9.0+cu128 - OS: Linux </details> ### Additional context Happy to open a PR if you agree with the fix or if you have another idea :)