Reset min_max normalization values at the start of validation epoch

[abc-125]

📝 Description

Currently, min_max values for normalization are updated every validation epoch. This update keeps irrelevant values from previous epochs by comparing new and old ones:
self.max = torch.max(self.max, torch.max(predictions))

It seems like it does not affect most of the models, but with at least EfficientAD, the final anomaly maps after training are normalized the wrong way (see #2139 or this discussion).

This fix resets min_max values at the beginning of every validation epoch to ensure that only values from the recent one will be used for normalization.

🛠️ Fixes #2027, thank you @CarlosNacher for pointing this problem out! Partially fixes #2139.

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[abc-125]

Sorry, this was already solved in this PR.