Strange behavior when calculating precision and recall with torchmetrics compared to manually calculated metrics

[ikaryss]

Greetings!
While training and testing my classification model I've faced with such a problem:
The model was trained on a perfectly balanced dataset (binary classification with 50%/50% classes distribution). I got pretty high accuracy on training set as well as on val/test sets. However, despite the fact that training precision and recall reach those highs too, on val/test sets these metrics drop significantly.
I got confused, so I decided to calculate these metrics manually using raw stat scores

...
stat_scores = BinaryStatScores().to(device)  
tp, fp, tn, fn, _ = stat_scores(y_logits, y_real)
...

Compared to original metrics accuracy seems to be pretty similar, but precision and recall distinguish drastically. The comparison is depicted below. (calc - calculated manually, x axis is a list of epochs)

and here is some comparisons between calculated metrics in each split

my code for calculating metrics in current batch:

def batch_metrics(
    y_real: torch.Tensor,
    y_logits: torch.Tensor,
    device: torch.device,
):
    """Calculates all necessary metrics on a single batch

    Tracked metrics: accuracy, precision, recall
    Args:
        y_real (torch.Tensor): torch tensor with real classes
        y_logits (torch.Tensor): torch tensor with predicted classes
        device (torch.device):  A target device to compute on (e.g. "cuda" or "cpu").

    Returns:
        Tuple[float, float, float, float, float, float, float, float]: A tuple of metrics
    """
    thresholds = None
    # get probabilities-like logits
    y_logits = torch.sigmoid(y_logits)

    # Accuracy
    accuracy_metric = BinaryAccuracy().to(device)
    acc = accuracy_metric(y_logits, y_real).item()

    # Precision
    precision_metric = BinaryPrecision().to(device)
    precision = precision_metric(y_logits, y_real).item()

    # Recall
    recall_metric = BinaryRecall().to(device)
    recall = recall_metric(y_logits, y_real).item()

    #  Area Under the Receiver Operating Characteristic Curve
    roc_metric = BinaryAUROC(thresholds=thresholds).to(device)
    auroc = roc_metric(y_logits, y_real).item()

    # Stat scores
    stat_scores = BinaryStatScores().to(device)
    tp, fp, tn, fn, _ = stat_scores(y_logits, y_real)

    return (
        acc,
        precision,
        recall,
        auroc,
        tp.item(),
        fp.item(),
        tn.item(),
        fn.item(),
    )

So my question is where is the ground truth?:) and maybe you can give me some hints regarding my mistakes?

[ikaryss]

I've figured out that I forgot to shuffle data in my test\val sets so metrics per batch were erroneous...