IQ.Pilot Prebuilt Release @ ab07000

tinygrad_repo/test/external/mlperf_retinanet/focal_loss.py

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+# Copied from https://github.com/mlcommons/training/blob/cdd928d4596c142c15a7d86b2eeadbac718c8da2/single_stage_detector/ssd/model/focal_loss.py
+
+import torch
+import torch.nn.functional as F
+
+
+def sigmoid_focal_loss(
+    inputs: torch.Tensor,
+    targets: torch.Tensor,
+    alpha: float = 0.25,
+    gamma: float = 2,
+    reduction: str = "none",
+):
+    """
+    Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py .
+    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
+
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+        alpha: (optional) Weighting factor in range (0,1) to balance
+                positive vs negative examples or -1 for ignore. Default = 0.25
+        gamma: Exponent of the modulating factor (1 - p_t) to
+               balance easy vs hard examples.
+        reduction: 'none' | 'mean' | 'sum'
+                 'none': No reduction will be applied to the output.
+                 'mean': The output will be averaged.
+                 'sum': The output will be summed.
+    Returns:
+        Loss tensor with the reduction option applied.
+    """
+    p = torch.sigmoid(inputs)
+    ce_loss = F.binary_cross_entropy_with_logits(
+        inputs, targets, reduction="none"
+    )
+    p_t = p * targets + (1 - p) * (1 - targets)
+    loss = ce_loss * ((1 - p_t) ** gamma)
+
+    if alpha >= 0:
+        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
+        loss = alpha_t * loss
+
+    if reduction == "mean":
+        loss = loss.mean()
+    elif reduction == "sum":
+        loss = loss.sum()
+
+    return loss