Remove cuda version focal loss (#3431)

src/otx/algo/detection/losses/cross_focal_loss.py

@@ -11,7 +11,7 @@
 from torch import Tensor, nn
 from torch.cuda.amp import custom_fwd
 
-from otx.algo.detection.losses.focal_loss import py_sigmoid_focal_loss, sigmoid_focal_loss
+from otx.algo.detection.losses.focal_loss import py_sigmoid_focal_loss
 
 
 def cross_sigmoid_focal_loss(
@@ -36,13 +36,10 @@ def cross_sigmoid_focal_loss(
         avg_factor: average factors.
         valid_label_mask: ignore label mask.
     """
-    if torch.cuda.is_available() and inputs.is_cuda:
-        calculate_loss_func = sigmoid_focal_loss
-    else:
-        inputs_size = inputs.size(1)
-        targets = torch.nn.functional.one_hot(targets, num_classes=inputs_size + 1)
-        targets = targets[:, :inputs_size]
-        calculate_loss_func = py_sigmoid_focal_loss
+    inputs_size = inputs.size(1)
+    targets = torch.nn.functional.one_hot(targets, num_classes=inputs_size + 1)
+    targets = targets[:, :inputs_size]
+    calculate_loss_func = py_sigmoid_focal_loss
 
     loss = calculate_loss_func(
         inputs,

src/otx/algo/detection/losses/focal_loss.py

@@ -12,10 +12,6 @@
 import torch
 import torch.nn.functional
 
-# TODO(Eugene): replace mmcv.sigmoid_focal_loss with torchvision
-# https://github.com/openvinotoolkit/training_extensions/pull/3281
-from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
-
 from otx.algo.detection.losses.weighted_loss import weight_reduce_loss
 
 if TYPE_CHECKING:
@@ -74,52 +70,3 @@ def py_sigmoid_focal_loss(
             msg = "The number of dimensions in weight should be equal to the number of dimensions in loss."
             raise ValueError(msg)
     return weight_reduce_loss(loss, weight, reduction, avg_factor)
-
-
-def sigmoid_focal_loss(
-    pred: Tensor,
-    target: Tensor,
-    weight: None | Tensor = None,
-    gamma: float = 2.0,
-    alpha: float = 0.25,
-    reduction: str = "mean",
-    avg_factor: int | None = None,
-) -> torch.Tensor:
-    r"""A wrapper of cuda version `Focal Loss <https://arxiv.org/abs/1708.02002>`_.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C), C is the number
-            of classes.
-        target (torch.Tensor): The learning label of the prediction.
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        gamma (float, optional): The gamma for calculating the modulating
-            factor. Defaults to 2.0.
-        alpha (float, optional): A balanced form for Focal Loss.
-            Defaults to 0.25.
-        reduction (str, optional): The method used to reduce the loss into
-            a scalar. Defaults to 'mean'. Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-    """
-    # Function.apply does not accept keyword arguments, so the decorator
-    # "weighted_loss" is not applicable
-    loss = _sigmoid_focal_loss(pred.contiguous(), target.contiguous(), gamma, alpha, None, "none")
-    if weight is not None:
-        if weight.shape != loss.shape:
-            if weight.size(0) == loss.size(0):
-                # For most cases, weight is of shape (num_priors, ),
-                #  which means it does not have the second axis num_class
-                weight = weight.view(-1, 1)
-            else:
-                # Sometimes, weight per anchor per class is also needed. e.g.
-                #  in FSAF. But it may be flattened of shape
-                #  (num_priors x num_class, ), while loss is still of shape
-                #  (num_priors, num_class).
-                if weight.numel() != loss.numel():
-                    msg = "The number of elements in weight should be equal to the number of elements in loss."
-                    raise ValueError(msg)
-                weight = weight.view(loss.size(0), -1)
-        if weight.ndim != loss.ndim:
-            msg = "The number of dimensions in weight should be equal to the number of dimensions in loss."
-            raise ValueError(msg)
-    return weight_reduce_loss(loss, weight, reduction, avg_factor)