add training losses

pcdet/utils/loss_utils.py

+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from . import box_utils
+
+
+class SigmoidFocalClassificationLoss(nn.Module):
+    """
+    Sigmoid focal cross entropy loss.
+    """
+
+    def __init__(self, gamma: float = 2.0, alpha: float = 0.25):
+        """
+        Args:
+            gamma: Weighting parameter to balance loss for hard and easy examples.
+            alpha: Weighting parameter to balance loss for positive and negative examples.
+        """
+        super(SigmoidFocalClassificationLoss, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+
+    @staticmethod
+    def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor):
+        """ PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:
+            max(x, 0) - x * z + log(1 + exp(-abs(x))) in
+            https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
+
+        Args:
+            input: (B, #anchors, #classes) float tensor.
+                Predicted logits for each class
+            target: (B, #anchors, #classes) float tensor.
+                One-hot encoded classification targets
+
+        Returns:
+            loss: (B, #anchors, #classes) float tensor.
+                Sigmoid cross entropy loss without reduction
+        """
+        loss = torch.clamp(input, min=0) - input * target + \
+               torch.log1p(torch.exp(-torch.abs(input)))
+        return loss
+
+    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
+        """
+        Args:
+            input: (B, #anchors, #classes) float tensor.
+                Predicted logits for each class
+            target: (B, #anchors, #classes) float tensor.
+                One-hot encoded classification targets
+            weights: (B, #anchors) float tensor.
+                Anchor-wise weights.
+
+        Returns:
+            weighted_loss: (B, #anchors, #classes) float tensor after weighting.
+        """
+        pred_sigmoid = torch.sigmoid(input)
+        alpha_weight = target * self.alpha + (1 - target) * (1 - self.alpha)
+        pt = target * (1.0 - pred_sigmoid) + (1.0 - target) * pred_sigmoid
+        focal_weight = alpha_weight * torch.pow(pt, self.gamma)
+
+        bce_loss = self.sigmoid_cross_entropy_with_logits(input, target)
+
+        loss = focal_weight * bce_loss
+
+        if weights.shape.__len__() == 2 or \
+                (weights.shape.__len__() == 1 and target.shape.__len__() == 2):
+            weights = weights.unsqueeze(-1)
+
+        assert weights.shape.__len__() == loss.shape.__len__()
+
+        return loss * weights
+
+
+class WeightedSmoothL1Loss(nn.Module):
+    """
+    Code-wise Weighted Smooth L1 Loss modified based on fvcore.nn.smooth_l1_loss
+    https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/smooth_l1_loss.py
+                  | 0.5 * x ** 2 / beta   if abs(x) < beta
+    smoothl1(x) = |
+                  | abs(x) - 0.5 * beta   otherwise,
+    where x = input - target.
+    """
+    def __init__(self, beta: float = 1.0 / 9.0, code_weights: list = None):
+        """
+        Args:
+            beta: Scalar float.
+                L1 to L2 change point.
+                For beta values < 1e-5, L1 loss is computed.
+            code_weights: (#codes) float list if not None.
+                Code-wise weights.
+        """
+        super(WeightedSmoothL1Loss, self).__init__()
+        self.beta = beta
+        if code_weights is not None:
+            self.code_weights = np.array(code_weights, dtype=np.float32)
+            self.code_weights = torch.from_numpy(self.code_weights).cuda()
+
+    @staticmethod
+    def smooth_l1_loss(diff, beta):
+        if beta < 1e-5:
+            loss = torch.abs(diff)
+        else:
+            n = torch.abs(diff)
+            loss = torch.where(n < beta, 0.5 * n ** 2 / beta, n - 0.5 * beta)
+
+        return loss
+
+    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor = None):
+        """
+        Args:
+            input: (B, #anchors, #codes) float tensor.
+                Ecoded predicted locations of objects.
+            target: (B, #anchors, #codes) float tensor.
+                Regression targets.
+            weights: (B, #anchors) float tensor if not None.
+
+        Returns:
+            loss: (B, #anchors) float tensor.
+                Weighted smooth l1 loss without reduction.
+        """
+        diff = input - target
+        # code-wise weighting
+        if self.code_weights is not None:
+            diff = diff * self.code_weights.view(1, 1, -1)
+
+        loss = self.smooth_l1_loss(diff, self.beta)
+
+        # anchor-wise weighting
+        if weights is not None:
+            assert weights.shape[0] == loss.shape[0] and weights.shape[1] == loss.shape[1]
+            loss = loss * weights.unsqueeze(-1)
+
+        return loss
+
+
+class WeightedCrossEntropyLoss(nn.Module):
+    """
+    Transform input to fit the fomation of PyTorch offical cross entropy loss
+    with anchor-wise weighting.
+    """
+    def __init__(self):
+        super(WeightedCrossEntropyLoss, self).__init__()
+
+    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
+        """
+        Args:
+            input: (B, #anchors, #classes) float tensor.
+                Predited logits for each class.
+            target: (B, #anchors, #classes) float tensor.
+                One-hot classification targets.
+            weights: (B, #anchors) float tensor.
+                Anchor-wise weights.
+
+        Returns:
+            loss: (B, #anchors) float tensor.
+                Weighted cross entropy loss without reduction
+        """
+        input = input.permute(0, 2, 1)
+        target = target.argmax(dim=-1)
+        loss = F.cross_entropy(input, target, reduction='none') * weights
+        return loss
+
+
+def get_corner_loss_lidar(pred_bbox3d: torch.Tensor, gt_bbox3d: torch.Tensor):
+    """
+    Args:
+        pred_bbox3d: (N, 7) float Tensor.
+        gt_bbox3d: (N, 7) float Tensor.
+
+    Returns:
+        corner_loss: (N) float Tensor.
+    """
+    assert pred_bbox3d.shape[0] == gt_bbox3d.shape[0]
+
+    pred_box_corners = box_utils.boxes_to_corners_3d(pred_bbox3d)
+    gt_box_corners = box_utils.boxes_to_corners_3d(gt_bbox3d)
+
+    gt_bbox3d_flip = gt_bbox3d.clone()
+    gt_bbox3d_flip[:, 6] += np.pi
+    gt_box_corners_flip = box_utils.boxes_to_corners_3d(gt_bbox3d_flip)
+    # (N, 8)
+    corner_dist = torch.min(torch.norm(pred_box_corners - gt_box_corners, dim=2),
+                            torch.norm(pred_box_corners - gt_box_corners_flip, dim=2))
+    # (N, 8)
+    corner_loss = WeightedSmoothL1Loss.smooth_l1_loss(corner_dist, beta=1.0)
+
+    return corner_loss.mean(dim=1)
\ No newline at end of file