[Feature] Support Tversky Loss

mmseg/models/losses/__init__.py

@@ -5,11 +5,12 @@
 from .dice_loss import DiceLoss
 from .focal_loss import FocalLoss
 from .lovasz_loss import LovaszLoss
+from .tversky_loss import TverskyLoss
 from .utils import reduce_loss, weight_reduce_loss, weighted_loss
 
 __all__ = [
     'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy',
     'mask_cross_entropy', 'CrossEntropyLoss', 'reduce_loss',
     'weight_reduce_loss', 'weighted_loss', 'LovaszLoss', 'DiceLoss',
-    'FocalLoss'
+    'FocalLoss', 'TverskyLoss'
 ]

mmseg/models/losses/tversky_loss.py

@@ -0,0 +1,137 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Modified from
+https://github.com/JunMa11/SegLoss/blob/master/losses_pytorch/dice_loss.py#L333
+(Apache-2.0 License)"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+from .utils import get_class_weight, weighted_loss
+
+
+@weighted_loss
+def tversky_loss(pred,
+                 target,
+                 valid_mask,
+                 alpha=0.3,
+                 beta=0.7,
+                 smooth=1,
+                 class_weight=None,
+                 ignore_index=255):
+    assert pred.shape[0] == target.shape[0]
+    total_loss = 0
+    num_classes = pred.shape[1]
+    for i in range(num_classes):
+        if i != ignore_index:
+            tversky_loss = binary_tversky_loss(
+                pred[:, i],
+                target[..., i],
+                valid_mask=valid_mask,
+                alpha=alpha,
+                beta=beta,
+                smooth=smooth)
+            if class_weight is not None:
+                tversky_loss *= class_weight[i]
+            total_loss += tversky_loss
+    return total_loss / num_classes
+
+
+@weighted_loss
+def binary_tversky_loss(pred,
+                        target,
+                        valid_mask,
+                        alpha=0.3,
+                        beta=0.7,
+                        smooth=1):
+    assert pred.shape[0] == target.shape[0]
+    pred = pred.reshape(pred.shape[0], -1)
+    target = target.reshape(target.shape[0], -1)
+    valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
+
+    TP = torch.sum(torch.mul(pred, target) * valid_mask, dim=1)
+    FP = torch.sum(torch.mul(pred, 1 - target) * valid_mask, dim=1)
+    FN = torch.sum(torch.mul(1 - pred, target) * valid_mask, dim=1)
+    tversky = (TP + smooth) / (TP + alpha * FP + beta * FN + smooth)
+
+    return 1 - tversky
+
+
+@LOSSES.register_module()
+class TverskyLoss(nn.Module):
+    """TverskyLoss. This loss is proposed in `Tversky loss function for image
+    segmentation using 3D fully convolutional deep networks.
+
+    <https://arxiv.org/abs/1706.05721>`_.
+    Args:
+        smooth (float): A float number to smooth loss, and avoid NaN error.
+            Default: 1.
+        class_weight (list[float] | str, optional): Weight of each class. If in
+            str format, read them from a file. Defaults to None.
+        loss_weight (float, optional): Weight of the loss. Default to 1.0.
+        ignore_index (int | None): The label index to be ignored. Default: 255.
+        alpha(float, in [0, 1]):
+            The coefficient of false positives. Default: 0.3.
+        beta (float, in [0, 1]):
+            The coefficient of false negatives. Default: 0.7.
+            Note: alpha + beta = 1.
+        loss_name (str, optional): Name of the loss item. If you want this loss
+            item to be included into the backward graph, `loss_` must be the
+            prefix of the name. Defaults to 'loss_tversky'.
+    """
+
+    def __init__(self,
+                 smooth=1,
+                 class_weight=None,
+                 loss_weight=1.0,
+                 ignore_index=255,
+                 alpha=0.3,
+                 beta=0.7,
+                 loss_name='loss_tversky'):
+        super(TverskyLoss, self).__init__()
+        self.smooth = smooth
+        self.class_weight = get_class_weight(class_weight)
+        self.loss_weight = loss_weight
+        self.ignore_index = ignore_index
+        assert (alpha + beta == 1.0), 'Sum of alpha and beta but be 1.0!'
+        self.alpha = alpha
+        self.beta = beta
+        self._loss_name = loss_name
+
+    def forward(self, pred, target, **kwargs):
+        if self.class_weight is not None:
+            class_weight = pred.new_tensor(self.class_weight)
+        else:
+            class_weight = None
+
+        pred = F.softmax(pred, dim=1)
+        num_classes = pred.shape[1]
+        one_hot_target = F.one_hot(
+            torch.clamp(target.long(), 0, num_classes - 1),
+            num_classes=num_classes)
+        valid_mask = (target != self.ignore_index).long()
+
+        loss = self.loss_weight * tversky_loss(
+            pred,
+            one_hot_target,
+            valid_mask=valid_mask,
+            alpha=self.alpha,
+            beta=self.beta,
+            smooth=self.smooth,
+            class_weight=class_weight,
+            ignore_index=self.ignore_index)
+        return loss
+
+    @property
+    def loss_name(self):
+        """Loss Name.
+
+        This function must be implemented and will return the name of this
+        loss function. This name will be used to combine different loss items
+        by simple sum operation. In addition, if you want this loss item to be
+        included into the backward graph, `loss_` must be the prefix of the
+        name.
+        Returns:
+            str: The name of this loss item.
+        """
+        return self._loss_name

tests/test_models/test_losses/test_tversky_loss.py

@@ -0,0 +1,76 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+
+def test_tversky_lose():
+    from mmseg.models import build_loss
+
+    # test alpha + beta != 1
+    with pytest.raises(AssertionError):
+        loss_cfg = dict(
+            type='TverskyLoss',
+            class_weight=[1.0, 2.0, 3.0],
+            loss_weight=1.0,
+            alpha=0.4,
+            beta=0.7,
+            loss_name='loss_tversky')
+        tversky_loss = build_loss(loss_cfg)
+        logits = torch.rand(8, 3, 4, 4)
+        labels = (torch.rand(8, 4, 4) * 3).long()
+        tversky_loss(logits, labels, ignore_index=1)
+
+    # test tversky loss
+    loss_cfg = dict(
+        type='TverskyLoss',
+        class_weight=[1.0, 2.0, 3.0],
+        loss_weight=1.0,
+        ignore_index=1,
+        loss_name='loss_tversky')
+    tversky_loss = build_loss(loss_cfg)
+    logits = torch.rand(8, 3, 4, 4)
+    labels = (torch.rand(8, 4, 4) * 3).long()
+    tversky_loss(logits, labels)
+
+    # test loss with class weights from file
+    import os
+    import tempfile
+
+    import mmcv
+    import numpy as np
+    tmp_file = tempfile.NamedTemporaryFile()
+
+    mmcv.dump([1.0, 2.0, 3.0], f'{tmp_file.name}.pkl', 'pkl')  # from pkl file
+    loss_cfg = dict(
+        type='TverskyLoss',
+        class_weight=f'{tmp_file.name}.pkl',
+        loss_weight=1.0,
+        ignore_index=1,
+        loss_name='loss_tversky')
+    tversky_loss = build_loss(loss_cfg)
+    tversky_loss(logits, labels)
+
+    np.save(f'{tmp_file.name}.npy', np.array([1.0, 2.0, 3.0]))  # from npy file
+    loss_cfg = dict(
+        type='TverskyLoss',
+        class_weight=f'{tmp_file.name}.pkl',
+        loss_weight=1.0,
+        ignore_index=1,
+        loss_name='loss_tversky')
+    tversky_loss = build_loss(loss_cfg)
+    tversky_loss(logits, labels)
+    tmp_file.close()
+    os.remove(f'{tmp_file.name}.pkl')
+    os.remove(f'{tmp_file.name}.npy')
+
+    # test tversky loss has name `loss_tversky`
+    loss_cfg = dict(
+        type='TverskyLoss',
+        smooth=2,
+        loss_weight=1.0,
+        ignore_index=1,
+        alpha=0.3,
+        beta=0.7,
+        loss_name='loss_tversky')
+    tversky_loss = build_loss(loss_cfg)
+    assert tversky_loss.loss_name == 'loss_tversky'