[Feature] Support multiple losses during training (#818)

* multiple losses

* fix lint error

* fix typos

* fix typos

* Adding Attribute

* Fixing loss_ prefix

* Fixing loss_ prefix

* Fixing loss_ prefix

* Add Same

* loss_name must has 'loss_' prefix

* Fix unittest

* Fix unittest

* Fix unittest

* Update mmseg/models/decode_heads/decode_head.py

Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>

docs/tutorials/training_tricks.md

@@ -50,3 +50,21 @@ model=dict(
 ```
 
 `class_weight` will be passed into `CrossEntropyLoss` as `weight` argument. Please refer to [PyTorch Doc](https://pytorch.org/docs/stable/nn.html?highlight=crossentropy#torch.nn.CrossEntropyLoss) for details.
+
+## Multiple Losses
+
+For loss calculation, we support multiple losses training concurrently. Here is an example config of training `unet` on `DRIVE` dataset, whose loss function is `1:3` weighted sum of `CrossEntropyLoss` and `DiceLoss`:
+
+```python
+_base_ = './fcn_unet_s5-d16_64x64_40k_drive.py'
+model = dict(
+    decode_head=dict(loss_decode=[dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
+            dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)]),
+    auxiliary_head=dict(loss_decode=[dict(type='CrossEntropyLoss', loss_name='loss_ce',loss_weight=1.0),
+            dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)]),
+    )
+```
+
+In this way, `loss_weight` and `loss_name` will be weight and name in training log of corresponding loss, respectively.
+
+Note: If you want this loss item to be included into the backward graph, `loss_` must be the prefix of the name.

docs_zh-CN/tutorials/training_tricks.md

@@ -49,3 +49,22 @@ model=dict(
 ```
 
 `class_weight` 将被作为 `weight` 参数，传递给 `CrossEntropyLoss`。详细信息请参照 [PyTorch 文档](https://pytorch.org/docs/stable/nn.html?highlight=crossentropy#torch.nn.CrossEntropyLoss) 。
+
+## 同时使用多种损失函数 (Multiple Losses)
+
+对于训练时损失函数的计算，我们目前支持多个损失函数同时使用。 以 `unet` 使用 `DRIVE` 数据集训练为例，
+使用 `CrossEntropyLoss` 和 `DiceLoss` 的 `1:3` 的加权和作为损失函数。配置文件写为:
+
+```python
+_base_ = './fcn_unet_s5-d16_64x64_40k_drive.py'
+model = dict(
+    decode_head=dict(loss_decode=[dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
+            dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)]),
+    auxiliary_head=dict(loss_decode=[dict(type='CrossEntropyLoss', loss_name='loss_ce',loss_weight=1.0),
+            dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)]),
+    )
+```
+
+通过这种方式，确定训练过程中损失函数的权重 `loss_weight` 和在训练日志里的名字 `loss_name`。
+
+注意： `loss_name` 的名字必须带有 `loss_` 前缀，这样它才能被包括在反传的图里。

mmseg/core/seg/sampler/ohem_pixel_sampler.py

@@ -62,12 +62,14 @@ class OHEMPixelSampler(BasePixelSampler):
                 threshold = max(min_threshold, self.thresh)
                 valid_seg_weight[seg_prob[valid_mask] < threshold] = 1.
             else:
-                losses = self.context.loss_decode(
-                    seg_logit,
-                    seg_label,
-                    weight=None,
-                    ignore_index=self.context.ignore_index,
-                    reduction_override='none')
+                losses = 0.0
+                for loss_module in self.context.loss_decode:
+                    losses += loss_module(
+                        seg_logit,
+                        seg_label,
+                        weight=None,
+                        ignore_index=self.context.ignore_index,
+                        reduction_override='none')
                 # faster than topk according to https://github.com/pytorch/pytorch/issues/22812  # noqa
                 _, sort_indices = losses[valid_mask].sort(descending=True)
                 valid_seg_weight[sort_indices[:batch_kept]] = 1.

mmseg/models/decode_heads/decode_head.py

@@ -33,10 +33,17 @@ class BaseDecodeHead(BaseModule, metaclass=ABCMeta):
                 a list and passed into decode head.
             None: Only one select feature map is allowed.
             Default: None.
-        loss_decode (dict): Config of decode loss.
+        loss_decode (dict | Sequence[dict]): Config of decode loss.
+            The `loss_name` is property of corresponding loss function which
+            could be shown in training log. If you want this loss
+            item to be included into the backward graph, `loss_` must be the
+            prefix of the name. Defaults to 'loss_ce'.
+             e.g. dict(type='CrossEntropyLoss'),
+             [dict(type='CrossEntropyLoss', loss_name='loss_ce'),
+              dict(type='DiceLoss', loss_name='loss_dice')]
             Default: dict(type='CrossEntropyLoss').
         ignore_index (int | None): The label index to be ignored. When using
-            masked BCE loss, ignore_index should be set to None. Default: 255
+            masked BCE loss, ignore_index should be set to None. Default: 255.
         sampler (dict|None): The config of segmentation map sampler.
             Default: None.
         align_corners (bool): align_corners argument of F.interpolate.
@@ -73,9 +80,20 @@ class BaseDecodeHead(BaseModule, metaclass=ABCMeta):
         self.norm_cfg = norm_cfg
         self.act_cfg = act_cfg
         self.in_index = in_index
-        self.loss_decode = build_loss(loss_decode)
+
         self.ignore_index = ignore_index
         self.align_corners = align_corners
+        self.loss_decode = nn.ModuleList()
+
+        if isinstance(loss_decode, dict):
+            self.loss_decode.append(build_loss(loss_decode))
+        elif isinstance(loss_decode, (list, tuple)):
+            for loss in loss_decode:
+                self.loss_decode.append(build_loss(loss))
+        else:
+            raise TypeError(f'loss_decode must be a dict or sequence of dict,\
+                but got {type(loss_decode)}')
+
         if sampler is not None:
             self.sampler = build_pixel_sampler(sampler, context=self)
         else:
@@ -224,10 +242,19 @@ class BaseDecodeHead(BaseModule, metaclass=ABCMeta):
         else:
             seg_weight = None
         seg_label = seg_label.squeeze(1)
-        loss['loss_seg'] = self.loss_decode(
-            seg_logit,
-            seg_label,
-            weight=seg_weight,
-            ignore_index=self.ignore_index)
+        for loss_decode in self.loss_decode:
+            if loss_decode.loss_name not in loss:
+                loss[loss_decode.loss_name] = loss_decode(
+                    seg_logit,
+                    seg_label,
+                    weight=seg_weight,
+                    ignore_index=self.ignore_index)
+            else:
+                loss[loss_decode.loss_name] += loss_decode(
+                    seg_logit,
+                    seg_label,
+                    weight=seg_weight,
+                    ignore_index=self.ignore_index)
+
         loss['acc_seg'] = accuracy(seg_logit, seg_label)
         return loss

mmseg/models/decode_heads/point_head.py

@@ -249,8 +249,9 @@ class PointHead(BaseCascadeDecodeHead):
     def losses(self, point_logits, point_label):
         """Compute segmentation loss."""
         loss = dict()
-        loss['loss_point'] = self.loss_decode(
-            point_logits, point_label, ignore_index=self.ignore_index)
+        for loss_module in self.loss_decode:
+            loss['point' + loss_module.loss_name] = loss_module(
+                point_logits, point_label, ignore_index=self.ignore_index)
         loss['acc_point'] = accuracy(point_logits, point_label)
         return loss
 

mmseg/models/losses/cross_entropy_loss.py

@@ -150,6 +150,9 @@ class CrossEntropyLoss(nn.Module):
         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. Defaults to 1.0.
+        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_ce'.
     """
 
     def __init__(self,
@@ -157,7 +160,8 @@ class CrossEntropyLoss(nn.Module):
                  use_mask=False,
                  reduction='mean',
                  class_weight=None,
-                 loss_weight=1.0):
+                 loss_weight=1.0,
+                 loss_name='loss_ce'):
         super(CrossEntropyLoss, self).__init__()
         assert (use_sigmoid is False) or (use_mask is False)
         self.use_sigmoid = use_sigmoid
@@ -172,6 +176,7 @@ class CrossEntropyLoss(nn.Module):
             self.cls_criterion = mask_cross_entropy
         else:
             self.cls_criterion = cross_entropy
+        self._loss_name = loss_name
 
     def forward(self,
                 cls_score,
@@ -197,3 +202,17 @@ class CrossEntropyLoss(nn.Module):
             avg_factor=avg_factor,
             **kwargs)
         return loss_cls
+
+    @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

mmseg/models/losses/dice_loss.py

@@ -68,6 +68,9 @@ class DiceLoss(nn.Module):
             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.
+        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_dice'.
     """
 
     def __init__(self,
@@ -77,6 +80,7 @@ class DiceLoss(nn.Module):
                  class_weight=None,
                  loss_weight=1.0,
                  ignore_index=255,
+                 loss_name='loss_dice',
                  **kwards):
         super(DiceLoss, self).__init__()
         self.smooth = smooth
@@ -85,6 +89,7 @@ class DiceLoss(nn.Module):
         self.class_weight = get_class_weight(class_weight)
         self.loss_weight = loss_weight
         self.ignore_index = ignore_index
+        self._loss_name = loss_name
 
     def forward(self,
                 pred,
@@ -118,3 +123,17 @@ class DiceLoss(nn.Module):
             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

mmseg/models/losses/lovasz_loss.py

@@ -244,6 +244,9 @@ class LovaszLoss(nn.Module):
         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. Defaults to 1.0.
+        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_lovasz'.
     """
 
     def __init__(self,
@@ -252,7 +255,8 @@ class LovaszLoss(nn.Module):
                  per_image=False,
                  reduction='mean',
                  class_weight=None,
-                 loss_weight=1.0):
+                 loss_weight=1.0,
+                 loss_name='loss_lovasz'):
         super(LovaszLoss, self).__init__()
         assert loss_type in ('binary', 'multi_class'), "loss_type should be \
                                                     'binary' or 'multi_class'."
@@ -271,6 +275,7 @@ class LovaszLoss(nn.Module):
         self.reduction = reduction
         self.loss_weight = loss_weight
         self.class_weight = get_class_weight(class_weight)
+        self._loss_name = loss_name
 
     def forward(self,
                 cls_score,
@@ -302,3 +307,17 @@ class LovaszLoss(nn.Module):
             avg_factor=avg_factor,
             **kwargs)
         return loss_cls
+
+    @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_heads/test_decode_head.py

@@ -74,3 +74,92 @@ def test_decode_head():
     assert head.input_transform == 'resize_concat'
     transformed_inputs = head._transform_inputs(inputs)
     assert transformed_inputs.shape == (1, 48, 45, 45)
+
+    # test multi-loss, loss_decode is dict
+    with pytest.raises(TypeError):
+        # loss_decode must be a dict or sequence of dict.
+        BaseDecodeHead(3, 16, num_classes=19, loss_decode=['CrossEntropyLoss'])
+
+    inputs = torch.randn(2, 19, 8, 8).float()
+    target = torch.ones(2, 1, 64, 64).long()
+    head = BaseDecodeHead(
+        3,
+        16,
+        num_classes=19,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
+    if torch.cuda.is_available():
+        head, inputs = to_cuda(head, inputs)
+        head, target = to_cuda(head, target)
+    loss = head.losses(seg_logit=inputs, seg_label=target)
+    assert 'loss_ce' in loss
+
+    # test multi-loss, loss_decode is list of dict
+    inputs = torch.randn(2, 19, 8, 8).float()
+    target = torch.ones(2, 1, 64, 64).long()
+    head = BaseDecodeHead(
+        3,
+        16,
+        num_classes=19,
+        loss_decode=[
+            dict(type='CrossEntropyLoss', loss_name='loss_1'),
+            dict(type='CrossEntropyLoss', loss_name='loss_2')
+        ])
+    if torch.cuda.is_available():
+        head, inputs = to_cuda(head, inputs)
+        head, target = to_cuda(head, target)
+    loss = head.losses(seg_logit=inputs, seg_label=target)
+    assert 'loss_1' in loss
+    assert 'loss_2' in loss
+
+    # 'loss_decode' must be a dict or sequence of dict
+    with pytest.raises(TypeError):
+        BaseDecodeHead(3, 16, num_classes=19, loss_decode=['CrossEntropyLoss'])
+    with pytest.raises(TypeError):
+        BaseDecodeHead(3, 16, num_classes=19, loss_decode=0)
+
+    # test multi-loss, loss_decode is list of dict
+    inputs = torch.randn(2, 19, 8, 8).float()
+    target = torch.ones(2, 1, 64, 64).long()
+    head = BaseDecodeHead(
+        3,
+        16,
+        num_classes=19,
+        loss_decode=(dict(type='CrossEntropyLoss', loss_name='loss_1'),
+                     dict(type='CrossEntropyLoss', loss_name='loss_2'),
+                     dict(type='CrossEntropyLoss', loss_name='loss_3')))
+    if torch.cuda.is_available():
+        head, inputs = to_cuda(head, inputs)
+        head, target = to_cuda(head, target)
+    loss = head.losses(seg_logit=inputs, seg_label=target)
+    assert 'loss_1' in loss
+    assert 'loss_2' in loss
+    assert 'loss_3' in loss
+
+    # test multi-loss, loss_decode is list of dict, names of them are identical
+    inputs = torch.randn(2, 19, 8, 8).float()
+    target = torch.ones(2, 1, 64, 64).long()
+    head = BaseDecodeHead(
+        3,
+        16,
+        num_classes=19,
+        loss_decode=(dict(type='CrossEntropyLoss', loss_name='loss_ce'),
+                     dict(type='CrossEntropyLoss', loss_name='loss_ce'),
+                     dict(type='CrossEntropyLoss', loss_name='loss_ce')))
+    if torch.cuda.is_available():
+        head, inputs = to_cuda(head, inputs)
+        head, target = to_cuda(head, target)
+    loss_3 = head.losses(seg_logit=inputs, seg_label=target)
+
+    head = BaseDecodeHead(
+        3,
+        16,
+        num_classes=19,
+        loss_decode=(dict(type='CrossEntropyLoss', loss_name='loss_ce')))
+    if torch.cuda.is_available():
+        head, inputs = to_cuda(head, inputs)
+        head, target = to_cuda(head, target)
+    loss = head.losses(seg_logit=inputs, seg_label=target)
+    assert 'loss_ce' in loss
+    assert 'loss_ce' in loss_3
+    assert loss_3['loss_ce'] == 3 * loss['loss_ce']

tests/test_models/test_losses/test_ce_loss.py

@@ -20,7 +20,8 @@ def test_ce_loss():
         type='CrossEntropyLoss',
         use_sigmoid=False,
         class_weight=[0.8, 0.2],
-        loss_weight=1.0)
+        loss_weight=1.0,
+        loss_name='loss_ce')
     loss_cls = build_loss(loss_cls_cfg)
     fake_pred = torch.Tensor([[100, -100]])
     fake_label = torch.Tensor([1]).long()
@@ -38,7 +39,8 @@ def test_ce_loss():
         type='CrossEntropyLoss',
         use_sigmoid=False,
         class_weight=f'{tmp_file.name}.pkl',
-        loss_weight=1.0)
+        loss_weight=1.0,
+        loss_name='loss_ce')
     loss_cls = build_loss(loss_cls_cfg)
     assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.))
 
@@ -47,7 +49,8 @@ def test_ce_loss():
         type='CrossEntropyLoss',
         use_sigmoid=False,
         class_weight=f'{tmp_file.name}.npy',
-        loss_weight=1.0)
+        loss_weight=1.0,
+        loss_name='loss_ce')
     loss_cls = build_loss(loss_cls_cfg)
     assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.))
     tmp_file.close()
@@ -74,4 +77,12 @@ def test_ce_loss():
         torch.tensor(0.9354),
         atol=1e-4)
 
+    # test cross entropy loss has name `loss_ce`
+    loss_cls_cfg = dict(
+        type='CrossEntropyLoss',
+        use_sigmoid=False,
+        loss_weight=1.0,
+        loss_name='loss_ce')
+    loss_cls = build_loss(loss_cls_cfg)
+    assert loss_cls.loss_name == 'loss_ce'
     # TODO test use_mask

tests/test_models/test_losses/test_dice_loss.py

@@ -11,7 +11,8 @@ def test_dice_lose():
         reduction='none',
         class_weight=[1.0, 2.0, 3.0],
         loss_weight=1.0,
-        ignore_index=1)
+        ignore_index=1,
+        loss_name='loss_dice')
     dice_loss = build_loss(loss_cfg)
     logits = torch.rand(8, 3, 4, 4)
     labels = (torch.rand(8, 4, 4) * 3).long()
@@ -30,7 +31,8 @@ def test_dice_lose():
         reduction='none',
         class_weight=f'{tmp_file.name}.pkl',
         loss_weight=1.0,
-        ignore_index=1)
+        ignore_index=1,
+        loss_name='loss_dice')
     dice_loss = build_loss(loss_cfg)
     dice_loss(logits, labels, ignore_index=None)
 
@@ -40,7 +42,8 @@ def test_dice_lose():
         reduction='none',
         class_weight=f'{tmp_file.name}.pkl',
         loss_weight=1.0,
-        ignore_index=1)
+        ignore_index=1,
+        loss_name='loss_dice')
     dice_loss = build_loss(loss_cfg)
     dice_loss(logits, labels, ignore_index=None)
     tmp_file.close()
@@ -54,8 +57,21 @@ def test_dice_lose():
         exponent=3,
         reduction='sum',
         loss_weight=1.0,
-        ignore_index=0)
+        ignore_index=0,
+        loss_name='loss_dice')
     dice_loss = build_loss(loss_cfg)
     logits = torch.rand(8, 2, 4, 4)
     labels = (torch.rand(8, 4, 4) * 2).long()
     dice_loss(logits, labels)
+
+    # test dice loss has name `loss_dice`
+    loss_cfg = dict(
+        type='DiceLoss',
+        smooth=2,
+        exponent=3,
+        reduction='sum',
+        loss_weight=1.0,
+        ignore_index=0,
+        loss_name='loss_dice')
+    dice_loss = build_loss(loss_cfg)
+    assert dice_loss.loss_name == 'loss_dice'

tests/test_models/test_losses/test_lovasz_loss.py

@@ -12,16 +12,24 @@ def test_lovasz_loss():
             type='LovaszLoss',
             loss_type='Binary',
             reduction='none',
-            loss_weight=1.0)
+            loss_weight=1.0,
+            loss_name='loss_lovasz')
         build_loss(loss_cfg)
 
     # reduction should be 'none' when per_image is False.
     with pytest.raises(AssertionError):
-        loss_cfg = dict(type='LovaszLoss', loss_type='multi_class')
+        loss_cfg = dict(
+            type='LovaszLoss',
+            loss_type='multi_class',
+            loss_name='loss_lovasz')
         build_loss(loss_cfg)
 
     # test lovasz loss with loss_type = 'multi_class' and per_image = False
-    loss_cfg = dict(type='LovaszLoss', reduction='none', loss_weight=1.0)
+    loss_cfg = dict(
+        type='LovaszLoss',
+        reduction='none',
+        loss_weight=1.0,
+        loss_name='loss_lovasz')
     lovasz_loss = build_loss(loss_cfg)
     logits = torch.rand(1, 3, 4, 4)
     labels = (torch.rand(1, 4, 4) * 2).long()
@@ -33,7 +41,8 @@ def test_lovasz_loss():
         per_image=True,
         reduction='mean',
         class_weight=[1.0, 2.0, 3.0],
-        loss_weight=1.0)
+        loss_weight=1.0,
+        loss_name='loss_lovasz')
     lovasz_loss = build_loss(loss_cfg)
     logits = torch.rand(1, 3, 4, 4)
     labels = (torch.rand(1, 4, 4) * 2).long()
@@ -52,7 +61,8 @@ def test_lovasz_loss():
         per_image=True,
         reduction='mean',
         class_weight=f'{tmp_file.name}.pkl',
-        loss_weight=1.0)
+        loss_weight=1.0,
+        loss_name='loss_lovasz')
     lovasz_loss = build_loss(loss_cfg)
     lovasz_loss(logits, labels, ignore_index=None)
 
@@ -62,7 +72,8 @@ def test_lovasz_loss():
         per_image=True,
         reduction='mean',
         class_weight=f'{tmp_file.name}.npy',
-        loss_weight=1.0)
+        loss_weight=1.0,
+        loss_name='loss_lovasz')
     lovasz_loss = build_loss(loss_cfg)
     lovasz_loss(logits, labels, ignore_index=None)
     tmp_file.close()
@@ -74,7 +85,8 @@ def test_lovasz_loss():
         type='LovaszLoss',
         loss_type='binary',
         reduction='none',
-        loss_weight=1.0)
+        loss_weight=1.0,
+        loss_name='loss_lovasz')
     lovasz_loss = build_loss(loss_cfg)
     logits = torch.rand(2, 4, 4)
     labels = (torch.rand(2, 4, 4)).long()
@@ -86,8 +98,20 @@ def test_lovasz_loss():
         loss_type='binary',
         per_image=True,
         reduction='mean',
-        loss_weight=1.0)
+        loss_weight=1.0,
+        loss_name='loss_lovasz')
     lovasz_loss = build_loss(loss_cfg)
     logits = torch.rand(2, 4, 4)
     labels = (torch.rand(2, 4, 4)).long()
     lovasz_loss(logits, labels, ignore_index=None)
+
+    # test lovasz loss has name `loss_lovasz`
+    loss_cfg = dict(
+        type='LovaszLoss',
+        loss_type='binary',
+        per_image=True,
+        reduction='mean',
+        loss_weight=1.0,
+        loss_name='loss_lovasz')
+    lovasz_loss = build_loss(loss_cfg)
+    assert lovasz_loss.loss_name == 'loss_lovasz'