[vec][loss] add FocalLoss to deal with class imbalances, test=doc fix #1721

paddlespeech/vector/modules/loss.py

@@ -132,7 +132,7 @@ class NCELoss(nn.Layer):
 
     def forward(self, output, target):
         """Forward inference
-        
+
         Args:
             output (tensor): the model output, which is the input of loss function
         """
@@ -161,7 +161,7 @@ class NCELoss(nn.Layer):
         """Post processing the score of post model(output of nn) of batchsize data
         """
         scores = self.get_scores(idx, scores)
-        scale = paddle.to_tensor([self.Z_offset], dtype='float32')
+        scale = paddle.to_tensor([self.Z_offset], dtype='float64')
         scores = paddle.add(scores, -scale)
         prob = paddle.exp(scores)
         if sep_target:
@@ -225,3 +225,65 @@ class NCELoss(nn.Layer):
         loss = -(model_loss + noise_loss)
 
         return loss
+
+
+class FocalLoss(nn.Layer):
+    """This criterion is a implemenation of Focal Loss, which is proposed in 
+    Focal Loss for Dense Object Detection.
+
+        Loss(x, class) = - \alpha (1-softmax(x)[class])^gamma \log(softmax(x)[class])
+
+    The losses are averaged across observations for each minibatch.
+
+    Args:
+        alpha(1D Tensor, Variable) : the scalar factor for this criterion
+        gamma(float, double) : gamma > 0; reduces the relative loss for well-classified examples (p > .5), 
+                                putting more focus on hard, misclassified examples
+        size_average(bool): By default, the losses are averaged over observations for each minibatch.
+                            However, if the field size_average is set to False, the losses are
+                            instead summed for each minibatch.
+    """
+
+    def __init__(self, alpha=1, gamma=0, size_average=True, ignore_index=-100):
+        super(FocalLoss, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+        self.size_average = size_average
+        self.ce = nn.CrossEntropyLoss(
+            ignore_index=ignore_index, reduction="none")
+
+    def forward(self, outputs, targets):
+        """Forword inference.
+
+        Args:
+            outputs: input tensor
+            target: target label tensor
+        """
+        ce_loss = self.ce(outputs, targets)
+        pt = paddle.exp(-ce_loss)
+        focal_loss = self.alpha * (1 - pt)**self.gamma * ce_loss
+        if self.size_average:
+            return focal_loss.mean()
+        else:
+            return focal_loss.sum()
+
+
+if __name__ == "__main__":
+    import numpy as np
+    from paddlespeech.vector.utils.vector_utils import Q_from_tokens
+    paddle.set_device("cpu")
+
+    input_data = paddle.uniform([5, 100], dtype="float64")
+    label_data = np.random.randint(0, 100, size=(5)).astype(np.int64)
+
+    input = paddle.to_tensor(input_data)
+    label = paddle.to_tensor(label_data)
+
+    loss1 = FocalLoss()
+    loss = loss1.forward(input, label)
+    print("loss: %.5f" % (loss))
+
+    Q = Q_from_tokens(100)
+    loss2 = NCELoss(Q)
+    loss = loss2.forward(input, label)
+    print("loss: %.5f" % (loss))

paddlespeech/vector/utils/vector_utils.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import paddle
 
 
 def get_chunks(seg_dur, audio_id, audio_duration):