Merge pull request #2532 from Zth9730/wav2vec2.0

[s2t] fix wav2vec2 report loss bug

paddlespeech/s2t/exps/wav2vec2/model.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 """Contains wav2vec2 model."""
 import json
+import math
 import os
 import time
 from collections import defaultdict
@@ -46,25 +47,20 @@ logger = Log(__name__).getlog()
 class Wav2Vec2ASRTrainer(Trainer):
     def __init__(self, config, args):
         super().__init__(config, args)
-        self.avg_train_loss = 0
+        self.avg_train_loss = 0.0
 
-    def update_average(self, batch_index, loss, avg_loss):
+    def update_average(self, batch_index, loss):
         """Update running average of the loss.
         Arguments
         ---------
+        batch_index : int
+            current batch index
         loss : paddle.tensor
             detached loss, a single float value.
-        avg_loss : float
-            current running average.
-        Returns
-        -------
-        avg_loss : float
-            The average loss.
         """
-        if paddle.isfinite(loss):
-            avg_loss -= avg_loss / (batch_index + 1)
-            avg_loss += float(loss) / (batch_index + 1)
-        return avg_loss
+        if math.isfinite(loss):
+            self.avg_train_loss -= self.avg_train_loss / (batch_index + 1)
+            self.avg_train_loss += loss / (batch_index + 1)
 
     def train_batch(self, batch_index, batch, msg):
         train_conf = self.config
@@ -80,8 +76,8 @@ class Wav2Vec2ASRTrainer(Trainer):
         # loss div by `batch_size * accum_grad`
         loss /= train_conf.accum_grad
 
-        self.avg_train_loss = self.update_average(batch_index, loss,
-                                                  self.avg_train_loss)
+        # update self.avg_train_loss
+        self.update_average(batch_index, float(loss))
 
         # loss backward
         if (batch_index + 1) % train_conf.accum_grad != 0:
@@ -106,7 +102,7 @@ class Wav2Vec2ASRTrainer(Trainer):
             self.lr_scheduler.step()
             self.iteration += 1
 
-        losses_np = {'loss': float(self.avg_train_loss) * train_conf.accum_grad}
+        losses_np = {'loss': self.avg_train_loss * train_conf.accum_grad}
         iteration_time = time.time() - start
         for k, v in losses_np.items():
             report(k, v)