修复与pytorch1.1中的padsequence的兼容问题; 修改Trainer的pbar

fastNLP/core/callback.py

@@ -130,7 +130,8 @@ class Callback(object):
     
     @property
     def pbar(self):
-        """如果在Callback中需要打印内容，请使用self.pbar.write(str)。否则可能出现命令行显示效果不太好的问题。"""
+        """如果在Callback中需要打印内容，请使用self.pbar.write(str)。否则可能出现命令行显示效果不太好的问题。在
+        on_train_begin(), on_train_end(), on_exception()中请不要使用该属性，通过print输出即可。"""
         return self._trainer.pbar
     
     @property
@@ -440,7 +441,7 @@ class LRScheduler(Callback):
             raise ValueError(f"Expect torch.optim.lr_scheduler for LRScheduler. Got {type(lr_scheduler)}.")
     
     def on_epoch_begin(self):
-        self.scheduler.step()
+        self.scheduler.step(self.epoch)
 
 
 class ControlC(Callback):
@@ -526,7 +527,7 @@ class LRFinder(Callback):
             if torch.isnan(loss) or self.stop is True:
                 self.stop = True
                 return
-            loss_val = loss.detach().cpu().data
+            loss_val = loss.detach().mean().item()
             self.loss_history.append(loss_val)
             self.smooth_value.add_value(loss_val)
             if self.best_loss == 0. or self.smooth_value.smooth < self.best_loss:
@@ -548,7 +549,7 @@ class LRFinder(Callback):
             self.find = False
             # reset model
             ModelLoader().load_pytorch(self.trainer.model, "tmp")
-            print("Model reset. \nFind best lr={}".format(self.best_lr))
+            self.pbar.write("Model reset. \nFind best lr={}".format(self.best_lr))
 
 
 class TensorboardCallback(Callback):

fastNLP/core/trainer.py

@@ -558,7 +558,7 @@ class Trainer(object):
         start = time.time()
         
         with inner_tqdm(total=self.n_steps, postfix='loss:{0:<6.5f}', leave=False, dynamic_ncols=True) as pbar:
-            self.pbar = pbar if isinstance(pbar, tqdm) else None
+            self.pbar = pbar
             avg_loss = 0
             data_iterator = Batch(self.train_data, batch_size=self.batch_size, sampler=self.sampler, as_numpy=False,
                                   prefetch=self.prefetch)

fastNLP/modules/encoder/variational_rnn.py

@@ -43,7 +43,7 @@ class VarRnnCellWrapper(nn.Module):
                 return torch.cat([hi, h0[:h0_size]], dim=0)
             return hi[:size]
         is_lstm = isinstance(hidden, tuple)
-        input, batch_sizes = input_x
+        input, batch_sizes = input_x.data, input_x.batch_sizes
         output = []
         cell = self.cell
         if is_reversed:
@@ -148,10 +148,11 @@ class VarRNNBase(nn.Module):
             seq_len = x.size(1) if self.batch_first else x.size(0)
             max_batch_size = x.size(0) if self.batch_first else x.size(1)
             seq_lens = torch.LongTensor([seq_len for _ in range(max_batch_size)])
-            x, batch_sizes = pack_padded_sequence(x, seq_lens, batch_first=self.batch_first)
+            _tmp = pack_padded_sequence(x, seq_lens, batch_first=self.batch_first)
+            x, batch_sizes = _tmp.data, _tmp.batch_sizes
         else:
             max_batch_size = int(x.batch_sizes[0])
-            x, batch_sizes = x
+            x, batch_sizes = x.data, x.batch_sizes
 
         if hx is None:
             hx = x.new_zeros(self.num_layers * self.num_directions,