fix the resume bug: the lr is not related to iteration, but epoch

deepspeech/exps/deepspeech2/model.py

@@ -123,10 +123,6 @@ class DeepSpeech2Trainer(Trainer):
     def setup_model(self):
         config = self.config.clone()
         config.defrost()
-        assert (self.train_loader.collate_fn.feature_size ==
-                self.test_loader.collate_fn.feature_size)
-        assert (self.train_loader.collate_fn.vocab_size ==
-                self.test_loader.collate_fn.vocab_size)
         config.model.feat_size = self.train_loader.collate_fn.feature_size
         config.model.dict_size = self.train_loader.collate_fn.vocab_size
         config.freeze()

deepspeech/training/trainer.py

@@ -29,37 +29,37 @@ logger = Log(__name__).getlog()
 
 class Trainer():
     """
-    An experiment template in order to structure the training code and take 
+    An experiment template in order to structure the training code and take
-    care of saving, loading, logging, visualization stuffs. It's intended to 
+    care of saving, loading, logging, visualization stuffs. It's intended to
-    be flexible and simple. 
+    be flexible and simple.
-    
+
-    So it only handles output directory (create directory for the output, 
+    So it only handles output directory (create directory for the output,
-    create a checkpoint directory, dump the config in use and create 
+    create a checkpoint directory, dump the config in use and create
     visualizer and logger) in a standard way without enforcing any
-    input-output protocols to the model and dataloader. It leaves the main 
+    input-output protocols to the model and dataloader. It leaves the main
-    part for the user to implement their own (setup the model, criterion, 
+    part for the user to implement their own (setup the model, criterion,
-    optimizer, define a training step, define a validation function and 
+    optimizer, define a training step, define a validation function and
     customize all the text and visual logs).
-    It does not save too much boilerplate code. The users still have to write 
+    It does not save too much boilerplate code. The users still have to write
-    the forward/backward/update mannually, but they are free to add 
+    the forward/backward/update mannually, but they are free to add
     non-standard behaviors if needed.
     We have some conventions to follow.
-    1. Experiment should have ``model``, ``optimizer``, ``train_loader`` and 
+    1. Experiment should have ``model``, ``optimizer``, ``train_loader`` and
     ``valid_loader``, ``config`` and ``args`` attributes.
-    2. The config should have a ``training`` field, which has 
+    2. The config should have a ``training`` field, which has
-    ``valid_interval``, ``save_interval`` and ``max_iteration`` keys. It is 
+    ``valid_interval``, ``save_interval`` and ``max_iteration`` keys. It is
-    used as the trigger to invoke validation, checkpointing and stop of the 
+    used as the trigger to invoke validation, checkpointing and stop of the
     experiment.
-    3. There are four methods, namely ``train_batch``, ``valid``, 
+    3. There are four methods, namely ``train_batch``, ``valid``,
     ``setup_model`` and ``setup_dataloader`` that should be implemented.
-    Feel free to add/overwrite other methods and standalone functions if you 
+    Feel free to add/overwrite other methods and standalone functions if you
     need.
-    
+
     Parameters
     ----------
     config: yacs.config.CfgNode
         The configuration used for the experiment.
-    
+
     args: argparse.Namespace
         The parsed command line arguments.
     Examples
@@ -68,16 +68,16 @@ class Trainer():
     >>>     exp = Trainer(config, args)
     >>>     exp.setup()
     >>>     exp.run()
-    >>> 
+    >>>
     >>> config = get_cfg_defaults()
     >>> parser = default_argument_parser()
     >>> args = parser.parse_args()
-    >>> if args.config: 
+    >>> if args.config:
     >>>     config.merge_from_file(args.config)
     >>> if args.opts:
     >>>     config.merge_from_list(args.opts)
     >>> config.freeze()
-    >>> 
+    >>>
     >>> if args.nprocs > 1 and args.device == "gpu":
     >>>     dist.spawn(main_sp, args=(config, args), nprocs=args.nprocs)
     >>> else:
@@ -114,7 +114,7 @@ class Trainer():
 
     @property
     def parallel(self):
-        """A flag indicating whether the experiment should run with 
+        """A flag indicating whether the experiment should run with
         multiprocessing.
         """
         return self.args.device == "gpu" and self.args.nprocs > 1
@@ -144,9 +144,9 @@ class Trainer():
                                        self.optimizer, infos)
 
     def resume_or_scratch(self):
-        """Resume from latest checkpoint at checkpoints in the output 
+        """Resume from latest checkpoint at checkpoints in the output
         directory or load a specified checkpoint.
-        
+
         If ``args.checkpoint_path`` is not None, load the checkpoint, else
         resume training.
         """
@@ -181,8 +181,7 @@ class Trainer():
         if from_scratch:
             # save init model, i.e. 0 epoch
             self.save(tag='init', infos=None)
-
+        self.lr_scheduler.step(self.epoch)
-        self.lr_scheduler.step(self.iteration)
         if self.parallel:
             self.train_loader.batch_sampler.set_epoch(self.epoch)
 
@@ -254,7 +253,7 @@ class Trainer():
 
     def setup_checkpointer(self):
         """Create a directory used to save checkpoints into.
-        
+
         It is "checkpoints" inside the output directory.
         """
         # checkpoint dir
@@ -277,13 +276,13 @@ class Trainer():
     @mp_tools.rank_zero_only
     def setup_visualizer(self):
         """Initialize a visualizer to log the experiment.
-        
+
         The visual log is saved in the output directory.
-        
+
         Notes
         ------
-        Only the main process has a visualizer with it. Use multiple 
+        Only the main process has a visualizer with it. Use multiple
-        visualizers in multiprocess to write to a same log file may cause 
+        visualizers in multiprocess to write to a same log file may cause
         unexpected behaviors.
         """
         # visualizer
@@ -292,9 +291,9 @@ class Trainer():
 
     @mp_tools.rank_zero_only
     def dump_config(self):
-        """Save the configuration used for this experiment. 
+        """Save the configuration used for this experiment.
-        
+
-        It is saved in to ``config.yaml`` in the output directory at the 
+        It is saved in to ``config.yaml`` in the output directory at the
         beginning of the experiment.
         """
         with open(self.output_dir / "config.yaml", 'wt') as f:
@@ -312,13 +311,13 @@ class Trainer():
         raise NotImplementedError("valid should be implemented.")
 
     def setup_model(self):
-        """Setup model, criterion and optimizer, etc. A subclass should 
+        """Setup model, criterion and optimizer, etc. A subclass should
         implement this method.
         """
         raise NotImplementedError("setup_model should be implemented.")
 
     def setup_dataloader(self):
-        """Setup training dataloader and validation dataloader. A subclass 
+        """Setup training dataloader and validation dataloader. A subclass
         should implement this method.
         """
         raise NotImplementedError("setup_dataloader should be implemented.")