save best and test on tiny/s0

deepspeech/training/trainer.py

@@ -18,7 +18,7 @@ import paddle
 from paddle import distributed as dist
 from tensorboardX import SummaryWriter
 
-from deepspeech.utils import checkpoint
+from deepspeech.utils.checkpoint import KBestCheckpoint
 from deepspeech.utils import mp_tools
 from deepspeech.utils.log import Log
 
@@ -139,9 +139,12 @@ class Trainer():
             "epoch": self.epoch,
             "lr": self.optimizer.get_lr()
         })
-        checkpoint.save_parameters(self.checkpoint_dir, self.iteration
+        self.checkpoint.add_checkpoint(self.checkpoint_dir, self.iteration
                                    if tag is None else tag, self.model,
                                    self.optimizer, infos)
+        # checkpoint.save_parameters(self.checkpoint_dir, self.iteration
+        #                            if tag is None else tag, self.model,
+        #                            self.optimizer, infos)
 
     def resume_or_scratch(self):
         """Resume from latest checkpoint at checkpoints in the output 
@@ -151,7 +154,7 @@ class Trainer():
         resume training.
         """
         scratch = None
-        infos = checkpoint.load_parameters(
+        infos = self.checkpoint.load_parameters(
             self.model,
             self.optimizer,
             checkpoint_dir=self.checkpoint_dir,
@@ -180,7 +183,7 @@ class Trainer():
         from_scratch = self.resume_or_scratch()
         if from_scratch:
             # save init model, i.e. 0 epoch
-            self.save(tag='init')
+            self.save(tag='init', infos=None)
 
         self.lr_scheduler.step(self.iteration)
         if self.parallel:
@@ -263,6 +266,9 @@ class Trainer():
 
         self.checkpoint_dir = checkpoint_dir
 
+        self.checkpoint = KBestCheckpoint(max_size=self.config.training.max_epoch, 
+                            last_size=self.config.training.last_epoch)
+
     @mp_tools.rank_zero_only
     def destory(self):
         """Close visualizer to avoid hanging after training"""

deepspeech/utils/checkpoint.py

@@ -23,19 +23,110 @@ from paddle.optimizer import Optimizer
 from deepspeech.utils import mp_tools
 from deepspeech.utils.log import Log
 
+import glob
+
 logger = Log(__name__).getlog()
 
 __all__ = ["load_parameters", "save_parameters"]
 
+class KBestCheckpoint(object):
+    def __init__(self,
+                 max_size: int=5,
+                 last_size: int=1):
+        self.best_records: Mapping[Path, float] = {}
+        self.last_records = []
+        self.max_size = max_size
+        self.last_size = last_size
+        self._save_all = (max_size == -1)
+    
+    def should_save_best(self, metric: float) -> bool:
+        if not self.best_full():
+            return True
+
+        # already full
+        worst_record_path = max(self.best_records, key=self.best_records.get)
+        worst_metric = self.best_records[worst_record_path]
+        return metric < worst_metric
+
+    def best_full(self):
+        return (not self._save_all) and len(self.best_records) == self.max_size
+    
+    def last_full(self):
+        return len(self.last_records) == self.last_size
+
+    def add_checkpoint(self,
+                        checkpoint_dir, tag_or_iteration,
+                        model, optimizer, infos):
+        if("val_loss" not in infos.keys()):
+            self.save_parameters(checkpoint_dir, tag_or_iteration, 
+                                model, optimizer, infos)
+            return
+
+        #save best
+        if self.should_save_best(infos["val_loss"]):
+            self.save_checkpoint_and_update(infos["val_loss"], 
+                        checkpoint_dir, tag_or_iteration,
+                        model, optimizer, infos)
+        #save last
+        self.save_last_checkpoint_and_update(checkpoint_dir, tag_or_iteration,
+                        model, optimizer, infos)
+        
+        if isinstance(tag_or_iteration, int):
+            self._save_record(checkpoint_dir, tag_or_iteration)
+    
+    def save_checkpoint_and_update(self, metric, 
+                        checkpoint_dir, tag_or_iteration,
+                        model, optimizer, infos):
+        # remove the worst
+        if self.best_full():
+            worst_record_path = max(self.best_records,
+                                    key=self.best_records.get)
+            self.best_records.pop(worst_record_path)
+            if(worst_record_path not in self.last_records):
+                print('----to remove (best)----')
+                print(worst_record_path)
+                self.del_checkpoint(checkpoint_dir, worst_record_path)
+
+        # add the new one
+        self.save_parameters(checkpoint_dir, tag_or_iteration,
+                        model, optimizer, infos)
+        self.best_records[tag_or_iteration] = metric
+ 
+    def save_last_checkpoint_and_update(self, checkpoint_dir, tag_or_iteration,
+                        model, optimizer, infos):
+        # remove the old
+        if self.last_full():
+            to_del_fn = self.last_records.pop(0)
+            if(to_del_fn not in self.best_records.keys()):
+                print('----to remove (last)----')
+                print(to_del_fn)
+                self.del_checkpoint(checkpoint_dir, to_del_fn)
+        self.last_records.append(tag_or_iteration)
+
+        self.save_parameters(checkpoint_dir, tag_or_iteration,
+                    model, optimizer, infos)
+            # with open(os.path.join(checkpoint_dir, "checkpoint"), "w") as handle:
+            #     for iteration in self.best_records
+            #     handle.write("model_checkpoint_path:{}\n".format(iteration))
+
+
+    def del_checkpoint(self, checkpoint_dir, tag_or_iteration):
+        checkpoint_path = os.path.join(checkpoint_dir,
+                                    "{}".format(tag_or_iteration))
+        for filename in glob.glob(checkpoint_path+".*"):
+            os.remove(filename)
+            print("delete file: "+filename)
+        
 
-def _load_latest_checkpoint(checkpoint_dir: str) -> int:
+
+    def _load_latest_checkpoint(self, checkpoint_dir: str) -> int:
         """Get the iteration number corresponding to the latest saved checkpoint.
         Args:
             checkpoint_dir (str): the directory where checkpoint is saved.
         Returns:
             int: the latest iteration number. -1 for no checkpoint to load.
         """
-    checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
+        checkpoint_record = os.path.join(checkpoint_dir, "checkpoint_last")
         if not os.path.isfile(checkpoint_record):
             return -1
 
@@ -46,7 +137,7 @@ def _load_latest_checkpoint(checkpoint_dir: str) -> int:
         return iteration
 
 
-def _save_record(checkpoint_dir: str, iteration: int):
+    def _save_record(self, checkpoint_dir: str, iteration: int):
         """Save the iteration number of the latest model to be checkpoint record.
         Args:
             checkpoint_dir (str): the directory where checkpoint is saved.
@@ -54,13 +145,20 @@ def _save_record(checkpoint_dir: str, iteration: int):
         Returns:
             None
         """
-    checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
+        checkpoint_record_last = os.path.join(checkpoint_dir, "checkpoint_last")
+        checkpoint_record_best = os.path.join(checkpoint_dir, "checkpoint_best")
         # Update the latest checkpoint index.
-    with open(checkpoint_record, "a+") as handle:
-        handle.write("model_checkpoint_path:{}\n".format(iteration))
+        # with open(checkpoint_record, "a+") as handle:
+        #     handle.write("model_checkpoint_path:{}\n".format(iteration))
+        with open(checkpoint_record_best, "w") as handle:
+            for i in self.best_records.keys():
+                handle.write("model_checkpoint_path:{}\n".format(i))
+        with open(checkpoint_record_last, "w") as handle:
+            for i in self.last_records:
+                handle.write("model_checkpoint_path:{}\n".format(i))
 
 
-def load_parameters(model,
+    def load_parameters(self, model,
                         optimizer=None,
                         checkpoint_dir=None,
                         checkpoint_path=None):
@@ -81,7 +179,7 @@ def load_parameters(model,
         if checkpoint_path is not None:
             tag = os.path.basename(checkpoint_path).split(":")[-1]
         elif checkpoint_dir is not None:
-        iteration = _load_latest_checkpoint(checkpoint_dir)
+            iteration = self._load_latest_checkpoint(checkpoint_dir)
             if iteration == -1:
                 return configs
             checkpoint_path = os.path.join(checkpoint_dir, "{}".format(iteration))
@@ -111,8 +209,8 @@ def load_parameters(model,
         return configs
 
 
-@mp_tools.rank_zero_only
-def save_parameters(checkpoint_dir: str,
+    @mp_tools.rank_zero_only
+    def save_parameters(self, checkpoint_dir: str,
                         tag_or_iteration: Union[int, str],
                         model: paddle.nn.Layer,
                         optimizer: Optimizer=None,
@@ -148,5 +246,3 @@ def save_parameters(checkpoint_dir: str,
             data = json.dumps(infos)
             fout.write(data)
 
-    if isinstance(tag_or_iteration, int):
-        _save_record(checkpoint_dir, tag_or_iteration)

examples/tiny/s0/conf/deepspeech2.yaml

@@ -43,12 +43,15 @@ model:
   share_rnn_weights: True 
 
 training:
-  n_epoch: 24
+  n_epoch: 6
   lr: 1e-5 
   lr_decay: 1.0 
   weight_decay: 1e-06
   global_grad_clip: 5.0
   log_interval: 1
+  max_epoch: 3
+  last_epoch: 2
+
 
 decoding:
   batch_size: 128