exp with eval mode

deepspeech/exps/deepspeech2/model.py

@@ -167,6 +167,11 @@ class DeepSpeech2Trainer(Trainer):
 
         logger.info(f"{model}")
         layer_tools.print_params(model, logger.info)
+        self.model = model
+        logger.info("Setup model!")
+
+        if not self.train:
+            return
 
         grad_clip = ClipGradByGlobalNormWithLog(
             config.training.global_grad_clip)
@@ -180,74 +185,77 @@ class DeepSpeech2Trainer(Trainer):
             weight_decay=paddle.regularizer.L2Decay(
                 config.training.weight_decay),
             grad_clip=grad_clip)
-
-        self.model = model
         self.optimizer = optimizer
         self.lr_scheduler = lr_scheduler
-        logger.info("Setup model/optimizer/lr_scheduler!")
+        logger.info("Setup optimizer/lr_scheduler!")
+
 
     def setup_dataloader(self):
         config = self.config.clone()
         config.defrost()
-        config.collator.keep_transcription_text = False
-
-        config.data.manifest = config.data.train_manifest
-        train_dataset = ManifestDataset.from_config(config)
-
-        config.data.manifest = config.data.dev_manifest
-        dev_dataset = ManifestDataset.from_config(config)
-
-        config.data.manifest = config.data.test_manifest
-        test_dataset = ManifestDataset.from_config(config)
-
-        if self.parallel:
-            batch_sampler = SortagradDistributedBatchSampler(
+        if self.train:
+            # train
+            config.data.manifest = config.data.train_manifest
+            train_dataset = ManifestDataset.from_config(config)
+            if self.parallel:
+                batch_sampler = SortagradDistributedBatchSampler(
+                    train_dataset,
+                    batch_size=config.collator.batch_size,
+                    num_replicas=None,
+                    rank=None,
+                    shuffle=True,
+                    drop_last=True,
+                    sortagrad=config.collator.sortagrad,
+                    shuffle_method=config.collator.shuffle_method)
+            else:
+                batch_sampler = SortagradBatchSampler(
+                    train_dataset,
+                    shuffle=True,
+                    batch_size=config.collator.batch_size,
+                    drop_last=True,
+                    sortagrad=config.collator.sortagrad,
+                    shuffle_method=config.collator.shuffle_method)
+
+            config.collator.keep_transcription_text = False
+            collate_fn_train = SpeechCollator.from_config(config)
+            self.train_loader = DataLoader(
                 train_dataset,
-                batch_size=config.collator.batch_size,
-                num_replicas=None,
-                rank=None,
-                shuffle=True,
-                drop_last=True,
-                sortagrad=config.collator.sortagrad,
-                shuffle_method=config.collator.shuffle_method)
+                batch_sampler=batch_sampler,
+                collate_fn=collate_fn_train,
+                num_workers=config.collator.num_workers)
+
+            # dev
+            config.data.manifest = config.data.dev_manifest
+            dev_dataset = ManifestDataset.from_config(config)
+
+            config.collator.augmentation_config = ""
+            config.collator.keep_transcription_text = False
+            collate_fn_dev = SpeechCollator.from_config(config)
+            self.valid_loader = DataLoader(
+                dev_dataset,
+                batch_size=int(config.collator.batch_size),
+                shuffle=False,
+                drop_last=False,
+                collate_fn=collate_fn_dev,
+                num_workers=config.collator.num_workers)
+            logger.info("Setup train/valid  Dataloader!")
         else:
-            batch_sampler = SortagradBatchSampler(
-                train_dataset,
-                shuffle=True,
-                batch_size=config.collator.batch_size,
-                drop_last=True,
-                sortagrad=config.collator.sortagrad,
-                shuffle_method=config.collator.shuffle_method)
-
-        collate_fn_train = SpeechCollator.from_config(config)
-
-        config.collator.augmentation_config = ""
-        collate_fn_dev = SpeechCollator.from_config(config)
-
-        config.collator.keep_transcription_text = True
-        config.collator.augmentation_config = ""
-        collate_fn_test = SpeechCollator.from_config(config)
-
-        self.train_loader = DataLoader(
-            train_dataset,
-            batch_sampler=batch_sampler,
-            collate_fn=collate_fn_train,
-            num_workers=config.collator.num_workers)
-        self.valid_loader = DataLoader(
-            dev_dataset,
-            batch_size=int(config.collator.batch_size),
-            shuffle=False,
-            drop_last=False,
-            collate_fn=collate_fn_dev,
-            num_workers=config.collator.num_workers)
-        self.test_loader = DataLoader(
-            test_dataset,
-            batch_size=config.decoding.batch_size,
-            shuffle=False,
-            drop_last=False,
-            collate_fn=collate_fn_test,
-            num_workers=config.collator.num_workers)
-        logger.info("Setup train/valid/test  Dataloader!")
+            # test
+            config.data.manifest = config.data.test_manifest
+            test_dataset = ManifestDataset.from_config(config)
+
+            config.collator.augmentation_config = ""
+            config.collator.keep_transcription_text = True
+            collate_fn_test = SpeechCollator.from_config(config)
+
+            self.test_loader = DataLoader(
+                test_dataset,
+                batch_size=config.decoding.batch_size,
+                shuffle=False,
+                drop_last=False,
+                collate_fn=collate_fn_test,
+                num_workers=config.collator.num_workers)
+            logger.info("Setup test  Dataloader!")
 
 
 class DeepSpeech2Tester(DeepSpeech2Trainer):

deepspeech/exps/u2/model.py

@@ -172,7 +172,7 @@ class U2Trainer(Trainer):
             dist.get_rank(), total_loss / num_seen_utts))
         return total_loss, num_seen_utts
 
-    def train(self):
+    def do_train(self):
         """The training process control by step."""
         # !!!IMPORTANT!!!
         # Try to export the model by script, if fails, we should refine

deepspeech/exps/u2_kaldi/model.py

@@ -173,7 +173,7 @@ class U2Trainer(Trainer):
             dist.get_rank(), total_loss / num_seen_utts))
         return total_loss, num_seen_utts
 
-    def train(self):
+    def do_train(self):
         """The training process control by step."""
         # !!!IMPORTANT!!!
         # Try to export the model by script, if fails, we should refine

deepspeech/exps/u2_st/model.py

@@ -184,7 +184,7 @@ class U2STTrainer(Trainer):
             dist.get_rank(), total_loss / num_seen_utts))
         return total_loss, num_seen_utts
 
-    def train(self):
+    def do_train(self):
         """The training process control by step."""
         # !!!IMPORTANT!!!
         # Try to export the model by script, if fails, we should refine

deepspeech/training/trainer.py

@@ -134,6 +134,10 @@ class Trainer():
             logger.info(
                 f"Benchmark reset batch-size: {self.args.benchmark_batch_size}")
 
+    @property
+    def train(self):
+        return self._train
+
     @contextmanager
     def eval(self):
         self._train = False
@@ -248,7 +252,7 @@ class Trainer():
             sys.exit(
                 f"Reach benchmark-max-step: {self.args.benchmark_max_step}")
 
-    def train(self):
+    def do_train(self):
         """The training process control by epoch."""
         self.before_train()
 
@@ -321,7 +325,7 @@ class Trainer():
         """
         try:
             with Timer("Training Done: {}"):
-                self.train()
+                self.do_train()
         except KeyboardInterrupt:
             exit(-1)
         finally:
@@ -432,7 +436,7 @@ class Trainer():
         beginning of the experiment.
         """
         config_file = self.config_dir / "config.yaml"
-        if self._train and config_file.exists():
+        if self.train and config_file.exists():
             time_stamp = time.strftime("%Y_%m_%d_%H_%M_%s", time.gmtime())
             target_path = self.config_dir / ".".join(
                 [time_stamp, "config.yaml"])