Apply d2760687

dan/manager/ocr.py

@@ -46,8 +46,6 @@ class OCRDatasetManager:
         self.generator = torch.Generator()
         self.generator.manual_seed(0)
 
-        self.batch_size = self.get_batch_size_by_set()
-
         self.load_in_memory = (
             self.params["config"]["load_in_memory"]
             if "load_in_memory" in self.params["config"]
@@ -126,7 +124,7 @@ class OCRDatasetManager:
         """
         self.train_loader = DataLoader(
             self.train_dataset,
-            batch_size=self.batch_size["train"],
+            batch_size=self.params["batch_size"],
             shuffle=True if self.train_sampler is None else False,
             drop_last=False,
             batch_sampler=self.train_sampler,
@@ -141,7 +139,7 @@ class OCRDatasetManager:
         for key in self.valid_datasets.keys():
             self.valid_loaders[key] = DataLoader(
                 self.valid_datasets[key],
-                batch_size=self.batch_size["val"],
+                batch_size=1,
                 sampler=self.valid_samplers[key],
                 batch_sampler=self.valid_samplers[key],
                 shuffle=False,
@@ -197,7 +195,7 @@ class OCRDatasetManager:
 
         self.test_loaders[custom_name] = DataLoader(
             self.test_datasets[custom_name],
-            batch_size=self.batch_size["test"],
+            batch_size=1,
             sampler=self.test_samplers[custom_name],
             shuffle=False,
             num_workers=self.params["num_gpu"] * self.params["worker_per_gpu"],
@@ -243,20 +241,6 @@ class OCRDatasetManager:
             "pad": len(self.charset) + 2,
         }
 
-    def get_batch_size_by_set(self):
-        """
-        Return batch size for each set
-        """
-        return {
-            "train": self.params["batch_size"],
-            "val": self.params["valid_batch_size"]
-            if "valid_batch_size" in self.params
-            else self.params["batch_size"],
-            "test": self.params["test_batch_size"]
-            if "test_batch_size" in self.params
-            else 1,
-        }
-
 
 class OCRCollateFunction:
     """

dan/manager/training.py

@@ -84,14 +84,6 @@ class GenericTrainingManager:
         self.params["dataset_params"]["batch_size"] = self.params["training_params"][
             "batch_size"
         ]
-        if "valid_batch_size" in self.params["training_params"]:
-            self.params["dataset_params"]["valid_batch_size"] = self.params[
-                "training_params"
-            ]["valid_batch_size"]
-        if "test_batch_size" in self.params["training_params"]:
-            self.params["dataset_params"]["test_batch_size"] = self.params[
-                "training_params"
-            ]["test_batch_size"]
         self.params["dataset_params"]["num_gpu"] = self.params["training_params"][
             "nb_gpu"
         ]

dan/ocr/document/train.py

@@ -170,7 +170,6 @@ def get_config():
             "load_epoch": "last",  # ["best", "last"]: last to continue training, best to evaluate
             "interval_save_weights": None,  # None: keep best and last only
             "batch_size": 2,  # mini-batch size for training
-            "valid_batch_size": 4,  # mini-batch size for valdiation
             "use_ddp": False,  # Use DistributedDataParallel
             "ddp_port": "20027",
             "use_amp": True,  # Enable automatic mix-precision

docs/usage/train/parameters.md

@@ -162,7 +162,6 @@ For a detailed description of all augmentation transforms, see the [dedicated pa
 | `training_params.load_epoch`                            | Model to load. Should be either `"best"` (evaluation) or `last` (training). | `str`        | `"last"`                                    |
 | `training_params.interval_save_weights`                 | Step to save weights. Set to `None` to keep only best and last epochs.      | `int`        | `None`                                      |
 | `training_params.batch_size`                            | Mini-batch size for the training loop.                                      | `int`        | `2`                                         |
-| `training_params.valid_batch_size`                      | Mini-batch size for the valdiation loop.                                    | `int`        | `4`                                         |
 | `training_params.use_ddp`                               | Whether to use DistributedDataParallel.                                     | `bool`       | `False`                                     |
 | `training_params.ddp_port`                              | DDP port.                                                                   | `int`        | `20027`                                     |
 | `training_params.use_amp`                               | Whether to enable automatic mix-precision.                                  | `int`        | `torch.cuda.device_count()`                 |
@@ -184,6 +183,7 @@ For a detailed description of all augmentation transforms, see the [dedicated pa
 | `training_params.label_noise_scheduler.max_error_rate`  | Maximum ratio of teacher forcing.                                           | `float`      | `0.2`                                       |
 | `training_params.label_noise_scheduler.total_num_steps` | Number of steps before stopping teacher forcing.                            | `float`      | `5e4`                                       |
 
+During the validation stage, the batch size is set to 1. This avoids problems associated with image sizes that can be very different inside batches and lead to significant padding, resulting in performance degradations.
 
 ## MLFlow logging
 

tests/conftest.py

@@ -116,7 +116,6 @@ def training_config():
             "load_epoch": "last",  # ["best", "last"]: last to continue training, best to evaluate
             "interval_save_weights": None,  # None: keep best and last only
             "batch_size": 2,  # mini-batch size for training
-            "valid_batch_size": 2,  # mini-batch size for valdiation
             "use_ddp": False,  # Use DistributedDataParallel
             "nb_gpu": 0,
             "optimizers": {