diff --git a/dan/manager/ocr.py b/dan/manager/ocr.py
index 9425632335798b5eb7933d8462bd5da7e66b6158..8391eb7888ea3b23604d4d5dab815194e71c1b79 100644
--- a/dan/manager/ocr.py
+++ b/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:
"""
diff --git a/dan/manager/training.py b/dan/manager/training.py
index 3b472a2b77a491a64b02e9d6d4c3870d2ef34158..f231e372e6cf5c76c84281da03fc2051f31c6fb2 100644
--- a/dan/manager/training.py
+++ b/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"
]
diff --git a/dan/ocr/document/train.py b/dan/ocr/document/train.py
index 189bc2b993c2cbcd37a2d21097ba40a72b32c773..7ba39de33f2043016653f88fe4f92de2bcd43562 100644
--- a/dan/ocr/document/train.py
+++ b/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
diff --git a/docs/usage/train/parameters.md b/docs/usage/train/parameters.md
index de9339d2efe7f1b87cb963745cc7df297ed916fa..ce10c3299fd5d0ab8624cf15e30a668246be295f 100644
--- a/docs/usage/train/parameters.md
+++ b/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
diff --git a/tests/conftest.py b/tests/conftest.py
index a38d6a01fa4282106e67d9193822a0e131704147..e398d9df0563fea16ae13478ec6f57b2c055abca 100644
--- a/tests/conftest.py
+++ b/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": {