diff --git a/dan/manager/ocr.py b/dan/manager/ocr.py
index 383ac455aea8cd6ed104f81be85eb17bafec8952..39dc48117c8b1e5b7df90f618a661922a3b325f6 100644
--- a/dan/manager/ocr.py
+++ b/dan/manager/ocr.py
@@ -39,8 +39,6 @@ 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"]
@@ -116,7 +114,7 @@ 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,
@@ -131,7 +129,7 @@ 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,
@@ -185,7 +183,7 @@ class OCRDatasetManager:
self.test_samplers[custom_name] = None
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"],
@@ -231,20 +229,6 @@ 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 562966dc2a57360faa233e73236704cad9842cb4..5e2b8b293a097998120da31bd27957079b5b36ea 100644
--- a/docs/usage/train/parameters.md
+++ b/docs/usage/train/parameters.md
@@ -153,7 +153,6 @@ 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()` |
@@ -175,6 +174,7 @@ 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": {