dan/manager/dataset.py

@@ -18,7 +18,6 @@ class DatasetManager:
     def __init__(self, params, device: str):
         self.params = params
         self.dataset_class = None
-        self.img_padding_value = params["config"]["padding_value"]
 
         self.my_collate_function = None
         # Whether data should be copied on GPU via https://pytorch.org/docs/stable/generated/torch.Tensor.pin_memory.html
@@ -224,13 +223,6 @@ class GenericDataset(Dataset):
         if self.load_in_memory:
             self.apply_preprocessing(params["config"]["preprocessings"])
 
-        self.padding_value = params["config"]["padding_value"]
-        if self.padding_value == "mean":
-            if self.mean is None:
-                _, _ = self.compute_std_mean()
-            self.padding_value = self.mean
-            self.params["config"]["padding_value"] = self.padding_value
-
         self.curriculum_config = None
 
     def __len__(self):

dan/manager/ocr.py

@@ -25,14 +25,9 @@ class OCRDatasetManager(DatasetManager):
             params["charset"] if "charset" in params else self.get_merged_charsets()
         )
 
-        self.tokens = {
-            "pad": params["config"]["padding_token"],
-        }
+        self.tokens = {"pad": len(self.charset) + 2}
         self.tokens["end"] = len(self.charset)
         self.tokens["start"] = len(self.charset) + 1
-        self.tokens["pad"] = (
-            self.tokens["pad"] if self.tokens["pad"] else len(self.charset) + 2
-        )
         self.params["config"]["padding_token"] = self.tokens["pad"]
 
     def get_merged_charsets(self):
@@ -77,9 +72,8 @@ class OCRDataset(GenericDataset):
         super(OCRDataset, self).__init__(params, set_name, custom_name, paths_and_sets)
         self.charset = None
         self.tokens = None
-        self.reduce_dims_factor = np.array(
-            [params["config"]["height_divisor"], params["config"]["width_divisor"], 1]
-        )
+        # Factor to reduce the height and width of the feature vector before feeding the decoder.
+        self.reduce_dims_factor = np.array([32, 8, 1])
         self.collate_function = OCRCollateFunction
 
     def __getitem__(self, idx):
@@ -143,7 +137,6 @@ class OCRDataset(GenericDataset):
 
                 sample["img"], sample["img_position"] = pad_image(
                     sample["img"],
-                    padding_value=self.padding_value,
                     new_width=self.params["config"]["padding"]["min_width"],
                     new_height=self.params["config"]["padding"]["min_height"],
                     pad_width=pad_width,
@@ -177,7 +170,6 @@ class OCRCollateFunction:
     """
 
     def __init__(self, config):
-        self.img_padding_value = float(config["padding_value"])
         self.label_padding_value = config["padding_token"]
         self.config = config
 
@@ -190,9 +182,7 @@ class OCRCollateFunction:
             self.config["padding_mode"] if "padding_mode" in self.config else "br"
         )
         imgs = [batch_data[i]["img"] for i in range(len(batch_data))]
-        imgs = pad_images(
-            imgs, padding_value=self.img_padding_value, padding_mode=padding_mode
-        )
+        imgs = pad_images(imgs, padding_mode=padding_mode)
         imgs = torch.tensor(imgs).float().permute(0, 3, 1, 2)
 
         formatted_batch_data = {

dan/ocr/document/train.py

@@ -105,10 +105,6 @@ def get_config():
             "config": {
                 "load_in_memory": True,  # Load all images in CPU memory
                 "worker_per_gpu": 4,  # Num of parallel processes per gpu for data loading
-                "width_divisor": 8,  # Image width will be divided by 8
-                "height_divisor": 32,  # Image height will be divided by 32
-                "padding_value": 0,  # Image padding value
-                "padding_token": None,  # Label padding value
                 "preprocessings": [
                     {
                         "type": "to_RGB",

dan/utils.py

@@ -25,7 +25,7 @@ def pad_sequences_1D(data, padding_value):
     return padded_data
 
 
-def pad_images(data, padding_value, padding_mode="br"):
+def pad_images(data, padding_mode="br"):
     """
     data: list of numpy array
     mode: "br"/"tl"/"random" (bottom-right, top-left, random)
@@ -34,9 +34,7 @@ def pad_images(data, padding_value, padding_mode="br"):
     y_lengths = [x.shape[1] for x in data]
     longest_x = max(x_lengths)
     longest_y = max(y_lengths)
-    padded_data = (
-        np.ones((len(data), longest_x, longest_y, data[0].shape[2])) * padding_value
-    )
+    padded_data = np.zeros((len(data), longest_x, longest_y, data[0].shape[2]))
     for i, xy_len in enumerate(zip(x_lengths, y_lengths)):
         x_len, y_len = xy_len
         if padding_mode == "br":
@@ -56,7 +54,6 @@ def pad_images(data, padding_value, padding_mode="br"):
 
 def pad_image(
     image,
-    padding_value,
     new_height=None,
     new_width=None,
     pad_width=None,
@@ -90,7 +87,7 @@ def pad_image(
     )
 
     if not (pad_width == 0 and pad_height == 0):
-        padded_image = np.ones((h + pad_height, w + pad_width, c)) * padding_value
+        padded_image = np.zeros((h + pad_height, w + pad_width, c))
         if padding_mode == "br":
             hi, wi = 0, 0
         elif padding_mode == "tl":

docs/usage/train/parameters.md

@@ -14,10 +14,6 @@ All hyperparameters are specified and editable in the training scripts (meaning
 | `dataset_params.config.datasets`        | Dataset dictionary with the dataset name as key and dataset path as value.             | `dict`       |                                                |
 | `dataset_params.config.load_in_memory`  | Load all images in CPU memory.                                                         | `str`        | `True`                                         |
 | `dataset_params.config.worker_per_gpu`  | Number of parallel processes per gpu for data loading.                                 | `int`        | `4`                                            |
-| `dataset_params.config.height_divisor`  | Factor to reduce the width of the feature vector before feeding the decoder.           | `int`        | `8`                                            |
-| `dataset_params.config.width_divisor`   | Factor to reduce the height of the feature vector before feeding the decoder.          | `int`        | `32`                                           |
-| `dataset_params.config.padding_value`   | Image padding value.                                                                   | `int`        | `0`                                            |
-| `dataset_params.config.padding_token`   | Transcription padding value.                                                           | `int`        | `None`                                         |
 | `dataset_params.config.preprocessings`  | List of pre-processing functions to apply to input images.                             | `list`       | (see [dedicated section](#data-preprocessing)) |
 | `dataset_params.config.augmentation`    | Configuration for data augmentation.                                                   | `dict`       | (see [dedicated section](#data-augmentation))  |
 

tests/conftest.py

@@ -68,10 +68,6 @@ def training_config():
             },
             "config": {
                 "load_in_memory": True,  # Load all images in CPU memory
-                "width_divisor": 8,  # Image width will be divided by 8
-                "height_divisor": 32,  # Image height will be divided by 32
-                "padding_value": 0,  # Image padding value
-                "padding_token": None,  # Label padding value
                 "preprocessings": [
                     {
                         "type": "to_RGB",