Remove padding for ctc loss

dan/manager/ocr.py

@@ -14,7 +14,6 @@ from dan.ocr.utils import LM_str_to_ind
 from dan.utils import (
     pad_image,
     pad_image_width_random,
-    pad_image_width_right,
     pad_images,
     pad_sequences_1D,
     rand,
@@ -154,37 +153,7 @@ class OCRDataset(GenericDataset):
             sample["img_shape"] / self.reduce_dims_factor
         ).astype(int)
 
-        # Padding to handle CTC requirements
         if self.set_name == "train":
-            max_label_len = 0
-            height = 1
-            ctc_padding = False
-            if "CTC_line" in self.params["config"]["constraints"]:
-                max_label_len = sample["label_len"]
-                ctc_padding = True
-            if "CTC_va" in self.params["config"]["constraints"]:
-                max_label_len = max(sample["line_label_len"])
-                ctc_padding = True
-            if "CTC_pg" in self.params["config"]["constraints"]:
-                max_label_len = sample["label_len"]
-                height = max(sample["img_reduced_shape"][0], 1)
-                ctc_padding = True
-            if (
-                ctc_padding
-                and 2 * max_label_len + 1 > sample["img_reduced_shape"][1] * height
-            ):
-                sample["img"] = pad_image_width_right(
-                    sample["img"],
-                    int(
-                        np.ceil((2 * max_label_len + 1) / height)
-                        * self.reduce_dims_factor[1]
-                    ),
-                    self.padding_value,
-                )
-                sample["img_shape"] = sample["img"].shape
-                sample["img_reduced_shape"] = np.ceil(
-                    sample["img_shape"] / self.reduce_dims_factor
-                ).astype(int)
             sample["img_reduced_shape"] = [
                 max(1, t) for t in sample["img_reduced_shape"]
             ]