add arabic vocabs and some modification for the detection model so the errors are more clear

doctr/datasets/vocabs.py

@@ -13,7 +13,7 @@
     # Arabic & Persian
     "arabic_diacritics": "ًٌٍَُِّْ",
     "arabic_digits": "٠١٢٣٤٥٦٧٨٩",
-    "arabic_letters": "ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىي",
+    "arabic_letters": "- ء آ أ ؤ إ ئ ا ٪ ب ت ث ج ح خ د ذ ر ز س ش ص ض ط ظ ع غ ف ق ك  ٰیٕ٪ ل م ن ه ة و ي پ چ ڢ ڤ گ ﻻ ﻷ ﻹ ﻵ ﺀ ﺁ ﺃ ﺅ ﺇ ﺉ ﺍ ﺏ ﺕ ﺙ ﺝ ﺡ ﺥ ﺩ ﺫ ﺭ ﺯ ﺱ ﺵ ﺹ ﺽ ﻁ ﻅ ﻉ ﻍ ﻑ ﻕ ﻙ ﻝ ﻡ ﻥ ﻩ ﻩ ﻭ ﻱ ﺑ ﺗ ﺛ ﺟ ﺣ ﺧ ﺳ ﺷ ﺻ ﺿ ﻃ ﻇ ﻋ ﻏ ﻓ ﻗ ﻛ ﻟ ﻣ ﻧ ﻫ ﻳ ﺒ ﺘ ﺜ ﺠ ﺤ ﺨ ﺴ ﺸ ﺼ ﺾ ﻄ ﻈ ﻌ ﻐ ﻔ ﻘ ﻜ ﻠ ﻤ ﻨ ﻬ ﻴ ﺎ ﺐ ﺖ ﺚ ﺞ ﺢ ﺦ ﺪ ﺬ ﺮ ﺰ ﺲ ﺶ ﺺ ﺾ ﻂ ﻆ ﻊ ﻎ ﻒ ﻖ ﻚ ﻞ ﻢ ﻦ ﻪ ﺔ ﺓﺋ ﺓﺋ ى ﻼوفرّٕ  ﺊ ﻯ ﻀ ﻯ ﻼ ﺋ ﺊﺓى ﻀال ص ح x ـ ـوx  ﻰ ﻮ ﻲ ً ٌ  ؟ ؛ « » — !  # $ % & ' ( ) * + , - . / : ; < = > ? @ [ ] ^ _  { | } ~",
     "arabic_punctuation": "؟؛«»—",
     "persian_letters": "پچڢڤگ",
     # Bangla
@@ -786,7 +786,8 @@
 VOCABS["multilingual"] = "".join(
     dict.fromkeys(
         # latin_based
-        VOCABS["english"]
+        VOCABS["arabic"]
+        +VOCABS["english"]
         + VOCABS["albanian"]
         + VOCABS["afrikaans"]
         + VOCABS["azerbaijani"]

doctr/models/detection/differentiable_binarization/base.py

 def test_detection_dataset(mock_image_folder, mock_detection_label): 
 def test_detection_dataset(mock_image_folder, mock_detection_label): 
 def test_detection_dataset(mock_image_folder, mock_detection_label): 
@@ -28,6 +28,17 @@
         bin_thresh: threshold used to binzarized p_map at inference time
 
     """
+    class InvalidCoordinatesError(Exception):
+     def __init__(self, image_name, class_name, min_val, max_val):
+        self.image_name = image_name
+        self.class_name = class_name
+        self.min_val = min_val
+        self.max_val = max_val
+        message = (
+            f"Invalid box coordinates in {image_name}, class '{class_name}': "
+            f"values should be between 0 & 1, but found range [{min_val:.4f}, {max_val:.4f}]."
+        )
+        super().__init__(message)
 
     def __init__(
         self,
@@ -270,11 +281,24 @@
         target: list[dict[str, np.ndarray]],
         output_shape: tuple[int, int, int],
         channels_last: bool = True,
+        image_names: list[str] = None,  # Add optional parameter for image names
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
         if any(t.dtype != np.float32 for tgt in target for t in tgt.values()):
             raise AssertionError("the expected dtype of target 'boxes' entry is 'np.float32'.")
-        if any(np.any((t[:, :4] > 1) | (t[:, :4] < 0)) for tgt in target for t in tgt.values()):
-            raise ValueError("the 'boxes' entry of the target is expected to take values between 0 & 1.")
+
+        # Enhanced error checking with image identification
+        for idx, tgt in enumerate(target):
+            for class_name, t in tgt.items():
+                if np.any((t[:, :4] > 1) | (t[:, :4] < 0)):
+                    image_id = f"image #{idx}" if image_names is None else image_names[idx]
+                    # Find the actual values that are out of range for better debugging
+                    min_val = t[:, :4].min()
+                    max_val = t[:, :4].max()
+                    raise ValueError(
+                        f"Invalid box coordinates in {image_id}, class '{class_name}': "
+                        f"values should be between 0 & 1, but found range [{min_val:.4f}, {max_val:.4f}]. "
+                        f"Please normalize your coordinates by dividing x by image width and y by image height."
+                    )
 
         input_dtype = next(iter(target[0].values())).dtype if len(target) > 0 else np.float32
 
@@ -362,4 +386,4 @@
         thresh_target = thresh_target.astype(input_dtype)
         thresh_mask = thresh_mask.astype(bool)
 
-        return seg_target, seg_mask, thresh_target, thresh_mask
+        return seg_target, seg_mask, thresh_target, thresh_mask

doctr/models/detection/differentiable_binarization/pytorch.py

@@ -4,8 +4,7 @@
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
 
 from collections.abc import Callable
-from typing import Any
-
+from typing import Any, List, Optional
 import numpy as np
 import torch
 from torch import nn
@@ -185,6 +184,7 @@
         target: list[np.ndarray] | None = None,
         return_model_output: bool = False,
         return_preds: bool = False,
+        image_names: Optional[List[str]] = None,  # Added parameter for image names
     ) -> dict[str, torch.Tensor]:
         # Extract feature maps at different stages
         feats = self.feat_extractor(x)
@@ -218,7 +218,7 @@
 
         if target is not None:
             thresh_map = self.thresh_head(feat_concat)
-            loss = self.compute_loss(logits, thresh_map, target)
+            loss = self.compute_loss(logits, thresh_map, target, image_names=image_names)  # Pass image_names to compute_loss
             out["loss"] = loss
 
         return out
@@ -231,6 +231,7 @@
         gamma: float = 2.0,
         alpha: float = 0.5,
         eps: float = 1e-8,
+        image_names: Optional[List[str]] = None,  # Paramètre pour les noms d'images
     ) -> torch.Tensor:
         """Compute a batch of gts, masks, thresh_gts, thresh_masks from a list of boxes
         and a list of masks for each image. From there it computes the loss with the model output
@@ -242,6 +243,7 @@
             gamma: modulating factor in the focal loss formula
             alpha: balancing factor in the focal loss formula
             eps: epsilon factor in dice loss
+            image_names: list of image filenames for error reporting
 
         Returns:
             A loss tensor
@@ -252,13 +254,26 @@
         prob_map = torch.sigmoid(out_map)
         thresh_map = torch.sigmoid(thresh_map)
 
-        targets = self.build_target(target, out_map.shape[1:], False)  # type: ignore[arg-type]
+        try:
+            targets = self.build_target(target, out_map.shape[1:], False, image_names=image_names)
+        except ValueError as e:
+            # Re-raise with more context about which images caused the problem
+            if "Invalid box coordinates" in str(e) and image_names:
+                batch_info = ", ".join(image_names)
+                raise ValueError(f"{str(e)} Images in batch: {batch_info}")
+            else:
+                raise
 
         seg_target, seg_mask = torch.from_numpy(targets[0]), torch.from_numpy(targets[1])
         seg_target, seg_mask = seg_target.to(out_map.device), seg_mask.to(out_map.device)
         thresh_target, thresh_mask = torch.from_numpy(targets[2]), torch.from_numpy(targets[3])
         thresh_target, thresh_mask = thresh_target.to(out_map.device), thresh_mask.to(out_map.device)
 
+        # Initialize all loss components
+        focal_loss = torch.tensor(0.0, device=out_map.device)
+        dice_loss = torch.tensor(0.0, device=out_map.device)
+        l1_loss = torch.tensor(0.0, device=out_map.device)
+
         if torch.any(seg_mask):
             # Focal loss
             focal_scale = 10.0
@@ -269,7 +284,7 @@
             # Unreduced version
             focal_loss = alpha_t * (1 - p_t) ** gamma * bce_loss
             # Class reduced
-            focal_loss = (seg_mask * focal_loss).sum((0, 1, 2, 3)) / seg_mask.sum((0, 1, 2, 3))
+            focal_loss = (seg_mask * focal_loss).sum((0, 1, 2, 3)) / (seg_mask.sum((0, 1, 2, 3)) + eps)
 
             # Compute dice loss for each class or for approx binary_map
             if len(self.class_names) > 1:
@@ -429,4 +444,4 @@
             "thresh_head.6.bias",
         ],
         **kwargs,
-    )
+    )