[FIX] db loss TF and PT also for training with rotated samples

doctr/models/detection/differentiable_binarization/base.py

@@ -38,7 +38,7 @@ def __init__(
         assume_straight_pages: bool = True,
     ) -> None:
         super().__init__(box_thresh, bin_thresh, assume_straight_pages)
-        self.unclip_ratio = 1.5 if assume_straight_pages else 2.2
+        self.unclip_ratio = 1.5
 
     def polygon_to_box(
         self,
@@ -93,7 +93,7 @@ def bitmap_to_boxes(
         pred: np.ndarray,
         bitmap: np.ndarray,
     ) -> np.ndarray:
-        """Compute boxes from a bitmap/pred_map
+        """Compute boxes from a bitmap/pred_map: find connected components then filter boxes
 
         Args:
         ----
@@ -108,7 +108,7 @@ def bitmap_to_boxes(
                 containing x, y, w, h, score for the box
         """
         height, width = bitmap.shape[:2]
-        min_size_box = 1 + int(height / 512)
+        min_size_box = 2
         boxes: List[Union[np.ndarray, List[float]]] = []
         # get contours from connected components on the bitmap
         contours, _ = cv2.findContours(bitmap.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
@@ -180,7 +180,7 @@ def compute_distance(
         ys: np.ndarray,
         a: np.ndarray,
         b: np.ndarray,
-        eps: float = 1e-7,
+        eps: float = 1e-6,
     ) -> float:
         """Compute the distance for each point of the map (xs, ys) to the (a, b) segment
 
@@ -201,9 +201,10 @@ def compute_distance(
         square_dist_2 = np.square(xs - b[0]) + np.square(ys - b[1])
         square_dist = np.square(a[0] - b[0]) + np.square(a[1] - b[1])
         cosin = (square_dist - square_dist_1 - square_dist_2) / (2 * np.sqrt(square_dist_1 * square_dist_2) + eps)
+        cosin = np.clip(cosin, -1.0, 1.0)
         square_sin = 1 - np.square(cosin)
         square_sin = np.nan_to_num(square_sin)
-        result = np.sqrt(square_dist_1 * square_dist_2 * square_sin / square_dist)
+        result = np.sqrt(square_dist_1 * square_dist_2 * square_sin / square_dist + eps)
         result[cosin < 0] = np.sqrt(np.fmin(square_dist_1, square_dist_2))[cosin < 0]
         return result
 
@@ -265,7 +266,10 @@ def draw_thresh_map(
 
         # Fill the canvas with the distances computed inside the valid padded polygon
         canvas[ymin_valid : ymax_valid + 1, xmin_valid : xmax_valid + 1] = np.fmax(
-            1 - distance_map[ymin_valid - ymin : ymax_valid - ymin + 1, xmin_valid - xmin : xmax_valid - xmin + 1],
+            1
+            - distance_map[
+                ymin_valid - ymin : ymax_valid - ymax + height, xmin_valid - xmin : xmax_valid - xmax + width
+            ],
             canvas[ymin_valid : ymax_valid + 1, xmin_valid : xmax_valid + 1],
         )
 
@@ -274,7 +278,7 @@ def draw_thresh_map(
     def build_target(
         self,
         target: List[Dict[str, np.ndarray]],
-        output_shape: Tuple[int, int, int, int],
+        output_shape: Tuple[int, int, int],
         channels_last: bool = True,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
         if any(t.dtype != np.float32 for tgt in target for t in tgt.values()):
@@ -284,23 +288,24 @@ def build_target(
 
         input_dtype = next(iter(target[0].values())).dtype if len(target) > 0 else np.float32
 
+        h: int
+        w: int
         if channels_last:
-            h, w = output_shape[1:-1]
-            target_shape = (output_shape[0], output_shape[-1], h, w)  # (Batch_size, num_classes, h, w)
+            h, w, num_classes = output_shape
         else:
-            h, w = output_shape[-2:]
-            target_shape = output_shape  # (Batch_size, num_classes, h, w)
+            num_classes, h, w = output_shape
+        target_shape = (len(target), num_classes, h, w)
+
         seg_target: np.ndarray = np.zeros(target_shape, dtype=np.uint8)
         seg_mask: np.ndarray = np.ones(target_shape, dtype=bool)
         thresh_target: np.ndarray = np.zeros(target_shape, dtype=np.float32)
-        thresh_mask: np.ndarray = np.ones(target_shape, dtype=np.uint8)
+        thresh_mask: np.ndarray = np.zeros(target_shape, dtype=np.uint8)
 
         for idx, tgt in enumerate(target):
             for class_idx, _tgt in enumerate(tgt.values()):
                 # Draw each polygon on gt
                 if _tgt.shape[0] == 0:
                     # Empty image, full masked
-                    # seg_mask[idx, :, :, class_idx] = False
                     seg_mask[idx, class_idx] = False
 
                 # Absolute bounding boxes
@@ -326,10 +331,9 @@ def build_target(
                     )
                     boxes_size = np.minimum(abs_boxes[:, 2] - abs_boxes[:, 0], abs_boxes[:, 3] - abs_boxes[:, 1])
 
-                for box, box_size, poly in zip(abs_boxes, boxes_size, polys):
+                for poly, box, box_size in zip(polys, abs_boxes, boxes_size):
                     # Mask boxes that are too small
                     if box_size < self.min_size_box:
-                        # seg_mask[idx, box[1] : box[3] + 1, box[0] : box[2] + 1, class_idx] = False
                         seg_mask[idx, class_idx, box[1] : box[3] + 1, box[0] : box[2] + 1] = False
                         continue
 
@@ -339,19 +343,17 @@ def build_target(
                     subject = [tuple(coor) for coor in poly]
                     padding = pyclipper.PyclipperOffset()
                     padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
-                    shrinked = padding.Execute(-distance)
+                    shrunken = padding.Execute(-distance)
 
                     # Draw polygon on gt if it is valid
-                    if len(shrinked) == 0:
-                        # seg_mask[idx, box[1] : box[3] + 1, box[0] : box[2] + 1, class_idx] = False
+                    if len(shrunken) == 0:
                         seg_mask[idx, class_idx, box[1] : box[3] + 1, box[0] : box[2] + 1] = False
                         continue
-                    shrinked = np.array(shrinked[0]).reshape(-1, 2)
-                    if shrinked.shape[0] <= 2 or not Polygon(shrinked).is_valid:
-                        # seg_mask[idx, box[1] : box[3] + 1, box[0] : box[2] + 1, class_idx] = False
+                    shrunken = np.array(shrunken[0]).reshape(-1, 2)
+                    if shrunken.shape[0] <= 2 or not Polygon(shrunken).is_valid:
                         seg_mask[idx, class_idx, box[1] : box[3] + 1, box[0] : box[2] + 1] = False
                         continue
-                    cv2.fillPoly(seg_target[idx, class_idx], [shrinked.astype(np.int32)], 1.0)  # type: ignore[call-overload]
+                    cv2.fillPoly(seg_target[idx, class_idx], [shrunken.astype(np.int32)], 1.0)  # type: ignore[call-overload]
 
                     # Draw on both thresh map and thresh mask
                     poly, thresh_target[idx, class_idx], thresh_mask[idx, class_idx] = self.draw_thresh_map(

doctr/models/detection/differentiable_binarization/pytorch.py

@@ -213,7 +213,15 @@
 
         return out
 
-    def compute_loss(self, out_map: torch.Tensor, thresh_map: torch.Tensor, target: List[np.ndarray]) -> torch.Tensor:
+    def compute_loss(
+        self,
+        out_map: torch.Tensor,
+        thresh_map: torch.Tensor,
+        target: List[np.ndarray],
+        gamma: float = 2.0,
+        alpha: float = 0.5,
+        eps: float = 1e-8,
+    ) -> 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
 
@@ -222,56 +230,50 @@
             out_map: output feature map of the model of shape (N, C, H, W)
             thresh_map: threshold map of shape (N, C, H, W)
             target: list of dictionary where each dict has a `boxes` and a `flags` entry
+            gamma: modulating factor in the focal loss formula
+            alpha: balancing factor in the focal loss formula
+            eps: epsilon factor in dice loss
 
         Returns:
         -------
             A loss tensor
         """
+        if gamma < 0:
+            raise ValueError("Value of gamma should be greater than or equal to zero.")
+
         prob_map = torch.sigmoid(out_map)
         thresh_map = torch.sigmoid(thresh_map)
 
-        targets = self.build_target(target, prob_map.shape, False)  # type: ignore[arg-type]
+        targets = self.build_target(target, out_map.shape[1:], False)  # type: ignore[arg-type]
 
         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)
 
-        # Compute balanced BCE loss for proba_map
-        bce_scale = 5.0
-        balanced_bce_loss = torch.zeros(1, device=out_map.device)
-        dice_loss = torch.zeros(1, device=out_map.device)
-        l1_loss = torch.zeros(1, device=out_map.device)
         if torch.any(seg_mask):
-            bce_loss = F.binary_cross_entropy_with_logits(
-                out_map,
-                seg_target,
-                reduction="none",
-            )[seg_mask]
-
-            neg_target = 1 - seg_target[seg_mask]
-            positive_count = seg_target[seg_mask].sum()
-            negative_count = torch.minimum(neg_target.sum(), 3.0 * positive_count)
-            negative_loss = bce_loss * neg_target
-            negative_loss = negative_loss.sort().values[-int(negative_count.item()) :]
-            sum_losses = torch.sum(bce_loss * seg_target[seg_mask]) + torch.sum(negative_loss)
-            balanced_bce_loss = sum_losses / (positive_count + negative_count + 1e-6)
-
-            # Compute dice loss for approxbin_map
-            bin_map = 1 / (1 + torch.exp(-50.0 * (prob_map[seg_mask] - thresh_map[seg_mask])))
-
-            bce_min = bce_loss.min()
-            weights = (bce_loss - bce_min) / (bce_loss.max() - bce_min) + 1.0
-            inter = torch.sum(bin_map * seg_target[seg_mask] * weights)
-            union = torch.sum(bin_map) + torch.sum(seg_target[seg_mask]) + 1e-8  # type: ignore[call-overload]
-            dice_loss = 1 - 2.0 * inter / union
+            # Focal loss
+            focal_scale = 10.0
+            bce_loss = F.binary_cross_entropy_with_logits(out_map, seg_target, reduction="none")
+
+            p_t = prob_map * seg_target + (1 - prob_map) * (1 - seg_target)
+            alpha_t = alpha * seg_target + (1 - alpha) * (1 - seg_target)
+            # Unreduced version
+            focal_loss = alpha_t * (1 - p_t) ** gamma * bce_loss
+            # Class reduced
+            focal_loss = (seg_mask * focal_loss).sum() / seg_mask.sum()
+
+            # Compute dice loss for approx binary_map
+            binary_map = 1 / (1 + torch.exp(-50.0 * (prob_map - thresh_map)))
+            inter = (seg_mask * binary_map * seg_target).sum()  # type: ignore[attr-defined]
+            cardinality = (seg_mask * (binary_map + seg_target)).sum()  # type: ignore[attr-defined]
+            dice_loss = 1 - 2 * (inter + eps) / (cardinality + eps)
 
         # Compute l1 loss for thresh_map
-        l1_scale = 10.0
         if torch.any(thresh_mask):
-            l1_loss = torch.mean(torch.abs(thresh_map[thresh_mask] - thresh_target[thresh_mask]))
+            l1_loss = (torch.abs(thresh_map - thresh_target) * thresh_mask).sum() / (thresh_mask.sum() + eps)
 
-        return l1_scale * l1_loss + bce_scale * balanced_bce_loss + dice_loss  # type: ignore[return-value]
+        return l1_loss + focal_scale * focal_loss + dice_loss
 
 
 def _dbnet(

doctr/models/detection/differentiable_binarization/tensorflow.py

@@ -166,6 +166,9 @@
         out_map: tf.Tensor,
         thresh_map: tf.Tensor,
         target: List[Dict[str, np.ndarray]],
+        gamma: float = 2.0,
+        alpha: float = 0.5,
+        eps: float = 1e-8,
     ) -> tf.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
@@ -175,53 +178,55 @@
             out_map: output feature map of the model of shape (N, H, W, C)
             thresh_map: threshold map of shape (N, H, W, C)
             target: list of dictionary where each dict has a `boxes` and a `flags` entry
+            gamma: modulating factor in the focal loss formula
+            alpha: balancing factor in the focal loss formula
+            eps: epsilon factor in dice loss
 
         Returns:
         -------
             A loss tensor
         """
+        if gamma < 0:
+            raise ValueError("Value of gamma should be greater than or equal to zero.")
+
         prob_map = tf.math.sigmoid(out_map)
         thresh_map = tf.math.sigmoid(thresh_map)
 
-        seg_target, seg_mask, thresh_target, thresh_mask = self.build_target(target, out_map.shape, True)
+        seg_target, seg_mask, thresh_target, thresh_mask = self.build_target(target, out_map.shape[1:], True)
         seg_target = tf.convert_to_tensor(seg_target, dtype=out_map.dtype)
         seg_mask = tf.convert_to_tensor(seg_mask, dtype=tf.bool)
+        seg_mask = tf.cast(seg_mask, tf.float32)
         thresh_target = tf.convert_to_tensor(thresh_target, dtype=out_map.dtype)
         thresh_mask = tf.convert_to_tensor(thresh_mask, dtype=tf.bool)
 
-        # Compute balanced BCE loss for proba_map
-        bce_scale = 5.0
-        bce_loss = tf.keras.losses.binary_crossentropy(
-            seg_target[..., None],
-            out_map[..., None],
-            from_logits=True,
-        )[seg_mask]
-
-        neg_target = 1 - seg_target[seg_mask]
-        positive_count = tf.math.reduce_sum(seg_target[seg_mask])
-        negative_count = tf.math.reduce_min([tf.math.reduce_sum(neg_target), 3.0 * positive_count])
-        negative_loss = bce_loss * neg_target
-        negative_loss, _ = tf.nn.top_k(negative_loss, tf.cast(negative_count, tf.int32))
-        sum_losses = tf.math.reduce_sum(bce_loss * seg_target[seg_mask]) + tf.math.reduce_sum(negative_loss)
-        balanced_bce_loss = sum_losses / (positive_count + negative_count + 1e-6)
-
-        # Compute dice loss for approxbin_map
-        bin_map = 1 / (1 + tf.exp(-50.0 * (prob_map[seg_mask] - thresh_map[seg_mask])))
-
-        bce_min = tf.math.reduce_min(bce_loss)
-        weights = (bce_loss - bce_min) / (tf.math.reduce_max(bce_loss) - bce_min) + 1.0
-        inter = tf.math.reduce_sum(bin_map * seg_target[seg_mask] * weights)
-        union = tf.math.reduce_sum(bin_map) + tf.math.reduce_sum(seg_target[seg_mask]) + 1e-8
-        dice_loss = 1 - 2.0 * inter / union
+        # Focal loss
+        focal_scale = 10.0
+        bce_loss = tf.keras.losses.binary_crossentropy(seg_target[..., None], out_map[..., None], from_logits=True)
+
+        # Convert logits to prob, compute gamma factor
+        p_t = (seg_target * prob_map) + ((1 - seg_target) * (1 - prob_map))
+        alpha_t = seg_target * alpha + (1 - seg_target) * (1 - alpha)
+        # Unreduced loss
+        focal_loss = alpha_t * (1 - p_t) ** gamma * bce_loss
+        # Class reduced
+        focal_loss = tf.reduce_sum(seg_mask * focal_loss, (0, 1, 2, 3)) / tf.reduce_sum(seg_mask, (0, 1, 2, 3))
+
+        # Compute dice loss for approx binary_map
+        binary_map = 1.0 / (1.0 + tf.exp(-50 * (prob_map - thresh_map)))
+        inter = tf.reduce_sum(seg_mask * binary_map * seg_target, (0, 1, 2, 3))
+        cardinality = tf.reduce_sum((binary_map + seg_target), (0, 1, 2, 3))
+        dice_loss = 1 - 2 * (inter + eps) / (cardinality + eps)
 
         # Compute l1 loss for thresh_map
-        l1_scale = 10.0
         if tf.reduce_any(thresh_mask):
-            l1_loss = tf.math.reduce_mean(tf.math.abs(thresh_map[thresh_mask] - thresh_target[thresh_mask]))
+            thresh_mask = tf.cast(thresh_mask, tf.float32)
+            l1_loss = tf.reduce_sum(tf.abs(thresh_map - thresh_target) * thresh_mask) / (
+                tf.reduce_sum(thresh_mask) + eps
+            )
         else:
             l1_loss = tf.constant(0.0)
 
-        return l1_scale * l1_loss + bce_scale * balanced_bce_loss + dice_loss
+        return l1_loss + focal_scale * focal_loss + dice_loss
 
     def call(
         self,

doctr/models/detection/linknet/base.py

@@ -36,7 +36,7 @@ def __init__(
         assume_straight_pages: bool = True,
     ) -> None:
         super().__init__(box_thresh, bin_thresh, assume_straight_pages)
-        self.unclip_ratio = 1.2
+        self.unclip_ratio = 1.5
 
     def polygon_to_box(
         self,