[Bug] Fix eval scripts + possible overflow in Resize

api/app/vision.py

@@ -6,7 +6,7 @@
 
 import tensorflow as tf
 
-gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+gpu_devices = tf.config.list_physical_devices("GPU")
 if any(gpu_devices):
     tf.config.experimental.set_memory_growth(gpu_devices[0], True)
 

doctr/transforms/modules/pytorch.py

@@ -74,33 +74,34 @@
                 if self.symmetric_pad:
                     half_pad = (math.ceil(_pad[1] / 2), math.ceil(_pad[3] / 2))
                     _pad = (half_pad[0], _pad[1] - half_pad[0], half_pad[1], _pad[3] - half_pad[1])
+                # Pad image
                 img = pad(img, _pad)
 
             # In case boxes are provided, resize boxes if needed (for detection task if preserve aspect ratio)
             if target is not None:
+                if self.symmetric_pad:
+                    offset = half_pad[0] / img.shape[-1], half_pad[1] / img.shape[-2]
+
                 if self.preserve_aspect_ratio:
                     # Get absolute coords
                     if target.shape[1:] == (4,):
                         if isinstance(self.size, (tuple, list)) and self.symmetric_pad:
-                            if np.max(target) <= 1:
-                                offset = half_pad[0] / img.shape[-1], half_pad[1] / img.shape[-2]
                             target[:, [0, 2]] = offset[0] + target[:, [0, 2]] * raw_shape[-1] / img.shape[-1]
                             target[:, [1, 3]] = offset[1] + target[:, [1, 3]] * raw_shape[-2] / img.shape[-2]
                         else:
                             target[:, [0, 2]] *= raw_shape[-1] / img.shape[-1]
                             target[:, [1, 3]] *= raw_shape[-2] / img.shape[-2]
                     elif target.shape[1:] == (4, 2):
                         if isinstance(self.size, (tuple, list)) and self.symmetric_pad:
-                            if np.max(target) <= 1:
-                                offset = half_pad[0] / img.shape[-1], half_pad[1] / img.shape[-2]
                             target[..., 0] = offset[0] + target[..., 0] * raw_shape[-1] / img.shape[-1]
                             target[..., 1] = offset[1] + target[..., 1] * raw_shape[-2] / img.shape[-2]
                         else:
                             target[..., 0] *= raw_shape[-1] / img.shape[-1]
                             target[..., 1] *= raw_shape[-2] / img.shape[-2]
                     else:
-                        raise AssertionError
-                return img, target
+                        raise AssertionError("Boxes should be in the format (n_boxes, 4, 2) or (n_boxes, 4)")
+
+                return img, np.clip(target, 0, 1)
 
             return img
 

doctr/transforms/modules/tensorflow.py

@@ -107,46 +107,50 @@
         target: Optional[np.ndarray] = None,
     ) -> Union[tf.Tensor, Tuple[tf.Tensor, np.ndarray]]:
         input_dtype = img.dtype
+        self.output_size = (
+            (self.output_size, self.output_size) if isinstance(self.output_size, int) else self.output_size
+        )
 
         img = tf.image.resize(img, self.wanted_size, self.method, self.preserve_aspect_ratio, self.antialias)
         # It will produce an un-padded resized image, with a side shorter than wanted if we preserve aspect ratio
         raw_shape = img.shape[:2]
+        if self.symmetric_pad:
+            half_pad = (int((self.output_size[0] - img.shape[0]) / 2), 0)
         if self.preserve_aspect_ratio:
             if isinstance(self.output_size, (tuple, list)):
                 # In that case we need to pad because we want to enforce both width and height
                 if not self.symmetric_pad:
-                    offset = (0, 0)
+                    half_pad = (0, 0)
                 elif self.output_size[0] == img.shape[0]:
-                    offset = (0, int((self.output_size[1] - img.shape[1]) / 2))
-                else:
-                    offset = (int((self.output_size[0] - img.shape[0]) / 2), 0)
-                img = tf.image.pad_to_bounding_box(img, *offset, *self.output_size)
+                    half_pad = (0, int((self.output_size[1] - img.shape[1]) / 2))
+                # Pad image
+                img = tf.image.pad_to_bounding_box(img, *half_pad, *self.output_size)
 
         # In case boxes are provided, resize boxes if needed (for detection task if preserve aspect ratio)
         if target is not None:
+            if self.symmetric_pad:
+                offset = half_pad[0] / img.shape[0], half_pad[1] / img.shape[1]
+
             if self.preserve_aspect_ratio:
                 # Get absolute coords
                 if target.shape[1:] == (4,):
                     if isinstance(self.output_size, (tuple, list)) and self.symmetric_pad:
-                        if np.max(target) <= 1:
-                            offset = offset[0] / img.shape[0], offset[1] / img.shape[1]
                         target[:, [0, 2]] = offset[1] + target[:, [0, 2]] * raw_shape[1] / img.shape[1]
                         target[:, [1, 3]] = offset[0] + target[:, [1, 3]] * raw_shape[0] / img.shape[0]
                     else:
                         target[:, [0, 2]] *= raw_shape[1] / img.shape[1]
                         target[:, [1, 3]] *= raw_shape[0] / img.shape[0]
                 elif target.shape[1:] == (4, 2):
                     if isinstance(self.output_size, (tuple, list)) and self.symmetric_pad:
-                        if np.max(target) <= 1:
-                            offset = offset[0] / img.shape[0], offset[1] / img.shape[1]
                         target[..., 0] = offset[1] + target[..., 0] * raw_shape[1] / img.shape[1]
                         target[..., 1] = offset[0] + target[..., 1] * raw_shape[0] / img.shape[0]
                     else:
                         target[..., 0] *= raw_shape[1] / img.shape[1]
                         target[..., 1] *= raw_shape[0] / img.shape[0]
                 else:
-                    raise AssertionError
-            return tf.cast(img, dtype=input_dtype), target
+                    raise AssertionError("Boxes should be in the format (n_boxes, 4, 2) or (n_boxes, 4)")
+
+            return tf.cast(img, dtype=input_dtype), np.clip(target, 0, 1)
 
         return tf.cast(img, dtype=input_dtype)
 

references/classification/latency_tensorflow.py

@@ -20,7 +20,7 @@
 
 def main(args):
     if args.gpu:
-        gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+        gpu_devices = tf.config.list_physical_devices("GPU")
         if any(gpu_devices):
             tf.config.experimental.set_memory_growth(gpu_devices[0], True)
         else:

references/classification/train_tensorflow_character.py

@@ -18,7 +18,7 @@
 
 from doctr.models import login_to_hub, push_to_hf_hub
 
-gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+gpu_devices = tf.config.list_physical_devices("GPU")
 if any(gpu_devices):
     tf.config.experimental.set_memory_growth(gpu_devices[0], True)
 

references/classification/train_tensorflow_orientation.py

@@ -18,7 +18,7 @@
 
 from doctr.models import login_to_hub, push_to_hf_hub
 
-gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+gpu_devices = tf.config.list_physical_devices("GPU")
 if any(gpu_devices):
     tf.config.experimental.set_memory_growth(gpu_devices[0], True)
 

references/detection/evaluate_tensorflow.py

@@ -17,7 +17,7 @@
 from tensorflow.keras import mixed_precision
 from tqdm import tqdm
 
-gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+gpu_devices = tf.config.list_physical_devices("GPU")
 if any(gpu_devices):
     tf.config.experimental.set_memory_growth(gpu_devices[0], True)
 

references/detection/latency_tensorflow.py

@@ -20,7 +20,7 @@
 
 def main(args):
     if args.gpu:
-        gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+        gpu_devices = tf.config.list_physical_devices("GPU")
         if any(gpu_devices):
             tf.config.experimental.set_memory_growth(gpu_devices[0], True)
         else:

references/detection/train_tensorflow.py

@@ -19,7 +19,7 @@
 
 from doctr.models import login_to_hub, push_to_hf_hub
 
-gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+gpu_devices = tf.config.list_physical_devices("GPU")
 if any(gpu_devices):
     tf.config.experimental.set_memory_growth(gpu_devices[0], True)
 

references/recognition/evaluate_tensorflow.py

@@ -14,7 +14,7 @@
 from tensorflow.keras import mixed_precision
 from tqdm import tqdm
 
-gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+gpu_devices = tf.config.list_physical_devices("GPU")
 if any(gpu_devices):
     tf.config.experimental.set_memory_growth(gpu_devices[0], True)
 

references/recognition/latency_tensorflow.py

@@ -20,7 +20,7 @@
 
 def main(args):
     if args.gpu:
-        gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+        gpu_devices = tf.config.list_physical_devices("GPU")
         if any(gpu_devices):
             tf.config.experimental.set_memory_growth(gpu_devices[0], True)
         else:

references/recognition/train_tensorflow.py

@@ -20,7 +20,7 @@
 
 from doctr.models import login_to_hub, push_to_hf_hub
 
-gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+gpu_devices = tf.config.list_physical_devices("GPU")
 if any(gpu_devices):
     tf.config.experimental.set_memory_growth(gpu_devices[0], True)
 

scripts/analyze.py

@@ -16,7 +16,7 @@
 if is_tf_available():
     import tensorflow as tf
 
-    gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+    gpu_devices = tf.config.list_physical_devices("GPU")
     if any(gpu_devices):
         tf.config.experimental.set_memory_growth(gpu_devices[0], True)
 

scripts/detect_text.py

@@ -20,7 +20,7 @@
 if is_tf_available():
     import tensorflow as tf
 
-    gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+    gpu_devices = tf.config.list_physical_devices("GPU")
     if any(gpu_devices):
         tf.config.experimental.set_memory_growth(gpu_devices[0], True)
 

scripts/evaluate.py

@@ -11,6 +11,7 @@
 from tqdm import tqdm
 
 from doctr import datasets
+from doctr import transforms as T
 from doctr.file_utils import is_tf_available
 from doctr.models import ocr_predictor
 from doctr.utils.geometry import extract_crops, extract_rcrops
@@ -20,7 +21,7 @@
 if is_tf_available():
     import tensorflow as tf
 
-    gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+    gpu_devices = tf.config.list_physical_devices("GPU")
     if any(gpu_devices):
         tf.config.experimental.set_memory_growth(gpu_devices[0], True)
 else:
@@ -35,24 +36,47 @@ def main(args):
     if not args.rotation:
         args.eval_straight = True
 
+    input_shape = (args.size, args.size)
+
+    # We define a transformation function which does transform the annotation
+    # to the required format for the Resize transformation
+    def _transform(img, target):
+        boxes = target["boxes"]
+        transformed_img, transformed_boxes = T.Resize(
+            input_shape, preserve_aspect_ratio=args.keep_ratio, symmetric_pad=args.symmetric_pad
+        )(img, boxes)
+        return transformed_img, {"boxes": transformed_boxes, "labels": target["labels"]}
+
     predictor = ocr_predictor(
         args.detection,
         args.recognition,
         pretrained=True,
         reco_bs=args.batch_size,
-        preserve_aspect_ratio=False,
+        preserve_aspect_ratio=False,  # we handle the transformation directly in the dataset so this is set to False
+        symmetric_pad=False,  # we handle the transformation directly in the dataset so this is set to False
         assume_straight_pages=not args.rotation,
     )
 
     if args.img_folder and args.label_file:
         testset = datasets.OCRDataset(
             img_folder=args.img_folder,
             label_file=args.label_file,
+            sample_transforms=_transform,
         )
         sets = [testset]
     else:
-        train_set = datasets.__dict__[args.dataset](train=True, download=True, use_polygons=not args.eval_straight)
-        val_set = datasets.__dict__[args.dataset](train=False, download=True, use_polygons=not args.eval_straight)
+        train_set = datasets.__dict__[args.dataset](
+            train=True,
+            download=True,
+            use_polygons=not args.eval_straight,
+            sample_transforms=_transform,
+        )
+        val_set = datasets.__dict__[args.dataset](
+            train=False,
+            download=True,
+            use_polygons=not args.eval_straight,
+            sample_transforms=_transform,
+        )
         sets = [train_set, val_set]
 
     reco_metric = TextMatch()
@@ -190,6 +214,9 @@ def parse_args():
     parser.add_argument("--label_file", type=str, default=None, help="Only for local sets, path to labels")
     parser.add_argument("--rotation", dest="rotation", action="store_true", help="run rotated OCR + postprocessing")
     parser.add_argument("-b", "--batch_size", type=int, default=32, help="batch size for recognition")
+    parser.add_argument("--size", type=int, default=1024, help="model input size, H = W")
+    parser.add_argument("--keep_ratio", action="store_true", help="keep the aspect ratio of the input image")
+    parser.add_argument("--symmetric_pad", action="store_true", help="pad the image symmetrically")
     parser.add_argument("--samples", type=int, default=None, help="evaluate only on the N first samples")
     parser.add_argument(
         "--eval-straight",

scripts/evaluate_kie.py

@@ -13,6 +13,7 @@
 from tqdm import tqdm
 
 from doctr import datasets
+from doctr import transforms as T
 from doctr.file_utils import is_tf_available
 from doctr.models import kie_predictor
 from doctr.utils.geometry import extract_crops, extract_rcrops
@@ -22,7 +23,7 @@
 if is_tf_available():
     import tensorflow as tf
 
-    gpu_devices = tf.config.experimental.list_physical_devices("GPU")
+    gpu_devices = tf.config.list_physical_devices("GPU")
     if any(gpu_devices):
         tf.config.experimental.set_memory_growth(gpu_devices[0], True)
 else:
@@ -37,24 +38,47 @@ def main(args):
     if not args.rotation:
         args.eval_straight = True
 
+    input_shape = (args.size, args.size)
+
+    # We define a transformation function which does transform the annotation
+    # to the required format for the Resize transformation
+    def _transform(img, target):
+        boxes = target["boxes"]
+        transformed_img, transformed_boxes = T.Resize(
+            input_shape, preserve_aspect_ratio=args.keep_ratio, symmetric_pad=args.symmetric_pad
+        )(img, boxes)
+        return transformed_img, {"boxes": transformed_boxes, "labels": target["labels"]}
+
     predictor = kie_predictor(
         args.detection,
         args.recognition,
         pretrained=True,
         reco_bs=args.batch_size,
-        preserve_aspect_ratio=False,
+        preserve_aspect_ratio=False,  # we handle the transformation directly in the dataset so this is set to False
+        symmetric_pad=False,  # we handle the transformation directly in the dataset so this is set to False
         assume_straight_pages=not args.rotation,
     )
 
     if args.img_folder and args.label_file:
         testset = datasets.OCRDataset(
             img_folder=args.img_folder,
             label_file=args.label_file,
+            sample_transforms=_transform,
         )
         sets = [testset]
     else:
-        train_set = datasets.__dict__[args.dataset](train=True, download=True, use_polygons=not args.eval_straight)
-        val_set = datasets.__dict__[args.dataset](train=False, download=True, use_polygons=not args.eval_straight)
+        train_set = datasets.__dict__[args.dataset](
+            train=True,
+            download=True,
+            use_polygons=not args.eval_straight,
+            sample_transforms=_transform,
+        )
+        val_set = datasets.__dict__[args.dataset](
+            train=False,
+            download=True,
+            use_polygons=not args.eval_straight,
+            sample_transforms=_transform,
+        )
         sets = [train_set, val_set]
 
     reco_metric = TextMatch()
@@ -187,6 +211,9 @@ def parse_args():
     parser.add_argument("--label_file", type=str, default=None, help="Only for local sets, path to labels")
     parser.add_argument("--rotation", dest="rotation", action="store_true", help="run rotated OCR + postprocessing")
     parser.add_argument("-b", "--batch_size", type=int, default=32, help="batch size for recognition")
+    parser.add_argument("--size", type=int, default=1024, help="model input size, H = W")
+    parser.add_argument("--keep_ratio", action="store_true", help="keep the aspect ratio of the input image")
+    parser.add_argument("--symmetric_pad", action="store_true", help="pad the image symmetrically")
     parser.add_argument("--samples", type=int, default=None, help="evaluate only on the N first samples")
     parser.add_argument(
         "--eval-straight",

tests/pytorch/test_transforms_pt.py

@@ -66,6 +66,22 @@ def test_resize():
     out = transfo(input_t)
     assert out.dtype == torch.float16
 
+    # --- Test with target (bounding boxes) ---
+
+    target_boxes = np.array([[0.1, 0.1, 0.9, 0.9], [0.2, 0.2, 0.8, 0.8]])
+    output_size = (64, 64)
+
+    transfo = Resize(output_size, preserve_aspect_ratio=True)
+    input_t = torch.ones((3, 32, 64), dtype=torch.float32)
+    out, new_target = transfo(input_t, target_boxes)
+
+    assert out.shape[-2:] == output_size
+    assert new_target.shape == target_boxes.shape
+    assert np.all(new_target >= 0) and np.all(new_target <= 1)
+
+    out = transfo(input_t)
+    assert out.shape[-2:] == output_size
+
 
 @pytest.mark.parametrize(
     "rgb_min",