[models] Change Resize kwargs to args for each zoo predictor

CONTRIBUTING.md

@@ -41,7 +41,7 @@ Install all additional dependencies with the following command:
 
 ```shell
 python -m pip install --upgrade pip
-pip install -e .[dev]
+pip install -e '.[dev]'
 pre-commit install
 ```
 

doctr/models/classification/zoo.py

@@ -63,7 +63,7 @@ def _orientation_predictor(
 
 
 def crop_orientation_predictor(
-    arch: Any = "mobilenet_v3_small_crop_orientation", pretrained: bool = False, **kwargs: Any
+    arch: Any = "mobilenet_v3_small_crop_orientation", pretrained: bool = False, batch_size: int = 128, **kwargs: Any
 ) -> OrientationPredictor:
     """Crop orientation classification architecture.
 
@@ -77,17 +77,18 @@ def crop_orientation_predictor(
     ----
         arch: name of the architecture to use (e.g. 'mobilenet_v3_small_crop_orientation')
         pretrained: If True, returns a model pre-trained on our recognition crops dataset
+        batch_size: number of samples the model processes in parallel
         **kwargs: keyword arguments to be passed to the OrientationPredictor
 
     Returns:
     -------
         OrientationPredictor
     """
-    return _orientation_predictor(arch, pretrained, model_type="crop", **kwargs)
+    return _orientation_predictor(arch=arch, pretrained=pretrained, batch_size=batch_size, model_type="crop", **kwargs)
 
 
 def page_orientation_predictor(
-    arch: Any = "mobilenet_v3_small_page_orientation", pretrained: bool = False, **kwargs: Any
+    arch: Any = "mobilenet_v3_small_page_orientation", pretrained: bool = False, batch_size: int = 4, **kwargs: Any
 ) -> OrientationPredictor:
     """Page orientation classification architecture.
 
@@ -101,10 +102,11 @@ def page_orientation_predictor(
     ----
         arch: name of the architecture to use (e.g. 'mobilenet_v3_small_page_orientation')
         pretrained: If True, returns a model pre-trained on our recognition crops dataset
+        batch_size: number of samples the model processes in parallel
         **kwargs: keyword arguments to be passed to the OrientationPredictor
 
     Returns:
     -------
         OrientationPredictor
     """
-    return _orientation_predictor(arch, pretrained, model_type="page", **kwargs)
+    return _orientation_predictor(arch=arch, pretrained=pretrained, batch_size=batch_size, model_type="page", **kwargs)

doctr/models/detection/zoo.py

@@ -79,6 +79,9 @@ def detection_predictor(
     arch: Any = "fast_base",
     pretrained: bool = False,
     assume_straight_pages: bool = True,
+    preserve_aspect_ratio: bool = True,
+    symmetric_pad: bool = True,
+    batch_size: int = 2,
     **kwargs: Any,
 ) -> DetectionPredictor:
     """Text detection architecture.
@@ -94,10 +97,22 @@ def detection_predictor(
         arch: name of the architecture or model itself to use (e.g. 'db_resnet50')
         pretrained: If True, returns a model pre-trained on our text detection dataset
         assume_straight_pages: If True, fit straight boxes to the page
+        preserve_aspect_ratio: If True, pad the input document image to preserve the aspect ratio before
+            running the detection model on it
+        symmetric_pad: if True, pad the image symmetrically instead of padding at the bottom-right
+        batch_size: number of samples the model processes in parallel
         **kwargs: optional keyword arguments passed to the architecture
 
     Returns:
     -------
         Detection predictor
     """
-    return _predictor(arch, pretrained, assume_straight_pages, **kwargs)
+    return _predictor(
+        arch=arch,
+        pretrained=pretrained,
+        assume_straight_pages=assume_straight_pages,
+        preserve_aspect_ratio=preserve_aspect_ratio,
+        symmetric_pad=symmetric_pad,
+        batch_size=batch_size,
+        **kwargs,
+    )

doctr/models/preprocessor/pytorch.py

@@ -27,6 +27,7 @@ class PreProcessor(nn.Module):
         batch_size: the size of page batches
         mean: mean value of the training distribution by channel
         std: standard deviation of the training distribution by channel
+        **kwargs: additional arguments for the resizing operation
     """
 
     def __init__(

doctr/models/preprocessor/tensorflow.py

@@ -25,6 +25,7 @@ class PreProcessor(NestedObject):
         batch_size: the size of page batches
         mean: mean value of the training distribution by channel
         std: standard deviation of the training distribution by channel
+        **kwargs: additional arguments for the resizing operation
     """
 
     _children_names: List[str] = ["resize", "normalize"]

doctr/models/recognition/zoo.py

@@ -52,7 +52,13 @@ def _predictor(arch: Any, pretrained: bool, **kwargs: Any) -> RecognitionPredict
     return predictor
 
 
-def recognition_predictor(arch: Any = "crnn_vgg16_bn", pretrained: bool = False, **kwargs: Any) -> RecognitionPredictor:
+def recognition_predictor(
+    arch: Any = "crnn_vgg16_bn",
+    pretrained: bool = False,
+    symmetric_pad: bool = False,
+    batch_size: int = 128,
+    **kwargs: Any,
+) -> RecognitionPredictor:
     """Text recognition architecture.
 
     Example::
@@ -66,10 +72,12 @@ def recognition_predictor(arch: Any = "crnn_vgg16_bn", pretrained: bool = False,
     ----
         arch: name of the architecture or model itself to use (e.g. 'crnn_vgg16_bn')
         pretrained: If True, returns a model pre-trained on our text recognition dataset
+        symmetric_pad: if True, pad the image symmetrically instead of padding at the bottom-right
+        batch_size: number of samples the model processes in parallel
         **kwargs: optional parameters to be passed to the architecture
 
     Returns:
     -------
         Recognition predictor
     """
-    return _predictor(arch, pretrained, **kwargs)
+    return _predictor(arch=arch, pretrained=pretrained, symmetric_pad=symmetric_pad, batch_size=batch_size, **kwargs)