Add LimitLong Transform (PaddlePaddle#803)

paddleseg/core/infer.py

@@ -42,6 +42,23 @@ def get_reverse_list(ori_shape, transforms):
         if op.__class__.__name__ in ['Padding']:
             reverse_list.append(('padding', (h, w)))
             w, h = op.target_size[0], op.target_size[1]
+        if op.__class__.__name__ in ['LimitLong']:
+            long_edge = max(h, w)
+            short_edge = min(h, w)
+            if ((op.max_long is not None) and (long_edge > op.max_long)):
+                reverse_list.append(('resize', (h, w)))
+                long_edge = op.max_long
+                short_edge = int(round(short_edge * op.max_long / long_edge))
+            elif ((op.min_long is not None) and (long_edge < op.min_long)):
+                reverse_list.append(('resize', (h, w)))
+                long_edge = op.min_long
+                short_edge = int(round(short_edge * op.min_long / long_edge))
+            if h > w:
+                h = long_edge
+                w = short_edge
+            else:
+                w = long_edge
+                h = short_edge
     return reverse_list
 
 

paddleseg/core/val.py

@@ -123,7 +123,8 @@ def evaluate(model,
                 intersect_area_list = []
                 pred_area_list = []
                 label_area_list = []
-                paddle.distributed.all_gather(intersect_area_list, intersect_area)
+                paddle.distributed.all_gather(intersect_area_list,
+                                              intersect_area)
                 paddle.distributed.all_gather(pred_area_list, pred_area)
                 paddle.distributed.all_gather(label_area_list, label_area)
 
@@ -135,7 +136,8 @@ def evaluate(model,
                     label_area_list = label_area_list[:valid]
 
                 for i in range(len(intersect_area_list)):
-                    intersect_area_all = intersect_area_all + intersect_area_list[i]
+                    intersect_area_all = intersect_area_all + intersect_area_list[
+                        i]
                     pred_area_all = pred_area_all + pred_area_list[i]
                     label_area_all = label_area_all + label_area_list[i]
             else:

paddleseg/transforms/transforms.py

@@ -228,6 +228,71 @@ def __call__(self, im, label=None):
             return (im, label)
 
 
+@manager.TRANSFORMS.add_component
+class LimitLong:
+    """
+    Limit the long edge of image.
+
+    If the long edge is larger than max_long, resize the long edge
+    to max_long, while scale the short edge proportionally.
+
+    If the long edge is smaller than min_long, resize the long edge
+    to min_long, while scale the short edge proportionally.
+
+    Args:
+        max_long (int, optional): If the long edge of image is larger than max_long,
+            it will be resize to max_long. Default: None.
+        min_long (int, optional): If the long edge of image is smaller than min_long,
+            it will be resize to min_long. Default: None.
+    """
+
+    def __init__(self, max_long=None, min_long=None):
+        if max_long is not None:
+            if not isinstance(max_long, int):
+                raise TypeError(
+                    "Type of `max_long` is invalid. It should be int, but it is {}"
+                    .format(type(max_long)))
+        if min_long is not None:
+            if not isinstance(min_long, int):
+                raise TypeError(
+                    "Type of `min_long` is invalid. It should be int, but it is {}"
+                    .format(type(min_long)))
+        if (max_long is not None) and (min_long is not None):
+            if min_long > max_long:
+                raise ValueError(
+                    '`max_long should not smaller than min_long, but they are {} and {}'
+                    .format(max_long, min_long))
+        self.max_long = max_long
+        self.min_long = min_long
+
+    def __call__(self, im, label=None):
+        """
+        Args:
+            im (np.ndarray): The Image data.
+            label (np.ndarray, optional): The label data. Default: None.
+
+        Returns:
+            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
+        """
+        h, w = im.shape[0], im.shape[1]
+        long_edge = max(h, w)
+        target = long_edge
+        if (self.max_long is not None) and (long_edge > self.max_long):
+            target = self.max_long
+        elif (self.min_long is not None) and (long_edge < self.min_long):
+            target = self.min_long
+
+        if target != long_edge:
+            im = functional.resize_long(im, target)
+            if label is not None:
+                label = functional.resize_long(label, target, cv2.INTER_NEAREST)
+
+        if label is None:
+            return (im, )
+        else:
+            return (im, label)
+
+
 @manager.TRANSFORMS.add_component
 class ResizeRangeScaling:
     """