[Dy2St] transforms.RandomResizedCrop Support static mode

python/paddle/tests/test_transforms_static.py

@@ -158,5 +158,14 @@ def set_trans_api(self):
         self.api = transforms.RandomRotation(degree_tuple, expand=True, fill=3)
 
 
+class TestRandomResizedCrop(TestTransformUnitTestBase):
+    def set_trans_api(self, eps=10e-5):
+        c, h, w = self.get_shape()
+        size = h, w
+        scale = (1 - eps, 1.0)
+        ratio = (1 - eps, 1.0)
+        self.api = transforms.RandomResizedCrop(size, scale=scale, ratio=ratio)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/vision/transforms/transforms.py

@@ -494,7 +494,7 @@ def __init__(
         self.ratio = ratio
         self.interpolation = interpolation
 
-    def _get_param(self, image, attempts=10):
+    def _dynamic_get_param(self, image, attempts=10):
         width, height = _get_image_size(image)
         area = height * width
 
@@ -527,8 +527,106 @@ def _get_param(self, image, attempts=10):
         j = (width - w) // 2
         return i, j, h, w
 
+    def _static_get_param(self, image, attempts=10):
+        width, height = _get_image_size(image)
+        area = height * width
+        log_ratio = tuple(math.log(x) for x in self.ratio)
+
+        counter = paddle.full(
+            shape=[1], fill_value=0, dtype='int32'
+        )  # loop counter
+
+        ten = paddle.full(
+            shape=[1], fill_value=10, dtype='int32'
+        )  # loop length
+
+        i = paddle.zeros([1], dtype="int32")
+        j = paddle.zeros([1], dtype="int32")
+        h = paddle.ones([1], dtype="int32") * (height + 1)
+        w = paddle.ones([1], dtype="int32") * (width + 1)
+
+        def cond(counter, ten, i, j, h, w):
+            return (counter < ten) and (w > width or h > height)
+
+        def body(counter, ten, i, j, h, w):
+            target_area = (
+                paddle.uniform(shape=[1], min=self.scale[0], max=self.scale[1])
+                * area
+            )
+            aspect_ratio = paddle.exp(
+                paddle.uniform(shape=[1], min=log_ratio[0], max=log_ratio[1])
+            )
+
+            w = paddle.round(paddle.sqrt(target_area * aspect_ratio)).astype(
+                'int32'
+            )
+            h = paddle.round(paddle.sqrt(target_area / aspect_ratio)).astype(
+                'int32'
+            )
+
+            i = paddle.static.nn.cond(
+                0 < w <= width and 0 < h <= height,
+                lambda: paddle.uniform(shape=[1], min=0, max=height - h).astype(
+                    "int32"
+                ),
+                lambda: i,
+            )
+
+            j = paddle.static.nn.cond(
+                0 < w <= width and 0 < h <= height,
+                lambda: paddle.uniform(shape=[1], min=0, max=width - w).astype(
+                    "int32"
+                ),
+                lambda: j,
+            )
+
+            counter += 1
+
+            return counter, ten, i, j, h, w
+
+        counter, ten, i, j, h, w = paddle.static.nn.while_loop(
+            cond, body, [counter, ten, i, j, h, w]
+        )
+
+        def central_crop(width, height):
+
+            height = paddle.assign([height]).astype("float32")
+            width = paddle.assign([width]).astype("float32")
+
+            # Fallback to central crop
+            in_ratio = width / height
+
+            w, h = paddle.static.nn.cond(
+                in_ratio < self.ratio[0],
+                lambda: [
+                    width.astype("int32"),
+                    paddle.round(width / self.ratio[0]).astype("int32"),
+                ],
+                lambda: paddle.static.nn.cond(
+                    in_ratio > self.ratio[1],
+                    lambda: [
+                        paddle.round(height * self.ratio[1]),
+                        height.astype("int32"),
+                    ],
+                    lambda: [width.astype("int32"), height.astype("int32")],
+                ),
+            )
+            i = (height.astype("int32") - h) // 2
+            j = (width.astype("int32") - w) // 2
+
+            return i, j, h, w, counter
+
+        return paddle.static.nn.cond(
+            0 < w <= width and 0 < h <= height,
+            lambda: [i, j, h, w, counter],
+            lambda: central_crop(width, height),
+        )
+
     def _apply_image(self, img):
-        i, j, h, w = self._get_param(img)
+        if paddle.in_dynamic_mode():
+            i, j, h, w = self._dynamic_get_param(img)
+        else:
+            i, j, h, w, counter = self._static_get_param(img)
 
         cropped_img = F.crop(img, i, j, h, w)
         return F.resize(cropped_img, self.size, self.interpolation)