[Dy2St] transforms.RandomCrop Support static mode (#49057)

* add RandomCrop

* 10e-5  =>  eps

* add same shape test

python/paddle/tests/test_transforms_static.py

@@ -60,6 +60,8 @@ class TestTransformUnitTestBase(unittest.TestCase):
 
     def test_transform(self):
         dy_res = self.dynamic_transform()
+        if isinstance(dy_res, paddle.Tensor):
+            dy_res = dy_res.numpy()
         st_res = self.static_transform()
 
         np.testing.assert_almost_equal(dy_res, st_res)
@@ -98,5 +100,47 @@ class TestRandomVerticalFlip1(TestTransformUnitTestBase):
         self.api = transforms.RandomVerticalFlip(prob=1)
 
 
+class TestRandomCrop_random(TestTransformUnitTestBase):
+    def get_shape(self):
+        return (3, 240, 240)
+
+    def set_trans_api(self):
+        self.crop_size = (224, 224)
+        self.api = transforms.RandomCrop(self.crop_size)
+
+    def assert_test_random_equal(self, res, eps=10e-5):
+
+        _, h, w = self.get_shape()
+        c_h, c_w = self.crop_size
+        res_assert = True
+        for y in range(h - c_h):
+            for x in range(w - c_w):
+                diff_abs_sum = np.abs(
+                    (self.img[:, y : y + c_h, x : x + c_w] - res)
+                ).sum()
+                if diff_abs_sum < eps:
+                    res_assert = False
+                    break
+            if not res_assert:
+                break
+        assert not res_assert
+
+    def test_transform(self):
+        dy_res = self.dynamic_transform().numpy()
+        st_res = self.static_transform()
+
+        self.assert_test_random_equal(dy_res)
+        self.assert_test_random_equal(st_res)
+
+
+class TestRandomCrop_same(TestTransformUnitTestBase):
+    def get_shape(self):
+        return (3, 224, 224)
+
+    def set_trans_api(self):
+        self.crop_size = (224, 224)
+        self.api = transforms.RandomCrop(self.crop_size)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/vision/transforms/transforms.py

@@ -1138,8 +1138,12 @@ class RandomCrop(BaseTransform):
         if w == tw and h == th:
             return 0, 0, h, w
 
-        i = random.randint(0, h - th)
-        j = random.randint(0, w - tw)
+        if paddle.in_dynamic_mode():
+            i = random.randint(0, h - th)
+            j = random.randint(0, w - tw)
+        else:
+            i = paddle.randint(low=0, high=h - th)
+            j = paddle.randint(low=0, high=w - tw)
         return i, j, th, tw
 
     def _apply_image(self, img):