【PIR API adaptor No.15-17】 Migrate RandomHorizontalFlip、RandomVerticalFlip、RandomErasing into pir

python/paddle/static/nn/control_flow.py

@@ -1420,22 +1420,20 @@ def get_expected_precision(out_with_blocks):
             return new_outs
 
         if all(isinstance(out, paddle.pir.Value) for out in outs):
-            if in_pir_mode():
-                amp_attrs = core._get_amp_attrs()
-                amp_level = amp_attrs._amp_level
-                apply_amp_level_list = [
-                    core.AmpLevel.O0,
-                    core.AmpLevel.O1,
-                    core.AmpLevel.O2,
-                ]
-                if (amp_level in apply_amp_level_list) and (
-                    not all_has_same_dtype(outs)
-                ):
-                    warnings.warn(
-                        f"Return results from different branches in cond has different type: true value is '{outs[0]}' and false value is '{outs[1]}', "
-                        "so we will promote the lower precision to the higher one."
-                    )
-                    return promote_precision(out_with_blocks)
+            amp_attrs = core._get_amp_attrs()
+            amp_level = amp_attrs._amp_level
+            apply_amp_level_list = [
+                core.AmpLevel.O1,
+                core.AmpLevel.O2,
+            ]
+            if (amp_level in apply_amp_level_list) and (
+                not all_has_same_dtype(outs)
+            ):
+                warnings.warn(
+                    f"Return results from different branches in cond has different dtype: true value dtype is '{outs[0].dtype}' and false value dtype is '{outs[1].dtype}', "
+                    "so we will promote the lower precision to the higher one."
+                )
+                return promote_precision(out_with_blocks)
             return outs
 
         if all(arg is None for arg in outs):

python/paddle/tensor/linalg.py

@@ -5187,7 +5187,7 @@ def cond(i, _):
         return paddle.static.nn.cond(
             is_finite,
             lambda: paddle.less_than(i, max_squaring),
-            lambda: paddle.full((), False),
+            lambda: paddle.full((), False, dtype=paddle.bool),
         )
 
     def body(i, result):

python/paddle/vision/transforms/functional.py

@@ -25,6 +25,7 @@
 from paddle._typing import unreached
 
 from ...base.framework import Variable
+from ...base.libpaddle.pir import Value
 from . import (
     functional_cv2 as F_cv2,
     functional_pil as F_pil,
@@ -64,7 +65,7 @@ def _is_tensor_image(img: _ImageDataType) -> TypeGuard[Tensor]:
     """
     Return True if img is a Tensor for dynamic mode or Variable for static graph mode.
     """
-    return isinstance(img, (paddle.Tensor, Variable))
+    return isinstance(img, (paddle.Tensor, Variable, Value))
 
 
 def _is_numpy_image(img: _ImageDataType) -> TypeGuard[npt.NDArray[Any]]:

python/paddle/vision/transforms/functional_tensor.py

@@ -21,13 +21,14 @@
 import paddle.nn.functional as F
 
 from ...base.framework import Variable
+from ...base.libpaddle.pir import Value
 
 __all__ = []
 
 
 def _assert_image_tensor(img, data_format):
     if (
-        not isinstance(img, (paddle.Tensor, Variable))
+        not isinstance(img, (paddle.Tensor, Variable, Value))
         or img.ndim < 3
         or img.ndim > 4
         or data_format.lower() not in ('chw', 'hwc')

python/paddle/vision/transforms/transforms.py

@@ -619,7 +619,13 @@ def _static_get_param(self, image, attempts=10):
         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)
+            return paddle.logical_and(
+                counter < ten,
+                paddle.logical_or(
+                    w > width,
+                    h > height,
+                ),
+            )
 
         def body(counter, ten, i, j, h, w):
             target_area = (
@@ -638,15 +644,21 @@ def body(counter, ten, i, j, h, w):
             )
 
             i = paddle.static.nn.cond(
-                0 < w <= width and 0 < h <= height,
+                paddle.logical_and(
+                    paddle.logical_and(0 < h, h <= height),
+                    paddle.logical_and(0 < w, w <= width),
+                ),
                 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,
+                paddle.logical_and(
+                    paddle.logical_and(0 < h, h <= height),
+                    paddle.logical_and(0 < w, w <= width),
+                ),
                 lambda: paddle.uniform(shape=[1], min=0, max=width - w).astype(
                     "int32"
                 ),
@@ -677,7 +689,7 @@ def central_crop(width, height):
                 lambda: paddle.static.nn.cond(
                     in_ratio > self.ratio[1],
                     lambda: [
-                        paddle.round(height * self.ratio[1]),
+                        paddle.round(height * self.ratio[1]).astype("int32"),
                         height.astype("int32"),
                     ],
                     lambda: [width.astype("int32"), height.astype("int32")],
@@ -689,7 +701,10 @@ def central_crop(width, height):
             return i, j, h, w, counter
 
         return paddle.static.nn.cond(
-            0 < w <= width and 0 < h <= height,
+            paddle.logical_and(
+                paddle.logical_and(0 < h, h <= height),
+                paddle.logical_and(0 < w, w <= width),
+            ),
             lambda: [i, j, h, w, counter],
             lambda: central_crop(width, height),
         )
@@ -2188,7 +2203,13 @@ def _static_get_param(self, img, scale, ratio, value):
         log_ratio = np.log(np.array(ratio))
 
         def cond(counter, ten, erase_h, erase_w):
-            return counter < ten and (erase_h >= h or erase_w >= w)
+            return paddle.logical_and(
+                counter < ten,
+                paddle.logical_or(
+                    erase_h >= h,
+                    erase_w > w,
+                ),
+            )
 
         def body(counter, ten, erase_h, erase_w):
             erase_area = (
@@ -2228,31 +2249,35 @@ def body(counter, ten, erase_h, erase_w):
 
         zero = paddle.zeros([1]).astype("int32")
         top = paddle.static.nn.cond(
-            erase_h < h and erase_w < w,
+            paddle.logical_and(erase_h < h, erase_w < w),
             lambda: paddle.uniform(
                 shape=[1], min=0, max=h - erase_h + 1
             ).astype("int32"),
             lambda: zero,
         )
 
         left = paddle.static.nn.cond(
-            erase_h < h and erase_w < w,
+            paddle.logical_and(erase_h < h, erase_w < w),
             lambda: paddle.uniform(
                 shape=[1], min=0, max=w - erase_w + 1
             ).astype("int32"),
             lambda: zero,
         )
 
         erase_h = paddle.static.nn.cond(
-            erase_h < h and erase_w < w, lambda: erase_h, lambda: h
+            paddle.logical_and(erase_h < h, erase_w < w),
+            lambda: erase_h,
+            lambda: h,
         )
 
         erase_w = paddle.static.nn.cond(
-            erase_h < h and erase_w < w, lambda: erase_w, lambda: w
+            paddle.logical_and(erase_h < h, erase_w < w),
+            lambda: erase_w,
+            lambda: w,
         )
 
         v = paddle.static.nn.cond(
-            erase_h < h and erase_w < w, lambda: v, lambda: img
+            paddle.logical_and(erase_h < h, erase_w < w), lambda: v, lambda: img
         )
 
         return top, left, erase_h, erase_w, v, counter

test/legacy_test/test_transforms_static.py

@@ -28,6 +28,13 @@ def setUp(self):
             np.float32
         )
         self.set_trans_api()
+        self.init_dy_res()
+
+    def init_dy_res(self):
+        # Obtain the dynamic transform result first before test_transform.
+        self.dy_res = self.dynamic_transform()
+        if isinstance(self.dy_res, paddle.Tensor):
+            self.dy_res = self.dy_res.numpy()
 
     def get_shape(self):
         return (3, 64, 64)
@@ -59,12 +66,8 @@ def static_transform(self):
         return res[0]
 
     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)
+        np.testing.assert_almost_equal(self.dy_res, st_res)
 
 
 class TestResize(TestTransformUnitTestBase):
@@ -138,10 +141,9 @@ def assert_test_random_equal(self, res, eps=1e-4):
         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(self.dy_res)
         self.assert_test_random_equal(st_res)
 
 
@@ -180,12 +182,9 @@ def set_trans_api(self):
         )
 
     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()
 
-        self.assert_test_erasing(dy_res)
+        self.assert_test_erasing(self.dy_res)
         self.assert_test_erasing(st_res)
 
     def assert_test_erasing(self, arr):