remove fluid.layers.affine_grid API (#47851)

python/paddle/fluid/layers/nn.py

@@ -177,7 +177,6 @@ __all__ = [
     'mul',
     'maxout',
     'space_to_depth',
-    'affine_grid',
     'affine_channel',
     'similarity_focus',
     'hash',
@@ -9693,90 +9692,6 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
     return out
 
 
-def affine_grid(theta, out_shape, name=None):
-    """
-    :alias_main: paddle.nn.functional.affine_grid
-        :alias: paddle.nn.functional.affine_grid,paddle.nn.functional.vision.affine_grid
-        :old_api: paddle.fluid.layers.affine_grid
-
-    It generates a grid of (x,y) coordinates using the parameters of
-    the affine transformation that correspond to a set of points where
-    the input feature map should be sampled to produce the transformed
-    output feature map.
-
-    Args:
-        theta (Variable) - A Tensor with shape [N, 2, 3]. It contains a batch of affine transform parameters.
-                           The data type can be float32 or float64.
-        out_shape (Variable | list | tuple): The shape of target output with format [batch_size, channel, height, width].
-                                             ``out_shape`` can be a Tensor or a list or tuple. The data
-                                             type must be int32.
-        name(str|None): The default value is None.  Normally there is no need for user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
-
-    Returns:
-        Variable: A Tensor with shape [batch_size, H, W, 2] while 'H' and 'W' are the height and width of feature map in affine transformation. The data type is the same as `theta`.
-
-    Raises:
-        ValueError: If the type of arguments is not supported.
-
-    Examples:
-
-        .. code-block:: python
-
-            import paddle.fluid as fluid
-            import numpy as np
-            place = fluid.CPUPlace()
-            theta = fluid.data(name="x", shape=[None, 2, 3], dtype="float32")
-            out_shape = fluid.data(name="y", shape=[4], dtype="int32")
-            grid_0 = fluid.layers.affine_grid(theta, out_shape)
-            grid_1 = fluid.layers.affine_grid(theta, [5, 3, 28, 28])
-            batch_size=2
-            exe = fluid.Executor(place)
-            exe.run(fluid.default_startup_program())
-            output= exe.run(feed={"x": np.random.rand(batch_size,2,3).astype("float32"),
-                                  "y": np.array([5, 3, 28, 28]).astype("int32")},
-                                  fetch_list=[grid_0.name, grid_1.name])
-            print(output[0])
-            print(output[1])
-    """
-    helper = LayerHelper('affine_grid')
-
-    check_variable_and_dtype(
-        theta, 'theta', ['float32', 'float64'], 'affine_grid'
-    )
-
-    if not (
-        isinstance(out_shape, list)
-        or isinstance(out_shape, tuple)
-        or isinstance(out_shape, Variable)
-    ):
-        raise ValueError("The out_shape should be a list, tuple or Variable.")
-
-    if not isinstance(theta, Variable):
-        raise ValueError("The theta should be a Variable.")
-
-    out = helper.create_variable_for_type_inference(theta.dtype)
-    ipts = {'Theta': theta}
-    attrs = {}
-    if isinstance(out_shape, Variable):
-        ipts['OutputShape'] = out_shape
-        check_variable_and_dtype(
-            out_shape, 'out_shape', ['int32'], 'affine_grid'
-        )
-    else:
-        attrs['output_shape'] = out_shape
-    if core.is_compiled_with_rocm():
-        # ROCM platform do not have MIOPEN kernel for affine_grid
-        attrs['use_cudnn'] = False
-
-    helper.append_op(
-        type='affine_grid',
-        inputs=ipts,
-        outputs={'Output': out},
-        attrs=None if len(attrs) == 0 else attrs,
-    )
-    return out
-
-
 def pad2d(
     input,
     paddings=[0, 0, 0, 0],

python/paddle/fluid/tests/unittests/test_affine_grid_function.py

@@ -52,7 +52,9 @@ class AffineGridTestCase(unittest.TestCase):
                 theta_var = fluid.data(
                     "input", self.theta_shape, dtype=self.dtype
                 )
-                y_var = fluid.layers.affine_grid(theta_var, self.output_shape)
+                y_var = paddle.nn.functional.affine_grid(
+                    theta_var, self.output_shape
+                )
         feed_dict = {"input": self.theta}
         exe = fluid.Executor(place)
         exe.run(start)

python/paddle/fluid/tests/unittests/test_layers.py

@@ -3278,7 +3278,9 @@ class TestBook(LayerTest):
             fluid.default_main_program(), fluid.default_startup_program()
         ):
             theta = self._get_data("theta", shape=[2, 3], dtype='float32')
-            x = fluid.layers.affine_grid(theta, out_shape=[2, 3, 244, 244])
+            x = paddle.nn.functional.affine_grid(
+                theta, out_shape=[2, 3, 244, 244]
+            )
             return layers.pool2d(
                 x, pool_size=[5, 3], pool_stride=[1, 2], pool_padding=(2, 1)
             )
@@ -4175,8 +4177,8 @@ class TestBook(LayerTest):
 
             theta = layers.data(name="theta", shape=[2, 3], dtype="float32")
             out_shape = layers.data(name="out_shape", shape=[-1], dtype="int32")
-            data_0 = layers.affine_grid(theta, out_shape)
-            data_1 = layers.affine_grid(theta, [5, 3, 28, 28])
+            data_0 = paddle.nn.functional.affine_grid(theta, out_shape)
+            data_1 = paddle.nn.functional.affine_grid(theta, [5, 3, 28, 28])
 
             self.assertIsNotNone(data_0)
             self.assertIsNotNone(data_1)