Revert "Add masked select api (#21172)" (#21456)

This reverts commit 007c9975.

python/paddle/fluid/layers/nn.py

@@ -28,7 +28,7 @@ from ..framework import Variable, OpProtoHolder, in_dygraph_mode
 from ..dygraph import base
 from ..param_attr import ParamAttr
 from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
-from .tensor import concat, assign, fill_constant, zeros, cast
+from .tensor import concat, assign, fill_constant, zeros
 from . import utils
 from .. import unique_name
 from functools import reduce
@@ -183,7 +183,6 @@ __all__ = [
     'hard_swish',
     'gather_tree',
     'uniform_random',
-    'masked_select',
 ]
 
 
@@ -13620,63 +13619,3 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
         outputs={"Out": out})
 
     return helper.append_activation(out)
-
-
-def masked_select(input, mask):
-    """
-    This OP selects elements of the input tensor according to the mask tensor.
-    The shapes of the mask tensor don't have to match shapes of input tensor, but they must be broadcastable, and the result is a new 1-D tensor.
-
-    NOTE: The meaning of broadcastable is consistent with expand_as.
-
-    Parameters:
-
-        input(Variable): The input tensor, the data type should be int32, float32, float64.
-        mask(Variable): The boolean mask tensor, the data type should be bool.
-
-    Returns:
-        Variable: masked select tensor, its data type is same as the input.
-
-    Examples:
-        .. code-block:: python
-
-            import paddle.fluid as fluid
-            import numpy as np
-            mask_shape = [4,1]
-            shape = [4,4]
-            data = np.random.random(mask_shape).astype("float32")
-            input_data = np.random.randint(5,size=shape).astype("float32")
-            mask_data = data > 0.5
-
-            # print(input_data)
-            # [[0.38972723 0.36218056 0.7892614  0.50122297]
-            #  [0.14408113 0.85540855 0.30984417 0.7577004 ]
-            #  [0.97263193 0.5248062  0.07655851 0.75549215]
-            #  [0.26214206 0.32359877 0.6314582  0.2128865 ]]
-
-            # print(mask_data)
-            # [[ True]
-            #  [ True]
-            #  [False]
-            #  [ True]]
-
-            input = fluid.data(name="input",shape=[4,4],dtype="float32")
-            mask = fluid.data(name="mask",shape=[4,1],dtype="bool")
-            result = fluid.layers.masked_select(input=input, mask=mask)
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            start = fluid.default_startup_program()
-            main = fluid.default_main_program()
-            exe.run(start)
-            masked_select_result= exe.run(main, feed={'input':input_data, 'mask':mask_data}, fetch_list=[result])
-            # print(masked_select_result)
-            # [0.38972723 0.36218056 0.7892614  0.50122297 0.14408113 0.85540855
-            #   0.30984417 0.7577004  0.26214206 0.32359877 0.6314582  0.2128865 ]
-
-    """
-    mask_cast = cast(x=mask, dtype=input.dtype)
-    mask_expand = expand_as(x=mask_cast, target_tensor=input)
-    mask_expand_cast_back_bool = cast(x=mask_expand, dtype="bool")
-    select = where(mask_expand_cast_back_bool)
-    result = gather_nd(input, select)
-    return result

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

@@ -1640,9 +1640,6 @@ class TestBook(LayerTest):
         elif dtype == 'int64':
             return np.random.randint(self._low_data_bound,
                                      self._high_data_bound, shape).astype(dtype)
-        elif dtype == 'bool':
-            return np.random.randint(self._low_data_bound,
-                                     self._high_data_bound, shape).astype(dtype)
 
     def _get_data(self,
                   name,
@@ -2574,14 +2571,6 @@ class TestBook(LayerTest):
             out = layers.square_error_cost(input=x, label=y)
             return (out)
 
-    def make_masked_select(self):
-        with program_guard(fluid.default_main_program(),
-                           fluid.default_startup_program()):
-            x = self._get_data(name="X", shape=[4, 4], dtype="float32")
-            y = self._get_data(name="Y", shape=[1, 4], dtype="bool")
-            out = layers.masked_select(input=x, mask=y)
-            return (out)
-
     def test_dynamic_lstmp(self):
         # TODO(minqiyang): dygraph do not support lod now
         with self.static_graph():

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

-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import print_function
-
-import unittest
-import numpy as np
-import sys
-import paddle.fluid.core as core
-import paddle.fluid as fluid
-import paddle.fluid.layers as layers
-from paddle.fluid.executor import Executor
-
-
-class TestMaskedSelect(unittest.TestCase):
-    def test_masked_select(self):
-
-        mask_shape = [4, 1]
-        shape = [4, 4]
-        data = np.random.random(mask_shape).astype("float32")
-        input_data = np.random.random(shape).astype("float32")
-        mask_data = data > 0.5
-        mask_data_b = np.broadcast_to(mask_data, shape)
-        npresult = input_data[np.where(mask_data_b)]
-
-        input_var = layers.create_tensor(dtype="float32", name="input")
-        mask_var = layers.create_tensor(dtype="bool", name="mask")
-
-        output = layers.masked_select(input=input_var, mask=mask_var)
-        for use_cuda in ([False, True]
-                         if core.is_compiled_with_cuda() else [False]):
-            place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-            exe = Executor(place)
-            result = exe.run(fluid.default_main_program(),
-                             feed={"input": input_data,
-                                   "mask": mask_data},
-                             fetch_list=[output])
-
-            self.assertTrue(np.isclose(npresult, result).all())
-
-
-if __name__ == "__main__":
-    unittest.main()