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249 lines
8.2 KiB
249 lines
8.2 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid as fluid
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import paddle
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class TestGatherNdOpWithEmptyIndex(OpTest):
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#Index has empty element, which means copy entire tensor
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def setUp(self):
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self.op_type = "gather_nd"
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xnp = np.random.random((5, 20)).astype("float64")
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self.inputs = {'X': xnp, 'Index': np.array([[], []]).astype("int32")}
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self.outputs = {
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'Out': np.vstack((xnp[np.newaxis, :], xnp[np.newaxis, :]))
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}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestGatherNdOpWithIndex1(OpTest):
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def setUp(self):
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self.op_type = "gather_nd"
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xnp = np.random.random((5, 20)).astype("float64")
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self.inputs = {'X': xnp, 'Index': np.array([1]).astype("int32")}
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self.outputs = {'Out': self.inputs["X"][self.inputs["Index"]]}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestGatherNdOpWithLowIndex(OpTest):
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#Index has low rank, X has high rank
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def setUp(self):
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self.op_type = "gather_nd"
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xnp = np.random.uniform(0, 100, (10, 10)).astype("float64")
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index = np.array([[1], [2]]).astype("int64")
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self.inputs = {'X': xnp, 'Index': index}
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self.outputs = {'Out': xnp[tuple(index.T)]} #[[14, 25, 1], [76, 22, 3]]
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestGatherNdOpIndex1(OpTest):
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#Index has low rank, X has high rank
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def setUp(self):
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self.op_type = "gather_nd"
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xnp = np.random.uniform(0, 100, (10, 10)).astype("float64")
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index = np.array([1, 2]).astype("int64")
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self.inputs = {'X': xnp, 'Index': index}
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self.outputs = {'Out': xnp[tuple(index.T)]}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestGatherNdOpWithSameIndexAsX(OpTest):
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#Index has same rank as X's rank
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def setUp(self):
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self.op_type = "gather_nd"
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xnp = np.random.uniform(0, 100, (10, 10)).astype("float64")
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index = np.array([[1, 1], [2, 1]]).astype("int64")
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self.inputs = {'X': xnp, 'Index': index}
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self.outputs = {'Out': xnp[tuple(index.T)]} #[25, 22]
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestGatherNdOpWithHighRankSame(OpTest):
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#Both Index and X have high rank, and Rank(Index) = Rank(X)
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def setUp(self):
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self.op_type = "gather_nd"
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shape = (5, 2, 3, 1, 10)
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xnp = np.random.rand(*shape).astype("float64")
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index = np.vstack([np.random.randint(0, s, size=2) for s in shape]).T
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self.inputs = {'X': xnp, 'Index': index.astype("int32")}
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self.outputs = {'Out': xnp[tuple(index.T)]}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestGatherNdOpWithHighRankDiff(OpTest):
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#Both Index and X have high rank, and Rank(Index) < Rank(X)
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def setUp(self):
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self.op_type = "gather_nd"
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shape = (2, 3, 4, 1, 10)
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xnp = np.random.rand(*shape).astype("float64")
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index = np.vstack([np.random.randint(0, s, size=200) for s in shape]).T
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index_re = index.reshape([20, 5, 2, 5])
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self.inputs = {'X': xnp, 'Index': index_re.astype("int32")}
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self.outputs = {'Out': xnp[tuple(index.T)].reshape([20, 5, 2])}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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#Test Python API
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class TestGatherNdOpAPI(unittest.TestCase):
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def test_case1(self):
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x1 = fluid.layers.data(
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name='x1', shape=[30, 40, 50, 60], dtype='float32')
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index1 = fluid.layers.data(name='index1', shape=[2, 4], dtype='int32')
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output1 = fluid.layers.gather_nd(x1, index1)
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def test_case2(self):
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x2 = fluid.layers.data(name='x2', shape=[30, 40, 50], dtype='float32')
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index2 = fluid.layers.data(name='index2', shape=[2, 2], dtype='int64')
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output2 = fluid.layers.gather_nd(x2, index2)
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def test_case3(self):
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x3 = fluid.layers.data(name='x3', shape=[3, 4, 5], dtype='float32')
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index3 = fluid.layers.data(name='index3', shape=[2, 1], dtype='int32')
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output3 = fluid.layers.gather_nd(x3, index3, name="gather_nd_layer")
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#Test Raise Index Error
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class TestGatherNdOpRaise(unittest.TestCase):
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def test_check_raise(self):
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def check_raise_is_test():
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try:
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x = fluid.layers.data(
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name='x', shape=[3, 4, 5], dtype='float32')
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index = fluid.layers.data(
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name='index', shape=[2, 10], dtype='int32')
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output = fluid.layers.gather_nd(x, index)
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except Exception as e:
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t = \
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"Input(Index).shape[-1] should be no greater than Input(X).rank"
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if t in str(e):
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raise IndexError
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self.assertRaises(IndexError, check_raise_is_test)
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class TestGatherNdError(unittest.TestCase):
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def test_error(self):
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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shape = [8, 9, 6]
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x = paddle.fluid.data(shape=shape, dtype='float32', name='x')
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index = paddle.fluid.data(shape=shape, dtype='bool', name='index')
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index_float = paddle.fluid.data(
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shape=shape, dtype='float32', name='index_float')
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np_x = np.random.random(shape).astype('float32')
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np_index = np.array(np.random.randint(2, size=shape, dtype=bool))
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def test_x_type():
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paddle.gather_nd(np_x, index)
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self.assertRaises(TypeError, test_x_type)
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def test_index_type():
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paddle.gather_nd(x, np_index)
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self.assertRaises(TypeError, test_index_type)
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def test_index_dtype():
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paddle.gather_nd(x, index_float)
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self.assertRaises(TypeError, test_index_dtype)
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class TestGatherNdAPI2(unittest.TestCase):
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def test_static(self):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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data1 = fluid.layers.data('data1', shape=[-1, 2], dtype='float64')
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index = fluid.layers.data('index', shape=[-1, 1], dtype='int32')
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out = paddle.gather_nd(data1, index)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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input = np.array([[1, 2], [3, 4], [5, 6]])
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index_1 = np.array([[1]])
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result, = exe.run(feed={"data1": input,
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"index": index_1},
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fetch_list=[out])
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expected_output = np.array([[3, 4]])
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self.assertTrue(np.allclose(result, expected_output))
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def test_imperative(self):
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paddle.disable_static()
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input_1 = np.array([[1, 2], [3, 4], [5, 6]])
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index_1 = np.array([[1]])
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input = fluid.dygraph.to_variable(input_1)
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index = fluid.dygraph.to_variable(index_1)
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output = paddle.fluid.layers.gather(input, index)
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output_np = output.numpy()
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expected_output = np.array([3, 4])
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self.assertTrue(np.allclose(output_np, expected_output))
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paddle.enable_static()
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if __name__ == "__main__":
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unittest.main()
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