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136 lines
5.0 KiB
136 lines
5.0 KiB
# Copyright (c) 2020 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 paddle
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import numpy as np
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import paddle.fluid.core as core
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from op_test import OpTest
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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class TestIndexSelectOp(OpTest):
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def setUp(self):
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self.op_type = "index_select"
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self.init_dtype_type()
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index_np = np.random.randint(
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low=0, high=self.x_shape[self.dim], size=self.index_size)
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x_np = np.random.random(self.x_shape).astype(self.x_type)
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self.inputs = {'X': x_np, 'Index': index_np}
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self.attrs = {'dim': self.dim}
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outer_loop = np.prod(self.x_shape[:self.dim])
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x_reshape = [outer_loop] + list(self.x_shape[self.dim:])
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x_np_reshape = np.reshape(x_np, tuple(x_reshape))
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out_list = []
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for i in range(outer_loop):
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for j in range(self.index_size):
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out_list.append(x_np_reshape[i, index_np[j]])
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self.out_shape = list(self.x_shape)
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self.out_shape[self.dim] = self.index_size
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self.out_shape = tuple(self.out_shape)
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out = np.reshape(out_list, self.out_shape)
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self.outputs = {'Out': out}
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def init_dtype_type(self):
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self.dim = 1
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self.x_type = np.float64
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self.index_type = np.int64
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self.x_shape = (100, 4, 5)
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self.index_size = 100
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['X'], 'Out')
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class TestIndexSelectOpCase2(TestIndexSelectOp):
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def init_dtype_type(self):
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self.x_type = np.float32
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self.index_type = np.int32
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self.dim = -2
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self.x_shape = (10, 10, 4, 10)
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self.index_size = 10
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class TestIndexSelectAPI(unittest.TestCase):
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def input_data(self):
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self.data_x = np.array([[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0],
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[9.0, 10.0, 11.0, 12.0]])
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self.data_index = np.array([0, 1, 1]).astype('int32')
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def test_index_select_api(self):
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self.input_data()
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# case 1:
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with program_guard(Program(), Program()):
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x = fluid.layers.data(name='x', shape=[-1, 4])
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index = fluid.layers.data(
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name='index', shape=[3], dtype='int32', append_batch_size=False)
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z = paddle.index_select(x, index, axis=1)
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exe = fluid.Executor(fluid.CPUPlace())
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res, = exe.run(feed={'x': self.data_x,
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'index': self.data_index},
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fetch_list=[z.name],
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return_numpy=False)
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expect_out = np.array([[1.0, 2.0, 2.0], [5.0, 6.0, 6.0],
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[9.0, 10.0, 10.0]])
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self.assertTrue(np.allclose(expect_out, np.array(res)))
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# case 2:
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with program_guard(Program(), Program()):
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x = fluid.layers.data(name='x', shape=[-1, 4])
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index = fluid.layers.data(
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name='index', shape=[3], dtype='int32', append_batch_size=False)
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z = paddle.index_select(x, index)
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exe = fluid.Executor(fluid.CPUPlace())
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res, = exe.run(feed={'x': self.data_x,
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'index': self.data_index},
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fetch_list=[z.name],
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return_numpy=False)
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expect_out = np.array(
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[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [5.0, 6.0, 7.0, 8.0]])
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self.assertTrue(np.allclose(expect_out, np.array(res)))
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def test_dygraph_api(self):
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self.input_data()
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# case 1:
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with fluid.dygraph.guard():
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x = fluid.dygraph.to_variable(self.data_x)
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index = fluid.dygraph.to_variable(self.data_index)
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z = paddle.index_select(x, index)
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np_z = z.numpy()
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expect_out = np.array(
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[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [5.0, 6.0, 7.0, 8.0]])
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self.assertTrue(np.allclose(expect_out, np_z))
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# case 2:
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with fluid.dygraph.guard():
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x = fluid.dygraph.to_variable(self.data_x)
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index = fluid.dygraph.to_variable(self.data_index)
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z = paddle.index_select(x, index, axis=1)
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np_z = z.numpy()
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expect_out = np.array([[1.0, 2.0, 2.0], [5.0, 6.0, 6.0],
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[9.0, 10.0, 10.0]])
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self.assertTrue(np.allclose(expect_out, np_z))
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if __name__ == '__main__':
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unittest.main()
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