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146 lines
4.2 KiB
146 lines
4.2 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 paddle.fluid as fluid
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import numpy as np
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from op_test import OpTest
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class TestIndexSampleOp(OpTest):
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def setUp(self):
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self.op_type = "index_sample"
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self.config()
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xnp = np.random.random(self.x_shape).astype(self.x_type)
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indexnp = np.random.randint(
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low=0, high=self.x_shape[1],
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size=self.index_shape).astype(self.index_type)
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self.inputs = {'X': xnp, 'Index': indexnp}
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index_array = []
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for i in range(self.index_shape[0]):
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for j in indexnp[i]:
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index_array.append(xnp[i, j])
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index_array = np.array(index_array).astype(self.x_type)
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out = np.reshape(index_array, self.index_shape)
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self.outputs = {'Out': out}
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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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def config(self):
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"""
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For multi-dimension input
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"""
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self.x_shape = (10, 20)
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self.x_type = "float64"
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self.index_shape = (10, 10)
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self.index_type = "int32"
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class TestCase1(TestIndexSampleOp):
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def config(self):
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"""
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For one dimension input
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"""
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self.x_shape = (100, 1)
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self.x_type = "float64"
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self.index_shape = (100, 1)
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self.index_type = "int32"
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class TestCase2(TestIndexSampleOp):
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def config(self):
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"""
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For int64_t index type
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"""
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self.x_shape = (10, 100)
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self.x_type = "float64"
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self.index_shape = (10, 10)
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self.index_type = "int64"
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class TestCase3(TestIndexSampleOp):
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def config(self):
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"""
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For int index type
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"""
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self.x_shape = (10, 100)
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self.x_type = "float64"
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self.index_shape = (10, 10)
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self.index_type = "int32"
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class TestCase4(TestIndexSampleOp):
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def config(self):
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"""
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For int64 index type
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"""
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self.x_shape = (10, 128)
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self.x_type = "float64"
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self.index_shape = (10, 64)
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self.index_type = "int64"
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class TestIndexSampleShape(unittest.TestCase):
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def test_shape(self):
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paddle.enable_static()
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# create x value
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x_shape = (2, 5)
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x_type = "float64"
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x_np = np.random.random(x_shape).astype(x_type)
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# create index value
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index_shape = (2, 3)
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index_type = "int32"
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index_np = np.random.randint(
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low=0, high=x_shape[1], size=index_shape).astype(index_type)
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x = fluid.data(name='x', shape=[-1, 5], dtype='float64')
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index = fluid.data(name='index', shape=[-1, 3], dtype='int32')
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output = paddle.index_sample(x=x, index=index)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place=place)
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exe.run(fluid.default_startup_program())
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feed = {'x': x_np, 'index': index_np}
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res = exe.run(feed=feed, fetch_list=[output])
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class TestIndexSampleDynamic(unittest.TestCase):
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def test_result(self):
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with fluid.dygraph.guard():
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x = paddle.to_tensor(
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[[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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dtype='float32')
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index = paddle.to_tensor(
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[[0, 1, 2], [1, 2, 3], [0, 0, 0]], dtype='int32')
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out_z1 = paddle.index_sample(x, index)
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except_output = np.array(
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[[1.0, 2.0, 3.0], [6.0, 7.0, 8.0], [9.0, 9.0, 9.0]])
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assert out_z1.numpy().all() == except_output.all()
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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