131 lines
4.1 KiB
131 lines
4.1 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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def bcast(x, target_tensor):
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x_dims = x.shape
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y_dims = target_tensor.shape
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bcast_dims = []
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for i in range(len(x_dims)):
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bcast_dims.append(int(y_dims[i] / x_dims[i]))
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bcast_dims = np.array(bcast_dims).astype("int64")
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return bcast_dims
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class TestExpandAsOpRank1(OpTest):
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def setUp(self):
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self.op_type = "expand_as"
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x = np.random.rand(12).astype("float64")
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target_tensor = np.random.rand(24).astype("float64")
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self.inputs = {'X': x, 'target_tensor': target_tensor}
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self.attrs = {}
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bcast_dims = bcast(x, target_tensor)
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output = np.tile(self.inputs['X'], bcast_dims)
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self.outputs = {'Out': output}
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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 TestExpandAsOpRank2(OpTest):
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def setUp(self):
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self.op_type = "expand_as"
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x = np.random.rand(2, 3).astype("float64")
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target_tensor = np.random.rand(4, 6).astype("float64")
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self.inputs = {'X': x, 'target_tensor': target_tensor}
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self.attrs = {}
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bcast_dims = bcast(x, target_tensor)
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output = np.tile(self.inputs['X'], bcast_dims)
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self.outputs = {'Out': output}
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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 TestExpandAsOpRank3(OpTest):
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def setUp(self):
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self.op_type = "expand_as"
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x = np.random.rand(2, 3, 3).astype("float64")
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target_tensor = np.random.rand(4, 6, 6).astype("float64")
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self.inputs = {'X': x, 'target_tensor': target_tensor}
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self.attrs = {}
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bcast_dims = bcast(x, target_tensor)
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output = np.tile(self.inputs['X'], bcast_dims)
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self.outputs = {'Out': output}
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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 TestExpandAsOpRank4(OpTest):
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def setUp(self):
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self.op_type = "expand_as"
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x = np.random.rand(1, 1, 3, 16).astype("float64")
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target_tensor = np.random.rand(4, 6, 6, 32).astype("float64")
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self.inputs = {'X': x, 'target_tensor': target_tensor}
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self.attrs = {}
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bcast_dims = bcast(x, target_tensor)
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output = np.tile(self.inputs['X'], bcast_dims)
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self.outputs = {'Out': output}
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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 TestExpandAPI(OpTest):
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def test_api(self):
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input1 = np.random.random([12, 14]).astype("float32")
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input2 = np.random.random([48, 14]).astype("float32")
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x = fluid.layers.data(
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name='x', shape=[12, 14], append_batch_size=False, dtype="float32")
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y = fluid.layers.data(
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name='target_tensor',
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shape=[48, 14],
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append_batch_size=False,
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dtype="float32")
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out_1 = fluid.layers.expand_as(x, target_tensor=y)
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exe = fluid.Executor(place=fluid.CPUPlace())
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res_1 = exe.run(fluid.default_main_program(),
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feed={"x": input1,
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"target_tensor": input2},
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fetch_list=[out_1])
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assert np.array_equal(res_1[0], np.tile(input1, (4, 1)))
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if __name__ == "__main__":
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unittest.main()
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