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76 lines
2.9 KiB
76 lines
2.9 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 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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from paddle.fluid import compiler, Program, program_guard
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import paddle
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paddle.enable_static()
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class TestBroadcastToError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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x1 = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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shape = [2, 2]
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self.assertRaises(TypeError, paddle.tensor.broadcast_to, x1, shape)
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x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
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self.assertRaises(TypeError, paddle.tensor.broadcast_to, x2, shape)
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x3 = fluid.layers.data(name='x3', shape=[4], dtype="bool")
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x3.stop_gradient = False
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self.assertRaises(ValueError, paddle.tensor.broadcast_to, x3, shape)
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# Test python API
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class TestBroadcastToAPI(unittest.TestCase):
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def test_api(self):
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input = np.random.random([12, 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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positive_2 = fluid.layers.fill_constant([1], "int32", 12)
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expand_shape = fluid.layers.data(
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name="expand_shape",
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shape=[2],
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append_batch_size=False,
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dtype="int32")
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out_1 = paddle.broadcast_to(x, shape=[12, 14])
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out_2 = paddle.broadcast_to(x, shape=[positive_2, 14])
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out_3 = paddle.broadcast_to(x, shape=expand_shape)
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g0 = fluid.backward.calc_gradient(out_2, x)
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exe = fluid.Executor(place=fluid.CPUPlace())
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res_1, res_2, res_3 = exe.run(fluid.default_main_program(),
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feed={
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"x": input,
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"expand_shape":
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np.array([12, 14]).astype("int32")
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},
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fetch_list=[out_1, out_2, out_3])
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assert np.array_equal(res_1, np.tile(input, (1, 1)))
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assert np.array_equal(res_2, np.tile(input, (1, 1)))
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assert np.array_equal(res_3, np.tile(input, (1, 1)))
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if __name__ == "__main__":
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unittest.main()
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