wehub-resource-sync
121 lines
3.7 KiB
Python
121 lines
3.7 KiB
Python
# 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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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
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from paddle import base
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def size_wrapper(input):
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return paddle.numel(paddle.to_tensor(input))
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class TestSizeOp(OpTest):
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def setUp(self):
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self.op_type = "size"
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self.python_api = size_wrapper
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self.shape = []
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self.config()
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input = np.zeros(self.shape, dtype='bool')
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self.inputs = {'Input': input}
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self.outputs = {'Out': np.array(np.size(input), dtype='int64')}
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def config(self):
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pass
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def test_check_output(self):
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self.check_output()
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class TestRank1Tensor(TestSizeOp):
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def config(self):
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self.shape = [2]
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class TestRank2Tensor(TestSizeOp):
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def config(self):
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self.shape = [2, 3]
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class TestRank3Tensor(TestSizeOp):
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def config(self):
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self.shape = [2, 3, 100]
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class TestLargeTensor(TestSizeOp):
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def config(self):
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self.shape = [2**10]
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class TestSizeAPI(unittest.TestCase):
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def test_size_static(self):
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main_program = base.Program()
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startup_program = base.Program()
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with base.program_guard(main_program, startup_program):
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shape1 = [2, 1, 4, 5]
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shape2 = [1, 4, 5]
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x_1 = paddle.static.data(shape=shape1, dtype='int32', name='x_1')
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x_2 = paddle.static.data(shape=shape2, dtype='int32', name='x_2')
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input_1 = np.random.random(shape1).astype("int32")
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input_2 = np.random.random(shape2).astype("int32")
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out_1 = paddle.numel(x_1)
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out_2 = paddle.numel(x_2)
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exe = paddle.static.Executor(place=paddle.CPUPlace())
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res_1, res_2 = exe.run(
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feed={
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"x_1": input_1,
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"x_2": input_2,
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},
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fetch_list=[out_1, out_2],
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)
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np.testing.assert_array_equal(
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res_1, np.array(np.size(input_1)).astype("int64")
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)
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np.testing.assert_array_equal(
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res_2, np.array(np.size(input_2)).astype("int64")
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)
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def test_size_imperative(self):
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paddle.disable_static(paddle.CPUPlace())
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input_1 = np.random.random([2, 1, 4, 5]).astype("int32")
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input_2 = np.random.random([1, 4, 5]).astype("int32")
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x_1 = paddle.to_tensor(input_1)
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x_2 = paddle.to_tensor(input_2)
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out_1 = paddle.numel(x_1)
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out_2 = paddle.numel(x_2)
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np.testing.assert_array_equal(out_1.numpy().item(0), np.size(input_1))
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np.testing.assert_array_equal(out_2.numpy().item(0), np.size(input_2))
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paddle.enable_static()
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def test_error(self):
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main_program = base.Program()
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startup_program = base.Program()
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with base.program_guard(main_program, startup_program):
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def test_x_type():
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shape = [1, 4, 5]
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input_1 = np.random.random(shape).astype("int32")
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out_1 = paddle.numel(input_1)
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self.assertRaises(TypeError, test_x_type)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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