wehub-resource-sync
150 lines
4.6 KiB
Python
150 lines
4.6 KiB
Python
# 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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import unittest
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import numpy as np
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from op_test import get_device, get_device_place, is_custom_device
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import paddle
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from paddle import base, rand
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from paddle.base import Program, core, program_guard
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class TestRandOpError(unittest.TestCase):
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"""
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This class test the input type check.
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"""
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def test_errors(self):
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main_prog = Program()
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start_prog = Program()
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with program_guard(main_prog, start_prog):
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def test_Variable():
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x1 = base.create_lod_tensor(
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np.zeros((4, 784)), [[1, 1, 1, 1]], base.CPUPlace()
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)
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rand(x1)
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self.assertRaises(TypeError, test_Variable)
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def test_dtype():
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dim_1 = paddle.tensor.fill_constant([1], "int64", 3)
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dim_2 = paddle.tensor.fill_constant([1], "int32", 5)
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rand(shape=[dim_1, dim_2], dtype='int32')
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self.assertRaises(TypeError, test_dtype)
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class TestRandOp(unittest.TestCase):
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"""
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This class test the common usages of randop.
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"""
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def run_net(self, use_cuda=False):
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place = get_device_place() if use_cuda else base.CPUPlace()
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exe = base.Executor(place)
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train_program = base.Program()
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startup_program = base.Program()
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with base.program_guard(train_program, startup_program):
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result_0 = rand([3, 4])
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result_1 = rand([3, 4], 'float64')
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dim_1 = paddle.tensor.fill_constant([1], "int64", 3)
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dim_2 = paddle.tensor.fill_constant([1], "int32", 5)
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result_2 = rand(shape=[dim_1, dim_2])
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var_shape = paddle.static.data(
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name='var_shape', shape=[2], dtype="int64"
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)
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result_3 = rand(var_shape)
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var_shape_int32 = paddle.static.data(
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name='var_shape_int32', shape=[2], dtype="int32"
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)
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result_4 = rand(var_shape_int32)
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exe.run(startup_program)
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x1 = np.array([3, 2]).astype('int64')
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x2 = np.array([4, 3]).astype('int32')
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ret = exe.run(
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train_program,
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feed={"var_shape": x1, "var_shape_int32": x2},
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fetch_list=[result_1, result_1, result_2, result_3, result_4],
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)
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def test_run(self):
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self.run_net(False)
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if core.is_compiled_with_cuda() or is_custom_device():
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self.run_net(True)
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class TestRandOpForDygraph(unittest.TestCase):
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"""
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This class test the common usages of randop.
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"""
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def run_net(self, use_cuda=False):
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place = get_device_place() if use_cuda else base.CPUPlace()
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with base.dygraph.guard(place):
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rand([3, 4])
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rand([3, 4], 'float64')
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dim_1 = paddle.tensor.fill_constant([1], "int64", 3)
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dim_2 = paddle.tensor.fill_constant([1], "int32", 5)
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rand(shape=[dim_1, dim_2])
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var_shape = paddle.to_tensor(np.array([3, 4]))
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rand(var_shape)
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def test_run(self):
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self.run_net(False)
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if core.is_compiled_with_cuda() or is_custom_device():
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self.run_net(True)
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class TestRandDtype(unittest.TestCase):
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def test_default_dtype(self):
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paddle.disable_static()
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def test_default_fp16():
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paddle.framework.set_default_dtype('float16')
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out = paddle.tensor.random.rand([2, 3])
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self.assertEqual(out.dtype, paddle.float16)
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def test_default_fp32():
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paddle.framework.set_default_dtype('float32')
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out = paddle.tensor.random.rand([2, 3])
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self.assertEqual(out.dtype, paddle.float32)
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def test_default_fp64():
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paddle.framework.set_default_dtype('float64')
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out = paddle.tensor.random.rand([2, 3])
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self.assertEqual(out.dtype, paddle.float64)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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paddle.set_device(get_device())
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test_default_fp16()
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test_default_fp64()
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test_default_fp32()
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paddle.enable_static()
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if __name__ == "__main__":
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unittest.main()
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