forked from mindspore-Ecosystem/mindspore
138 lines
4.2 KiB
Python
138 lines
4.2 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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""" test loss """
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import numpy as np
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import pytest
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import mindspore as ms
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore import Tensor
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from mindspore.ops import operations as P
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def func_single_output(x1, x2):
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return x1 - x2
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def func_multi_output(x1, x2):
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return (x1 + x2), (x1 - x2)
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output = 0
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def func_no_output(x1, x2):
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global output
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output = x1 + x2
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class PyFuncNet(nn.Cell):
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def __init__(self, fn, in_types, in_shapes, out_types, out_shapes):
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super().__init__()
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self.func = P.PyFunc(fn, in_types, in_shapes, out_types, out_shapes)
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self.relu = P.ReLU()
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def construct(self, x1, x2):
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x = self.func((x1, x2))
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return self.relu(x[0])
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def func_with_dtype(ms_dtype, np_dtype):
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shape = (40, 40)
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np.random.seed(42)
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x1 = np.random.randint(-5, 5, size=shape).astype(np_dtype)
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x2 = np.random.randint(-5, 5, size=shape).astype(np_dtype)
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expect = func_single_output(x1, x2)
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expect = P.ReLU()(Tensor(expect))
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net = PyFuncNet(func_single_output, [ms_dtype, ms_dtype], [shape, shape], [ms_dtype], [shape])
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x = net(Tensor(x1), Tensor(x2))
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assert np.allclose(x.asnumpy(), expect.asnumpy())
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pyfunc_single_output():
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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func_with_dtype(ms.float16, np.float16)
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func_with_dtype(ms.float32, np.float32)
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func_with_dtype(ms.float64, np.float64)
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func_with_dtype(ms.int32, np.int32)
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func_with_dtype(ms.int64, np.int64)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pyfunc_multi_output():
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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shape = (40, 40)
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dtype = ms.float32
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np.random.seed(42)
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x1 = np.random.randint(-5, 5, size=shape).astype(np.float32)
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x2 = np.random.randint(-5, 5, size=shape).astype(np.float32)
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expect, _ = func_multi_output(x1, x2)
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expect = P.ReLU()(Tensor(expect))
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net = PyFuncNet(func_multi_output, [dtype, dtype], [shape, shape], [dtype, dtype], [shape, shape])
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x = net(Tensor(x1), Tensor(x2))
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assert np.allclose(x.asnumpy(), expect.asnumpy())
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class PyFuncGraph(nn.Cell):
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def __init__(self, fn, in_types, in_shapes, out_types, out_shapes):
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super().__init__()
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self.func = P.PyFunc(fn, in_types, in_shapes, out_types, out_shapes)
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def construct(self, x1, x2):
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return self.func((x1, x2))
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pyfunc_no_output():
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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shape = (40, 40)
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dtype = ms.float32
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np.random.seed(42)
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x1 = np.random.randint(-5, 5, size=shape).astype(np.float32)
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x2 = np.random.randint(-5, 5, size=shape).astype(np.float32)
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func_no_output(x1, x2)
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global output
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expect = output
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net = PyFuncGraph(func_no_output, [dtype, dtype], [shape, shape], [], [])
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net(Tensor(x1), Tensor(x2))
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net_output = output
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assert np.allclose(net_output, expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pyfunc_scalar():
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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shape = ()
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ms_dtype = ms.int32
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x1 = int(10)
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x2 = int(5)
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expect = func_single_output(x1, x2)
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net = PyFuncGraph(func_single_output, [ms_dtype, ms_dtype], [shape, shape], [ms_dtype], [shape])
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x = net(Tensor(x1), Tensor(x2))
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assert np.allclose(x[0].asnumpy(), expect)
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