forked from mindspore-Ecosystem/mindspore
138 lines
4.2 KiB
Python
138 lines
4.2 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
""" test loss """
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore as ms
|
|
import mindspore.nn as nn
|
|
import mindspore.context as context
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
|
|
def func_single_output(x1, x2):
|
|
return x1 - x2
|
|
|
|
def func_multi_output(x1, x2):
|
|
return (x1 + x2), (x1 - x2)
|
|
|
|
output = 0
|
|
def func_no_output(x1, x2):
|
|
global output
|
|
output = x1 + x2
|
|
|
|
class PyFuncNet(nn.Cell):
|
|
def __init__(self, fn, in_types, in_shapes, out_types, out_shapes):
|
|
super().__init__()
|
|
self.func = P.PyFunc(fn, in_types, in_shapes, out_types, out_shapes)
|
|
self.relu = P.ReLU()
|
|
|
|
def construct(self, x1, x2):
|
|
x = self.func((x1, x2))
|
|
return self.relu(x[0])
|
|
|
|
|
|
def func_with_dtype(ms_dtype, np_dtype):
|
|
shape = (40, 40)
|
|
np.random.seed(42)
|
|
x1 = np.random.randint(-5, 5, size=shape).astype(np_dtype)
|
|
x2 = np.random.randint(-5, 5, size=shape).astype(np_dtype)
|
|
|
|
expect = func_single_output(x1, x2)
|
|
expect = P.ReLU()(Tensor(expect))
|
|
|
|
net = PyFuncNet(func_single_output, [ms_dtype, ms_dtype], [shape, shape], [ms_dtype], [shape])
|
|
x = net(Tensor(x1), Tensor(x2))
|
|
assert np.allclose(x.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_pyfunc_single_output():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
func_with_dtype(ms.float16, np.float16)
|
|
func_with_dtype(ms.float32, np.float32)
|
|
func_with_dtype(ms.float64, np.float64)
|
|
func_with_dtype(ms.int32, np.int32)
|
|
func_with_dtype(ms.int64, np.int64)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_pyfunc_multi_output():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
shape = (40, 40)
|
|
dtype = ms.float32
|
|
|
|
np.random.seed(42)
|
|
x1 = np.random.randint(-5, 5, size=shape).astype(np.float32)
|
|
x2 = np.random.randint(-5, 5, size=shape).astype(np.float32)
|
|
expect, _ = func_multi_output(x1, x2)
|
|
expect = P.ReLU()(Tensor(expect))
|
|
|
|
net = PyFuncNet(func_multi_output, [dtype, dtype], [shape, shape], [dtype, dtype], [shape, shape])
|
|
x = net(Tensor(x1), Tensor(x2))
|
|
|
|
assert np.allclose(x.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
class PyFuncGraph(nn.Cell):
|
|
def __init__(self, fn, in_types, in_shapes, out_types, out_shapes):
|
|
super().__init__()
|
|
self.func = P.PyFunc(fn, in_types, in_shapes, out_types, out_shapes)
|
|
|
|
def construct(self, x1, x2):
|
|
return self.func((x1, x2))
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_pyfunc_no_output():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
shape = (40, 40)
|
|
dtype = ms.float32
|
|
|
|
np.random.seed(42)
|
|
x1 = np.random.randint(-5, 5, size=shape).astype(np.float32)
|
|
x2 = np.random.randint(-5, 5, size=shape).astype(np.float32)
|
|
func_no_output(x1, x2)
|
|
global output
|
|
expect = output
|
|
|
|
net = PyFuncGraph(func_no_output, [dtype, dtype], [shape, shape], [], [])
|
|
net(Tensor(x1), Tensor(x2))
|
|
net_output = output
|
|
|
|
assert np.allclose(net_output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_pyfunc_scalar():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
shape = ()
|
|
ms_dtype = ms.int32
|
|
|
|
x1 = int(10)
|
|
x2 = int(5)
|
|
expect = func_single_output(x1, x2)
|
|
|
|
net = PyFuncGraph(func_single_output, [ms_dtype, ms_dtype], [shape, shape], [ms_dtype], [shape])
|
|
x = net(Tensor(x1), Tensor(x2))
|
|
assert np.allclose(x[0].asnumpy(), expect)
|