forked from mindspore-Ecosystem/mindspore
147 lines
4.9 KiB
Python
147 lines
4.9 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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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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class NetArgminWithValue(nn.Cell):
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def __init__(self):
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super(NetArgminWithValue, self).__init__()
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axis1 = 0
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axis2 = -1
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self.argmin1 = P.ArgMinWithValue(axis1)
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self.argmin2 = P.ArgMinWithValue(axis2)
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self.argmin3 = P.ArgMinWithValue()
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def construct(self, x):
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return (self.argmin1(x), self.argmin2(x), self.argmin3(x))
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class NetArgminWithValueBig(nn.Cell):
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def __init__(self, axis=0):
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super(NetArgminWithValueBig, self).__init__()
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self.argmin = P.ArgMinWithValue(axis)
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def construct(self, x):
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return self.argmin(x)
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def argminwithvalue_base(data_type):
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x = Tensor(np.array([[1., 20., 5.],
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[67., 8., 9.],
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[130., 24., 15.],
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[0.3, -0.4, -15.]]).astype(data_type))
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expect1 = np.array([3, 3, 3]).astype(data_type)
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expect2 = np.array([0, 1, 2, 2]).astype(data_type)
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expect11 = np.array([0.3, -0.4, -15.]).astype(data_type)
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expect22 = np.array([1., 8., 15., -15.]).astype(data_type)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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argmin = NetArgminWithValue()
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output = argmin(x)
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assert (output[0][0].asnumpy() == expect1).all()
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assert (output[0][1].asnumpy() == expect11).all()
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assert (output[1][0].asnumpy() == expect2).all()
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assert (output[1][1].asnumpy() == expect22).all()
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assert (output[2][0].asnumpy() == expect1).all()
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assert (output[2][1].asnumpy() == expect11).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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argmin = NetArgminWithValue()
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output = argmin(x)
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assert (output[0][0].asnumpy() == expect1).all()
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assert (output[0][1].asnumpy() == expect11).all()
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assert (output[1][0].asnumpy() == expect2).all()
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assert (output[1][1].asnumpy() == expect22).all()
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assert (output[2][0].asnumpy() == expect1).all()
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assert (output[2][1].asnumpy() == expect11).all()
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def argminwithvalue_3d(data_type, shape_x):
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np.random.seed(2)
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x_np = np.random.random(shape_x).astype(data_type)
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x = Tensor(x_np)
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argmin = NetArgminWithValueBig(0)
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output = argmin(x)
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expect1 = np.argmin(x_np, axis=0)
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expect2 = np.minimum.reduce(x_np, 0)
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assert (output[0].asnumpy() == expect1).all()
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assert (output[1].asnumpy() == expect2).all()
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argmin = NetArgminWithValueBig(1)
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output = argmin(x)
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expect1 = np.argmin(x_np, axis=1)
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expect2 = np.minimum.reduce(x_np, 1)
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assert (output[0].asnumpy() == expect1).all()
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assert (output[1].asnumpy() == expect2).all()
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argmin = NetArgminWithValueBig(2)
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output = argmin(x)
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expect1 = np.argmin(x_np, axis=2)
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expect2 = np.minimum.reduce(x_np, 2)
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assert (output[0].asnumpy() == expect1).all()
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assert (output[1].asnumpy() == expect2).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_argminwithvalue_base_float32():
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argminwithvalue_base(np.float32)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_argminwithvalue_base_float16():
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argminwithvalue_base(np.float16)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_argminwithvalue_3d_float32():
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shape_x = (2, 32, 256)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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argminwithvalue_3d(np.float32, shape_x)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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argminwithvalue_3d(np.float32, shape_x)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_argminwithvalue_3d_float16():
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shape_x = (2, 64, 128)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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argminwithvalue_3d(np.float16, shape_x)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_argminwithvalue_3d_big_float32():
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shape_x = (128, 1024, 1)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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argminwithvalue_3d(np.float32, shape_x)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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argminwithvalue_3d(np.float32, shape_x)
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