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186 lines
6.3 KiB
186 lines
6.3 KiB
# Copyright 2020 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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from mindspore.common.tensor import Tensor
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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class ConstScalarAndTensorMinimum(Cell):
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def __init__(self):
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super(ConstScalarAndTensorMinimum, self).__init__()
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self.min = P.Minimum()
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self.x = 20
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def construct(self, y):
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return self.min(self.x, y)
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class TwoTensorsMinimum(Cell):
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def __init__(self):
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super(TwoTensorsMinimum, self).__init__()
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self.min = P.Minimum()
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def construct(self, x, y):
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return self.min(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_minimum_constScalar_tensor_int():
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x = Tensor(np.array([[2, 3, 4], [100, 200, 300]]).astype(np.int32))
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expect = [[2, 3, 4], [20, 20, 20]]
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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min_op = ConstScalarAndTensorMinimum()
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output = min_op(x)
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assert np.all(output.asnumpy() == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_minimum_two_tensors_Not_Broadcast_int():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(3, 4, 5).astype(np.int32) * prop
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y = np.random.randn(3, 4, 5).astype(np.int32) * prop
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expect = np.minimum(x, y).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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min_op = TwoTensorsMinimum()
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output = min_op(Tensor(x), Tensor(y))
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assert np.all(output.asnumpy() == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_minimum_two_tensors_Broadcast_int():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(3, 4, 5).astype(np.int32) * prop
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y = np.random.randn(3, 1, 1).astype(np.int32) * prop
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expect = np.minimum(x, y).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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min_op = TwoTensorsMinimum()
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output = min_op(Tensor(x), Tensor(y))
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assert np.all(output.asnumpy() == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_minimum_two_tensors_Broadcast_oneDimension_int():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(3).astype(np.int32) * prop
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y = np.random.randn(3).astype(np.int32) * prop
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expect = np.minimum(x, y).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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min_op = TwoTensorsMinimum()
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output = min_op(Tensor(x), Tensor(y))
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assert np.all(output.asnumpy() == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_minimum_two_tensors_notBroadcast_all_oneDimension_int():
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x = Tensor(np.array([[2]]).astype(np.int32))
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y = Tensor(np.array([[100]]).astype(np.int32))
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expect = [[2]]
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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min_op = TwoTensorsMinimum()
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output = min_op(x, y)
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assert np.all(output.asnumpy() == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_minimum_two_tensors_notBroadcast_float32():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(3, 4, 5).astype(np.float32) * prop
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y = np.random.randn(3, 4, 5).astype(np.float32) * prop
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expect = np.minimum(x, y).astype(np.float32)
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error = np.ones(shape=expect.shape) * 1.0e-5
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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min_op = TwoTensorsMinimum()
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output = min_op(Tensor(x), Tensor(y))
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diff = output.asnumpy() - expect
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assert np.all(np.abs(diff) < error)
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assert output.shape == expect.shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_minimum_two_tensors_notBroadcast_float16():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(3, 4, 5).astype(np.float16) * prop
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y = np.random.randn(3, 4, 5).astype(np.float16) * prop
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expect = np.minimum(x, y).astype(np.float16)
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error = np.ones(shape=expect.shape) * 1.0e-5
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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min_op = TwoTensorsMinimum()
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output = min_op(Tensor(x), Tensor(y))
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diff = output.asnumpy() - expect
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assert np.all(np.abs(diff) < error)
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assert output.shape == expect.shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_minimum_two_tensors_Broadcast_float16():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(3, 4, 5).astype(np.float16) * prop
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y = np.random.randn(3, 4, 1).astype(np.float16) * prop
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expect = np.minimum(x, y).astype(np.float16)
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error = np.ones(shape=expect.shape) * 1.0e-5
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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min_op = TwoTensorsMinimum()
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output = min_op(Tensor(x), Tensor(y))
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diff = output.asnumpy() - expect
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assert np.all(np.abs(diff) < error)
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assert output.shape == expect.shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_minimum_two_tensors_notBroadcast_float64():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(3, 4, 1).astype(np.float64) * prop
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y = np.random.randn(3, 4, 5).astype(np.float64) * prop
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expect = np.minimum(x, y).astype(np.float64)
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error = np.ones(shape=expect.shape) * 1.0e-5
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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min_op = TwoTensorsMinimum()
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output = min_op(Tensor(x), Tensor(y))
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diff = output.asnumpy() - expect
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assert np.all(np.abs(diff) < error)
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assert output.shape == expect.shape
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