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82 lines
2.9 KiB
82 lines
2.9 KiB
5 years ago
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# 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 pytest
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from mindspore.ops import operations as P
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from mindspore.nn import Cell
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from mindspore.common.tensor import Tensor
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import mindspore.common.dtype as mstype
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import mindspore.context as context
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import numpy as np
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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_nobroadcast():
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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x1_np = np.random.rand(10, 20).astype(np.float32)
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x2_np = np.random.rand(10, 20).astype(np.float32)
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output_ms = P.Minimum()(Tensor(x1_np), Tensor(x2_np))
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output_np = np.minimum(x1_np, x2_np)
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assert np.allclose(output_ms.asnumpy(), output_np)
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output_ms = P.Maximum()(Tensor(x1_np), Tensor(x2_np))
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output_np = np.maximum(x1_np, x2_np)
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assert np.allclose(output_ms.asnumpy(), output_np)
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output_ms = P.Greater()(Tensor(x1_np), Tensor(x2_np))
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output_np = x1_np > x2_np
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assert np.allclose(output_ms.asnumpy(), output_np)
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output_ms = P.Less()(Tensor(x1_np), Tensor(x2_np))
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output_np = x1_np < x2_np
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assert np.allclose(output_ms.asnumpy(), output_np)
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output_ms = P.Pow()(Tensor(x1_np), Tensor(x2_np))
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output_np = np.power(x1_np, x2_np)
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assert np.allclose(output_ms.asnumpy(), output_np)
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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_broadcast():
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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x1_np = np.random.rand(3, 1, 5, 1).astype(np.float32)
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x2_np = np.random.rand(1, 4, 1, 6).astype(np.float32)
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output_ms = P.Minimum()(Tensor(x1_np), Tensor(x2_np))
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output_np = np.minimum(x1_np, x2_np)
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assert np.allclose(output_ms.asnumpy(), output_np)
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output_ms = P.Maximum()(Tensor(x1_np), Tensor(x2_np))
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output_np = np.maximum(x1_np, x2_np)
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assert np.allclose(output_ms.asnumpy(), output_np)
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output_ms = P.Greater()(Tensor(x1_np), Tensor(x2_np))
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output_np = x1_np > x2_np
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assert np.allclose(output_ms.asnumpy(), output_np)
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output_ms = P.Less()(Tensor(x1_np), Tensor(x2_np))
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output_np = x1_np < x2_np
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assert np.allclose(output_ms.asnumpy(), output_np)
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output_ms = P.Pow()(Tensor(x1_np), Tensor(x2_np))
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output_np = np.power(x1_np, x2_np)
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assert np.allclose(output_ms.asnumpy(), output_np)
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