forked from mindspore-Ecosystem/mindspore
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
# Copyright 2020-2021 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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from mindspore.common.tensor import Tensor
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from mindspore.ops import operations as P
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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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shape = (3, 4, 5, 6)
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x_np = np.random.rand(3, 1, 5, 1).astype(np.float32)
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output = P.BroadcastTo(shape)(Tensor(x_np))
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expect = np.broadcast_to(x_np, shape)
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assert np.allclose(output.asnumpy(), expect)
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x1_np = np.random.rand(3, 1, 5, 1).astype(np.float16)
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output = P.BroadcastTo(shape)(Tensor(x1_np))
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expect = np.broadcast_to(x1_np, shape)
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assert np.allclose(output.asnumpy(), expect)
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shape = (2, 3, 4, 5)
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x1_np = np.random.rand(4, 5).astype(np.float32)
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output = P.BroadcastTo(shape)(Tensor(x1_np))
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expect = np.broadcast_to(x1_np, shape)
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assert np.allclose(output.asnumpy(), 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_broadcast_dyn_init():
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"""
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Test running the op with -1's in the init shape to support varied inputs.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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ms_shape = (-1, -1, 5, 6)
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np_shape = (3, 4, 5, 6)
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x_np = np.random.rand(3, 1, 5, 1).astype(np.float32)
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output = P.BroadcastTo(ms_shape)(Tensor(x_np))
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expect = np.broadcast_to(x_np, np_shape)
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assert np.allclose(output.asnumpy(), expect)
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x1_np = np.random.rand(3, 1, 5, 1).astype(np.float16)
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output = P.BroadcastTo(ms_shape)(Tensor(x1_np))
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expect = np.broadcast_to(x1_np, np_shape)
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assert np.allclose(output.asnumpy(), expect)
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ms_shape = (2, 3, -1, -1)
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np_shape = (2, 3, 4, 5)
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x1_np = np.random.rand(4, 5).astype(np.float32)
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output = P.BroadcastTo(ms_shape)(Tensor(x1_np))
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expect = np.broadcast_to(x1_np, np_shape)
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assert np.allclose(output.asnumpy(), 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_broadcast_dyn_invalid_init():
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"""
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Test running the op with -1's in the init shape in incorrect positions.
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Expected to fail.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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ms_shape = (2, -1, 4, 5)
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x_np = np.random.rand(4, 5).astype(np.float32)
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with pytest.raises(ValueError):
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P.BroadcastTo(ms_shape)(Tensor(x_np))
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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ms_shape = (-1, 1, -1, -1)
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x_np = np.random.rand(4, 5).astype(np.float32)
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with pytest.raises(ValueError):
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P.BroadcastTo(ms_shape)(Tensor(x_np))
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