122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
# Copyright 2022 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 Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.expand_dims = P.ExpandDims()
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def construct(self, tensor):
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return self.expand_dims(tensor, -1)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize("data_type",
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[np.bool, np.int8, np.uint8, np.int16, np.uint16, np.int32, np.uint32, np.int64,
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np.uint64, np.float16, np.float32, np.float64])
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def test_sqeeze_net_ascend(data_type):
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"""
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Feature: Test ExpandDims DynamicShape.
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Description: The input data type contains common valid types including bool
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Expectation: match to np benchmark.
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"""
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x_np = np.random.randn(1, 16, 1, 1).astype(data_type)
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x = Tensor(x_np)
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net = Net()
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input_dyn = Tensor(shape=[1, None, 1, 1], dtype=x.dtype)
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net.set_inputs(input_dyn)
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expected = np.expand_dims(x_np, -1)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == expected)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == expected)
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize("data_type",
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[np.bool, np.int8, np.uint8, np.int16, np.uint16, np.int32, np.uint32, np.int64,
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np.uint64, np.float16, np.float32, np.float64, np.complex64, np.complex128])
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def test_sqeeze_net_cpu(data_type):
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"""
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Feature: Test ExpandDims DynamicShape.
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Description: The input data type contains common valid types including bool
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Expectation: match to np benchmark.
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"""
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x_np = np.random.randn(1, 16, 1, 1).astype(data_type)
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x = Tensor(x_np)
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net = Net()
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input_dyn = Tensor(shape=[1, None, 1, 1], dtype=x.dtype)
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net.set_inputs(input_dyn)
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expected = np.expand_dims(x_np, -1)
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == expected)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == expected)
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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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@pytest.mark.parametrize("data_type",
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[np.bool, np.int8, np.uint8, np.int16, np.uint16, np.int32, np.uint32, np.int64,
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np.uint64, np.float16, np.float32, np.float64, np.complex64, np.complex128])
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def test_sqeeze_net_gpu(data_type):
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"""
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Feature: Test ExpandDims DynamicShape.
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Description: The input data type contains common valid types including bool
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Expectation: match to np benchmark.
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"""
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x_np = np.random.randn(1, 16, 1, 1).astype(data_type)
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x = Tensor(x_np)
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net = Net()
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input_dyn = Tensor(shape=[1, None, 1, 1], dtype=x.dtype)
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net.set_inputs(input_dyn)
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expected = np.expand_dims(x_np, -1)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == expected)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == expected)
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