forked from mindspore-Ecosystem/mindspore
118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
# 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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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _inner_ops as inner
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import mindspore.nn as nn
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import mindspore.context as context
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class DynamicShapeNet(nn.Cell):
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def __init__(self):
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super(DynamicShapeNet, self).__init__()
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self.convert_to_dynamic_shape_op = inner.GpuConvertToDynamicShape()
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self.dynamic_shape_op = P.DynamicShape()
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def construct(self, x):
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x_dynamic_shape = self.convert_to_dynamic_shape_op(x)
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return self.dynamic_shape_op(x_dynamic_shape)
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def dynamic_shape(np_type):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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dynamic_shape_net = DynamicShapeNet()
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shape = (1,)
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x = Tensor(np.zeros(shape).astype(np_type))
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ms_out = dynamic_shape_net(x).asnumpy()
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expected = np.array(shape)
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np.testing.assert_array_equal(ms_out, expected)
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shape = (7,)
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x = Tensor(np.zeros(shape).astype(np_type))
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ms_out = dynamic_shape_net(x).asnumpy()
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expected = np.array(shape)
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np.testing.assert_array_equal(ms_out, expected)
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shape = (1, 1)
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x = Tensor(np.zeros(shape).astype(np_type))
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ms_out = dynamic_shape_net(x).asnumpy()
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expected = np.array(shape)
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np.testing.assert_array_equal(ms_out, expected)
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shape = (1, 7)
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x = Tensor(np.zeros(shape).astype(np_type))
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ms_out = dynamic_shape_net(x).asnumpy()
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expected = np.array(shape)
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np.testing.assert_array_equal(ms_out, expected)
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shape = (3, 1)
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x = Tensor(np.zeros(shape).astype(np_type))
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ms_out = dynamic_shape_net(x).asnumpy()
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expected = np.array(shape)
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np.testing.assert_array_equal(ms_out, expected)
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shape = (2, 4)
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x = Tensor(np.zeros(shape).astype(np_type))
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ms_out = dynamic_shape_net(x).asnumpy()
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expected = np.array(shape)
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np.testing.assert_array_equal(ms_out, expected)
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shape = (1, 1, 1)
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x = Tensor(np.zeros(shape).astype(np_type))
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ms_out = dynamic_shape_net(x).asnumpy()
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expected = np.array(shape)
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np.testing.assert_array_equal(ms_out, expected)
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shape = (1, 5, 3)
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x = Tensor(np.zeros(shape).astype(np_type))
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ms_out = dynamic_shape_net(x).asnumpy()
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expected = np.array(shape)
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np.testing.assert_array_equal(ms_out, expected)
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shape = (2, 3, 1, 3, 1)
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x = Tensor(np.zeros(shape).astype(np_type))
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ms_out = dynamic_shape_net(x).asnumpy()
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expected = np.array(shape)
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np.testing.assert_array_equal(ms_out, 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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def test_dynamic_shape_int32():
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dynamic_shape(np.int32)
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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_dynamic_shape_float16():
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dynamic_shape(np.float16)
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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_dynamic_shape_float32():
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dynamic_shape(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_dynamic_shape_bool():
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dynamic_shape(np.bool)
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