forked from mindspore-Ecosystem/mindspore
313 lines
12 KiB
Python
313 lines
12 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
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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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class NumpyGetItem():
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def __init__(self, index1, index2):
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super(NumpyGetItem, self).__init__()
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self.index1 = index1
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self.index2 = index2
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def __call__(self, tensor1, tensor2):
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return tensor1[self.index1], tensor2[self.index2]
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class TensorGetItem(nn.Cell):
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def __init__(self, index1, index2):
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super(TensorGetItem, self).__init__()
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self.index1 = index1
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self.index2 = index2
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def construct(self, tensor1, tensor2):
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return tensor1[self.index1], tensor2[self.index2]
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def common_func(ms_net, np_net):
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x = Tensor(shape=[8, None, 32], dtype=mindspore.float32)
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y = Tensor(shape=[None, 32, 32], dtype=mindspore.float32)
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ms_net.set_inputs(x, y)
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input_np1 = np.arange(8 * 16 * 32).reshape(8, 16, 32).astype(np.float32)
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input_np2 = np.arange(16 * 32 * 32).reshape(16, 32, 32).astype(np.float32)
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out0, out1 = ms_net(Tensor(input_np1), Tensor(input_np2))
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out_np0, out_np1 = np_net(input_np1, input_np2)
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assert np.all(out0.asnumpy() == out_np0)
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assert np.all(out1.asnumpy() == out_np1)
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@pytest.mark.level0
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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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def test_dynamic_getitem_int_negative():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is negative int.
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Expectation: Assert the result is equal the numpy result.
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"""
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index1 = -2
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index2 = -1
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ms_net = TensorGetItem(index1, index2)
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np_net = NumpyGetItem(index1, index2)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_int():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is int.
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Expectation: Assert the result is equal the numpy result.
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"""
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index1 = 2
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index2 = 1
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ms_net = TensorGetItem(index1, index2)
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np_net = NumpyGetItem(index1, index2)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_tuple_basic():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is basic tuple.
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Expectation: Assert the result is equal the numpy result.
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"""
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index1 = (1, slice(0, 1, 1), ...)
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index2 = (slice(2, None, None), 1, slice(3, 4, None))
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ms_net = TensorGetItem(index1, index2)
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np_net = NumpyGetItem(index1, index2)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_tuple_basic_neg():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is basic tuple(int is negative).
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Expectation: Assert the result is equal the numpy result.
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"""
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index1 = (slice(0, 1, 1), ..., -1)
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index2 = (-2, slice(2, None, None), slice(3, 4, None))
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ms_net = TensorGetItem(index1, index2)
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np_net = NumpyGetItem(index1, index2)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_tuple():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is tuple.
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Expectation: Assert the result is equal the numpy result.
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"""
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tensor_index = Tensor(np.array([[1, 2, 1], [0, 3, 2]]), mindspore.int32)
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index1 = (slice(2, None, None), (0, 2, 1), tensor_index)
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index2 = (-1, slice(0, 1, None), tensor_index)
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ms_net = TensorGetItem(index1, index2)
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index3 = (slice(2, None, None), (0, 2, 1), tensor_index.asnumpy())
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index4 = (-1, slice(0, 1, None), tensor_index.asnumpy())
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np_net = NumpyGetItem(index3, index4)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_bool():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is bool.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = True
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ms_net = TensorGetItem(index, index)
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np_net = NumpyGetItem(index, index)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_none():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is none.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = None
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ms_net = TensorGetItem(index, index)
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np_net = NumpyGetItem(index, index)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_ellipsis():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is ellipsis.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = ...
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ms_net = TensorGetItem(index, index)
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np_net = NumpyGetItem(index, index)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_slice():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is slice.
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Expectation: Assert the result is equal the numpy result.
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"""
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index1 = slice(1, 5, 1)
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index2 = slice(1, None, None)
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ms_net = TensorGetItem(index1, index2)
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np_net = NumpyGetItem(index1, index2)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_slice_neg():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is negative slice.
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Expectation: Assert the result is equal the numpy result.
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"""
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index1 = slice(-3, -1, 1)
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index2 = slice(-1, None, None)
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ms_net = TensorGetItem(index1, index2)
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np_net = NumpyGetItem(index1, index2)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_tensor():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is tensor.
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Expectation: Assert the result is equal the numpy result.
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"""
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index1 = Tensor(np.array([[1, 2], [0, 3]]), mindspore.int32)
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index2 = Tensor(np.array([[1, 2]]), mindspore.int32)
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ms_net = TensorGetItem(index1, index2)
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np_net = NumpyGetItem(index1.asnumpy(), index2.asnumpy())
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_list():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is list.
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Expectation: Assert the result is equal the numpy result.
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"""
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index1 = [True, 2, True]
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index2 = [1, 2, 0]
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ms_net = TensorGetItem(index1, index2)
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np_net = NumpyGetItem(index1, index2)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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@pytest.mark.level0
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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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def test_dynamic_getitem_slice_startoversize():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is slice and start is over size.
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Expectation: Assert the result is equal the numpy result.
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"""
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index1 = slice(8, None, 1)
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index2 = slice(30, None, None)
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ms_net = TensorGetItem(index1, index2)
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np_net = NumpyGetItem(index1, index2)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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common_func(ms_net, np_net)
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