forked from mindspore-Ecosystem/mindspore
161 lines
4.7 KiB
Python
161 lines
4.7 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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import os
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import context, jit
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from mindspore.common.tensor import Tensor
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from mindspore.train.serialization import export, load
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class SingleWhileNet(nn.Cell):
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def construct(self, x, y):
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x += 1
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while x < y:
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x += 1
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y += 2 * x
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return y
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_single_while():
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context.set_context(mode=context.GRAPH_MODE)
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network = SingleWhileNet()
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x = Tensor(np.array([1]).astype(np.float32))
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y = Tensor(np.array([2]).astype(np.float32))
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origin_out = network(x, y)
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file_name = "while_net"
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export(network, x, y, file_name=file_name, file_format='MINDIR')
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mindir_name = file_name + ".mindir"
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assert os.path.exists(mindir_name)
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graph = load(mindir_name)
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loaded_net = nn.GraphCell(graph)
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outputs_after_load = loaded_net(x, y)
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assert origin_out == outputs_after_load
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_jit_function_while():
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"""
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Features: Control flow.
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Description: Test while in @jit decorated function.
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Expectation: No exception.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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network = SingleWhileNet()
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x = Tensor(np.array([1]).astype(np.float32))
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y = Tensor(np.array([2]).astype(np.float32))
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origin_out = network(x, y)
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file_name = "while_net"
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export(network, x, y, file_name=file_name, file_format='MINDIR')
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mindir_name = file_name + ".mindir"
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assert os.path.exists(mindir_name)
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graph = load(mindir_name)
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loaded_net = nn.GraphCell(graph)
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context.set_context(mode=context.PYNATIVE_MODE)
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@jit
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def run_graph(x, y):
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outputs = loaded_net(x, y)
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return outputs
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outputs_after_load = run_graph(x, y)
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assert origin_out == outputs_after_load
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class SingleWhileInlineNet(nn.Cell):
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def construct(self, x, y):
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x += 1
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while x < y:
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x += 1
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y += x
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return y
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_single_while_inline_export():
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context.set_context(mode=context.GRAPH_MODE)
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network = SingleWhileInlineNet()
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x = Tensor(np.array([1]).astype(np.float32))
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y = Tensor(np.array([2]).astype(np.float32))
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file_name = "while_inline_net"
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export(network, x, y, file_name=file_name, file_format='MINDIR')
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mindir_name = file_name + ".mindir"
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assert os.path.exists(mindir_name)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_single_while_inline_load():
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context.set_context(mode=context.GRAPH_MODE)
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network = SingleWhileInlineNet()
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x = Tensor(np.array([1]).astype(np.float32))
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y = Tensor(np.array([2]).astype(np.float32))
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file_name = "while_inline_net"
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export(network, x, y, file_name=file_name, file_format='MINDIR')
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mindir_name = file_name + ".mindir"
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assert os.path.exists(mindir_name)
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load(mindir_name)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_single_while_inline():
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context.set_context(mode=context.GRAPH_MODE)
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network = SingleWhileInlineNet()
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x = Tensor(np.array([1]).astype(np.float32))
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y = Tensor(np.array([2]).astype(np.float32))
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origin_out = network(x, y)
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file_name = "while_inline_net"
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export(network, x, y, file_name=file_name, file_format='MINDIR')
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mindir_name = file_name + ".mindir"
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assert os.path.exists(mindir_name)
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graph = load(mindir_name)
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loaded_net = nn.GraphCell(graph)
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outputs_after_load = loaded_net(x, y)
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assert origin_out == outputs_after_load
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