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146 lines
3.9 KiB
146 lines
3.9 KiB
# 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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""" test_fix_bug """
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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 Tensor
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from mindspore.common import dtype as ms
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from mindspore.common.api import _executor
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class assignment1_Net(nn.Cell):
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""" assignment1_Net definition """
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def __init__(self, number):
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super().__init__()
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self.number = number
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self.relu = nn.ReLU()
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def construct(self, x):
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y = self.number
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for _ in [1, y]:
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x = self.relu(x)
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return x
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class assignment2_Net(nn.Cell):
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""" assignment2_Net definition """
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def __init__(self, number):
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super().__init__()
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self.number = number
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self.relu = nn.ReLU()
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def construct(self, x):
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a, b = self.number
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for _ in [a, b]:
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x = self.relu(x)
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return x
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def assignment_operator_base(number):
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""" assignment_operator_base """
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input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me = Tensor(input_np)
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x = number
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if isinstance(x, int):
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net = assignment1_Net(x)
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else:
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net = assignment2_Net(x)
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_executor.compile(net, input_me)
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def test_ME_assignment_operator_0010():
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""" test_ME_assignment_operator_0010 """
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assignment_operator_base(3)
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def test_ME_assignment_operator_0020():
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""" test_ME_assignment_operator_0020 """
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assignment_operator_base((1, 3))
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class unsupported_method_net(nn.Cell):
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""" unsupported_method_net definition """
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def __init__(self):
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super().__init__()
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self.relu = nn.ReLU()
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def construct(self, x):
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with open("a.txt") as f:
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f.read()
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return x
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def test_compile_unspported():
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""" test_compile_unspported """
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input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me = Tensor(input_np)
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net = unsupported_method_net()
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with pytest.raises(RuntimeError):
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_executor.compile(net, input_me)
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def test_parser_map_0002():
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class NetMap0002(nn.Cell):
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def __init__(self):
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super().__init__()
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self.relu = nn.ReLU()
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self.hypermap = C.Map()
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def mul(self, x=2, y=4):
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return x * y
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def construct(self, x):
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if map(self.mul) == 8:
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x = self.relu(x)
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return x
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input_np_x = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me_x = Tensor(input_np_x)
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net = NetMap0002()
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with pytest.raises(TypeError):
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net(input_me_x)
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def test_fix_expanddims_loss_scale():
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class ControlOneIfOneScaleOneScale(nn.Cell):
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def __init__(self):
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super().__init__()
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self.op = P.ExpandDims()
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def construct(self, x, y, data):
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if x > y:
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out = 1
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else:
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out = 2
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if x > y:
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out = self.op(data, out)
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else:
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out = self.op(data, out)
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return out
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net = ControlOneIfOneScaleOneScale()
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x = Tensor(1, ms.float32)
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y = Tensor(0, ms.float32)
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input_shape = (1024, 512, 7, 7)
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input_data = np.random.randn(*input_shape).astype(np.float32)
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net = ControlOneIfOneScaleOneScale()
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net(x, y, Tensor(input_data))
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