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127 lines
3.6 KiB
127 lines
3.6 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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import pytest
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import numpy as np
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import mindspore as ms
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from mindspore import context
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from mindspore.nn import ReLU
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from mindspore.nn import Cell
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from mindspore.common.tensor import Tensor
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from mindspore.ops import operations as P
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def setup_module():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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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_parser_tensor_assign_slice():
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class Net(Cell):
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def __init__(self, U):
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super(Net, self).__init__()
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self.relu = ReLU()
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self.U = U
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def construct(self, x):
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x = self.relu(x)
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x[..., :2] = U
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return x
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input_np_x = np.random.rand(4, 4, 4)
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input_me_x = Tensor(input_np_x, ms.float32)
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U = 1.0
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net = Net(U)
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out_me = net(input_me_x)
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input_np_x[..., :2] = U
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assert np.allclose(out_me.asnumpy(), input_np_x, rtol=0.01, atol=0.01)
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def test_parser_tensor_assign_slice_002():
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class Net(Cell):
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def __init__(self, U):
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super(Net, self).__init__()
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self.relu = ReLU()
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self.U = U
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def construct(self, x):
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x = self.relu(x)
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x[::, :, :1] = self.U
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return x
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input_np_x = np.random.rand(4, 4, 4)
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input_me_x = Tensor(input_np_x, ms.float32)
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U = 1.0
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net = Net(U)
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out_me = net(input_me_x)
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input_np_x[::, :, :1] = U
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assert np.allclose(out_me.asnumpy(), input_np_x, rtol=0.01, atol=0.01)
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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_parser_tensor_assign_bool():
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class Net(Cell):
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def __init__(self, U):
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super(Net, self).__init__()
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self.relu = ReLU()
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self.U = U
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def construct(self, x, tensorB):
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x = self.relu(x)
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x[tensorB] = self.U
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return x
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input_np_x = np.random.rand(4, 4, 4)
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input_me_x = Tensor(input_np_x, ms.float32)
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numpy_B = np.random.randn(4, 4, 4) > 0
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tensor_B = Tensor(numpy_B)
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U = np.array([1])
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net = Net(Tensor(U))
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out_me = net(input_me_x, tensor_B)
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input_np_x[numpy_B] = U
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assert np.allclose(out_me.asnumpy(), input_np_x, rtol=0.01, atol=0.01)
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def test_parser_tensor_assign_bool_002():
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class Net(Cell):
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def __init__(self, U):
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super(Net, self).__init__()
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self.relu = ReLU()
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self.U = U
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self.fill = P.Fill()
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def construct(self, x, tensorB):
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x = self.relu(x)
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x[tensorB] = self.U
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return x
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input_np_x = np.random.rand(2, 2, 2)
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input_me_x = Tensor(input_np_x, ms.float32)
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numpy_B = np.random.randn(2, 2, 2) > 0
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tensor_B = Tensor(numpy_B)
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U = 1
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net = Net(U)
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out_me = net(input_me_x, tensor_B)
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input_np_x[numpy_B] = U
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assert np.allclose(out_me.asnumpy(), input_np_x, rtol=0.01, atol=0.01)
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