forked from huawei/mindspore2022
77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
# Copyright 2020-2021 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.nn as nn
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from mindspore.ops import composite as C
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from mindspore.nn import Momentum
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from mindspore import context, Tensor
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from mindspore.common.api import ms_function
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grad_all = C.GradOperation(get_all=True)
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class CellBprop(nn.Cell):
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def __init__(self):
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super(CellBprop, self).__init__()
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def construct(self, x, y):
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return 2 * x * x + y * y
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@ms_function
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def bprop(self, x, y, out, dout):
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return dout, 2 * y
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def test_cell_bprop_grad():
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input_x = Tensor(np.random.randn(2, 2).astype(np.float32))
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input_y = Tensor(np.random.randn(2, 2).astype(np.float32))
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context.set_context(mode=context.PYNATIVE_MODE)
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net = CellBprop()
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with pytest.raises(RuntimeError):
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grad_all(net)(input_x, input_y)
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class ConvNet(nn.Cell):
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def __init__(self):
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super(ConvNet, self).__init__()
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self.conv = nn.Conv2d(1, 2, kernel_size=2, stride=1, padding=0, weight_init="ones", pad_mode="valid")
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def construct(self, x):
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out = self.conv(x)
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return out
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class MomentumWithMsFunc(nn.Cell):
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def __init__(self, net):
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super(MomentumWithMsFunc, self).__init__()
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self.net = net
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self.optimizer = Momentum(filter(lambda x: x.requires_grad, self.net.get_parameters()), 0.1, 0.9)
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@ms_function
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def construct(self, grads):
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ret = self.optimizer(grads)
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return ret
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def test_ms_func_decorate_forward():
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context.set_context(mode=context.PYNATIVE_MODE)
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input_x = Tensor(np.random.randn(1, 1, 2, 2).astype(np.float32))
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net = ConvNet()
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grad_out = grad_all(net)(input_x)
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opt = MomentumWithMsFunc(net)
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opt(grad_out)
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