forked from mindspore-Ecosystem/mindspore
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
# Copyright 2019 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 numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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from mindspore import context
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from mindspore.common import dtype as mstype
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from mindspore.common.api import _cell_graph_executor
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from mindspore.nn.loss.loss import LossBase
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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 tests.ut.python.ops.test_math_ops import VirtualLoss
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grad_all = C.GradOperation(get_all=True)
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class NetWithLoss(nn.Cell):
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def __init__(self, network):
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super(NetWithLoss, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x, y):
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predict = self.network(x, y)
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return self.loss(predict)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x, y):
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return grad_all(self.network)(x, y)
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class CustomMatMul(nn.Cell):
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def __init__(self, transpose_a=False, transpose_b=False):
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super(CustomMatMul, self).__init__()
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self.fc = P.MatMul(transpose_a=transpose_a, transpose_b=transpose_b)
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def construct(self, x1, x2):
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out = self.fc(x1, x2)
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return out
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class MarginCE(LossBase):
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def __init__(self):
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super(MarginCE, self).__init__()
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self.fc = CustomMatMul(transpose_b=True)
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self.fc1 = CustomMatMul(transpose_b=True)
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self.fc2 = CustomMatMul(transpose_b=True)
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self.fc3 = CustomMatMul(transpose_b=True)
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self.fc4 = CustomMatMul(transpose_b=True)
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self.param = Parameter(Tensor(np.ones([512, 512]), dtype=mstype.float32), name="param", requires_grad=False)
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self.param2 = Parameter(Tensor(np.ones([512, 512]), dtype=mstype.float32), name="param", requires_grad=False)
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def construct(self, feature, label):
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fc_out = self.fc(feature, label)
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fc1_out = self.fc1(self.param2, self.param)
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fc2_out = self.fc2(fc1_out, fc_out)
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fc3_out = self.fc3(fc1_out, fc_out)
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fc4_out = self.fc4(fc2_out, fc3_out)
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return fc4_out
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def test_marin_loss():
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context.set_auto_parallel_context(device_num=4, global_rank=0)
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x = Tensor(np.ones([512, 512]), dtype=ms.float32)
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y = Tensor(np.ones([512, 512]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(MarginCE()))
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context.set_auto_parallel_context(parallel_mode="auto_parallel")
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net.set_auto_parallel()
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net.set_train()
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_cell_graph_executor.compile(net, x, y)
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