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88 lines
3.0 KiB
88 lines
3.0 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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""" auto mixed precision """
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import numpy as np
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from mindspore import amp
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from mindspore import nn
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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import mindspore.context as context
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from mindspore.model_zoo.resnet import resnet50
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def setup_module(module):
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context.set_context(mode=context.GRAPH_MODE)
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class Net(nn.Cell):
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def __init__(self, in_features, out_features):
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super(Net, self).__init__()
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self.dense = nn.Dense(in_features, out_features)
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self.loss = nn.MSELoss()
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def construct(self, input_x, label):
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output = self.dense(input_x)
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loss = self.loss(output, label)
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return loss
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class NetNoLoss(nn.Cell):
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def __init__(self, in_features, out_features):
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super(NetNoLoss, self).__init__()
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self.dense = nn.Dense(in_features, out_features)
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def construct(self, input_x):
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return self.dense(input_x)
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def test_amp_o0():
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inputs = Tensor(np.ones([16, 16]).astype(np.float32))
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label = Tensor(np.zeros([16, 16]).astype(np.float32))
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net = Net(16, 16)
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optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_network = amp.build_train_network(net, optimizer, level="O0")
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output = train_network(inputs, label)
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def test_amp_o2():
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inputs = Tensor(np.ones([16, 16]).astype(np.float32))
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label = Tensor(np.zeros([16, 16]).astype(np.float32))
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net = Net(16, 16)
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optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_network = amp.build_train_network(net, optimizer, level="O2")
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output = train_network(inputs, label)
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def test_amp_o2_loss():
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inputs = Tensor(np.ones([16, 16]).astype(np.float32))
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label = Tensor(np.zeros([16, 16]).astype(np.float32))
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net = NetNoLoss(16, 16)
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loss = nn.MSELoss()
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optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_network = amp.build_train_network(net, optimizer, loss, level="O2")
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output = train_network(inputs, label)
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def test_amp_o0_loss():
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inputs = Tensor(np.ones([16, 16]).astype(np.float32))
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label = Tensor(np.zeros([16, 16]).astype(np.float32))
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net = NetNoLoss(16, 16)
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loss = nn.MSELoss()
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optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_network = amp.build_train_network(net, optimizer, loss)
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output = train_network(inputs, label)
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