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90 lines
3.8 KiB
90 lines
3.8 KiB
4 years ago
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# 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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"""Test network turn on mix_precision."""
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import pytest
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import numpy as np
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from mindspore import nn
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from mindspore import ops
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from mindspore import amp
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from mindspore import Tensor
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from mindspore import context
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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class Net(nn.Cell):
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def __init__(self, in_c, out_c):
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super().__init__()
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self.relu = nn.ReLU()
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self.bn1 = nn.BatchNorm2d(num_features=in_c,
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gamma_init='ones',
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beta_init='zeros',
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moving_mean_init='zeros',
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moving_var_init='ones')
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self.bn2 = nn.BatchNorm2d(num_features=out_c,
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gamma_init='ones',
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beta_init='zeros',
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moving_mean_init='zeros',
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moving_var_init='ones')
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self.conv = nn.Conv2d(in_channels=in_c,
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out_channels=out_c,
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kernel_size=3,
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stride=1,
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has_bias=True,
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pad_mode='same',
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weight_init='ones',
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bias_init='ones')
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self.mean = ops.ReduceMean(keep_dims=False)
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def construct(self, x):
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x = self.relu(x)
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x = self.bn1(x)
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x = self.conv(x)
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x = self.bn2(x)
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x = self.relu(x)
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x = self.mean(x, (2, 3))
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return x
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_auto_mix_precision():
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input_data = np.random.randn(32, 3, 224, 224).astype(np.float64)
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label_data = np.random.randn(32, 10).astype(np.float32)
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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net = Net(3, 10)
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opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.001, momentum=0.0009, weight_decay=0.001,
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loss_scale=0.0001)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
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train_network = amp.build_train_network(net, opt, loss, level="O3",
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loss_scale_manager=FixedLossScaleManager(drop_overflow_update=False))
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out = train_network(Tensor(input_data), Tensor(label_data))
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# pynative mode
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context.set_context(mode=context.PYNATIVE_MODE)
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net_pynative = Net(3, 10)
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opt_pynative = nn.Momentum(params=net_pynative.trainable_params(), learning_rate=0.001, momentum=0.0009,
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weight_decay=0.001,
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loss_scale=0.0001)
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loss_pynative = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
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train_network_pynative = amp.build_train_network(net_pynative, opt_pynative, loss_pynative, level="O3",
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loss_scale_manager=FixedLossScaleManager(
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drop_overflow_update=False))
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out_pynative = train_network_pynative(Tensor(input_data), Tensor(label_data))
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assert np.allclose(out.asnumpy(), out_pynative.asnumpy(), 0.001, 0.001)
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