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40 lines
1.2 KiB
40 lines
1.2 KiB
import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, context
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from mindspore.nn import Momentum
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from mindspore.nn import WithLossCell, TrainOneStepCell
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from mindspore.ops import operations as P
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from mindspore.parallel._cost_model_context import set_cost_model_context
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class Net(nn.Cell):
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def __init__(self, input_ch, out_ch):
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super(Net, self).__init__()
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self.dense = nn.Dense(input_ch, out_ch)
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self.relu = P.ReLU()
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def construct(self, x):
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x = self.dense(x)
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x = self.relu(x)
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return x
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def test_inference_phase():
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="auto_parallel")
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set_cost_model_context(run_phase=1)
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net = Net(512, 128)
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predict = Tensor(np.ones([64, 512]).astype(np.float32) * 0.001)
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label = Tensor(np.ones([64, 128]).astype(np.float32))
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loss = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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train_network.set_train()
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train_network.set_auto_parallel()
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_ = train_network(predict, label)
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