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99 lines
3.1 KiB
99 lines
3.1 KiB
import os
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import numpy as np
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.common.tensor import Tensor
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from mindspore.common.initializer import TruncatedNormal
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from mindspore.common.parameter import ParameterTuple
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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from mindspore.train.serialization import export
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def weight_variable():
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return TruncatedNormal(0.02)
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode="valid")
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def fc_with_initialize(input_channels, out_channels):
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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class LeNet5(nn.Cell):
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def __init__(self):
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super(LeNet5, self).__init__()
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self.batch_size = 32
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self.conv1 = conv(1, 6, 5)
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self.conv2 = conv(6, 16, 5)
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self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
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self.fc2 = fc_with_initialize(120, 84)
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self.fc3 = fc_with_initialize(84, 10)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.reshape = P.Reshape()
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.reshape(x, (self.batch_size, -1))
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.relu(x)
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x = self.fc3(x)
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return x
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class WithLossCell(nn.Cell):
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def __init__(self, network):
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super(WithLossCell, self).__init__(auto_prefix=False)
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self.loss = nn.SoftmaxCrossEntropyWithLogits()
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self.network = network
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def construct(self, x, label):
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predict = self.network(x)
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return self.loss(predict, label)
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class TrainOneStepCell(nn.Cell):
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def __init__(self, network):
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super(TrainOneStepCell, self).__init__(auto_prefix=False)
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self.network = network
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self.network.set_train()
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self.weights = ParameterTuple(network.trainable_params())
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self.optimizer = nn.Momentum(self.weights, 0.1, 0.9)
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self.hyper_map = C.HyperMap()
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self.grad = C.GradOperation(get_by_list=True)
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def construct(self, x, label):
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weights = self.weights
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grads = self.grad(self.network, weights)(x, label)
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return self.optimizer(grads)
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def test_export_lenet_grad_mindir():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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network = LeNet5()
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network.set_train()
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predict = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.zeros([32, 10]).astype(np.float32))
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net = TrainOneStepCell(WithLossCell(network))
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file_name = "lenet_grad"
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export(net, predict, label, file_name=file_name, file_format='MINDIR')
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verify_name = file_name + ".mindir"
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assert os.path.exists(verify_name)
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os.remove(verify_name)
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