mindspore/tests/ut/python/utils/test_export.py

import os
import numpy as np

import mindspore.nn as nn
from mindspore import context
from mindspore.common.tensor import Tensor
from mindspore.common.initializer import TruncatedNormal
from mindspore.common.parameter import ParameterTuple
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore.train.serialization import export


def weight_variable():
    return TruncatedNormal(0.02)


def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
    weight = weight_variable()
    return nn.Conv2d(in_channels, out_channels,
                     kernel_size=kernel_size, stride=stride, padding=padding,
                     weight_init=weight, has_bias=False, pad_mode="valid")


def fc_with_initialize(input_channels, out_channels):
    weight = weight_variable()
    bias = weight_variable()
    return nn.Dense(input_channels, out_channels, weight, bias)


class LeNet5(nn.Cell):
    def __init__(self):
        super(LeNet5, self).__init__()
        self.batch_size = 32
        self.conv1 = conv(1, 6, 5)
        self.conv2 = conv(6, 16, 5)
        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
        self.fc2 = fc_with_initialize(120, 84)
        self.fc3 = fc_with_initialize(84, 10)
        self.relu = nn.ReLU()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
        self.reshape = P.Reshape()

    def construct(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        x = self.reshape(x, (self.batch_size, -1))
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.relu(x)
        x = self.fc3(x)
        return x


class WithLossCell(nn.Cell):
    def __init__(self, network):
        super(WithLossCell, self).__init__(auto_prefix=False)
        self.loss = nn.SoftmaxCrossEntropyWithLogits()
        self.network = network

    def construct(self, x, label):
        predict = self.network(x)
        return self.loss(predict, label)


class TrainOneStepCell(nn.Cell):
    def __init__(self, network):
        super(TrainOneStepCell, self).__init__(auto_prefix=False)
        self.network = network
        self.network.set_train()
        self.weights = ParameterTuple(network.trainable_params())
        self.optimizer = nn.Momentum(self.weights, 0.1, 0.9)
        self.hyper_map = C.HyperMap()
        self.grad = C.GradOperation(get_by_list=True)

    def construct(self, x, label):
        weights = self.weights
        grads = self.grad(self.network, weights)(x, label)
        return self.optimizer(grads)


def test_export_lenet_grad_mindir():
    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
    network = LeNet5()
    network.set_train()
    predict = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
    label = Tensor(np.zeros([32, 10]).astype(np.float32))
    net = TrainOneStepCell(WithLossCell(network))
    file_name = "lenet_grad"
    export(net, predict, label, file_name=file_name, file_format='MINDIR')
    verify_name = file_name + ".mindir"
    assert os.path.exists(verify_name)
    os.remove(verify_name)