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

# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Test export"""
import os
import numpy as np

import mindspore.nn as nn
import mindspore.dataset as ds
import mindspore.dataset.vision as CV
import mindspore.dataset.transforms as CT
from mindspore.dataset.vision import Inter
from mindspore import context
from mindspore.common import dtype as mstype
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)


def create_dataset():
    # define dataset
    mnist_ds = ds.MnistDataset("../data/dataset/testMnistData")

    resize_height, resize_width = 32, 32
    rescale = 1.0 / 255.0
    shift = 0.0
    rescale_nml = 1 / 0.3081
    shift_nml = -1 * 0.1307 / 0.3081

    # define map operations
    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)
    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
    rescale_op = CV.Rescale(rescale, shift)
    hwc2chw_op = CV.HWC2CHW()
    type_cast_op = CT.TypeCast(mstype.int32)

    # apply map operations on images
    mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label")
    mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image")
    mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image")
    mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image")
    mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image")

    # apply DatasetOps
    mnist_ds = mnist_ds.batch(batch_size=32, drop_remainder=True)

    return mnist_ds

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():
    """
    Feature: Export LeNet to MindIR
    Description: Test export API to save network into MindIR
    Expectation: save successfully
    """
    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)


def test_export_lenet_with_dataset():
    """
    Feature: Export LeNet with data preprocess to MindIR
    Description: Test export API to save network and dataset into MindIR
    Expectation: save successfully
    """
    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
    network = LeNet5()
    network.set_train()
    dataset = create_dataset()
    file_name = "lenet_preprocess"

    export(network, dataset, file_name=file_name, file_format='MINDIR')
    verify_name = file_name + ".mindir"
    assert os.path.exists(verify_name)
    os.remove(verify_name)