mindspore/tests/st/networks/test_gpu_lenet.py

# Copyright 2019 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.
# ============================================================================

import os

import numpy as np
import pytest

import mindspore.context as context
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms.vision.c_transforms as CV
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.dataset.transforms.vision import Inter
from mindspore.model_zoo.lenet import LeNet5
from mindspore.nn import Dense, TrainOneStepCell, WithLossCell
from mindspore.nn.metrics import Accuracy
from mindspore.nn.optim import Momentum
from mindspore.ops import operations as P
from mindspore.train import Model
from mindspore.train.callback import LossMonitor

context.set_context(mode=context.GRAPH_MODE, device_target="GPU")


class LeNet(nn.Cell):
    def __init__(self):
        super(LeNet, self).__init__()
        self.relu = P.ReLU()
        self.batch_size = 1
        weight1 = Tensor(np.ones([6, 3, 5, 5]).astype(np.float32) * 0.01)
        weight2 = Tensor(np.ones([16, 6, 5, 5]).astype(np.float32) * 0.01)
        self.conv1 = nn.Conv2d(3, 6, (5, 5), weight_init=weight1, stride=1, padding=0, pad_mode='valid')
        self.conv2 = nn.Conv2d(6, 16, (5, 5), weight_init=weight2, pad_mode='valid', stride=1, padding=0)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="valid")

        self.reshape = P.Reshape()
        self.reshape1 = P.Reshape()

        self.fc1 = Dense(400, 120)
        self.fc2 = Dense(120, 84)
        self.fc3 = Dense(84, 10)

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


def multisteplr(total_steps, gap, base_lr=0.9, gamma=0.1, dtype=mstype.float32):
    lr = []
    for step in range(total_steps):
        lr_ = base_lr * gamma ** (step // gap)
        lr.append(lr_)
    return Tensor(np.array(lr), dtype)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_train_lenet():
    epoch = 100
    net = LeNet()
    momentum = 0.9
    learning_rate = multisteplr(epoch, 30)

    optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
    criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
    net_with_criterion = WithLossCell(net, criterion)
    train_network = TrainOneStepCell(net_with_criterion, optimizer)  # optimizer
    train_network.set_train()
    losses = []
    for i in range(epoch):
        data = Tensor(np.ones([net.batch_size, 3, 32, 32]).astype(np.float32) * 0.01)
        label = Tensor(np.ones([net.batch_size]).astype(np.int32))
        loss = train_network(data, label)
        losses.append(loss)
    print(losses)


def create_dataset(data_path, batch_size=32, repeat_size=1,
                   num_parallel_workers=1):
    """
    create dataset for train or test
    """
    # define dataset
    mnist_ds = ds.MnistDataset(data_path)

    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)  # Bilinear mode
    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
    rescale_op = CV.Rescale(rescale, shift)
    hwc2chw_op = CV.HWC2CHW()
    type_cast_op = C.TypeCast(mstype.int32)

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

    # apply DatasetOps
    buffer_size = 10000
    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
    mnist_ds = mnist_ds.repeat(repeat_size)

    return mnist_ds


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_train_and_eval_lenet():
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    network = LeNet5(10)
    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
    net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})

    print("============== Starting Training ==============")
    ds_train = create_dataset(os.path.join('/home/workspace/mindspore_dataset/mnist', "train"), 32, 1)
    model.train(1, ds_train, callbacks=[LossMonitor()], dataset_sink_mode=True)

    print("============== Starting Testing ==============")
    ds_eval = create_dataset(os.path.join('/home/workspace/mindspore_dataset/mnist', "test"), 32, 1)
    acc = model.eval(ds_eval, dataset_sink_mode=True)
    print("============== Accuracy:{} ==============".format(acc))