基于自动机器学习工具 NNI 的创新性科研扩展项目 (MSRA)+源代码+文档说明

## Task 2 ### Task 2.1 CIFAR10 此处同样使用了示例中的模板协助学习 ```yaml '''Train CIFAR10 with PyTorch.''' from __future__ import print_function import argparse import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import torch.backends.cudnn as cudnn import torchvision import torchvision.transforms as transforms import os import argparse import logging from models import * from utils import progress_bar import nni _logger = logging.getLogger("cifar10_pytorch_automl") trainloader = None testloader = None net = None criterion = None optimizer = None device = 'cuda' if torch.cuda.is_available() else 'cpu' #此处调用了CUDA作为运算 如无CUDA设备则使用CPU #不过感觉CPU运行还是慢不少的? best_acc = 0.0 # best test accuracy start_epoch = 0 # start from epoch 0 or last checkpoint epoch def prepare(args): global trainloader global testloader global net global criterion global optimizer # Data print('==> Preparing data..') transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train) trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2) #classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') # Model #输入模型 print('==> Building model..') if args['model'] == 'vgg': net = VGG('VGG19') if args['model'] == 'resnet18': net = ResNet18() if args['model'] == 'googlenet': net = GoogLeNet() if args['model'] == 'densenet121': net = DenseNet121() if args['model'] == 'mobilenet': net = MobileNet() if args['model'] == 'dpn92': net = DPN92() if args['model'] == 'shufflenetg2': net = ShuffleNetG2() if args['model'] == 'senet18': net = SENet18() net = net.to(device) if device == 'cuda': net = torch.nn.DataParallel(net) cudnn.benchmark = True criterion = nn.CrossEntropyLoss() #optimizer = optim.SGD(net.parameters(), lr=args['lr'], momentum=0.9, weight_decay=5e-4) if args['optimizer'] == 'SGD': optimizer = optim.SGD(net.parameters(), lr=args['lr'], momentum=0.9, weight_decay=5e-4) if args['optimizer'] == 'Adadelta': optimizer = optim.Adadelta(net.parameters(), lr=args['lr']) if args['optimizer'] == 'Adagrad': optimizer = optim.Adagrad(net.parameters(), lr=args['lr']) if args['optimizer'] == 'Adam': optimizer = optim.Adam(net.parameters(), lr=args['lr']) if args['optimizer'] == 'Adamax': optimizer = optim.Adam(net.parameters(), lr=args['lr']) # Training def train(epoch, batches=-1): global trainloader global testloader global net global criterion global optimizer print('\nEpoch: %d' % epoch) net.train() train_loss = 0 correct = 0 total = 0 for batch_idx, (inputs, targets) in enumerate(trainloader): inputs, targets = inputs.to(device), targets.to(device) optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() train_loss += loss.item() _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() acc = 100.*correct/total progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' % (train_loss/(batch_idx+1), 100.*correct/total, correct, total)) if batches > 0 and (batch_idx+1) >= batches: return def test(epoch): global best_acc global trainloader global testloader global net global criterion global optimizer net.eval() test_loss = 0 correct = 0 total = 0 with torch.no_grad(): for batch_idx, (inputs, targets) in enumerate(testloader): inputs, targets = inputs.to(device), targets.to(device) outputs = net(inputs) loss = criterion(outputs, targets) test_loss += loss.item() _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() acc = 100.*correct/total progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' % (test_loss/(batch_idx+1), 100.*correct/total, correct, total)) # Save checkpoint. acc = 100.*correct/total if acc > best_acc: print('Saving..') state = { 'net': net.state_dict(), 'acc': acc, 'epoch': epoch, } if not os.path.isdir('checkpoint'): os.mkdir('checkpoint') torch.save(state, './checkpoint/ckpt.t7') best_acc = acc return acc, best_acc if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("--epochs", type=int, default=200) # Maximum mini-batches per epoch, for code testing purpose parser.add_argument("--batches", type=int, default=-1) args, _ = parser.parse_known_args() try: RCV_CONFIG = nni.get_next_parameter() #RCV_CONFIG = {'lr': 0.1, 'optimizer': 'Adam', 'model':'senet18'} _logger.debug(RCV_CONFIG) prepare(RCV_CONFIG) acc = 0.0 best_acc = 0.0 for epoch in range(start_epoch, start_epoch+args.epochs): train(epoch, args.batches) acc, best_acc = test(epoch) nni.report_intermediate_result(acc) nni.report_final_result(best_acc) except Exception as exception: _logger.exception(exception) raise ``` ### Task 2.2