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| """ | ||
| @author: Baixu Chen | ||
| @contact: [email protected] | ||
| """ | ||
| import random | ||
| import time | ||
| import warnings | ||
| import sys | ||
| import argparse | ||
| import shutil | ||
|
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||
| import torch | ||
| import torch.nn as nn | ||
| import torch.backends.cudnn as cudnn | ||
| from torch.optim import SGD | ||
| from torch.utils.data import DataLoader | ||
| import torchvision.transforms as T | ||
| import torch.nn.functional as F | ||
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| sys.path.append('../..') | ||
| from common.modules.classifier import Classifier | ||
| from common.vision.transforms import ResizeImage | ||
| from common.utils.metric import accuracy | ||
| from common.utils.meter import AverageMeter, ProgressMeter | ||
| from common.utils.data import ForeverDataIterator | ||
| from common.utils.logger import CompleteLogger | ||
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| sys.path.append('.') | ||
| import utils | ||
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| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | ||
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| def main(args: argparse.Namespace): | ||
| logger = CompleteLogger(args.log, args.phase) | ||
| print(args) | ||
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| if args.seed is not None: | ||
| random.seed(args.seed) | ||
| torch.manual_seed(args.seed) | ||
| cudnn.deterministic = True | ||
| warnings.warn('You have chosen to seed training. ' | ||
| 'This will turn on the CUDNN deterministic setting, ' | ||
| 'which can slow down your training considerably! ' | ||
| 'You may see unexpected behavior when restarting ' | ||
| 'from checkpoints.') | ||
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| cudnn.benchmark = True | ||
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| # Data loading code | ||
| normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | ||
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| train_transform = T.Compose([ | ||
| T.RandomResizedCrop(224, scale=(0.2, 1.)), | ||
| T.RandomHorizontalFlip(), | ||
| T.ToTensor(), | ||
| normalize | ||
| ]) | ||
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| val_transform = T.Compose([ | ||
| ResizeImage(256), | ||
| T.CenterCrop(224), | ||
| T.ToTensor(), | ||
| normalize | ||
| ]) | ||
|
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| # get dataset | ||
| labeled_train_dataset, _, val_dataset = utils.get_dataset(args.data, args.root, args.sample_rate, train_transform, | ||
| val_transform) | ||
| labeled_train_loader = DataLoader(labeled_train_dataset, batch_size=args.batch_size, shuffle=True, | ||
| num_workers=args.workers, drop_last=True) | ||
| labeled_train_iter = ForeverDataIterator(labeled_train_loader) | ||
| val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers) | ||
|
|
||
| # create model | ||
| print("=> using pre-trained model '{}'".format(args.arch)) | ||
| backbone = utils.get_model(args.arch) | ||
| num_classes = labeled_train_dataset.num_classes | ||
| pool_layer = nn.Identity() if args.no_pool else None | ||
| classifier = Classifier(backbone, num_classes, pool_layer=pool_layer, finetune=not args.scratch).to(device) | ||
|
|
||
| # define optimizer | ||
| optimizer = SGD(classifier.get_parameters(args.lr), args.lr, momentum=0.9, weight_decay=args.wd, nesterov=True) | ||
|
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||
| # resume from the best checkpoint | ||
| if args.phase == 'test': | ||
| checkpoint = torch.load(logger.get_checkpoint_path('best'), map_location='cpu') | ||
| classifier.load_state_dict(checkpoint) | ||
| acc1 = utils.validate(val_loader, classifier, args, device) | ||
| print(acc1) | ||
| return | ||
|
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| # start training | ||
| best_acc1 = 0.0 | ||
| for epoch in range(args.epochs): | ||
| # train for one epoch | ||
| train(labeled_train_iter, classifier, optimizer, epoch, args) | ||
| # evaluate on validation set | ||
| with torch.no_grad(): | ||
| acc1 = utils.validate(val_loader, classifier, args, device) | ||
|
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| # remember best acc@1 and save checkpoint | ||
| torch.save(classifier.state_dict(), logger.get_checkpoint_path('latest')) | ||
| if acc1 > best_acc1: | ||
| shutil.copy(logger.get_checkpoint_path('latest'), logger.get_checkpoint_path('best')) | ||
| best_acc1 = max(acc1, best_acc1) | ||
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| print("best_acc1 = {:3.1f}".format(best_acc1)) | ||
| logger.close() | ||
|
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| def train(labeled_train_iter: ForeverDataIterator, model, optimizer: SGD, epoch: int, args: argparse.Namespace): | ||
| batch_time = AverageMeter('Time', ':2.2f') | ||
| data_time = AverageMeter('Data', ':2.1f') | ||
| losses = AverageMeter('Loss', ':3.2f') | ||
| cls_accs = AverageMeter('Acc', ':3.1f') | ||
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| progress = ProgressMeter( | ||
| args.iters_per_epoch, | ||
| [batch_time, data_time, losses, cls_accs], | ||
| prefix="Epoch: [{}]".format(epoch)) | ||
|
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| # switch to train mode | ||
| model.train() | ||
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| end = time.time() | ||
| for i in range(args.iters_per_epoch): | ||
| labeled_x, labels = next(labeled_train_iter) | ||
| labeled_x = labeled_x.to(device) | ||
| batch_size = labeled_x.shape[0] | ||
| labels = labels.to(device) | ||
|
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| # measure data loading time | ||
| data_time.update(time.time() - end) | ||
|
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| # compute output | ||
| labeled_y, f = model(labeled_x) | ||
| # cross entropy loss | ||
| loss = F.cross_entropy(labeled_y, labels) | ||
|
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| # measure accuracy and record loss | ||
| losses.update(loss.item(), batch_size) | ||
| cls_acc = accuracy(labeled_y, labels)[0] | ||
| cls_accs.update(cls_acc.item(), batch_size) | ||
|
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| # compute gradient and do SGD step | ||
| optimizer.zero_grad() | ||
| loss.backward() | ||
| optimizer.step() | ||
|
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| # measure elapsed time | ||
| batch_time.update(time.time() - end) | ||
| end = time.time() | ||
|
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| if i % args.print_freq == 0: | ||
| progress.display(i) | ||
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| if __name__ == '__main__': | ||
| parser = argparse.ArgumentParser(description='Baseline for Semi Supervised Learning') | ||
| # dataset parameters | ||
| parser.add_argument('root', metavar='DIR', | ||
| help='root path of dataset') | ||
| parser.add_argument('-d', '--data', metavar='DATA', | ||
| help='dataset: ' + ' | '.join(utils.get_dataset_names())) | ||
| parser.add_argument('-sr', '--sample-rate', default=100, type=int, | ||
| metavar='N', | ||
| help='sample rate of training dataset (default: 100)') | ||
| # model parameters | ||
| parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50', | ||
| choices=utils.get_model_names(), | ||
| help='backbone architecture: ' + | ||
| ' | '.join(utils.get_model_names()) + | ||
| ' (default: resnet50)') | ||
| parser.add_argument('--no-pool', action='store_true', | ||
| help='no pool layer after the feature extractor.') | ||
| parser.add_argument('--scratch', action='store_true', help='whether train from scratch.') | ||
| # training parameters | ||
| parser.add_argument('-b', '--batch-size', default=48, type=int, | ||
| metavar='N', | ||
| help='mini-batch size (default: 48)') | ||
| parser.add_argument('--lr', '--learning-rate', default=0.01, type=float, | ||
| metavar='LR', help='initial learning rate', dest='lr') | ||
| parser.add_argument('--wd', '--weight-decay', default=5e-4, type=float, | ||
| metavar='W', help='weight decay (default:5e-4)') | ||
| parser.add_argument('-j', '--workers', default=2, type=int, metavar='N', | ||
| help='number of data loading workers (default: 4)') | ||
| parser.add_argument('--epochs', default=5, type=int, metavar='N', | ||
| help='number of total epochs to run') | ||
| parser.add_argument('-i', '--iters-per-epoch', default=500, type=int, | ||
| help='Number of iterations per epoch') | ||
| parser.add_argument('-p', '--print-freq', default=100, type=int, | ||
| metavar='N', help='print frequency (default: 100)') | ||
| parser.add_argument('--seed', default=None, type=int, | ||
| help='seed for initializing training. ') | ||
| parser.add_argument("--log", type=str, default='baseline', | ||
| help="Where to save logs, checkpoints and debugging images.") | ||
| parser.add_argument("--phase", type=str, default='train', choices=['train', 'test'], | ||
| help="When phase is 'test', only test the model.") | ||
| args = parser.parse_args() | ||
| main(args) |
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| @@ -0,0 +1,15 @@ | ||
| #!/usr/bin/env bash | ||
| # ResNet50, CUB200 | ||
| CUDA_VISIBLE_DEVICES=0 python baseline.py data/cub200 -d CUB200 -sr 15 --seed 0 --log logs/baseline/cub200_15 | ||
| CUDA_VISIBLE_DEVICES=0 python baseline.py data/cub200 -d CUB200 -sr 30 --seed 0 --log logs/baseline/cub200_30 | ||
| CUDA_VISIBLE_DEVICES=0 python baseline.py data/cub200 -d CUB200 -sr 50 --seed 0 --log logs/baseline/cub200_50 | ||
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| # ResNet50, StanfordCars | ||
| CUDA_VISIBLE_DEVICES=0 python baseline.py data/stanford_cars -d StanfordCars -sr 15 --seed 0 --log logs/baseline/car_15 | ||
| CUDA_VISIBLE_DEVICES=0 python baseline.py data/stanford_cars -d StanfordCars -sr 30 --seed 0 --log logs/baseline/car_30 | ||
| CUDA_VISIBLE_DEVICES=0 python baseline.py data/stanford_cars -d StanfordCars -sr 50 --seed 0 --log logs/baseline/car_50 | ||
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| # ResNet50, Aircraft | ||
| CUDA_VISIBLE_DEVICES=0 python baseline.py data/aircraft -d Aircraft -sr 15 --seed 0 --log logs/baseline/aircraft_15 | ||
| CUDA_VISIBLE_DEVICES=0 python baseline.py data/aircraft -d Aircraft -sr 30 --seed 0 --log logs/baseline/aircraft_30 | ||
| CUDA_VISIBLE_DEVICES=0 python baseline.py data/aircraft -d Aircraft -sr 50 --seed 0 --log logs/baseline/aircraft_50 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,29 @@ | ||
| """ | ||
| @author: Baixu Chen | ||
| @contact: [email protected] | ||
| """ | ||
| import numpy as np | ||
| from PIL import Image | ||
| from torchvision import datasets | ||
|
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|
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| class CIFAR100(datasets.CIFAR100): | ||
| def __init__(self, root, idxes, train=True, | ||
| transform=None, target_transform=None, | ||
| download=False): | ||
| super().__init__(root, train=train, transform=transform, target_transform=target_transform, download=download) | ||
| if idxes is not None: | ||
| self.data = self.data[idxes] | ||
| self.targets = np.array(self.targets)[idxes] | ||
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| def __getitem__(self, index): | ||
| img, target = self.data[index], self.targets[index] | ||
| img = Image.fromarray(img) | ||
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| if self.transform is not None: | ||
| img = self.transform(img) | ||
|
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| if self.target_transform is not None: | ||
| target = self.target_transform(target) | ||
|
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| return img, target |
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