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main.py
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import os
os.environ['OMP_NUM_THREADS'] = '1'
import argparse
import sys
import shutil
from distutils.dir_util import copy_tree
import datetime
import tqdm
import numpy as np
import torch
import torch.optim as optim
import torchvision.transforms as T
from multiview_detector.datasets import *
from multiview_detector.loss.gaussian_mse import GaussianMSE
from multiview_detector.models.persp_trans_detector import PerspTransDetector
from multiview_detector.models.image_proj_variant import ImageProjVariant
from multiview_detector.models.res_proj_variant import ResProjVariant
from multiview_detector.models.no_joint_conv_variant import NoJointConvVariant
from multiview_detector.utils.logger import Logger
from multiview_detector.utils.draw_curve import draw_curve
from multiview_detector.utils.image_utils import img_color_denormalize
from multiview_detector.trainer import PerspectiveTrainer
def main(args):
# seed
if args.seed is not None:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
torch.backends.cudnn.benchmark = True
else:
torch.backends.cudnn.benchmark = True
# dataset
normalize = T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
denormalize = img_color_denormalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
train_trans = T.Compose([T.Resize([720, 1280]), T.ToTensor(), normalize, ])
if 'wildtrack' in args.dataset:
data_path = os.path.expanduser('~/Data/Wildtrack')
base = Wildtrack(data_path)
elif 'multiviewx' in args.dataset:
data_path = os.path.expanduser('~/Data/MultiviewX')
base = MultiviewX(data_path)
else:
raise Exception('must choose from [wildtrack, multiviewx]')
train_set = frameDataset(base, train=True, transform=train_trans, grid_reduce=4)
test_set = frameDataset(base, train=False, transform=train_trans, grid_reduce=4)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True)
# model
if args.variant == 'default':
model = PerspTransDetector(train_set, args.arch)
elif args.variant == 'img_proj':
model = ImageProjVariant(train_set, args.arch)
elif args.variant == 'res_proj':
model = ResProjVariant(train_set, args.arch)
elif args.variant == 'no_joint_conv':
model = NoJointConvVariant(train_set, args.arch)
else:
raise Exception('no support for this variant')
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=args.lr, steps_per_epoch=len(train_loader),
epochs=args.epochs)
# loss
criterion = GaussianMSE().cuda()
# logging
logdir = f'logs/{args.dataset}_frame/{args.variant}/' + datetime.datetime.today().strftime('%Y-%m-%d_%H-%M-%S') \
if not args.resume else f'logs/{args.dataset}_frame/{args.variant}/{args.resume}'
if args.resume is None:
os.makedirs(logdir, exist_ok=True)
copy_tree('./multiview_detector', logdir + '/scripts/multiview_detector')
for script in os.listdir('.'):
if script.split('.')[-1] == 'py':
dst_file = os.path.join(logdir, 'scripts', os.path.basename(script))
shutil.copyfile(script, dst_file)
sys.stdout = Logger(os.path.join(logdir, 'log.txt'), )
print('Settings:')
print(vars(args))
# draw curve
x_epoch = []
train_loss_s = []
train_prec_s = []
test_loss_s = []
test_prec_s = []
test_moda_s = []
trainer = PerspectiveTrainer(model, criterion, logdir, denormalize, args.cls_thres, args.alpha)
# learn
if args.resume is None:
print('Testing...')
trainer.test(test_loader, os.path.join(logdir, 'test.txt'), train_set.gt_fpath, True)
for epoch in tqdm.tqdm(range(1, args.epochs + 1)):
print('Training...')
train_loss, train_prec = trainer.train(epoch, train_loader, optimizer, args.log_interval, scheduler)
print('Testing...')
test_loss, test_prec, moda = trainer.test(test_loader, os.path.join(logdir, 'test.txt'),
train_set.gt_fpath, True)
x_epoch.append(epoch)
train_loss_s.append(train_loss)
train_prec_s.append(train_prec)
test_loss_s.append(test_loss)
test_prec_s.append(test_prec)
test_moda_s.append(moda)
draw_curve(os.path.join(logdir, 'learning_curve.jpg'), x_epoch, train_loss_s, train_prec_s,
test_loss_s, test_prec_s, test_moda_s)
# save
torch.save(model.state_dict(), os.path.join(logdir, 'MultiviewDetector.pth'))
else:
resume_dir = f'logs/{args.dataset}_frame/{args.variant}/' + args.resume
resume_fname = resume_dir + '/MultiviewDetector.pth'
model.load_state_dict(torch.load(resume_fname))
model.eval()
print('Test loaded model...')
trainer.test(test_loader, os.path.join(logdir, 'test.txt'), train_set.gt_fpath, True)
if __name__ == '__main__':
# settings
parser = argparse.ArgumentParser(description='Multiview detector')
parser.add_argument('--reID', action='store_true')
parser.add_argument('--cls_thres', type=float, default=0.4)
parser.add_argument('--alpha', type=float, default=1.0, help='ratio for per view loss')
parser.add_argument('--variant', type=str, default='default',
choices=['default', 'img_proj', 'res_proj', 'no_joint_conv'])
parser.add_argument('--arch', type=str, default='resnet18', choices=['vgg11', 'resnet18'])
parser.add_argument('-d', '--dataset', type=str, default='wildtrack', choices=['wildtrack', 'multiviewx'])
parser.add_argument('-j', '--num_workers', type=int, default=4)
parser.add_argument('-b', '--batch_size', type=int, default=1, metavar='N',
help='input batch size for training (default: 1)')
parser.add_argument('--epochs', type=int, default=10, metavar='N', help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.1, metavar='LR', help='learning rate (default: 0.1)')
parser.add_argument('--weight_decay', type=float, default=5e-4)
parser.add_argument('--momentum', type=float, default=0.5, metavar='M', help='SGD momentum (default: 0.5)')
parser.add_argument('--log_interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--resume', type=str, default=None)
parser.add_argument('--visualize', action='store_true')
parser.add_argument('--seed', type=int, default=1, help='random seed (default: None)')
args = parser.parse_args()
main(args)