update
This commit is contained in:
Xu Ma
parent
eb59980e47
commit
36fa3171ba
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@ -1,4 +1,3 @@
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import argparse
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import os
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import logging
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@ -26,10 +25,10 @@ def parse_args():
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parser.add_argument('--msg', type=str, help='message after checkpoint')
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parser.add_argument('--batch_size', type=int, default=32, help='batch size in training')
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parser.add_argument('--model', default='PointNet', help='model name [default: pointnet_cls]')
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parser.add_argument('--epoch', default=200, type=int, help='number of epoch in training')
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parser.add_argument('--epoch', default=300, type=int, help='number of epoch in training')
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parser.add_argument('--num_points', type=int, default=1024, help='Point Number')
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parser.add_argument('--learning_rate', default=0.1, type=float, help='learning rate in training')
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parser.add_argument('--weight_decay', type=float, default=1e-4, help='decay rate')
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parser.add_argument('--weight_decay', type=float, default=2e-4, help='decay rate')
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parser.add_argument('--seed', type=int, help='random seed')
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parser.add_argument('--workers', default=8, type=int, help='workers')
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return parser.parse_args()
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@ -56,8 +55,8 @@ def main():
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if args.msg is None:
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message = time_str
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else:
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message = "-"+args.msg
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args.checkpoint = 'checkpoints/' + args.model + message + '-'+str(args.seed)
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message = "-" + args.msg
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args.checkpoint = 'checkpoints/' + args.model + message + '-' + str(args.seed)
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if not os.path.isdir(args.checkpoint):
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mkdir_p(args.checkpoint)
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@ -68,12 +67,11 @@ def main():
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file_handler.setLevel(logging.INFO)
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file_handler.setFormatter(formatter)
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screen_logger.addHandler(file_handler)
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def printf(str):
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screen_logger.info(str)
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print(str)
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# Model
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printf(f"args: {args}")
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printf('==> Building model..')
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@ -94,7 +92,6 @@ def main():
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start_epoch = 0 # start from epoch 0 or last checkpoint epoch
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optimizer_dict = None
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if not os.path.isfile(os.path.join(args.checkpoint, "last_checkpoint.pth")):
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save_args(args)
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logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title="ModelNet" + args.model)
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@ -116,19 +113,16 @@ def main():
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logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title="ModelNet" + args.model, resume=True)
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optimizer_dict = checkpoint['optimizer']
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printf('==> Preparing data..')
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train_loader = DataLoader(ModelNet40(partition='train', num_points=args.num_points), num_workers=args.workers,
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batch_size=args.batch_size, shuffle=True, drop_last=True)
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test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=args.workers,
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batch_size=args.batch_size//2, shuffle=False, drop_last=False)
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batch_size=args.batch_size // 2, shuffle=False, drop_last=False)
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optimizer = torch.optim.SGD(net.parameters(), lr=args.learning_rate, momentum=0.9, weight_decay=args.weight_decay)
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if optimizer_dict is not None:
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optimizer.load_state_dict(optimizer_dict)
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scheduler = CosineAnnealingLR(optimizer, args.epoch, eta_min=1e-3, last_epoch=start_epoch-1)
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scheduler = CosineAnnealingLR(optimizer, args.epoch, eta_min=1e-3, last_epoch=start_epoch - 1)
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for epoch in range(start_epoch, args.epoch):
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printf('Epoch(%d/%s) Learning Rate %s:' % (epoch + 1, args.epoch, optimizer.param_groups[0]['lr']))
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@ -149,16 +143,15 @@ def main():
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best_test_loss = test_out["loss"] if (test_out["loss"] < best_test_loss) else best_test_loss
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best_train_loss = train_out["loss"] if (train_out["loss"] < best_train_loss) else best_train_loss
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save_model(
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net, epoch, path=args.checkpoint, acc=test_out["acc"], is_best=is_best,
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best_test_acc=best_test_acc, # best test accuracy
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best_train_acc = best_train_acc,
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best_test_acc_avg = best_test_acc_avg,
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best_train_acc_avg = best_train_acc_avg,
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best_test_loss = best_test_loss,
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best_train_loss = best_train_loss,
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optimizer = optimizer.state_dict()
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best_train_acc=best_train_acc,
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best_test_acc_avg=best_test_acc_avg,
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best_train_acc_avg=best_train_acc_avg,
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best_test_loss=best_test_loss,
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best_train_loss=best_train_loss,
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optimizer=optimizer.state_dict()
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)
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logger.append([epoch, optimizer.param_groups[0]['lr'],
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train_out["loss"], train_out["acc_avg"], train_out["acc"],
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@ -178,8 +171,6 @@ def main():
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printf(f"++++++++" * 5)
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def train(net, trainloader, optimizer, criterion, device):
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net.train()
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train_loss = 0
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@ -1,6 +1,3 @@
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"""
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nohup python voting.py --model model31A --msg 20210818204651 > model31A_20210818204651_voting.out &
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"""
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import argparse
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import os
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import datetime
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@ -32,10 +29,7 @@ def parse_args():
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parser.add_argument('--batch_size', type=int, default=32, help='batch size in training')
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parser.add_argument('--model', default='model31A', help='model name [default: pointnet_cls]')
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parser.add_argument('--num_classes', default=40, type=int, choices=[10, 40], help='training on ModelNet10/40')
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parser.add_argument('--epoch', default=350, type=int, help='number of epoch in training')
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parser.add_argument('--num_points', type=int, default=1024, help='Point Number')
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parser.add_argument('--learning_rate', default=0.01, type=float, help='learning rate in training')
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parser.add_argument('--weight_decay', type=float, default=1e-4, help='decay rate')
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parser.add_argument('--seed', type=int, help='random seed (default: 1)')
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# Voting evaluation, referring: https://github.com/CVMI-Lab/PAConv/blob/main/obj_cls/eval_voting.py
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@ -59,6 +53,7 @@ class PointcloudScale(object): # input random scaling
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return pc
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def main():
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args = parse_args()
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print(f"args: {args}")
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@ -82,12 +77,12 @@ def main():
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if args.msg is None:
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message = str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S'))
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else:
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message = "-"+args.msg
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message = "-" + args.msg
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args.checkpoint = 'checkpoints/' + args.model + message
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print('==> Preparing data..')
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test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=4,
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batch_size=args.batch_size//2, shuffle=False, drop_last=False)
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batch_size=args.batch_size // 2, shuffle=False, drop_last=False)
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# Model
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print('==> Building model..')
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net = models.__dict__[args.model]()
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@ -115,8 +110,6 @@ def main():
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voting(net, test_loader, device, args)
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def validate(net, testloader, criterion, device):
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net.eval()
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test_loss = 0
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@ -152,11 +145,11 @@ def validate(net, testloader, criterion, device):
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def voting(net, testloader, device, args):
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name ='/evaluate_voting'+str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S'))+'seed_'+str(args.seed)+'.log'
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name = '/evaluate_voting' + str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S')) + 'seed_' + str(
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args.seed) + '.log'
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io = IOStream(args.checkpoint + name)
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io.cprint(str(args))
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net.eval()
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best_acc = 0
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best_mean_acc = 0
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@ -199,11 +192,5 @@ def voting(net, testloader, device, args):
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io.cprint(final_outstr)
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if __name__ == '__main__':
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main()
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