update

classification_ModelNet40/main.py

@@ -1,4 +1,3 @@
-
 import argparse
 import os
 import logging
@@ -26,10 +25,10 @@ def parse_args():
     parser.add_argument('--msg', type=str, help='message after checkpoint')
     parser.add_argument('--batch_size', type=int, default=32, help='batch size in training')
     parser.add_argument('--model', default='PointNet', help='model name [default: pointnet_cls]')
-    parser.add_argument('--epoch', default=200, type=int, help='number of epoch in training')
+    parser.add_argument('--epoch', default=300, type=int, help='number of epoch in training')
     parser.add_argument('--num_points', type=int, default=1024, help='Point Number')
     parser.add_argument('--learning_rate', default=0.1, type=float, help='learning rate in training')
-    parser.add_argument('--weight_decay', type=float, default=1e-4, help='decay rate')
+    parser.add_argument('--weight_decay', type=float, default=2e-4, help='decay rate')
     parser.add_argument('--seed', type=int, help='random seed')
     parser.add_argument('--workers', default=8, type=int, help='workers')
     return parser.parse_args()
@@ -68,12 +67,11 @@ def main():
     file_handler.setLevel(logging.INFO)
     file_handler.setFormatter(formatter)
     screen_logger.addHandler(file_handler)
+
     def printf(str):
         screen_logger.info(str)
         print(str)
 
-
-
     # Model
     printf(f"args: {args}")
     printf('==> Building model..')
@@ -94,7 +92,6 @@ def main():
     start_epoch = 0  # start from epoch 0 or last checkpoint epoch
     optimizer_dict = None
 
-
     if not os.path.isfile(os.path.join(args.checkpoint, "last_checkpoint.pth")):
         save_args(args)
         logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title="ModelNet" + args.model)
@@ -116,8 +113,6 @@ def main():
         logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title="ModelNet" + args.model, resume=True)
         optimizer_dict = checkpoint['optimizer']
 
-
-
     printf('==> Preparing data..')
     train_loader = DataLoader(ModelNet40(partition='train', num_points=args.num_points), num_workers=args.workers,
                               batch_size=args.batch_size, shuffle=True, drop_last=True)
@@ -129,7 +124,6 @@ def main():
         optimizer.load_state_dict(optimizer_dict)
     scheduler = CosineAnnealingLR(optimizer, args.epoch, eta_min=1e-3, last_epoch=start_epoch - 1)
 
-
     for epoch in range(start_epoch, args.epoch):
         printf('Epoch(%d/%s) Learning Rate %s:' % (epoch + 1, args.epoch, optimizer.param_groups[0]['lr']))
         train_out = train(net, train_loader, optimizer, criterion, device)  # {"loss", "acc", "acc_avg", "time"}
@@ -149,7 +143,6 @@ def main():
         best_test_loss = test_out["loss"] if (test_out["loss"] < best_test_loss) else best_test_loss
         best_train_loss = train_out["loss"] if (train_out["loss"] < best_train_loss) else best_train_loss
 
-
         save_model(
             net, epoch, path=args.checkpoint, acc=test_out["acc"], is_best=is_best,
             best_test_acc=best_test_acc,  # best test accuracy
@@ -178,8 +171,6 @@ def main():
     printf(f"++++++++" * 5)
 
 
-
-
 def train(net, trainloader, optimizer, criterion, device):
     net.train()
     train_loss = 0

classification_ModelNet40/voting.py

@@ -1,6 +1,3 @@
-"""
-nohup python voting.py --model model31A --msg 20210818204651 > model31A_20210818204651_voting.out &
-"""
 import argparse
 import os
 import datetime
@@ -32,10 +29,7 @@ def parse_args():
     parser.add_argument('--batch_size', type=int, default=32, help='batch size in training')
     parser.add_argument('--model', default='model31A', help='model name [default: pointnet_cls]')
     parser.add_argument('--num_classes', default=40, type=int, choices=[10, 40], help='training on ModelNet10/40')
-    parser.add_argument('--epoch', default=350, type=int, help='number of epoch in training')
     parser.add_argument('--num_points', type=int, default=1024, help='Point Number')
-    parser.add_argument('--learning_rate', default=0.01, type=float, help='learning rate in training')
-    parser.add_argument('--weight_decay', type=float, default=1e-4, help='decay rate')
     parser.add_argument('--seed', type=int, help='random seed (default: 1)')
 
     # Voting evaluation, referring: https://github.com/CVMI-Lab/PAConv/blob/main/obj_cls/eval_voting.py
@@ -59,6 +53,7 @@ class PointcloudScale(object): # input random scaling
 
         return pc
 
+
 def main():
     args = parse_args()
     print(f"args: {args}")
@@ -115,8 +110,6 @@ def main():
     voting(net, test_loader, device, args)
 
 
-
-
 def validate(net, testloader, criterion, device):
     net.eval()
     test_loss = 0
@@ -152,11 +145,11 @@ def validate(net, testloader, criterion, device):
 
 
 def voting(net, testloader, device, args):
-    name ='/evaluate_voting'+str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S'))+'seed_'+str(args.seed)+'.log'
+    name = '/evaluate_voting' + str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S')) + 'seed_' + str(
+        args.seed) + '.log'
     io = IOStream(args.checkpoint + name)
     io.cprint(str(args))
 
-
     net.eval()
     best_acc = 0
     best_mean_acc = 0
@@ -199,11 +192,5 @@ def voting(net, testloader, device, args):
     io.cprint(final_outstr)
 
 
-
-
-
-
-
-
 if __name__ == '__main__':
     main()