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Xu Ma 2021-10-04 04:00:01 -04:00
parent eb59980e47
commit 36fa3171ba
2 changed files with 19 additions and 41 deletions
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35
classification_ModelNet40/main.py
View file

@ -1,4 +1,3 @@
import argparse import argparse
import os import os
import logging import logging
@ -26,10 +25,10 @@ def parse_args():
parser.add_argument('--msg', type=str, help='message after checkpoint') 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('--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('--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('--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('--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('--seed', type=int, help='random seed')
parser.add_argument('--workers', default=8, type=int, help='workers') parser.add_argument('--workers', default=8, type=int, help='workers')
return parser.parse_args() return parser.parse_args()
@ -56,8 +55,8 @@ def main():
if args.msg is None: if args.msg is None:
message = time_str message = time_str
else: else:
message = "-"+args.msg message = "-" + args.msg
args.checkpoint = 'checkpoints/' + args.model + message + '-'+str(args.seed) args.checkpoint = 'checkpoints/' + args.model + message + '-' + str(args.seed)
if not os.path.isdir(args.checkpoint): if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint) mkdir_p(args.checkpoint)
@ -68,12 +67,11 @@ def main():
file_handler.setLevel(logging.INFO) file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter) file_handler.setFormatter(formatter)
screen_logger.addHandler(file_handler) screen_logger.addHandler(file_handler)
def printf(str): def printf(str):
screen_logger.info(str) screen_logger.info(str)
print(str) print(str)
# Model # Model
printf(f"args: {args}") printf(f"args: {args}")
printf('==> Building model..') printf('==> Building model..')
@ -94,7 +92,6 @@ def main():
start_epoch = 0 # start from epoch 0 or last checkpoint epoch start_epoch = 0 # start from epoch 0 or last checkpoint epoch
optimizer_dict = None optimizer_dict = None
if not os.path.isfile(os.path.join(args.checkpoint, "last_checkpoint.pth")): if not os.path.isfile(os.path.join(args.checkpoint, "last_checkpoint.pth")):
save_args(args) save_args(args)
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title="ModelNet" + args.model) logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title="ModelNet" + args.model)
@ -116,19 +113,16 @@ def main():
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title="ModelNet" + args.model, resume=True) logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title="ModelNet" + args.model, resume=True)
optimizer_dict = checkpoint['optimizer'] optimizer_dict = checkpoint['optimizer']
printf('==> Preparing data..') printf('==> Preparing data..')
train_loader = DataLoader(ModelNet40(partition='train', num_points=args.num_points), num_workers=args.workers, 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) batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=args.workers, test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=args.workers,
batch_size=args.batch_size//2, shuffle=False, drop_last=False) batch_size=args.batch_size // 2, shuffle=False, drop_last=False)
optimizer = torch.optim.SGD(net.parameters(), lr=args.learning_rate, momentum=0.9, weight_decay=args.weight_decay) optimizer = torch.optim.SGD(net.parameters(), lr=args.learning_rate, momentum=0.9, weight_decay=args.weight_decay)
if optimizer_dict is not None: if optimizer_dict is not None:
optimizer.load_state_dict(optimizer_dict) optimizer.load_state_dict(optimizer_dict)
scheduler = CosineAnnealingLR(optimizer, args.epoch, eta_min=1e-3, last_epoch=start_epoch-1) scheduler = CosineAnnealingLR(optimizer, args.epoch, eta_min=1e-3, last_epoch=start_epoch - 1)
for epoch in range(start_epoch, args.epoch): for epoch in range(start_epoch, args.epoch):
printf('Epoch(%d/%s) Learning Rate %s:' % (epoch + 1, args.epoch, optimizer.param_groups[0]['lr'])) printf('Epoch(%d/%s) Learning Rate %s:' % (epoch + 1, args.epoch, optimizer.param_groups[0]['lr']))
@ -149,16 +143,15 @@ def main():
best_test_loss = test_out["loss"] if (test_out["loss"] < best_test_loss) else best_test_loss 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 best_train_loss = train_out["loss"] if (train_out["loss"] < best_train_loss) else best_train_loss
save_model( save_model(
net, epoch, path=args.checkpoint, acc=test_out["acc"], is_best=is_best, net, epoch, path=args.checkpoint, acc=test_out["acc"], is_best=is_best,
best_test_acc=best_test_acc, # best test accuracy best_test_acc=best_test_acc, # best test accuracy
best_train_acc = best_train_acc, best_train_acc=best_train_acc,
best_test_acc_avg = best_test_acc_avg, best_test_acc_avg=best_test_acc_avg,
best_train_acc_avg = best_train_acc_avg, best_train_acc_avg=best_train_acc_avg,
best_test_loss = best_test_loss, best_test_loss=best_test_loss,
best_train_loss = best_train_loss, best_train_loss=best_train_loss,
optimizer = optimizer.state_dict() optimizer=optimizer.state_dict()
) )
logger.append([epoch, optimizer.param_groups[0]['lr'], logger.append([epoch, optimizer.param_groups[0]['lr'],
train_out["loss"], train_out["acc_avg"], train_out["acc"], train_out["loss"], train_out["acc_avg"], train_out["acc"],
@ -178,8 +171,6 @@ def main():
printf(f"++++++++" * 5) printf(f"++++++++" * 5)
def train(net, trainloader, optimizer, criterion, device): def train(net, trainloader, optimizer, criterion, device):
net.train() net.train()
train_loss = 0 train_loss = 0

25
classification_ModelNet40/voting.py
View file

@ -1,6 +1,3 @@
"""
nohup python voting.py --model model31A --msg 20210818204651 > model31A_20210818204651_voting.out &
"""
import argparse import argparse
import os import os
import datetime 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('--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('--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('--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('--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)') 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 # Voting evaluation, referring: https://github.com/CVMI-Lab/PAConv/blob/main/obj_cls/eval_voting.py
@ -46,7 +40,7 @@ def parse_args():
return parser.parse_args() return parser.parse_args()
class PointcloudScale(object): # input random scaling class PointcloudScale(object): # input random scaling
def __init__(self, scale_low=2. / 3., scale_high=3. / 2.): def __init__(self, scale_low=2. / 3., scale_high=3. / 2.):
self.scale_low = scale_low self.scale_low = scale_low
self.scale_high = scale_high self.scale_high = scale_high
@ -59,6 +53,7 @@ class PointcloudScale(object): # input random scaling
return pc return pc
def main(): def main():
args = parse_args() args = parse_args()
print(f"args: {args}") print(f"args: {args}")
@ -82,12 +77,12 @@ def main():
if args.msg is None: if args.msg is None:
message = str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S')) message = str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S'))
else: else:
message = "-"+args.msg message = "-" + args.msg
args.checkpoint = 'checkpoints/' + args.model + message args.checkpoint = 'checkpoints/' + args.model + message
print('==> Preparing data..') print('==> Preparing data..')
test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=4, test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=4,
batch_size=args.batch_size//2, shuffle=False, drop_last=False) batch_size=args.batch_size // 2, shuffle=False, drop_last=False)
# Model # Model
print('==> Building model..') print('==> Building model..')
net = models.__dict__[args.model]() net = models.__dict__[args.model]()
@ -115,8 +110,6 @@ def main():
voting(net, test_loader, device, args) voting(net, test_loader, device, args)
def validate(net, testloader, criterion, device): def validate(net, testloader, criterion, device):
net.eval() net.eval()
test_loss = 0 test_loss = 0
@ -152,11 +145,11 @@ def validate(net, testloader, criterion, device):
def voting(net, testloader, device, args): 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 = IOStream(args.checkpoint + name)
io.cprint(str(args)) io.cprint(str(args))
net.eval() net.eval()
best_acc = 0 best_acc = 0
best_mean_acc = 0 best_mean_acc = 0
@ -199,11 +192,5 @@ def voting(net, testloader, device, args):
io.cprint(final_outstr) io.cprint(final_outstr)
if __name__ == '__main__': if __name__ == '__main__':
main() main()