-
Notifications
You must be signed in to change notification settings - Fork 188
/
run_root_cls.py
184 lines (161 loc) · 9.36 KB
/
run_root_cls.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
#-------------------------------------------------------------------------------
# Name: run_root_cls.py
# Purpose: Train a network (rootnet) to predict which joint is the root
# RigNet Copyright 2020 University of Massachusetts
# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
#-------------------------------------------------------------------------------
import os
import sys
sys.path.append("./")
import shutil
import argparse
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from torch_geometric.data import DataLoader
from models.ROOT_GCN import ROOTNET
from utils.log_utils import AverageMeter
from utils.os_utils import isdir, mkdir_p, isfile
from torch.utils.tensorboard import SummaryWriter
from datasets.skeleton_dataset import GraphDataset
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar', snapshot=None):
filepath = os.path.join(checkpoint, filename)
torch.save(state, filepath)
if snapshot and state['epoch'] % snapshot == 0:
shutil.copyfile(filepath, os.path.join(checkpoint, 'checkpoint_{}.pth.tar'.format(state['epoch'])))
if is_best:
shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar'))
def main(args):
global device
best_acc = 0.0
# create checkpoint dir and log dir
if not isdir(args.checkpoint):
print("Create new checkpoint folder " + args.checkpoint)
mkdir_p(args.checkpoint)
if not args.resume:
if isdir(args.logdir):
shutil.rmtree(args.logdir)
mkdir_p(args.logdir)
# create model
model = ROOTNET()
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr = optimizer.param_groups[0]['lr']
print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters()) / 1000000.0))
train_loader = DataLoader(GraphDataset(root=args.train_folder), batch_size=args.train_batch, shuffle=True, follow_batch=['joints'])
val_loader = DataLoader(GraphDataset(root=args.val_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints'])
test_loader = DataLoader(GraphDataset(root=args.test_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints'])
if args.evaluate:
print('\nEvaluation only')
test_loss, test_acc = test(test_loader, model)
print('test_loss {:.8f}. test_acc: {:.6f}'.format(test_loss, test_acc))
return
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.schedule, gamma=args.gamma)
logger = SummaryWriter(log_dir=args.logdir)
for epoch in range(args.start_epoch, args.epochs):
lr = scheduler.get_last_lr()
print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr[0]))
train_loss = train(train_loader, model, optimizer, args)
val_loss, val_acc = test(val_loader, model)
test_loss, test_acc = test(test_loader, model)
scheduler.step()
print('Epoch{:d}. train_loss: {:.6f}.'.format(epoch + 1, train_loss))
print('Epoch{:d}. val_loss: {:.6f}. val_acc: {:.6f}'.format(epoch + 1, val_loss, val_acc))
print('Epoch{:d}. test_loss: {:.6f}. test_acc: {:.6f}'.format(epoch + 1, test_loss, test_acc))
# remember best acc and save checkpoint
is_best = val_acc > best_acc
best_acc = max(val_acc, best_acc)
save_checkpoint({'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_acc': best_acc,
'optimizer': optimizer.state_dict()}, is_best, checkpoint=args.checkpoint)
info = {'train_loss': train_loss, 'val_loss': val_loss, 'val_accuracy': val_acc,
'test_loss': test_loss, 'test_accuracy': test_acc}
for tag, value in info.items():
logger.add_scalar(tag, value, epoch + 1)
print("=> loading checkpoint '{}'".format(os.path.join(args.checkpoint, 'model_best.pth.tar')))
checkpoint = torch.load(os.path.join(args.checkpoint, 'model_best.pth.tar'))
best_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(os.path.join(args.checkpoint, 'model_best.pth.tar'), best_epoch))
test_loss, test_acc = test(test_loader, model)
print('Best epoch:\n test_loss {:8f} test_acc {:8f}'.format(test_loss, test_acc))
def train(train_loader, model, optimizer, args):
global device
model.train() # switch to train mode
loss_meter = AverageMeter()
for data in train_loader:
#print(data.name)
data = data.to(device)
optimizer.zero_grad()
pre_label, label = model(data)
loss_1 = torch.nn.functional.binary_cross_entropy_with_logits(pre_label, label, reduction='none')
topk_val, _ = torch.topk(loss_1.view(-1), k=int(args.topk * len(pre_label)), dim=0, sorted=False)
loss2 = topk_val.mean()
#loss_3 = torch.nn.functional.binary_cross_entropy_with_logits(pre_label, label)
loss = loss_1.mean() + loss2
loss.backward()
optimizer.step()
loss_meter.update(loss.item())
return loss_meter.avg
def test(test_loader, model):
global device
model.eval() # switch to test mode
loss_meter = AverageMeter()
acc_total = 0.0
count = 0.0
for data in test_loader:
#print(data.name)
data = data.to(device)
with torch.no_grad():
pre_label, label = model(data)
loss = torch.nn.functional.binary_cross_entropy_with_logits(pre_label, label.float())
loss_meter.update(loss.item())
for i in range(len(torch.unique(data.batch))):
pred_root_id = torch.argmax(pre_label[data.joints_batch==i]).item()
gt_root_id = torch.argmax(label[data.joints_batch==i]).item()
if pred_root_id == gt_root_id:
acc_total += 1.0
count += 1.0
return loss_meter.avg, acc_total/count
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='network for picking root')
parser.add_argument('--arch', default='rootnet') # rootnet_fc, rootnet_nogt, rootnet_nogs
parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)')
parser.add_argument('--weight_decay', '--wd', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--gamma', type=float, default=0.2, help='LR is multiplied by gamma on schedule.')
parser.add_argument('-j', '--workers', default=1, type=int, metavar='N', help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=300, type=int, metavar='N', help='number of total epochs to run')
parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, metavar='LR', help='initial learning rate')
parser.add_argument('--schedule', type=int, nargs='+', default=[200], help='Decrease learning rate at these epochs.')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set')
####################################################################################################################
parser.add_argument('--train_batch', default=3, type=int, metavar='N', help='train batchsize')
parser.add_argument('--test_batch', default=3, type=int, metavar='N', help='test batchsize')
parser.add_argument('-c', '--checkpoint', default='checkpoints/test', type=str, metavar='PATH',
help='path to save checkpoint (default: checkpoint)')
parser.add_argument('--logdir', default='logs/test', type=str, metavar='LOG', help='directory to save logs')
parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)')
parser.add_argument('--train_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/train/',
type=str, help='folder of training data')
parser.add_argument('--val_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/val/',
type=str, help='folder of validation data')
parser.add_argument('--test_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/test/',
type=str, help='folder of testing data')
parser.add_argument('--pos_weight', default=10.0, type=float, help='weight for positive class')
parser.add_argument('--topk', default=0.3, type=float, help='topk ratio for ohem')
print(parser.parse_args())
main(parser.parse_args())