/
main_semseg.py
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/
main_semseg.py
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from __future__ import print_function
import os
import argparse
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR, StepLR
from data import S3DIS
from model.sempvt import pvt_semseg
import numpy as np
from torch.utils.data import DataLoader
from util import cal_loss, IOStream
import sklearn.metrics as metrics
import time
def _init_():
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
if not os.path.exists('checkpoints/'+args.exp_name):
os.makedirs('checkpoints/'+args.exp_name)
os.system('cp main_semseg.py checkpoints'+'/'+args.exp_name+'/'+'main_semseg.py.backup')
os.system('cp model/sempvt.py checkpoints' + '/' + args.exp_name + '/' + 'sempvt.py.backup')
os.system('cp util.py checkpoints' + '/' + args.exp_name + '/' + 'util.py.backup')
os.system('cp data.py checkpoints' + '/' + args.exp_name + '/' + 'data.py.backup')
def calculate_sem_IoU(pred_np, seg_np):
I_all = np.zeros(13)
U_all = np.zeros(13)
for sem_idx in range(seg_np.shape[0]):
for sem in range(13):
I = np.sum(np.logical_and(pred_np[sem_idx] == sem, seg_np[sem_idx] == sem))
U = np.sum(np.logical_or(pred_np[sem_idx] == sem, seg_np[sem_idx] == sem))
I_all[sem] += I
U_all[sem] += U
return I_all / U_all
def train(args, io):
train_loader = DataLoader(S3DIS(partition='train', num_points=args.num_points, test_area=args.test_area),
num_workers=8, batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(S3DIS(partition='test', num_points=args.num_points, test_area=args.test_area),
num_workers=8, batch_size=args.test_batch_size, shuffle=False, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
# Try to load models
if args.model == 'pvt':
model = pvt_semseg(args).to(device)
else:
raise Exception("Not implemented")
print("Let's use", torch.cuda.device_count(), "GPUs!")
if args.use_sgd:
print("Use SGD")
opt = optim.SGD(model.parameters(), lr=args.lr * 100, momentum=args.momentum, weight_decay=1e-4)
else:
print("Use Adam")
opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
if args.scheduler == 'cos':
scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=1e-3)
elif args.scheduler == 'step':
scheduler = StepLR(opt, 20, 0.5, args.epochs)
criterion = cal_loss
best_test_iou = 0
for epoch in range(args.epochs):
####################
# Train
####################
model.train()
for data, seg in train_loader:
data, seg = data.to(device), seg.to(device)
opt.zero_grad()
seg_pred = model(data)
seg_pred = seg_pred.permute(0, 2, 1).contiguous()
loss = criterion(seg_pred.view(-1, 13), seg.view(-1, 1).squeeze())
loss.backward()
opt.step()
if args.scheduler == 'cos':
scheduler.step()
elif args.scheduler == 'step':
if opt.param_groups[0]['lr'] > 1e-5:
scheduler.step()
if opt.param_groups[0]['lr'] < 1e-5:
for param_group in opt.param_groups:
param_group['lr'] = 1e-5
####################
# Test
####################
test_area = args.test_area
test_loss = 0.0
count = 0.0
model.eval()
test_true_cls = []
test_pred_cls = []
test_true_seg = []
test_pred_seg = []
for data, seg in test_loader:
data, seg = data.to(device), seg.to(device)
batch_size = data.size()[0]
seg_pred = model(data)
seg_pred = seg_pred.permute(0, 2, 1).contiguous()
loss = criterion(seg_pred.view(-1, 13), seg.view(-1, 1).squeeze())
pred = seg_pred.max(dim=2)[1]
count += batch_size
test_loss += loss.item() * batch_size
seg_np = seg.cpu().numpy()
pred_np = pred.detach().cpu().numpy()
test_true_cls.append(seg_np.reshape(-1))
test_pred_cls.append(pred_np.reshape(-1))
test_true_seg.append(seg_np)
test_pred_seg.append(pred_np)
# sys.exit(0)
test_true_cls = np.concatenate(test_true_cls)
test_pred_cls = np.concatenate(test_pred_cls)
test_acc = metrics.accuracy_score(test_true_cls, test_pred_cls)
avg_per_class_acc = metrics.balanced_accuracy_score(test_true_cls, test_pred_cls)
test_true_seg = np.concatenate(test_true_seg, axis=0)
test_pred_seg = np.concatenate(test_pred_seg, axis=0)
test_ious = calculate_sem_IoU(test_pred_seg, test_true_seg)
outstr = 'test area: %s, loss: %.6f, test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (test_area,
test_loss * 1.0 / count,
test_acc,
avg_per_class_acc,
np.mean(test_ious))
io.cprint(outstr)
if np.mean(test_ious) >= best_test_iou:
best_test_iou = np.mean(test_ious)
torch.save(model.state_dict(), 'checkpoints/%s/model_seg_%s.t7' % (args.exp_name, args.test_area))
def test(args, io):
all_true_cls = []
all_pred_cls = []
all_true_seg = []
all_pred_seg = []
for test_area in range(1,7):
test_area = str(test_area)
if (args.test_area == 'all') or (test_area == args.test_area):
test_loader = DataLoader(S3DIS(partition='test', num_points=args.num_points, test_area=test_area),
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
#Try to load models
if args.model == 'pvt':
model = pvt_semseg(args).to(device)
else:
raise Exception("Not implemented")
model.load_state_dict(torch.load(os.path.join(args.model_root, 'model_seg_%s.t7' % test_area)))
model = model.eval()
test_true_cls = []
test_pred_cls = []
test_true_seg = []
test_pred_seg = []
for data, seg in test_loader:
data, seg = data.to(device), seg.to(device)
seg_pred = model(data)
seg_pred = seg_pred.permute(0, 2, 1).contiguous()
pred = seg_pred.max(dim=2)[1]
seg_np = seg.cpu().numpy()
pred_np = pred.detach().cpu().numpy()
test_true_cls.append(seg_np.reshape(-1))
test_pred_cls.append(pred_np.reshape(-1))
test_true_seg.append(seg_np)
test_pred_seg.append(pred_np)
# sys.exit(0)
test_true_cls = np.concatenate(test_true_cls)
test_pred_cls = np.concatenate(test_pred_cls)
test_acc = metrics.accuracy_score(test_true_cls, test_pred_cls)
avg_per_class_acc = metrics.balanced_accuracy_score(test_true_cls, test_pred_cls)
test_true_seg = np.concatenate(test_true_seg, axis=0)
test_pred_seg = np.concatenate(test_pred_seg, axis=0)
test_ious = calculate_sem_IoU(test_pred_seg, test_true_seg)
outstr = 'Test :: test area: %s, test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (test_area,
test_acc,
avg_per_class_acc,
np.mean(test_ious))
io.cprint(outstr)
all_true_cls.append(test_true_cls)
all_pred_cls.append(test_pred_cls)
all_true_seg.append(test_true_seg)
all_pred_seg.append(test_pred_seg)
if args.test_area == 'all':
all_true_cls = np.concatenate(all_true_cls)
all_pred_cls = np.concatenate(all_pred_cls)
all_acc = metrics.accuracy_score(all_true_cls, all_pred_cls)
avg_per_class_acc = metrics.balanced_accuracy_score(all_true_cls, all_pred_cls)
all_true_seg = np.concatenate(all_true_seg, axis=0)
all_pred_seg = np.concatenate(all_pred_seg, axis=0)
all_ious = calculate_sem_IoU(all_pred_seg, all_true_seg)
outstr = 'Overall Test :: test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (all_acc,
avg_per_class_acc,
np.mean(all_ious))
io.cprint(outstr)
if __name__ == "__main__":
# Training settings
parser = argparse.ArgumentParser(description='Point Cloud Part Segmentation')
parser.add_argument('--exp_name', type=str, default='semseg', metavar='N',
help='Name of the experiment')
parser.add_argument('--model', type=str, default='pvt', metavar='N',
choices=['pvt'],
help='Model to use, [pvt]')
parser.add_argument('--dataset', type=str, default='S3DIS', metavar='N',
choices=['S3DIS'])
parser.add_argument('--test_area', type=str, default='5', metavar='N',
choices=['1', '2', '3', '4', '5', '6', 'all'])
parser.add_argument('--batch_size', type=int, default=8, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--test_batch_size', type=int, default=8, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--epochs', type=int, default=50, metavar='N',
help='number of episode to train ')
parser.add_argument('--use_sgd', type=bool, default=True,
help='Use SGD')
parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate (default: 0.001, 0.1 if using sgd)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--scheduler', type=str, default='cos', metavar='N',
choices=['cos', 'step'],
help='Scheduler to use, [cos, step]')
parser.add_argument('--no_cuda', type=bool, default=False,
help='enables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--eval', type=bool, default=False,
help='evaluate the model')
parser.add_argument('--num_points', type=int, default=4096,
help='num of points to use')
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout rate')
parser.add_argument('--model_root', type=str, default='checkpoints/semseg', metavar='N',
help='Pretrained model root')
args = parser.parse_args()
_init_()
io = IOStream('checkpoints/' + args.exp_name + '/semrun.log')
io.cprint(str(args))
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
io.cprint(
'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices')
torch.cuda.manual_seed(args.seed)
else:
io.cprint('Using CPU')
if not args.eval:
train(args, io)
else:
test(args, io)