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import os
import sys
import logging
import numpy
import numpy as np
import torch
import torch.utils.data
import torchvision
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from tqdm import tqdm
# Only if the files are in example folder.
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
if BASE_DIR[-8:] == 'examples':
sys.path.append(os.path.join(BASE_DIR, os.pardir))
os.chdir(os.path.join(BASE_DIR, os.pardir))
from learning3d.models import PointNet
from learning3d.models import iPCRNet
from learning3d.losses import ChamferDistanceLoss
from learning3d.data_utils import RegistrationData, ModelNet40Data
def _init_(args):
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
if not os.path.exists('checkpoints/' + args.exp_name):
os.makedirs('checkpoints/' + args.exp_name)
if not os.path.exists('checkpoints/' + args.exp_name + '/' + 'models'):
os.makedirs('checkpoints/' + args.exp_name + '/' + 'models')
os.system('cp main.py checkpoints' + '/' + args.exp_name + '/' + 'main.py.backup')
os.system('cp model.py checkpoints' + '/' + args.exp_name + '/' + 'model.py.backup')
class IOStream:
def __init__(self, path):
self.f = open(path, 'a')
def cprint(self, text):
print(text)
self.f.write(text + '\n')
self.f.flush()
def close(self):
self.f.close()
def test_one_epoch(device, model, test_loader):
model.eval()
test_loss = 0.0
pred = 0.0
count = 0
for i, data in enumerate(tqdm(test_loader)):
template, source, igt = data
template = template.to(device)
source = source.to(device)
igt = igt.to(device)
output = model(template, source)
loss_val = ChamferDistanceLoss()(template, output['transformed_source'])
test_loss += loss_val.item()
count += 1
test_loss = float(test_loss)/count
return test_loss
def test(args, model, test_loader, textio):
test_loss, test_accuracy = test_one_epoch(args.device, model, test_loader)
textio.cprint('Validation Loss: %f & Validation Accuracy: %f'%(test_loss, test_accuracy))
def train_one_epoch(device, model, train_loader, optimizer):
model.train()
train_loss = 0.0
pred = 0.0
count = 0
for i, data in enumerate(tqdm(train_loader)):
template, source, igt = data
template = template.to(device)
source = source.to(device)
igt = igt.to(device)
output = model(template, source)
loss_val = ChamferDistanceLoss()(template, output['transformed_source'])
# print(loss_val.item())
# forward + backward + optimize
optimizer.zero_grad()
loss_val.backward()
optimizer.step()
train_loss += loss_val.item()
count += 1
train_loss = float(train_loss)/count
return train_loss
def train(args, model, train_loader, test_loader, boardio, textio, checkpoint):
learnable_params = filter(lambda p: p.requires_grad, model.parameters())
if args.optimizer == 'Adam':
optimizer = torch.optim.Adam(learnable_params)
else:
optimizer = torch.optim.SGD(learnable_params, lr=0.1)
if checkpoint is not None:
min_loss = checkpoint['min_loss']
optimizer.load_state_dict(checkpoint['optimizer'])
best_test_loss = np.inf
for epoch in range(args.start_epoch, args.epochs):
train_loss = train_one_epoch(args.device, model, train_loader, optimizer)
test_loss = test_one_epoch(args.device, model, test_loader)
if test_loss<best_test_loss:
best_test_loss = test_loss
snap = {'epoch': epoch + 1,
'model': model.state_dict(),
'min_loss': best_test_loss,
'optimizer' : optimizer.state_dict(),}
torch.save(snap, 'checkpoints/%s/models/best_model_snap.t7' % (args.exp_name))
torch.save(model.state_dict(), 'checkpoints/%s/models/best_model.t7' % (args.exp_name))
torch.save(model.feature_model.state_dict(), 'checkpoints/%s/models/best_ptnet_model.t7' % (args.exp_name))
torch.save(snap, 'checkpoints/%s/models/model_snap.t7' % (args.exp_name))
torch.save(model.state_dict(), 'checkpoints/%s/models/model.t7' % (args.exp_name))
torch.save(model.feature_model.state_dict(), 'checkpoints/%s/models/ptnet_model.t7' % (args.exp_name))
boardio.add_scalar('Train Loss', train_loss, epoch+1)
boardio.add_scalar('Test Loss', test_loss, epoch+1)
boardio.add_scalar('Best Test Loss', best_test_loss, epoch+1)
textio.cprint('EPOCH:: %d, Traininig Loss: %f, Testing Loss: %f, Best Loss: %f'%(epoch+1, train_loss, test_loss, best_test_loss))
def options():
parser = argparse.ArgumentParser(description='Point Cloud Registration')
parser.add_argument('--exp_name', type=str, default='exp_ipcrnet', metavar='N',
help='Name of the experiment')
parser.add_argument('--dataset_path', type=str, default='ModelNet40',
metavar='PATH', help='path to the input dataset') # like '/path/to/ModelNet40'
parser.add_argument('--eval', type=bool, default=False, help='Train or Evaluate the network.')
# settings for input data
parser.add_argument('--dataset_type', default='modelnet', choices=['modelnet', 'shapenet2'],
metavar='DATASET', help='dataset type (default: modelnet)')
parser.add_argument('--num_points', default=1024, type=int,
metavar='N', help='points in point-cloud (default: 1024)')
# settings for PointNet
parser.add_argument('--pointnet', default='tune', type=str, choices=['fixed', 'tune'],
help='train pointnet (default: tune)')
parser.add_argument('--emb_dims', default=1024, type=int,
metavar='K', help='dim. of the feature vector (default: 1024)')
parser.add_argument('--symfn', default='max', choices=['max', 'avg'],
help='symmetric function (default: max)')
# settings for on training
parser.add_argument('--seed', type=int, default=1234)
parser.add_argument('-j', '--workers', default=4, type=int,
metavar='N', help='number of data loading workers (default: 4)')
parser.add_argument('-b', '--batch_size', default=20, type=int,
metavar='N', help='mini-batch size (default: 32)')
parser.add_argument('--epochs', default=200, type=int,
metavar='N', help='number of total epochs to run')
parser.add_argument('--start_epoch', default=0, type=int,
metavar='N', help='manual epoch number (useful on restarts)')
parser.add_argument('--optimizer', default='Adam', choices=['Adam', 'SGD'],
metavar='METHOD', help='name of an optimizer (default: Adam)')
parser.add_argument('--resume', default='', type=str,
metavar='PATH', help='path to latest checkpoint (default: null (no-use))')
parser.add_argument('--pretrained', default='', type=str,
metavar='PATH', help='path to pretrained model file (default: null (no-use))')
parser.add_argument('--device', default='cuda:0', type=str,
metavar='DEVICE', help='use CUDA if available')
args = parser.parse_args()
return args
def main():
args = options()
torch.backends.cudnn.deterministic = True
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
boardio = SummaryWriter(log_dir='checkpoints/' + args.exp_name)
_init_(args)
textio = IOStream('checkpoints/' + args.exp_name + '/run.log')
textio.cprint(str(args))
trainset = RegistrationData('PCRNet', ModelNet40Data(train=True))
testset = RegistrationData('PCRNet', ModelNet40Data(train=False))
train_loader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=args.workers)
test_loader = DataLoader(testset, batch_size=args.batch_size, shuffle=False, drop_last=False, num_workers=args.workers)
if not torch.cuda.is_available():
args.device = 'cpu'
args.device = torch.device(args.device)
# Create PointNet Model.
ptnet = PointNet(emb_dims=args.emb_dims)
model = iPCRNet(feature_model=ptnet)
model = model.to(args.device)
checkpoint = None
if args.resume:
assert os.path.isfile(args.resume)
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['model'])
if args.pretrained:
assert os.path.isfile(args.pretrained)
model.load_state_dict(torch.load(args.pretrained, map_location='cpu'))
model.to(args.device)
if args.eval:
test(args, model, test_loader, textio)
else:
train(args, model, train_loader, test_loader, boardio, textio, checkpoint)
if __name__ == '__main__':
main() |