File size: 8,530 Bytes
97aa5af | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 | import argparse
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 RPMNet, PPFNet
from learning3d.losses import FrobeniusNormLoss, RMSEFeaturesLoss
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 = FrobeniusNormLoss()(output['est_T'], igt) + RMSEFeaturesLoss()(output['r'])
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 = FrobeniusNormLoss()(output['est_T'], igt) + RMSEFeaturesLoss()(output['r'])
# 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_rpmnet', 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('--fine_tune_pointnet', default='tune', type=str, choices=['fixed', 'tune'],
help='train pointnet (default: tune)')
parser.add_argument('--transfer_ptnet_weights', default='./checkpoints/exp_classifier/models/best_ptnet_model.t7', type=str,
metavar='PATH', help='path to pointnet features file')
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=10, 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('RPMNet', ModelNet40Data(train=True, num_points=args.num_points, use_normals=True), partial_source=True, partial_template=True)
testset = RegistrationData('RPMNet', ModelNet40Data(train=False, num_points=args.num_points, use_normals=True), partial_source=True, partial_template=True)
train_loader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=args.workers)
test_loader = DataLoader(testset, batch_size=8, 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 RPMNet Model.
model = RPMNet(feature_model=PPFNet())
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() |