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main.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import os
import gc
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import MultiStepLR
from data import ModelNet40
import numpy as np
from torch.utils.data import DataLoader
from model import PRNet
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')
os.system('cp data.py checkpoints' + '/' + args.exp_name + '/' + 'data.py.backup')
def train(args, net, train_loader, test_loader):
if args.use_sgd:
print("Use SGD")
opt = optim.SGD(net.parameters(), lr=args.lr * 100, momentum=args.momentum, weight_decay=1e-4)
else:
print("Use Adam")
opt = optim.Adam(net.parameters(), lr=args.lr, weight_decay=1e-4)
epoch_factor = args.epochs / 100.0
scheduler = MultiStepLR(opt,
milestones=[int(30*epoch_factor), int(60*epoch_factor), int(80*epoch_factor)],
gamma=0.1)
info_test_best = None
for epoch in range(args.epochs):
scheduler.step()
info_train = net._train_one_epoch(epoch=epoch, train_loader=train_loader, opt=opt)
info_test = net._test_one_epoch(epoch=epoch, test_loader=test_loader)
if info_test_best is None or info_test_best['loss'] > info_test['loss']:
info_test_best = info_test
info_test_best['stage'] = 'best_test'
net.save('checkpoints/%s/models/model.best.t7' % args.exp_name)
net.logger.write(info_test_best)
net.save('checkpoints/%s/models/model.%d.t7' % (args.exp_name, epoch))
gc.collect()
def main():
parser = argparse.ArgumentParser(description='Point Cloud Registration')
parser.add_argument('--exp_name', type=str, default='exp', metavar='N',
help='Name of the experiment')
parser.add_argument('--model', type=str, default='prnet', metavar='N',
choices=['prnet'],
help='Model to use, [prnet]')
parser.add_argument('--emb_nn', type=str, default='dgcnn', metavar='N',
choices=['pointnet', 'dgcnn'],
help='Embedding to use, [pointnet, dgcnn]')
parser.add_argument('--attention', type=str, default='transformer', metavar='N',
choices=['identity', 'transformer'],
help='Head to use, [identity, transformer]')
parser.add_argument('--head', type=str, default='svd', metavar='N',
choices=['mlp', 'svd'],
help='Head to use, [mlp, svd]')
parser.add_argument('--n_emb_dims', type=int, default=512, metavar='N',
help='Dimension of embeddings')
parser.add_argument('--n_blocks', type=int, default=1, metavar='N',
help='Num of blocks of encoder&decoder')
parser.add_argument('--n_heads', type=int, default=4, metavar='N',
help='Num of heads in multiheadedattention')
parser.add_argument('--n_iters', type=int, default=3, metavar='N',
help='Num of iters to run inference')
parser.add_argument('--discount_factor', type=float, default=0.9, metavar='N',
help='Discount factor to compute the loss')
parser.add_argument('--n_ff_dims', type=int, default=1024, metavar='N',
help='Num of dimensions of fc in transformer')
parser.add_argument('--n_keypoints', type=int, default=512, metavar='N',
help='Num of keypoints to use')
parser.add_argument('--temp_factor', type=float, default=100, metavar='N',
help='Factor to control the softmax precision')
parser.add_argument('--cat_sampler', type=str, default='gumbel_softmax', choices=['softmax', 'gumbel_softmax'],
metavar='N', help='use gumbel_softmax to get the categorical sample')
parser.add_argument('--dropout', type=float, default=0.0, metavar='N',
help='Dropout ratio in transformer')
parser.add_argument('--batch_size', type=int, default=36, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--test_batch_size', type=int, default=12, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--epochs', type=int, default=100, metavar='N',
help='number of episode to train ')
parser.add_argument('--use_sgd', type=bool, default=False,
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('--no_cuda', action='store_true', default=False,
help='enables CUDA training')
parser.add_argument('--seed', type=int, default=1234, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--eval', action='store_true', default=False,
help='evaluate the model')
parser.add_argument('--cycle_consistency_loss', type=float, default=0.1, metavar='N',
help='cycle consistency loss')
parser.add_argument('--feature_alignment_loss', type=float, default=0.1, metavar='N',
help='feature alignment loss')
parser.add_argument('--gaussian_noise', type=bool, default=False, metavar='N',
help='Wheter to add gaussian noise')
parser.add_argument('--unseen', type=bool, default=False, metavar='N',
help='Wheter to test on unseen category')
parser.add_argument('--n_points', type=int, default=1024, metavar='N',
help='Num of points to use')
parser.add_argument('--n_subsampled_points', type=int, default=768, metavar='N',
help='Num of subsampled points to use')
parser.add_argument('--dataset', type=str, default='modelnet40', choices=['modelnet40'], metavar='N',
help='dataset to use')
parser.add_argument('--rot_factor', type=float, default=4, metavar='N',
help='Divided factor of rotation')
parser.add_argument('--model_path', type=str, default='', metavar='N',
help='Pretrained model path')
args = parser.parse_args()
torch.backends.cudnn.deterministic = True
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
_init_(args)
if args.dataset == 'modelnet40':
train_loader = DataLoader(ModelNet40(num_points=args.n_points,
num_subsampled_points=args.n_subsampled_points,
partition='train', gaussian_noise=args.gaussian_noise,
unseen=args.unseen, rot_factor=args.rot_factor),
batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=6)
test_loader = DataLoader(ModelNet40(num_points=args.n_points,
num_subsampled_points=args.n_subsampled_points,
partition='test', gaussian_noise=args.gaussian_noise,
unseen=args.unseen, rot_factor=args.rot_factor),
batch_size=args.test_batch_size, shuffle=False, drop_last=False, num_workers=6)
else:
raise Exception("not implemented")
if args.model == 'prnet':
net = PRNet(args).cuda()
if args.eval:
if args.model_path is '':
model_path = 'checkpoints' + '/' + args.exp_name + '/models/model.best.t7'
else:
model_path = args.model_path
if not os.path.exists(model_path):
print("can't find pretrained model")
return
else:
raise Exception('Not implemented')
if not args.eval:
train(args, net, train_loader, test_loader)
print('FINISH')
if __name__ == '__main__':
main()