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train_seg.py
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# -*- coding: utf-8 -*-
import argparse
import os
import pdb
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import lera
from datasets import PartDataset
from datasets import S3dDataset
from pointnet import PointNetSeg
def train(config):
print('Random seed: %d' % int(config.seed))
torch.manual_seed(config.seed)
torch.backends.cudnn.benchmark = True
dset = config.dataset
if dset == 'shapenet16':
dataset = PartDataset(root=config.root, npoints=config.npoints, class_choice=[config.classname], train=True)
test_dataset = PartDataset(root=config.root, npoints=config.npoints, class_choice=[config.classname], train=False)
elif dset == 's3dis':
dataset = S3dDataset(root=config.root, npoints=config.npoints, train=True)
test_dataset = S3dDataset(root=config.root, npoints=config.npoints, train=False)
else:
raise NotImplementedError('Dataset not supported.')
print('Selected %s' % dset)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.batchsize, shuffle=True,
num_workers=config.workers)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=config.batchsize, shuffle=True,
num_workers=config.workers)
num_classes = dataset.num_classes
print('number of classes: %d' % num_classes)
print('train set size: %d | test set size: %d' % (len(dataset), len(test_dataset)))
try:
os.makedirs(config.outf)
except:
pass
blue = lambda x: '\033[94m' + x + '\033[0m'
yellow = lambda x: '\033[93m' + x + '\033[0m'
red = lambda x: '\033[91m' + x + '\033[0m'
classifier = PointNetSeg(k=num_classes)
if config.model != '':
classifier.load_state_dict(torch.load(config.model))
optimizer = optim.SGD(classifier.parameters(), lr=config.lr, momentum=config.momentum)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
classifier.to(device)
if config.mgpu:
classifier = torch.nn.DataParallel(classifier, device_ids=config.gpuids)
num_batch = len(dataset) / config.batchsize
lera.log_hyperparams({
'title': dset,
'classname': config.classname,
'batchsize': config.batchsize,
'epochs': config.nepochs,
'optimizer': 'SGD',
'lr': config.lr,
'npoints': config.npoints
})
for epoch in range(config.nepochs):
train_acc_epoch, train_iou_epoch, test_acc_epoch, test_iou_epoch = [], [], [], []
for i, data in enumerate(dataloader):
points, labels = data
points = points.transpose(2, 1)
points, labels = points.to(device), labels.to(device)
optimizer.zero_grad()
classifier = classifier.train()
pred, _ = classifier(points)
pred = pred.view(-1, num_classes)
# print(pred.size(), labels.size())
if dset == 'shapenet16':
labels = labels.view(-1, 1)[:, 0] - 1
else:
labels = labels.view(-1, 1)[:, 0]
# print(pred.size(), labels.size())
# pdb.set_trace()
loss = F.nll_loss(pred, labels)
loss.backward()
optimizer.step()
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(labels.data).cpu().sum()
train_acc = correct.item() / float(config.batchsize*config.npoints)
train_iou = correct.item() / float(2*config.batchsize*config.npoints-correct.item())
print('epoch %d: %d/%d | train loss: %f | train acc: %f | train iou: %f' % (epoch+1, i+1, num_batch+1, loss.item(), train_acc, train_iou))
train_acc_epoch.append(train_acc)
train_iou_epoch.append(train_iou)
lera.log({
'train loss': loss.item(),
'train acc': train_acc,
'train IoU': train_iou}
)
if (i+1) % 10 == 0:
j, data = next(enumerate(test_dataloader, 0))
points, labels = data
points = points.transpose(2, 1)
points, labels = points.to(device), labels.to(device)
classifier = classifier.eval()
with torch.no_grad():
pred, _ = classifier(points)
pred = pred.view(-1, num_classes)
if dset == 'shapenet16':
labels = labels.view(-1, 1)[:, 0] - 1
else:
labels = labels.view(-1, 1)[:, 0]
loss = F.nll_loss(pred, labels)
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(labels.data).cpu().sum()
test_acc = correct.item() / float(config.batchsize*config.npoints)
test_iou = correct.item() / float(2*config.batchsize*config.npoints-correct.item())
print(blue('epoch %d: %d/%d | test loss: %f | test acc: %f | test iou: %f') % (epoch+1, i+1, num_batch+1, loss.item(), test_acc, test_iou))
test_acc_epoch.append(test_acc)
test_iou_epoch.append(test_iou)
lera.log({
'test loss': loss.item(),
'test acc': test_acc,
'test IoU': test_iou})
print(yellow('epoch %d | mean train acc: %f | mean train IoU: %f') % (epoch+1, np.mean(train_acc_epoch), np.mean(train_iou_epoch)))
print(red('epoch %d | mean test acc: %f | mean test IoU: %f') % (epoch+1, np.mean(test_acc_epoch), np.mean(test_iou_epoch)))
lera.log({
'mean train acc': np.mean(train_acc_epoch),
'mean train iou': np.mean(train_iou_epoch),
'mean test acc': np.mean(test_acc_epoch),
'mean test iou': np.mean(test_iou_epoch)})
torch.save(classifier.state_dict(), '%s/%s_model_%d.pth' % (config.outf, config.dataset, epoch))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--seed', type=int, help='random seed')
parser.add_argument('-dset', '--dataset', type=str, required=True, help='dataset to train on, one of modelnet, shapenet16 and s3dis')
parser.add_argument('-c', '--classname', type=str, default='Chair', help='one of 16 categories on shapenet16')
parser.add_argument('-r', '--root', type=str, required=True, help='path to dataset')
parser.add_argument('-np', '--npoints', type=int, help='number of points to sample')
parser.add_argument('-bs', '--batchsize', type=int, default=32, help='batch size')
parser.add_argument('-ws', '--workers', type=int, default=4, help='number of workers')
parser.add_argument('-out', '--outf', type=str, default='./checkpoints', help='path to save model checkpoints')
parser.add_argument('--model', type=str, default='', help='checkpoint dir')
parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum in SGD')
parser.add_argument('--mgpu', type=bool, default=False, help='whether to utilize multiple gpus')
parser.add_argument('--gpuids', nargs='+', type=int, help='which gpus to use')
parser.add_argument('--nepochs', type=int, default=100, help='epochs to train')
config = parser.parse_args()
train(config)