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train_custom_cls.py
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train_custom_cls.py
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import argparse
import numpy as np
import os
import time
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from models.pointnet2_cls import pointnet2_cls_ssg, pointnet2_cls_msg, cls_loss
from data.CustomDataset import CustomDataset
from utils.common import setup_seed
def train_one_epoch(train_loader, model, loss_func, optimizer, device):
losses, total_seen, total_correct = [], 0, 0
for data, labels in train_loader:
optimizer.zero_grad() # Important
labels = labels.to(device)
xyz, points = data[:, :, :3], data[:, :, 3:]
pred = model(xyz.to(device), points.to(device))
loss = loss_func(pred, labels)
loss.backward()
optimizer.step()
pred = torch.max(pred, dim=-1)[1]
total_correct += torch.sum(pred == labels)
total_seen += xyz.shape[0]
losses.append(loss.item())
return np.mean(losses), total_correct, total_seen, total_correct / float(total_seen)
def test_one_epoch(test_loader, model, loss_func, device):
losses, total_seen, total_correct = [], 0, 0
for data, labels in test_loader:
labels = labels.to(device)
xyz, points = data[:, :, :3], data[:, :, 3:]
with torch.no_grad():
pred = model(xyz.to(device), points.to(device))
loss = loss_func(pred, labels)
pred = torch.max(pred, dim=-1)[1]
total_correct += torch.sum(pred == labels)
total_seen += xyz.shape[0]
losses.append(loss.item())
return np.mean(losses), total_correct, total_seen, total_correct / float(total_seen)
def train(train_loader, test_loader, model, loss_func, optimizer, scheduler, device, ngpus, nepoches, log_interval, log_dir, checkpoint_interval):
if not os.path.exists(log_dir):
os.makedirs(log_dir)
checkpoint_dir = os.path.join(log_dir, 'checkpoints')
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
tensorboard_dir = os.path.join(log_dir, 'tensorboard')
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
writer = SummaryWriter(tensorboard_dir)
for epoch in range(nepoches):
if epoch % checkpoint_interval == 0:
print('='*40)
if ngpus > 1:
torch.save(model.module.state_dict(), os.path.join(checkpoint_dir, "pointnet2_cls_%d.pth" % epoch))
else:
torch.save(model.state_dict(), os.path.join(checkpoint_dir, "pointnet2_cls_%d.pth" % epoch))
model.eval()
lr = optimizer.state_dict()['param_groups'][0]['lr']
loss, total_correct, total_seen, acc = test_one_epoch(test_loader, model, loss_func, device)
print('Test Epoch: {} / {}, lr: {:.6f}, Loss: {:.2f}, Corr: {}, Total: {}, Acc: {:.4f}'.format(epoch, nepoches, lr, loss, total_correct, total_seen, acc))
writer.add_scalar('test loss', loss, epoch)
writer.add_scalar('test acc', acc, epoch)
model.train()
loss, total_correct, total_seen, acc = train_one_epoch(train_loader, model, loss_func, optimizer, device)
writer.add_scalar('train loss', loss, epoch)
writer.add_scalar('train acc', acc, epoch)
if epoch % log_interval == 0:
lr = optimizer.state_dict()['param_groups'][0]['lr']
print('Train Epoch: {} / {}, lr: {:.6f}, Loss: {:.2f}, Corr: {}, Total: {}, Acc: {:.4f}'.format(epoch, nepoches, lr, loss, total_correct, total_seen, acc))
scheduler.step()
if __name__ == '__main__':
Models = {
'pointnet2_cls_ssg': pointnet2_cls_ssg,
'pointnet2_cls_msg': pointnet2_cls_msg
}
parser = argparse.ArgumentParser()
parser.add_argument('--data_root', type=str, required=True, help='Root to the dataset')
parser.add_argument('--batch_size', type=int, default=32, help='Batch size')
parser.add_argument('--npoints', type=int, default=1024, help='Number of the training points')
parser.add_argument('--nclasses', type=int, required=True, help='Number of classes')
parser.add_argument('--augment', type=bool, default=False, help='Augment the train data')
parser.add_argument('--dp', type=bool, default=False, help='Random input dropout during training')
parser.add_argument('--model', type=str, default='pointnet2_cls_ssg', help='Model name')
parser.add_argument('--gpus', type=str, default='0', help='Cuda ids')
parser.add_argument('--lr', type=float, default=0.001, help='Initial learing rate')
parser.add_argument('--decay_rate', type=float, default=1e-4, help='Initial learing rate')
parser.add_argument('--nepoches', type=int, default=251, help='Number of traing epoches')
parser.add_argument('--step_size', type=int, default=20, help='StepLR step size')
parser.add_argument('--gamma', type=float, default=0.7, help='StepLR gamma')
parser.add_argument('--seed', type=int, default=1234, help='random seed')
parser.add_argument('--log_interval', type=int, default=10, help='Print iterval')
parser.add_argument('--log_dir', type=str, default='work_dirs', help='Train/val loss and accuracy logs')
parser.add_argument('--checkpoint_interval', type=int, default=10, help='Checkpoint saved interval')
args = parser.parse_args()
print(args)
setup_seed(args.seed)
device_ids = list(map(int, args.gpus.strip().split(','))) if ',' in args.gpus else [int(args.gpus)]
ngpus = len(device_ids)
custom_train = CustomDataset(data_root=args.data_root, split='train', npoints=args.npoints, augment=args.augment, dp=args.dp)
custom_test = CustomDataset(data_root=args.data_root, split='test', npoints=-1)
train_loader = DataLoader(dataset=custom_train, batch_size=args.batch_size // ngpus, shuffle=True, num_workers=4)
test_loader = DataLoader(dataset=custom_test, batch_size=1, shuffle=False, num_workers=1)
print('Train set: {}'.format(len(custom_train)))
print('Test set: {}'.format(len(custom_test)))
Model = Models[args.model]
model = Model(6, args.nclasses)
# Mutli-gpus
device = torch.device("cuda:{}".format(device_ids[0]) if torch.cuda.is_available() else "cpu")
if ngpus > 1 and torch.cuda.device_count() > 1:
model = nn.DataParallel(model, device_ids=device_ids)
model = model.to(device)
loss = cls_loss().to(device)
#optimizer = torch.optim.SGD(model.parameters(), lr=args.init_lr, momentum=args.momentum)
optimizer = torch.optim.Adam(
model.parameters(),
lr=args.lr,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=args.decay_rate
)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.step_size, gamma=0.7)
tic = time.time()
train(train_loader=train_loader,
test_loader=test_loader,
model=model,
loss_func=loss,
optimizer=optimizer,
scheduler=scheduler,
device=device,
ngpus=ngpus,
nepoches=args.nepoches,
log_interval=args.log_interval,
log_dir=args.log_dir,
checkpoint_interval=args.checkpoint_interval,
)
toc = time.time()
print('Training completed, {:.2f} minutes'.format((toc - tic) / 60))