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train_completion.py
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train_completion.py
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# Copyright (c) 2020. Hanchen Wang, [email protected]
# Ref: https://github.com/wentaoyuan/pcn/blob/master/train.py
# Ref: https://github.com/WangYueFt/dgcnn/blob/master/tensorflow/train.py
# For DGCNN Encoder, We Also Use Adam + StepLR for the Unity and Simplicity
import os, sys, time, torch, shutil, argparse, datetime, importlib, numpy as np
sys.path.append('utils')
sys.path.append('models')
from TrainLogger import TrainLogger
from LMDB_DataFlow import lmdb_dataflow
from Torch_Utility import copy_parameters
# from torch.optim.lr_scheduler import StepLR
from Visu_Utility import plot_pcd_three_views
from torch.utils.tensorboard import SummaryWriter
def parse_args():
parser = argparse.ArgumentParser('Point Cloud Completion')
''' === Training Setting === '''
parser.add_argument('--log_dir', type=str, help='log folder [default: ]')
parser.add_argument('--gpu', type=str, default='0', help='GPU [default: 0]')
parser.add_argument('--batch_size', type=int, default=32, help='batch size [default: 32]')
parser.add_argument('--epoch', type=int, default=50, help='number of epoch [default: 50]')
parser.add_argument('--lr', type=float, default=0.0001, help='learning rate [default: 1e-4]')
parser.add_argument('--lr_decay', type=float, default=0.7, help='lr decay rate [default: 0.7]')
parser.add_argument('--step_size', type=int, default=20, help='lr decay step [default: 20 epoch]')
parser.add_argument('--dataset', type=str, default='modelnet', help='dataset [default: modelnet]')
parser.add_argument('--restore', action='store_true', help='loaded from restore [default: False]')
parser.add_argument('--restore_path', type=str, help='path to saved pre-trained model [default: ]')
parser.add_argument('--steps_print', type=int, default=100, help='# steps to print [default: 100]')
parser.add_argument('--steps_visu', type=int, default=3456, help='# steps to visual [default: 3456]')
parser.add_argument('--steps_eval', type=int, default=1000, help='# steps to evaluate [default: 1e3]')
parser.add_argument('--epochs_save', type=int, default=5, help='# epochs to save [default: 5 epochs]')
''' === Model Setting === '''
parser.add_argument('--model', type=str, default='pcn_occo', help='model [pcn_occo]')
parser.add_argument('--k', type=int, default=20, help='# nearest neighbors in DGCNN [20]')
parser.add_argument('--grid_size', type=int, default=4, help='edge length of the 2D grid [4]')
parser.add_argument('--grid_scale', type=float, default=0.5, help='scale of the 2D grid [0.5]')
parser.add_argument('--num_coarse', type=int, default=1024, help='# points in coarse gt [1024]')
parser.add_argument('--emb_dims', type=int, default=1024, help='# dimension of DGCNN encoder [1024]')
parser.add_argument('--input_pts', type=int, default=1024, help='# points of occluded inputs [1024]')
parser.add_argument('--gt_pts', type=int, default=16384, help='# points of ground truth inputs [16384]')
return parser.parse_args()
def main(args):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
''' === Set up Loggers and Load Data === '''
MyLogger = TrainLogger(args, name=args.model.upper(), subfold='completion')
os.makedirs(os.path.join(MyLogger.experiment_dir, 'plots'), exist_ok=True)
writer = SummaryWriter(os.path.join(MyLogger.experiment_dir, 'runs'))
MyLogger.logger.info('Load dataset %s' % args.dataset)
if args.dataset == 'modelnet':
lmdb_train = './data/modelnet/train.lmdb'
lmdb_valid = './data/modelnet/test.lmdb'
elif args.dataset == 'shapenet':
lmdb_train = 'data/shapenet/train.lmdb'
lmdb_valid = 'data/shapenet/valid.lmdb'
else:
raise ValueError("Dataset is not available, it should be either ModelNet or ShapeNet")
assert (args.gt_pts == args.grid_size ** 2 * args.num_coarse)
df_train, num_train = lmdb_dataflow(
lmdb_train, args.batch_size, args.input_pts, args.gt_pts, is_training=True)
df_valid, num_valid = lmdb_dataflow(
lmdb_valid, args.batch_size, args.input_pts, args.gt_pts, is_training=False)
train_gen, valid_gen = df_train.get_data(), df_valid.get_data()
total_steps = num_train // args.batch_size * args.epoch
''' === Load Model and Backup Scripts === '''
MODEL = importlib.import_module(args.model)
shutil.copy(os.path.abspath(__file__), MyLogger.log_dir)
shutil.copy('./models/%s.py' % args.model, MyLogger.log_dir)
# multiple GPUs usage
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
completer = MODEL.get_model(args=args, grid_size=args.grid_size,
grid_scale=args.grid_scale, num_coarse=args.num_coarse).to(device)
criterion = MODEL.get_loss().to(device)
completer = torch.nn.DataParallel(completer)
# nn.DataParallel has its own issues (slow, memory expensive), bearable
# some optional advanced solutions: https://zhuanlan.zhihu.com/p/145427849
print('=' * 33)
print('Using %d GPU,' % torch.cuda.device_count(), 'Indices are: %s' % args.gpu)
print('=' * 33)
''' === Restore Model from Checkpoints, If there is any === '''
if args.restore:
checkpoint = torch.load(args.restore_path)
completer = copy_parameters(completer, checkpoint, verbose=True)
MyLogger.logger.info('Use pre-trained model from %s' % args.restore_path)
MyLogger.step, MyLogger.epoch = checkpoint['step'], checkpoint['epoch']
else:
MyLogger.logger.info('No pre-trained model, start training from scratch...')
''' IMPORTANT: for completion, no weight decay in Adam, no batch norm in decoder!'''
optimizer = torch.optim.Adam(
completer.parameters(),
lr=args.lr,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0)
# weight_decay=1e-4)
# For the sake of simplicity, we save the momentum decay in the batch norm
# scheduler = StepLR(optimizer, step_size=20, gamma=0.7) -> instead we define these manually
LEARNING_RATE_CLIP = 0.01 * args.lr
def vary2fix(inputs, npts, batch_size=args.batch_size, num_point=args.input_pts):
"""upsample/downsample varied input points into fixed length
:param inputs: input points cloud
:param npts: describe how many points of each input object
:param batch_size: training batch size
:param num_point: number of points of per occluded object
:return: fixed length of points of each object
"""
inputs_ls = np.split(inputs[0], npts.cumsum())
ret_inputs = np.zeros((1, batch_size * num_point, 3))
ret_npts = npts.copy()
for idx, obj in enumerate(inputs_ls[:-1]):
if len(obj) <= num_point:
select_idx = np.concatenate([
np.arange(len(obj)), np.random.choice(len(obj), num_point - len(obj))])
else:
select_idx = np.arange(len(obj))
np.random.shuffle(select_idx)
ret_inputs[0][idx * num_point:(idx + 1) * num_point] = obj[select_idx].copy()
ret_npts[idx] = num_point
return ret_inputs, ret_npts
def piecewise_constant(global_step, boundaries, values):
"""substitute for tf.train.piecewise_constant:
https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/piecewise_constant
global_step can be either training epoch or training step
"""
if len(boundaries) != len(values) - 1:
raise ValueError(
"The length of boundaries should be 1 less than the length of values")
if global_step <= boundaries[0]:
return values[0]
elif global_step > boundaries[-1]:
return values[-1]
else:
for low, high, v in zip(boundaries[:-1], boundaries[1:], values[1:-1]):
if (global_step > low) & (global_step <= high):
return v
total_time, train_start = 0, time.time()
for step in range(MyLogger.step + 1, total_steps + 1):
''' === Training === '''
start = time.time()
epoch = step * args.batch_size // num_train + 1
lr = max(args.lr * (args.lr_decay ** (epoch // args.step_size)), LEARNING_RATE_CLIP)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# follow the original alpha setting for ShapeNet Dataset in PCN paper:
alpha = piecewise_constant(step, [10000, 20000, 50000], [0.01, 0.1, 0.5, 1.0])
writer.add_scalar('Learning Rate', lr, step)
writer.add_scalar('Alpha', alpha, step)
ids, inputs, npts, gt = next(train_gen)
if args.dataset == 'shapenet':
inputs, _ = vary2fix(inputs, npts)
completer.train()
optimizer.zero_grad()
inputs = inputs.reshape(args.batch_size, args.input_pts, 3)
inputs, gt = torch.Tensor(inputs).transpose(2, 1).cuda(), torch.Tensor(gt).cuda()
pred_coarse, pred_fine = completer(inputs)
loss = criterion(pred_coarse, pred_fine, gt, alpha)
loss.backward()
optimizer.step()
total_time += time.time() - start
writer.add_scalar('Loss', loss, step)
if step % args.steps_print == 0:
MyLogger.logger.info('epoch %d step %d alpha %.2f loss %.8f time per step %.2f s' %
(epoch, step, alpha, loss, total_time / args.steps_print))
total_time = 0
''' === Validating === '''
if step % args.steps_eval == 0:
with torch.no_grad():
completer.eval()
MyLogger.logger.info('Testing...')
num_eval_steps, eval_loss, eval_time = num_valid // args.batch_size, 0, 0
for eval_step in range(num_eval_steps):
start = time.time()
_, inputs, npts, gt = next(valid_gen)
if args.dataset == 'shapenet':
inputs, _ = vary2fix(inputs, npts)
inputs = inputs.reshape(args.batch_size, args.input_pts, 3)
inputs, gt = torch.Tensor(inputs).transpose(2, 1).cuda(), torch.Tensor(gt).cuda()
pred_coarse, pred_fine = completer(inputs)
loss = criterion(pred_coarse, pred_fine, gt, alpha)
eval_loss += loss
eval_time += time.time() - start
MyLogger.logger.info('epoch %d step %d validation loss %.8f time per step %.2f s' %
(epoch, step, eval_loss / num_eval_steps, eval_time / num_eval_steps))
''' === Visualisation === '''
if step % args.steps_visu == 0:
all_pcds = [item.detach().cpu().numpy() for item in [
inputs.transpose(2, 1), pred_coarse, pred_fine, gt]]
for i in range(args.batch_size):
plot_path = os.path.join(MyLogger.experiment_dir, 'plots',
'epoch_%d_step_%d_%s.png' % (epoch, step, ids[i]))
pcds = [x[i] for x in all_pcds]
plot_pcd_three_views(plot_path, pcds,
['input', 'coarse output', 'fine output', 'ground truth'])
trained_epoch = epoch - 1
if (trained_epoch % args.epochs_save == 0) and (trained_epoch != 0) and \
not os.path.exists(os.path.join(MyLogger.checkpoints_dir,
'model_epoch_%d.pth' % trained_epoch)):
state = {
'step': step,
'epoch': epoch,
'model_state_dict': completer.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}
torch.save(state, os.path.join(MyLogger.checkpoints_dir,
"model_epoch_%d.pth" % trained_epoch))
MyLogger.logger.info('Model saved at %s/model_epoch_%d.pth\n'
% (MyLogger.checkpoints_dir, trained_epoch))
MyLogger.logger.info('Training Finished, Total Time: ' +
str(datetime.timedelta(seconds=time.time() - train_start)))
if __name__ == '__main__':
args = parse_args()
main(args)