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main.py
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main.py
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import os
import sys
import time
import random
from tqdm import tqdm
from argparse import ArgumentParser
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
if torch.__version__ == 'parrots':
from pavi import SummaryWriter
else:
from torch.utils.tensorboard import SummaryWriter
from data import PlanningDataset, SequencePlanningDataset, Comma2k19SequenceDataset
from model import PlaningNetwork, MultipleTrajectoryPredictionLoss, SequencePlanningNetwork
from utils import draw_trajectory_on_ax, get_val_metric, get_val_metric_keys
def get_hyperparameters(parser: ArgumentParser):
parser.add_argument('--batch_size', type=int, default=6)
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--n_workers', type=int, default=4)
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--log_per_n_step', type=int, default=20)
parser.add_argument('--val_per_n_epoch', type=int, default=1)
parser.add_argument('--resume', type=str, default='')
parser.add_argument('--M', type=int, default=5)
parser.add_argument('--num_pts', type=int, default=33)
parser.add_argument('--mtp_alpha', type=float, default=1.0)
parser.add_argument('--optimizer', type=str, default='sgd')
parser.add_argument('--sync_bn', type=bool, default=True)
parser.add_argument('--tqdm', type=bool, default=False)
parser.add_argument('--optimize_per_n_step', type=int, default=40)
try:
exp_name = os.environ["SLURM_JOB_ID"]
except KeyError:
exp_name = str(time.time())
parser.add_argument('--exp_name', type=str, default=exp_name)
return parser
def setup(rank, world_size):
torch.cuda.set_device(rank)
dist.init_process_group('nccl', init_method='tcp://localhost:%s' % os.environ['PORT'], rank=rank, world_size=world_size)
print('[%.2f]' % time.time(), 'DDP Initialized at %s:%s' % ('localhost', os.environ['PORT']), rank, 'of', world_size, flush=True)
def get_dataloader(rank, world_size, batch_size, pin_memory=False, num_workers=0):
train = Comma2k19SequenceDataset('data/comma2k19_train_non_overlap.txt', 'data/comma2k19/','train', use_memcache=False)
val = Comma2k19SequenceDataset('data/comma2k19_val_non_overlap.txt', 'data/comma2k19/','demo', use_memcache=False)
if torch.__version__ == 'parrots':
dist_sampler_params = dict(num_replicas=world_size, rank=rank, shuffle=True)
else:
dist_sampler_params = dict(num_replicas=world_size, rank=rank, shuffle=True, drop_last=True)
train_sampler = DistributedSampler(train, **dist_sampler_params)
val_sampler = DistributedSampler(val, **dist_sampler_params)
loader_args = dict(num_workers=num_workers, persistent_workers=True if num_workers > 0 else False, prefetch_factor=2, pin_memory=pin_memory)
train_loader = DataLoader(train, batch_size, sampler=train_sampler, **loader_args)
val_loader = DataLoader(val, batch_size=1, sampler=val_sampler, **loader_args)
return train_loader, val_loader
def cleanup():
dist.destroy_process_group()
class SequenceBaselineV1(nn.Module):
def __init__(self, M, num_pts, mtp_alpha, lr, optimizer, optimize_per_n_step=40) -> None:
super().__init__()
self.M = M
self.num_pts = num_pts
self.mtp_alpha = mtp_alpha
self.lr = lr
self.optimizer = optimizer
self.net = SequencePlanningNetwork(M, num_pts)
self.optimize_per_n_step = optimize_per_n_step # for the gru module
@staticmethod
def configure_optimizers(args, model):
if args.optimizer == 'sgd':
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=0.01)
elif args.optimizer == 'adam':
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=0.01)
elif args.optimizer == 'adamw':
optimizer = optim.AdamW(model.parameters(), lr=args.lr, )
else:
raise NotImplementedError
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, 20, 0.9)
return optimizer, lr_scheduler
def forward(self, x, hidden=None):
if hidden is None:
hidden = torch.zeros((2, x.size(0), 512)).to(self.device)
return self.net(x, hidden)
def main(rank, world_size, args):
if rank == 0:
writer = SummaryWriter()
train_dataloader, val_dataloader = get_dataloader(rank, world_size, args.batch_size, False, args.n_workers)
model = SequenceBaselineV1(args.M, args.num_pts, args.mtp_alpha, args.lr, args.optimizer, args.optimize_per_n_step)
use_sync_bn = args.sync_bn
if use_sync_bn:
model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.cuda()
optimizer, lr_scheduler = model.configure_optimizers(args, model)
model: SequenceBaselineV1
if args.resume and rank == 0:
print('Loading weights from', args.resume)
model.load_state_dict(torch.load(args.resume), strict=True)
dist.barrier()
model = nn.parallel.DistributedDataParallel(model, device_ids=[rank], find_unused_parameters=True, broadcast_buffers=False)
loss = MultipleTrajectoryPredictionLoss(args.mtp_alpha, args.M, args.num_pts, distance_type='angle')
num_steps = 0
disable_tqdm = (not args.tqdm) or (rank != 0)
for epoch in tqdm(range(args.epochs), disable=disable_tqdm, position=0):
train_dataloader.sampler.set_epoch(epoch)
for batch_idx, data in enumerate(tqdm(train_dataloader, leave=False, disable=disable_tqdm, position=1)):
seq_inputs, seq_labels = data['seq_input_img'].cuda(), data['seq_future_poses'].cuda()
bs = seq_labels.size(0)
seq_length = seq_labels.size(1)
hidden = torch.zeros((2, bs, 512)).cuda()
total_loss = 0
for t in tqdm(range(seq_length), leave=False, disable=disable_tqdm, position=2):
num_steps += 1
inputs, labels = seq_inputs[:, t, :, :, :], seq_labels[:, t, :, :]
pred_cls, pred_trajectory, hidden = model(inputs, hidden)
cls_loss, reg_loss = loss(pred_cls, pred_trajectory, labels)
total_loss += (cls_loss + args.mtp_alpha * reg_loss.mean()) / model.module.optimize_per_n_step
if rank == 0 and (num_steps + 1) % args.log_per_n_step == 0:
# TODO: add a customized log function
writer.add_scalar('train/epoch', epoch, num_steps)
writer.add_scalar('loss/cls', cls_loss, num_steps)
writer.add_scalar('loss/reg', reg_loss.mean(), num_steps)
writer.add_scalar('loss/reg_x', reg_loss[0], num_steps)
writer.add_scalar('loss/reg_y', reg_loss[1], num_steps)
writer.add_scalar('loss/reg_z', reg_loss[2], num_steps)
writer.add_scalar('param/lr', optimizer.param_groups[0]['lr'], num_steps)
if (t + 1) % model.module.optimize_per_n_step == 0:
hidden = hidden.clone().detach()
optimizer.zero_grad()
total_loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) # TODO: move to args
optimizer.step()
if rank == 0:
writer.add_scalar('loss/total', total_loss, num_steps)
total_loss = 0
if not isinstance(total_loss, int):
optimizer.zero_grad()
total_loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) # TODO: move to args
optimizer.step()
if rank == 0:
writer.add_scalar('loss/total', total_loss, num_steps)
lr_scheduler.step()
if (epoch + 1) % args.val_per_n_epoch == 0:
if rank == 0:
# save model
ckpt_path = os.path.join(writer.log_dir, 'epoch_%d.pth' % epoch)
torch.save(model.module.state_dict(), ckpt_path)
print('[Epoch %d] checkpoint saved at %s' % (epoch, ckpt_path))
model.eval()
with torch.no_grad():
saved_metric_epoch = get_val_metric_keys()
for batch_idx, data in enumerate(tqdm(val_dataloader, leave=False, disable=disable_tqdm, position=1)):
seq_inputs, seq_labels = data['seq_input_img'].cuda(), data['seq_future_poses'].cuda()
bs = seq_labels.size(0)
seq_length = seq_labels.size(1)
hidden = torch.zeros((2, bs, 512), device=seq_inputs.device)
for t in tqdm(range(seq_length), leave=False, disable=True, position=2):
inputs, labels = seq_inputs[:, t, :, :, :], seq_labels[:, t, :, :]
pred_cls, pred_trajectory, hidden = model(inputs, hidden)
metrics = get_val_metric(pred_cls, pred_trajectory.view(-1, args.M, args.num_pts, 3), labels)
for k, v in metrics.items():
saved_metric_epoch[k].append(v.float().mean().item())
dist.barrier() # Wait for all processes
# sync
metric_single = torch.zeros((len(saved_metric_epoch), ), dtype=torch.float32, device='cuda')
counter_single = torch.zeros((len(saved_metric_epoch), ), dtype=torch.int32, device='cuda')
# From Python 3.6 onwards, the standard dict type maintains insertion order by default.
# But, programmers should not rely on it.
for i, k in enumerate(sorted(saved_metric_epoch.keys())):
metric_single[i] = np.mean(saved_metric_epoch[k])
counter_single[i] = len(saved_metric_epoch[k])
metric_gather = [torch.zeros((len(saved_metric_epoch), ), dtype=torch.float32, device='cuda')[None] for _ in range(world_size)]
counter_gather = [torch.zeros((len(saved_metric_epoch), ), dtype=torch.int32, device='cuda')[None] for _ in range(world_size)]
dist.all_gather(metric_gather, metric_single[None])
dist.all_gather(counter_gather, counter_single[None])
if rank == 0:
metric_gather = torch.cat(metric_gather, dim=0) # [world_size, num_metric_keys]
counter_gather = torch.cat(counter_gather, dim=0) # [world_size, num_metric_keys]
metric_gather_weighted_mean = (metric_gather * counter_gather).sum(0) / counter_gather.sum(0)
for i, k in enumerate(sorted(saved_metric_epoch.keys())):
writer.add_scalar(k, metric_gather_weighted_mean[i], num_steps)
dist.barrier()
model.train()
cleanup()
if __name__ == "__main__":
print('[%.2f]' % time.time(), 'starting job...', os.environ['SLURM_PROCID'], 'of', os.environ['SLURM_NTASKS'], flush=True)
parser = ArgumentParser()
parser = get_hyperparameters(parser)
args = parser.parse_args()
setup(rank=int(os.environ['SLURM_PROCID']), world_size=int(os.environ['SLURM_NTASKS']))
main(rank=int(os.environ['SLURM_PROCID']), world_size=int(os.environ['SLURM_NTASKS']), args=args)