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train_AE.py
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import os
from os.path import join as pjoin
import torch
import numpy as np
import random
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch.optim as optim
from models.AE import AE_models
from utils.evaluators import Evaluators
from utils.datasets import AEDataset, Text2MotionDataset, collate_fn
import time
from collections import OrderedDict, defaultdict
from utils.train_utils import update_lr_warm_up, def_value, save, print_current_loss
from utils.eval_utils import evaluation_ae
import argparse
def main(args):
#################################################################################
# Seed #
#################################################################################
torch.backends.cudnn.benchmark = False
os.environ["OMP_NUM_THREADS"] = "1"
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
#################################################################################
# Train Data #
#################################################################################
if args.dataset_name == "t2m":
data_root = f'{args.dataset_dir}/HumanML3D/'
joints_num = 22
dim_pose = 67
else:
data_root = f'{args.dataset_dir}/KIT-ML/'
joints_num = 21
dim_pose = 64
motion_dir = pjoin(data_root, 'new_joint_vecs')
text_dir = pjoin(data_root, 'texts')
max_motion_length = 196
mean = np.load(pjoin(data_root, 'Mean.npy'))
std = np.load(pjoin(data_root, 'Std.npy'))
train_split_file = pjoin(data_root, 'train.txt')
val_split_file = pjoin(data_root, 'val.txt')
train_dataset = AEDataset(mean, std, motion_dir, args.window_size, train_split_file)
val_dataset = AEDataset(mean, std, motion_dir, args.window_size, val_split_file)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, drop_last=True, num_workers=args.num_workers,
shuffle=True, pin_memory=True)
val_loader = DataLoader(val_dataset, batch_size=args.batch_size, drop_last=True, num_workers=args.num_workers,
shuffle=True, pin_memory=True)
#################################################################################
# Eval Data #
#################################################################################
eval_mean = np.load(f'utils/eval_mean_std/{args.dataset_name}/eval_mean.npy')
eval_std = np.load(f'utils/eval_mean_std/{args.dataset_name}/eval_std.npy')
split_file = pjoin(data_root, 'val.txt')
eval_dataset = Text2MotionDataset(eval_mean, eval_std, split_file, args.dataset_name, motion_dir, text_dir,
4, max_motion_length, 20, evaluation=True)
eval_loader = DataLoader(eval_dataset, batch_size=32, num_workers=args.num_workers, drop_last=True,
collate_fn=collate_fn, shuffle=True)
#################################################################################
# Models #
#################################################################################
model_dir = pjoin(args.checkpoints_dir, args.dataset_name, args.name, 'model')
os.makedirs(model_dir, exist_ok=True)
ae = AE_models[args.model](input_width=dim_pose)
print(ae)
pc_vae = sum(param.numel() for param in ae.parameters())
print('Total parameters of all models: {}M'.format(pc_vae / 1000_000))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
eval_wrapper = Evaluators(args.dataset_name, device=device)
#################################################################################
# Training Loop #
#################################################################################
logger = SummaryWriter(model_dir)
if args.recons_loss == 'l1_smooth':
criterion = torch.nn.SmoothL1Loss()
else:
criterion = torch.nn.MSELoss()
ae.to(device)
optimizer = optim.AdamW(ae.parameters(), lr=args.lr, betas=(0.9, 0.99), weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.milestones, gamma=args.lr_decay)
epoch = 0
it = 0
if args.is_continue:
model_dir = pjoin(model_dir, 'latest.tar')
checkpoint = torch.load(model_dir, map_location=device)
ae.load_state_dict(checkpoint['ae'])
optimizer.load_state_dict(checkpoint[f'opt_ae'])
scheduler.load_state_dict(checkpoint['scheduler'])
epoch, it = checkpoint['ep'], checkpoint['total_it']
print("Load model epoch:%d iterations:%d" % (epoch, it))
start_time = time.time()
total_iters = args.epoch * len(train_loader)
print(f'Total Epochs: {args.epoch}, Total Iters: {total_iters}')
print('Iters Per Epoch, Training: %04d, Validation: %03d' % (len(train_loader), len(val_loader)))
current_lr = args.lr
logs = defaultdict(def_value, OrderedDict())
best_fid, best_div, best_top1, best_top2, best_top3, best_matching, mpjpe = 1000, 0, 0, 0, 0, 100, 100
while epoch < args.epoch:
ae.train()
for i, batch_data in enumerate(train_loader):
it += 1
if it < args.warm_up_iter:
current_lr = update_lr_warm_up(it, args.warm_up_iter, optimizer, args.lr)
motions = batch_data.detach().to(device).float()
pred_motion = ae(motions)
loss_rec = criterion(pred_motion, motions)
pred_local_pos = pred_motion[..., 4: (joints_num - 1) * 3 + 4]
local_pos = motions[..., 4: (joints_num - 1) * 3 + 4]
loss_explicit = criterion(pred_local_pos, local_pos)
loss = loss_rec + args.aux_loss_joints * loss_explicit
optimizer.zero_grad()
loss.backward()
optimizer.step()
if it >= args.warm_up_iter:
scheduler.step()
logs['loss'] += loss.item()
logs['loss_rec'] += loss_rec.item()
logs['loss_vel'] += loss_explicit.item()
logs['lr'] += optimizer.param_groups[0]['lr']
if it % args.log_every == 0:
mean_loss = OrderedDict()
for tag, value in logs.items():
logger.add_scalar('Train/%s' % tag, value / args.log_every, it)
mean_loss[tag] = value / args.log_every
logs = defaultdict(def_value, OrderedDict())
print_current_loss(start_time, it, total_iters, mean_loss, epoch=epoch, inner_iter=i)
save(pjoin(model_dir, 'latest.tar'), epoch, ae, optimizer, scheduler, it, 'ae')
epoch += 1
#################################################################################
# Eval Loop #
#################################################################################
print('Validation time:')
ae.eval()
val_loss_rec = []
val_loss_vel = []
val_loss = []
with torch.no_grad():
for i, batch_data in enumerate(val_loader):
motions = batch_data.detach().to(device).float()
pred_motion = ae(motions)
loss_rec = criterion(pred_motion, motions)
pred_local_pos = pred_motion[..., 4: (joints_num - 1) * 3 + 4]
local_pos = motions[..., 4: (joints_num - 1) * 3 + 4]
loss_explicit = criterion(pred_local_pos, local_pos)
loss = loss_rec + args.aux_loss_joints * loss_explicit
val_loss.append(loss.item())
val_loss_rec.append(loss_rec.item())
val_loss_vel.append(loss_explicit.item())
logger.add_scalar('Val/loss', sum(val_loss) / len(val_loss), epoch)
logger.add_scalar('Val/loss_rec', sum(val_loss_rec) / len(val_loss_rec), epoch)
logger.add_scalar('Val/loss_vel', sum(val_loss_vel) / len(val_loss_vel), epoch)
print('Validation Loss: %.5f, Reconstruction: %.5f, Velocity: %.5f,' %
(sum(val_loss) / len(val_loss), sum(val_loss_rec) / len(val_loss), sum(val_loss_vel) / len(val_loss)))
best_fid, best_div, best_top1, best_top2, best_top3, best_matching, mpjpe, writer = evaluation_ae(
model_dir, eval_loader, ae, logger, epoch-1, device=device, num_joint=joints_num, best_fid=best_fid,
best_div=best_div, best_top1=best_top1, best_top2=best_top2, best_top3=best_top3,
train_mean=mean, train_std=std, best_matching=best_matching, eval_wrapper=eval_wrapper)
print(f'best fid {best_fid}')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--name', type=str, default='AE')
parser.add_argument('--model', type=str, default='AE_Model')
parser.add_argument('--dataset_dir', type=str, default='./datasets')
parser.add_argument('--dataset_name', type=str, default='t2m')
parser.add_argument('--batch_size', default=256, type=int)
parser.add_argument('--window_size', type=int, default=64)
parser.add_argument('--epoch', default=50, type=int)
parser.add_argument('--warm_up_iter', default=2000, type=int)
parser.add_argument('--lr', default=2e-4, type=float)
parser.add_argument('--milestones', default=[150000, 250000], nargs="+", type=int)
parser.add_argument('--lr_decay', default=0.1, type=float)
parser.add_argument('--weight_decay', default=0.0, type=float)
parser.add_argument('--aux_loss_joints', type=float, default=1)
parser.add_argument('--recons_loss', type=str, default='l1_smooth')
parser.add_argument("--seed", type=int, default=3407)
parser.add_argument("--num_workers", type=int, default=4)
parser.add_argument('--is_continue', action="store_true")
parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints')
parser.add_argument('--log_every', default=10, type=int)
arg = parser.parse_args()
main(arg)