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import torch
from collections import defaultdict
import torch.optim as optim
# import tensorflow as tf
from torch.utils.tensorboard import SummaryWriter
from collections import OrderedDict
from utils.utils import *
from os.path import join as pjoin
from utils.eval_t2m import evaluation_mask_transformer, evaluation_res_transformer
from models.mask_transformer.tools import *
from einops import rearrange, repeat
def def_value():
return 0.0
class MaskTransformerTrainer:
def __init__(self, args, t2m_transformer, vq_model):
self.opt = args
self.t2m_transformer = t2m_transformer
self.vq_model = vq_model
self.device = args.device
self.vq_model.eval()
if args.is_train:
self.logger = SummaryWriter(args.log_dir)
def update_lr_warm_up(self, nb_iter, warm_up_iter, lr):
current_lr = lr * (nb_iter + 1) / (warm_up_iter + 1)
for param_group in self.opt_t2m_transformer.param_groups:
param_group["lr"] = current_lr
return current_lr
def forward(self, batch_data):
conds, motion, m_lens = batch_data
motion = motion.detach().float().to(self.device)
m_lens = m_lens.detach().long().to(self.device)
# (b, n, q)
code_idx, _ = self.vq_model.encode(motion)
m_lens = m_lens // 4
conds = conds.to(self.device).float() if torch.is_tensor(conds) else conds
# loss_dict = {}
# self.pred_ids = []
# self.acc = []
_loss, _pred_ids, _acc = self.t2m_transformer(code_idx[..., 0], conds, m_lens)
return _loss, _acc
def update(self, batch_data):
loss, acc = self.forward(batch_data)
self.opt_t2m_transformer.zero_grad()
loss.backward()
self.opt_t2m_transformer.step()
self.scheduler.step()
return loss.item(), acc
def save(self, file_name, ep, total_it):
t2m_trans_state_dict = self.t2m_transformer.state_dict()
clip_weights = [e for e in t2m_trans_state_dict.keys() if e.startswith('clip_model.')]
for e in clip_weights:
del t2m_trans_state_dict[e]
state = {
't2m_transformer': t2m_trans_state_dict,
'opt_t2m_transformer': self.opt_t2m_transformer.state_dict(),
'scheduler':self.scheduler.state_dict(),
'ep': ep,
'total_it': total_it,
}
torch.save(state, file_name)
def resume(self, model_dir):
checkpoint = torch.load(model_dir, map_location=self.device)
missing_keys, unexpected_keys = self.t2m_transformer.load_state_dict(checkpoint['t2m_transformer'], strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') for k in missing_keys])
try:
self.opt_t2m_transformer.load_state_dict(checkpoint['opt_t2m_transformer']) # Optimizer
self.scheduler.load_state_dict(checkpoint['scheduler']) # Scheduler
except:
print('Resume wo optimizer')
return checkpoint['ep'], checkpoint['total_it']
def train(self, train_loader, val_loader, eval_val_loader, eval_wrapper, plot_eval):
self.t2m_transformer.to(self.device)
self.vq_model.to(self.device)
self.opt_t2m_transformer = optim.AdamW(self.t2m_transformer.parameters(), betas=(0.9, 0.99), lr=self.opt.lr, weight_decay=1e-5)
self.scheduler = optim.lr_scheduler.MultiStepLR(self.opt_t2m_transformer,
milestones=self.opt.milestones,
gamma=self.opt.gamma)
epoch = 0
it = 0
if self.opt.is_continue:
model_dir = pjoin(self.opt.model_dir, 'latest.tar') # TODO
epoch, it = self.resume(model_dir)
print("Load model epoch:%d iterations:%d"%(epoch, it))
start_time = time.time()
total_iters = self.opt.max_epoch * len(train_loader)
print(f'Total Epochs: {self.opt.max_epoch}, Total Iters: {total_iters}')
print('Iters Per Epoch, Training: %04d, Validation: %03d' % (len(train_loader), len(val_loader)))
logs = defaultdict(def_value, OrderedDict())
best_fid, best_div, best_top1, best_top2, best_top3, best_matching, writer = evaluation_mask_transformer(
self.opt.save_root, eval_val_loader, self.t2m_transformer, self.vq_model, self.logger, epoch,
best_fid=100, best_div=100,
best_top1=0, best_top2=0, best_top3=0,
best_matching=100, eval_wrapper=eval_wrapper,
plot_func=plot_eval, save_ckpt=False, save_anim=False
)
best_acc = 0.
while epoch < self.opt.max_epoch:
self.t2m_transformer.train()
self.vq_model.eval()
for i, batch in enumerate(train_loader):
it += 1
if it < self.opt.warm_up_iter:
self.update_lr_warm_up(it, self.opt.warm_up_iter, self.opt.lr)
loss, acc = self.update(batch_data=batch)
logs['loss'] += loss
logs['acc'] += acc
logs['lr'] += self.opt_t2m_transformer.param_groups[0]['lr']
if it % self.opt.log_every == 0:
mean_loss = OrderedDict()
# self.logger.add_scalar('val_loss', val_loss, it)
# self.l
for tag, value in logs.items():
self.logger.add_scalar('Train/%s'%tag, value / self.opt.log_every, it)
mean_loss[tag] = value / self.opt.log_every
logs = defaultdict(def_value, OrderedDict())
print_current_loss(start_time, it, total_iters, mean_loss, epoch=epoch, inner_iter=i)
if it % self.opt.save_latest == 0:
self.save(pjoin(self.opt.model_dir, 'latest.tar'), epoch, it)
self.save(pjoin(self.opt.model_dir, 'latest.tar'), epoch, it)
epoch += 1
print('Validation time:')
self.vq_model.eval()
self.t2m_transformer.eval()
val_loss = []
val_acc = []
with torch.no_grad():
for i, batch_data in enumerate(val_loader):
loss, acc = self.forward(batch_data)
val_loss.append(loss.item())
val_acc.append(acc)
print(f"Validation loss:{np.mean(val_loss):.3f}, accuracy:{np.mean(val_acc):.3f}")
self.logger.add_scalar('Val/loss', np.mean(val_loss), epoch)
self.logger.add_scalar('Val/acc', np.mean(val_acc), epoch)
if np.mean(val_acc) > best_acc:
print(f"Improved accuracy from {best_acc:.02f} to {np.mean(val_acc)}!!!")
self.save(pjoin(self.opt.model_dir, 'net_best_acc.tar'), epoch, it)
best_acc = np.mean(val_acc)
best_fid, best_div, best_top1, best_top2, best_top3, best_matching, writer = evaluation_mask_transformer(
self.opt.save_root, eval_val_loader, self.t2m_transformer, self.vq_model, self.logger, epoch, best_fid=best_fid,
best_div=best_div, best_top1=best_top1, best_top2=best_top2, best_top3=best_top3,
best_matching=best_matching, eval_wrapper=eval_wrapper,
plot_func=plot_eval, save_ckpt=True, save_anim=(epoch%self.opt.eval_every_e==0)
)
class ResidualTransformerTrainer:
def __init__(self, args, res_transformer, vq_model):
self.opt = args
self.res_transformer = res_transformer
self.vq_model = vq_model
self.device = args.device
self.vq_model.eval()
if args.is_train:
self.logger = SummaryWriter(args.log_dir)
# self.l1_criterion = torch.nn.SmoothL1Loss()
def update_lr_warm_up(self, nb_iter, warm_up_iter, lr):
current_lr = lr * (nb_iter + 1) / (warm_up_iter + 1)
for param_group in self.opt_res_transformer.param_groups:
param_group["lr"] = current_lr
return current_lr
def forward(self, batch_data):
conds, motion, m_lens = batch_data
motion = motion.detach().float().to(self.device)
m_lens = m_lens.detach().long().to(self.device)
# (b, n, q), (q, b, n ,d)
code_idx, all_codes = self.vq_model.encode(motion)
m_lens = m_lens // 4
conds = conds.to(self.device).float() if torch.is_tensor(conds) else conds
ce_loss, pred_ids, acc = self.res_transformer(code_idx, conds, m_lens)
return ce_loss, acc
def update(self, batch_data):
loss, acc = self.forward(batch_data)
self.opt_res_transformer.zero_grad()
loss.backward()
self.opt_res_transformer.step()
self.scheduler.step()
return loss.item(), acc
def save(self, file_name, ep, total_it):
res_trans_state_dict = self.res_transformer.state_dict()
clip_weights = [e for e in res_trans_state_dict.keys() if e.startswith('clip_model.')]
for e in clip_weights:
del res_trans_state_dict[e]
state = {
'res_transformer': res_trans_state_dict,
'opt_res_transformer': self.opt_res_transformer.state_dict(),
'scheduler':self.scheduler.state_dict(),
'ep': ep,
'total_it': total_it,
}
torch.save(state, file_name)
def resume(self, model_dir):
checkpoint = torch.load(model_dir, map_location=self.device)
missing_keys, unexpected_keys = self.res_transformer.load_state_dict(checkpoint['res_transformer'], strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') for k in missing_keys])
try:
self.opt_res_transformer.load_state_dict(checkpoint['opt_res_transformer']) # Optimizer
self.scheduler.load_state_dict(checkpoint['scheduler']) # Scheduler
except:
print('Resume wo optimizer')
return checkpoint['ep'], checkpoint['total_it']
def train(self, train_loader, val_loader, eval_val_loader, eval_wrapper, plot_eval):
self.res_transformer.to(self.device)
self.vq_model.to(self.device)
self.opt_res_transformer = optim.AdamW(self.res_transformer.parameters(), betas=(0.9, 0.99), lr=self.opt.lr, weight_decay=1e-5)
self.scheduler = optim.lr_scheduler.MultiStepLR(self.opt_res_transformer,
milestones=self.opt.milestones,
gamma=self.opt.gamma)
epoch = 0
it = 0
if self.opt.is_continue:
model_dir = pjoin(self.opt.model_dir, 'latest.tar') # TODO
epoch, it = self.resume(model_dir)
print("Load model epoch:%d iterations:%d"%(epoch, it))
start_time = time.time()
total_iters = self.opt.max_epoch * len(train_loader)
print(f'Total Epochs: {self.opt.max_epoch}, Total Iters: {total_iters}')
print('Iters Per Epoch, Training: %04d, Validation: %03d' % (len(train_loader), len(val_loader)))
logs = defaultdict(def_value, OrderedDict())
best_fid, best_div, best_top1, best_top2, best_top3, best_matching, writer = evaluation_res_transformer(
self.opt.save_root, eval_val_loader, self.res_transformer, self.vq_model, self.logger, epoch,
best_fid=100, best_div=100,
best_top1=0, best_top2=0, best_top3=0,
best_matching=100, eval_wrapper=eval_wrapper,
plot_func=plot_eval, save_ckpt=False, save_anim=False
)
best_loss = 100
best_acc = 0
while epoch < self.opt.max_epoch:
self.res_transformer.train()
self.vq_model.eval()
for i, batch in enumerate(train_loader):
it += 1
if it < self.opt.warm_up_iter:
self.update_lr_warm_up(it, self.opt.warm_up_iter, self.opt.lr)
loss, acc = self.update(batch_data=batch)
logs['loss'] += loss
logs["acc"] += acc
logs['lr'] += self.opt_res_transformer.param_groups[0]['lr']
if it % self.opt.log_every == 0:
mean_loss = OrderedDict()
# self.logger.add_scalar('val_loss', val_loss, it)
# self.l
for tag, value in logs.items():
self.logger.add_scalar('Train/%s'%tag, value / self.opt.log_every, it)
mean_loss[tag] = value / self.opt.log_every
logs = defaultdict(def_value, OrderedDict())
print_current_loss(start_time, it, total_iters, mean_loss, epoch=epoch, inner_iter=i)
if it % self.opt.save_latest == 0:
self.save(pjoin(self.opt.model_dir, 'latest.tar'), epoch, it)
epoch += 1
self.save(pjoin(self.opt.model_dir, 'latest.tar'), epoch, it)
print('Validation time:')
self.vq_model.eval()
self.res_transformer.eval()
val_loss = []
val_acc = []
with torch.no_grad():
for i, batch_data in enumerate(val_loader):
loss, acc = self.forward(batch_data)
val_loss.append(loss.item())
val_acc.append(acc)
print(f"Validation loss:{np.mean(val_loss):.3f}, Accuracy:{np.mean(val_acc):.3f}")
self.logger.add_scalar('Val/loss', np.mean(val_loss), epoch)
self.logger.add_scalar('Val/acc', np.mean(val_acc), epoch)
if np.mean(val_loss) < best_loss:
print(f"Improved loss from {best_loss:.02f} to {np.mean(val_loss)}!!!")
self.save(pjoin(self.opt.model_dir, 'net_best_loss.tar'), epoch, it)
best_loss = np.mean(val_loss)
if np.mean(val_acc) > best_acc:
print(f"Improved acc from {best_acc:.02f} to {np.mean(val_acc)}!!!")
# self.save(pjoin(self.opt.model_dir, 'net_best_loss.tar'), epoch, it)
best_acc = np.mean(val_acc)
best_fid, best_div, best_top1, best_top2, best_top3, best_matching, writer = evaluation_res_transformer(
self.opt.save_root, eval_val_loader, self.res_transformer, self.vq_model, self.logger, epoch, best_fid=best_fid,
best_div=best_div, best_top1=best_top1, best_top2=best_top2, best_top3=best_top3,
best_matching=best_matching, eval_wrapper=eval_wrapper,
plot_func=plot_eval, save_ckpt=True, save_anim=(epoch%self.opt.eval_every_e==0)
) |