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import os
import sys
import logging
import datetime
import os.path as osp
from tqdm.auto import tqdm
from omegaconf import OmegaConf
import torch
import diffusers
import transformers
from torch.utils.tensorboard import SummaryWriter
from diffusers.optimization import get_scheduler
from mld.config import parse_args
from mld.data.get_data import get_datasets
from mld.models.modeltype.mld import MLD
from mld.utils.utils import print_table, set_seed, move_batch_to_device
os.environ["TOKENIZERS_PARALLELISM"] = "false"
def main():
cfg = parse_args()
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
set_seed(cfg.TRAIN.SEED_VALUE)
name_time_str = osp.join(cfg.NAME, datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S"))
output_dir = osp.join(cfg.FOLDER, name_time_str)
os.makedirs(output_dir, exist_ok=False)
os.makedirs(f"{output_dir}/checkpoints", exist_ok=False)
writer = SummaryWriter(output_dir)
stream_handler = logging.StreamHandler(sys.stdout)
file_handler = logging.FileHandler(osp.join(output_dir, 'output.log'))
handlers = [file_handler, stream_handler]
logging.basicConfig(level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=handlers)
logger = logging.getLogger(__name__)
OmegaConf.save(cfg, osp.join(output_dir, 'config.yaml'))
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
assert cfg.model.is_controlnet, "cfg.model.is_controlnet must be true for controlling!"
datasets = get_datasets(cfg)[0]
train_dataloader = datasets.train_dataloader()
val_dataloader = datasets.val_dataloader()
logger.info(f"Loading pretrained model: {cfg.TRAIN.PRETRAINED}")
state_dict = torch.load(cfg.TRAIN.PRETRAINED, map_location="cpu")["state_dict"]
lcm_key = 'denoiser.time_embedding.cond_proj.weight'
is_lcm = False
if lcm_key in state_dict:
is_lcm = True
time_cond_proj_dim = state_dict[lcm_key].shape[1]
cfg.model.denoiser.params.time_cond_proj_dim = time_cond_proj_dim
logger.info(f'Is LCM: {is_lcm}')
model = MLD(cfg, datasets)
logger.info(model.load_state_dict(state_dict, strict=False))
logger.info(model.controlnet.load_state_dict(model.denoiser.state_dict(), strict=False))
model.vae.requires_grad_(False)
model.text_encoder.requires_grad_(False)
model.denoiser.requires_grad_(False)
model.vae.eval()
model.text_encoder.eval()
model.denoiser.eval()
model.to(device)
controlnet_params = list(model.controlnet.parameters())
traj_encoder_params = list(model.traj_encoder.parameters())
params = controlnet_params + traj_encoder_params
params_to_optimize = [{'params': controlnet_params, 'lr': cfg.TRAIN.learning_rate},
{'params': traj_encoder_params, 'lr': cfg.TRAIN.learning_rate_spatial}]
logger.info("learning_rate: {}, learning_rate_spatial: {}".
format(cfg.TRAIN.learning_rate, cfg.TRAIN.learning_rate_spatial))
optimizer = torch.optim.AdamW(
params_to_optimize,
betas=(cfg.TRAIN.adam_beta1, cfg.TRAIN.adam_beta2),
weight_decay=cfg.TRAIN.adam_weight_decay,
eps=cfg.TRAIN.adam_epsilon)
if cfg.TRAIN.max_train_steps == -1:
assert cfg.TRAIN.max_train_epochs != -1
cfg.TRAIN.max_train_steps = cfg.TRAIN.max_train_epochs * len(train_dataloader)
if cfg.TRAIN.checkpointing_steps == -1:
assert cfg.TRAIN.checkpointing_epochs != -1
cfg.TRAIN.checkpointing_steps = cfg.TRAIN.checkpointing_epochs * len(train_dataloader)
if cfg.TRAIN.validation_steps == -1:
assert cfg.TRAIN.validation_epochs != -1
cfg.TRAIN.validation_steps = cfg.TRAIN.validation_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
cfg.TRAIN.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=cfg.TRAIN.lr_warmup_steps,
num_training_steps=cfg.TRAIN.max_train_steps)
# Train!
logger.info("***** Running training *****")
logging.info(f" Num examples = {len(train_dataloader.dataset)}")
logging.info(f" Num Epochs = {cfg.TRAIN.max_train_epochs}")
logging.info(f" Instantaneous batch size per device = {cfg.TRAIN.BATCH_SIZE}")
logging.info(f" Total optimization steps = {cfg.TRAIN.max_train_steps}")
global_step = 0
first_epoch = 0
progress_bar = tqdm(range(0, cfg.TRAIN.max_train_steps), desc="Steps")
@torch.no_grad()
def validation():
model.controlnet.eval()
model.traj_encoder.eval()
for val_batch in tqdm(val_dataloader):
val_batch = move_batch_to_device(val_batch, device)
model.allsplit_step('test', val_batch)
metrics = model.allsplit_epoch_end()
min_val_km = metrics['Metrics/kps_mean_err(m)']
min_val_tj = metrics['Metrics/traj_fail_50cm']
print_table(f'Metrics@Step-{global_step}', metrics)
for k, v in metrics.items():
writer.add_scalar(k, v, global_step=global_step)
model.controlnet.train()
model.traj_encoder.train()
return min_val_km, min_val_tj
min_km, min_tj = validation()
for epoch in range(first_epoch, cfg.TRAIN.max_train_epochs):
for step, batch in enumerate(train_dataloader):
batch = move_batch_to_device(batch, device)
loss_dict = model.allsplit_step('train', batch)
diff_loss = loss_dict['diff_loss']
cond_loss = loss_dict['cond_loss']
rot_loss = loss_dict['rot_loss']
loss = diff_loss + cond_loss + rot_loss
loss.backward()
torch.nn.utils.clip_grad_norm_(params, cfg.TRAIN.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
progress_bar.update(1)
global_step += 1
if global_step % cfg.TRAIN.checkpointing_steps == 0:
save_path = os.path.join(output_dir, 'checkpoints', f"checkpoint-{global_step}.ckpt")
ckpt = dict(state_dict=model.state_dict())
model.on_save_checkpoint(ckpt)
torch.save(ckpt, save_path)
logger.info(f"Saved state to {save_path}")
if global_step % cfg.TRAIN.validation_steps == 0:
cur_km, cur_tj = validation()
if cur_km < min_km:
min_km = cur_km
save_path = os.path.join(output_dir, 'checkpoints', f"checkpoint-{global_step}-km-{round(cur_km, 3)}.ckpt")
ckpt = dict(state_dict=model.state_dict())
model.on_save_checkpoint(ckpt)
torch.save(ckpt, save_path)
logger.info(f"Saved state to {save_path} with km:{round(cur_km, 3)}")
if cur_tj < min_tj:
min_tj = cur_tj
save_path = os.path.join(output_dir, 'checkpoints', f"checkpoint-{global_step}-tj-{round(cur_tj, 3)}.ckpt")
ckpt = dict(state_dict=model.state_dict())
model.on_save_checkpoint(ckpt)
torch.save(ckpt, save_path)
logger.info(f"Saved state to {save_path} with tj:{round(cur_tj, 3)}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0],
"diff_loss": diff_loss.detach().item(), 'cond_loss': cond_loss.detach().item(), 'rot_loss': rot_loss.detach().item()}
progress_bar.set_postfix(**logs)
for k, v in logs.items():
writer.add_scalar(k, v, global_step=global_step)
if global_step >= cfg.TRAIN.max_train_steps:
break
save_path = os.path.join(output_dir, 'checkpoints', f"checkpoint-last.ckpt")
ckpt = dict(state_dict=model.state_dict())
model.on_save_checkpoint(ckpt)
torch.save(ckpt, save_path)
if __name__ == "__main__":
main()
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