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import torch
import argparse
import copy
from glob import glob
from torch.utils.data import DataLoader, random_split
from tensorboardX import SummaryWriter
from data.dataloader_nba import NBADatasetMinMax as NBADatasetMinMax
from data.dataloader_nba import seq_collate_nba, seq_collate_imle_train
from utils.config import Config
from utils.utils import back_up_code_git, set_random_seed, log_config_to_file
from models.flow_matching import FlowMatcher
from models.imle import IMLE
from models.backbone import IMLETransformer
from trainer.imle_trainers import IMLETrainer
def parse_config():
"""
Parse the command line arguments and return the configuration options.
"""
parser = argparse.ArgumentParser()
# Basic configuration
parser.add_argument('--ckpt_path', type=str, default=None, help='Path to the checkpoint to load the model from.')
parser.add_argument('--cfg', default='auto', type=str, help="Config file path")
parser.add_argument('--exp', default='', type=str, help='Experiment description for each run, name of the saving folder.')
parser.add_argument('--save_samples', default=False, action='store_true', help='Save the samples during evaluation.')
parser.add_argument('--eval_on_train', default=False, action='store_true', help='Evaluate the model on the training set.')
# Data configuration
parser.add_argument('--batch_size', default=None, type=int, help='Override the batch size in the config file.')
parser.add_argument('--data_dir', type=str, default='./data/nba', help='Directory where the data is stored.')
parser.add_argument('--n_train', type=int, default=32500, help='Number training scenes used.')
parser.add_argument('--n_test', type=int, default=12500, help='Number testing scenes used.')
parser.add_argument('--rotate', default=False, action='store_true', help='Whether to rotate the data to canonical x-axis or not.')
parser.add_argument('--data_norm', default='min_max', choices=['min_max', 'sqrt'], help='Normalization method for the data.')
# Reproducibility configuration
parser.add_argument('--fix_random_seed', action='store_true', default=False, help='fix random seed for reproducibility')
parser.add_argument('--seed', type=int, default=42, help='Set the random seed to split the testing set for training evaluation.')
return parser.parse_args()
def init_basics(args):
"""
Init the basic configurations for the experiment.
"""
"""Load the config file"""
result_dir = os.path.abspath(os.path.join(args.ckpt_path, '../../'))
if args.cfg == 'auto':
yml_ls = glob(result_dir+'/*.yml')
assert len(yml_ls) >= 1, 'At least one config file should be found in the directory.'
yml_path = [f for f in yml_ls if '_updated.yml' in os.path.basename(f)][0]
args.cfg = yml_path
cfg = Config(args.cfg, f'{args.exp}', train_mode=False)
tag = '_'
### Update data configuration ###
def _update_data_params(args, cfg, tag):
if args.n_train != 32500:
tag += f'_subset{args.n_train}'
return cfg, tag
cfg, tag = _update_data_params(args, cfg, tag)
def _update_optimization_params(args, cfg, tag):
if args.batch_size is not None:
# override the batch size
cfg.train_batch_size = args.batch_size
cfg.test_batch_size = args.batch_size
return cfg, tag
cfg, tag = _update_optimization_params(args, cfg, tag)
### voila, create the saving directory ###
tag += '_train_set' if args.eval_on_train else '_test_set'
tag = tag.replace('__', '_')
cfg.device = 'cuda' if torch.cuda.is_available() else 'cpu'
logger = cfg.create_dirs(tag_suffix=tag)
"""fix random seed"""
if args.fix_random_seed:
set_random_seed(args.seed)
"""set up tensorboard and text log"""
tb_dir = os.path.abspath(os.path.join(cfg.log_dir, '../tb_eval'))
os.makedirs(tb_dir, exist_ok=True)
tb_log = SummaryWriter(log_dir=tb_dir)
"""print the config file"""
log_config_to_file(cfg.yml_dict, logger=logger)
return cfg, logger, tb_log
def build_data_loader(cfg, args):
"""
Build the data loader for the NBA dataset.
"""
train_dset = NBADatasetMinMax(
data_dir=args.data_dir,
obs_len=cfg.past_frames,
pred_len=cfg.future_frames,
training=True,
num_scenes=args.n_train,
overfit=False,
cfg=cfg,
rotate=args.rotate,
data_norm=args.data_norm,
imle=True)
train_loader = DataLoader(
train_dset,
batch_size=cfg.train_batch_size,
shuffle=True,
num_workers=0,
collate_fn=seq_collate_imle_train,
pin_memory=True)
test_dset = NBADatasetMinMax(
data_dir=args.data_dir,
obs_len=cfg.past_frames,
pred_len=cfg.future_frames,
training=False,
overfit=False,
test_scenes=args.n_test,
cfg=cfg,
rotate=args.rotate,
data_norm=args.data_norm,
imle=False)
test_loader = DataLoader(
test_dset,
batch_size=cfg.test_batch_size, ### change it from 500
shuffle=False,
num_workers=4,
collate_fn=seq_collate_nba,
pin_memory=True)
return train_loader, test_loader
def build_network(cfg, args, logger):
"""
Build the network for the denoising model.
"""
model = IMLETransformer(
model_config=cfg.MODEL,
logger=logger,
config=cfg,
)
imle_model = IMLE(
cfg=cfg,
model=model,
logger=logger,
)
return imle_model
def main():
"""
Main function to train the model.
"""
"""Init everything"""
args = parse_config()
cfg, logger, tb_log = init_basics(args)
train_loader, test_loader = build_data_loader(cfg, args)
imle_model = build_network(cfg, args, logger)
"""Train or evaluate the model"""
trainer = IMLETrainer(
cfg,
imle_model,
train_loader,
test_loader,
tb_log=tb_log,
logger=logger,
gradient_accumulate_every=1,
ema_decay = 0.995,
ema_update_every = 1,
save_samples=args.save_samples
) ### grid search
trainer.test(mode='best', eval_on_train=args.eval_on_train)
if __name__ == "__main__":
main()
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