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adapt ae_body to cape

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luni
2023-05-21 19:45:24 +02:00
parent 75df8a09cc
commit 70162cf8ac
3 changed files with 108 additions and 24 deletions
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scripts/ae_body_train.py
+17 -16
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@@ -8,14 +8,16 @@ import importlib
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str)
parser.add_argument('--config', type=str)
parser.add_argument('--debug', action='store_true')
args = vars(parser.parse_args())
config_module = importlib.import_module(args['config'])
config = config_module.config
# from configs.ae_body_1 import config
# from configs.ae_body_cape_m import config
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
if args['debug']:
os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
import time
import wandb
@@ -155,37 +157,31 @@ def train():
if step == 0:
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
wandb.log(to_log, step=step)
if not args['debug']:
wandb.log(to_log, step=step)
train_accumulator.reset()
valid_accumulator.reset()
print(step)
if step % 10000 == 0:
ckpt_saver_enc(model.encoder, step, optimizer)
ckpt_saver_dec(model.decoder, step, optimizer)
ckpt_saver_enc(model.encoder, step, optimizer, scaler=None)
ckpt_saver_dec(model.decoder, step, optimizer, scaler=None)
step += 1
if __name__ == '__main__':
# init_wandb()
# device = "cuda" if torch.cuda.is_available() else "cpu"
if not args['debug']:
init_wandb()
# parser = argparse.ArgumentParser(description='')
# parser.add_argument('--name', type=str)
# args = vars(parser.parse_args())
resolution_modifier = ''
if config['resolution'] != 512:
resolution_modifier = '_' + str(config['resolution'])
skirt_modifier = '_skirt' if config['is_skirt'] else ''
uv_mask_body = np.load('custom_utils/mask_256x512' + ('_skirt' if config['is_skirt'] else '') + '.npy')
uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2)
uv_mask_body = torch.from_numpy(uv_mask_body).float().to(config['device'])
dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file=config['train_frames'], use_cache=config['dataset_in_memory'])
dataset_valid = Dataset(config['valid_dir'], config['signature'], device=config['device'], frames_file=config['valid_frames'], use_cache=config['dataset_in_memory'])
dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file=config['train_frames'], use_cache=config['dataset_in_memory'], is_cape=config['is_cape'])
dataset_valid = Dataset(config['valid_dir'], config['signature'], device=config['device'], frames_file=config['valid_frames'], use_cache=config['dataset_in_memory'], is_cape=config['is_cape'])
train_dataloader = DataLoader(dataset_train, batch_size=4, drop_last=True, shuffle=True)
valid_dataloader = DataLoader(dataset_valid, batch_size=4, drop_last=True, shuffle=True)
train_accumulator = Accumulator()
@@ -206,6 +202,11 @@ if __name__ == '__main__':
run_name = config['name']
print(f'started training: {run_name}')
if not args['debug']:
# model = torch.compile(model)
wandb.watch(model, log='all', log_freq=1000, log_graph=True)
train()