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luni
2023-01-25 12:25:59 +00:00
parent 1bf224841f
commit 7470ca9fb6
139 changed files with 82069 additions and 811 deletions
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auto_encoder_static.py
+35 -10
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@@ -1,4 +1,5 @@
import os
import sys
from configs.config_ae_static import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
@@ -32,7 +33,7 @@ else:
from tf_utils import check_tf_version, allow_memory_growth
from dataset import DatasetFactory, StaticDataset
from custom_utils.custom_utils import batch_to_dict, plot_3d
from models.cond_encoder import AutoEncoder
from models.cond_encoder import AutoEncoderNew as AutoEncoder
def init_wandb():
@@ -40,12 +41,13 @@ def init_wandb():
artifact = wandb.Artifact('code', type='code')
artifact.add_file('auto_encoder_static.py')
artifact.add_file('losses.py')
artifact.add_file('configs/config_base.py')
artifact.add_file('configs/config_ae_static.py')
artifact.add_file('models/base.py')
artifact.add_file('models/cond_encoder.py')
artifact.add_file('dataset.py')
artifact.add_file('convnext/models/convnext_tf.py')
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
for config_file_name in config_file_names:
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
run.log_artifact(artifact)
uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@@ -184,7 +186,7 @@ def test():
if __name__ == '__main__':
init_wandb()
allow_memory_growth()
# allow_memory_growth()
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str)
args = vars(parser.parse_args())
@@ -198,10 +200,33 @@ if __name__ == '__main__':
factory = DatasetFactory()
dataset_train = factory.get_dataset(folder=config['train_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=config['dataset_in_memory'], is_cape=config['is_cape'])
static_dataset_train = StaticDataset(folder=config['train_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=True, is_cape=config['is_cape'])
dataset_valid = factory.get_dataset(folder=config['valid_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=config['dataset_in_memory'], is_cape=config['is_cape'])
static_dataset_valid = StaticDataset(folder=config['valid_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=True, is_cape=config['is_cape'])
dataset_train = factory.get_dataset(folder=config['train_dir'],
batch_size=config['batch_size'],
signature=config['signature_dynamic'],
resolutions=[config['resolution']],
in_memory=config['dataset_in_memory'],
use_no_orient=config['remove_global_orientation'],
frames_file=config['frames_file'] if 'frames_file' in config.keys() else None,
is_cape=config['is_cape'])
static_dataset_train = StaticDataset(folder=config['train_dir'],
signature=config['signature_static'],
resolutions=[config['resolution']],
in_memory=True,
is_cape=config['is_cape'])
dataset_valid = factory.get_dataset(folder=config['valid_dir'],
batch_size=config['batch_size'],
signature=config['signature_dynamic'],
resolutions=[config['resolution']],
in_memory=config['dataset_in_memory'],
use_no_orient=config['remove_global_orientation'],
frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None,
is_cape=config['is_cape'])
static_dataset_valid = StaticDataset(folder=config['valid_dir'],
signature=config['signature_static'],
resolutions=[config['resolution']],
in_memory=True,
is_cape=config['is_cape'])
model : AutoEncoder = AutoEncoder(config['cond_ae'])
optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
@@ -211,9 +236,9 @@ if __name__ == '__main__':
load_model(model.decoder, config['pretrained_model'] + '_dec')
ckpt_enc = tf.train.Checkpoint(model=model.encoder)
manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/' + config['name'] + '_enc', max_to_keep=20)
manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/post/' + config['name'] + '_enc', max_to_keep=20)
ckpt_dec = tf.train.Checkpoint(model=model.decoder)
manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/' + config['name'] + '_dec', max_to_keep=20)
manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/post/' + config['name'] + '_dec', max_to_keep=20)
# test()
train()