luni/cvpr
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

commit all

Browse Source
This commit is contained in:
luni
2023-01-25 12:25:59 +00:00
parent 1bf224841f
commit 7470ca9fb6
139 changed files with 82069 additions and 811 deletions
Download Patch File Download Diff File Expand all files Collapse all files
auto_encoder_body.py
+35 -10
View File
@@ -1,5 +1,6 @@
import os
from configs.config_ae_body import config
import sys
from configs.config_ae_body_post import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
@@ -29,7 +30,7 @@ from tf_utils import check_tf_version, allow_memory_growth
from losses import edge_loss, uv_mask_loss
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():
@@ -37,12 +38,13 @@ def init_wandb():
artifact = wandb.Artifact('code', type='code')
artifact.add_file('auto_encoder_body.py')
artifact.add_file('losses.py')
artifact.add_file('configs/config_base.py')
artifact.add_file('configs/config_ae_body.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_smpl_orig=config['is_cape'], is_skirt=config['is_skirt'])
@@ -164,10 +166,33 @@ 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(config['cond_ae'])
optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
@@ -176,8 +201,8 @@ if __name__ == '__main__':
# load_model(model.decoder, 'checkpoints/body_1_skirt_2_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)
train()