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training orig with 10p and curv_10

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luni
2023-01-28 13:08:52 +00:00
parent 0543657e4e
commit af336370d4
3 changed files with 32 additions and 34 deletions
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aaa_helpers.txt
+2
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@@ -1,3 +1,5 @@
nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
one final convolutional layer for smoothing, on the final coordinates
skirts!!!!
configs/config_abl_all_10p_curv_10_r2.py
+27 -31
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@@ -57,6 +57,7 @@ if config['is_cape']:
config['pretrained_model'] = ''
config['latent_dim'] = 128
###############
config['lr'] = 0.0001# 0.0001
@@ -88,42 +89,37 @@ config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.001
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256, 64]
config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae_static'] = dict()
config['cond_ae_static']['is_label'] = False
config['cond_ae_static']['kernel_size'] = 7
config['cond_ae_static']['bottleneck_size'] = 8
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 9, 3]
config['cond_ae_static']['encoder_dims'] = [16, 32, 64, 64, config['cond_ae_static']['bottleneck_size']]
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
config['cond_ae_static']['activation'] = 'tanh'
config['cond_ae_curv'] = dict()
config['cond_ae_curv']['is_label'] = True
config['cond_ae_curv']['kernel_size'] = 7
config['cond_ae_curv']['bottleneck_size'] = 40
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
config['cond_ae_curv']['activation'] = 'tanh'
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['is_label'] = False
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['bottleneck_size'] = 16
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
config['cond_ae_body']['activation'] = 'tanh'
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
cvae.py
+3 -3
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@@ -41,7 +41,7 @@ from losses import collision_loss as collision_penalty
from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
from dataset import DatasetFactory, StaticDataset
from custom_utils.smpl import SMPL
from models.cvae_model import CVAENew as CVAE
from models.cvae_model import CVAE
def init_wandb():
@@ -73,7 +73,7 @@ def kl_loss(mean, logvar):
# 3d loss on posed !!!!!!!!!!!!
# @tf.function
@tf.function
def train_step(batch):
with tf.GradientTape() as tape:
@@ -476,7 +476,7 @@ def test():
if __name__ == '__main__':
# init_wandb()
init_wandb()
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str)
args = vars(parser.parse_args())