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resnet condition encoders

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luni
2023-02-27 17:41:47 +00:00
parent bb7128f589
commit aa3defbf07
10 changed files with 151 additions and 111 deletions
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auto_encoder_body.py
+31 -21
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@@ -1,6 +1,6 @@
import os
import sys
from configs.review.config_ae_body import config
from configs.iccv.config_ae_body import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
@@ -31,10 +31,11 @@ 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 AutoEncoderNew as AutoEncoder
from custom_utils.custom_utils import Accumulator
def init_wandb():
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp', tags=config['tags'])
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
artifact = wandb.Artifact('code', type='code')
artifact.add_file('auto_encoder_body.py')
artifact.add_file('losses.py')
@@ -42,9 +43,11 @@ def init_wandb():
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.review.')]
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')
file_name = '/'.join(config_file_name.split('.')) + '.py'
if os.path.exists(file_name):
artifact.add_file(file_name)
run.log_artifact(artifact)
uv2mesh = UV2Mesh(res=256, is_smpl_orig=config['is_cape'], is_skirt=config['is_skirt'])
@@ -60,7 +63,8 @@ def train_step(batch):
e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, tf.ones((pred.shape[0], 3638 if config['is_cape'] else 14475))))
l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_body_posed' + resolution_modifier] - pred))), axis=1))
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=2, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3))
loss = 1e3 * (1 - ms_ssim)
@@ -80,7 +84,8 @@ def valid_step(batch):
e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, tf.ones((pred.shape[0], 3638 if config['is_cape'] else 14475))))
l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_body_posed' + resolution_modifier] - pred))), axis=1))
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=2, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3))
loss = 1e3 * (1 - ms_ssim)
@@ -124,24 +129,27 @@ def train():
for batch in dataset_train:
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
train_loss = train_step(batch)
train_accumulator.update(train_loss)
data_valid = valid_iterator.get_next()
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
valid_loss = valid_step(data_valid)
if step % 50 == 0:
data_valid = valid_iterator.get_next()
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
valid_loss = valid_step(data_valid)
valid_accumulator.update(valid_loss)
to_log = { **train_loss, **valid_loss, **{ 'current_step': step} }
if step % 5000 == 0 or (step < 5000 and step % 500 == 0):
train_figs = plot_everything(batch, 'train')
valid_figs = plot_everything(data_valid, 'valid')
to_log = { **to_log, **train_figs, **valid_figs }
if step == 0:
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
train_figs = plot_everything(batch, 'train')
valid_figs = plot_everything(data_valid, 'valid')
to_log = { **to_log, **train_figs, **valid_figs }
if step == 0:
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
wandb.log(to_log, step=step)
train_accumulator.reset()
valid_accumulator.reset()
if step % 1000 == 0:
wandb.log(to_log, step=step, commit=True)
else:
wandb.log(to_log, step=step, commit=False)
if step % 10000 == 0:
manager_enc.save(checkpoint_number=step)
manager_dec.save(checkpoint_number=step)
@@ -207,5 +215,7 @@ if __name__ == '__main__':
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/post/' + config['name'] + '_dec', max_to_keep=20)
train_accumulator = Accumulator()
valid_accumulator = Accumulator()
train()