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

apply for body and curv

Browse Source
This commit is contained in:
luni
2023-01-25 19:13:18 +00:00
parent 8514032837
commit 655c70413b
7 changed files with 125 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files
aaa_helpers.txt
+2
View File
@@ -1,4 +1,6 @@
nohup python auto_encoder_static.py --name="lhunor_ae_static_r7" > nohup/review/ae_static_r7.out 2>&1 &
nohup python auto_encoder_body.py --name="lhunor_ae_body_r7" > nohup/review/ae_body_r7.out 2>&1 &
nohup python auto_encoder_curv.py --name="lhunor_ae_curv_r7" > nohup/review/ae_curv_r7.out 2>&1 &
one final convolutional layer for smoothing, on the final coordinates
auto_encoder_body.py
+7 -4
View File
@@ -1,6 +1,6 @@
import os
import sys
from configs.config_ae_body_post import config
from configs.review.config_ae_body import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
@@ -34,7 +34,7 @@ from models.cond_encoder import AutoEncoderNew as AutoEncoder
def init_wandb():
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp', tags=config['tags'])
artifact = wandb.Artifact('code', type='code')
artifact.add_file('auto_encoder_body.py')
artifact.add_file('losses.py')
@@ -42,7 +42,7 @@ 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.')]
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.review.')]
for config_file_name in config_file_names:
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
run.log_artifact(artifact)
@@ -137,7 +137,10 @@ def train():
if step == 0:
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
wandb.log(to_log, step=step)
if step % 1000:
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)
auto_encoder_curv.py
+5 -5
View File
@@ -1,6 +1,6 @@
import os
import sys
from configs.config_ae_curv_post import config
from configs.review.config_ae_curv import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
@@ -37,7 +37,7 @@ from models.cond_encoder import AutoEncoderNew as AutoEncoder
testing = False
def init_wandb():
if not testing:
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp/')
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp', tags=config['tags'])
artifact = wandb.Artifact('code', type='code')
artifact.add_file('auto_encoder_curv.py')
artifact.add_file('losses.py')
@@ -45,7 +45,7 @@ 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.')]
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.review.')]
for config_file_name in config_file_names:
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
run.log_artifact(artifact)
@@ -178,7 +178,7 @@ def train():
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
wandb.log(to_log, step=step)
print(step)
# print(step)
if step % 10000 == 0:
manager_enc.save(checkpoint_number=step)
@@ -206,7 +206,7 @@ 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())
configs/review/config_ae_body.py
+51
View File
@@ -0,0 +1,51 @@
config = dict()
config['batch_size'] = 4
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['use_filled'] = False
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['remove_global_orientation'] = True
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
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'] = 16
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
config['cond_ae']['activation'] = 'tanh'
config['gpu'] = '4'
config['lr'] = 0.0001
config['name'] = 'body_r7'
config['notes'] = 'resnet18'
config['tags'] = ['review_7']
config['cond_ae']['upscale_output'] = False
config['pretrained_model'] = ''
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (3, 512, 256,),
'mesh_body_posed': (config['smpl_res'], 3,),
}
config['signature_static'] = {
'mesh_mask': (config['smpl_res'],),
}
configs/review/config_ae_curv.py
+50
View File
@@ -0,0 +1,50 @@
config = dict()
config['batch_size'] = 4
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['use_filled'] = False
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['remove_global_orientation'] = True
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = True
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'] = 40
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
config['cond_ae']['activation'] = 'tanh'
config['gpu'] = '3'
config['lr'] = 0.001
config['name'] = 'curv_r7'
config['notes'] = 'resnet18'
config['tags'] = ['review_7']
config['cond_ae']['upscale_output'] = False
config['pretrained_model'] = ''
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'uv_curvature': (512, 256, 7),
'mesh_curvature': (config['smpl_res'])
}
config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
'uv_mask': (512, 256,),
}
models/base.py
+1 -3
View File
@@ -152,7 +152,6 @@ class AEEncoderNew(tf.keras.Model):
def __init__(self, depths, dims, kernel_size=7, activation='tanh'):
super(AEEncoderNew, self).__init__()
# self.encoder = ConvNeXt(depths=depths, dims=dims, include_top=False, kernel_size=kernel_size)
self.encoder = resnet_18()
self.act = tf.keras.layers.Activation(activation)
@@ -180,10 +179,9 @@ class DecoderNew(tf.keras.Model):
self.is_label = is_label
if not is_label:
# self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size)
self.decoder = resnet_18(reversed=True)
else:
self.decoder = ConvNeXtReverseLabels(depths=depths, dims=dims, nr_of_classes=7)
self.decoder = resnet_18(reversed=True, labels=6)
def call(self, inputs, training):
models/resnet.py
+9 -5
View File
@@ -116,9 +116,10 @@ def make_bottleneck_layer(filter_num, blocks, stride=1):
class ResNetTypeI(tf.keras.Model):
def __init__(self, layer_params, reversed=False):
def __init__(self, layer_params, reversed=False, labels=None):
super(ResNetTypeI, self).__init__()
self.reversed = reversed
self.labels = labels
if not reversed:
self.conv1 = tf.keras.layers.Conv2D(filters=64,
@@ -130,7 +131,7 @@ class ResNetTypeI(tf.keras.Model):
strides=2,
padding="same")
else:
self.conv1 = tf.keras.layers.Conv2DTranspose(filters=3,
self.conv1 = tf.keras.layers.Conv2DTranspose(filters=3 if labels is None else labels,
kernel_size=(7, 7),
strides=2,
padding="same")
@@ -177,7 +178,10 @@ class ResNetTypeI(tf.keras.Model):
x = self.bn1(x, training=training)
# x = tf.nn.relu(x)
x = self.conv1(x)
x = tf.nn.tanh(x)
if self.labels is None:
x = tf.nn.tanh(x)
# else:
# x = tf.nn.softmax(x)
return x
@@ -224,8 +228,8 @@ class ResNetTypeII(tf.keras.Model):
return output
def resnet_18(reversed=False):
return ResNetTypeI(layer_params=[2, 2, 2, 2], reversed=reversed)
def resnet_18(reversed=False, labels=None):
return ResNetTypeI(layer_params=[2, 2, 2, 2], reversed=reversed, labels=labels)
def resnet_34():