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configs save

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luni
2023-07-02 01:06:14 +02:00
parent 05fc297808
commit d66e6d5a77
9 changed files with 555 additions and 12 deletions
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configs/iccvw/ae_body_1_10p.py
+78
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from utils.convnext_utils import CNBlockConfig
config = dict()
config['gpu'] = '5'
config['name'] = 'body_iccvw_1_10p'
config['notes'] = ' '
config['tags'] = ['torch', 'body']
config['script'] = 'scripts/ae_body_train.py'
config['pretrained_model'] = ''
config['batch_size'] = 4
config['train_dir'] = '/data-local/127/cloth3d_processed'
config['valid_dir'] = '/data-local/127/cloth3d_processed'
config['test_dir'] = '/data-net/dataset/test_ready'
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['remove_global_orientation'] = True
config['device'] = 'cuda'
if config['is_skirt']:
config['train_frames'] = 'frames_train_skirt_10p.txt'
config['valid_frames'] = 'frames_valid_skirt.txt'
else:
config['train_frames'] = 'frames_train_10p.txt'
config['valid_frames'] = 'frames_valid.txt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['bottleneck_scale'] = 2
config['cond_ae'] = dict()
config['cond_ae']['device'] = config['device']
config['cond_ae']['is_label'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 16 * config['bottleneck_scale']
block_setting = [
CNBlockConfig(16, 32, 3),
CNBlockConfig(32, 64, 3),
CNBlockConfig(64, 128, 9),
CNBlockConfig(128, config['cond_ae']['bottleneck_size'], 9),
CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
]
block_setting_reverse = [
CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
CNBlockConfig(128, 64, 9),
CNBlockConfig(64, 32, 9),
CNBlockConfig(32, 16, 3),
CNBlockConfig(16, 3, 3),
# CNBlockConfig(3, 3, 3),
]
config['cond_ae']['encoder_block_settings'] = block_setting
config['cond_ae']['decoder_block_settings'] = block_setting_reverse
config['cond_ae']['activation'] = 'tanh'
config['lr'] = 0.0001
config['cond_ae']['upscale_output'] = False
config['signature'] = { 'id',
'frame_nr',
'pose_shape',
'uv_body_posed',
'mesh_mask',
'mesh_body_posed',
}
configs/iccvw/ae_body_1_25p.py
+78
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from utils.convnext_utils import CNBlockConfig
config = dict()
config['gpu'] = '5'
config['name'] = 'body_iccvw_1_25p'
config['notes'] = ' '
config['tags'] = ['torch', 'body']
config['script'] = 'scripts/ae_body_train.py'
config['pretrained_model'] = ''
config['batch_size'] = 4
config['train_dir'] = '/data-local/127/cloth3d_processed'
config['valid_dir'] = '/data-local/127/cloth3d_processed'
config['test_dir'] = '/data-net/dataset/test_ready'
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['remove_global_orientation'] = True
config['device'] = 'cuda'
if config['is_skirt']:
config['train_frames'] = 'frames_train_skirt_25p.txt'
config['valid_frames'] = 'frames_valid_skirt.txt'
else:
config['train_frames'] = 'frames_train_25p.txt'
config['valid_frames'] = 'frames_valid.txt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['bottleneck_scale'] = 2
config['cond_ae'] = dict()
config['cond_ae']['device'] = config['device']
config['cond_ae']['is_label'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 16 * config['bottleneck_scale']
block_setting = [
CNBlockConfig(16, 32, 3),
CNBlockConfig(32, 64, 3),
CNBlockConfig(64, 128, 9),
CNBlockConfig(128, config['cond_ae']['bottleneck_size'], 9),
CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
]
block_setting_reverse = [
CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
CNBlockConfig(128, 64, 9),
CNBlockConfig(64, 32, 9),
CNBlockConfig(32, 16, 3),
CNBlockConfig(16, 3, 3),
# CNBlockConfig(3, 3, 3),
]
config['cond_ae']['encoder_block_settings'] = block_setting
config['cond_ae']['decoder_block_settings'] = block_setting_reverse
config['cond_ae']['activation'] = 'tanh'
config['lr'] = 0.0001
config['cond_ae']['upscale_output'] = False
config['signature'] = { 'id',
'frame_nr',
'pose_shape',
'uv_body_posed',
'mesh_mask',
'mesh_body_posed',
}
configs/iccvw/ae_body_1_50p.py
+78
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@@ -0,0 +1,78 @@
from utils.convnext_utils import CNBlockConfig
config = dict()
config['gpu'] = '5'
config['name'] = 'body_iccvw_1_50p'
config['notes'] = ' '
config['tags'] = ['torch', 'body']
config['script'] = 'scripts/ae_body_train.py'
config['pretrained_model'] = ''
config['batch_size'] = 4
config['train_dir'] = '/data-local/127/cloth3d_processed'
config['valid_dir'] = '/data-local/127/cloth3d_processed'
config['test_dir'] = '/data-net/dataset/test_ready'
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['remove_global_orientation'] = True
config['device'] = 'cuda'
if config['is_skirt']:
config['train_frames'] = 'frames_train_skirt_50p.txt'
config['valid_frames'] = 'frames_valid_skirt.txt'
else:
config['train_frames'] = 'frames_train_50p.txt'
config['valid_frames'] = 'frames_valid.txt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['bottleneck_scale'] = 2
config['cond_ae'] = dict()
config['cond_ae']['device'] = config['device']
config['cond_ae']['is_label'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 16 * config['bottleneck_scale']
block_setting = [
CNBlockConfig(16, 32, 3),
CNBlockConfig(32, 64, 3),
CNBlockConfig(64, 128, 9),
CNBlockConfig(128, config['cond_ae']['bottleneck_size'], 9),
CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
]
block_setting_reverse = [
CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
CNBlockConfig(128, 64, 9),
CNBlockConfig(64, 32, 9),
CNBlockConfig(32, 16, 3),
CNBlockConfig(16, 3, 3),
# CNBlockConfig(3, 3, 3),
]
config['cond_ae']['encoder_block_settings'] = block_setting
config['cond_ae']['decoder_block_settings'] = block_setting_reverse
config['cond_ae']['activation'] = 'tanh'
config['lr'] = 0.0001
config['cond_ae']['upscale_output'] = False
config['signature'] = { 'id',
'frame_nr',
'pose_shape',
'uv_body_posed',
'mesh_mask',
'mesh_body_posed',
}
configs/iccvw/ae_body_1_skirt.py
+78
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@@ -0,0 +1,78 @@
from utils.convnext_utils import CNBlockConfig
config = dict()
config['gpu'] = '1'
config['name'] = 'body_iccvw_1_skirt'
config['notes'] = ' '
config['tags'] = ['torch', 'body']
config['script'] = 'scripts/ae_body_train.py'
config['pretrained_model'] = ''
config['batch_size'] = 4
config['train_dir'] = '/data-local/127/cloth3d_processed'
config['valid_dir'] = '/data-local/127/cloth3d_processed'
config['test_dir'] = '/data-net/dataset/test_ready'
config['resolution'] = 512
config['is_skirt'] = True
config['dataset_in_memory'] = False
config['is_cape'] = False
config['remove_global_orientation'] = True
config['device'] = 'cuda'
if config['is_skirt']:
config['train_frames'] = 'frames_train_skirt.txt'
config['valid_frames'] = 'frames_valid_skirt.txt'
else:
config['train_frames'] = 'frames_train.txt'
config['valid_frames'] = 'frames_valid.txt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['bottleneck_scale'] = 2
config['cond_ae'] = dict()
config['cond_ae']['device'] = config['device']
config['cond_ae']['is_label'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 16 * config['bottleneck_scale']
block_setting = [
CNBlockConfig(16, 32, 3),
CNBlockConfig(32, 64, 3),
CNBlockConfig(64, 128, 9),
CNBlockConfig(128, config['cond_ae']['bottleneck_size'], 9),
CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
]
block_setting_reverse = [
CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
CNBlockConfig(128, 64, 9),
CNBlockConfig(64, 32, 9),
CNBlockConfig(32, 16, 3),
CNBlockConfig(16, 3, 3),
# CNBlockConfig(3, 3, 3),
]
config['cond_ae']['encoder_block_settings'] = block_setting
config['cond_ae']['decoder_block_settings'] = block_setting_reverse
config['cond_ae']['activation'] = 'tanh'
config['lr'] = 0.0001
config['cond_ae']['upscale_output'] = False
config['signature'] = { 'id',
'frame_nr',
'pose_shape',
'uv_body_posed',
'mesh_mask',
'mesh_body_posed',
}
configs/iccvw/ae_norm_1_skirt.py
+78
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@@ -0,0 +1,78 @@
from utils.convnext_utils import CNBlockConfig
config = dict()
config['gpu'] = '4'
config['name'] = 'norm_iccvw_1_skirt'
config['notes'] = ' '
config['tags'] = ['torch', 'norm']
config['script'] = 'scripts/ae_norm_train.py'
config['pretrained_model'] = ''
config['batch_size'] = 4
config['train_dir'] = '/data-local/127/cloth3d_processed'
config['valid_dir'] = '/data-local/127/cloth3d_processed'
config['test_dir'] = '/data-net/dataset/test_ready'
config['resolution'] = 512
config['is_skirt'] = True
config['dataset_in_memory'] = False
config['is_cape'] = False
config['remove_global_orientation'] = True
config['device'] = 'cuda'
if config['is_skirt']:
config['train_frames'] = 'frames_train_skirt.txt'
config['valid_frames'] = 'frames_valid_skirt.txt'
else:
config['train_frames'] = 'frames_train.txt'
config['valid_frames'] = 'frames_valid.txt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['bottleneck_scale'] = 2
config['cond_ae'] = dict()
config['cond_ae']['device'] = config['device']
config['cond_ae']['is_label'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 40 * config['bottleneck_scale']
block_setting = [
CNBlockConfig(16, 32, 3),
CNBlockConfig(32, 64, 3),
CNBlockConfig(64, 128, 9),
CNBlockConfig(128, config['cond_ae']['bottleneck_size'], 9),
CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
]
block_setting_reverse = [
CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
CNBlockConfig(128, 64, 9),
CNBlockConfig(64, 32, 9),
CNBlockConfig(32, 16, 3),
CNBlockConfig(16, 3, 3),
# CNBlockConfig(3, 3, 3),
]
config['cond_ae']['encoder_block_settings'] = block_setting
config['cond_ae']['decoder_block_settings'] = block_setting_reverse
config['cond_ae']['activation'] = 'tanh'
config['lr'] = 0.0001
config['cond_ae']['upscale_output'] = False
config['signature'] = { 'id',
'frame_nr',
'uv_mask',
'uv_normals',
'mesh_mask',
'mesh_normals',
}
configs/iccvw/ae_static_1_skirt.py
+78
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@@ -0,0 +1,78 @@
from utils.convnext_utils import CNBlockConfig
config = dict()
config['gpu'] = '3'
config['name'] = 'static_iccvw_1_skirt'
config['notes'] = ' '
config['tags'] = ['torch', 'static']
config['script'] = 'scripts/ae_static_train.py'
config['pretrained_model'] = ''
config['batch_size'] = 4
config['train_dir'] = '/data-local/127/cloth3d_processed'
config['valid_dir'] = '/data-local/127/cloth3d_processed'
config['test_dir'] = '/data-net/dataset/test_ready'
config['resolution'] = 512
config['is_skirt'] = True
config['dataset_in_memory'] = False
config['is_cape'] = False
config['remove_global_orientation'] = True
config['device'] = 'cuda'
if config['is_skirt']:
config['train_frames'] = 'frames_train_skirt.txt'
config['valid_frames'] = 'frames_valid_skirt.txt'
else:
config['train_frames'] = 'frames_train.txt'
config['valid_frames'] = 'frames_valid.txt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['bottleneck_scale'] = 2
config['cond_ae'] = dict()
config['cond_ae']['device'] = config['device']
config['cond_ae']['is_label'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 8 * config['bottleneck_scale']
block_setting = [
CNBlockConfig(8, 16, 3),
CNBlockConfig(16, 32, 3),
CNBlockConfig(32, 64, 3),
CNBlockConfig(64, config['cond_ae']['bottleneck_size'], 9),
CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
]
block_setting_reverse = [
CNBlockConfig(config['cond_ae']['bottleneck_size'], 64, 3),
CNBlockConfig(64, 32, 9),
CNBlockConfig(32, 16, 3),
CNBlockConfig(16, 8, 3),
CNBlockConfig(8, 3, 3),
# CNBlockConfig(3, 3, 3),
]
config['cond_ae']['encoder_block_settings'] = block_setting
config['cond_ae']['decoder_block_settings'] = block_setting_reverse
config['cond_ae']['activation'] = 'tanh'
config['lr'] = 0.0005
config['cond_ae']['upscale_output'] = False
config['signature'] = { 'id',
'frame_nr',
'uv_static',
'mesh_static',
'mesh_mask',
'uv_mask',
}
configs/iccvw/ae_static_5.py
+78
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@@ -0,0 +1,78 @@
from utils.convnext_utils import CNBlockConfig
config = dict()
config['gpu'] = '5'
config['name'] = 'static_iccvw_5'
config['notes'] = 'lower learning rate'
config['tags'] = ['torch', 'static']
config['script'] = 'scripts/ae_static_train.py'
config['pretrained_model'] = 'checkpoints/iccvw/static_iccvw_3'
config['batch_size'] = 4
config['train_dir'] = '/data-local/127/cloth3d_processed'
config['valid_dir'] = '/data-local/127/cloth3d_processed'
config['test_dir'] = '/data-net/dataset/test_ready'
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['remove_global_orientation'] = True
config['device'] = 'cuda'
if config['is_skirt']:
config['train_frames'] = 'frames_train_skirt.txt'
config['valid_frames'] = 'frames_valid_skirt.txt'
else:
config['train_frames'] = 'frames_train.txt'
config['valid_frames'] = 'frames_valid.txt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['bottleneck_scale'] = 2
config['cond_ae'] = dict()
config['cond_ae']['device'] = config['device']
config['cond_ae']['is_label'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 8 * config['bottleneck_scale']
block_setting = [
CNBlockConfig(8, 16, 3),
CNBlockConfig(16, 32, 3),
CNBlockConfig(32, 64, 3),
CNBlockConfig(64, config['cond_ae']['bottleneck_size'], 9),
CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
]
block_setting_reverse = [
CNBlockConfig(config['cond_ae']['bottleneck_size'], 64, 3),
CNBlockConfig(64, 32, 9),
CNBlockConfig(32, 16, 3),
CNBlockConfig(16, 8, 3),
CNBlockConfig(8, 3, 3),
# CNBlockConfig(3, 3, 3),
]
config['cond_ae']['encoder_block_settings'] = block_setting
config['cond_ae']['decoder_block_settings'] = block_setting_reverse
config['cond_ae']['activation'] = 'tanh'
config['lr'] = 0.00005
config['cond_ae']['upscale_output'] = False
config['signature'] = { 'id',
'frame_nr',
'uv_static',
'mesh_static',
'mesh_mask',
'uv_mask',
}
scripts/ae_body_train.py
+8 -11
View File
@@ -11,10 +11,12 @@ parser.add_argument('--config', type=str)
parser.add_argument('--debug', action='store_true')
args = vars(parser.parse_args())
config_module = importlib.import_module(args['config'])
config = config_module.config
if args['debug']:
from configs.iccvw.ae_body_cape_m import config
else:
config_module = importlib.import_module(args['config'])
config = config_module.config
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
if args['debug']:
@@ -32,10 +34,7 @@ import plotly.graph_objects as go
import plotly.express as ex
import torch.optim as optim
if config['is_skirt']:
from losses.losses import edge_loss_skirt as edge_loss
else:
from losses.losses import edge_loss
from losses.losses import edge_loss
from data.dataset import Dataset
# from torch.utils.data import DataLoader
@@ -65,7 +64,7 @@ def init_wandb():
artifact.add_file(file_name)
run.log_artifact(artifact)
uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
uv2mesh = UV2Mesh(res=256, is_skirt=False, is_smpl_orig=config['is_cape'])
@@ -122,7 +121,7 @@ def plot_everything(batch: dict, type: str):
img = torch.concat((orig_img, pred_img), axis=2)
imgs = [wandb.Image((img[i].cpu().numpy() + 1) / 2.0) for i in range(4)]
F = np.load("smpl/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
F = np.load("smpl/smpl_faces.npy")
garment_unposed, faces = mesh_pred.cpu().numpy(), F
gt = [mesh_orig[i].cpu().numpy() for i in range(pred.shape[0])]
@@ -176,9 +175,7 @@ if __name__ == '__main__':
if not args['debug']:
init_wandb()
skirt_modifier = '_skirt' if config['is_skirt'] else ''
uv_mask_body = np.load('custom_utils/mask_256x512' + ('_skirt' if config['is_skirt'] else '') + '.npy')
uv_mask_body = np.load('custom_utils/mask_256x512.npy')
uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2)
uv_mask_body = torch.from_numpy(uv_mask_body).float().to(config['device'])
scripts/ae_static_train.py
+1 -1
View File
@@ -94,7 +94,7 @@ def train_step(batch):
mesh_orig = batch['mesh_static']
l1_loss = torch.mean(torch.sum((torch.abs(batch['uv_static'] - pred)), axis=1))
e_loss = 1e-2 * torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred.detach()) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim, }