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import os |
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import yaml |
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import torch |
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import random |
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import argparse |
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import numpy as np |
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from loop import loop |
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def main(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--config', help='Path to config file', type=str, default='./example_config.yml') |
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parser.add_argument('--output_path', help='Output directory (will be created)', type=str, default=argparse.SUPPRESS) |
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parser.add_argument('--gpu', help='GPU index', type=int, default=argparse.SUPPRESS) |
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parser.add_argument('--seed', help='Random seed', type=int, default=argparse.SUPPRESS) |
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parser.add_argument('--text_prompt', help='Target text prompt', type=str, default=argparse.SUPPRESS) |
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parser.add_argument('--base_text_prompt', help='Base text prompt describing input mesh', type=str, default=argparse.SUPPRESS) |
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parser.add_argument('--clip_model', help='CLIP Model for text comparison', type=str, default=argparse.SUPPRESS) |
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parser.add_argument('--consistency_clip_model', help='CLIP Model for consistency', type=str, default=argparse.SUPPRESS) |
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parser.add_argument('--consistency_vit_stride', help='New stride for ViT patch interpolation', type=int, default=argparse.SUPPRESS) |
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parser.add_argument('--consistency_vit_layer', help='Which layer to take ViT patch features from (0-11)', type=int, default=argparse.SUPPRESS) |
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parser.add_argument('--mesh', help='Path to input mesh', type=str, default=argparse.SUPPRESS) |
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parser.add_argument('--retriangulate', help='Use isotropic remeshing', type=int, default=argparse.SUPPRESS, choices=[0, 1]) |
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parser.add_argument('--bsdf', help='Render technique', type=str, default=argparse.SUPPRESS, choices=['diffuse', 'pbr']) |
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parser.add_argument('--lr', help='Learning rate', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--epochs', help='Number of optimization steps', type=int, default=argparse.SUPPRESS) |
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parser.add_argument('--clip_weight', help='Weight for CLIP loss', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--delta_clip_weight', help='Wight for delta-CLIP loss', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--regularize_jacobians_weight', help='Weight for jacobian regularization', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--consistency_loss_weight', help='Weight for viewpoint consistency penalty', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--consistency_elev_filter', help='Elev. angle threshold for filtering out pairs of viewpoints for consistency loss', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--consistency_azim_filter', help='Azim. angle threshold for filtering out pairs of viewpoints for consistency loss', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--batch_size', help='Number of images rendered at the same time', type=int, default=argparse.SUPPRESS) |
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parser.add_argument('--train_res', help='Resolution of render before downscaling to CLIP size', type=int, default=argparse.SUPPRESS) |
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parser.add_argument('--resize_method', help='Image downsampling/upsampling method', type=str, default=argparse.SUPPRESS, choices=['cubic', 'linear', 'lanczos2', 'lanczos3']) |
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parser.add_argument('--fov_min', help='Minimum camera field of view angle during renders', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--fov_max', help='Maximum camera field of view angle during renders', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--dist_mi n', help= 'Minimum distance of camera from mesh during renders', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--dist_max', help='Maximum distance of camera from mesh during renders', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--light_power', help='Light intensity', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--elev_alpha', help='Alpha parameter for Beta distribution for elevation sampling', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--elev_beta', help='Beta parameter for Beta distribution for elevation sampling', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--elev_max', help='Maximum elevation anglez in degree', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--azim_min', help='Minimum azimuth angle in degree', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--azim_max', help='Maximum azimuth angle in degree', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--aug_loc', help='Offset mesh from center of image?', type=int, default=argparse.SUPPRESS, choices=[0, 1]) |
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parser.add_argument('--aug_light', help='Augment the direction of light around the camera', type=int, default=argparse.SUPPRESS, choices=[0, 1]) |
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parser.add_argument('--aug_bkg', help='Augment the background', type=int, default=argparse.SUPPRESS, choices=[0, 1]) |
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parser.add_argument('--adapt_dist', help='Adjust camera distance to account for scale of shape', type=int, default=argparse.SUPPRESS, choices=[0, 1]) |
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parser.add_argument('--log_inter val', help='Interval for logging, every X epochs', type=int, default=argparse.SUPPRESS) |
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parser.add_argument('--log_interval_im', help='Interval for logging renders image, every X epochs', type=int, default=argparse.SUPPRESS) |
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parser.add_argument('--log_elev', help='Logging elevation angle', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--log_fov', help='Logging field of view', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--log_dist', help='Logging distance from object', type=float, default=argparse.SUPPRESS) |
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parser.add_argument('--log_res', help='Logging render resolution', type=int, default=argparse.SUPPRESS) |
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parser.add_argument('--log_light_power', help='Light intensity for logging', type=float, default=argparse.SUPPRESS) |
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args = parser.parse_args() |
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if args.config is not None: |
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with open(args.config, 'r') as f: |
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try: |
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cfg = yaml.safe_load(f) |
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except yaml.YAMLError as e: |
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print(e) |
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for key in vars(args): |
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cfg[key] = vars(args)[key] |
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print(yaml.dump(cfg, default_flow_style=False)) |
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random.seed(cfg['seed']) |
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os.environ['PYTHONHASHSEED'] = str(cfg['seed']) |
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np.random.seed(cfg['seed']) |
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torch.manual_seed(cfg['seed']) |
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torch.cuda.manual_seed(cfg['seed']) |
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torch.backends.cudnn.deterministic = True |
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loop(cfg) |
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print('Done') |
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if __name__ == '__main__': |
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main() |
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