| import os |
| import torch |
| import json |
| from tqdm import tqdm |
| import argparse |
| from helpers import params2cpu, save_params |
| from external import densify |
| from train_utils import initialize_params, initialize_optimizer, initialize_per_timestep, initialize_post_first_timestep, get_batch, get_loss, report_progress, get_custom_dataset |
|
|
|
|
| def train(seq, exp, remove_threshold, remove_thresh_5k, weight_params, num_knn, scale_scene_radius, |
| metadata_path, init_pt_cld_path): |
| |
| md = json.load(open(f"./data/{seq}/{metadata_path}", 'r')) |
| num_timesteps = len(md['fn']) |
| params, variables = initialize_params(seq, md, init_pt_cld_path) |
| optimizer = initialize_optimizer(params, variables) |
| output_params = [] |
| for t in range(num_timesteps): |
| dataset = get_custom_dataset(t, md, seq) |
| todo_dataset = [] |
| is_initial_timestep = (t == 0) |
| if not is_initial_timestep: |
| params, variables = initialize_per_timestep(params, variables, optimizer) |
| num_iter_per_timestep = 10000 if is_initial_timestep else 2000 |
| progress_bar = tqdm(range(num_iter_per_timestep), desc=f"timestep {t}") |
| for i in range(num_iter_per_timestep): |
| curr_data = get_batch(todo_dataset, dataset) |
| loss, variables = get_loss( |
| params, curr_data, variables, is_initial_timestep, |
| weight_params['soft_col_cons'], |
| weight_params['im'], |
| weight_params['seg'], |
| weight_params['rigid'], |
| weight_params['bg'], |
| weight_params['iso'], |
| weight_params['rot']) |
| loss.backward() |
| with torch.no_grad(): |
| report_progress(params, dataset[0], i, progress_bar) |
| if is_initial_timestep: |
| params, variables, num_pts = densify(params, variables, optimizer, i, remove_threshold, remove_thresh_5k, scale_scene_radius) |
| os.makedirs(f"./output/{exp}/{seq}", exist_ok=True) |
| with open(f"./output/{exp}/{seq}/num_pts.txt", 'w') as f: |
| f.write(f"Number of points: {num_pts}\n") |
| optimizer.step() |
| optimizer.zero_grad(set_to_none=True) |
| progress_bar.close() |
| output_params.append(params2cpu(params, is_initial_timestep)) |
| if is_initial_timestep: |
| variables = initialize_post_first_timestep(params, variables, optimizer, num_knn) |
| if (t % 5 == 0 and t > 0) or t == num_timesteps - 1: |
| save_params(output_params, seq, exp) |
| print(f"Saved ckpts at timestep {t}") |
|
|
| if __name__ == "__main__": |
| |
| parser = argparse.ArgumentParser(description='Run training with given sequence and experiment name.') |
| parser.add_argument('--exp_name', type=str, required=True, help='The experiment name.') |
| parser.add_argument('--sequence', type=str, required=True, help='The sequence to train on.') |
| parser.add_argument('--remove_threshold', type=float, default=0.005, help='The threshold for removing points.') |
| parser.add_argument('--remove_thresh_5k', type=float, default=0.25, help='The threshold for removing points at 5k iterations.') |
| parser.add_argument('--weight_soft_col_cons', type=float, default=0.01, help='The weight for soft color consistency loss.') |
| parser.add_argument('--weight_im', type=float, default=50.0, help='The weight for image loss.') |
| parser.add_argument('--weight_seg', type=float, default=200.0, help='The weight for segmentation loss.') |
| parser.add_argument('--weight_rigid', type=float, default=200.0, help='The weight for rigid loss.') |
| parser.add_argument('--weight_bg', type=float, default=200.0, help='The weight for background loss.') |
| parser.add_argument('--weight_iso', type=float, default=1000.0, help='The weight for isotropic loss.') |
| parser.add_argument('--weight_rot', type=float, default=4.0, help='The weight for rotational loss.') |
| parser.add_argument('--num_knn', type=int, default=20, help='The number of nearest neighbors to use for loss calculation.') |
| parser.add_argument('--scale_scene_radius', type=float, default=0.05, help='The scale factor for the scene radius.') |
| parser.add_argument('--metadata_path', type=str, required=True, help='The path to the metadata file.') |
| parser.add_argument('--init_pt_cld_path', type=str, required=True, help='The path to the initial point cloud file.') |
|
|
| |
| args = parser.parse_args() |
|
|
| weight_params = { |
| 'soft_col_cons': args.weight_soft_col_cons, |
| 'im': args.weight_im, |
| 'seg': args.weight_seg, |
| 'rigid': args.weight_rigid, |
| 'bg': args.weight_bg, |
| 'iso': args.weight_iso, |
| 'rot': args.weight_rot |
| } |
| |
| train(args.sequence, args.exp_name, args.remove_threshold, args.remove_thresh_5k, |
| weight_params, args.num_knn, args.scale_scene_radius, |
| args.metadata_path, args.init_pt_cld_path) |
| torch.cuda.empty_cache() |
| |
