File size: 5,091 Bytes
e4c5b8d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | 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')) # metadata for custom dataset
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__":
# Set up the argument parser
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.')
# Parse the arguments
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()
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