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train_renderer.py
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train_renderer.py
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import argparse
import imageio
import numpy as np
import torch
import torch.nn.functional as F
from pathlib import Path
import trimesh
from omegaconf import OmegaConf
from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor, Callback
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning import Trainer
from skimage.io import imsave
from tqdm import tqdm
import mcubes
from ldm.base_utils import read_pickle, output_points
from renderer.renderer import NeuSRenderer, DEFAULT_SIDE_LENGTH
from ldm.util import instantiate_from_config
class ResumeCallBacks(Callback):
def __init__(self):
pass
def on_train_start(self, trainer, pl_module):
pl_module.optimizers().param_groups = pl_module.optimizers()._optimizer.param_groups
def render_images(model, output,):
# render from model
n = 180
azimuths = (np.arange(n) / n * np.pi * 2).astype(np.float32)
elevations = np.deg2rad(np.asarray([30] * n).astype(np.float32))
K, _, _, _, poses = read_pickle(f'meta_info/camera-16.pkl')
output_points
h, w = 256, 256
default_size = 256
K = np.diag([w/default_size,h/default_size,1.0]) @ K
imgs = []
for ni in tqdm(range(n)):
# R = euler2mat(azimuths[ni], elevations[ni], 0, 'szyx')
# R = np.asarray([[0,-1,0],[0,0,-1],[1,0,0]]) @ R
e, a = elevations[ni], azimuths[ni]
row1 = np.asarray([np.sin(e)*np.cos(a),np.sin(e)*np.sin(a),-np.cos(e)])
row0 = np.asarray([-np.sin(a),np.cos(a), 0])
row2 = np.cross(row0, row1)
R = np.stack([row0,row1,row2],0)
t = np.asarray([0,0,1.5])
pose = np.concatenate([R,t[:,None]],1)
pose_ = torch.from_numpy(pose.astype(np.float32)).unsqueeze(0)
K_ = torch.from_numpy(K.astype(np.float32)).unsqueeze(0) # [1,3,3]
coords = torch.stack(torch.meshgrid(torch.arange(h), torch.arange(w)), -1)[:, :, (1, 0)] # h,w,2
coords = coords.float()[None, :, :, :].repeat(1, 1, 1, 1) # imn,h,w,2
coords = coords.reshape(1, h * w, 2)
coords = torch.cat([coords, torch.ones(1, h * w, 1, dtype=torch.float32)], 2) # imn,h*w,3
# imn,h*w,3 @ imn,3,3 => imn,h*w,3
rays_d = coords @ torch.inverse(K_).permute(0, 2, 1)
R, t = pose_[:, :, :3], pose_[:, :, 3:]
rays_d = rays_d @ R
rays_d = F.normalize(rays_d, dim=-1)
rays_o = -R.permute(0, 2, 1) @ t # imn,3,3 @ imn,3,1
rays_o = rays_o.permute(0, 2, 1).repeat(1, h * w, 1) # imn,h*w,3
ray_batch = {
'rays_o': rays_o.reshape(-1,3).cuda(),
'rays_d': rays_d.reshape(-1,3).cuda(),
}
with torch.no_grad():
image = model.renderer.render(ray_batch,False,5000)['rgb'].reshape(h,w,3)
image = (image.cpu().numpy() * 255).astype(np.uint8)
imgs.append(image)
imageio.mimsave(f'{output}/rendering.mp4', imgs, fps=30)
def extract_fields(bound_min, bound_max, resolution, query_func, batch_size=64, outside_val=1.0):
N = batch_size
X = torch.linspace(bound_min[0], bound_max[0], resolution).split(N)
Y = torch.linspace(bound_min[1], bound_max[1], resolution).split(N)
Z = torch.linspace(bound_min[2], bound_max[2], resolution).split(N)
u = np.zeros([resolution, resolution, resolution], dtype=np.float32)
with torch.no_grad():
for xi, xs in enumerate(X):
for yi, ys in enumerate(Y):
for zi, zs in enumerate(Z):
xx, yy, zz = torch.meshgrid(xs, ys, zs)
pts = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1).cuda()
val = query_func(pts).detach()
outside_mask = torch.norm(pts,dim=-1)>=1.0
val[outside_mask]=outside_val
val = val.reshape(len(xs), len(ys), len(zs)).cpu().numpy()
u[xi * N: xi * N + len(xs), yi * N: yi * N + len(ys), zi * N: zi * N + len(zs)] = val
return u
def extract_geometry(bound_min, bound_max, resolution, threshold, query_func, color_func, outside_val=1.0):
u = extract_fields(bound_min, bound_max, resolution, query_func, outside_val=outside_val)
vertices, triangles = mcubes.marching_cubes(u, threshold)
b_max_np = bound_max.detach().cpu().numpy()
b_min_np = bound_min.detach().cpu().numpy()
vertices = vertices / (resolution - 1.0) * (b_max_np - b_min_np)[None, :] + b_min_np[None, :]
vertex_colors = color_func(vertices)
return vertices, triangles, vertex_colors
def extract_mesh(model, output, resolution=512):
if not isinstance(model.renderer, NeuSRenderer): return
bbox_min = -torch.ones(3)*DEFAULT_SIDE_LENGTH
bbox_max = torch.ones(3)*DEFAULT_SIDE_LENGTH
with torch.no_grad():
vertices, triangles, vertex_colors = extract_geometry(bbox_min, bbox_max, resolution, 0, lambda x: model.renderer.sdf_network.sdf(x), lambda x: model.renderer.get_vertex_colors(x))
# output geometry
mesh = trimesh.Trimesh(vertices, triangles, vertex_colors=vertex_colors)
mesh.export(str(f'{output}/mesh.ply'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--image_path', type=str, required=True)
parser.add_argument('-n', '--name', type=str, required=True)
parser.add_argument('-b', '--base', type=str, default='configs/neus.yaml')
parser.add_argument('-l', '--log', type=str, default='output/renderer')
parser.add_argument('-s', '--seed', type=int, default=6033)
parser.add_argument('-g', '--gpus', type=str, default='0,')
parser.add_argument('-r', '--resume', action='store_true', default=False, dest='resume')
parser.add_argument('--fp16', action='store_true', default=False, dest='fp16')
opt = parser.parse_args()
# seed_everything(opt.seed)
# configs
cfg = OmegaConf.load(opt.base)
name = opt.name
log_dir, ckpt_dir = Path(opt.log) / name, Path(opt.log) / name / 'ckpt'
cfg.model.params['image_path'] = opt.image_path
cfg.model.params['log_dir'] = log_dir
# setup
log_dir.mkdir(exist_ok=True, parents=True)
ckpt_dir.mkdir(exist_ok=True, parents=True)
trainer_config = cfg.trainer
callback_config = cfg.callbacks
model_config = cfg.model
data_config = cfg.data
data_config.params.seed = opt.seed
data = instantiate_from_config(data_config)
data.prepare_data()
data.setup('fit')
model = instantiate_from_config(model_config,)
model.cpu()
model.learning_rate = model_config.base_lr
# logger
logger = TensorBoardLogger(save_dir=log_dir, name='tensorboard_logs')
callbacks=[]
callbacks.append(LearningRateMonitor(logging_interval='step'))
callbacks.append(ModelCheckpoint(dirpath=ckpt_dir, filename="{epoch:06}", verbose=True, save_last=True, every_n_train_steps=callback_config.save_interval))
# trainer
trainer_config.update({
"accelerator": "cuda", "check_val_every_n_epoch": None,
"benchmark": True, "num_sanity_val_steps": 0,
"devices": 1, "gpus": opt.gpus,
})
if opt.fp16:
trainer_config['precision']=16
if opt.resume:
callbacks.append(ResumeCallBacks())
trainer_config['resume_from_checkpoint'] = str(ckpt_dir / 'last.ckpt')
else:
if (ckpt_dir / 'last.ckpt').exists():
raise RuntimeError(f"checkpoint {ckpt_dir / 'last.ckpt'} existing ...")
trainer = Trainer.from_argparse_args(args=argparse.Namespace(), **trainer_config, logger=logger, callbacks=callbacks)
trainer.fit(model, data)
model = model.cuda().eval()
render_images(model, log_dir)
extract_mesh(model, log_dir)
if __name__=="__main__":
main()