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train_mlp_nerf.py
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train_mlp_nerf.py
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"""
Copyright (c) 2022 Ruilong Li, UC Berkeley.
"""
import argparse
import math
import os
import time
import imageio
import numpy as np
import torch
import torch.nn.functional as F
import tqdm
from radiance_fields.mlp import VanillaNeRFRadianceField
from utils import render_image, set_random_seed
from nerfacc import ContractionType, OccupancyGrid
if __name__ == "__main__":
device = "cuda:0"
set_random_seed(42)
parser = argparse.ArgumentParser()
parser.add_argument(
"--train_split",
type=str,
default="trainval",
choices=["train", "trainval"],
help="which train split to use",
)
parser.add_argument(
"--scene",
type=str,
default="lego",
choices=[
# nerf synthetic
"chair",
"drums",
"ficus",
"hotdog",
"lego",
"materials",
"mic",
"ship",
# mipnerf360 unbounded
"garden",
],
help="which scene to use",
)
parser.add_argument(
"--aabb",
type=lambda s: [float(item) for item in s.split(",")],
default="-1.5,-1.5,-1.5,1.5,1.5,1.5",
help="delimited list input",
)
parser.add_argument(
"--test_chunk_size",
type=int,
default=8192,
)
parser.add_argument(
"--unbounded",
action="store_true",
help="whether to use unbounded rendering",
)
parser.add_argument("--cone_angle", type=float, default=0.0)
args = parser.parse_args()
render_n_samples = 1024
# setup the scene bounding box.
if args.unbounded:
print("Using unbounded rendering")
contraction_type = ContractionType.UN_BOUNDED_SPHERE
# contraction_type = ContractionType.UN_BOUNDED_TANH
scene_aabb = None
near_plane = 0.2
far_plane = 1e4
render_step_size = 1e-2
else:
contraction_type = ContractionType.AABB
scene_aabb = torch.tensor(args.aabb, dtype=torch.float32, device=device)
near_plane = None
far_plane = None
render_step_size = (
(scene_aabb[3:] - scene_aabb[:3]).max()
* math.sqrt(3)
/ render_n_samples
).item()
# setup the radiance field we want to train.
max_steps = 50000
grad_scaler = torch.cuda.amp.GradScaler(1)
radiance_field = VanillaNeRFRadianceField().to(device)
optimizer = torch.optim.Adam(radiance_field.parameters(), lr=5e-4)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[
max_steps // 2,
max_steps * 3 // 4,
max_steps * 5 // 6,
max_steps * 9 // 10,
],
gamma=0.33,
)
# setup the dataset
train_dataset_kwargs = {}
test_dataset_kwargs = {}
if args.scene == "garden":
from datasets.nerf_360_v2 import SubjectLoader
data_root_fp = "/home/ruilongli/data/360_v2/"
target_sample_batch_size = 1 << 16
train_dataset_kwargs = {"color_bkgd_aug": "random", "factor": 4}
test_dataset_kwargs = {"factor": 4}
grid_resolution = 128
else:
from datasets.nerf_synthetic import SubjectLoader
data_root_fp = "/home/ruilongli/data/nerf_synthetic/"
target_sample_batch_size = 1 << 16
grid_resolution = 128
train_dataset = SubjectLoader(
subject_id=args.scene,
root_fp=data_root_fp,
split=args.train_split,
num_rays=target_sample_batch_size // render_n_samples,
**train_dataset_kwargs,
)
train_dataset.images = train_dataset.images.to(device)
train_dataset.camtoworlds = train_dataset.camtoworlds.to(device)
train_dataset.K = train_dataset.K.to(device)
test_dataset = SubjectLoader(
subject_id=args.scene,
root_fp=data_root_fp,
split="test",
num_rays=None,
**test_dataset_kwargs,
)
test_dataset.images = test_dataset.images.to(device)
test_dataset.camtoworlds = test_dataset.camtoworlds.to(device)
test_dataset.K = test_dataset.K.to(device)
occupancy_grid = OccupancyGrid(
roi_aabb=args.aabb,
resolution=grid_resolution,
contraction_type=contraction_type,
).to(device)
# training
step = 0
tic = time.time()
for epoch in range(10000000):
for i in range(len(train_dataset)):
radiance_field.train()
data = train_dataset[i]
render_bkgd = data["color_bkgd"]
rays = data["rays"]
pixels = data["pixels"]
# update occupancy grid
occupancy_grid.every_n_step(
step=step,
occ_eval_fn=lambda x: radiance_field.query_opacity(
x, render_step_size
),
)
# render
rgb, acc, depth, n_rendering_samples = render_image(
radiance_field,
occupancy_grid,
rays,
scene_aabb,
# rendering options
near_plane=near_plane,
far_plane=far_plane,
render_step_size=render_step_size,
render_bkgd=render_bkgd,
cone_angle=args.cone_angle,
)
if n_rendering_samples == 0:
continue
# dynamic batch size for rays to keep sample batch size constant.
num_rays = len(pixels)
num_rays = int(
num_rays
* (target_sample_batch_size / float(n_rendering_samples))
)
train_dataset.update_num_rays(num_rays)
alive_ray_mask = acc.squeeze(-1) > 0
# compute loss
loss = F.smooth_l1_loss(rgb[alive_ray_mask], pixels[alive_ray_mask])
optimizer.zero_grad()
# do not unscale it because we are using Adam.
grad_scaler.scale(loss).backward()
optimizer.step()
scheduler.step()
if step % 5000 == 0:
elapsed_time = time.time() - tic
loss = F.mse_loss(rgb[alive_ray_mask], pixels[alive_ray_mask])
print(
f"elapsed_time={elapsed_time:.2f}s | {step=} | "
f"loss={loss:.5f} | "
f"alive_ray_mask={alive_ray_mask.long().sum():d} | "
f"n_rendering_samples={n_rendering_samples:d} | num_rays={len(pixels):d} |"
)
if step >= 0 and step % max_steps == 0 and step > 0:
# evaluation
radiance_field.eval()
psnrs = []
with torch.no_grad():
for i in tqdm.tqdm(range(len(test_dataset))):
data = test_dataset[i]
render_bkgd = data["color_bkgd"]
rays = data["rays"]
pixels = data["pixels"]
# rendering
rgb, acc, depth, _ = render_image(
radiance_field,
occupancy_grid,
rays,
scene_aabb,
# rendering options
near_plane=None,
far_plane=None,
render_step_size=render_step_size,
render_bkgd=render_bkgd,
cone_angle=args.cone_angle,
# test options
test_chunk_size=args.test_chunk_size,
)
mse = F.mse_loss(rgb, pixels)
psnr = -10.0 * torch.log(mse) / np.log(10.0)
psnrs.append(psnr.item())
# imageio.imwrite(
# "acc_binary_test.png",
# ((acc > 0).float().cpu().numpy() * 255).astype(np.uint8),
# )
# imageio.imwrite(
# "rgb_test.png",
# (rgb.cpu().numpy() * 255).astype(np.uint8),
# )
# break
psnr_avg = sum(psnrs) / len(psnrs)
print(f"evaluation: {psnr_avg=}")
train_dataset.training = True
if step == max_steps:
print("training stops")
exit()
step += 1