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eval_geo_nerf.py
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import argparse
import glob
import os
import time
import pickle
import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision
import yaml
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm, trange
from nerf import (CfgNode, get_embedding_function, get_ray_bundle, img2mse,
load_blender_data, load_llff_data, meshgrid_xy, models,
mse2psnr, run_one_iter_of_nerf, sample_geodesics)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--config", type=str, required=True, help="Path to (.yml) config file."
)
parser.add_argument(
"--load-checkpoint",
type=str,
default="",
help="Path to load saved checkpoint from.",
)
configargs = parser.parse_args()
# Read config file.
cfg = None
with open(configargs.config, "r") as f:
cfg_dict = yaml.load(f, Loader=yaml.FullLoader)
cfg = CfgNode(cfg_dict)
# # (Optional:) enable this to track autograd issues when debugging
# torch.autograd.set_detect_anomaly(True)
# If a pre-cached dataset is available, skip the dataloader.
USE_CACHED_DATASET = False
train_paths, validation_paths = None, None
images, poses, render_poses, hwf, i_split = None, None, None, None, None
H, W, focal, i_train, i_val, i_test = None, None, None, None, None, None
"""
# Disable Cache dir stuff
if hasattr(cfg.dataset, "cachedir") and os.path.exists(cfg.dataset.cachedir):
train_paths = glob.glob(os.path.join(cfg.dataset.cachedir, "train", "*.data"))
validation_paths = glob.glob(
os.path.join(cfg.dataset.cachedir, "val", "*.data")
)
USE_CACHED_DATASET = True
print("using cache!")
else:
"""
if True:
# Load dataset
images, poses, render_poses, hwf = None, None, None, None
assert(cfg.dataset.type.lower() == "blender")
images, poses, render_poses, hwf, i_split = load_blender_data(
cfg.dataset.basedir,
half_res=cfg.dataset.half_res,
testskip=cfg.dataset.testskip,
)
i_train, i_val, i_test = i_split
H, W, focal = hwf
H, W = int(H), int(W)
hwf = [H, W, focal]
if cfg.nerf.train.white_background:
images = images[..., :3] * images[..., -1:] + (1.0 - images[..., -1:])
# Seed experiment for repeatability
seed = cfg.experiment.randomseed
np.random.seed(seed)
torch.manual_seed(seed)
# Device on which to run.
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
encode_position_fn = get_embedding_function(
num_encoding_functions=cfg.models.coarse.num_encoding_fn_xyz,
include_input=cfg.models.coarse.include_input_xyz,
log_sampling=cfg.models.coarse.log_sampling_xyz,
)
encode_direction_fn = None
assert(cfg.models.coarse.use_viewdirs == False)
if cfg.models.coarse.use_viewdirs:
encode_direction_fn = get_embedding_function(
num_encoding_functions=cfg.models.coarse.num_encoding_fn_dir,
include_input=cfg.models.coarse.include_input_dir,
log_sampling=cfg.models.coarse.log_sampling_dir,
)
# Initialize a coarse-resolution model.
model_coarse = getattr(models, cfg.models.coarse.type)(
num_encoding_fn_xyz=cfg.models.coarse.num_encoding_fn_xyz,
num_encoding_fn_dir=cfg.models.coarse.num_encoding_fn_dir,
include_input_xyz=cfg.models.coarse.include_input_xyz,
include_input_dir=cfg.models.coarse.include_input_dir,
use_viewdirs=cfg.models.coarse.use_viewdirs,
)
# If a fine-resolution model is specified, initialize it.
model_coarse.to(device)
model_fine = None
if hasattr(cfg.models, "fine"):
model_fine = getattr(models, cfg.models.fine.type)(
num_encoding_fn_xyz=cfg.models.fine.num_encoding_fn_xyz,
num_encoding_fn_dir=cfg.models.fine.num_encoding_fn_dir,
include_input_xyz=cfg.models.fine.include_input_xyz,
include_input_dir=cfg.models.fine.include_input_dir,
use_viewdirs=cfg.models.fine.use_viewdirs,
)
model_fine.to(device)
# Setup logging.
logdir = os.path.join(cfg.experiment.logdir, cfg.experiment.id)
os.makedirs(logdir, exist_ok=True)
writer = SummaryWriter(logdir)
# Write out config parameters.
with open(os.path.join(logdir, "config.yml"), "w") as f:
f.write(cfg.dump()) # cfg, f, default_flow_style=False)
# Load an existing checkpoint, if a path is specified.
if os.path.exists(configargs.load_checkpoint):
checkpoint = torch.load(configargs.load_checkpoint)
model_coarse.load_state_dict(checkpoint["model_coarse_state_dict"])
if checkpoint["model_fine_state_dict"]:
model_fine.load_state_dict(checkpoint["model_fine_state_dict"])
# optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
geodesics = pickle.load(open(cfg.geonerf.cache_filename, 'rb'))
geo_nerf_model = models.GeoNeRF(128)
geo_nerf_model.set_nerf(model_coarse)
geo_nerf_model.to(device)
state = torch.load(cfg.geonerf.out_model)
geo_nerf_model.load_state_dict(state)
# Initialize optimizer.
geo_nerf_model.eval()
N = geodesics['V'].shape[0]
D = np.ndarray((N, N))
print("Final evaluation")
X1 = geodesics['V'].copy().astype(np.float32)
X1 = torch.from_numpy(X1).to(device)
X1 = encode_position_fn(X1)
with torch.no_grad():
for i in trange(N):
x0 = geodesics['V'][i].reshape((1, 3)).astype(np.float32)
X0 = np.repeat(x0, N, axis=0)
X0 = torch.from_numpy(X0).to(device)
X0 = encode_position_fn(X0)
pred = geo_nerf_model(X0, X1).cpu().numpy().reshape((-1,))
D[i] = pred
np.save(cfg.geonerf.out_filename, D)
plt.imshow(D); plt.show()
def cast_to_image(tensor):
# Input tensor is (H, W, 3). Convert to (3, H, W).
tensor = tensor.permute(2, 0, 1)
# Conver to PIL Image and then np.array (output shape: (H, W, 3))
img = np.array(torchvision.transforms.ToPILImage()(tensor.detach().cpu()))
# Map back to shape (3, H, W), as tensorboard needs channels first.
img = np.moveaxis(img, [-1], [0])
return img
if __name__ == "__main__":
main()