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#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import os
import warnings
import hydra
import numpy as np
import torch
from nerf.dataset import get_nerf_datasets, trivial_collate
from nerf.eval_video_utils import generate_eval_video_cameras
from nerf.nerf_renderer import RadianceFieldRenderer
from nerf.stats import Stats
from omegaconf import DictConfig
from PIL import Image
CONFIG_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "configs")
@hydra.main(config_path=CONFIG_DIR, config_name="lego")
def main(cfg: DictConfig):
# Device on which to run.
if torch.cuda.is_available():
device = "cuda"
else:
warnings.warn(
"Please note that although executing on CPU is supported,"
+ "the testing is unlikely to finish in reasonable time."
)
device = "cpu"
# Initialize the Radiance Field model.
model = RadianceFieldRenderer(
image_size=cfg.data.image_size,
n_pts_per_ray=cfg.raysampler.n_pts_per_ray,
n_pts_per_ray_fine=cfg.raysampler.n_pts_per_ray,
n_rays_per_image=cfg.raysampler.n_rays_per_image,
min_depth=cfg.raysampler.min_depth,
max_depth=cfg.raysampler.max_depth,
stratified=cfg.raysampler.stratified,
stratified_test=cfg.raysampler.stratified_test,
chunk_size_test=cfg.raysampler.chunk_size_test,
n_harmonic_functions_xyz=cfg.implicit_function.n_harmonic_functions_xyz,
n_harmonic_functions_dir=cfg.implicit_function.n_harmonic_functions_dir,
n_hidden_neurons_xyz=cfg.implicit_function.n_hidden_neurons_xyz,
n_hidden_neurons_dir=cfg.implicit_function.n_hidden_neurons_dir,
n_layers_xyz=cfg.implicit_function.n_layers_xyz,
density_noise_std=cfg.implicit_function.density_noise_std,
)
# Move the model to the relevant device.
model.to(device)
# Resume from the checkpoint.
checkpoint_path = os.path.join(hydra.utils.get_original_cwd(), cfg.checkpoint_path)
if not os.path.isfile(checkpoint_path):
raise ValueError(f"Model checkpoint {checkpoint_path} does not exist!")
print(f"Loading checkpoint {checkpoint_path}.")
loaded_data = torch.load(checkpoint_path)
# Do not load the cached xy grid.
# - this allows setting an arbitrary evaluation image size.
state_dict = {
k: v
for k, v in loaded_data["model"].items()
if "_grid_raysampler._xy_grid" not in k
}
model.load_state_dict(state_dict, strict=False)
# Load the test data.
if cfg.test.mode == "evaluation":
_, _, test_dataset = get_nerf_datasets(
dataset_name=cfg.data.dataset_name,
image_size=cfg.data.image_size,
)
elif cfg.test.mode == "export_video":
train_dataset, _, _ = get_nerf_datasets(
dataset_name=cfg.data.dataset_name,
image_size=cfg.data.image_size,
)
test_dataset = generate_eval_video_cameras(
train_dataset,
trajectory_type=cfg.test.trajectory_type,
up=cfg.test.up,
scene_center=cfg.test.scene_center,
n_eval_cams=cfg.test.n_frames,
trajectory_scale=cfg.test.trajectory_scale,
)
# store the video in directory (checkpoint_file - extension + '_video')
export_dir = os.path.splitext(checkpoint_path)[0] + "_video"
os.makedirs(export_dir, exist_ok=True)
else:
raise ValueError(f"Unknown test mode {cfg.test_mode}.")
# Init the test dataloader.
test_dataloader = torch.utils.data.DataLoader(
test_dataset,
batch_size=1,
shuffle=False,
num_workers=0,
collate_fn=trivial_collate,
)
if cfg.test.mode == "evaluation":
# Init the test stats object.
eval_stats = ["mse_coarse", "mse_fine", "psnr_coarse", "psnr_fine", "sec/it"]
stats = Stats(eval_stats)
stats.new_epoch()
elif cfg.test.mode == "export_video":
# Init the frame buffer.
frame_paths = []
# Set the model to the eval mode.
model.eval()
# Run the main testing loop.
for batch_idx, test_batch in enumerate(test_dataloader):
test_image, test_camera, camera_idx = test_batch[0].values()
if test_image is not None:
test_image = test_image.to(device)
test_camera = test_camera.to(device)
# Activate eval mode of the model (lets us do a full rendering pass).
model.eval()
with torch.no_grad():
test_nerf_out, test_metrics = model(
None, # we do not use pre-cached cameras
test_camera,
test_image,
)
if cfg.test.mode == "evaluation":
# Update stats with the validation metrics.
stats.update(test_metrics, stat_set="test")
stats.print(stat_set="test")
elif cfg.test.mode == "export_video":
# Store the video frame.
frame = test_nerf_out["rgb_fine"][0].detach().cpu()
frame_path = os.path.join(export_dir, f"frame_{batch_idx:05d}.png")
print(f"Writing {frame_path}.")
Image.fromarray((frame.numpy() * 255.0).astype(np.uint8)).save(frame_path)
frame_paths.append(frame_path)
if cfg.test.mode == "evaluation":
print(f"Final evaluation metrics on '{cfg.data.dataset_name}':")
for stat in eval_stats:
stat_value = stats.stats["test"][stat].get_epoch_averages()[0]
print(f"{stat:15s}: {stat_value:1.4f}")
elif cfg.test.mode == "export_video":
# Convert the exported frames to a video.
video_path = os.path.join(export_dir, "video.mp4")
ffmpeg_bin = "ffmpeg"
frame_regexp = os.path.join(export_dir, "frame_%05d.png")
ffmcmd = (
"%s -r %d -i %s -vcodec h264 -f mp4 -y -b 2000k -pix_fmt yuv420p %s"
% (ffmpeg_bin, cfg.test.fps, frame_regexp, video_path)
)
ret = os.system(ffmcmd)
if ret != 0:
raise RuntimeError("ffmpeg failed!")
if __name__ == "__main__":
main()
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