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render.py
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render.py
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#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact [email protected]
#
import torch
from scene import Scene, DeformModel
import os
from tqdm import tqdm
from os import makedirs
from gaussian_renderer import render
import torchvision
from utils.general_utils import safe_state
from utils.pose_utils import pose_spherical
from argparse import ArgumentParser
from arguments import ModelParams, PipelineParams, get_combined_args, OptimizationParams
from gaussian_renderer import GaussianModel
import imageio
import numpy as np
from pytorch_msssim import ms_ssim
from piq import LPIPS
lpips = LPIPS()
from utils.image_utils import ssim as ssim_func
from utils.image_utils import psnr, lpips, alex_lpips
def render_set(model_path, load2gpt_on_the_fly, name, iteration, views, gaussians, pipeline, background, deform):
render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
depth_path = os.path.join(model_path, name, "ours_{}".format(iteration), "depth")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
makedirs(depth_path, exist_ok=True)
# Measurement
psnr_list, ssim_list, lpips_list = [], [], []
ms_ssim_list, alex_lpips_list = [], []
to8b = lambda x: (255 * np.clip(x, 0, 1)).astype(np.uint8)
renderings = []
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
if load2gpt_on_the_fly:
view.load2device()
fid = view.fid
xyz = gaussians.get_xyz
if deform.name == 'mlp':
time_input = fid.unsqueeze(0).expand(xyz.shape[0], -1)
elif deform.name == 'node':
time_input = deform.deform.expand_time(fid)
d_values = deform.step(xyz.detach(), time_input, feature=gaussians.feature, motion_mask=gaussians.motion_mask)
d_xyz, d_rotation, d_scaling, d_opacity, d_color = d_values['d_xyz'], d_values['d_rotation'], d_values['d_scaling'], d_values['d_opacity'], d_values['d_color']
results = render(view, gaussians, pipeline, background, d_xyz, d_rotation, d_scaling, d_opacity=d_opacity, d_color=d_color, d_rot_as_res=deform.d_rot_as_res)
alpha = results["alpha"]
rendering = torch.clamp(torch.cat([results["render"], alpha]), 0.0, 1.0)
# Measurement
image = rendering[:3]
gt_image = torch.clamp(view.original_image.to("cuda"), 0.0, 1.0)
psnr_list.append(psnr(image[None], gt_image[None]).mean())
ssim_list.append(ssim_func(image[None], gt_image[None], data_range=1.).mean())
lpips_list.append(lpips(image[None], gt_image[None]).mean())
ms_ssim_list.append(ms_ssim(image[None], gt_image[None], data_range=1.).mean())
alex_lpips_list.append(alex_lpips(image[None], gt_image[None]).mean())
renderings.append(to8b(rendering.cpu().numpy()))
depth = results["depth"]
depth = depth / (depth.max() + 1e-5)
gt = view.original_image[0:4, :, :]
torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(depth, os.path.join(depth_path, '{0:05d}'.format(idx) + ".png"))
renderings = np.stack(renderings, 0).transpose(0, 2, 3, 1)
imageio.mimwrite(os.path.join(render_path, 'video.mp4'), renderings, fps=30, quality=8)
# Measurement
psnr_test = torch.stack(psnr_list).mean()
ssim_test = torch.stack(ssim_list).mean()
lpips_test = torch.stack(lpips_list).mean()
ms_ssim_test = torch.stack(ms_ssim_list).mean()
alex_lpips_test = torch.stack(alex_lpips_list).mean()
print("\n[ITER {}] Evaluating {}: PSNR {} SSIM {} LPIPS {} MS SSIM{} ALEX_LPIPS {}".format(iteration, name, psnr_test, ssim_test, lpips_test, ms_ssim_test, alex_lpips_test))
def interpolate_time(model_path, load2gpt_on_the_fly, name, iteration, views, gaussians, pipeline, background, deform):
render_path = os.path.join(model_path, name, "interpolate_{}".format(iteration), "renders")
depth_path = os.path.join(model_path, name, "interpolate_{}".format(iteration), "depth")
makedirs(render_path, exist_ok=True)
makedirs(depth_path, exist_ok=True)
to8b = lambda x: (255 * np.clip(x, 0, 1)).astype(np.uint8)
frame = 150
idx = torch.randint(0, len(views), (1,)).item()
view = views[idx]
renderings = []
for t in tqdm(range(0, frame, 1), desc="Rendering progress"):
fid = torch.Tensor([t / (frame - 1)]).cuda()
xyz = gaussians.get_xyz
if deform.name == 'deform':
time_input = fid.unsqueeze(0).expand(xyz.shape[0], -1)
elif deform.name == 'node':
time_input = deform.deform.expand_time(fid)
d_values = deform.step(xyz.detach(), time_input, feature=gaussians.feature, motion_mask=gaussians.motion_mask)
d_xyz, d_rotation, d_scaling, d_opacity, d_color = d_values['d_xyz'], d_values['d_rotation'], d_values['d_scaling'], d_values['d_opacity'], d_values['d_color']
results = render(view, gaussians, pipeline, background, d_xyz, d_rotation, d_scaling, d_opacity=d_opacity, d_color=d_color, d_rot_as_res=deform.d_rot_as_res)
rendering = results["render"]
renderings.append(to8b(rendering.cpu().numpy()))
depth = results["depth"]
depth = depth / (depth.max() + 1e-5)
torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(t) + ".png"))
torchvision.utils.save_image(depth, os.path.join(depth_path, '{0:05d}'.format(t) + ".png"))
renderings = np.stack(renderings, 0).transpose(0, 2, 3, 1)
imageio.mimwrite(os.path.join(render_path, 'video.mp4'), renderings, fps=30, quality=8)
def interpolate_all(model_path, load2gpt_on_the_fly, name, iteration, views, gaussians, pipeline, background, deform):
render_path = os.path.join(model_path, name, "interpolate_all_{}".format(iteration), "renders")
depth_path = os.path.join(model_path, name, "interpolate_all_{}".format(iteration), "depth")
makedirs(render_path, exist_ok=True)
makedirs(depth_path, exist_ok=True)
frame = 150
render_poses = torch.stack([pose_spherical(angle, -30.0, 4.0) for angle in np.linspace(-180, 180, frame + 1)[:-1]], 0)
to8b = lambda x: (255 * np.clip(x, 0, 1)).astype(np.uint8)
idx = torch.randint(0, len(views), (1,)).item()
view = views[idx] # Choose a specific time for rendering
renderings = []
for i, pose in enumerate(tqdm(render_poses, desc="Rendering progress")):
fid = torch.Tensor([i / (frame - 1)]).cuda()
matrix = np.linalg.inv(np.array(pose))
R = -np.transpose(matrix[:3, :3])
R[:, 0] = -R[:, 0]
T = -matrix[:3, 3]
view.reset_extrinsic(R, T)
xyz = gaussians.get_xyz
if deform.name == 'mlp':
time_input = fid.unsqueeze(0).expand(xyz.shape[0], -1)
elif deform.name == 'node':
time_input = deform.deform.expand_time(fid)
d_values = deform.step(xyz.detach(), time_input, feature=gaussians.feature, motion_mask=gaussians.motion_mask)
d_xyz, d_rotation, d_scaling, d_opacity, d_color = d_values['d_xyz'], d_values['d_rotation'], d_values['d_scaling'], d_values['d_opacity'], d_values['d_color']
results = render(view, gaussians, pipeline, background, d_xyz, d_rotation, d_scaling, d_opacity=d_opacity, d_color=d_color, d_rot_as_res=deform.d_rot_as_res)
rendering = torch.clamp(results["render"], 0.0, 1.0)
renderings.append(to8b(rendering.cpu().numpy()))
depth = results["depth"]
depth = depth / (depth.max() + 1e-5)
torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(i) + ".png"))
torchvision.utils.save_image(depth, os.path.join(depth_path, '{0:05d}'.format(i) + ".png"))
renderings = np.stack(renderings, 0).transpose(0, 2, 3, 1)
imageio.mimwrite(os.path.join(render_path, 'video.mp4'), renderings, fps=30, quality=8)
def render_sets(dataset: ModelParams, iteration: int, pipeline: PipelineParams, skip_train: bool, skip_test: bool, mode: str, load2device_on_the_fly=False):
with torch.no_grad():
deform = DeformModel(K=dataset.K, deform_type=dataset.deform_type, is_blender=dataset.is_blender, skinning=dataset.skinning, hyper_dim=dataset.hyper_dim, node_num=dataset.node_num, pred_opacity=dataset.pred_opacity, pred_color=dataset.pred_color, use_hash=dataset.use_hash, hash_time=dataset.hash_time, d_rot_as_res=dataset.d_rot_as_res, local_frame=dataset.local_frame, progressive_brand_time=dataset.progressive_brand_time, max_d_scale=dataset.max_d_scale)
deform.load_weights(dataset.model_path, iteration=iteration)
gs_fea_dim = deform.deform.node_num if dataset.skinning and deform.name == 'node' else dataset.hyper_dim
gaussians = GaussianModel(dataset.sh_degree, fea_dim=gs_fea_dim, with_motion_mask=dataset.gs_with_motion_mask)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
if mode == "render":
render_func = render_set
elif mode == "time":
render_func = interpolate_time
else:
render_func = interpolate_all
if not skip_train:
render_func(dataset.model_path, load2device_on_the_fly, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background, deform)
if not skip_test:
render_func(dataset.model_path, load2device_on_the_fly, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background, deform)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)
pipeline = PipelineParams(parser)
op = OptimizationParams(parser)
parser.add_argument("--iteration", default=-1, type=int)
parser.add_argument("--skip_train", action="store_true")
parser.add_argument("--skip_test", action="store_true")
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--mode", default='render', choices=['render', 'time', 'view', 'all', 'pose', 'original'])
parser.add_argument('--gui', action='store_true', help="start a GUI")
parser.add_argument('--W', type=int, default=800, help="GUI width")
parser.add_argument('--H', type=int, default=800, help="GUI height")
parser.add_argument('--elevation', type=float, default=0, help="default GUI camera elevation")
parser.add_argument('--radius', type=float, default=5, help="default GUI camera radius from center")
parser.add_argument('--fovy', type=float, default=50, help="default GUI camera fovy")
parser.add_argument('--ip', type=str, default="127.0.0.1")
parser.add_argument('--port', type=int, default=6009)
parser.add_argument('--detect_anomaly', action='store_true', default=False)
parser.add_argument("--test_iterations", nargs="+", type=int,
default=[5000, 6000, 7_000] + list(range(10000, 80_0001, 1000)))
parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 10_000, 20_000, 30_000, 40000])
# parser.add_argument("--quiet", action="store_true")
parser.add_argument("--deform-type", type=str, default='mlp')
args = get_combined_args(parser)
if not args.model_path.endswith(args.deform_type):
args.model_path = os.path.join(os.path.dirname(os.path.normpath(args.model_path)), os.path.basename(os.path.normpath(args.model_path)) + f'_{args.deform_type}')
print("Rendering " + args.model_path)
# Initialize system state (RNG)
safe_state(args.quiet)
render_sets(model.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test, args.mode, load2device_on_the_fly=args.load2gpu_on_the_fly)