-
Notifications
You must be signed in to change notification settings - Fork 26
/
render.py
executable file
·236 lines (192 loc) · 12.3 KB
/
render.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
#
# 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 george.drettakis@inria.fr
#
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)