-
Notifications
You must be signed in to change notification settings - Fork 0
/
render.py
341 lines (291 loc) · 12.8 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
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
import os
import time
import numpy as np
import shutil
import torch
import torch.utils.data.distributed
from torch.nn import functional as F
from torch.utils.data import DataLoader
from gpnerf.data_loaders import dataset_dict
from gpnerf.render_ray import render_rays
from gpnerf.render_image import render_single_image
from gpnerf.model import GPNeRFModel
from gpnerf.ibrnet import IBRNetModel
from gpnerf.sample_ray import RaySamplerSingleImage
from gpnerf.criterion import SemanticCriterion
from utils import img_HWC2CHW, img2psnr, colorize, img2psnr, lpips, ssim
from gpnerf.loss import RenderLoss, SemanticLoss, IoU, DepthLoss
import config
import torch.distributed as dist
from gpnerf.projection import Projector
import imageio
import logging
def setup_for_distributed(is_master):
"""
This function disables printing when not in master process
"""
import builtins as __builtin__
builtin_print = __builtin__.print
def print(*args, **kwargs):
force = kwargs.pop('force', False)
if is_master or force:
builtin_print(*args, **kwargs)
__builtin__.print = print
def init_distributed_mode(args):
if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
args.rank = int(os.environ["RANK"])
args.world_size = int(os.environ['WORLD_SIZE'])
args.gpu = int(os.environ['LOCAL_RANK'])
elif 'SLURM_PROCID' in os.environ:
args.rank = int(os.environ['SLURM_PROCID'])
args.gpu = args.rank % torch.cuda.device_count()
else:
print('Not using distributed mode')
args.distributed = False
args.rank=0
return
args.distributed = True
torch.cuda.set_device(args.gpu)
args.dist_backend = 'nccl'
print('| distributed init (rank {}): {}'.format(
args.rank, args.dist_url), flush=True)
torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
torch.distributed.barrier()
setup_for_distributed(args.rank == 0)
def worker_init_fn(worker_id):
np.random.seed(np.random.get_state()[1][0] + worker_id)
def synchronize():
"""
Helper function to synchronize (barrier) among all processes when
using distributed training
"""
if not dist.is_available():
return
if not dist.is_initialized():
return
world_size = dist.get_world_size()
if world_size == 1:
return
dist.barrier()
def render(args):
device = "cuda:{}".format(args.local_rank)
out_folder = os.path.join(args.rootdir, "out", args.expname)
print("outputs will be saved to {}".format(out_folder))
os.makedirs(out_folder, exist_ok=True)
# save the args and config files
f = os.path.join(out_folder, "args.txt")
with open(f, "w") as file:
for arg in sorted(vars(args)):
attr = getattr(args, arg)
file.write("{} = {}\n".format(arg, attr))
if args.config is not None:
f = os.path.join(out_folder, "config.txt")
if not os.path.isfile(f):
shutil.copy(args.config, f)
# create finetuning dataset for each scene
train_set_lists, val_set_lists, scene_set_names= [], [], []
ft_scenes = np.loadtxt(args.val_set_list, dtype=str).tolist()
for name in ft_scenes:
train_dataset = dataset_dict['val_scannet'](args, is_train=True, scenes=name)
train_sampler = (
torch.utils.data.distributed.DistributedSampler(train_dataset)
if args.distributed
else None
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=1,
worker_init_fn=lambda _: np.random.seed(),
num_workers=args.num_workers,
pin_memory=True,
sampler=train_sampler,
shuffle=True if train_sampler is None else False,
)
train_set_lists.append(train_loader)
val_dataset = dataset_dict['val_scannet'](args, is_train=False, scenes=name)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1)
val_set_lists.append(val_loader)
scene_set_names.append(name.split('/')[1])
os.makedirs(out_folder + '/' + name.split('/')[1], exist_ok=True)
print(f'{name} val set len {len(val_loader)}')
# create projector
projector = Projector(device=device)
# Create criterion
render_criterion = RenderLoss(args)
semantic_criterion = SemanticLoss(args)
depth_criterion = DepthLoss(args)
iou_criterion = IoU(args)
for val_loader, scene_name in zip(val_set_lists, scene_set_names):
args.ckpt_path = f'./out/{args.expname}/model_219999.pth'
model = GPNeRFModel(args, load_opt=not args.no_load_opt, load_scheduler=not args.no_load_scheduler)
logging.basicConfig(format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s',
level=logging.CRITICAL,
filename=os.path.join(out_folder, scene_name, "result.log"),
filemode='a')
print("Evaluating...")
indx = 0
psnr_scores,lpips_scores,ssim_scores, iou_scores, depth_scores = [],[],[],[],[]
for val_data in val_loader:
tmp_ray_sampler = RaySamplerSingleImage(val_data, device, render_stride=args.render_stride)
H, W = tmp_ray_sampler.H, tmp_ray_sampler.W
gt_img = tmp_ray_sampler.rgb.reshape(H, W, 3)
gt_depth = val_data['true_depth'][0]
psnr_curr_img, lpips_curr_img, ssim_curr_img, iou_metric, depth_metric = log_view(
indx,
args,
model,
tmp_ray_sampler,
projector,
gt_img,
gt_depth,
evaluator=[iou_criterion, semantic_criterion, depth_criterion],
render_stride=args.render_stride,
prefix="val/",
out_folder=out_folder,
ret_alpha=args.N_importance > 0,
single_net=args.single_net,
val_name = scene_name
)
psnr_scores.append(psnr_curr_img)
lpips_scores.append(lpips_curr_img)
ssim_scores.append(ssim_curr_img)
iou_scores.append(iou_metric)
depth_scores.append(depth_metric)
torch.cuda.empty_cache()
indx += 1
scene_psnr = np.mean(psnr_scores)
scene_iou = np.mean(iou_scores)
scene_psnr = np.mean(psnr_scores)
scene_lpips = np.mean(lpips_scores)
scene_ssim = np.mean(ssim_scores)
scene_depth = np.mean(depth_scores)
print("Average {} PSNR: {}, LPIPS: {}, SSIM: {}, IoU: {}, Depth: {}".format(
scene_name,scene_psnr ,scene_iou ,scene_psnr ,scene_lpips,scene_ssim,scene_depth))
@torch.no_grad()
def log_view(
global_step,
args,
model,
ray_sampler,
projector,
gt_img,
gt_depth,
evaluator,
render_stride=1,
prefix="",
out_folder="",
ret_alpha=False,
single_net=True,
val_name = None,
):
model.switch_to_eval()
with torch.no_grad():
ray_batch = ray_sampler.get_all()
ref_coarse_feats, fine_feats, ref_deep_semantics = model.feature_net(ray_batch["src_rgbs"].squeeze(0).permute(0, 3, 1, 2))
ref_deep_semantics = model.feature_fpn(ref_deep_semantics)
device = ref_deep_semantics.device
_, _, que_deep_semantics = model.feature_net(gt_img.unsqueeze(0).permute(0, 3, 1, 2).to(ref_coarse_feats.device))
que_deep_semantics = model.feature_fpn(que_deep_semantics)
ret = render_single_image(
ray_sampler=ray_sampler,
ray_batch=ray_batch,
model=model,
projector=projector,
chunk_size=args.chunk_size,
N_samples=args.N_samples,
inv_uniform=args.inv_uniform,
det=True,
N_importance=args.N_importance,
white_bkgd=args.white_bkgd,
render_stride=render_stride,
featmaps=ref_coarse_feats,
deep_semantics=ref_deep_semantics, # encoder的语义输出
ret_alpha=ret_alpha,
single_net=single_net,
)
ret['outputs_coarse']['sems'] = model.sem_seg_head(ret['outputs_coarse']['feats_out'].permute(2,0,1).unsqueeze(0).to(device), None, None).permute(0,2,3,1)
ret['outputs_fine']['sems'] = model.sem_seg_head(ret['outputs_fine']['feats_out'].permute(2,0,1).unsqueeze(0).to(device), None, None).permute(0,2,3,1)
ret['que_sems'] = model.sem_seg_head(que_deep_semantics, None, None).permute(0,2,3,1)
average_im = ray_sampler.src_rgbs.cpu().mean(dim=(0, 1))
if args.render_stride != 1:
gt_img = gt_img[::render_stride, ::render_stride]
gt_depth = gt_depth[::render_stride, ::render_stride]
average_im = average_im[::render_stride, ::render_stride]
rgb_gt = img_HWC2CHW(gt_img)
average_im = img_HWC2CHW(average_im)
rgb_pred = img_HWC2CHW(ret["outputs_coarse"]["rgb"].detach().cpu())
h_max = max(rgb_gt.shape[-2], rgb_pred.shape[-2], average_im.shape[-2])
w_max = max(rgb_gt.shape[-1], rgb_pred.shape[-1], average_im.shape[-1])
rgb_im = torch.zeros(3, h_max, 3 * w_max)
rgb_im[:, : average_im.shape[-2], : average_im.shape[-1]] = average_im
rgb_im[:, : rgb_gt.shape[-2], w_max : w_max + rgb_gt.shape[-1]] = rgb_gt
rgb_im[:, : rgb_pred.shape[-2], 2 * w_max : 2 * w_max + rgb_pred.shape[-1]] = rgb_pred
if "depth" in ret["outputs_coarse"].keys():
depth_pred = ret["outputs_coarse"]["depth"].detach().cpu()
depth_pred = torch.cat((colorize(gt_depth.squeeze(-1).detach().cpu(), cmap_name="jet"), colorize(depth_pred, cmap_name="jet")), dim=1)
depth_im = img_HWC2CHW(depth_pred)
else:
depth_im = None
if ret["outputs_fine"] is not None:
rgb_fine = img_HWC2CHW(ret["outputs_fine"]["rgb"].detach().cpu())
rgb_fine_ = torch.zeros(3, h_max, w_max)
rgb_fine_[:, : rgb_fine.shape[-2], : rgb_fine.shape[-1]] = rgb_fine
rgb_im = torch.cat((rgb_im, rgb_fine_), dim=-1)
depth_pred = torch.cat((depth_pred, colorize(ret["outputs_fine"]["depth"].detach().cpu(), cmap_name="jet")), dim=1)
depth_im = img_HWC2CHW(depth_pred)
rgb_im = rgb_im.permute(1, 2, 0).detach().cpu().numpy()
filename = os.path.join(out_folder, val_name, "rgb_{:03d}.png".format(global_step))
imageio.imwrite(filename, rgb_im)
if depth_im is not None:
depth_im = depth_im.permute(1, 2, 0).detach().cpu().numpy()
filename = os.path.join(out_folder, val_name, "depth_{:03d}.png".format(global_step))
imageio.imwrite(filename, depth_im)
# write scalar
pred_rgb = (
ret["outputs_fine"]["rgb"]
if ret["outputs_fine"] is not None else ret["outputs_coarse"]["rgb"]
)
lpips_curr_img = lpips(pred_rgb, gt_img, format="HWC").item()
ssim_curr_img = ssim(pred_rgb, gt_img, format="HWC").item()
psnr_curr_img = img2psnr(pred_rgb.detach().cpu(), gt_img)
iou_metric = evaluator[0](ret, ray_batch, global_step)
ret["outputs_fine"]['que_sems'] = ret["que_sems"]
sem_imgs = evaluator[1].plot_semantic_results(ret["outputs_fine"], ray_batch, global_step, val_name, vis=True)
evaluator[1].plot_pca_features(ret, ray_batch, global_step, val_name, vis=True)
print(prefix + "psnr_image: ", psnr_curr_img)
print(prefix + "lpips_image: ", lpips_curr_img)
print(prefix + "ssim_image: ", ssim_curr_img)
print(prefix + "iou: ", iou_metric['miou'].item())
logging.critical("{}-No.{:03d} PSNR: {}, LPIPS: {}, SSIM: {}, IoU: {}".format(val_name, global_step, psnr_curr_img, lpips_curr_img, ssim_curr_img, iou_metric['miou'].item()))
if 'que_miou' in iou_metric.keys():
print(prefix + "que_miou: ", iou_metric['que_miou'].item())
return psnr_curr_img, lpips_curr_img, ssim_curr_img, iou_metric['miou'].item(), iou_metric['que_miou'].item()
if __name__ == "__main__":
parser = config.config_parser()
args = parser.parse_args()
args.semantic_color_map=[
[174, 199, 232], # wall
[152, 223, 138], # floor
[31, 119, 180], # cabinet
[255, 187, 120], # bed
[188, 189, 34], # chair
[140, 86, 75], # sofa
[255, 152, 150], # table
[214, 39, 40], # door
[197, 176, 213], # window
[148, 103, 189], # bookshelf
[196, 156, 148], # picture
[23, 190, 207], # counter
[247, 182, 210], # desk
[219, 219, 141], # curtain
[255, 127, 14], # refrigerator
[91, 163, 138], # shower curtain
[44, 160, 44], # toilet
[112, 128, 144], # sink
[227, 119, 194], # bathtub
[82, 84, 163], # otherfurn
[248, 166, 116] # invalid
]
init_distributed_mode(args)
render(args)