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eval_gpnerf.py
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eval_gpnerf.py
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import os
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_image import render_single_image
from gpnerf.model import GPNeRFModel
from gpnerf.sample_ray import RaySamplerSingleImage
from utils import img_HWC2CHW, colorize, img2psnr, lpips, ssim
import config
import torch.distributed as dist
from gpnerf.projection import Projector
import imageio
from gpnerf.loss import RenderLoss, SemanticLoss, IoU, DepthLoss
import logging
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()
@torch.no_grad()
def eval(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)
logging.basicConfig(format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s',
level=logging.CRITICAL,
filename=os.path.join(out_folder, "result.log"),
filemode='a')
# create validation dataset
val_set_lists, val_set_names = [], []
val_scenes = np.loadtxt(args.val_set_list, dtype=str).tolist()
for name in val_scenes:
val_dataset = dataset_dict[args.eval_dataset](args, is_train=False, scenes=name)
val_loader = DataLoader(val_dataset, batch_size=1)
val_set_lists.append(val_loader)
if 'scannet' not in args.eval_dataset:
val_set_names.append(name)
os.makedirs(out_folder + '/' + name, exist_ok=True)
else:
val_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 gpnerf model
model = GPNeRFModel(
args, load_opt=not args.no_load_opt, load_scheduler=not args.no_load_scheduler
)
# create projector
projector = Projector(device=device)
iou_criterion = IoU(args)
semantic_criterion = SemanticLoss(args)
all_psnr_scores,all_lpips_scores,all_ssim_scores, = [],[],[]
all_iou_scores, all_tot_acc_scores, all_avg_acc_scores = [], [], []
for val_loader, scene_name in zip(val_set_lists, val_set_names):
indx = 0
psnr_scores, lpips_scores, ssim_scores, = [],[],[]
iou_scores, tot_acc_scores, avg_acc_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, tot_acc_metric, avg_acc_metric = log_view(
indx,
args,
model,
tmp_ray_sampler,
projector,
gt_img,
gt_depth,
evaluator=[iou_criterion, semantic_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)
tot_acc_scores.append(tot_acc_metric)
avg_acc_scores.append(avg_acc_metric)
torch.cuda.empty_cache()
indx += 1
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_tot_acc = np.mean(tot_acc_scores)
scene_avg_acc = np.mean(avg_acc_scores)
all_psnr_scores.append(scene_psnr)
all_lpips_scores.append(scene_lpips)
all_ssim_scores.append(scene_ssim)
all_tot_acc_scores.append(scene_tot_acc)
all_avg_acc_scores.append(scene_avg_acc)
all_iou_scores.append(scene_iou)
print("Average {} PSNR: {}, LPIPS: {}, SSIM: {}, IoU: {}, TotAcc: {}, AvgAcc: {}".format(
scene_name,scene_psnr, scene_lpips, scene_ssim, scene_iou, scene_tot_acc, scene_avg_acc))
logging.critical("Average {} PSNR: {}, LPIPS: {}, SSIM: {}, IoU: {}, TotAcc: {}, AvgAcc: {}".format(
scene_name,scene_psnr, scene_lpips, scene_ssim, scene_iou, scene_tot_acc, scene_avg_acc))
print("Overall PSNR: {}, LPIPS: {}, SSIM: {}, IoU: {}".format(
np.mean(all_psnr_scores),
np.mean(all_lpips_scores),
np.mean(all_ssim_scores),
np.mean(all_iou_scores)))
logging.critical("Overall PSNR: {}, LPIPS: {}, SSIM: {}, IoU: {}".format(
np.mean(all_psnr_scores),
np.mean(all_lpips_scores),
np.mean(all_ssim_scores),
np.mean(all_iou_scores)))
@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,
)
if args.render_stride == 1:
ret['outputs_coarse']['sems'] = model.sem_seg_head(ret['outputs_coarse']['feats_out'][::2, ::2, :].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'][::2, ::2, :].permute(2,0,1).unsqueeze(0).to(device), None, None).permute(0,2,3,1)
else:
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)
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()
rgb_im = (255 * np.clip(rgb_im, a_min=0, a_max=1.)).astype(np.uint8)
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()
depth_im = (255 * np.clip(depth_im, a_min=0, a_max=1.)).astype(np.uint8)
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)
sem_imgs = evaluator[1].plot_semantic_results(ret["outputs_fine"], ray_batch, global_step, val_name, vis=True)
if args.render_stride != 1:
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())
model.switch_to_train()
return psnr_curr_img, lpips_curr_img, ssim_curr_img, iou_metric['miou'].item(), iou_metric['total_accuracy'].item(), iou_metric['class_average_accuracy'].item()
if __name__ == "__main__":
parser = config.config_parser()
parser.add_argument("--run_val", action="store_true", help="run on val set")
args = parser.parse_args()
if args.train_dataset == 'train_replica' and args.eval_dataset == 'val_replica':
import imgviz
args.semantic_color_map = imgviz.label_colormap(args.num_classes + 1)
else:
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
]
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl", init_method="env://")
synchronize()
eval(args)