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eval2.py
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eval2.py
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import torch
import os
import numpy as np
from collections import defaultdict
from tqdm import tqdm
import imageio
from argparse import ArgumentParser
from models.rendering import render_rays1,render_rays
from models.nerf import *
from utils import load_ckpt
import metrics
from datasets import dataset_dict
from datasets.depth_utils import *
import configargparse
torch.backends.cudnn.benchmark = True
def config_parser(cmd=None):
parser = ArgumentParser()
parser = configargparse.ArgumentParser()
parser.add_argument('--root_dir', type=str,
default='/data1/liufengyi/all_datasets/multi-view',
help='root directory of dataset')
parser.add_argument('--dataset_name', type=str, default='mvcam_llff1',
choices=['blender', 'llff','blender1', 'mvcam_change','mvcam_llff', 'mvcam_llff1'],
help='which dataset to validate')
parser.add_argument('--scene_name', type=str, default='test_final',
help='scene name, used as output folder name')
parser.add_argument('--split', type=str, default='test',
help='test or test_train')
parser.add_argument('--img_wh', nargs="+", type=int, default=[360, 640],
help='resolution (img_w, img_h) of the image')
parser.add_argument('--spheric_poses', default=False, action="store_true",
help='whether images are taken in spheric poses (for llff)')
parser.add_argument('--N_samples', type=int, default=64,
help='number of coarse samples')
parser.add_argument('--N_importance', type=int, default=128,
help='number of additional fine samples')
parser.add_argument('--use_disp', default=False, action="store_true",
help='use disparity depth sampling')
parser.add_argument('--chunk', type=int, default=32*1024*4,
help='chunk size to split the input to avoid OOM')
parser.add_argument('--ckpt_path', type=str, default = '/data1/liufengyi/get_results/nerfpl_t/runs_new/mvcam_final/ckpts/final.tar',
help='pretrained checkpoint path to load')
parser.add_argument('--save_depth', default=False, action="store_true",
help='whether to save depth prediction')
parser.add_argument('--depth_format', type=str, default='pfm',
choices=['pfm', 'bytes'],
help='which format to save')
if cmd is not None:
return parser.parse_args(cmd)
else:
return parser.parse_args(args=[])
@torch.no_grad()
def batched_inference(models, embeddings,
rays, N_samples, N_importance, use_disp,
chunk,
white_back,
t_normalize):
"""Do batched inference on rays using chunk."""
B = rays.shape[0]
chunk = 1024*32
results = defaultdict(list)
for i in range(0, B, chunk):
rendered_ray_chunks = \
render_rays1( models,
embeddings,
rays[i:i+chunk],
N_samples,
use_disp,
0,
0,
N_importance,
chunk,
dataset.white_back,
t_normalize = t_normalize,
test_time=True
)
for k, v in rendered_ray_chunks.items():
results[k] += [v]
for k, v in results.items():
results[k] = torch.cat(v, 0)
return results
if __name__ == "__main__":
args = config_parser()
h, w = args.img_wh
kwargs = {'root_dir': args.root_dir,
'split': args.split,
'img_wh': tuple(args.img_wh)}
if args.dataset_name == 'llff':
kwargs['spheric_poses'] = args.spheric_poses
# dataset = dataset_dict[args.dataset_name](**kwargs)
val_dir = args.root_dir
dataset = dataset_dict[args.dataset_name](root_dir=val_dir, split='test', max_len=10)
# embedding_xyz = Embedding(3, 10)
embedding_xyz = Embedding(4, 10)
embedding_dir = Embedding(3, 4)
nerf_coarse = NeRF()
nerf_fine = NeRF()
if args.ckpt_path is not None and args.ckpt_path != 'None':
ckpts = [args.ckpt_path]
print('Found ckpts', ckpts)
if len(ckpts) > 0 :
ckpt_path = ckpts[-1]
print('Reloading from', ckpt_path)
ckpt = torch.load(ckpt_path)
nerf_coarse.load_state_dict(ckpt['nerf_coarse_state_dict'])
nerf_fine.load_state_dict(ckpt['nerf_fine_state_dict'])
# load_ckpt(nerf_coarse, args.ckpt_path, model_name='nerf_coarse')
# load_ckpt(nerf_fine, args.ckpt_path, model_name='nerf_fine')
nerf_coarse.cuda().eval()
nerf_fine.cuda().eval()
models = [nerf_coarse, nerf_fine]
embeddings = [embedding_xyz, embedding_dir]
imgs = []
psnrs = []
dir_name = f'/data1/liufengyi/get_results/nerfpl_t/results_1/{args.dataset_name}/{args.scene_name}'
os.makedirs(dir_name, exist_ok=True)
for i in tqdm(range(len(dataset))):
sample = dataset[i]
rays = sample['rays'].cuda()
rays = rays.squeeze()
rgbs = sample['rgbs'].squeeze().cuda
scene_t = sample['scene_t']
t_num1 = sample['t_num1']
t_num1 = 100
scene_t = torch.tensor(scene_t).float().cuda()
t_num1 = torch.tensor(t_num1).float().cuda()
t_normalize = 2*scene_t/(t_num1-1)-1
# t_normalize = 2*scene_t.type(torch.FloatTensor)/(t_num1.type(torch.FloatTensor)-1)-1
results = batched_inference(models, embeddings, rays.float(),
args.N_samples, args.N_importance, args.use_disp,
args.chunk,
dataset.white_back,
t_normalize = t_normalize)
img_pred1 = results['rgb_fine'].view(h, w, 3)
img_pred = img_pred1.cpu().numpy()
if args.save_depth:
depth_pred = results['depth_fine'].view(h, w).cpu().numpy()
depth_pred = np.nan_to_num(depth_pred)
if args.depth_format == 'pfm':
save_pfm(os.path.join(dir_name, f'depth_{i:03d}.pfm'), depth_pred)
else:
with open(f'depth_{i:03d}', 'wb') as f:
f.write(depth_pred.tobytes())
img_pred_ = (img_pred*255).astype(np.uint8)
imgs += [img_pred_]
imageio.imwrite(os.path.join(dir_name, f'liu_{i:03d}.png'), img_pred_)
if 'rgbs' in sample:
rgbs = sample['rgbs']
img_gt = rgbs.view(h, w, 3)
img_gt = img_gt.unsqueeze(0)
img_pred1 = img_pred1.unsqueeze(0) #[1,h,w,3]
img_cha = abs(img_gt - img_pred1.cpu())
# img_cha = 1.-(img_gt - img_gt)
img_vis = torch.cat((img_gt,img_pred1.cpu(),img_cha),dim=0).permute(1,0,2,3).reshape(img_gt.shape[1],-1,3).numpy()
# imageio.imwrite(os.path.join(dir_name, f'liu_{i:03d}_compare.png'), (img_vis*255).astype(np.uint8))
imageio.imwrite(os.path.join(dir_name, f'liu_{i:03d}_compare.png'), (img_vis*255).astype(np.uint8))
psnrs += [metrics.psnr(img_gt, img_pred).item()]
imageio.mimsave(os.path.join(dir_name, f'{args.scene_name}.gif'), imgs, fps=30)
if psnrs:
mean_psnr = np.mean(psnrs)
print(f'Mean PSNR : {mean_psnr:.2f}')