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eval.py
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eval.py
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import os
import cv2
from collections import defaultdict
from tqdm import tqdm
import imageio
from argparse import ArgumentParser
from models.rendering import render_rays
from models.nerf import *
from utils import load_ckpt
import metrics
from datasets import dataset_dict
from datasets.depth_utils import *
from datasets.carla_utils.utils import *
torch.backends.cudnn.benchmark = True
def get_opts():
parser = ArgumentParser()
parser.add_argument('--root_dir', type=str,
default='/home/ubuntu/data/nerf_example_data/nerf_synthetic/lego',
help='root directory of dataset')
parser.add_argument('--dataset_name', type=str, default='blender',
choices=['blender', 'llff','carla'],
help='which dataset to validate')
parser.add_argument('--scene_name', type=str, default='test',
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=[800, 800],
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, required=True,
help='pretrained checkpoint path to load')
parser.add_argument('--save_depth', default=True, 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')
return parser.parse_args()
@torch.no_grad()
def batched_inference(models, embeddings,
rays,segs, N_samples, N_importance, use_disp,
chunk):
"""Do batched inference on rays using chunk."""
B = rays.shape[0]
results = defaultdict(list)
for i in range(0, B, chunk):
rendered_ray_chunks = \
render_rays(models,
embeddings,
rays[i:i+chunk],
segs[i:i+chunk],
N_samples,
use_disp,
0,
0,
N_importance,
chunk,
dataset.white_back,
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 = get_opts()
w, h = args.img_wh
kwargs = {'root_dir': args.root_dir,
'split': args.split,
'img_wh': tuple(args.img_wh)}
fps =5 if args.split == 'test_train' else 30
if args.dataset_name == 'llff':
kwargs['spheric_poses'] = args.spheric_poses
dataset = dataset_dict[args.dataset_name](**kwargs)
embedding_xyz = Embedding(3, 10)
embedding_dir = Embedding(3, 4)
nerf_coarse = NeRF()
load_ckpt(nerf_coarse, args.ckpt_path, model_name='nerf_coarse')
nerf_coarse.cuda().eval()
models = {'coarse': nerf_coarse}
embeddings = {'xyz': embedding_xyz, 'dir': embedding_dir}
if args.N_importance > 0:
nerf_fine = NeRF()
load_ckpt(nerf_fine, args.ckpt_path, model_name='nerf_fine')
nerf_fine.cuda().eval()
models['fine'] = nerf_fine
imgs ,depth_maps, psnrs, input_segs = [], [], [], []
dir_name = f'results/{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()
segs_onehot = sample['segs_onehot'].cuda()
segs = sample['segs'].cpu().view(h,w,1).squeeze(-1)
save_semantic = SaveSemantics('carla')
save_semantic(segs,os.path.join(dir_name, f'inputSeg_{i:03d}.png'))
input_segs += [save_semantic.to_color(segs)]
results = batched_inference(models, embeddings, rays, segs_onehot,
args.N_samples, args.N_importance, args.use_disp,
args.chunk)
typ = 'fine' if 'rgb_fine' in results else 'coarse'
img_pred = np.clip(results[f'rgb_{typ}'].view(h, w, 3).cpu().numpy(), 0, 1)
if args.save_depth:
depth_pred = results[f'depth_{typ}'].view(h, w).cpu().numpy()
depth_maps += [depth_pred]
img_pred_ = (img_pred * 255).astype(np.uint8)
imgs += [img_pred_]
imageio.imwrite(os.path.join(dir_name, f'{i:03d}.png'), img_pred_)
if 'rgbs' in sample:
rgbs = sample['rgbs']
img_gt = rgbs.view(h, w, 3)
psnrs += [metrics.psnr(img_gt, img_pred).item()]
imageio.mimsave(os.path.join(dir_name, f'{args.scene_name}.gif'), imgs, fps=fps)
imageio.mimsave(os.path.join(dir_name, f'inputSeg_{args.scene_name}.gif'), input_segs, fps=fps)
if args.save_depth:
depth_imgs = (depth_maps - np.min(depth_maps)) / (max(np.max(depth_maps) - np.min(depth_maps), 1e-8))
depth_imgs_ = [cv2.applyColorMap((img * 255).astype(np.uint8), cv2.COLORMAP_JET) for img in depth_imgs]
for i,img in enumerate(depth_imgs_):
imageio.imwrite(os.path.join(dir_name, f'{i:03d}_depth.png'), img)
imageio.mimsave(os.path.join(dir_name, f'{args.scene_name}_depth.gif'), depth_imgs_, fps=fps)
if psnrs:
mean_psnr = np.mean(psnrs)
print(f'Mean PSNR : {mean_psnr:.2f}')