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eval.py
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eval.py
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import os
import numpy as np
import shutil
import torch
import torch.utils.data.distributed
from torch.utils.data import DataLoader
from evenerf.data_loaders import dataset_dict
from evenerf.render_image import render_single_image
from evenerf.model import EVENeRF
from evenerf.sample_ray import RaySamplerSingleImage
from utils import img_HWC2CHW, colorize, img2psnr, lpips, get_ssim, show_attention_map
import config
import torch.distributed as dist
from evenerf.projection import Projector
from evenerf.data_loaders.create_training_dataset import create_training_dataset
import imageio
import tqdm
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)
if args.run_val == False:
# create training dataset
dataset, sampler = create_training_dataset(args)
# currently only support batch_size=1 (i.e., one set of target and source views) for each GPU node
# please use distributed parallel on multiple GPUs to train multiple target views per batch
loader = torch.utils.data.DataLoader(
dataset,
batch_size=1,
worker_init_fn=lambda _: np.random.seed(),
num_workers=args.workers,
pin_memory=True,
sampler=sampler,
shuffle=True if sampler is None else False,
)
iterator = iter(loader)
else:
# create validation dataset
dataset = dataset_dict[args.eval_dataset](args, "validation", scenes=args.eval_scenes)
loader = DataLoader(dataset, batch_size=1)
iterator = iter(loader)
# Create GNT model
model = EVENeRF(
args, load_opt=not args.no_load_opt, load_scheduler=not args.no_load_scheduler
)
# create projector
projector = Projector(device=device)
psnr_scores = []
lpips_scores = []
ssim_scores = []
for indx, data in enumerate(tqdm.tqdm(loader, leave=True)):
if args.local_rank == 0:
tmp_ray_sampler = RaySamplerSingleImage(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)
psnr_curr_img, lpips_curr_img, ssim_curr_img = log_view(
indx,
args,
model,
tmp_ray_sampler,
projector,
gt_img,
render_stride=args.render_stride,
prefix="val/" if args.run_val else "train/",
out_folder=out_folder,
vis_depth=args.vis_depth,
)
psnr_scores.append(psnr_curr_img)
lpips_scores.append(lpips_curr_img)
ssim_scores.append(ssim_curr_img)
torch.cuda.empty_cache()
print("Average PSNR: ", np.mean(psnr_scores))
print("Average LPIPS: ", np.mean(lpips_scores))
print("Average SSIM: ", np.mean(ssim_scores))
txt_filename = os.path.join(out_folder, "result.txt")
with open(txt_filename, "w", encoding="utf-8") as f:
f.write("Average PSNR: " + str(np.mean(psnr_scores)))
f.write("\n")
f.write("Average LPIPS: " + str(np.mean(lpips_scores)))
f.write("\n")
f.write("Average SSIM: " + str(np.mean(ssim_scores)))
@torch.no_grad()
def log_view(
global_step,
args,
model,
ray_sampler,
projector,
gt_img,
render_stride=1,
prefix="",
out_folder="",
vis_depth=False,
):
model.switch_to_eval()
with torch.no_grad():
ray_batch = ray_sampler.get_all()
if model.feature_net is not None:
featmaps = model.feature_net(ray_batch["src_rgbs"].squeeze(0).permute(0, 3, 1, 2))
else:
featmaps = [None, None]
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,
white_bkgd=args.white_bkgd,
render_stride=render_stride,
featmaps=featmaps,
)
average_im = ray_sampler.src_rgbs.cpu().mean(dim=(0, 1))
if args.render_stride != 1:
gt_img = gt_img[::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 = ret["outputs_render"]["rgb"].detach().cpu()
rgb_show = img_HWC2CHW(rgb_pred)
rgb_show = rgb_show.permute(1, 2, 0).numpy()
filename = os.path.join(out_folder, prefix[:-1] + "_{:03d}_render.png".format(global_step))
imageio.imwrite(filename, rgb_show)
if vis_depth:
depth_pred = ret["outputs_render"]["depth"].detach().cpu()
depth_show = img_HWC2CHW(colorize(depth_pred, cmap_name="jet"))
depth_show = depth_show.permute(1, 2, 0).detach().cpu().numpy()
filename = os.path.join(
out_folder, prefix[:-1] + "_{:03d}_depth.png".format(global_step)
)
imageio.imwrite(filename, depth_show)
# write scalar
pred_rgb = torch.clip(rgb_pred, 0.0, 1.0)
lpips_curr_img = lpips(pred_rgb*2.-1., gt_img*2.-1., format="HWC").item()
ssim_curr_img = get_ssim(pred_rgb, gt_img)
psnr_curr_img = img2psnr(pred_rgb.detach().cpu(), gt_img)
print(prefix + "psnr_image: ", psnr_curr_img)
print(prefix + "lpips_image: ", lpips_curr_img)
print(prefix + "ssim_image: ", ssim_curr_img)
return psnr_curr_img, lpips_curr_img, ssim_curr_img
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.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl", init_method="env://")
synchronize()
eval(args)