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evaluate.py
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import os
import json
import hydra
import torch
import numpy as np
from tqdm import tqdm
from pathlib import Path
from omegaconf import DictConfig
from hydra.core.hydra_config import HydraConfig
from matplotlib import pyplot as plt
import torchvision.transforms.functional as TF
from models.model import GaussianPredictor, to_device
from evaluation.evaluator import Evaluator
from datasets.util import create_datasets
from misc.util import add_source_frame_id
from misc.visualise_3d import save_ply
def get_model_instance(model):
"""
unwraps model from EMA object
"""
return model.ema_model if type(model).__name__ == "EMA" else model
def evaluate(model, cfg, evaluator, dataloader, device=None, save_vis=False):
model_model = get_model_instance(model)
model_model.set_eval()
score_dict = {}
match cfg.dataset.name:
case "re10k" | "nyuv2":
# override the frame idxs used for eval
target_frame_ids = [1, 2, 3]
eval_frames = ["src", "tgt5", "tgt10", "tgt_rand"]
for fid, target_name in zip(add_source_frame_id(target_frame_ids),
eval_frames):
score_dict[fid] = { "ssim": [],
"psnr": [],
"lpips": [],
"name": target_name }
case "kitti":
if cfg.dataset.stereo:
eval_frames = ["s0"]
target_frame_ids = ["s0"]
all_frames = add_source_frame_id(eval_frames)
else:
eval_frames = [1, 2]
target_frame_ids = eval_frames
all_frames = add_source_frame_id(target_frame_ids)
for fid in all_frames:
score_dict[fid] = { "ssim": [],
"psnr": [],
"lpips": [],
"name": fid}
dataloader_iter = iter(dataloader)
for k in tqdm([i for i in range(len(dataloader.dataset) // cfg.data_loader.batch_size)]):
try:
inputs = next(dataloader_iter)
except Exception as e:
if cfg.dataset.name=="re10k":
if cfg.dataset.test_split in ["pixelsplat_ctx1",
"pixelsplat_ctx2",
"latentsplat_ctx1",
"latentsplat_ctx2"]:
print("Failed to read example {}".format(k))
continue
raise e
if save_vis:
out_dir = Path("/work/cxzheng/3D/splatvideo/eldar/visual_results/images")
out_dir.mkdir(exist_ok=True)
print(f"saving images to: {out_dir.resolve()}")
seq_name = inputs[("frame_id", 0)][0].split("+")[1]
out_out_dir = out_dir / seq_name
out_out_dir.mkdir(exist_ok=True)
out_pred_dir = out_out_dir / f"pred"
out_pred_dir.mkdir(exist_ok=True)
out_gt_dir = out_out_dir / f"gt"
out_gt_dir.mkdir(exist_ok=True)
out_dir_ply = out_out_dir / "ply"
out_dir_ply.mkdir(exist_ok=True)
with torch.no_grad():
if device is not None:
to_device(inputs, device)
inputs["target_frame_ids"] = target_frame_ids
outputs = model(inputs)
for f_id in score_dict.keys():
pred = outputs[('color_gauss', f_id, 0)]
if cfg.dataset.name == "dtu":
gt = inputs[('color_orig_res', f_id, 0)]
pred = TF.resize(pred, gt.shape[-2:])
else:
gt = inputs[('color', f_id, 0)]
# should work in for B>1, however be careful of reduction
out = evaluator(pred, gt)
if save_vis:
save_ply(outputs, out_dir_ply / f"{f_id}.ply", gaussians_per_pixel=model.cfg.model.gaussians_per_pixel)
pred = pred[0].clip(0.0, 1.0).permute(1, 2, 0).detach().cpu().numpy()
gt = gt[0].clip(0.0, 1.0).permute(1, 2, 0).detach().cpu().numpy()
plt.imsave(str(out_pred_dir / f"{f_id:03}.png"), pred)
plt.imsave(str(out_gt_dir / f"{f_id:03}.png"), gt)
for metric_name, v in out.items():
score_dict[f_id][metric_name].append(v)
metric_names = ["psnr", "ssim", "lpips"]
score_dict_by_name = {}
for f_id in score_dict.keys():
score_dict_by_name[score_dict[f_id]["name"]] = {}
for metric_name in metric_names:
# compute mean
score_dict[f_id][metric_name] = sum(score_dict[f_id][metric_name]) / len(score_dict[f_id][metric_name])
# original dict has frame ids as integers, for json out dict we want to change them
# to the meaningful names stored in dict
score_dict_by_name[score_dict[f_id]["name"]][metric_name] = score_dict[f_id][metric_name]
for metric in metric_names:
vals = [score_dict_by_name[f_id][metric] for f_id in eval_frames]
print(f"{metric}:", np.mean(np.array(vals)))
return score_dict_by_name
@hydra.main(
config_path="configs",
config_name="config",
version_base=None
)
def main(cfg: DictConfig):
print("current directory:", os.getcwd())
hydra_cfg = HydraConfig.get()
output_dir = hydra_cfg['runtime']['output_dir']
os.chdir(output_dir)
print("Working dir:", output_dir)
cfg.data_loader.batch_size = 1
cfg.data_loader.num_workers = 1
model = GaussianPredictor(cfg)
device = torch.device("cuda:0")
model.to(device)
if (ckpt_dir := model.checkpoint_dir()).exists():
# resume training
model.load_model(ckpt_dir, ckpt_ids=0)
evaluator = Evaluator(crop_border=cfg.dataset.crop_border)
evaluator.to(device)
split = "test"
save_vis = cfg.eval.save_vis
dataset, dataloader = create_datasets(cfg, split=split)
score_dict_by_name = evaluate(model, cfg, evaluator, dataloader,
device=device, save_vis=save_vis)
print(json.dumps(score_dict_by_name, indent=4))
if cfg.dataset.name=="re10k":
with open("metrics_{}_{}_{}.json".format(cfg.dataset.name, split, cfg.dataset.test_split), "w") as f:
json.dump(score_dict_by_name, f, indent=4)
with open("metrics_{}_{}.json".format(cfg.dataset.name, split), "w") as f:
json.dump(score_dict_by_name, f, indent=4)
if __name__ == "__main__":
main()