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from typing import Callable
import lpips
import torch
import torch.nn as nn
import torch.nn.functional as F
import ignite.distributed as idst
from scenedino.common.geometry import distance_to_z
import scenedino.common.metrics as metrics
def create_depth_eval(
model: nn.Module,
scaling_function: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]
| None = None,
):
def _compute_depth_metrics(
data,
# TODO: maybe integrate model
# model: nn.Module,
):
return metrics.compute_depth_metrics(
data["depths"][0], data["coarse"][0]["depth"][:, :1], scaling_function
)
return _compute_depth_metrics
def create_nvs_eval(model: nn.Module):
lpips_fn = lpips.LPIPS().to(idst.device())
def _compute_nvs_metrics(
data,
# model: nn.Module,
):
return metrics.compute_nvs_metrics(data, lpips_fn)
return _compute_nvs_metrics
def create_dino_eval(model: nn.Module):
def _compute_dino_metrics(
data,
):
return metrics.compute_dino_metrics(data)
return _compute_dino_metrics
def create_seg_eval(model: nn.Module, n_classes: int, gt_classes: int):
def _compute_seg_metrics(
data,
):
return metrics.compute_seg_metrics(data, n_classes, gt_classes) # Why is this necessary?
return _compute_seg_metrics
def create_stego_eval(model: nn.Module):
def _compute_stego_metrics(
data,
):
return metrics.compute_stego_metrics(data) # Why is this necessary?
return _compute_stego_metrics
# code for saving voxel grid
# def pack(uncompressed):
# """convert a boolean array into a bitwise array."""
# uncompressed_r = uncompressed.reshape(-1, 8)
# compressed = uncompressed_r.dot(
# 1 << np.arange(uncompressed_r.shape[-1] - 1, -1, -1)
# )
# return compressed
# if self.save_bin_path:
# # base_file = "/storage/user/hank/methods_test/semantic-kitti-api/bts_test/sequences/00/voxels"
# outside_frustum = (
# (
# (cam_pts[:, 0] < -1.0)
# | (cam_pts[:, 0] > 1.0)
# | (cam_pts[:, 1] < -1.0)
# | (cam_pts[:, 0] > 1.0)
# )
# .reshape(q_pts_shape)
# .permute(1, 2, 0)
# .detach()
# .cpu()
# .numpy()
# )
# is_occupied_numpy = (
# is_occupied_pred.reshape(q_pts_shape)
# .permute(1, 2, 0)
# .detach()
# .cpu()
# .numpy()
# .astype(np.float32)
# )
# is_occupied_numpy[outside_frustum] = 0.0
# ## carving out the invisible regions out of view-frustum
# # for i_ in range(
# # (is_occupied_numpy.shape[0]) // 2
# # ): ## left | right half of the space
# # for j_ in range(i_ + 1):
# # is_occupied_numpy[i_, j_] = 0
# pack(np.flip(is_occupied_numpy, (0, 1, 2)).reshape(-1)).astype(
# np.uint8
# ).tofile(
# # f"{base_file}/{self.counter:0>6}.bin"
# f"{self.save_bin_path}/{self.counter:0>6}.bin"
# )
# # for idx_i, image in enumerate(images[0]):
# # torchvision.utils.save_image(
# # image, f"{self.save_bin_path}/{self.counter:0>6}_{idx_i}.png"
# # )
def project_into_cam(pts, proj, pose):
pts = torch.cat((pts, torch.ones_like(pts[:, :1])), dim=-1)
cam_pts = (proj @ (torch.inverse(pose).squeeze()[:3, :] @ pts.T)).T
cam_pts[:, :2] /= cam_pts[:, 2:3]
dist = cam_pts[:, 2]
return cam_pts, dist
def create_occ_eval(
model: nn.Module,
occ_threshold: float,
query_batch_size: int,
):
# TODO: deal with other models such as IBRnet
def _compute_occ_metrics(
data,
):
projs = torch.stack(data["projs"], dim=1)
images = torch.stack(data["imgs"], dim=1)
_, _, _, h, w = images.shape
poses = torch.stack(data["poses"], dim=1)
device = poses.device
# TODO: get occ points and occupation from dataset
occ_pts = data["occ_pts"].permute(0, 2, 1, 3).contiguous()
occ_pts = occ_pts.to(device).view(-1, 3)
pred_depth = distance_to_z(data["coarse"]["depth"], projs[:1, :1])
# is visible? Check whether point is closer than the computed pseudo depth
cam_pts, dists = project_into_cam(occ_pts, projs[0, 0], poses[0, 0])
pred_dist = F.grid_sample(
pred_depth.view(1, 1, h, w),
cam_pts[:, :2].view(1, 1, -1, 2),
mode="nearest",
padding_mode="border",
align_corners=True,
).view(-1)
is_visible_pred = dists <= pred_dist
depth_plus4meters = False
if depth_plus4meters:
mask = (dists >= pred_dist) & (dists < pred_dist + 4)
densities = torch.zeros_like(occ_pts[..., 0])
densities[mask] = 1.0
is_occupied_pred = densities > occ_threshold
else:
# Query the density of the query points from the density field
densities = []
for i_from in range(0, len(occ_pts), query_batch_size):
i_to = min(i_from + query_batch_size, len(occ_pts))
q_pts_ = occ_pts[i_from:i_to]
_, _, densities_, _ = model(
q_pts_.unsqueeze(0), only_density=True
) ## ! occupancy estimation
densities.append(densities_.squeeze(0))
densities = torch.cat(densities, dim=0).squeeze()
is_occupied_pred = densities > occ_threshold
is_occupied = data["is_occupied"]
is_visible = data["is_visible"]
return metrics.compute_occ_metrics(is_occupied_pred, is_occupied, is_visible)
return _compute_occ_metrics
def make_eval_fn(
model: nn.Module,
conf,
):
eval_type = conf["type"]
eval_fn = globals().get(f"create_{eval_type}_eval", None)
if eval_fn:
if conf.get("args", None):
return eval_fn(model, **conf["args"])
else:
return eval_fn(model)
else:
return None
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