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"""
Author: Luigi Piccinelli
Licensed under the CC-BY NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/)
"""
# We prefer not to install PyTorch3D in the package
# Code commented is how 3D metrics are computed
from collections import defaultdict
from functools import partial
import torch
import torch.nn.functional as F
# from chamfer_distance import ChamferDistance
from unidepth.utils.constants import DEPTH_BINS
# chamfer_cls = ChamferDistance()
# def chamfer_dist(tensor1, tensor2):
# x_lengths = torch.tensor((tensor1.shape[1],), device=tensor1.device)
# y_lengths = torch.tensor((tensor2.shape[1],), device=tensor2.device)
# dist1, dist2, idx1, idx2 = chamfer_cls(
# tensor1, tensor2, x_lengths=x_lengths, y_lengths=y_lengths
# )
# return (torch.sqrt(dist1) + torch.sqrt(dist2)) / 2
# def auc(tensor1, tensor2, thresholds):
# x_lengths = torch.tensor((tensor1.shape[1],), device=tensor1.device)
# y_lengths = torch.tensor((tensor2.shape[1],), device=tensor2.device)
# dist1, dist2, idx1, idx2 = chamfer_cls(
# tensor1, tensor2, x_lengths=x_lengths, y_lengths=y_lengths
# )
# # compute precision recall
# precisions = [(dist1 < threshold).sum() / dist1.numel() for threshold in thresholds]
# recalls = [(dist2 < threshold).sum() / dist2.numel() for threshold in thresholds]
# auc_value = torch.trapz(
# torch.tensor(precisions, device=tensor1.device),
# torch.tensor(recalls, device=tensor1.device),
# )
# return auc_value
def delta(tensor1, tensor2, exponent):
inlier = torch.maximum((tensor1 / tensor2), (tensor2 / tensor1))
return (inlier < 1.25**exponent).to(torch.float32).mean()
def ssi(tensor1, tensor2, qtl=0.05):
stability_mat = 1e-9 * torch.eye(2, device=tensor1.device)
error = (tensor1 - tensor2).abs()
mask = error < torch.quantile(error, 1 - qtl)
tensor1_mask = tensor1[mask]
tensor2_mask = tensor2[mask]
tensor2_one = torch.stack(
[tensor2_mask.detach(), torch.ones_like(tensor2_mask).detach()], dim=1
)
scale_shift = torch.inverse(tensor2_one.T @ tensor2_one + stability_mat) @ (
tensor2_one.T @ tensor1_mask.unsqueeze(1)
)
scale, shift = scale_shift.squeeze().chunk(2, dim=0)
return tensor2 * scale + shift
# tensor2_one = torch.stack([tensor2.detach(), torch.ones_like(tensor2).detach()], dim=1)
# scale_shift = torch.inverse(tensor2_one.T @ tensor2_one + stability_mat) @ (tensor2_one.T @ tensor1.unsqueeze(1))
# scale, shift = scale_shift.squeeze().chunk(2, dim=0)
# return tensor2 * scale + shift
def d1_ssi(tensor1, tensor2):
delta_ = delta(tensor1, ssi(tensor1, tensor2), 1.0)
return delta_
def d_auc(tensor1, tensor2):
exponents = torch.linspace(0.01, 5.0, steps=100, device=tensor1.device)
deltas = [delta(tensor1, tensor2, exponent) for exponent in exponents]
return torch.trapz(torch.tensor(deltas, device=tensor1.device), exponents) / 5.0
# def f1_score(tensor1, tensor2, thresholds):
# x_lengths = torch.tensor((tensor1.shape[1],), device=tensor1.device)
# y_lengths = torch.tensor((tensor2.shape[1],), device=tensor2.device)
# dist1, dist2, idx1, idx2 = chamfer_cls(
# tensor1, tensor2, x_lengths=x_lengths, y_lengths=y_lengths
# )
# # compute precision recall
# precisions = [(dist1 < threshold).sum() / dist1.numel() for threshold in thresholds]
# recalls = [(dist2 < threshold).sum() / dist2.numel() for threshold in thresholds]
# precisions = torch.tensor(precisions, device=tensor1.device)
# recalls = torch.tensor(recalls, device=tensor1.device)
# f1_thresholds = 2 * precisions * recalls / (precisions + recalls)
# f1_thresholds = torch.where(
# torch.isnan(f1_thresholds), torch.zeros_like(f1_thresholds), f1_thresholds
# )
# f1_value = torch.trapz(f1_thresholds) / len(thresholds)
# return f1_value
DICT_METRICS = {
"d1": partial(delta, exponent=1.0),
"d2": partial(delta, exponent=2.0),
"d3": partial(delta, exponent=3.0),
"rmse": lambda gt, pred: torch.sqrt(((gt - pred) ** 2).mean()),
"rmselog": lambda gt, pred: torch.sqrt(
((torch.log(gt) - torch.log(pred)) ** 2).mean()
),
"arel": lambda gt, pred: (torch.abs(gt - pred) / gt).mean(),
"sqrel": lambda gt, pred: (((gt - pred) ** 2) / gt).mean(),
"log10": lambda gt, pred: torch.abs(torch.log10(pred) - torch.log10(gt)).mean(),
"silog": lambda gt, pred: 100 * torch.std(torch.log(pred) - torch.log(gt)).mean(),
"medianlog": lambda gt, pred: 100
* (torch.log(pred) - torch.log(gt)).median().abs(),
"d_auc": d_auc,
"d1_ssi": d1_ssi,
}
# DICT_METRICS_3D = {
# "chamfer": lambda gt, pred, thresholds: chamfer_dist(
# gt.unsqueeze(0).permute(0, 2, 1), pred.unsqueeze(0).permute(0, 2, 1)
# ),
# "F1": lambda gt, pred, thresholds: f1_score(
# gt.unsqueeze(0).permute(0, 2, 1),
# pred.unsqueeze(0).permute(0, 2, 1),
# thresholds=thresholds,
# ),
# }
DICT_METRICS_D = {
"a1": lambda gt, pred: (torch.maximum((gt / pred), (pred / gt)) > 1.25**1.0).to(
torch.float32
),
"abs_rel": lambda gt, pred: (torch.abs(gt - pred) / gt),
}
def eval_depth(
gts: torch.Tensor, preds: torch.Tensor, masks: torch.Tensor, max_depth=None
):
summary_metrics = defaultdict(list)
preds = F.interpolate(preds, gts.shape[-2:], mode="bilinear")
for i, (gt, pred, mask) in enumerate(zip(gts, preds, masks)):
if max_depth is not None:
mask = torch.logical_and(mask, gt <= max_depth)
for name, fn in DICT_METRICS.items():
summary_metrics[name].append(fn(gt[mask], pred[mask]).mean())
return {name: torch.stack(vals, dim=0) for name, vals in summary_metrics.items()}
# def eval_3d(
# gts: torch.Tensor, preds: torch.Tensor, masks: torch.Tensor, thresholds=None
# ):
# summary_metrics = defaultdict(list)
# w_max = min(gts.shape[-1] // 4, 400)
# gts = F.interpolate(
# gts, (int(w_max * gts.shape[-2] / gts.shape[-1]), w_max), mode="nearest"
# )
# preds = F.interpolate(preds, gts.shape[-2:], mode="nearest")
# masks = F.interpolate(
# masks.to(torch.float32), gts.shape[-2:], mode="nearest"
# ).bool()
# for i, (gt, pred, mask) in enumerate(zip(gts, preds, masks)):
# if not torch.any(mask):
# continue
# for name, fn in DICT_METRICS_3D.items():
# summary_metrics[name].append(
# fn(gt[:, mask.squeeze()], pred[:, mask.squeeze()], thresholds).mean()
# )
# return {name: torch.stack(vals, dim=0) for name, vals in summary_metrics.items()}
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