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from typing import Literal
import numpy as np
import torch
import torch.nn.functional as F
from torchmetrics.functional.image.lpips import _NoTrainLpips
from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure
from torchmetrics.metric import Metric
from torchmetrics.utilities import dim_zero_cat
from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE
def compute_psnr(
preds: torch.Tensor,
targets: torch.Tensor,
masks: torch.Tensor | None = None,
) -> float:
"""
Args:
preds (torch.Tensor): (..., 3) predicted images in [0, 1].
targets (torch.Tensor): (..., 3) target images in [0, 1].
masks (torch.Tensor | None): (...,) optional binary masks where the
1-regions will be taken into account.
Returns:
psnr (float): Peak signal-to-noise ratio.
"""
if masks is None:
masks = torch.ones_like(preds[..., 0])
return (
-10.0
* torch.log(
F.mse_loss(
preds * masks[..., None],
targets * masks[..., None],
reduction="sum",
)
/ masks.sum().clamp(min=1.0)
/ 3.0
)
/ np.log(10.0)
).item()
def compute_pose_errors(
preds: torch.Tensor, targets: torch.Tensor
) -> tuple[float, float, float]:
"""
Args:
preds: (N, 4, 4) predicted camera poses.
targets: (N, 4, 4) target camera poses.
Returns:
ate (float): Absolute trajectory error.
rpe_t (float): Relative pose error in translation.
rpe_r (float): Relative pose error in rotation (degree).
"""
# Compute ATE.
ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item()
# Compute RPE_t and RPE_r.
# NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r.
# torch has numerical issues for acos when the value is close to 1.0, i.e.
# RPE_r is supposed to be very small, and will result in artificially large
# error.
preds = preds.detach().cpu().numpy()
targets = targets.detach().cpu().numpy()
pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:]
pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:]
target_rels = np.linalg.inv(targets[:-1]) @ targets[1:]
error_rels = np.linalg.inv(target_rels) @ pred_rels
traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1)
rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item()
rpe_r = (
np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item()
/ np.pi
* 180.0
)
return ate, rpe_t, rpe_r
class mPSNR(PeakSignalNoiseRatio):
sum_squared_error: list[torch.Tensor]
total: list[torch.Tensor]
def __init__(self, **kwargs) -> None:
super().__init__(
data_range=1.0,
base=10.0,
dim=None,
reduction="elementwise_mean",
**kwargs,
)
self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat")
self.add_state("total", default=[], dist_reduce_fx="cat")
def __len__(self) -> int:
return len(self.total)
def update(
self,
preds: torch.Tensor,
targets: torch.Tensor,
masks: torch.Tensor | None = None,
):
"""Update state with predictions and targets.
Args:
preds (torch.Tensor): (..., 3) float32 predicted images.
targets (torch.Tensor): (..., 3) float32 target images.
masks (torch.Tensor | None): (...,) optional binary masks where the
1-regions will be taken into account.
"""
if masks is None:
masks = torch.ones_like(preds[..., 0])
self.sum_squared_error.append(
torch.sum(torch.pow((preds - targets) * masks[..., None], 2))
)
self.total.append(masks.sum().to(torch.int64) * 3)
def compute(self) -> torch.Tensor:
"""Compute peak signal-to-noise ratio over state."""
sum_squared_error = dim_zero_cat(self.sum_squared_error)
total = dim_zero_cat(self.total)
return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0)
class mSSIM(StructuralSimilarityIndexMeasure):
similarity: list
def __init__(self, **kwargs) -> None:
super().__init__(
reduction=None,
data_range=1.0,
return_full_image=False,
**kwargs,
)
assert isinstance(self.sigma, float)
def __len__(self) -> int:
return sum([s.shape[0] for s in self.similarity])
def update(
self,
preds: torch.Tensor,
targets: torch.Tensor,
masks: torch.Tensor | None = None,
):
"""Update state with predictions and targets.
Args:
preds (torch.Tensor): (B, H, W, 3) float32 predicted images.
targets (torch.Tensor): (B, H, W, 3) float32 target images.
masks (torch.Tensor | None): (B, H, W) optional binary masks where
the 1-regions will be taken into account.
"""
if masks is None:
masks = torch.ones_like(preds[..., 0])
# Construct a 1D Gaussian blur filter.
assert isinstance(self.kernel_size, int)
hw = self.kernel_size // 2
shift = (2 * hw - self.kernel_size + 1) / 2
assert isinstance(self.sigma, float)
f_i = (
(torch.arange(self.kernel_size, device=preds.device) - hw + shift)
/ self.sigma
) ** 2
filt = torch.exp(-0.5 * f_i)
filt /= torch.sum(filt)
# Blur in x and y (faster than the 2D convolution).
def convolve2d(z, m, f):
# z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf).
z = z.permute(0, 3, 1, 2)
m = m[:, None]
f = f[None, None].expand(z.shape[1], -1, -1, -1)
z_ = torch.nn.functional.conv2d(
z * m, f, padding="valid", groups=z.shape[1]
)
m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid")
return torch.where(
m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0
).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype)
filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None])
filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :])
filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m))
mu0 = filt_fn(preds, masks)[0]
mu1 = filt_fn(targets, masks)[0]
mu00 = mu0 * mu0
mu11 = mu1 * mu1
mu01 = mu0 * mu1
sigma00 = filt_fn(preds**2, masks)[0] - mu00
sigma11 = filt_fn(targets**2, masks)[0] - mu11
sigma01 = filt_fn(preds * targets, masks)[0] - mu01
# Clip the variances and covariances to valid values.
# Variance must be non-negative:
sigma00 = sigma00.clamp(min=0.0)
sigma11 = sigma11.clamp(min=0.0)
sigma01 = torch.sign(sigma01) * torch.minimum(
torch.sqrt(sigma00 * sigma11), torch.abs(sigma01)
)
assert isinstance(self.data_range, float)
c1 = (self.k1 * self.data_range) ** 2
c2 = (self.k2 * self.data_range) ** 2
numer = (2 * mu01 + c1) * (2 * sigma01 + c2)
denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2)
ssim_map = numer / denom
self.similarity.append(ssim_map.mean(dim=(1, 2, 3)))
def compute(self) -> torch.Tensor:
"""Compute final SSIM metric."""
return torch.cat(self.similarity).mean()
class mLPIPS(Metric):
sum_scores: list[torch.Tensor]
total: list[torch.Tensor]
def __init__(
self,
net_type: Literal["vgg", "alex", "squeeze"] = "alex",
**kwargs,
):
super().__init__(**kwargs)
if not _TORCHVISION_AVAILABLE:
raise ModuleNotFoundError(
"LPIPS metric requires that torchvision is installed."
" Either install as `pip install torchmetrics[image]` or `pip install torchvision`."
)
valid_net_type = ("vgg", "alex", "squeeze")
if net_type not in valid_net_type:
raise ValueError(
f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}."
)
self.net = _NoTrainLpips(net=net_type, spatial=True)
self.add_state("sum_scores", [], dist_reduce_fx="cat")
self.add_state("total", [], dist_reduce_fx="cat")
def __len__(self) -> int:
return len(self.total)
def update(
self,
preds: torch.Tensor,
targets: torch.Tensor,
masks: torch.Tensor | None = None,
):
"""Update internal states with lpips scores.
Args:
preds (torch.Tensor): (B, H, W, 3) float32 predicted images.
targets (torch.Tensor): (B, H, W, 3) float32 target images.
masks (torch.Tensor | None): (B, H, W) optional float32 binary
masks where the 1-regions will be taken into account.
"""
if masks is None:
masks = torch.ones_like(preds[..., 0])
scores = self.net(
(preds * masks[..., None]).permute(0, 3, 1, 2),
(targets * masks[..., None]).permute(0, 3, 1, 2),
normalize=True,
)
self.sum_scores.append((scores * masks[:, None]).sum())
self.total.append(masks.sum().to(torch.int64))
def compute(self) -> torch.Tensor:
"""Compute final perceptual similarity metric."""
return (
torch.tensor(self.sum_scores, device=self.device)
/ torch.tensor(self.total, device=self.device)
).mean()
class PCK(Metric):
correct: list[torch.Tensor]
total: list[int]
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.add_state("correct", default=[], dist_reduce_fx="cat")
self.add_state("total", default=[], dist_reduce_fx="cat")
def __len__(self) -> int:
return len(self.total)
def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float):
"""Update internal states with PCK scores.
Args:
preds (torch.Tensor): (N, 2) predicted 2D keypoints.
targets (torch.Tensor): (N, 2) targets 2D keypoints.
threshold (float): PCK threshold.
"""
self.correct.append(
(torch.linalg.norm(preds - targets, dim=-1) < threshold).sum()
)
self.total.append(preds.shape[0])
def compute(self) -> torch.Tensor:
"""Compute PCK over state."""
return (
torch.tensor(self.correct, device=self.device)
/ torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8)
).mean()
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