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# Copyright (C) 2022-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
# --------------------------------------------------------
# Losses, metrics per batch, metrics per dataset
# --------------------------------------------------------
import torch
import torch.nn.functional as F
from torch import nn
def _get_gtnorm(gt):
if gt.size(1) == 1: # stereo
return gt
# flow
return torch.sqrt(torch.sum(gt**2, dim=1, keepdims=True)) # Bx1xHxW
############ losses without confidence
class L1Loss(nn.Module):
def __init__(self, max_gtnorm=None):
super().__init__()
self.max_gtnorm = max_gtnorm
self.with_conf = False
def _error(self, gt, predictions):
return torch.abs(gt - predictions)
def forward(self, predictions, gt, inspect=False):
mask = torch.isfinite(gt)
if self.max_gtnorm is not None:
mask *= _get_gtnorm(gt).expand(-1, gt.size(1), -1, -1) < self.max_gtnorm
if inspect:
return self._error(gt, predictions)
return self._error(gt[mask], predictions[mask]).mean()
############## losses with confience
## there are several parametrizations
class LaplacianLoss(nn.Module): # used for CroCo-Stereo on ETH3D, d'=exp(d)
def __init__(self, max_gtnorm=None):
super().__init__()
self.max_gtnorm = max_gtnorm
self.with_conf = True
def forward(self, predictions, gt, conf):
mask = torch.isfinite(gt)
mask = mask[:, 0, :, :]
if self.max_gtnorm is not None:
mask *= _get_gtnorm(gt)[:, 0, :, :] < self.max_gtnorm
conf = conf.squeeze(1)
return (
torch.abs(gt - predictions).sum(dim=1)[mask] / torch.exp(conf[mask])
+ conf[mask]
).mean() # + torch.log(2) => which is a constant
class LaplacianLossBounded(
nn.Module
): # used for CroCo-Flow ; in the equation of the paper, we have a=1/b
def __init__(self, max_gtnorm=10000.0, a=0.25, b=4.0):
super().__init__()
self.max_gtnorm = max_gtnorm
self.with_conf = True
self.a, self.b = a, b
def forward(self, predictions, gt, conf):
mask = torch.isfinite(gt)
mask = mask[:, 0, :, :]
if self.max_gtnorm is not None:
mask *= _get_gtnorm(gt)[:, 0, :, :] < self.max_gtnorm
conf = conf.squeeze(1)
conf = (self.b - self.a) * torch.sigmoid(conf) + self.a
return (
torch.abs(gt - predictions).sum(dim=1)[mask] / conf[mask]
+ torch.log(conf)[mask]
).mean() # + torch.log(2) => which is a constant
class LaplacianLossBounded2(
nn.Module
): # used for CroCo-Stereo (except for ETH3D) ; in the equation of the paper, we have a=b
def __init__(self, max_gtnorm=None, a=3.0, b=3.0):
super().__init__()
self.max_gtnorm = max_gtnorm
self.with_conf = True
self.a, self.b = a, b
def forward(self, predictions, gt, conf):
mask = torch.isfinite(gt)
mask = mask[:, 0, :, :]
if self.max_gtnorm is not None:
mask *= _get_gtnorm(gt)[:, 0, :, :] < self.max_gtnorm
conf = conf.squeeze(1)
conf = 2 * self.a * (torch.sigmoid(conf / self.b) - 0.5)
return (
torch.abs(gt - predictions).sum(dim=1)[mask] / torch.exp(conf[mask])
+ conf[mask]
).mean() # + torch.log(2) => which is a constant
############## metrics per batch
class StereoMetrics(nn.Module):
def __init__(self, do_quantile=False):
super().__init__()
self.bad_ths = [0.5, 1, 2, 3]
self.do_quantile = do_quantile
def forward(self, predictions, gt):
B = predictions.size(0)
metrics = {}
gtcopy = gt.clone()
mask = torch.isfinite(gtcopy)
gtcopy[
~mask
] = 999999.0 # we make a copy and put a non-infinite value, such that it does not become nan once multiplied by the mask value 0
Npx = mask.view(B, -1).sum(dim=1)
L1error = (torch.abs(gtcopy - predictions) * mask).view(B, -1)
L2error = (torch.square(gtcopy - predictions) * mask).view(B, -1)
# avgerr
metrics["avgerr"] = torch.mean(L1error.sum(dim=1) / Npx)
# rmse
metrics["rmse"] = torch.sqrt(L2error.sum(dim=1) / Npx).mean(dim=0)
# err > t for t in [0.5,1,2,3]
for ths in self.bad_ths:
metrics["bad@{:.1f}".format(ths)] = (
((L1error > ths) * mask.view(B, -1)).sum(dim=1) / Npx
).mean(dim=0) * 100
return metrics
class FlowMetrics(nn.Module):
def __init__(self):
super().__init__()
self.bad_ths = [1, 3, 5]
def forward(self, predictions, gt):
B = predictions.size(0)
metrics = {}
mask = torch.isfinite(gt[:, 0, :, :]) # both x and y would be infinite
Npx = mask.view(B, -1).sum(dim=1)
gtcopy = (
gt.clone()
) # to compute L1/L2 error, we need to have non-infinite value, the error computed at this locations will be ignored
gtcopy[:, 0, :, :][~mask] = 999999.0
gtcopy[:, 1, :, :][~mask] = 999999.0
L1error = (torch.abs(gtcopy - predictions).sum(dim=1) * mask).view(B, -1)
L2error = (
torch.sqrt(torch.sum(torch.square(gtcopy - predictions), dim=1)) * mask
).view(B, -1)
metrics["L1err"] = torch.mean(L1error.sum(dim=1) / Npx)
metrics["EPE"] = torch.mean(L2error.sum(dim=1) / Npx)
for ths in self.bad_ths:
metrics["bad@{:.1f}".format(ths)] = (
((L2error > ths) * mask.view(B, -1)).sum(dim=1) / Npx
).mean(dim=0) * 100
return metrics
############## metrics per dataset
## we update the average and maintain the number of pixels while adding data batch per batch
## at the beggining, call reset()
## after each batch, call add_batch(...)
## at the end: call get_results()
class StereoDatasetMetrics(nn.Module):
def __init__(self):
super().__init__()
self.bad_ths = [0.5, 1, 2, 3]
def reset(self):
self.agg_N = 0 # number of pixels so far
self.agg_L1err = torch.tensor(0.0) # L1 error so far
self.agg_Nbad = [0 for _ in self.bad_ths] # counter of bad pixels
self._metrics = None
def add_batch(self, predictions, gt):
assert predictions.size(1) == 1, predictions.size()
assert gt.size(1) == 1, gt.size()
if (
gt.size(2) == predictions.size(2) * 2
and gt.size(3) == predictions.size(3) * 2
): # special case for Spring ...
L1err = torch.minimum(
torch.minimum(
torch.minimum(
torch.sum(torch.abs(gt[:, :, 0::2, 0::2] - predictions), dim=1),
torch.sum(torch.abs(gt[:, :, 1::2, 0::2] - predictions), dim=1),
),
torch.sum(torch.abs(gt[:, :, 0::2, 1::2] - predictions), dim=1),
),
torch.sum(torch.abs(gt[:, :, 1::2, 1::2] - predictions), dim=1),
)
valid = torch.isfinite(L1err)
else:
valid = torch.isfinite(gt[:, 0, :, :]) # both x and y would be infinite
L1err = torch.sum(torch.abs(gt - predictions), dim=1)
N = valid.sum()
Nnew = self.agg_N + N
self.agg_L1err = (
float(self.agg_N) / Nnew * self.agg_L1err
+ L1err[valid].mean().cpu() * float(N) / Nnew
)
self.agg_N = Nnew
for i, th in enumerate(self.bad_ths):
self.agg_Nbad[i] += (L1err[valid] > th).sum().cpu()
def _compute_metrics(self):
if self._metrics is not None:
return
out = {}
out["L1err"] = self.agg_L1err.item()
for i, th in enumerate(self.bad_ths):
out["bad@{:.1f}".format(th)] = (
float(self.agg_Nbad[i]) / self.agg_N
).item() * 100.0
self._metrics = out
def get_results(self):
self._compute_metrics() # to avoid recompute them multiple times
return self._metrics
class FlowDatasetMetrics(nn.Module):
def __init__(self):
super().__init__()
self.bad_ths = [0.5, 1, 3, 5]
self.speed_ths = [(0, 10), (10, 40), (40, torch.inf)]
def reset(self):
self.agg_N = 0 # number of pixels so far
self.agg_L1err = torch.tensor(0.0) # L1 error so far
self.agg_L2err = torch.tensor(0.0) # L2 (=EPE) error so far
self.agg_Nbad = [0 for _ in self.bad_ths] # counter of bad pixels
self.agg_EPEspeed = [
torch.tensor(0.0) for _ in self.speed_ths
] # EPE per speed bin so far
self.agg_Nspeed = [0 for _ in self.speed_ths] # N pixels per speed bin so far
self._metrics = None
self.pairname_results = {}
def add_batch(self, predictions, gt):
assert predictions.size(1) == 2, predictions.size()
assert gt.size(1) == 2, gt.size()
if (
gt.size(2) == predictions.size(2) * 2
and gt.size(3) == predictions.size(3) * 2
): # special case for Spring ...
L1err = torch.minimum(
torch.minimum(
torch.minimum(
torch.sum(torch.abs(gt[:, :, 0::2, 0::2] - predictions), dim=1),
torch.sum(torch.abs(gt[:, :, 1::2, 0::2] - predictions), dim=1),
),
torch.sum(torch.abs(gt[:, :, 0::2, 1::2] - predictions), dim=1),
),
torch.sum(torch.abs(gt[:, :, 1::2, 1::2] - predictions), dim=1),
)
L2err = torch.minimum(
torch.minimum(
torch.minimum(
torch.sqrt(
torch.sum(
torch.square(gt[:, :, 0::2, 0::2] - predictions), dim=1
)
),
torch.sqrt(
torch.sum(
torch.square(gt[:, :, 1::2, 0::2] - predictions), dim=1
)
),
),
torch.sqrt(
torch.sum(
torch.square(gt[:, :, 0::2, 1::2] - predictions), dim=1
)
),
),
torch.sqrt(
torch.sum(torch.square(gt[:, :, 1::2, 1::2] - predictions), dim=1)
),
)
valid = torch.isfinite(L1err)
gtspeed = (
torch.sqrt(torch.sum(torch.square(gt[:, :, 0::2, 0::2]), dim=1))
+ torch.sqrt(torch.sum(torch.square(gt[:, :, 0::2, 1::2]), dim=1))
+ torch.sqrt(torch.sum(torch.square(gt[:, :, 1::2, 0::2]), dim=1))
+ torch.sqrt(torch.sum(torch.square(gt[:, :, 1::2, 1::2]), dim=1))
) / 4.0 # let's just average them
else:
valid = torch.isfinite(gt[:, 0, :, :]) # both x and y would be infinite
L1err = torch.sum(torch.abs(gt - predictions), dim=1)
L2err = torch.sqrt(torch.sum(torch.square(gt - predictions), dim=1))
gtspeed = torch.sqrt(torch.sum(torch.square(gt), dim=1))
N = valid.sum()
Nnew = self.agg_N + N
self.agg_L1err = (
float(self.agg_N) / Nnew * self.agg_L1err
+ L1err[valid].mean().cpu() * float(N) / Nnew
)
self.agg_L2err = (
float(self.agg_N) / Nnew * self.agg_L2err
+ L2err[valid].mean().cpu() * float(N) / Nnew
)
self.agg_N = Nnew
for i, th in enumerate(self.bad_ths):
self.agg_Nbad[i] += (L2err[valid] > th).sum().cpu()
for i, (th1, th2) in enumerate(self.speed_ths):
vv = (gtspeed[valid] >= th1) * (gtspeed[valid] < th2)
iNspeed = vv.sum()
if iNspeed == 0:
continue
iNnew = self.agg_Nspeed[i] + iNspeed
self.agg_EPEspeed[i] = (
float(self.agg_Nspeed[i]) / iNnew * self.agg_EPEspeed[i]
+ float(iNspeed) / iNnew * L2err[valid][vv].mean().cpu()
)
self.agg_Nspeed[i] = iNnew
def _compute_metrics(self):
if self._metrics is not None:
return
out = {}
out["L1err"] = self.agg_L1err.item()
out["EPE"] = self.agg_L2err.item()
for i, th in enumerate(self.bad_ths):
out["bad@{:.1f}".format(th)] = (
float(self.agg_Nbad[i]) / self.agg_N
).item() * 100.0
for i, (th1, th2) in enumerate(self.speed_ths):
out[
"s{:d}{:s}".format(th1, "-" + str(th2) if th2 < torch.inf else "+")
] = self.agg_EPEspeed[i].item()
self._metrics = out
def get_results(self):
self._compute_metrics() # to avoid recompute them multiple times
return self._metrics
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