Spaces:
Running
on
Zero
Running
on
Zero
File size: 10,281 Bytes
05fb4ab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 |
import math
import numpy as np
import torch
import torch.nn.functional as F
from matplotlib import pyplot as plt
from validation.metrics_flow import Fl_kitti_2015, correct_correspondences
def compute_uncertainty_per_image(uncertainty_est, flow_gt, flow_est, mask_valid, dict_list_uncertainties):
# uncert shape is #number_of_elements
for uncertainty_name in uncertainty_est.keys():
if uncertainty_name == 'inference_parameters' or uncertainty_name == 'log_var_map' or \
uncertainty_name == 'weight_map' or uncertainty_name == 'warping_mask':
continue
if 'p_r' == uncertainty_name:
# convert confidence map to uncertainty
uncert = (1.0 / (uncertainty_est['p_r'] + 1e-6))[mask_valid.unsqueeze(1)]
else:
uncert = uncertainty_est[uncertainty_name][mask_valid.unsqueeze(1)]
# compute metrics based on uncertainty
uncertainty_metric_dict = compute_aucs(flow_gt, flow_est, uncert, intervals=50)
if uncertainty_name not in dict_list_uncertainties.keys():
# for first image, create the list for each possible uncertainty type
dict_list_uncertainties[uncertainty_name] = []
dict_list_uncertainties[uncertainty_name].append(uncertainty_metric_dict)
return dict_list_uncertainties
def estimate_average_variance_of_mixture_density(log_var_map):
if isinstance(log_var_map[0], list):
# several uncertainties estimation
log_var = log_var_map[0][-1]
proba_map = torch.nn.functional.softmax(log_var_map[1][-1], dim=1)
else:
log_var = log_var_map[0]
proba_map = torch.nn.functional.softmax(log_var_map[1], dim=1)
avg_variance = torch.sum(proba_map * torch.exp(log_var), dim=1, keepdim=True) # shape is b,1, h, w
R = 1
var = torch.exp(log_var)
I = torch.sum(proba_map * (1 - torch.exp(- math.sqrt(2)*R/torch.sqrt(var)))**2, dim=1, keepdim=True)
return avg_variance, I
def estimate_probability_of_confidence_interval_of_mixture_density(log_var_map, list_item=-1, R = 1.0):
if isinstance(log_var_map[0], list):
# several uncertainties estimation
log_var = log_var_map[0][list_item]
proba_map = torch.nn.functional.softmax(log_var_map[1][list_item], dim=1)
else:
log_var = log_var_map[0]
proba_map = torch.nn.functional.softmax(log_var_map[1], dim=1)
var = torch.exp(log_var)
I = torch.sum(proba_map * (1 - torch.exp(- math.sqrt(2)*R/torch.sqrt(var)))**2, dim=1, keepdim=True)
return I
def save_sparsification_plot(output, save_path):
EPE_uncer = output["sparse_curve"]["EPE"]
EPE_perfect = output["opt_curve"]["EPE"]
x = output["quants"]
fig, axis = plt.subplots(1, 1, figsize=(25, 25))
axis.plot(x, np.float32(EPE_uncer) / EPE_uncer[0], marker='o', markersize=13, linewidth=5, label='estimated uncertainty')
axis.plot(x, np.float32(EPE_perfect) / EPE_uncer[0], marker='o', markersize=13, linewidth=5, label='optimal')
axis.set_ylabel('EPE normalized', fontsize='xx-large')
axis.set_xlabel('Removing x fraction of pixels', fontsize='xx-large')
axis.legend(bbox_to_anchor=(1.05, 1), loc=4, borderaxespad=0., fontsize='xx-large')
fig.savefig('{}.png'.format(save_path),
bbox_inches='tight')
plt.close(fig)
def compute_eigen_errors_v2(gt, pred, metrics=['EPE', 'PCK1', 'PCK5'], mask=None, reduce_mean=True):
"""Revised compute_eigen_errors function used for uncertainty metrics, with optional reduce_mean argument and (1-a1) computation
"""
results = []
# in shape (#number_elements, 2)
# mask shape #number_of_elements
if mask is not None:
pred = pred[mask]
gt = gt[mask]
if "EPE" in metrics:
epe = torch.norm(gt - pred, p=2, dim=1)
if reduce_mean:
epe = epe.mean().item()
results.append(epe)
if "Fl" in metrics:
Fl = Fl_kitti_2015(pred, gt) / pred.shape[0] if pred.shape[0] != 0 else 0
results.append(Fl)
if "PCK1" in metrics:
px_1 = correct_correspondences(pred, gt, alpha=1.0, img_size=1.0)
pck1 = px_1 / (pred.shape[0]) if pred.shape[0] != 0 else 0
results.append(pck1)
if "PCK5" in metrics:
px_5 = correct_correspondences(pred, gt, alpha=5.0, img_size=1.0)
pck5 = px_5 / (pred.shape[0]) if pred.shape[0] != 0 else 0
results.append(pck5)
return results
def compute_aucs(gt, pred, uncert, intervals=50):
"""
Computation of sparsification curve, oracle curve and auc metric (area below the difference of the two curves),
for each metrics (AEPE, PCK ..).
Args:
gt: gt flow field, shape #number elements, 2
pred: predicted flow field, shape #number elements, 2
uncert: predicted uncertainty measure, shape #number elements
intervals: number of intervals to compute the sparsification plot
Returns:
dictionary with sparsification, oracle and AUC for each metric (here EPE, PCK1 and PCK5).
"""
uncertainty_metrics = ['EPE', 'PCK1', 'PCK5']
value_for_no_pixels = {'EPE': 0.0, 'PCK1': 1.0, 'PCK5': 1.0}
# results dictionaries
AUSE = {'EPE': 0, 'PCK1': 0, 'PCK5': 0}
# revert order (high uncertainty first)
uncert = -uncert # shape #number_elements
# list the EPE, as the uncertainty. negative because we want high uncertainty first when taking percentile!
true_uncert = - torch.norm(gt - pred, p=2, dim=1)
# prepare subsets for sampling and for area computation
quants = [100. / intervals * t for t in range(0, intervals)]
plotx = [1. / intervals * t for t in range(0, intervals + 1)]
# get percentiles for sampling and corresponding subsets
thresholds = [np.percentile(uncert.cpu().numpy(), q) for q in quants]
subs = [(uncert.ge(t)) for t in thresholds]
# compute sparsification curves for each metric (add 0 for final sampling)
# calculates the metrics for each interval
sparse_curve = {
m: [compute_eigen_errors_v2(gt, pred, metrics=[m], mask=sub, reduce_mean=True)[0] for sub in subs] +
[value_for_no_pixels[m]] for m in uncertainty_metrics}
# human-readable call
'''
sparse_curve = {"rmse":[compute_eigen_errors_v2(gt,pred,metrics=["rmse"],mask=sub,reduce_mean=True)[0] for sub in subs]+[0],
"a1":[compute_eigen_errors_v2(gt,pred,metrics=["a1"],mask=sub,reduce_mean=True)[0] for sub in subs]+[0],
"abs_rel":[compute_eigen_errors_v2(gt,pred,metrics=["abs_rel"],mask=sub,reduce_mean=True)[0] for sub in subs]+[0]}
'''
# get percentiles for optimal sampling and corresponding subsets (based on real EPE)
opt_thresholds = [np.percentile(true_uncert.cpu().numpy(), q) for q in quants]
opt_subs = [(true_uncert.ge(o)) for o in opt_thresholds]
# compute sparsification curves for optimal sampling (add 0 for final sampling)
opt_curve = {m: [compute_eigen_errors_v2(gt, pred, metrics=[m], mask=opt_sub, reduce_mean=True)[0] for opt_sub in
opt_subs] + [value_for_no_pixels[m]] for m in uncertainty_metrics}
# compute error and gain metrics
for m in uncertainty_metrics:
max = np.array(opt_curve[m]).max() + 1e-6
# normalize both to 0-1 first
# error: subtract from method sparsification (first term) the oracle sparsification (second term)
AUSE[m] = np.abs(np.trapz(np.array(sparse_curve[m])/max, x=plotx) -
np.trapz(np.array(opt_curve[m])/max, x=plotx))
opt_curve[m] = np.array(opt_curve[m]) / max
sparse_curve[m] = np.array(sparse_curve[m]) / max # normalizes each curve to 0 and 1
# returns a dictionary with AUSE and AURG for each metric
return {'sparse_curve': sparse_curve, 'opt_curve': opt_curve, 'AUSE': AUSE}
def compute_average_of_uncertainty_metrics(list_uncertainty_metrics, intervals=50):
"""The sparsification and AUC were computed per image pair. We here compute the average over all image pairs of
a dataset.
Args:
list_uncertainty_metrics: list where each item corresponds to the output of compute_aucs for an image pair.
intervals: number of intervals to compute the sparsification plot
Returns:
dictionary with sparsification, oracle and AUC for each metric (here EPE, PCK1 and PCK5), averaged over all
elements of the provided list (should correspond to all image pairs of the dataset).
"""
# https://github.com/mattpoggi/mono-uncertainty/blob/master/evaluate.py
quants = [1. / intervals * t for t in range(0, intervals + 1)]
uncertainty_metrics = ['EPE', 'PCK1', 'PCK5']
keys = list(list_uncertainty_metrics[0].keys())
output_dict = {m: 0.0 for m in keys}
for key in keys:
# list all values
output_dict[key] = {m: [dict[key][m] for dict in list_uncertainty_metrics] for m in uncertainty_metrics}
if 'curve' in key:
# average the curve values
output_dict[key] = {m: np.array(output_dict[key][m], np.float64).mean(0).tolist()
for m in uncertainty_metrics}
else:
output_dict[key] = {m: np.array(output_dict[key][m], np.float64).mean() for m in uncertainty_metrics}
output_dict['quants'] = quants
'''
output_dict['sparse_curve'] = {m: [dict['sparse_curve'][m] for dict in list_uncertainty_metrics]
for m in uncertainty_metrics}
output_dict['opt_curve'] = {m: [dict['opt_curve'][m] for dict in list_uncertainty_metrics]
for m in uncertainty_metrics}
output_dict['AUSE'] = {m: [dict['AUSE'][m] for dict in list_uncertainty_metrics] for m in uncertainty_metrics}
output_dict['sparse_curve'] = {m: np.array(output_dict['sparse_curve'][m], np.float64).mean(0).tolist()
for m in uncertainty_metrics}
output_dict['opt_curve'] = {m: np.array(output_dict['opt_curve'][m], np.float64).mean(0).tolist() for m in
uncertainty_metrics}
output_dict['AUSE'] = {m: np.array(output_dict['AUSE'][m], np.float64).mean() for m in uncertainty_metrics}
'''
return output_dict |