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metrics.py
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metrics.py
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import itertools
import tensorflow as tf
import tensorflow.keras.backend as K
tf.compat.v1.enable_eager_execution()
def create_permutations(num_models):
"""
create_permutations(3) returns [[0,1,2],[0,2,1],[1,0,2],[1,2,0],...]
:param num_models:
:return: list of lists of permutations
"""
indexes = [i for i in range(0, num_models)]
perms = list(itertools.permutations(indexes))
perms = [list(perm) for perm in perms]
return perms
def find_best_perm_MM(y_true, y_pred):
"""
accuracy with variable models
:param y_true: (BS, NPOINTS, NMODELS)
:param y_pred: (BS, NPOINTS, NMODELS)
:return: best y pred permutation
"""
BS = y_pred.shape[0]
num_models = y_pred.shape[2]
inlierTresh = 0.5
y_pred_out = []
for sample in range(BS):
best_acc = 0.0
pred_with_best_permutation = tf.gather(y_pred[sample], [i for i in range(num_models)], axis=-1)
for perm in create_permutations(num_models):
pred = tf.gather(y_pred[sample], perm, axis=-1)
totAcc = 0.0
for model in range(num_models):
inliersProb = 1.0 - K.sigmoid(pred[:, model])
predictedInliers = K.cast(
K.greater(inliersProb, inlierTresh), "float32"
)
acc = K.mean(
K.cast(
K.not_equal(predictedInliers, y_true[sample, :, model]),
"float32",
)
)
totAcc += acc
totAcc = totAcc / num_models
if totAcc > best_acc:
pred_with_best_permutation = pred
best_acc = totAcc
y_pred_out.append(
K.reshape(
pred_with_best_permutation,
shape=(
1,
pred_with_best_permutation.shape[0],
pred_with_best_permutation.shape[1],
),
)
)
y_pred_out = tf.concat(y_pred_out, axis=0)
return y_pred_out
def precision(y_true, y_pred):
# TP / TP + FP
inliersGT = 1.0 - y_true
inlierTresh = 0.5
inliersProb = 1.0 - K.sigmoid(y_pred)
predictedInliers = K.cast(K.greater(inliersProb, inlierTresh), "float32")
trueInliers = predictedInliers * inliersGT
trueInliersCount = K.sum(trueInliers)
falseInliers = predictedInliers * (1.0 - inliersGT)
falseInliersCount = K.sum(falseInliers)
precision = trueInliersCount / (trueInliersCount + falseInliersCount)
return precision
def recall(y_true, y_pred):
# TP / TP + FN
inliersGT = 1.0 - y_true
inlierTresh = 0.5
inliersProb = 1.0 - K.sigmoid(y_pred)
predictedInliers = K.cast(K.greater(inliersProb, inlierTresh), "float32")
trueInliers = predictedInliers * inliersGT
trueInliersCount = K.sum(trueInliers)
falseOutliers = (1.0 - predictedInliers) * inliersGT
falseOutliersCount = K.sum(falseOutliers)
recall = trueInliersCount / (trueInliersCount + falseOutliersCount)
return recall
def acc(y_true, y_pred):
"""
accuracy
:param y_true:
:param y_pred:
:return:
"""
num_models = y_pred.shape[2]
y_true = y_true[:, :, 0:num_models]
y_p = find_best_perm_MM(y_true, y_pred)
inliersTresh = 0.5
totAcc = 0
for model in range(num_models):
inliersProb = 1.0 - K.sigmoid(y_p[..., model])
predictedInliers = K.cast(K.greater(inliersProb, inliersTresh), "float32")
temp = y_true[..., model]
acc = K.mean(K.cast(K.not_equal(predictedInliers, temp), "float32"))
totAcc += acc
totAcc /= num_models
return totAcc
def nspi(y_true, y_pred, avg=False):
"""
number of samples predicted inliers
:param y_true:
:param y_pred:
:param avg: explained below
:return:
avg = True -> average num of inliers for each model in the sample
avg = False -> total num of samples predicted inliers, no matter what model they are in
"""
n_coords = y_pred.shape[2]
y_true = y_true[:, :, 0:n_coords]
y_p = find_best_perm_MM(y_true, y_pred)
inliersTresh = 0.5
num_models = y_pred.shape[2]
tot_num_inliers = 0.0
for model in range(num_models):
inliersProb = 1.0 - K.sigmoid(y_p[:, :, model])
n_inliers = K.sum(K.cast(K.greater_equal(inliersProb, inliersTresh), "float32"), axis=1)
tot_num_inliers += n_inliers
if avg:
tot_num_inliers = tot_num_inliers / num_models
return tot_num_inliers
def idr(y_true, y_pred):
"""
inliers detection rate
:param y_true:
:param y_pred:
:return:
"""
nm = y_pred.shape[2]
y_true = y_true[:, :, 0:nm]
y_p = find_best_perm_MM(y_true, y_pred)
tot_inliers_det_rate = 0
for model in range(nm):
# model
correctPred = K.equal(
K.cast(K.greater_equal(K.sigmoid(y_p[:, :, model]), 0.5), "int64"),
K.cast(y_true[:, :, model], "int64"),
)
correctPred = K.cast(correctPred, "float32")
inliersGT = K.cast(K.less(y_true[:, :, model], 0.5), "float32")
correctInliers = correctPred * inliersGT
correctInliers = K.sum(K.cast(correctInliers, "float32")) / (K.sum(inliersGT))
inliersDetectionRate = K.mean(correctInliers)
tot_inliers_det_rate += inliersDetectionRate
tot_inliers_det_rate = tot_inliers_det_rate / nm
return tot_inliers_det_rate
def odr(y_true, y_pred):
"""
outliers detection rate
:param y_true:
:param y_pred:
:return:
"""
n_coords = y_pred.shape[2]
y_true = y_true[:, :, 0:n_coords]
y_p = find_best_perm_MM(y_true, y_pred)
num_models = y_pred.shape[2]
tot_outliers_det_rate_MM = 0
for model in range(num_models):
correctPred = K.equal(
K.cast(K.greater_equal(K.sigmoid(y_p[:, :, model]), 0.5), "int64"),
K.cast(y_true[:, :, model], "int64"),
)
correctPred = K.cast(correctPred, "float32")
outliersGT = K.cast(K.greater(y_true[:, :, model], 0.5), "float32")
correctOutliers = correctPred * outliersGT
correctOutliers = K.sum(K.cast(correctOutliers, "float32")) / (K.sum(outliersGT))
outliersDetectionRate = K.mean(correctOutliers)
tot_outliers_det_rate_MM += outliersDetectionRate
tot_outliers_det_rate_MM = tot_outliers_det_rate_MM / num_models
return tot_outliers_det_rate_MM
def f1(y_true, y_pred):
"""
f1 score
:param y_true:
:param y_pred:
:return:
"""
n_coords = y_pred.shape[2]
y_true = y_true[:, :, 0:n_coords]
y_pred_good_perm = find_best_perm_MM(y_true, y_pred)
num_models = y_pred.shape[2]
tot_f1_score_MM = 0
for model in range(num_models):
yt = y_true[..., model]
yp = y_pred_good_perm[..., model]
p = precision(yt, yp)
r = recall(yt, yp)
tot_f1_score_MM += 2 * (r * p) / (r + p)
f1_score_MM = tot_f1_score_MM / num_models
return f1_score_MM