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"""Helper for evaluation on the Labeled Faces in the Wild dataset | ||
""" | ||
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# MIT License | ||
# | ||
# Copyright (c) 2016 David Sandberg | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
# SOFTWARE. | ||
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import numpy as np | ||
from sklearn.model_selection import KFold | ||
from sklearn.decomposition import PCA | ||
import sklearn | ||
from scipy import interpolate | ||
import datetime | ||
import mxnet as mx | ||
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def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca=0): | ||
assert (embeddings1.shape[0] == embeddings2.shape[0]) | ||
assert (embeddings1.shape[1] == embeddings2.shape[1]) | ||
nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) | ||
nrof_thresholds = len(thresholds) | ||
k_fold = KFold(n_splits=nrof_folds, shuffle=False) | ||
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tprs = np.zeros((nrof_folds, nrof_thresholds)) | ||
fprs = np.zeros((nrof_folds, nrof_thresholds)) | ||
accuracy = np.zeros((nrof_folds)) | ||
best_thresholds = np.zeros((nrof_folds)) | ||
indices = np.arange(nrof_pairs) | ||
# print('pca', pca) | ||
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if pca == 0: | ||
diff = np.subtract(embeddings1, embeddings2) | ||
dist = np.sum(np.square(diff), 1) | ||
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for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): | ||
# print('train_set', train_set) | ||
# print('test_set', test_set) | ||
if pca > 0: | ||
print('doing pca on', fold_idx) | ||
embed1_train = embeddings1[train_set] | ||
embed2_train = embeddings2[train_set] | ||
_embed_train = np.concatenate((embed1_train, embed2_train), axis=0) | ||
# print(_embed_train.shape) | ||
pca_model = PCA(n_components=pca) | ||
pca_model.fit(_embed_train) | ||
embed1 = pca_model.transform(embeddings1) | ||
embed2 = pca_model.transform(embeddings2) | ||
embed1 = sklearn.preprocessing.normalize(embed1) | ||
embed2 = sklearn.preprocessing.normalize(embed2) | ||
# print(embed1.shape, embed2.shape) | ||
diff = np.subtract(embed1, embed2) | ||
dist = np.sum(np.square(diff), 1) | ||
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# Find the best threshold for the fold | ||
acc_train = np.zeros((nrof_thresholds)) | ||
for threshold_idx, threshold in enumerate(thresholds): | ||
_, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set]) | ||
best_threshold_index = np.argmax(acc_train) | ||
# print('best_threshold_index', best_threshold_index, acc_train[best_threshold_index]) | ||
best_thresholds[fold_idx] = thresholds[best_threshold_index] | ||
for threshold_idx, threshold in enumerate(thresholds): | ||
tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy(threshold, | ||
dist[test_set], | ||
actual_issame[ | ||
test_set]) | ||
_, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], | ||
actual_issame[test_set]) | ||
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tpr = np.mean(tprs, 0) | ||
fpr = np.mean(fprs, 0) | ||
return tpr, fpr, accuracy, best_thresholds | ||
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def calculate_accuracy(threshold, dist, actual_issame): | ||
predict_issame = np.less(dist, threshold) | ||
tp = np.sum(np.logical_and(predict_issame, actual_issame)) | ||
fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) | ||
tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame))) | ||
fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame)) | ||
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tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn) | ||
fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn) | ||
acc = float(tp + tn) / dist.size | ||
return tpr, fpr, acc | ||
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def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10): | ||
''' | ||
Copy from [insightface](https://github.com/deepinsight/insightface) | ||
:param thresholds: | ||
:param embeddings1: | ||
:param embeddings2: | ||
:param actual_issame: | ||
:param far_target: | ||
:param nrof_folds: | ||
:return: | ||
''' | ||
assert (embeddings1.shape[0] == embeddings2.shape[0]) | ||
assert (embeddings1.shape[1] == embeddings2.shape[1]) | ||
nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) | ||
nrof_thresholds = len(thresholds) | ||
k_fold = KFold(n_splits=nrof_folds, shuffle=False) | ||
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val = np.zeros(nrof_folds) | ||
far = np.zeros(nrof_folds) | ||
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diff = np.subtract(embeddings1, embeddings2) | ||
dist = np.sum(np.square(diff), 1) | ||
indices = np.arange(nrof_pairs) | ||
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for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): | ||
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# Find the threshold that gives FAR = far_target | ||
far_train = np.zeros(nrof_thresholds) | ||
for threshold_idx, threshold in enumerate(thresholds): | ||
_, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set]) | ||
if np.max(far_train) >= far_target: | ||
f = interpolate.interp1d(far_train, thresholds, kind='slinear') | ||
threshold = f(far_target) | ||
else: | ||
threshold = 0.0 | ||
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val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set]) | ||
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val_mean = np.mean(val) | ||
far_mean = np.mean(far) | ||
val_std = np.std(val) | ||
return val_mean, val_std, far_mean | ||
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def calculate_val_far(threshold, dist, actual_issame): | ||
predict_issame = np.less(dist, threshold) | ||
true_accept = np.sum(np.logical_and(predict_issame, actual_issame)) | ||
false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) | ||
n_same = np.sum(actual_issame) | ||
n_diff = np.sum(np.logical_not(actual_issame)) | ||
val = float(true_accept) / float(n_same) | ||
far = float(false_accept) / float(n_diff) | ||
return val, far | ||
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def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0): | ||
# Calculate evaluation metrics | ||
thresholds = np.arange(0, 4, 0.01) | ||
embeddings1 = embeddings[0::2] | ||
embeddings2 = embeddings[1::2] | ||
tpr, fpr, accuracy, best_thresholds = calculate_roc(thresholds, embeddings1, embeddings2, | ||
np.asarray(actual_issame), nrof_folds=nrof_folds, pca=pca) | ||
# thresholds = np.arange(0, 4, 0.001) | ||
# val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2, | ||
# np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds) | ||
# return tpr, fpr, accuracy, best_thresholds, val, val_std, far | ||
return tpr, fpr, accuracy, best_thresholds |