mirror of
https://github.com/deepinsight/insightface.git
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275 lines
9.4 KiB
Python
275 lines
9.4 KiB
Python
# MIT License
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#
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# Copyright (c) 2016 David Sandberg
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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import math
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import numpy as np
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import sklearn
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from sklearn.model_selection import KFold
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from sklearn.decomposition import PCA
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from scipy import interpolate
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class LFold:
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def __init__(self, n_splits=2, shuffle=False):
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self.n_splits = n_splits
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if self.n_splits > 1:
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self.k_fold = KFold(n_splits=n_splits, shuffle=shuffle)
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def split(self, indices):
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if self.n_splits > 1:
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return self.k_fold.split(indices)
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else:
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return [(indices, indices)]
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def distance(embeddings1, embeddings2, distance_metric=0):
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if distance_metric == 0:
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# Euclidian distance
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diff = np.subtract(embeddings1, embeddings2)
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dist = np.sum(np.square(diff), 1)
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elif distance_metric == 1:
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# Distance based on cosine similarity
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dot = np.sum(np.multiply(embeddings1, embeddings2), axis=1)
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norm = np.linalg.norm(embeddings1, axis=1) * np.linalg.norm(
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embeddings2, axis=1
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)
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similarity = dot / norm
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dist = np.arccos(similarity) / math.pi
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else:
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raise "Undefined distance metric %d" % distance_metric
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return dist
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def calculate_roc(
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thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca=0
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):
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assert embeddings1.shape[0] == embeddings2.shape[0]
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assert embeddings1.shape[1] == embeddings2.shape[1]
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nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
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nrof_thresholds = len(thresholds)
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k_fold = LFold(n_splits=nrof_folds, shuffle=False)
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tprs = np.zeros((nrof_folds, nrof_thresholds))
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fprs = np.zeros((nrof_folds, nrof_thresholds))
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accuracy = np.zeros((nrof_folds))
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indices = np.arange(nrof_pairs)
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# print('pca', pca)
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if pca == 0:
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diff = np.subtract(embeddings1, embeddings2)
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dist = np.sum(np.square(diff), 1)
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for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
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# print('train_set', train_set)
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# print('test_set', test_set)
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if pca > 0:
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print("doing pca on", fold_idx)
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embed1_train = embeddings1[train_set]
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embed2_train = embeddings2[train_set]
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_embed_train = np.concatenate((embed1_train, embed2_train), axis=0)
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# print(_embed_train.shape)
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pca_model = PCA(n_components=pca)
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pca_model.fit(_embed_train)
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embed1 = pca_model.transform(embeddings1)
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embed2 = pca_model.transform(embeddings2)
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embed1 = sklearn.preprocessing.normalize(embed1)
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embed2 = sklearn.preprocessing.normalize(embed2)
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# print(embed1.shape, embed2.shape)
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diff = np.subtract(embed1, embed2)
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dist = np.sum(np.square(diff), 1)
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# Find the best threshold for the fold
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acc_train = np.zeros((nrof_thresholds))
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for threshold_idx, threshold in enumerate(thresholds):
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_, _, acc_train[threshold_idx] = calculate_accuracy(
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threshold, dist[train_set], actual_issame[train_set]
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)
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best_threshold_index = np.argmax(acc_train)
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# print('threshold', thresholds[best_threshold_index])
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for threshold_idx, threshold in enumerate(thresholds):
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(
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tprs[fold_idx, threshold_idx],
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fprs[fold_idx, threshold_idx],
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_,
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) = calculate_accuracy(
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threshold, dist[test_set], actual_issame[test_set]
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)
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_, _, accuracy[fold_idx] = calculate_accuracy(
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thresholds[best_threshold_index],
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dist[test_set],
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actual_issame[test_set],
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)
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tpr = np.mean(tprs, 0)
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fpr = np.mean(fprs, 0)
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return tpr, fpr, accuracy
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def calculate_accuracy(threshold, dist, actual_issame):
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predict_issame = np.less(dist, threshold)
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tp = np.sum(np.logical_and(predict_issame, actual_issame))
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fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
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tn = np.sum(
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np.logical_and(
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np.logical_not(predict_issame), np.logical_not(actual_issame)
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)
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)
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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)
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fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)
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acc = float(tp + tn) / dist.size
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return tpr, fpr, acc
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def calculate_val(
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thresholds,
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embeddings1,
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embeddings2,
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actual_issame,
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far_target,
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nrof_folds=10,
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):
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assert embeddings1.shape[0] == embeddings2.shape[0]
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assert embeddings1.shape[1] == embeddings2.shape[1]
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nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
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nrof_thresholds = len(thresholds)
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k_fold = LFold(n_splits=nrof_folds, shuffle=False)
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val = np.zeros(nrof_folds)
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far = np.zeros(nrof_folds)
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diff = np.subtract(embeddings1, embeddings2)
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dist = np.sum(np.square(diff), 1)
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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
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far_train = np.zeros(nrof_thresholds)
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for threshold_idx, threshold in enumerate(thresholds):
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_, far_train[threshold_idx] = calculate_val_far(
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threshold, dist[train_set], actual_issame[train_set]
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)
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if np.max(far_train) >= far_target:
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f = interpolate.interp1d(far_train, thresholds, kind="slinear")
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threshold = f(far_target)
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else:
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threshold = 0.0
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val[fold_idx], far[fold_idx] = calculate_val_far(
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threshold, dist[test_set], actual_issame[test_set]
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)
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val_mean = np.mean(val)
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far_mean = np.mean(far)
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val_std = np.std(val)
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return val_mean, val_std, far_mean
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def calculate_val_far(threshold, dist, actual_issame):
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predict_issame = np.less(dist, threshold)
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true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
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false_accept = np.sum(
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np.logical_and(predict_issame, np.logical_not(actual_issame))
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)
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n_same = np.sum(actual_issame)
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n_diff = np.sum(np.logical_not(actual_issame))
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# print(true_accept, false_accept)
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# print("n_same, n_diff:",n_same, n_diff)
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val = float(true_accept) / float(n_same)
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far = float(false_accept) / float(n_diff)
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return val, far
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def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):
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# Calculate metrics
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thresholds = np.arange(0, 4, 0.01)
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embeddings1 = embeddings[0::2]
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embeddings2 = embeddings[1::2]
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tpr, fpr, accuracy = calculate_roc(
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thresholds,
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embeddings1,
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embeddings2,
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np.asarray(actual_issame),
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nrof_folds=nrof_folds,
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pca=pca,
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)
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thresholds = np.arange(0, 4, 0.001)
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val, val_std, far = calculate_val(
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thresholds,
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embeddings1,
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embeddings2,
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np.asarray(actual_issame),
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1e-3,
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nrof_folds=nrof_folds,
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)
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return tpr, fpr, accuracy, val, val_std, far
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def cal_validation_metrics(
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embeddings_list, issame_list, nrof_folds=10, no_flip=False
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):
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print("Embedding shape: {}".format(embeddings_list[0].shape))
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if no_flip:
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embeddings = embeddings_list[0]
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print("Reading {} embeddings.".format(len(embeddings)))
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embeddings = sklearn.preprocessing.normalize(embeddings)
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acc1 = 0.0
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std1 = 0.0
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_, _, accuracy, val, val_std, far = evaluate(
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embeddings, issame_list, nrof_folds=10
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)
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acc1, std1 = np.mean(accuracy), np.std(accuracy)
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print(
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"Validation rate: %2.5f+-%2.5f @ FAR=%2.5f" % (val, val_std, far)
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)
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else:
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# xnorm
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_xnorm = 0.0
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_xnorm_cnt = 0
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for embed in embeddings_list:
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for i in range(embed.shape[0]):
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_em = embed[i]
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_norm = np.linalg.norm(_em)
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# print(_em.shape, _norm)
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_xnorm += _norm
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_xnorm_cnt += 1
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_xnorm /= _xnorm_cnt
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# Evaluate on embeddings
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embeddings = embeddings_list[0] + embeddings_list[1]
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embeddings = sklearn.preprocessing.normalize(embeddings)
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_, _, accuracy, val, val_std, far = evaluate(
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embeddings, issame_list, nrof_folds=nrof_folds
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)
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acc2, std2 = np.mean(accuracy), np.std(accuracy)
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print("XNorm: %f" % (_xnorm))
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print("Accuracy-Flip: %1.5f+-%1.5f" % (acc2, std2))
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