202 lines
7.9 KiB
Python
202 lines
7.9 KiB
Python
# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from cmath import nan
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import numpy as np
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from sklearn.model_selection import KFold
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from sklearn.decomposition import PCA
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from sklearn.preprocessing import normalize
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from ppcls.utils import logger
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class FaceAccuracy(nn.Layer):
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"""
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This code is modified from https://github.com/deepinsight/insightface/blob/master/recognition/arcface_torch/eval/verification.py
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"""
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def __init__(self):
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super().__init__()
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self.embedding_left_list = []
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self.embedding_right_list = []
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self.label_list = []
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self.best_acc = 0.
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def forward(self, embeddings_left, embeddings_right, labels, *args):
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assert len(embeddings_left) == len(embeddings_right) == len(labels)
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self.embedding_left_list.append(normalize(embeddings_left.numpy()))
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self.embedding_right_list.append(normalize(embeddings_right.numpy()))
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self.label_list.append(labels.numpy())
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return {}
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def reset(self):
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self.embedding_left_list = []
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self.embedding_right_list = []
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self.label_list = []
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self.best_acc = 0.
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@property
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def avg(self):
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return self.best_acc
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@property
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def avg_info(self):
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embeddings_left = np.concatenate(self.embedding_left_list)
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embeddings_right = np.concatenate(self.embedding_right_list)
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labels = np.concatenate(self.label_list)
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num_samples = len(embeddings_left)
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thresholds = np.arange(0, 4, 0.01)
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_, _, accuracy, best_thresholds = self.calculate_roc(thresholds,
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embeddings_left,
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embeddings_right,
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labels)
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self.best_acc = accuracy.mean()
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return "best_threshold: {:.4f}, acc: {:.4f}, num_samples: {}".format(
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best_thresholds.mean(), accuracy.mean(), num_samples)
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@staticmethod
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def calculate_roc(thresholds,
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embeddings1,
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embeddings2,
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actual_issame,
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nrof_folds=10,
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pca=0):
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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 = KFold(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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best_thresholds = np.zeros((nrof_folds))
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indices = np.arange(nrof_pairs)
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# print('pca', pca)
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dist = None
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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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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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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 = normalize(embed1)
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embed2 = normalize(embed2)
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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] = FaceAccuracy.calculate_accuracy(
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threshold, dist[train_set], actual_issame[train_set])
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best_threshold_index = np.argmax(acc_train)
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best_thresholds[fold_idx] = thresholds[best_threshold_index]
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for threshold_idx, threshold in enumerate(thresholds):
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tprs[fold_idx, threshold_idx], fprs[
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fold_idx, threshold_idx], _ = FaceAccuracy.calculate_accuracy(
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threshold, dist[test_set], actual_issame[test_set])
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_, _, accuracy[fold_idx] = FaceAccuracy.calculate_accuracy(
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thresholds[best_threshold_index], dist[test_set],
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actual_issame[test_set])
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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, best_thresholds
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@staticmethod
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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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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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class FaceAccOnFiveDatasets(FaceAccuracy):
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dataname_to_idx = {
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"agedb_30": 0,
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"cfp_fp": 1,
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"lfw": 2,
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"cplfw": 3,
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"calfw": 4
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}
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idx_to_dataname = {v: k for k, v in dataname_to_idx.items()}
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def __init__(self):
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super().__init__()
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self.dataname_idx_list = []
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def forward(self, embeddings_left, embeddings_right, labels,
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dataname_idxs, *args):
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assert len(embeddings_left) == len(dataname_idxs)
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dataname_idxs = dataname_idxs.astype('int64').numpy()
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self.dataname_idx_list.append(dataname_idxs)
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return super().forward(embeddings_left, embeddings_right, labels)
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def reset(self):
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super().reset()
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self.dataname_idx_list = []
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@property
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def avg_info(self):
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results = {}
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all_embeddings_left = np.concatenate(self.embedding_left_list)
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all_embeddings_right = np.concatenate(self.embedding_right_list)
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all_labels = np.concatenate(self.label_list)
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dataname_idxs = np.concatenate(self.dataname_idx_list)
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acc = []
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for dataname_idx in np.unique(dataname_idxs):
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dataname = self.idx_to_dataname[dataname_idx]
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mask = dataname_idxs == dataname_idx
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embeddings_left = all_embeddings_left[mask]
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embeddings_right = all_embeddings_right[mask]
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labels = all_labels[mask]
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thresholds = np.arange(0, 4, 0.01)
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_, _, accuracy, best_thresholds = self.calculate_roc(
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thresholds, embeddings_left, embeddings_right, labels)
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acc.append(accuracy.mean())
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results[f'{dataname}-best_threshold'] = f'{best_thresholds.mean():.4f}'
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results[f'{dataname}-acc'] = f'{accuracy.mean():.4f}'
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results[f'{dataname}-num_samples'] = f'{len(embeddings_left)}'
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self.best_acc = np.mean(acc)
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results['avg_acc'] = f'{self.best_acc:.4f}'
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info = ", ".join([f"{k}: {v}" for k, v in results.items()])
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return info
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