"""Helper for evaluation on the Labeled Faces in the Wild dataset """ # 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import numpy as np from scipy import misc from sklearn.model_selection import KFold from scipy import interpolate import sklearn from sklearn.decomposition import PCA import mxnet as mx from mxnet import ndarray as nd 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) tprs = np.zeros((nrof_folds, nrof_thresholds)) fprs = np.zeros((nrof_folds, nrof_thresholds)) accuracy = np.zeros((nrof_folds)) indices = np.arange(nrof_pairs) #print('pca', pca) if pca == 0: diff = np.subtract(embeddings1, embeddings2) dist = np.sum(np.square(diff), 1) 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) # 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) 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]) tpr = np.mean(tprs, 0) fpr = np.mean(fprs, 0) return tpr, fpr, accuracy 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)) 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 def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10): 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) val = np.zeros(nrof_folds) far = np.zeros(nrof_folds) diff = np.subtract(embeddings1, embeddings2) dist = np.sum(np.square(diff), 1) indices = np.arange(nrof_pairs) for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): # 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 val[fold_idx], far[fold_idx] = calculate_val_far( threshold, dist[test_set], actual_issame[test_set]) val_mean = np.mean(val) far_mean = np.mean(far) val_std = np.std(val) return val_mean, val_std, far_mean 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 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 = 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, val, val_std, far def get_paths(lfw_dir, pairs, file_ext): nrof_skipped_pairs = 0 path_list = [] issame_list = [] for pair in pairs: if len(pair) == 3: path0 = os.path.join( lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1]) + '.' + file_ext) path1 = os.path.join( lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[2]) + '.' + file_ext) issame = True elif len(pair) == 4: path0 = os.path.join( lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1]) + '.' + file_ext) path1 = os.path.join( lfw_dir, pair[2], pair[2] + '_' + '%04d' % int(pair[3]) + '.' + file_ext) issame = False if os.path.exists(path0) and os.path.exists( path1): # Only add the pair if both paths exist path_list += (path0, path1) issame_list.append(issame) else: print('not exists', path0, path1) nrof_skipped_pairs += 1 if nrof_skipped_pairs > 0: print('Skipped %d image pairs' % nrof_skipped_pairs) return path_list, issame_list def read_pairs(pairs_filename): pairs = [] with open(pairs_filename, 'r') as f: for line in f.readlines()[1:]: pair = line.strip().split() pairs.append(pair) return np.array(pairs) def load_dataset(lfw_dir, image_size): lfw_pairs = read_pairs(os.path.join(lfw_dir, 'pairs.txt')) lfw_paths, issame_list = get_paths(lfw_dir, lfw_pairs, 'jpg') lfw_data_list = [] for flip in [0, 1]: lfw_data = nd.empty((len(lfw_paths), 3, image_size[0], image_size[1])) lfw_data_list.append(lfw_data) i = 0 for path in lfw_paths: with open(path, 'rb') as fin: _bin = fin.read() img = mx.image.imdecode(_bin) img = nd.transpose(img, axes=(2, 0, 1)) for flip in [0, 1]: if flip == 1: img = mx.ndarray.flip(data=img, axis=2) lfw_data_list[flip][i][:] = img i += 1 if i % 1000 == 0: print('loading lfw', i) print(lfw_data_list[0].shape) print(lfw_data_list[1].shape) return (lfw_data_list, issame_list) def test(lfw_set, mx_model, batch_size): print('testing lfw..') lfw_data_list = lfw_set[0] issame_list = lfw_set[1] model = mx_model embeddings_list = [] for i in range(len(lfw_data_list)): lfw_data = lfw_data_list[i] embeddings = None ba = 0 while ba < lfw_data.shape[0]: bb = min(ba + batch_size, lfw_data.shape[0]) _data = nd.slice_axis(lfw_data, axis=0, begin=ba, end=bb) _label = nd.ones((bb - ba, )) #print(_data.shape, _label.shape) db = mx.io.DataBatch(data=(_data, ), label=(_label, )) model.forward(db, is_train=False) net_out = model.get_outputs() #_arg, _aux = model.get_params() #__arg = {} #for k,v in _arg.iteritems(): # __arg[k] = v.as_in_context(_ctx) #_arg = __arg #_arg["data"] = _data.as_in_context(_ctx) #_arg["softmax_label"] = _label.as_in_context(_ctx) #for k,v in _arg.iteritems(): # print(k,v.context) #exe = sym.bind(_ctx, _arg ,args_grad=None, grad_req="null", aux_states=_aux) #exe.forward(is_train=False) #net_out = exe.outputs _embeddings = net_out[0].asnumpy() #print(_embeddings.shape) if embeddings is None: embeddings = np.zeros( (lfw_data.shape[0], _embeddings.shape[1])) embeddings[ba:bb, :] = _embeddings ba = bb embeddings_list.append(embeddings) _xnorm = 0.0 _xnorm_cnt = 0 for embed in embeddings_list: for i in range(embed.shape[0]): _em = embed[i] _norm = np.linalg.norm(_em) #print(_em.shape, _norm) _xnorm += _norm _xnorm_cnt += 1 _xnorm /= _xnorm_cnt embeddings = embeddings_list[0].copy() embeddings = sklearn.preprocessing.normalize(embeddings) _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10) acc1, std1 = np.mean(accuracy), np.std(accuracy) #print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far)) #embeddings = np.concatenate(embeddings_list, axis=1) embeddings = embeddings_list[0] + embeddings_list[1] embeddings = sklearn.preprocessing.normalize(embeddings) print(embeddings.shape) _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10) acc2, std2 = np.mean(accuracy), np.std(accuracy) return acc1, std1, acc2, std2, _xnorm, embeddings_list