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336 lines
13 KiB
Python
336 lines
13 KiB
Python
"""Helper for evaluation on the Labeled Faces in the Wild dataset
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"""
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# 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 all
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# 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import argparse
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import sys
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import numpy as np
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from scipy import misc
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from sklearn.model_selection import KFold
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from scipy import interpolate
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import sklearn
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import datetime
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import pickle
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from sklearn.decomposition import PCA
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import mxnet as mx
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from mxnet import ndarray as nd
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sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))
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import face_image
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def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, 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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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(threshold, dist[train_set], actual_issame[train_set])
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best_threshold_index = np.argmax(acc_train)
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for threshold_idx, threshold in enumerate(thresholds):
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tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set])
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_, _, 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)
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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(np.logical_and(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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def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):
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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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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(threshold, dist[train_set], actual_issame[train_set])
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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(threshold, dist[test_set], actual_issame[test_set])
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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(np.logical_and(predict_issame, np.logical_not(actual_issame)))
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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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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 evaluation 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(thresholds, embeddings1, embeddings2,
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np.asarray(actual_issame), nrof_folds=nrof_folds, pca = pca)
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thresholds = np.arange(0, 4, 0.001)
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val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,
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np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)
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return tpr, fpr, accuracy, val, val_std, far
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def load_bin(path, image_size):
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bins, issame_list = pickle.load(open(path, 'rb'))
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data_list = []
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for flip in [0,1]:
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data = nd.empty((len(issame_list)*2, 3, image_size[0], image_size[1]))
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data_list.append(data)
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i = 0
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for i in xrange(len(issame_list)*2):
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_bin = bins[i]
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img = mx.image.imdecode(_bin)
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img = nd.transpose(img, axes=(2, 0, 1))
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for flip in [0,1]:
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if flip==1:
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img = mx.ndarray.flip(data=img, axis=2)
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data_list[flip][i][:] = img
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i+=1
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if i%1000==0:
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print('loading bin', i)
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print(data_list[0].shape)
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return (data_list, issame_list)
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def test(data_set, mx_model, batch_size, data_extra = None):
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print('testing verification..')
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data_list = data_set[0]
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issame_list = data_set[1]
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model = mx_model
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embeddings_list = []
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if data_extra is not None:
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_data_extra = nd.array(data_extra)
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time_consumed = 0.0
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for i in xrange( len(data_list) ):
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data = data_list[i]
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embeddings = None
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ba = 0
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while ba<data.shape[0]:
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bb = min(ba+batch_size, data.shape[0])
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count = bb-ba
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_data = nd.slice_axis(data, axis=0, begin=bb-batch_size, end=bb)
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_label = nd.ones( (batch_size,) )
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#print(_data.shape, _label.shape)
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time0 = datetime.datetime.now()
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if data_extra is None:
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db = mx.io.DataBatch(data=(_data,), label=(_label,))
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else:
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db = mx.io.DataBatch(data=(_data,_data_extra), label=(_label,))
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model.forward(db, is_train=False)
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net_out = model.get_outputs()
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#_arg, _aux = model.get_params()
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#__arg = {}
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#for k,v in _arg.iteritems():
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# __arg[k] = v.as_in_context(_ctx)
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#_arg = __arg
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#_arg["data"] = _data.as_in_context(_ctx)
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#_arg["softmax_label"] = _label.as_in_context(_ctx)
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#for k,v in _arg.iteritems():
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# print(k,v.context)
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#exe = sym.bind(_ctx, _arg ,args_grad=None, grad_req="null", aux_states=_aux)
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#exe.forward(is_train=False)
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#net_out = exe.outputs
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_embeddings = net_out[0].asnumpy()
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time_now = datetime.datetime.now()
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diff = time_now - time0
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time_consumed+=diff.total_seconds()
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#print(_embeddings.shape)
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if embeddings is None:
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embeddings = np.zeros( (data.shape[0], _embeddings.shape[1]) )
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embeddings[ba:bb,:] = _embeddings[(batch_size-count):,:]
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ba = bb
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embeddings_list.append(embeddings)
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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 xrange(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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embeddings = embeddings_list[0].copy()
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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(embeddings, issame_list, nrof_folds=10)
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#acc1, std1 = np.mean(accuracy), np.std(accuracy)
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#print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
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#embeddings = np.concatenate(embeddings_list, axis=1)
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embeddings = embeddings_list[0] + embeddings_list[1]
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embeddings = sklearn.preprocessing.normalize(embeddings)
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print(embeddings.shape)
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print('infer time', time_consumed)
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_, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10)
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acc2, std2 = np.mean(accuracy), np.std(accuracy)
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return acc1, std1, acc2, std2, _xnorm, embeddings_list
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='do verification')
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# general
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parser.add_argument('--data-dir', default='', help='')
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parser.add_argument('--model', default='../model/softmax,50', help='path to load model.')
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parser.add_argument('--target', default='lfw,cfp_ff,cfp_fp,agedb_30', help='test targets.')
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parser.add_argument('--gpu', default=0, type=int, help='gpu id')
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parser.add_argument('--batch-size', default=32, type=int, help='')
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parser.add_argument('--max', default='', type=str, help='')
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args = parser.parse_args()
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prop = face_image.load_property(args.data_dir)
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image_size = prop.image_size
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print('image_size', image_size)
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ctx = mx.gpu(args.gpu)
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nets = []
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vec = args.model.split(',')
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prefix = args.model.split(',')[0]
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epochs = []
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if len(vec)==1:
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pdir = os.path.dirname(prefix)
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for fname in os.listdir(pdir):
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if not fname.endswith('.params'):
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continue
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_file = os.path.join(pdir, fname)
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if _file.startswith(prefix):
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epoch = int(fname.split('.')[0].split('-')[1])
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epochs.append(epoch)
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epochs = sorted(epochs, reverse=True)
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if len(args.max)>0:
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_max = [int(x) for x in args.max.split(',')]
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assert len(_max)==2
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if len(epochs)>_max[1]:
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epochs = epochs[_max[0]:_max[1]]
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else:
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epochs = [int(x) for x in vec[1].split('|')]
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print('model number', len(epochs))
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time0 = datetime.datetime.now()
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for epoch in epochs:
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print('loading',prefix, epoch)
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model = mx.mod.Module.load(prefix, epoch, context = ctx)
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model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
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#model.init_params()
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nets.append(model)
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time_now = datetime.datetime.now()
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diff = time_now - time0
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print('model loading time', diff.total_seconds())
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ver_list = []
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ver_name_list = []
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for name in args.target.split(','):
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path = os.path.join(args.data_dir,name+".bin")
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if os.path.exists(path):
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print('loading.. ', name)
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data_set = load_bin(path, image_size)
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ver_list.append(data_set)
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ver_name_list.append(name)
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for i in xrange(len(ver_list)):
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results = []
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for model in nets:
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acc1, std1, acc2, std2, xnorm, embeddings_list = test(ver_list[i], model, args.batch_size)
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print('[%s]XNorm: %f' % (ver_name_list[i], xnorm))
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print('[%s]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], acc1, std1))
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print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], acc2, std2))
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results.append(acc2)
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print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results)))
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