evaluate_utils.py

import bcolz
import numpy as np
import os
import json
import numpy as np
from sklearn.model_selection import KFold
from sklearn.decomposition import PCA
import sklearn
from scipy import interpolate

def get_val_data(data_path):
    agedb_30, agedb_30_issame = get_val_pair(data_path, 'agedb_30')
    cfp_fp, cfp_fp_issame = get_val_pair(data_path, 'cfp_fp')
    lfw, lfw_issame = get_val_pair(data_path, 'lfw')
    cplfw, cplfw_issame = get_val_pair(data_path, 'cplfw')
    calfw, calfw_issame = get_val_pair(data_path, 'calfw')
    return agedb_30, cfp_fp, lfw, agedb_30_issame, cfp_fp_issame, lfw_issame, cplfw, cplfw_issame, calfw, calfw_issame

def get_val_pair(path, name, use_memfile=True):
    if use_memfile:
        mem_file_dir = os.path.join(path, name, 'memfile')
        mem_file_name = os.path.join(mem_file_dir, 'mem_file.dat')
        if os.path.isdir(mem_file_dir):
            print('laoding validation data memfile')
            np_array = read_memmap(mem_file_name)
        else:
            os.makedirs(mem_file_dir)
            carray = bcolz.carray(rootdir = os.path.join(path, name), mode='r')
            np_array = np.array(carray)
            mem_array = make_memmap(mem_file_name, np_array)
            del np_array, mem_array 
            np_array = read_memmap(mem_file_name)
    else:
        np_array = bcolz.carray(rootdir = os.path.join(path, name), mode='r')

    issame = np.load(os.path.join(path, '{}_list.npy'.format(name)))
    return np_array, issame


def make_memmap(mem_file_name, np_to_copy):
    memmap_configs = dict() 
    memmap_configs['shape'] = shape = tuple(np_to_copy.shape) 
    memmap_configs['dtype'] = dtype = str(np_to_copy.dtype)   
    json.dump(memmap_configs, open(mem_file_name+'.conf', 'w')) 
    # w+ mode: Create or overwrite existing file for reading and writing
    mm = np.memmap(mem_file_name, mode='w+', shape=shape, dtype=dtype)
    mm[:] = np_to_copy[:]
    mm.flush() # memmap data flush
    return mm

def read_memmap(mem_file_name):
    # r+ mode: Open existing file for reading and writing
    with open(mem_file_name+'.conf', 'r') as file:
        memmap_configs = json.load(file)
        return np.memmap(mem_file_name, mode='r+', \
                         shape=tuple(memmap_configs['shape']), \
                         dtype=memmap_configs['dtype'])


# 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.

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))
    best_thresholds = np.zeros((nrof_folds))
    indices = np.arange(nrof_pairs)
    # print('pca', pca)
    dist = None

    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)
        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])

    tpr = np.mean(tprs, 0)
    fpr = np.mean(fprs, 0)
    return tpr, fpr, accuracy, best_thresholds


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):
    '''
    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)

    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, best_thresholds = calculate_roc(thresholds, embeddings1, embeddings2,
                                       np.asarray(actual_issame), nrof_folds=nrof_folds, pca=pca)
    return tpr, fpr, accuracy, best_thresholds