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evaluation.py
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evaluation.py
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import numpy as np
import pickle
import cv2
import tensorflow as tf
import mxnet as mx
import datetime
import sklearn
from sklearn.model_selection import KFold
from sklearn.decomposition import PCA
from scipy import interpolate
from resnet import ResNet50, train_model
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 i in range(len(dist)):
# dist[i] = abs(np.dot(embeddings1[i], embeddings2[i]))
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)
# for i in range(len(dist)):
# dist[i] = abs(np.dot(embed1[i], embed1[i]))
# 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)
print('best_threshold_index', best_threshold_index,
acc_train[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
def calculate_accuracy(threshold, dist, actual_issame):
predict_issame = np.less(dist, threshold)
# predict_issame = np.less(threshold, dist)
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 = 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 load_bin(path, image_size):
bins, issame_list = pickle.load(
open(path, 'rb'), encoding='bytes')
data_list = []
for _ in [0, 1]:
data = np.empty(
(len(issame_list) * 2, image_size[0], image_size[1], 3))
data_list.append(data)
for i in range(len(issame_list) * 2):
_bin = bins[i]
img = mx.image.imdecode(_bin).asnumpy()
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
for flip in [0, 1]:
if flip == 1:
img = np.fliplr(img)
data_list[flip][i, ...] = img
i += 1
if i % 1000 == 0:
print('loading bin', i)
print(data_list[0].shape)
return data_list, issame_list
def data_iter(datasets, batch_size):
data_num = datasets.shape[0]
for i in range(0, data_num, batch_size):
yield datasets[i:min(i + batch_size, data_num), ...]
def test(data_set, batch_size, model):
'''
referenc official implementation [insightface](https://github.com/deepinsight/insightface)
:param data_set:
:param batch_size:
:param model:
:return:
'''
data_list = data_set[0]
issame_list = data_set[1]
embeddings_list = []
time_consumed = 0.0
for i in range(len(data_list)):
datas = data_list[i]
embeddings = None
for idx, data in enumerate(data_iter(datas, batch_size)):
data_tmp = data.copy() # fix issues #4
data_tmp -= 127.5
data_tmp *= 0.0078125
data_tmp = tf.cast(data_tmp, tf.float32)
time0 = datetime.datetime.now()
_embeddings = model(data_tmp)
time_now = datetime.datetime.now()
diff = time_now - time0
time_consumed += diff.total_seconds()
if embeddings is None:
embeddings = np.zeros((datas.shape[0], _embeddings.shape[1]))
try:
embeddings[idx * batch_size:min((idx + 1) *
batch_size, datas.shape[0]), ...] = _embeddings
except ValueError:
print('idx*batch_size value is %d min((idx+1)*batch_size, datas.shape[0]) %d, batch_size %d, data.shape[0] %d' %
(idx * batch_size, min((idx + 1) * batch_size, datas.shape[0]), batch_size, datas.shape[0]))
print('embedding shape is ', _embeddings.shape)
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
acc1 = 0.0
std1 = 0.0
embeddings = embeddings_list[0] + embeddings_list[1]
embeddings = sklearn.preprocessing.normalize(embeddings)
print(embeddings.shape)
print('infer time', time_consumed)
_, _, 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
def ver_test(data_set, dataset_name, batch_size, model):
for i in range(len(dataset_name)):
print('testing %s..' % (dataset_name[i]))
acc1, std1, acc2, std2, xnorm = test(data_set=data_set[i],
batch_size=batch_size,
model=model)
print('[%s]XNorm: %f' % (dataset_name[i], xnorm))
print('[%s]Accuracy: %1.5f+-%1.5f' % (dataset_name[i], acc2, std2))
tmodel = train_model()
dataset = []
dataset_name = []
dataset.append(load_bin('dataset/faces_webface_112x112/lfw.bin', [112, 112]))
dataset_name.append('lfw')
dataset.append(
load_bin('dataset/faces_webface_112x112/cfp_ff.bin', [112, 112]))
dataset_name.append('cfp_ff')
dataset.append(
load_bin('dataset/faces_webface_112x112/cfp_fp.bin', [112, 112]))
dataset_name.append('cfp_fp')
dataset.append(
load_bin('dataset/faces_webface_112x112/agedb_30.bin', [112, 112]))
dataset_name.append('agedb_30')
for i in [52000]:
tmodel.load_weights('output/ckpt/weights_step-%d' % i)
model = tmodel.resnet
ver_test(dataset, dataset_name, 16, model)