def run_index_pred_eval(args):
t0 = time()
run_index(args)
t1 = time()
run_pred(args)
t2 = time()
evaluate_recall(args)
print('run_index: %.1f mins, run_pred: %.1f mins' % ((t1 - t0) / 60, (t2 - t1) / 60))
def evaluation(feature_set, label_set, k_set=[1, 2, 4, 8, 16, 32], distance_type=None):
if distance_type == 'cosine':
squared_feature_set = np.sqrt(np.sum(np.power(feature_set, 2), axis=1, keepdims=True))
distM = np.divide(np.matmul(feature_set, np.transpose(feature_set, (1, 0))),
np.matmul(squared_feature_set, np.transpose(squared_feature_set, (1, 0))))
distM = 1. - distM
elif distance_type == "Euclidean":
distM = distance.cdist(feature_set, feature_set)
nn_av, ft_av, st_av, dcg_av, e_av, map_, p_points, pre, rec, rankArray = RetrievalEvaluation.RetrievalEvaluation(distM, label_set, label_set, testMode=2)
recall_k = evaluate_recall.evaluate_recall(feature_set, label_set, k_set)
for i, k in enumerate(k_set):
print("Recall@{:1d}: {:6.5f}".format(k_set[i], recall_k[i]))
normalized_mutual_information, RI, F = evaluate_clustering.evaluate_clustering(feature_set, label_set)
print("")
print("normalized_mutual_information = {}".format(normalized_mutual_information))
print("RI = {}".format(RI))
print("F_1 = {}".format(F))
print("")
print(('The NN is {:5.5f}\nThe FT is {:5.5f}\n' +
'The ST is {:5.5f}\nThe DCG is {:5.5f}\n' +
'The E is {:5.5f}\nThe MAP {:5.5f}\n').format(
nn_av, ft_av, st_av, dcg_av, e_av, map_))
rec = np.expand_dims(rec, axis=1)
pre = np.expand_dims(pre, axis=1)
rec_pre = np.concatenate([rec, pre], axis=1)
return rec_pre
def evaluate_online(self, sess, test_mode=1):
if test_mode == 1:
train_feature_set, train_label_set = \
get_feature_and_label(self.train_init_op, sess, self.features, self.labels)
test_feature_set, test_label_set = \
get_feature_and_label(self.test_init_op, sess, self.features, self.labels)
distM = distance.cdist(test_feature_set, train_feature_set)
nn_av, ft_av, st_av, dcg_av, e_av, map_, p_points, pre, rec, rankArray = RetrievalEvaluation.RetrievalEvaluation(
distM, train_label_set, test_label_set, testMode=1)
elif test_mode == 2:
batch_num = int(self.test_img_num / self.batch_size) + 2
test_feature_set, test_label_set = \
self.get_feature_and_label(self.test_init_op, sess, self.features, self.labels, batch_num)
distM = distance.cdist(test_feature_set, test_feature_set)
nn_av, ft_av, st_av, dcg_av, e_av, map_, p_points, pre, rec, rankArray = RetrievalEvaluation.RetrievalEvaluation(
distM, test_label_set, test_label_set, testMode=2)
recall_k = evaluate_recall.evaluate_recall(test_feature_set,
test_label_set,
self.k_set)
for i, k in enumerate(self.k_set):
print("Recall@{:1d}: {:6.5f}".format(self.k_set[i],
recall_k[i]))
normalized_mutual_information, RI, F = evaluate_clustering.evaluate_clustering(
test_feature_set, test_label_set)
print("")
print("normalized_mutual_information = {}".format(
normalized_mutual_information))
print("RI = {}".format(RI))
print("F_1 = {}".format(F))
print("")
print(('The NN is {:5.5f}\nThe FT is {:5.5f}\n' +
'The ST is {:5.5f}\nThe DCG is {:5.5f}\n' +
'The E is {:5.5f}\nThe MAP {:5.5f}\n').format(
nn_av, ft_av, st_av, dcg_av, e_av, map_))
def evaluate(self, sess, test_mode=2):
ckpt = self.get_checkpoint_file()
sess.run(tf.global_variables_initializer())
if ckpt is None:
raise IOError("No check point file found")
else:
self.saver.restore(sess, ckpt.model_checkpoint_path)
def get_feature_and_label(init_op, sess, feature_tensor, label_tensor):
feature_set = []
label_set = []
counter = 0
sess.run(init_op)
while True:
try:
features, labels = sess.run(
[self.features, self.labels],
feed_dict={self.is_training: False})
counter += 1
print("Processing the {:3d}-th batch".format(counter))
except tf.errors.OutOfRangeError:
break
feature_set.append(features)
label_set.append(labels)
feature_set = np.concatenate(feature_set, axis=0)
label_set = np.concatenate(label_set, axis=0)
return feature_set, label_set
if test_mode == 1:
train_feature_set, train_label_set = \
get_feature_and_label(self.train_init_op, sess, self.features, self.labels)
test_feature_set, test_label_set = \
get_feature_and_label(self.test_init_op, sess, self.features, self.labels)
distM = distance.cdist(test_feature_set, train_feature_set)
nn_av, ft_av, st_av, dcg_av, e_av, map_, p_points, pre, rec, rankArray = RetrievalEvaluation.RetrievalEvaluation(
distM, train_label_set, test_label_set, testMode=1)
elif test_mode == 2:
test_feature_set, test_label_set = \
get_feature_and_label(self.test_init_op, sess, self.features, self.labels)
distM = distance.cdist(test_feature_set, test_feature_set)
nn_av, ft_av, st_av, dcg_av, e_av, map_, p_points, pre, rec, rankArray = RetrievalEvaluation.RetrievalEvaluation(
distM, test_label_set, test_label_set, testMode=2)
recall_k = evaluate_recall.evaluate_recall(test_feature_set,
test_label_set,
self.k_set)
for i, k in enumerate(self.k_set):
print("Recall@{:1d}: {:6.5f}".format(self.k_set[i],
recall_k[i]))
normalized_mutual_information, RI, F = evaluate_clustering.evaluate_clustering(
test_feature_set, test_label_set)
print("")
print("normalized_mutual_information = {}".format(
normalized_mutual_information))
print("RI = {}".format(RI))
print("F_1 = {}".format(F))
print("")
print(('The NN is {:5.5f}\nThe FT is {:5.5f}\n' +
'The ST is {:5.5f}\nThe DCG is {:5.5f}\n' +
'The E is {:5.5f}\nThe MAP {:5.5f}\n').format(
nn_av, ft_av, st_av, dcg_av, e_av, map_))