def validation(self, img_query, img_database, R=100):
print("%s #validation# start validation" % (datetime.now()))
query_batch = int(ceil(img_query.n_samples / self.val_batch_size))
print("%s #validation# totally %d query in %d batches" % (datetime.now(), img_query.n_samples, query_batch))
for i in range(query_batch):
images, labels = img_query.next_batch(self.val_batch_size)
output, loss = self.sess.run([self.img_last_layer, self.cos_loss],
feed_dict={self.img: images, self.img_label: labels, self.stage: 1})
img_query.feed_batch_output(self.val_batch_size, output)
print('Cosine Loss: %s' % loss)
database_batch = int(ceil(img_database.n_samples / self.val_batch_size))
print("%s #validation# totally %d database in %d batches" %
(datetime.now(), img_database.n_samples, database_batch))
for i in range(database_batch):
images, labels = img_database.next_batch(self.val_batch_size)
output, loss = self.sess.run([self.img_last_layer, self.cos_loss],
feed_dict={self.img: images, self.img_label: labels, self.stage: 1})
img_database.feed_batch_output(self.val_batch_size, output)
# print output[:10, :10]
if i % 100 == 0:
print('Cosine Loss[%d/%d]: %s' % (i, database_batch, loss))
mAPs = MAPs(R)
self.sess.close()
prec, rec, mmap = mAPs.get_precision_recall_by_Hamming_Radius(img_database, img_query, 2)
return {
'i2i_by_feature': mAPs.get_mAPs_by_feature(img_database, img_query),
'i2i_after_sign': mAPs.get_mAPs_after_sign(img_database, img_query),
'i2i_prec_radius_2': prec,
'i2i_recall_radius_2': rec,
'i2i_map_radius_2': mmap
}
def validation(self, img_query, img_database, R=100):
print("%s #validation# start validation" % (datetime.now()))
query_batch = int(ceil(img_query.n_samples / self.val_batch_size))
print("%s #validation# totally %d query in %d batches" % (datetime.now(), img_query.n_samples, query_batch))
for i in range(query_batch):
images, labels = img_query.next_batch(self.val_batch_size)
output, loss = self.sess.run([self.img_last_layer, self.cos_loss],
feed_dict={self.img: images, self.img_label: labels, self.stage: 1})
img_query.feed_batch_output(self.val_batch_size, output)
print('Cosine Loss: %s' % loss)
database_batch = int(ceil(img_database.n_samples / self.val_batch_size))
print("%s #validation# totally %d database in %d batches" %
(datetime.now(), img_database.n_samples, database_batch))
for i in range(database_batch):
images, labels = img_database.next_batch(self.val_batch_size)
output, loss = self.sess.run([self.img_last_layer, self.cos_loss],
feed_dict={self.img: images, self.img_label: labels, self.stage: 1})
img_database.feed_batch_output(self.val_batch_size, output)
# print output[:10, :10]
if i % 100 == 0:
print('Cosine Loss[%d/%d]: %s' % (i, database_batch, loss))
mAPs = MAPs(R)
self.sess.close()
prec, rec, mmap = mAPs.get_precision_recall_by_Hamming_Radius(img_database, img_query, 2)
return {
'i2i_by_feature': mAPs.get_mAPs_by_feature(img_database, img_query),
'i2i_after_sign': mAPs.get_mAPs_after_sign(img_database, img_query),
'i2i_prec_radius_2': prec,
'i2i_recall_radius_2': rec,
'i2i_map_radius_2': mmap
}
def validation(self, img_query, img_database, R=100):
print("%s #validation# start validation" % (datetime.now()))
query_batch = int(ceil(img_query.n_samples / float(self.val_batch_size)))
img_query.finish_epoch()
print("%s #validation# totally %d query in %d batches" % (datetime.now(), img_query.n_samples, query_batch))
for i in range(query_batch):
images, labels = img_query.next_batch(self.val_batch_size)
output, loss = self.sess.run([self.img_last_layer, self.cos_loss],
feed_dict={self.img: images,
self.img_label: labels,
self.stage: 1})
img_query.feed_batch_output(self.val_batch_size, output)
print('Cosine Loss: %s'%loss)
database_batch = int(ceil(img_database.n_samples / float(self.val_batch_size)))
img_database.finish_epoch()
print("%s #validation# totally %d database in %d batches" % (datetime.now(), img_database.n_samples, database_batch))
for i in range(database_batch):
images, labels = img_database.next_batch(self.val_batch_size)
output, loss = self.sess.run([self.img_last_layer, self.cos_loss],
feed_dict={self.img: images,
self.img_label: labels,
self.stage: 1})
img_database.feed_batch_output(self.val_batch_size, output)
if i % 100 == 0:
print('Cosine Loss[%d/%d]: %s'%(i, database_batch, loss))
mAPs = MAPs(R)
self.sess.close()
prec, rec, mmap = mAPs.get_precision_recall_by_Hamming_Radius_All(img_database, img_query)
for i in range(self.output_dim+1):
#prec, rec, mmap = mAPs.get_precision_recall_by_Hamming_Radius(img_database, img_query, i)
plot.plot('prec', prec[i])
plot.plot('rec', rec[i])
plot.plot('mAP', mmap[i])
plot.tick()
print('Results ham dist [%d], prec:%s, rec:%s, mAP:%s'%(i, prec[i], rec[i], mmap[i]))
result_save_dir = os.path.join(self.save_dir, self.file_name)
if os.path.exists(result_save_dir) is False:
os.makedirs(result_save_dir)
plot.flush(result_save_dir)
prec, rec, mmap = mAPs.get_precision_recall_by_Hamming_Radius(img_database, img_query, 2)
return {
'i2i_by_feature': mAPs.get_mAPs_by_feature(img_database, img_query),
'i2i_after_sign': mAPs.get_mAPs_after_sign(img_database, img_query),
'i2i_prec_radius_2': prec,
'i2i_recall_radius_2': rec,
'i2i_map_radius_2': mmap,
}
def validation(self, img_query, img_database, R=100):
print("%s #validation# start validation" % (datetime.now()))
query_batch = int(ceil(img_query.n_samples / float(self.val_batch_size)))
img_query.finish_epoch()
print("%s #validation# totally %d query in %d batches" % (datetime.now(), img_query.n_samples, query_batch))
for i in range(query_batch):
images, labels = img_query.next_batch(self.val_batch_size)
output, loss = self.sess.run([self.img_last_layer, self.cos_loss],
feed_dict={self.img: images,
self.img_label: labels,
self.stage: 1})
img_query.feed_batch_output(self.val_batch_size, output)
print('Cosine Loss: %s'%loss)
database_batch = int(ceil(img_database.n_samples / float(self.val_batch_size)))
img_database.finish_epoch()
print("%s #validation# totally %d database in %d batches" % (datetime.now(), img_database.n_samples, database_batch))
for i in range(database_batch):
images, labels = img_database.next_batch(self.val_batch_size)
output, loss = self.sess.run([self.img_last_layer, self.cos_loss],
feed_dict={self.img: images,
self.img_label: labels,
self.stage: 1})
img_database.feed_batch_output(self.val_batch_size, output)
if i % 100 == 0:
print('Cosine Loss[%d/%d]: %s'%(i, database_batch, loss))
mAPs = MAPs(R)
self.sess.close()
prec, rec, mmap = mAPs.get_precision_recall_by_Hamming_Radius_All(img_database, img_query)
for i in range(self.output_dim+1):
#prec, rec, mmap = mAPs.get_precision_recall_by_Hamming_Radius(img_database, img_query, i)
plot.plot('prec', prec[i])
plot.plot('rec', rec[i])
plot.plot('mAP', mmap[i])
plot.tick()
print('Results ham dist [%d], prec:%s, rec:%s, mAP:%s'%(i, prec[i], rec[i], mmap[i]))
result_save_dir = os.path.join(self.save_dir, self.file_name)
if os.path.exists(result_save_dir) is False:
os.makedirs(result_save_dir)
plot.flush(result_save_dir)
prec, rec, mmap = mAPs.get_precision_recall_by_Hamming_Radius(img_database, img_query, 2)
return {
'i2i_by_feature': mAPs.get_mAPs_by_feature(img_database, img_query),
'i2i_after_sign': mAPs.get_mAPs_after_sign(img_database, img_query),
'i2i_prec_radius_2': prec,
'i2i_recall_radius_2': rec,
'i2i_map_radius_2': mmap,
}
def validation(self, img_query, img_database, R=100,config = None):
print("%s #validation# start validation" % (datetime.now()))
query_batch = int(ceil(img_query.n_samples / float(self.val_batch_size)))
img_query.finish_epoch()
print("%s #validation# totally %d query in %d batches" % (datetime.now(), img_query.n_samples, query_batch))
for i in tqdm(range(query_batch)):
images, labels = img_query.next_batch(self.val_batch_size)
output, loss = self.sess.run([self.img_last_layer, self.cos_loss],
feed_dict={self.img: images,
self.img_label: labels,
self.stage: 1})
img_query.feed_batch_output(self.val_batch_size, output)
print('Cosine Loss: %s'%loss)
database_batch = int(ceil(img_database.n_samples / float(self.val_batch_size)))
img_database.finish_epoch()
print("%s #validation# totally %d database in %d batches" % (datetime.now(), img_database.n_samples, database_batch))
for i in tqdm(range(database_batch)):
images, labels = img_database.next_batch(self.val_batch_size)
output, loss = self.sess.run([self.img_last_layer, self.cos_loss],
feed_dict={self.img: images,
self.img_label: labels,
self.stage: 1})
img_database.feed_batch_output(self.val_batch_size, output)
if i % 100 == 0:
print('Cosine Loss[%d/%d]: %s'%(i, database_batch, loss))
mAPs = MAPs(R)
self.sess.close()
cmc, mAP = get_mAPs_and_cmcs(img_database, img_query, config=config)
print('The cmc: Rank1:{},Rank2:{},Rank3:{},Rank4:{} Rank5:{},Rank6:{},Rank7:{},Rank8:{},Rank9:{}, Rank10:{}'
'Rank11:{},Rank12:{},Rank13:{},Rank14{},Rank15:{},Rank16:{},Rank17:{},Rank18:{},Rank19:{},Rank20:{},mAP is {}'.format(
cmc[0], cmc[1], cmc[2], cmc[3], cmc[4], cmc[5], cmc[6], cmc[7], cmc[8], cmc[9], cmc[10], cmc[11], cmc[12],
cmc[13], cmc[14],
cmc[15], cmc[16], cmc[17], cmc[18], cmc[19], mAP))
return cmc, mAP