def main():
args = parse_parameters()
print(args)
cuda_obj = CudaSpatialGrep()
bbox = prepare_bbox(args.bbox)
gz_file = gzip.open(args.f, 'rb')
out_file = open(args.o, 'wb')
while 1:
print("Loading data...")
(lines, coordinates) = read_in_batch(gz_file, int(args.batch_n))
if len(lines) == 0:
break
print("Spatial greping on CUDA...")
hits = cuda_obj.cuda_spatial_grep(bbox, coordinates)
idx = 0
for hit in hits:
if hit == 1:
out_file.write(lines[idx])
idx += 1
gz_file.close()
out_file.close()
def main():
args = parse_parameters()
(lsh, np_feature_vecs) = init_lsh(args)
lsh = run(args, lsh)
if args.c != 'y' and args.i != 'y' and args.e != None and args.s == 'random':
if args.p != 'y':
retrived = lsh.query(np_feature_vecs[1], num_results = int(args.k), expand_level = int(args.b), distance_func = 'hamming')
else:
retrived = lsh.query_in_compressed_domain(np_feature_vecs[1], num_results = int(args.k), expand_level = int(args.b), distance_func = 'hamming', gpu_mode = args.g, vlq_mode = args.l)
print retrived
def main():
args = parse_parameters()
(lsh, np_feature_vecs) = init_lsh(args)
lsh = run(args, lsh)
ground_truth = None
if args.gt != None:
(ground_truth, ground_truth_num) = load_ground_truth(args.gt, args.gtf)
if args.c != 'y' and args.i != 'y' and args.e != None and args.s == 'random':
client = cudaclient('net', {'host': args.host, 'port': 8080})
b_begin = int(args.b)
b_end = int(args.b) + 1
# when given a range of expanding levels
if args.b_begin != -1 and args.b_end != -1:
b_begin = int(args.b_begin)
b_end = int(args.b_end)
for cur_expand_level in range(b_begin, b_end):
client.send_query(['reset'])
client.send_query([args.title])
client.send_query(['expand_level: ' + str(cur_expand_level)])
total_found = {'10': 0, '100': 0}
for feature_idx in range(0, np_feature_vecs.shape[0]):
feature = np_feature_vecs[feature_idx]
if args.p != 'y':
retrived = lsh.query(feature, num_results = int(args.k), expand_level = cur_expand_level, distance_func = 'hamming')
else:
retrived = lsh.query_in_compressed_domain(feature, num_results = int(args.k), expand_level = cur_expand_level, distance_func = 'hamming', gpu_mode = args.g, vlq_mode = args.l)
total_found['10'] += cal_recall(retrived, ground_truth, feature_idx, int(args.k), topGT = 10)
total_found['100'] += cal_recall(retrived, ground_truth, feature_idx, int(args.k), topGT = 100)
recall_r = total_found['10'] / float(np_feature_vecs.shape[0])
recall_r_str = "recall@" + args.k + " GT@10: " + str(recall_r)
print recall_r_str
client.send_query([recall_r_str])
recall_r = total_found['100'] / float(np_feature_vecs.shape[0])
recall_r_str = "recall@" + args.k + " GT@100: " + str(recall_r)
print recall_r_str
client.send_query([recall_r_str])
def main():
args = parse_parameters()
(lsh, np_feature_vecs) = init_lsh(args)
lsh = run(args, lsh)
if args.c != 'y' and args.i != 'y' and args.e != None and args.s == 'random':
if args.p != 'y':
retrived = lsh.query(np_feature_vecs[1],
num_results=int(args.k),
expand_level=int(args.b),
distance_func='hamming')
else:
retrived = lsh.query_in_compressed_domain(np_feature_vecs[1],
num_results=int(args.k),
expand_level=int(args.b),
distance_func='hamming',
gpu_mode=args.g,
vlq_mode=args.l)
print retrived
def main():
args = parse_parameters()
(lsh, np_feature_vecs) = init_lsh(args)
lsh = run(args, lsh)
ground_truth = None
if args.gt != None:
(ground_truth, ground_truth_num) = load_ground_truth(args.gt, args.gtf)
if args.c != 'y' and args.i != 'y' and args.e != None and args.s == 'random':
client = cudaclient('net', {'host': args.host, 'port': 8080})
b_begin = int(args.b)
b_end = int(args.b) + 1
# when given a range of expanding levels
if args.b_begin != -1 and args.b_end != -1:
b_begin = int(args.b_begin)
b_end = int(args.b_end)
for cur_expand_level in range(b_begin, b_end):
client.send_query(['reset'])
client.send_query([args.title])
client.send_query(['expand_level: ' + str(cur_expand_level)])
total_found = {'10': 0, '100': 0}
for feature_idx in range(0, np_feature_vecs.shape[0]):
feature = np_feature_vecs[feature_idx]
if args.p != 'y':
retrived = lsh.query(feature,
num_results=int(args.k),
expand_level=cur_expand_level,
distance_func='hamming')
else:
retrived = lsh.query_in_compressed_domain(
feature,
num_results=int(args.k),
expand_level=cur_expand_level,
distance_func='hamming',
gpu_mode=args.g,
vlq_mode=args.l)
total_found['10'] += cal_recall(retrived,
ground_truth,
feature_idx,
int(args.k),
topGT=10)
total_found['100'] += cal_recall(retrived,
ground_truth,
feature_idx,
int(args.k),
topGT=100)
recall_r = total_found['10'] / float(np_feature_vecs.shape[0])
recall_r_str = "recall@" + args.k + " GT@10: " + str(recall_r)
print recall_r_str
client.send_query([recall_r_str])
recall_r = total_found['100'] / float(np_feature_vecs.shape[0])
recall_r_str = "recall@" + args.k + " GT@100: " + str(recall_r)
print recall_r_str
client.send_query([recall_r_str])
