def call_multi_iteration(client):
vec_a = None # the query
binary_codes = buffer('') # binary codes to match
if client.sendall('next') == None:
length = client.recv(1024)
if not length: raise ValueError('Socket Error')
#print 'length: ', int(length)
if client.sendall('next') == None:
# receive query
vec_a = client.recv(int(length))
if not vec_a: raise ValueError('Socket Error')
#print len(vec_a)
vec_a = numpy.frombuffer(vec_a, dtype = numpy.dtype(numpy.uint64))
if client.sendall('next') == None:
binary_codes = conn.recv_long_vector(client)
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
data = client.recv(1024)
if data != 'ready':
#print data
raise ValueError('Socket Error')
# hamming_distances: uint8 numpy array
hamming_distances = cuda_hamming_obj.multi_iteration(vec_a, binary_codes)
log_info(hamming_distances)
hamming_distances = hamming_distances[0].astype(numpy.uint8)
conn.send_long_vector(client, hamming_distances, 1)
def call_multi_iteration(client):
vec_a = None # the query
binary_codes = buffer('') # binary codes to match
if client.sendall('next') == None:
length = client.recv(1024)
if not length: raise ValueError('Socket Error')
#print 'length: ', int(length)
if client.sendall('next') == None:
# receive query
vec_a = client.recv(int(length))
if not vec_a: raise ValueError('Socket Error')
#print len(vec_a)
vec_a = numpy.frombuffer(vec_a, dtype=numpy.dtype(numpy.uint64))
if client.sendall('next') == None:
binary_codes = conn.recv_long_vector(client)
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
data = client.recv(1024)
if data != 'ready':
#print data
raise ValueError('Socket Error')
# hamming_distances: uint8 numpy array
hamming_distances = cuda_hamming_obj.multi_iteration(vec_a, binary_codes)
log_info(hamming_distances)
hamming_distances = hamming_distances[0].astype(numpy.uint8)
conn.send_long_vector(client, hamming_distances, 1)
def multi_iteration(self, vec_a, vec_b):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((self.host, self.port))
if s.sendall('multi_iteration') == None:
data = s.recv(1024)
if data != 'next': raise ValueError('Socket Error')
if s.sendall(str(vec_a.shape[0] * 8)) == None:
data = s.recv(1024)
if data != 'next': raise ValueError('Socket Error')
if s.sendall(vec_a) == None:
data = s.recv(1024)
if data != 'next': raise ValueError('Socket Error')
conn.send_long_vector(s, vec_b)
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
#print "ready to receive hamming results."
# receive results
if s.sendall('ready') == None:
distances = conn.recv_long_vector(s, numpy.uint8)
print distances
print distances.shape
return (distances, 0)
else:
raise ValueError('Socket Error')
def call_cuda_hamming_dist_in_compressed_domain(client):
vec_a = None
columns_vector = []
if client.sendall('next') == None:
length = client.recv(1024)
if not length: raise ValueError('Socket Error')
#print 'length: ', int(length)
if client.sendall('next') == None:
# receive query
vec_a = client.recv(int(length))
if not vec_a: raise ValueError('Socket Error')
#print len(vec_a)
vec_a = numpy.frombuffer(vec_a, dtype=numpy.dtype(numpy.uint64))
#print vec_a
if client.sendall('next') == None:
# begin to receive columns
cols_vec_length = client.recv(1024)
if not cols_vec_length: raise ValueError('Socket Error')
#print "cols_vec_length: ", cols_vec_length
cols_vec_length = int(cols_vec_length)
if client.sendall('next') == None:
while cols_vec_length > 0:
cols_length = client.recv(1024)
if not cols_length: raise ValueError('Socket Error')
#print "cols_length: ", cols_length
cols_length = int(cols_length)
if client.sendall('next') == None:
columns = conn.recv_long_vector(client, None)
columns_vector.append(columns)
cols_vec_length -= 1
else:
raise ValueError('Socket Error')
done = client.recv(1024)
if done != 'done': raise ValueError('Socket Error')
if client.sendall('ok') != None:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
image_ids_leng = client.recv(1024)
if not image_ids_leng: raise ValueError('Socket Error')
#print "image ids length: ", image_ids_leng
image_ids = numpy.zeros(int(image_ids_leng)).tolist()
if client.sendall('next') == None:
vlq_mode = client.recv(1024)
if not vlq_mode: raise ValueError('Socket Error')
if not client.sendall('done') == None:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
if client.recv(1024) != 'ready':
raise ValueError('Socket Error')
reshape_columns_vector = []
for columns in columns_vector:
idx = 0
reshape_columns = []
np_columns = numpy.frombuffer(columns, dtype=numpy.uint32)
while idx < np_columns.shape[0]:
length = np_columns[idx]
np_array = np_columns[idx + 1:idx + 1 + length]
if vlq_mode == 'y': np_array = np_array.astype(numpy.uint8)
reshape_columns.append(buffer(np_array))
idx = idx + 1 + length
reshape_columns_vector.append(reshape_columns)
# hamming_distances: uint8 numpy array
hamming_distances = cuda_hamming_obj.cuda_hamming_dist_in_compressed_domain(
vec_a, reshape_columns_vector, image_ids, vlq_mode)
log_info(hamming_distances)
hamming_distances = hamming_distances[0].astype(numpy.uint8)
conn.send_long_vector(client, hamming_distances, 1)
def cuda_hamming_dist_in_compressed_domain(self, vec_a, compressed_columns_vec, image_ids, vlq_mode):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((self.host, self.port))
if s.sendall('cuda_hamming_dist_in_compressed_domain') == None:
data = s.recv(1024)
if data != 'next': raise ValueError('Socket Error')
if s.sendall(str(vec_a.shape[0] * 8)) == None:
data = s.recv(1024)
if data != 'next': raise ValueError('Socket Error')
if s.sendall(vec_a) == None:
data = s.recv(1024)
if data != 'next': raise ValueError('Socket Error')
print "compressed_columns_vec len: ", len(compressed_columns_vec)
# tell server how many columns_vector to send
if s.sendall(str(len(compressed_columns_vec))) == None:
data = s.recv(1024)
if data != 'next': raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
for columns in compressed_columns_vec:
#print 'columns leg: ', len(columns)
# how many columns in a columns vector
# should be 64 columns for 64 bits
if s.sendall(str(len(columns))) == None:
data = s.recv(1024)
if data != 'next': raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
concate_columns = numpy.array([])
for column in columns:
if vlq_mode == 'y':
np_array = numpy.frombuffer(column, dtype = numpy.uint8)
np_array = np_array.astype(numpy.uint32)
concate_columns = numpy.concatenate((concate_columns, numpy.array([np_array.shape[0]]).astype(numpy.uint32)))
concate_columns = numpy.concatenate((concate_columns, np_array))
else:
np_array = numpy.frombuffer(column, dtype = numpy.uint32)
concate_columns = numpy.concatenate((concate_columns, numpy.array([np_array.shape[0]]).astype(numpy.uint32)))
concate_columns = numpy.concatenate((concate_columns, np_array))
conn.send_long_vector(s, concate_columns.astype(numpy.uint32), 4)
if s.sendall("done") == None:
data = s.recv(1024)
if data != 'ok': raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
# send image id length
if s.sendall(str(len(image_ids))) == None:
data = s.recv(1024)
if data != 'next': raise ValueError('Socket Error')
if s.sendall(vlq_mode) == None:
if s.recv(1024) != 'done': raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
# receive results
if s.sendall('ready') == None:
distances = conn.recv_long_vector(s, numpy.uint8)
print distances
print distances.shape
return (distances, 0)
else:
raise ValueError('Socket Error')
def call_cuda_hamming_dist_in_compressed_domain(client):
vec_a = None
columns_vector = []
if client.sendall('next') == None:
length = client.recv(1024)
if not length: raise ValueError('Socket Error')
#print 'length: ', int(length)
if client.sendall('next') == None:
# receive query
vec_a = client.recv(int(length))
if not vec_a: raise ValueError('Socket Error')
#print len(vec_a)
vec_a = numpy.frombuffer(vec_a, dtype = numpy.dtype(numpy.uint64))
#print vec_a
if client.sendall('next') == None:
# begin to receive columns
cols_vec_length = client.recv(1024)
if not cols_vec_length: raise ValueError('Socket Error')
#print "cols_vec_length: ", cols_vec_length
cols_vec_length = int(cols_vec_length)
if client.sendall('next') == None:
while cols_vec_length > 0:
cols_length = client.recv(1024)
if not cols_length: raise ValueError('Socket Error')
#print "cols_length: ", cols_length
cols_length = int(cols_length)
if client.sendall('next') == None:
columns = conn.recv_long_vector(client, None)
columns_vector.append(columns)
cols_vec_length -= 1
else:
raise ValueError('Socket Error')
done = client.recv(1024)
if done != 'done': raise ValueError('Socket Error')
if client.sendall('ok') != None:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
image_ids_leng = client.recv(1024)
if not image_ids_leng: raise ValueError('Socket Error')
#print "image ids length: ", image_ids_leng
image_ids = numpy.zeros(int(image_ids_leng)).tolist()
if client.sendall('next') == None:
vlq_mode = client.recv(1024)
if not vlq_mode: raise ValueError('Socket Error')
if not client.sendall('done') == None:
raise ValueError('Socket Error')
else:
raise ValueError('Socket Error')
if client.recv(1024) != 'ready':
raise ValueError('Socket Error')
reshape_columns_vector = []
for columns in columns_vector:
idx = 0
reshape_columns = []
np_columns = numpy.frombuffer(columns, dtype = numpy.uint32)
while idx < np_columns.shape[0]:
length = np_columns[idx]
np_array = np_columns[idx + 1: idx + 1 + length]
if vlq_mode == 'y': np_array = np_array.astype(numpy.uint8)
reshape_columns.append(buffer(np_array))
idx = idx + 1 + length
reshape_columns_vector.append(reshape_columns)
# hamming_distances: uint8 numpy array
hamming_distances = cuda_hamming_obj.cuda_hamming_dist_in_compressed_domain(vec_a, reshape_columns_vector, image_ids, vlq_mode)
log_info(hamming_distances)
hamming_distances = hamming_distances[0].astype(numpy.uint8)
conn.send_long_vector(client, hamming_distances, 1)
