def search(config, model, vocab, query, n_results=10):
device = torch.device(
f"cuda:{args.gpu_id}" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()
# device = next(model.parameters()).device
desc, desc_len = sent2indexes(
query, vocab_desc,
config['desc_len']) #convert query into word indices
desc = torch.from_numpy(desc).unsqueeze(0).to(device)
desc_len = torch.from_numpy(desc_len).clamp(
max=config['desc_len']).to(device)
with torch.no_grad():
desc_repr = model.desc_encoding(desc,
desc_len).data.cpu().numpy().astype(
np.float32) # [1 x dim]
# desc_repr = model.desc_encoding(desc, desc_len)
if config[
'sim_measure'] == 'cos': # normalizing vector for fast cosine computation
desc_repr = normalize(desc_repr) # [1 x dim]
results = []
threads = []
for i, codevecs_chunk in enumerate(codevecs):
t = threading.Thread(target=search_thread,
args=(results, desc_repr, codevecs_chunk, i,
n_results, config['sim_measure']))
threads.append(t)
for t in threads:
t.start()
for t in threads: #wait until all sub-threads have completed
t.join()
return results
def search(self, model, query, n_results=10):
desc = sent2indexes(
query, self.vocab_desc) # convert desc sentence into word indices
logger.debug("Description representation")
desc = np.expand_dims(desc, axis=0)
desc = gVar(desc)
logger.debug("Description embedding")
desc_repr = model.eval().desc_encoding(desc).data.cpu().numpy()
valued = []
threads = []
for i, codevecs_chunk in enumerate(self.codevecs):
# select the best n_results from each chunk
t = threading.Thread(target=self.search_thread,
args=(valued, desc_repr, codevecs_chunk, i,
n_results))
threads.append(t)
for t in threads:
t.start()
for t in threads: # wait until all sub-threads finish
t.join()
valued.sort(reverse=True)
return valued[:n_results]
def search(self,model,query,n_results=10):
desc=sent2indexes(query, self.vocab_desc)#convert desc sentence into word indices
desc = np.expand_dims(desc, axis=0)
desc=gVar(desc)
desc_repr=model.desc_encoding(desc).data.cpu().numpy()
codes=[]
sims=[]
threads=[]
for i, codevecs_chunk in enumerate(self.codevecs):
t = threading.Thread(target=self.search_thread, args = (codes,sims,desc_repr, codevecs_chunk,i,n_results))
threads.append(t)
for t in threads:
t.start()
for t in threads:#wait until all sub-threads finish
t.join()
return codes,sims
def search(config, model, vocab, query, n_results=10):
model.eval()
device = next(model.parameters()).device
desc, desc_len = sent2indexes(
query, vocab_desc,
config['desc_len']) #convert query into word indices
desc = torch.from_numpy(desc).unsqueeze(0).to(device)
desc_len = torch.from_numpy(desc_len).clamp(
max=config['desc_len']).to(device)
# data_path = './data/vocab.desc.json'
# vocab_desc = load_dict(data_path)
# nl_len = len(nl)
# for i in range(nl_len):
# nl[i] = vocab_desc.get(nl[i], 3)
# # print(nl)
# # print("\n")
# # nl2index, nl_len = sent2indexes(nl, vocab_desc, 30)
# nl2long = np.array(nl).astype(np.long)
# good_desc_len = min(int(nl_len), self.max_desc_len)
# good_desc = nl2long
# good_desc = self.pad_seq(good_desc, self.max_desc_len)
with torch.no_grad():
desc_repr = model.desc_encoding(desc,
desc_len).data.cpu().numpy().astype(
np.float32) # [1 x dim]
if config[
'sim_measure'] == 'cos': # normalizing vector for fast cosine computation
desc_repr = normalize(desc_repr) # [1 x dim]
results = []
threads = []
for i, codevecs_chunk in enumerate(codevecs):
t = threading.Thread(target=search_thread,
args=(results, desc_repr, codevecs_chunk, i,
n_results, config['sim_measure']))
threads.append(t)
for t in threads:
t.start()
for t in threads: #wait until all sub-threads have completed
t.join()
return results
def search(config, model, vocab, query, n_results=10):
model.eval()
device = next(model.parameters()).device
desc = sent2indexes(query, vocab_desc,
config['desc_len']) #convert query into word indices
desc = torch.from_numpy(desc).unsqueeze(0).to(device)
desc_len = torch.LongTensor(1).fill_(len(
query.split())).clamp(max=config['desc_len']).to(device)
with torch.no_grad():
desc_repr = model.desc_encoding(desc, desc_len).data.cpu().numpy()
codes, sims = [], []
threads = []
for i, codevecs_chunk in enumerate(codevecs):
t = threading.Thread(target=search_thread,
args=(codes, sims, desc_repr, codevecs_chunk, i,
n_results))
threads.append(t)
for t in threads:
t.start()
for t in threads: #wait until all sub-threads have completed
t.join()
return codes, sims
