vector_ids = []
id_query = {}
with open("embed.txt","r")as f:
for line in f:
line_lst = line.strip().split("\t")
vectors.append(list(map(float,(line_lst[2].split()))))
vector_ids.append(int(line_lst[1]))
id_query[int(line_lst[1])] = line_lst[0]
vectors = np.array(vectors)
vectors = vectors.astype("float32")
# 创建索引
quantizer = faiss.IndexFlatL2(vec_dim) # 使用欧式距离作为度量
nlist = 16384
faiss_index = faiss.IndexIVFFlat(quantizer, vec_dim, nlist, faiss.METRIC_L2)
faiss_index.nprobe = 16
#ssert not index.is_trained
faiss_index.train(vectors)
faiss_index.add(vectors)
faiss.write_index(faiss_index,"large.index")
# 查询向量 假设有5个
query_vectors = vectors[:20]
# 搜索结果
# 分别是 每条记录对应topk的距离和索引
# ndarray类型 。shape：len(query_vectors)*topk
res_distance, res_index = faiss_index.search(query_vectors, 5)
t = time.time()
def find_nearest_neighbors(
target, emb, k=5, metric="euclidean", gpu_id=None, exact=True
):
"""Find the nearest neighbors for each point.
:param emb: vectors for the points for which we find the nearest neighbors
:type emb: numpy.ndarray (num_entities, dim)
:param emb: vectors for the points from which we find the nearest neighbors.
:type emb: numpy.ndarray (num_entities, dim)
:param k: Number of nearest neighbors, defaults to 5
:type k: int, optional
:paramm metric: Distance metric for finding nearest neighbors. Available metric `metric="euclidean"`, `metric="cosine"` , `metric="dotsim"`
:type metric: str
:return: IDs of emb (indices), and similarity (distances)
:rtype: indices (numpy.ndarray), distances (numpy.ndarray)
.. highlight:: python
.. code-block:: python
>>> import emlens
>>> import numpy as np
>>> emb = np.random.randn(100, 20)
>>> target = np.random.randn(10, 20)
>>> A = emlens.find_nearest_neighbors(target, emb, k = 10)
"""
if emb.flags["C_CONTIGUOUS"]:
emb = emb.copy(order="C")
if target.flags["C_CONTIGUOUS"]:
target = target.copy(order="C")
emb = emb.astype(np.float32)
target = target.astype(np.float32)
# Find the nearest neighbors
if metric == "euclidean":
if exact:
index = faiss.IndexFlatL2(emb.shape[1])
else:
quantiser = faiss.IndexFlatL2(emb.shape[1])
nlist = int(np.ceil(10 * np.sqrt(emb.shape[0])))
index = faiss.IndexIVFFlat(quantiser, emb.shape[1], nlist, faiss.METRIC_L2)
index.train(emb)
elif metric == "cosine":
denom = np.array(np.linalg.norm(emb, axis=1)).reshape(-1)
denom[np.isclose(denom, 0)] = 1
emb = np.einsum("i,ij->ij", 1 / denom, emb)
denom = np.array(np.linalg.norm(target, axis=1)).reshape(-1)
denom[np.isclose(denom, 0)] = 1
target = np.einsum("i,ij->ij", 1 / denom, target)
if exact:
index = faiss.IndexFlatIP(emb.shape[1])
else:
quantiser = faiss.IndexFlatIP(emb.shape[1])
nlist = int(np.ceil(10 * np.sqrt(emb.shape[0])))
index = faiss.IndexIVFFlat(
quantiser, emb.shape[1], nlist, faiss.METRIC_INNER_PRODUCT
)
index.train(emb)
elif metric == "dotsim":
if exact:
index = faiss.IndexFlatIP(emb.shape[1])
else:
quantiser = faiss.IndexFlatIP(emb.shape[1])
nlist = int(np.ceil(10 * np.sqrt(emb.shape[0])))
index = faiss.IndexIVFFlat(
quantiser, emb.shape[1], nlist, faiss.METRIC_INNER_PRODUCT
)
index.train(emb)
else:
raise NotImplementedError("does not support metric: {}".format(metric))
if gpu_id is None:
gpu_id = 0
if k >= 2048: # if k is larger than that supported by GPU
index.add(emb)
else:
try:
res = faiss.StandardGpuResources()
index = faiss.index_cpu_to_gpu(res, gpu_id, index)
index.add(emb)
except (RuntimeError, AttributeError):
index.add(emb)
distances, neighbors = index.search(target, k=k)
assert distances.dtype == "float32"
assert neighbors.dtype == "int64"
nodes = (np.arange(target.shape[0]).reshape((-1, 1)) @ np.ones((1, k))).astype(int)
neighbors = neighbors.astype(int)
return nodes, neighbors, distances
print(index.ntotal) serarch_i = np.asarray([data[700], data[100]]) k = 4 # we want to see 4 nearest neighbors start = time.clock() D, I = index.search(serarch_i[:5], k) # sanity check end = time.clock() print end - start # ---------------------------------------------------------- # 加快搜索 nlist = 100 # 聚类中心的个数 k = 4 quantizer = faiss.IndexFlatL2(d) # the other index index = faiss.IndexIVFFlat(quantizer, d, nlist, faiss.METRIC_L2) # here we specify METRIC_L2, by default it performs inner-product search assert not index.is_trained index.train(data) assert index.is_trained start_k_suoyin = time.clock() index.add(data) # add may be a bit slower as well end_k_suoyin = time.clock() print 'the time in add kmeans:' print end_k_suoyin - start_k_suoyin serarch_a = np.asarray([data[700], data[200]]) # print serarch_a start = time.clock()
def __init__(self,
load_path: str,
word_to_idlist_filename: str,
entities_list_filename: str,
entities_ranking_filename: str,
vectorizer_filename: str,
faiss_index_filename: str,
chunker: NerChunker = None,
ner: Chainer = None,
ner_parser: EntityDetectionParser = None,
entity_ranker: RelRankerBertInfer = None,
num_faiss_candidate_entities: int = 20,
num_entities_for_bert_ranking: int = 50,
num_faiss_cells: int = 50,
use_gpu: bool = True,
save_path: str = None,
fit_vectorizer: bool = False,
max_tfidf_features: int = 1000,
include_mention: bool = False,
ngram_range: List[int] = None,
num_entities_to_return: int = 10,
lang: str = "ru",
use_descriptions: bool = True,
lemmatize: bool = False,
**kwargs) -> None:
"""
Args:
load_path: path to folder with inverted index files
word_to_idlist_filename: file with dict of words (keys) and start and end indices in
entities_list filename of the corresponding entity ids
entities_list_filename: file with the list of entity ids from the knowledge base
entities_ranking_filename: file with dict of entity ids (keys) and number of relations in Wikidata
for entities
vectorizer_filename: filename with TfidfVectorizer data
faiss_index_filename: file with Faiss index of words
chunker: component deeppavlov.models.kbqa.ner_chunker
ner: config for entity detection
ner_parser: component deeppavlov.models.kbqa.entity_detection_parser
entity_ranker: component deeppavlov.models.kbqa.rel_ranking_bert_infer
num_faiss_candidate_entities: number of nearest neighbors for the entity substring from the text
num_entities_for_bert_ranking: number of candidate entities for BERT ranking using description and context
num_faiss_cells: number of Voronoi cells for Faiss index
use_gpu: whether to use GPU for faster search of candidate entities
save_path: path to folder with inverted index files
fit_vectorizer: whether to build index with Faiss library
max_tfidf_features: maximal number of features for TfidfVectorizer
include_mention: whether to leave entity mention in the context (during BERT ranking)
ngram_range: char ngrams range for TfidfVectorizer
num_entities_to_return: number of candidate entities for the substring which are returned
lang: russian or english
use_description: whether to perform entity ranking by context and description
lemmatize: whether to lemmatize tokens
**kwargs:
"""
super().__init__(save_path=save_path, load_path=load_path)
self.morph = pymorphy2.MorphAnalyzer()
self.lemmatize = lemmatize
self.word_to_idlist_filename = word_to_idlist_filename
self.entities_list_filename = entities_list_filename
self.entities_ranking_filename = entities_ranking_filename
self.vectorizer_filename = vectorizer_filename
self.faiss_index_filename = faiss_index_filename
self.num_entities_for_bert_ranking = num_entities_for_bert_ranking
self.num_faiss_candidate_entities = num_faiss_candidate_entities
self.num_faiss_cells = num_faiss_cells
self.use_gpu = use_gpu
self.chunker = chunker
self.ner = ner
self.ner_parser = ner_parser
self.entity_ranker = entity_ranker
self.fit_vectorizer = fit_vectorizer
self.max_tfidf_features = max_tfidf_features
self.include_mention = include_mention
self.ngram_range = ngram_range
self.num_entities_to_return = num_entities_to_return
self.lang_str = f"@{lang}"
if self.lang_str == "@en":
self.stopwords = set(stopwords.words("english"))
elif self.lang_str == "@ru":
self.stopwords = set(stopwords.words("russian"))
self.use_descriptions = use_descriptions
self.load()
if self.fit_vectorizer:
self.vectorizer = TfidfVectorizer(
analyzer="char_wb",
ngram_range=tuple(self.ngram_range),
max_features=self.max_tfidf_features,
max_df=0.85)
self.vectorizer.fit(self.word_list)
self.matrix = self.vectorizer.transform(self.word_list)
self.dense_matrix = self.matrix.toarray()
if self.num_faiss_cells > 1:
quantizer = faiss.IndexFlatIP(self.max_tfidf_features)
self.faiss_index = faiss.IndexIVFFlat(quantizer,
self.max_tfidf_features,
self.num_faiss_cells)
self.faiss_index.train(self.dense_matrix.astype(np.float32))
else:
self.faiss_index = faiss.IndexFlatIP(self.max_tfidf_features)
if self.use_gpu:
res = faiss.StandardGpuResources()
self.faiss_index = faiss.index_cpu_to_gpu(
res, 0, self.faiss_index)
self.faiss_index.add(self.dense_matrix.astype(np.float32))
self.save_vectorizers_data()
result = {}
result["dim"] = d
result["nb"] = nb
result["k"] = k
print(result)
xb = np.random.random((nb, d)).astype('float32')
xb[:, 0] += np.arange(nb) / 1000.
xq = np.random.random((nq, d)).astype('float32')
xq[:, 0] += np.arange(nq) / 1000.
normalize_L2(xb)
normalize_L2(xq)
nlist = nb / 10000
quantizer = faiss.IndexFlatIP(d) # the other index
index = faiss.IndexIVFFlat(quantizer, d, nlist,
faiss.METRIC_INNER_PRODUCT)
index.nprobe = nlist / 4
index.verbose = True
assert not index.is_trained
index.train(xb)
assert index.is_trained
index.add(xb) # add may be a bit slower as well
spent = []
for i in range(100):
start = datetime.datetime.now()
D, I = index.search(xq[:1000], k) # actual search
end = datetime.datetime.now()
s = end - start
spent.append(s.total_seconds())
result["IVF_avg_spent"] = sum(spent) / 100
def predict_topk(biosyn,
eval_dictionary,
eval_queries,
topk,
score_mode='hybrid',
type_given=False):
"""
Parameters
----------
score_mode : str
hybrid, dense, sparse
"""
encoder = biosyn.get_dense_encoder()
tokenizer = biosyn.get_dense_tokenizer()
sparse_encoder = biosyn.get_sparse_encoder()
sparse_weight = biosyn.get_sparse_weight().item() # must be scalar value
# useful if we're conditioning on types
all_indv_types = [x for t in eval_dictionary[:, 1] for x in t.split('|')]
unique_types = np.unique(all_indv_types).tolist()
v_check_type = np.vectorize(check_label)
inv_idx = {
t: v_check_type(eval_dictionary[:, 1], t).nonzero()[0]
for t in unique_types
}
# embed dictionary
dict_sparse_embeds = biosyn.embed_sparse(names=eval_dictionary[:, 0],
show_progress=True)
dict_dense_embeds = biosyn.embed_dense(names=eval_dictionary[:, 0],
show_progress=True)
# build the sparse index
if not type_given:
sparse_index = nmslib.init(method='hnsw',
space='negdotprod_sparse_fast',
data_type=nmslib.DataType.SPARSE_VECTOR)
sparse_index.addDataPointBatch(dict_sparse_embeds)
sparse_index.createIndex({'post': 2}, print_progress=False)
else:
sparse_index = {}
for sty, indices in inv_idx.items():
sparse_index[sty] = nmslib.init(
method='hnsw',
space='negdotprod_sparse_fast',
data_type=nmslib.DataType.SPARSE_VECTOR)
sparse_index[sty].addDataPointBatch(dict_sparse_embeds[indices])
sparse_index[sty].createIndex({'post': 2}, print_progress=False)
# build the dense index
d = dict_dense_embeds.shape[1]
if not type_given:
nembeds = dict_dense_embeds.shape[0]
if nembeds < 10000: # if the number of embeddings is small, don't approximate
dense_index = faiss.IndexFlatIP(d)
dense_index.add(dict_dense_embeds)
else:
nlist = int(math.floor(
math.sqrt(nembeds))) # number of quantized cells
nprobe = int(math.floor(
math.sqrt(nlist))) # number of the quantized cells to probe
quantizer = faiss.IndexFlatIP(d)
dense_index = faiss.IndexIVFFlat(quantizer, d, nlist,
faiss.METRIC_INNER_PRODUCT)
dense_index.train(dict_dense_embeds)
dense_index.add(dict_dense_embeds)
dense_index.nprobe = nprobe
else:
dense_index = {}
for sty, indices in inv_idx.items():
sty_dict_dense_embeds = dict_dense_embeds[indices]
nembeds = sty_dict_dense_embeds.shape[0]
if nembeds < 10000: # if the number of embeddings is small, don't approximate
dense_index[sty] = faiss.IndexFlatIP(d)
dense_index[sty].add(sty_dict_dense_embeds)
else:
nlist = int(math.floor(
math.sqrt(nembeds))) # number of quantized cells
nprobe = int(math.floor(math.sqrt(
nlist))) # number of the quantized cells to probe
quantizer = faiss.IndexFlatIP(d)
dense_index[sty] = faiss.IndexIVFFlat(
quantizer, d, nlist, faiss.METRIC_INNER_PRODUCT)
dense_index[sty].train(sty_dict_dense_embeds)
dense_index[sty].add(sty_dict_dense_embeds)
dense_index[sty].nprobe = nprobe
# respond to mention queries
queries = []
for eval_query in tqdm(eval_queries, total=len(eval_queries)):
mentions = eval_query[0].replace("+", "|").split("|")
golden_cui = eval_query[1].replace("+", "|")
golden_sty = eval_query[2].replace("+", "|")
pmid = eval_query[3]
start_char = eval_query[4]
end_char = eval_query[5]
dict_mentions = []
for mention in mentions:
mention_sparse_embeds = biosyn.embed_sparse(
names=np.array([mention]))
mention_dense_embeds = biosyn.embed_dense(
names=np.array([mention]))
# search the sparse index
if not type_given:
sparse_nn = sparse_index.knnQueryBatch(mention_sparse_embeds,
k=topk,
num_threads=20)
else:
sparse_nn = sparse_index[golden_sty].knnQueryBatch(
mention_sparse_embeds, k=topk, num_threads=20)
sparse_idxs, _ = zip(*sparse_nn)
s_candidate_idxs = np.asarray(sparse_idxs)
if type_given:
# reverse mask index mapping
s_candidate_idxs = inv_idx[golden_sty][s_candidate_idxs]
s_candidate_idxs = s_candidate_idxs.astype(np.int64)
# search the dense index
if not type_given:
_, d_candidate_idxs = dense_index.search(
mention_dense_embeds, topk)
else:
_, d_candidate_idxs = dense_index[golden_sty].search(
mention_dense_embeds, topk)
# reverse mask index mapping
d_candidate_idxs = inv_idx[golden_sty][d_candidate_idxs]
d_candidate_idxs = d_candidate_idxs.astype(np.int64)
# get the reduced candidate set
reduced_candidate_idxs = np.unique(
np.hstack([
s_candidate_idxs.reshape(-1, ),
d_candidate_idxs.reshape(-1, )
]))
# get score matrix
sparse_score_matrix = biosyn.get_score_matrix(
query_embeds=mention_sparse_embeds,
dict_embeds=dict_sparse_embeds[
reduced_candidate_idxs, :]).todense()
dense_score_matrix = biosyn.get_score_matrix(
query_embeds=mention_dense_embeds,
dict_embeds=dict_dense_embeds[reduced_candidate_idxs, :])
if score_mode == 'hybrid':
score_matrix = sparse_weight * sparse_score_matrix + dense_score_matrix
elif score_mode == 'dense':
score_matrix = dense_score_matrix
elif score_mode == 'sparse':
score_matrix = sparse_score_matrix
else:
raise NotImplementedError()
# take care of getting the best indices
candidate_idxs = biosyn.retrieve_candidate(
score_matrix=score_matrix, topk=topk)
candidate_idxs = reduced_candidate_idxs[candidate_idxs]
np_candidates = eval_dictionary[candidate_idxs].squeeze()
dict_candidates = []
for np_candidate in np_candidates:
dict_candidates.append({
'name':
np_candidate[0],
'sty':
np_candidate[1],
'cui':
np_candidate[2],
'label':
check_label(np_candidate[2], golden_cui)
})
dict_mentions.append({
'mention': mention,
'golden_cui': golden_cui, # golden_cui can be composite cui
'pmid': pmid,
'start_char': start_char,
'end_char': end_char,
'candidates': dict_candidates
})
queries.append({'mentions': dict_mentions})
result = {'queries': queries}
return result
df.drop_duplicates(inplace=True, subset=["description"])
top_k_hits = 3
df.dropna(inplace=True, subset=["description"])
model = SentenceTransformer(
'bert-base-nli-stsb-mean-tokens') # BERT model fine tuned on STS dataset
embedding_size = 768 # Size of embeddings of each book description
top_k = 3 # Number of similarity matchings to output
embedding_cache_path = "data.pkl"
num_clusters = 200
# Define FAISS
quantizer = faiss.IndexFlatIP(embedding_size)
index = faiss.IndexIVFFlat(quantizer, embedding_size, num_clusters,
faiss.METRIC_INNER_PRODUCT)
index.nprobe = 3
if not os.path.exists(embedding_cache_path):
descriptions = []
titles = []
isbn13 = []
isbn = []
for row in df.itertuples():
descriptions.append(row.description)
titles.append(row.title)
isbn13.append(row.isbn13)
isbn.append(row.isbn)
def IVFFlatGpu(config):
print("IVFFlatGpu, ", config)
d = config['dimension'] # dimension
nb = config['db_size'] # database size
nq = config['query_num'] # nb of queries
topk = config['top_k']
nlist = config['nlist']
nprobe = config['nprobe']
search_repeat = 10
res = faiss.StandardGpuResources() # use a single GPU
# temp memory
if config["temp_memory"] == 0:
res.noTempMemory()
elif config["temp_memory"] != -1:
res.setTempMemory(config["temp_memory"] * 1024 * 1024)
index_list = []
create_ave_duration = 0
search_ave_duration = 0
if config['test_batch_write'] == True:
batch_write_ave_duration = 0
batch_write_num = config['write_batch_num']
batch_write_time = int(nb / config['write_batch_num'])
print("batch_write_time = ", batch_write_num)
for i in range(config['db_num']):
# Using an IVF index
quantizer = faiss.IndexFlatL2(d) # the other index
index_ivf = faiss.IndexIVFFlat(quantizer, d, nlist,
faiss.METRIC_L2)
gpu_index_ivf = faiss.index_cpu_to_gpu(res, 0, index_ivf)
batch_write_ave_one_lib = 0
for j in range(batch_write_time):
np.random.seed(i * batch_write_time + j)
xb = np.random.random((batch_write_num, d)).astype('float32')
xb[:, 0] += np.arange(batch_write_num) / 1000.
begin_time = time.time()
if gpu_index_ivf.is_trained == False:
print("train, j=", j)
gpu_index_ivf.train(xb)
gpu_index_ivf.add(xb)
duration = time.time() - begin_time
batch_write_ave_one_lib += duration
batch_write_ave_duration += duration
print(i, ",batch_write_ave_one_lib = ",
(batch_write_ave_one_lib / batch_write_time) * 1000 * 1000,
" us")
index_list.append(index_ivf)
print("batch_write_ave_duration = ",
(batch_write_ave_duration / len(index_list) / batch_write_time) *
1000 * 1000, " us")
return index_list
for i in range(config['db_num']):
np.random.seed(i) # make reproducible
xb = np.random.random((nb, d)).astype('float32')
xb[:, 0] += np.arange(nb) / 1000.
begin_time = time.time()
quantizer = faiss.IndexFlatL2(d) # the other index
index_ivf = faiss.IndexIVFFlat(quantizer, d, nlist, faiss.METRIC_L2)
# here we specify METRIC_L2, by default it performs inner-product search
# make it an IVF GPU index
gpu_index_ivf = faiss.index_cpu_to_gpu(res, 0, index_ivf)
assert not gpu_index_ivf.is_trained
gpu_index_ivf.train(xb) # add vectors to the index
assert gpu_index_ivf.is_trained
gpu_index_ivf.add(xb) # add vectors to the index
gpu_index_ivf.nprobe = nprobe
duration = time.time() - begin_time
create_ave_duration += duration
index_list.append(gpu_index_ivf)
if i == 0:
gpu_index_ivf.search(xb[:5], 4)
print("craete ave duration = ", create_ave_duration / len(index_list),
" s")
if len(index_list) == 0:
return index_list
for i in range(len(index_list)):
for j in range(search_repeat):
np.random.seed(i * search_repeat + j + config['db_num'])
xq = np.random.random((nq, d)).astype('float32')
xq[:, 0] += np.arange(nq) / 1000.
begin_time = time.time()
index_list[i].search(xq, topk) # actual search
duration = time.time() - begin_time
search_ave_duration += duration
print("search index aver time = ",
search_ave_duration / len(index_list) / search_repeat, " s")
return index_list
def build_index(self,
sentences_or_file_path: Union[str, List[str]],
use_faiss: bool = None,
faiss_fast: bool = False,
device: str = None,
batch_size: int = 64):
if use_faiss is None or use_faiss:
try:
import faiss
assert hasattr(faiss, "IndexFlatIP")
use_faiss = True
except:
logger.warning(
"Fail to import faiss. If you want to use faiss, install faiss through PyPI. Now the program continues with brute force search."
)
use_faiss = False
# if the input sentence is a string, we assume it's the path of file that stores various sentences
if isinstance(sentences_or_file_path, str):
sentences = []
with open(sentences_or_file_path, "r") as f:
logging.info("Loading sentences from %s ..." %
(sentences_or_file_path))
for line in tqdm(f):
sentences.append(line.rstrip())
sentences_or_file_path = sentences
logger.info("Encoding embeddings for sentences...")
embeddings = self.encode(sentences_or_file_path,
device=device,
batch_size=batch_size,
normalize_to_unit=True,
return_numpy=True)
logger.info("Building index...")
self.index = {"sentences": sentences_or_file_path}
if use_faiss:
quantizer = faiss.IndexFlatIP(embeddings.shape[1])
if faiss_fast:
index = faiss.IndexIVFFlat(
quantizer, embeddings.shape[1],
min(self.num_cells, len(sentences_or_file_path)))
else:
index = quantizer
if (self.device == "cuda" and device != "cpu") or device == "cuda":
if hasattr(faiss, "StandardGpuResources"):
logger.info("Use GPU-version faiss")
res = faiss.StandardGpuResources()
res.setTempMemory(20 * 1024 * 1024 * 1024)
index = faiss.index_cpu_to_gpu(res, 0, index)
else:
logger.info("Use CPU-version faiss")
else:
logger.info("Use CPU-version faiss")
if faiss_fast:
index.train(embeddings.astype(np.float32))
index.add(embeddings.astype(np.float32))
index.nprobe = min(self.num_cells_in_search,
len(sentences_or_file_path))
self.is_faiss_index = True
else:
index = embeddings
self.is_faiss_index = False
self.index["index"] = index
logger.info("Finished")
def train_faiss(item_vector):
quantizer = faiss.IndexFlatL2(item_vector.shape[1])
index = faiss.IndexIVFFlat(quantizer, item_vector.shape[1], 80)
index.train(item_vector)
index.add(item_vector)
return index
"hdfs://localhost:9000/database_embeddings/*",
recursiveFileLookup=True).select("features").toJSON().collect()
# writing databases to database_vector.pkl
list_all_db_vectors = []
list_all_db_vectors_index = []
i = 0
for data in databases:
data = json.loads(data)["features"]
data = get_features(data, "database")
data_index = [i] * len(data)
list_all_db_vectors += data
list_all_db_vectors_index.append(data_index)
list_all_db_vectors = np.asarray(list_all_db_vectors).astype('float32')
list_all_db_vectors = normalize(list_all_db_vectors, axis=1, norm='l2')
list_all_db_vectors_index = flatten(list_all_db_vectors_index)
with open("database_vector_index.pkl", "wb") as f:
pickle.dump(list_all_db_vectors_index, f)
# initializing database vectors
# with open("database_vector.pkl",'rb') as f :
# database_vector = pickle.load(f)
# initialization for FAISS algorithm
cluster = NUMBER_OF_CLUSTER
dimension = list_all_db_vectors[0].shape[0]
quantiser = faiss.IndexFlatIP(dimension)
index = faiss.IndexIVFFlat(quantiser, dimension, cluster,
faiss.METRIC_INNER_PRODUCT)
# training index on database vectors
index.train(list_all_db_vectors)
index.add(list_all_db_vectors)
faiss.write_index(index, "database_faiss.index")
def search(q, query_type):
videos = findVideos(q)
timings = {}
for each_video in videos:
df_embeddings = pd.read_csv("data/" + str(each_video) +
'/person_embeddings_mapping.csv',
sep='\t')
cols = list(df_embeddings)
cols.insert(0, cols.pop(cols.index('Embeddings')))
df_embeddings = df_embeddings.ix[:, cols]
x = str(df_embeddings['Embeddings'].tolist()).replace("\'", "")
x = ast.literal_eval(x)
y = numpy.array(x)
y = y.astype('float32')
d = 128
nlist = 1
k = 1
quantizer = faiss.IndexFlatL2(d) # the other index
index = faiss.IndexIVFFlat(quantizer, d, nlist, faiss.METRIC_L2)
index.train(y) # t and y ma k farak cha?
index.add(y) # add may be a bit slower as well
D, I = index.search(q, k) # actual search
pos = [0] * len(I)
p = [0] * len(I)
# if face is not present: then add to the list
if I == [[]]:
print("Not found")
else:
for i in range(len(I)):
pos[i] = I[i][0]
p[i] = df_embeddings.iloc[pos[i], 1]
df_person_bitmap = pd.read_csv("data/" + str(each_video) +
'/person_bitmap_vector.csv',
sep='\t')
person_bitmap = [0] * len(p)
for i in range(len(p)):
person_bitmap[i] = df_person_bitmap.loc[
df_person_bitmap['person_label'] == int(
p[i])]['BitMap'].values[0]
person_bitmap[i] = str(person_bitmap[i]).replace("\'", "")
person_bitmap[i] = json.loads(person_bitmap[i])
person_bitmap[i] = numpy.array(person_bitmap[i])
if query_type == 'next':
timings[each_video] = next(person_bitmap[0], person_bitmap[1])
if query_type == 'eventually':
timings[each_video] = eventually(person_bitmap[0],
person_bitmap[1])
if query_type == 'is_before':
timings[each_video] = is_a_before_b(person_bitmap[0],
person_bitmap[1])
if query_type == 'interval':
timings[each_video] = interval(person_bitmap)
return timings
def __init__(self) -> None:
quantizer = faiss.IndexFlatL2(self.d)
self.index = faiss.IndexIVFFlat(quantizer, self.d, self.nlist, faiss.METRIC_L2)
self.index.nprobe = self.nprobe
def create_index(dim: int = 512, cells: int = 100):
return faiss.IndexIVFFlat(faiss.IndexFlatL2(dim), dim, cells)
def main(sys):
np.seterr(over='ignore')
m = len(sys)
print ("The script has the name %s" % (sys[0]))
print("initiate: %s " % (sys[0]))
print ("Number of arguments: ", m, " arguments.")
input_file_dataset = sys[1]
input_file_queries = sys[2]
k = int(sys[3])
var = sys[4]
run = sys[5]
ground_truth_D = sys[6]
ground_truth_I = sys[7]
error = float(sys[8])
nlist = int(sys[9]) #number of clusters
nprobe = int(sys[10]) #how many times repeat search
print("check of the arguments")
for i in range(m):
print("arguments: %s " % (sys[i]))
dataset = os.path.realpath(input_file_dataset)
queryset = os.path.realpath(input_file_queries)
groundtruth_D = os.path.realpath(ground_truth_D)
groundtruth_I = os.path.realpath(ground_truth_I)
#ground_truth = os.path.realpath(output_file_gt)
a_vectors = np.loadtxt(dataset).astype(np.float32)
query_set = np.loadtxt(queryset).astype(np.float32)
GT_D = np.loadtxt(groundtruth_D).astype(np.float32)
GT_I = np.loadtxt(groundtruth_I).astype(np.float32)
n_db = len(a_vectors)
d = len(a_vectors[0]) #dimension of database
n_q = len(query_set)
# nlist = int(len(a_vectors) / k) #number of clusters
# nprobe = int((k/2)+1) #how many times repeat search
print("check of dimensions")
print("param n_db", n_db)
print("param d", d)
print("param k", k)
print("param n_q", n_q)
print("param nlist", nlist)
print("param nprobe", nprobe)
print("param error", error)
print("faiss ...")
start1 = time.clock()
quantizer = faiss.IndexFlatL2(d) # build the index
index = faiss.IndexIVFFlat(quantizer, d, nlist, faiss.METRIC_L2)
assert not index.is_trained
index.train(a_vectors)
assert index.is_trained
index.add( a_vectors)
stop1 = time.clock()
start2 = time.clock()
index.nprobe = nprobe # if set to nlist = same as brute force, part of autotuning performance - speed accuracy tradeoff
D, I = index.search(query_set, k) # actual search
stop2 = time.clock()
#run recall
recall_i = recall_similar_match( GT_I, I)
recall_d = recall_with_error( GT_D, D, error)
stringname_D = 'D' + sys[0][1:7] + '_' + var +'.txt'
stringname_I = 'I' + sys[0][1:7] + '_' + var +'.txt'
np.savetxt(stringname_D, D)
np.savetxt(stringname_I, I)
time1 = stop1 - start1
time2 = stop2 - start2
#run, filename, index_time, build_time, recall_D, recall_I, n_db, n_q, d, k
print_time(run, sys[0], time1, time2, recall_d, recall_i, n_db, n_q, d, k, error)
print("finish")
def create_faiss(self):
quantiser = faiss.IndexFlatL2(self.num_dimensions)
self.faiss_params = {}
if self.similarity_metric == 'cosine':
self.faiss_params['preprocess_opt'] = 'norm'
self.faiss_params['metric'] = faiss.METRIC_L2
elif self.similarity_metric == 'inner':
self.faiss_params['preprocess_opt'] = 'false'
self.faiss_params['metric'] = faiss.METRIC_INNER_PRODUCT
elif self.similarity_metric == 'euclidean':
self.faiss_params['preprocess_opt'] = 'false'
self.faiss_params['metric'] = faiss.METRIC_L2
elif self.similarity_metric == 'mahalanobis':
self.faiss_params['preprocess_opt'] = 'covar'
self.faiss_params['metric'] = faiss.METRIC_L2
self.faiss_indices = {
'title':
faiss.IndexIVFFlat(quantiser, self.num_dimensions,
self.num_centroids,
self.faiss_params['metric']),
'abstract':
faiss.IndexIVFFlat(quantiser, self.num_dimensions,
self.num_centroids,
self.faiss_params['metric']),
'body':
faiss.IndexIVFFlat(quantiser, self.num_dimensions,
self.num_centroids,
self.faiss_params['metric']),
}
# Title train
self.lookup_titles = {}
documents_with_titles = []
self.lookup_abstracts = {}
documents_with_abstracts = []
self.lookup_bodies = {}
documents_with_bodies = []
for doc in self.documents:
v_title = doc.mean_vector_title()
v_abstract = doc.mean_vector('abstract')
v_body = doc.mean_vector('body')
if not (v_title is None or len(v_title.shape) != 1
and v_title.shape[0] != self.num_dimensions):
self.lookup_titles[len(documents_with_titles)] = doc
documents_with_titles.append(v_title)
if not (v_abstract is None or len(v_abstract.shape) != 1
and v_abstract.shape[0] != self.num_dimensions):
self.lookup_abstracts[len(documents_with_abstracts)] = doc
documents_with_abstracts.append(v_abstract)
if not (v_body is None or len(v_body.shape) != 1
and v_body.shape[0] != self.num_dimensions):
self.lookup_bodies[len(documents_with_bodies)] = doc
documents_with_bodies.append(v_body)
vectors = self.search_preprocess(np.stack(documents_with_titles,
axis=0),
is_train=True)
self.faiss_indices['title'].train(vectors)
self.faiss_indices['title'].add(vectors)
# Train abstract
vectors = self.search_preprocess(np.stack(documents_with_abstracts,
axis=0),
is_train=True)
self.faiss_indices['abstract'].train(vectors)
self.faiss_indices['abstract'].add(vectors)
# Train full
vectors = self.search_preprocess(np.stack(documents_with_bodies,
axis=0),
is_train=True)
self.faiss_indices['body'].train(vectors)
self.faiss_indices['body'].add(vectors)
# define msg queue names
msg_queues = {}
msg_queues['crawler_notify'] = "CRAWLER_NOTIFY_QUE"
msg_queues['feature_detect'] = "FEATURE_DETECT_QUE"
msg_queues['detect_finish'] = "FEATURE_DETECT_FINISH_QUE"
# feature detect related config
static_image_feature_dir = "/Users/tonyyoung/test/feature/inception/image"
animated_image_feature_dir = "/Users/tonyyoung/test/feature/600"
static_dimension = 2048
animaed_dimension = 600
nlist = 20 # Number of clustering centers
quantizer_static = faiss.IndexFlatL2(static_dimension)
quantizer_animaed = faiss.IndexFlatL2(animaed_dimension)
static_image_index = faiss.IndexIVFFlat(quantizer_static, static_dimension,
nlist, faiss.METRIC_L2)
animated_image_index = faiss.IndexIVFFlat(quantizer_animaed, animaed_dimension,
nlist, faiss.METRIC_L2)
'''static_image_index = faiss.IndexFlatL2(static_dimension)
animated_image_index = faiss.IndexFlatL2(animaed_dimension)'''
duplicate_threshold = 10
# OSS account info.
clientId = 'LTAIW5NjZnlwWIjr'
clientSecret = 'BWajgSlWW32EtuQbTmDywvSf7pvwuj'
MEDIA_ROOT = "/home/ubuntu/workspace/smile_sv/smile/media"
# ========================= Redis operations ========================
'''sticker_md5 = "sticker_md5"
def __init__(self, dim=VECTOR_DIMENSION):
# if isfile(save_path):
# self._index = faiss.read_index(save_path)
# else:
quantizer = faiss.IndexFlatL2(dim)
self._index = faiss.IndexIVFFlat(quantizer, dim, N_LIST, faiss.METRIC_L2)
def ivf_search():
'''
IndexIVFFlat 倒排搜索 基于 直接搜索
要求：行需大于等于 行分割数nlist
优点：减少计算量速度提升明显,5百万8G 0.5秒
缺点：丢了点精度,数据倒排耗时
通过使用IndexIVFFlat索引，将数据集分割成多个，
我们在d维空间中定义Voronoi单元，每个数据库向量落在其中一个单元格中。在搜索时，只有查询x所在的单元格中包含的数据库向量y和几个相邻的数据库向量y与查询向量进行比较
:return:
'''
nlist = 4 # 行分割数-行分割成的单元格数,分割数越多分割越耗时
index = faiss.IndexFlatL2(d)
iv_index = faiss.IndexIVFFlat(index, d, nlist, faiss.METRIC_L2)
if not iv_index.is_trained:
iv_index.train(xb) # train xb
iv_index.add(xb) # 添加xb
# 动态新增数据
xb1 = np.random.random((1, d)).astype('float32')
xb1[:, 0] += np.arange(1) / 1000.
xb1[0][0] -= 1
print(xb1)
iv_index.train(xb1) # 需要训练
iv_index.add(xb1)
xb2 = np.ones((1, d)).astype('float32')
iv_index.train(xb2)
iv_index.add(xb2)
iv_index.nprobe = 3 # 搜索访问的单元格数,访问越大越精确越耗时 ：既 128维倒排切割成4份,只计算3份的维度相似
start = time.time()
print(type(xq))
D, I = iv_index.search(xq, K)
print('ivf_search_time', time.time() - start)
print('ivf_search_相似索引', I)
print('ivf_search_相似值', D)
xq3 = np.ones((1, d)).astype('float32')
xq3[:, 0] += np.arange(1) / 1000.
D, I = iv_index.search(xq3, K)
print('ivf_search_相似索引', I)
print('ivf_search_相似值', D)
vectors = MyRecognition.face_descriptors(
img_path=
'/home/stringk/PycharmProjects/pytorchDemo/demos/face_recognition_demo/img/0/0image0.jpg'
)
for v in vectors:
nv = np.array([v], dtype='float32')
print(nv)
print(type(nv))
print(nv[0][-1])
iv_index.train(nv)
iv_index.add(nv)
D, I = iv_index.search(nv, K)
print('ivf_search_相似索引', I)
print('ivf_search_相似值', D)
def build_index(cfg: DictConfig, model: object):
"""
Builds faiss index from index dataset specified in the config.
Args:
cfg (DictConfig): Config file specifying index parameters
model (object): Encoder model
"""
# Get index dataset embeddings
# PCA model exists and index embeddings have already been PCAed, no need to re-extract/PCA them
if cfg.apply_pca and os.path.isfile(
cfg.pca.pca_save_name) and os.path.isfile(
cfg.pca_embeddings_save_name):
logging.info("Loading reduced dimensionality embeddings")
embeddings = h5py.File(cfg.pca_embeddings_save_name, "r")
embeddings = embeddings[cfg.index_ds.name][:]
elif os.path.isfile(cfg.embedding_save_name):
logging.info("Loading previously extracted index dataset embeddings")
embeddings = h5py.File(cfg.embedding_save_name, "r")
embeddings = embeddings[cfg.index_ds.name][:]
else:
logging.info("Encoding index dataset, this may take a while")
index_dataloader = model.setup_dataloader(cfg.index_ds,
is_index_data=True)
embeddings, concept_ids = get_index_embeddings(cfg, index_dataloader,
model)
# Create pca model to reduce dimensionality of index dataset and decrease memory footprint
if cfg.apply_pca:
# Need to train PCA model and apply PCA transformation with newly trained model
if not os.path.isfile(cfg.pca.pca_save_name):
logging.info(
"Fitting PCA model for embedding dimensionality reduction")
pca_train_set = random.sample(
list(embeddings),
k=int(len(embeddings) * cfg.pca.sample_fraction))
pca = PCA(n_components=cfg.pca.output_dim)
pca.fit(pca_train_set)
pkl.dump(pca, open(cfg.pca.pca_save_name, "wb"))
embeddings = reduce_embedding_dim(pca, embeddings, cfg)
# PCA model already trained, just need to reduce dimensionality of all embeddings
elif not os.path.isfile(cfg.pca_embeddings_save_name):
pca = pkl.load(open(cfg.pca.pca_save_name, "rb"))
embeddings = reduce_embedding_dim(pca, embeddings, cfg)
# Build faiss index from embeddings
logging.info(
f"Training index with embedding dim size {cfg.dims} using {faiss.get_num_gpus()} gpus"
)
quantizer = faiss.IndexFlatL2(cfg.dims)
index = faiss.IndexIVFFlat(quantizer, cfg.dims, cfg.nlist)
index = faiss.index_cpu_to_all_gpus(index)
index.train(embeddings)
logging.info("Adding dataset embeddings to index")
for i in tqdm(range(0, embeddings.shape[0], cfg.index_batch_size)):
index.add(embeddings[i:i + cfg.index_batch_size])
logging.info("Saving index")
faiss.write_index(faiss.index_gpu_to_cpu(index), cfg.index_save_name)
logging.info("Index built and saved")
# features_astype = features.astype(np.float32)
# mat = faiss.PCAMatrix (1024, 128)
# mat.train(features_astype)
# assert mat.is_trained
# features_shape = mat.apply_py(features_astype)
# print(features_shape.shape)
# np.savetxt('PCA_features.txt',features_shape)
print(features_shape.shape)
dimension = 1024
n = 95276
nlist = 50
quantiser = faiss.IndexFlatL2(dimension)
index = faiss.IndexIVFFlat(quantiser, dimension, nlist, faiss.METRIC_L2)
# print(index.is_trained)
index.train(features_shape)
# print(index.ntotal)
index.add(features_shape)
# print(index.is_trained)
# print(index.ntotal)
nprobe = 10 # find 2 most similar clusters
k = 5 # return 3 nearest neighbours
a = np.reshape(features_shape[1], (1, -1))
distances, indices = index.search(a, k)
print(distances)
print(indices)
def serve(args):
# serve_demo: Load saved embeddings, serve question model. question in, results out.
# serve_question: only serve question model. question in, vector out.
# serve_context: only serve context model. context in, phrase-vector pairs out.
# serve: serve all three.
device = torch.device('cuda' if args.cuda else 'cpu')
pprint(args.__dict__)
interface = FileInterface(**args.__dict__)
# use cache for metadata
if args.cache:
out = interface.cache(preprocess, args)
processor = out['processor']
processed_metadata = out['processed_metadata']
else:
processor = Processor(**args.__dict__)
metadata = interface.load_metadata()
processed_metadata = processor.process_metadata(metadata)
model = Model(**args.__dict__).to(device)
model.init(processed_metadata)
interface.bind(processor, model)
interface.load(args.iteration, session=args.load_dir)
with torch.no_grad():
model.eval()
if args.mode == 'serve_demo':
phrases = []
paras = []
results = []
embs = []
idxs = []
iterator = interface.context_load(metadata=True, emb_type=args.emb_type)
for _, (cur_phrases, each_emb, metadata) in zip(range(args.num_train_mats), iterator):
embs.append(each_emb)
phrases.extend(cur_phrases)
for span in metadata['answer_spans']:
results.append([len(paras), span[0], span[1]])
idxs.append(len(idxs))
paras.append(metadata['context'])
if args.emb_type == 'dense':
import faiss
emb = np.concatenate(embs, 0)
d = 4 * args.hidden_size * args.num_heads
if args.metric == 'ip':
quantizer = faiss.IndexFlatIP(d) # Exact Search
elif args.metric == 'l2':
quantizer = faiss.IndexFlatL2(d)
else:
raise ValueError()
if args.nlist != args.nprobe:
# Approximate Search. nlist > nprobe makes it faster and less accurate
if args.bpv is None:
if args.metric == 'ip':
search_index = faiss.IndexIVFFlat(quantizer, d, args.nlist, faiss.METRIC_INNER_PRODUCT)
elif args.metric == 'l2':
search_index = faiss.IndexIVFFlat(quantizer, d, args.nlist)
else:
raise ValueError()
else:
assert args.metric == 'l2' # only l2 is supported for product quantization
search_index = faiss.IndexIVFPQ(quantizer, d, args.nlist, args.bpv, 8)
search_index.train(emb)
else:
search_index = quantizer
search_index.add(emb)
for cur_phrases, each_emb, metadata in iterator:
phrases.extend(cur_phrases)
for span in metadata['answer_spans']:
results.append([len(paras), span[0], span[1]])
paras.append(metadata['context'])
search_index.add(each_emb)
if args.nlist != args.nprobe:
search_index.nprobe = args.nprobe
def search(emb, k):
D, I = search_index.search(emb, k)
return D[0], I[0]
elif args.emb_type == 'sparse':
assert args.metric == 'l2' # currently only l2 is supported (couldn't find a good ip library)
import pysparnn.cluster_index as ci
cp = ci.MultiClusterIndex(embs, idxs)
for cur_phrases, each_emb, metadata in iterator:
phrases.extend(cur_phrases)
for span in metadata['answer_spans']:
results.append([len(paras), span[0], span[1]])
paras.append(metadata['context'])
for each_vec in each_emb:
cp.insert(each_vec, len(idxs))
idxs.append(len(idxs))
def search(emb, k):
return zip(*[each[0] for each in cp.search(emb, k=k)])
else:
raise ValueError()
def retrieve(question, k):
example = {'question': question, 'id': 'real', 'idx': 0}
dataset = (processor.preprocess(example), )
loader = DataLoader(dataset, batch_size=1, collate_fn=processor.collate)
batch = next(iter(loader))
question_output = model.get_question(**batch)
question_results = processor.postprocess_question_batch(dataset, batch, question_output)
id_, emb = question_results[0]
D, I = search(emb, k)
out = [(paras[results[i][0]], results[i][1], results[i][2], '%.4r' % d.item(),)
for d, i in zip(D, I)]
return out
if args.mem_info:
import psutil
import os
pid = os.getpid()
py = psutil.Process(pid)
info = py.memory_info()[0] / 2. ** 30
print('Memory Use: %.2f GB' % info)
# Demo server. Requires flask and tornado
from flask import Flask, request, jsonify
from flask_cors import CORS
from tornado.wsgi import WSGIContainer
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
app = Flask(__name__, static_url_path='/static')
app.config['JSONIFY_PRETTYPRINT_REGULAR'] = False
CORS(app)
@app.route('/')
def index():
return app.send_static_file('index.html')
@app.route('/files/<path:path>')
def static_files(path):
return app.send_static_file('files/' + path)
@app.route('/api', methods=['GET'])
def api():
query = request.args['query']
out = retrieve(query, 5)
return jsonify(out)
print('Starting server at %d' % args.port)
http_server = HTTPServer(WSGIContainer(app))
http_server.listen(args.port)
IOLoop.instance().start()
def test_IndexIVF_2(self):
index = faiss.IndexIVFFlat(faiss.IndexFlatL2(d), d, 10)
index.train(xt)
index.add(xb)
def _build_query_ann_index(
charge: int, mz_splits: np.ndarray, vectorize: Callable, n_probe: int,
batch_size: int, n_neighbors: int, n_neighbors_ann: int,
precursor_tol_mass: float, precursor_tol_mode: str,
distances: np.ndarray, indices: np.ndarray, indptr: np.ndarray,
work_dir: str) -> pd.DataFrame:
"""
Create ANN index(es) for spectra with the given charge per precursor m/z
split.
Parameters
----------
charge : int
Precursor charge of the spectra to be processed.
mz_splits : np.ndarray
M/z splits used to create separate ANN indexes.
vectorize : Callable
Function to convert the spectra to vectors.
batch_size : int
The number of vectors to be simultaneously added to the index.
work_dir : str
Directory to read and store (intermediate) results.
Returns
-------
pd.DataFrame
Metadata (identifier, precursor charge, precursor m/z) of the spectra
for which indexes were built.
"""
identifiers, precursor_mzs = [], []
indptr_i = 0
# Find neighbors per specified precursor m/z interval.
for mz in tqdm.tqdm(mz_splits, desc='Intervals queried', unit='index'):
pkl_filename = os.path.join(work_dir, 'spectra', f'{charge}_{mz}.pkl')
if not os.path.isfile(pkl_filename):
continue
# Read the spectra for the m/z split.
with open(pkl_filename, 'rb') as f_in:
spectra_split = pickle.load(f_in)
precursor_mzs_split = []
for spec in spectra_split:
identifiers.append(spec.identifier)
precursor_mzs_split.append(spec.precursor_mz)
precursor_mzs.append(np.asarray(precursor_mzs_split))
# Convert the spectra to vectors.
vectors_split = vectorize(spectra_split)
n_split, dim = len(spectra_split), vectors_split.shape[1]
# Figure out a decent value for the n_list hyperparameter based on
# the number of vectors.
# Rules of thumb from the Faiss wiki:
# https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index#how-big-is-the-dataset
if n_split == 0:
continue
if n_split < 10e2:
# Use a brute-force index instead of an ANN index when there
# are only a few items.
n_list = -1
elif n_split < 10e5:
n_list = 2**math.floor(math.log2(n_split / 39))
elif n_split < 10e6:
n_list = 2**16
elif n_split < 10e7:
n_list = 2**18
else:
n_list = 2**20
if n_split > 10e8:
logger.warning('More than 1B vectors to be indexed, consider '
'decreasing the ANN size')
# Create an ANN index using the inner product (proxy for cosine
# distance) for fast NN queries.
if n_list <= 0:
index = faiss.IndexIDMap(faiss.IndexFlatIP(dim))
else:
index = faiss.IndexIVFFlat(faiss.IndexFlatIP(dim), dim, n_list,
faiss.METRIC_INNER_PRODUCT)
index.nprobe = min(math.ceil(index.nlist / 8), n_probe)
# Compute cluster centroids.
# noinspection PyArgumentList
index.train(vectors_split)
# Add the vectors to the index in batches.
batch_size = min(n_split, batch_size)
for batch_start in range(0, n_split, batch_size):
batch_stop = min(batch_start + batch_size, n_split)
# noinspection PyArgumentList
index.add_with_ids(vectors_split[batch_start:batch_stop],
np.arange(batch_start, batch_stop))
# Query the index to calculate NN distances.
_dist_mz_interval(
index, vectors_split, precursor_mzs[-1], batch_size, n_neighbors,
n_neighbors_ann, precursor_tol_mass, precursor_tol_mode,
distances, indices, indptr, indptr_i)
indptr_i += vectors_split.shape[0]
return pd.DataFrame({'identifier': identifiers, 'precursor_charge': charge,
'precursor_mz': np.hstack(precursor_mzs)})
def main(sys):
np.seterr(over='ignore')
m = len(sys)
print("The script has the name %s" % (sys[0]))
print("initiate: %s " % (sys[0]))
print("Number of arguments: ", m, " arguments.")
input_file_dataset = sys[1]
input_file_queries = sys[2]
k = int(sys[3])
var = sys[4]
run = sys[5]
ground_truth_D = sys[6]
ground_truth_I = sys[7]
error = float(sys[8])
nlist = int(sys[9]) #number of clusters
nprobe = int(sys[10]) #how many times repeat search
print("check of the arguments")
for i in range(m):
print("arguments: %s " % (sys[i]))
dataset = os.path.realpath(input_file_dataset)
queryset = os.path.realpath(input_file_queries)
groundtruth_D = os.path.realpath(ground_truth_D)
groundtruth_I = os.path.realpath(ground_truth_I)
#ground_truth = os.path.realpath(output_file_gt)
a_vectors = np.loadtxt(dataset).astype(np.float32)
query_set = np.loadtxt(queryset).astype(np.float32)
GT_D = np.loadtxt(groundtruth_D).astype(np.float32)
GT_I = np.loadtxt(groundtruth_I).astype(np.float32)
#
# k = len(GT_D[0])
n_db = len(a_vectors)
d = len(a_vectors[0]) #dimension of database
n_q = len(query_set)
fo = len(a_vectors)
# nlist = int(float(fo) / k) #number of clusters
# nprobe = int((k/2)+1) #how many times repeat search
print("check of dimensions")
print("param n_db", n_db)
print("param d", d)
print("param k", k)
print("param n_q", n_q)
print("param nlist", nlist)
print("param nprobe", nprobe)
print("param error", error)
print("faiss ...")
start1 = time.clock()
quantizer = faiss.IndexHNSWFlat(d, 32)
index = faiss.IndexIVFFlat(quantizer, d, nlist)
index.cp.min_points_per_centroid = 10 # quiet warning
index.quantizer_trains_alone = 2
assert not index.is_trained
index.train(a_vectors)
assert index.is_trained
quantizer.hnsw.efSearch = nprobe #setting up the precission, higger the better but slower (def. is 40)
index.add(a_vectors)
stop1 = time.clock()
#----#start search
start2 = time.clock()
D, I = index.search(query_set, k) # actual search
stop2 = time.clock()
#---#end
#run recall
recall_i = recall_similar_match(GT_I, I)
recall_d = recall_with_error(GT_D, D, error)
stringname_D = 'D' + sys[0][1:7] + '_' + var + '.txt'
stringname_I = 'I' + sys[0][1:7] + '_' + var + '.txt'
np.savetxt(stringname_D, D)
np.savetxt(stringname_I, I)
time1 = stop1 - start1
time2 = stop2 - start2
#run, filename, index_time, build_time, recall_D, recall_I, n_db, n_q, d, k
print_time(run, sys[0], time1, time2, recall_d, recall_i, n_db, n_q, d, k,
error)
print("finish")
import numpy as np
import faiss # make faiss available
if __name__ == "__main__":
e = np.load('encodings.npy', allow_pickle=True).tolist()
vectorEncoding = np.array([x.get('encondings') for x in e],
dtype=np.float32)
vectorId = np.array([x.get('id') for x in e])
d = len(vectorEncoding[0])
print('Tamanho d do vetor: {}'.format(d))
index = faiss.IndexIVFFlat(d) # build the index
index2 = faiss.IndexIDMap(index)
index2.add_with_ids(vectorEncoding, vectorId)
print('Tamanho do indice: {}'.format(index2.ntotal))
faiss.write_index(index2, '10000.index')
print "add"
# to see progress
index.verbose = True
index.add(xb)
print "search"
for efSearch in 16, 32, 64, 128, 256:
print "efSearch", efSearch,
index.hnsw.efSearch = efSearch
evaluate(index)
if 'ivf' in todo:
print "Testing IVF Flat (baseline)"
quantizer = faiss.IndexFlatL2(d)
index = faiss.IndexIVFFlat(quantizer, d, 16384)
index.cp.min_points_per_centroid = 5 # quiet warning
# to see progress
index.verbose = True
print "training"
index.train(xt)
print "add"
index.add(xb)
print "search"
for nprobe in 1, 4, 16, 64, 256:
print "nprobe", nprobe,
index.nprobe = nprobe
"/home/jianx/results/passage_0__emb_p__data_obj_0.pb")
query_train_embeddings = obj_reader(
"/home/jianx/results/query_0__emb_p__data_obj_0.pb")
query_train_mapping = obj_reader(
"/datadrive/jianx/data/annoy/100_ance_query_train_map.dict")
pid_mapping = obj_reader(
"/datadrive/jianx/data/annoy/100_ance_passage_map.dict")
print_message("Building index")
faiss.omp_set_num_threads(16)
dim = passage_embeddings.shape[1]
if IS_FLAT:
cpu_index = faiss.IndexFlatIP(dim)
else:
quantizer = faiss.IndexFlatIP(dim)
cpu_index = faiss.IndexIVFFlat(quantizer, dim, NLIST)
assert not cpu_index.is_trained
cpu_index.train(passage_embeddings)
assert cpu_index.is_trained
cpu_index.add(passage_embeddings)
print_message("Searching for all queries")
with open(OUT_PATH, "w+") as f:
print_message("Writing to {}".format(OUT_PATH))
f.write("")
for starting in range(0, len(query_train_embeddings), BATCH_SIZE):
mini_batch = query_train_embeddings[starting:starting + BATCH_SIZE]
_, dev_I = cpu_index.search(mini_batch, RANK)
print_message("Batch No.{}/{}".format(
starting / BATCH_SIZE + 1,
len(query_train_embeddings) / BATCH_SIZE))
def test_dedup(self):
d = 10
nb = 1000
nq = 200
nt = 500
xt, xb, xq = get_dataset_2(d, nt, nb, nq)
# introduce duplicates
xb[500:900:2] = xb[501:901:2]
xb[901::4] = xb[900::4]
xb[902::4] = xb[900::4]
xb[903::4] = xb[900::4]
# also in the train set
xt[201::2] = xt[200::2]
quantizer = faiss.IndexFlatL2(d)
index_new = faiss.IndexIVFFlatDedup(quantizer, d, 20)
index_new.verbose = True
# should display
# IndexIVFFlatDedup::train: train on 350 points after dedup (was 500 points)
index_new.train(xt)
index_ref = faiss.IndexIVFFlat(quantizer, d, 20)
assert index_ref.is_trained
index_ref.nprobe = 5
index_ref.add(xb)
index_new.nprobe = 5
index_new.add(xb)
Dref, Iref = index_ref.search(xq, 20)
Dnew, Inew = index_new.search(xq, 20)
for i in range(nq):
ref = self.normalize_res(Dref[i], Iref[i])
new = self.normalize_res(Dnew[i], Inew[i])
assert ref == new
# test I/O
_, tmpfile = tempfile.mkstemp()
try:
faiss.write_index(index_new, tmpfile)
index_st = faiss.read_index(tmpfile)
finally:
if os.path.exists(tmpfile):
os.unlink(tmpfile)
Dst, Ist = index_st.search(xq, 20)
for i in range(nq):
new = self.normalize_res(Dnew[i], Inew[i])
st = self.normalize_res(Dst[i], Ist[i])
assert st == new
# test remove
toremove = np.hstack((np.arange(3, 1000, 5), np.arange(850, 950)))
index_ref.remove_ids(toremove)
index_new.remove_ids(toremove)
Dref, Iref = index_ref.search(xq, 20)
Dnew, Inew = index_new.search(xq, 20)
for i in range(nq):
ref = self.normalize_res(Dref[i], Iref[i])
new = self.normalize_res(Dnew[i], Inew[i])
assert ref == new
import faiss
import numpy
import numpy as np
if __name__ == '__main__':
nlist = 5
dimension = 512
quantizer = faiss.IndexFlatL2(dimension)
index = faiss.IndexIVFFlat(quantizer, dimension, nlist)
features_folder = "features/features.npy"
image_features = np.load(features_folder).astype('float32')
index.train(image_features)
index.add(image_features)
faiss.write_index(index, "image_index")
