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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import pickle
import time
try:
import faiss
except ImportError:
pass
from collections import defaultdict
from ..utils import get_local_rank, print_on_rank0
class VectorRetriever(object):
"""
How2 Video Retriver.
Reference usage of FAISS:
https://github.com/fairinternal/fairseq-py/blob/paraphrase_pretraining/fairseq/data/multilingual_faiss_dataset.py
"""
def __init__(self, hidden_size, cent, db_type, examples_per_cent_to_train):
if db_type == "flatl2":
quantizer = faiss.IndexFlatL2(hidden_size) # the other index
self.db = faiss.IndexIVFFlat(
quantizer, hidden_size, cent, faiss.METRIC_L2)
elif db_type == "pq":
self.db = faiss.index_factory(
hidden_size, f"IVF{cent}_HNSW32,PQ32"
)
else:
raise ValueError("unknown type of db", db_type)
self.train_thres = cent * examples_per_cent_to_train
self.train_cache = []
self.train_len = 0
self.videoid_to_vectoridx = {}
self.vectoridx_to_videoid = None
self.make_direct_maps_done = False
def make_direct_maps(self):
faiss.downcast_index(self.db).make_direct_map()
def __len__(self):
return self.db.ntotal
def save(self, out_dir):
faiss.write_index(
self.db,
os.path.join(out_dir, "faiss_idx")
)
with open(
os.path.join(
out_dir, "videoid_to_vectoridx.pkl"),
"wb") as fw:
pickle.dump(
self.videoid_to_vectoridx, fw,
protocol=pickle.HIGHEST_PROTOCOL
)
def load(self, out_dir):
fn = os.path.join(out_dir, "faiss_idx")
self.db = faiss.read_index(fn)
with open(
os.path.join(out_dir, "videoid_to_vectoridx.pkl"), "rb") as fr:
self.videoid_to_vectoridx = pickle.load(fr)
def add(self, hidden_states, video_ids, last=False):
assert len(hidden_states) == len(video_ids), "{}, {}".format(
str(len(hidden_states)), str(len(video_ids)))
assert len(hidden_states.shape) == 2
assert hidden_states.dtype == np.float32
valid_idx = []
for idx, video_id in enumerate(video_ids):
if video_id not in self.videoid_to_vectoridx:
valid_idx.append(idx)
self.videoid_to_vectoridx[video_id] = \
len(self.videoid_to_vectoridx)
hidden_states = hidden_states[valid_idx]
if not self.db.is_trained:
self.train_cache.append(hidden_states)
self.train_len += hidden_states.shape[0]
if self.train_len < self.train_thres:
return
self.finalize_training()
else:
self.db.add(hidden_states)
def finalize_training(self):
hidden_states = np.concatenate(self.train_cache, axis=0)
del self.train_cache
local_rank = get_local_rank()
if local_rank == 0:
start = time.time()
print("training db on", self.train_thres, "/", self.train_len)
self.db.train(hidden_states[:self.train_thres])
if local_rank == 0:
print("training db for", time.time() - start)
self.db.add(hidden_states)
def search(
self,
query_hidden_states,
orig_dist,
):
if len(self.videoid_to_vectoridx) != self.db.ntotal:
raise ValueError(
"cannot search: size mismatch in-between index and db",
len(self.videoid_to_vectoridx),
self.db.ntotal
)
if self.vectoridx_to_videoid is None:
self.vectoridx_to_videoid = {
self.videoid_to_vectoridx[videoid]: videoid
for videoid in self.videoid_to_vectoridx
}
assert len(self.vectoridx_to_videoid) \
== len(self.videoid_to_vectoridx)
# MultilingualFaissDataset uses the following; not sure the purpose.
# faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
queried_dist, index = self.db.search(query_hidden_states, 1)
queried_dist, index = queried_dist[:, 0], index[:, 0]
outputs = np.array(
[self.vectoridx_to_videoid[_index]
if _index != -1 else (-1, -1, -1) for _index in index],
dtype=np.int32)
outputs[queried_dist <= orig_dist] = -1
return outputs
def search_by_video_ids(
self,
video_ids,
retri_factor
):
if len(self.videoid_to_vectoridx) != self.db.ntotal:
raise ValueError(
len(self.videoid_to_vectoridx),
self.db.ntotal
)
if not self.make_direct_maps_done:
self.make_direct_maps()
if self.vectoridx_to_videoid is None:
self.vectoridx_to_videoid = {
self.videoid_to_vectoridx[videoid]: videoid
for videoid in self.videoid_to_vectoridx
}
assert len(self.vectoridx_to_videoid) \
== len(self.videoid_to_vectoridx)
query_hidden_states = []
vector_ids = []
for video_id in video_ids:
vector_id = self.videoid_to_vectoridx[video_id]
vector_ids.append(vector_id)
query_hidden_state = self.db.reconstruct(vector_id)
query_hidden_states.append(query_hidden_state)
query_hidden_states = np.stack(query_hidden_states)
# MultilingualFaissDataset uses the following; not sure the reason.
# faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
_, index = self.db.search(query_hidden_states, retri_factor)
outputs = []
for sample_idx, sample in enumerate(index):
# the first video_id is always the video itself.
cands = [video_ids[sample_idx]]
for vector_idx in sample:
if vector_idx >= 0 \
and vector_ids[sample_idx] != vector_idx:
cands.append(
self.vectoridx_to_videoid[vector_idx]
)
outputs.append(cands)
return outputs
class VectorRetrieverDM(VectorRetriever):
"""
with direct map.
How2 Video Retriver.
Reference usage of FAISS:
https://github.com/fairinternal/fairseq-py/blob/paraphrase_pretraining/fairseq/data/multilingual_faiss_dataset.py
"""
def __init__(
self,
hidden_size,
cent,
db_type,
examples_per_cent_to_train
):
super().__init__(
hidden_size, cent, db_type, examples_per_cent_to_train)
self.make_direct_maps_done = False
def make_direct_maps(self):
faiss.downcast_index(self.db).make_direct_map()
self.make_direct_maps_done = True
def search(
self,
query_hidden_states,
orig_dist,
):
if len(self.videoid_to_vectoridx) != self.db.ntotal:
raise ValueError(
len(self.videoid_to_vectoridx),
self.db.ntotal
)
if not self.make_direct_maps_done:
self.make_direct_maps()
if self.vectoridx_to_videoid is None:
self.vectoridx_to_videoid = {
self.videoid_to_vectoridx[videoid]: videoid
for videoid in self.videoid_to_vectoridx
}
assert len(self.vectoridx_to_videoid) \
== len(self.videoid_to_vectoridx)
# MultilingualFaissDataset uses the following; not sure the reason.
# faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
queried_dist, index = self.db.search(query_hidden_states, 1)
outputs = []
for sample_idx, sample in enumerate(index):
# and queried_dist[sample_idx] < thres \
if sample >= 0 \
and queried_dist[sample_idx] < orig_dist[sample_idx]:
outputs.append(self.vectoridx_to_videoid[sample])
else:
outputs.append(None)
return outputs
def search_by_video_ids(
self,
video_ids,
retri_factor=8
):
if len(self.videoid_to_vectoridx) != self.db.ntotal:
raise ValueError(
len(self.videoid_to_vectoridx),
self.db.ntotal
)
if not self.make_direct_maps_done:
self.make_direct_maps()
if self.vectoridx_to_videoid is None:
self.vectoridx_to_videoid = {
self.videoid_to_vectoridx[videoid]: videoid
for videoid in self.videoid_to_vectoridx
}
assert len(self.vectoridx_to_videoid) \
== len(self.videoid_to_vectoridx)
query_hidden_states = []
vector_ids = []
for video_id in video_ids:
vector_id = self.videoid_to_vectoridx[video_id]
vector_ids.append(vector_id)
query_hidden_state = self.db.reconstruct(vector_id)
query_hidden_states.append(query_hidden_state)
query_hidden_states = np.stack(query_hidden_states)
# MultilingualFaissDataset uses the following; not sure the reason.
# faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
_, index = self.db.search(query_hidden_states, retri_factor)
outputs = []
for sample_idx, sample in enumerate(index):
# the first video_id is always the video itself.
cands = [video_ids[sample_idx]]
for vector_idx in sample:
if vector_idx >= 0 \
and vector_ids[sample_idx] != vector_idx:
cands.append(
self.vectoridx_to_videoid[vector_idx]
)
outputs.append(cands)
return outputs
class MMVectorRetriever(VectorRetrieverDM):
"""
multimodal vector retriver:
text retrieve video or video retrieve text.
"""
def __init__(self, hidden_size, cent, db_type, examples_per_cent_to_train):
super().__init__(
hidden_size, cent, db_type, examples_per_cent_to_train)
video_db = self.db
super().__init__(
hidden_size, cent, db_type, examples_per_cent_to_train)
text_db = self.db
self.db = {"video": video_db, "text": text_db}
self.video_to_videoid = defaultdict(list)
def __len__(self):
assert self.db["video"].ntotal == self.db["text"].ntotal
return self.db["video"].ntotal
def make_direct_maps(self):
faiss.downcast_index(self.db["video"]).make_direct_map()
faiss.downcast_index(self.db["text"]).make_direct_map()
def save(self, out_dir):
faiss.write_index(
self.db["video"],
os.path.join(out_dir, "video_faiss_idx")
)
faiss.write_index(
self.db["text"],
os.path.join(out_dir, "text_faiss_idx")
)
with open(
os.path.join(
out_dir, "videoid_to_vectoridx.pkl"),
"wb") as fw:
pickle.dump(
self.videoid_to_vectoridx, fw,
protocol=pickle.HIGHEST_PROTOCOL
)
def load(self, out_dir):
fn = os.path.join(out_dir, "video_faiss_idx")
video_db = faiss.read_index(fn)
fn = os.path.join(out_dir, "text_faiss_idx")
text_db = faiss.read_index(fn)
self.db = {"video": video_db, "text": text_db}
with open(
os.path.join(out_dir, "videoid_to_vectoridx.pkl"), "rb") as fr:
self.videoid_to_vectoridx = pickle.load(fr)
self.video_to_videoid = defaultdict(list)
def add(self, hidden_states, video_ids):
"""hidden_states is a pair `(video, text)`"""
assert len(hidden_states) == len(video_ids), "{}, {}".format(
str(len(hidden_states)), str(len(video_ids)))
assert len(hidden_states.shape) == 3
assert len(self.video_to_videoid) == 0
valid_idx = []
for idx, video_id in enumerate(video_ids):
if video_id not in self.videoid_to_vectoridx:
valid_idx.append(idx)
self.videoid_to_vectoridx[video_id] = \
len(self.videoid_to_vectoridx)
batch_size = hidden_states.shape[0]
hidden_states = hidden_states[valid_idx]
hidden_states = np.transpose(hidden_states, (1, 0, 2)).copy()
if not self.db["video"].is_trained:
self.train_cache.append(hidden_states)
train_len = batch_size * len(self.train_cache)
if train_len < self.train_thres:
return
hidden_states = np.concatenate(self.train_cache, axis=1)
del self.train_cache
self.db["video"].train(hidden_states[0, :self.train_thres])
self.db["text"].train(hidden_states[1, :self.train_thres])
self.db["video"].add(hidden_states[0])
self.db["text"].add(hidden_states[1])
def get_clips_by_video_id(self, video_id):
if not self.video_to_videoid:
for video_id, video_clip, text_clip in self.videoid_to_vectoridx:
self.video_to_videoid[video_id].append(
(video_id, video_clip, text_clip))
return self.video_to_videoid[video_id]
def search(
self,
video_ids,
target_modality,
retri_factor=8
):
if len(self.videoid_to_vectoridx) != len(self):
raise ValueError(
len(self.videoid_to_vectoridx),
len(self)
)
if not self.make_direct_maps_done:
self.make_direct_maps()
if self.vectoridx_to_videoid is None:
self.vectoridx_to_videoid = {
self.videoid_to_vectoridx[videoid]: videoid
for videoid in self.videoid_to_vectoridx
}
assert len(self.vectoridx_to_videoid) \
== len(self.videoid_to_vectoridx)
src_modality = "text" if target_modality == "video" else "video"
query_hidden_states = []
vector_ids = []
for video_id in video_ids:
vector_id = self.videoid_to_vectoridx[video_id]
vector_ids.append(vector_id)
query_hidden_state = self.db[src_modality].reconstruct(vector_id)
query_hidden_states.append(query_hidden_state)
query_hidden_states = np.stack(query_hidden_states)
# MultilingualFaissDataset uses the following; not sure the reason.
# faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
_, index = self.db[target_modality].search(
query_hidden_states, retri_factor)
outputs = []
for sample_idx, sample in enumerate(index):
cands = []
for vector_idx in sample:
if vector_idx >= 0:
cands.append(
self.vectoridx_to_videoid[vector_idx]
)
outputs.append(cands)
return outputs
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