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import tarfile
from collections import defaultdict
from pathlib import Path
import faiss
import numpy as np
import pyarrow as pa
import requests
from tqdm import tqdm
__all__ = ["RetrievalDatabase", "download_retrieval_databases"]
RETRIEVAL_DATABASES_URLS = {
"cc12m": {
"url": "https://storage-cased.alessandroconti.me/cc12m.tar.gz",
"cache_subdir": "./cc12m/vit-l-14/",
},
}
def download_retrieval_databases(cache_dir: str):
"""Download data if needed.
Args:
cache_dir (str): Path to cache directory. Defaults to "~/.cache/cased".
"""
databases_path = Path(cache_dir, "databases")
for name, items in RETRIEVAL_DATABASES_URLS.items():
url = items["url"]
database_path = Path(databases_path, name)
if database_path.exists():
continue
# download data
target_path = Path(databases_path, name + ".tar.gz")
target_path.parent.mkdir(parents=True, exist_ok=True)
with requests.get(url, stream=True) as r:
r.raise_for_status()
total_bytes_size = int(r.headers.get("content-length", 0))
chunk_size = 8192
p_bar = tqdm(
desc="Downloading cc12m index",
total=total_bytes_size,
unit="iB",
unit_scale=True,
)
with open(target_path, "wb") as f:
for chunk in r.iter_content(chunk_size=chunk_size):
f.write(chunk)
p_bar.update(len(chunk))
p_bar.close()
# extract data
tar = tarfile.open(target_path, "r:gz")
tar.extractall(target_path.parent)
tar.close()
target_path.unlink()
class RetrievalDatabaseMetadataProvider:
"""Metadata provider for the retrieval database.
Args:
metadata_dir (str): Path to the metadata directory.
"""
def __init__(self, metadata_dir: str):
metadatas = [str(a) for a in sorted(Path(metadata_dir).glob("**/*")) if a.is_file()]
self.table = pa.concat_tables(
[
pa.ipc.RecordBatchFileReader(pa.memory_map(metadata, "r")).read_all()
for metadata in metadatas
]
)
def get(self, ids):
"""Get the metadata for the given ids.
Args:
ids (list): List of ids.
"""
columns = self.table.schema.names
end_ids = [i + 1 for i in ids]
t = pa.concat_tables([self.table[start:end] for start, end in zip(ids, end_ids)])
return t.select(columns).to_pandas().to_dict("records")
class RetrievalDatabase:
"""Retrieval database.
Args:
database_name (str): Name of the database.
cache_dir (str): Path to cache directory. Defaults to "~/.cache/cased".
"""
def __init__(self, database_name: str, cache_dir: str):
assert database_name in RETRIEVAL_DATABASES_URLS.keys(), (
f"Database name should be one of "
f"{list(RETRIEVAL_DATABASES_URLS.keys())}, got {database_name}."
)
database_dir = Path(cache_dir) / "databases"
database_dir = database_dir / RETRIEVAL_DATABASES_URLS[database_name]["cache_subdir"]
self._database_dir = database_dir
image_index_fp = Path(database_dir) / "image.index"
text_index_fp = Path(database_dir) / "text.index"
image_index = (
faiss.read_index(str(image_index_fp), faiss.IO_FLAG_MMAP | faiss.IO_FLAG_READ_ONLY)
if image_index_fp.exists()
else None
)
text_index = (
faiss.read_index(str(text_index_fp), faiss.IO_FLAG_MMAP | faiss.IO_FLAG_READ_ONLY)
if text_index_fp.exists()
else None
)
metadata_dir = str(Path(database_dir) / "metadata")
metadata_provider = RetrievalDatabaseMetadataProvider(metadata_dir)
self._image_index = image_index
self._text_index = text_index
self._metadata_provider = metadata_provider
def _map_to_metadata(self, indices: list, distances: list, embs: list, num_images: int):
"""Map the indices to metadata.
Args:
indices (list): List of indices.
distances (list): List of distances.
embs (list): List of results embeddings.
num_images (int): Number of images.
"""
results = []
metas = self._metadata_provider.get(indices[:num_images])
for key, (d, i, emb) in enumerate(zip(distances, indices, embs)):
output = {}
meta = None if key + 1 > len(metas) else metas[key]
if meta is not None:
output.update(self._meta_to_dict(meta))
output["id"] = i.item()
output["similarity"] = d.item()
output["sample_z"] = emb.tolist()
results.append(output)
return results
def _meta_to_dict(self, metadata):
"""Convert metadata to dict.
Args:
metadata (dict): Metadata.
"""
output = {}
for k, v in metadata.items():
if isinstance(v, bytes):
v = v.decode()
elif type(v).__module__ == np.__name__:
v = v.item()
output[k] = v
return output
def _get_connected_components(self, neighbors):
"""Find connected components in a graph.
Args:
neighbors (dict): Dictionary of neighbors.
"""
seen = set()
def component(node):
r = []
nodes = {node}
while nodes:
node = nodes.pop()
seen.add(node)
nodes |= set(neighbors[node]) - seen
r.append(node)
return r
u = []
for node in neighbors:
if node not in seen:
u.append(component(node))
return u
def _deduplicate_embeddings(self, embeddings, threshold=0.94):
"""Deduplicate embeddings.
Args:
embeddings (np.matrix): Embeddings to deduplicate.
threshold (float): Threshold to use for deduplication. Default is 0.94.
"""
index = faiss.IndexFlatIP(embeddings.shape[1])
index.add(embeddings)
l, _, indices = index.range_search(embeddings, threshold)
same_mapping = defaultdict(list)
for i in range(embeddings.shape[0]):
start = l[i]
end = l[i + 1]
for j in indices[start:end]:
same_mapping[int(i)].append(int(j))
groups = self._get_connected_components(same_mapping)
non_uniques = set()
for g in groups:
for e in g[1:]:
non_uniques.add(e)
return set(list(non_uniques))
def query(
self, query: np.matrix, modality: str = "text", num_samples: int = 10
) -> list[list[dict]]:
"""Query the database.
Args:
query (np.matrix): Query to search.
modality (str): Modality to search. One of `image` or `text`. Default to `text`.
num_samples (int): Number of samples to return. Default is 40.
"""
index = self._image_index if modality == "image" else self._text_index
distances, indices, embeddings = index.search_and_reconstruct(query, num_samples)
results = [indices[i] for i in range(len(indices))]
nb_results = [np.where(r == -1)[0] for r in results]
total_distances = []
total_indices = []
total_embeddings = []
for i in range(len(results)):
num_res = nb_results[i][0] if len(nb_results[i]) > 0 else len(results[i])
result_indices = results[i][:num_res]
result_distances = distances[i][:num_res]
result_embeddings = embeddings[i][:num_res]
# normalise embeddings
l2 = np.atleast_1d(np.linalg.norm(result_embeddings, 2, -1))
l2[l2 == 0] = 1
result_embeddings = result_embeddings / np.expand_dims(l2, -1)
# deduplicate embeddings
local_indices_to_remove = self._deduplicate_embeddings(result_embeddings)
indices_to_remove = set()
for local_index in local_indices_to_remove:
indices_to_remove.add(result_indices[local_index])
curr_indices = []
curr_distances = []
curr_embeddings = []
for ind, dis, emb in zip(result_indices, result_distances, result_embeddings):
if ind not in indices_to_remove:
indices_to_remove.add(ind)
curr_indices.append(ind)
curr_distances.append(dis)
curr_embeddings.append(emb)
total_indices.append(curr_indices)
total_distances.append(curr_distances)
total_embeddings.append(curr_embeddings)
if len(total_distances) == 0:
return []
total_results = []
for i in range(len(total_distances)):
results = self._map_to_metadata(
total_indices[i], total_distances[i], total_embeddings[i], num_samples
)
total_results.append(results)
return total_results
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