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import os
import tarfile
from pathlib import Path
from typing import Optional
import faiss
import numpy as np
import pyarrow as pa
import requests
import torch
from tqdm import tqdm
from transformers import CLIPModel, CLIPProcessor
from transformers.modeling_utils import PreTrainedModel
from .configuration_cased import CaSEDConfig
from .transforms_cased import default_vocabulary_transforms
DATABASES = {
"cc12m": {
"url": "https://storage-cased.alessandroconti.me/cc12m.tar.gz",
"cache_subdir": "./cc12m/vit-l-14/",
},
}
class MetadataProvider:
"""Metadata provider.
It uses arrow files to store metadata and retrieve it efficiently.
Code reference:
- https://github.dev/rom1504/clip-retrieval
"""
def __init__(self, arrow_folder: Path):
arrow_files = [str(a) for a in sorted(arrow_folder.glob("**/*")) if a.is_file()]
self.table = pa.concat_tables(
[
pa.ipc.RecordBatchFileReader(pa.memory_map(arrow_file, "r")).read_all()
for arrow_file in arrow_files
]
)
def get(self, ids: np.ndarray, cols: Optional[list] = None):
"""Get arrow metadata from ids.
Args:
ids (np.ndarray): Ids to retrieve.
cols (Optional[list], optional): Columns to retrieve. Defaults to None.
"""
if cols is None:
cols = self.table.schema.names
else:
cols = list(set(self.table.schema.names) & set(cols))
t = pa.concat_tables([self.table[i:j] for i, j in zip(ids, ids + 1)])
return t.select(cols).to_pandas().to_dict("records")
class CaSEDModel(PreTrainedModel):
"""Transformers module for Category Search from External Databases (CaSED).
Reference:
- Conti et al. Vocabulary-free Image Classification. arXiv 2023.
Args:
config (CaSEDConfig): Configuration class for CaSED.
"""
config_class = CaSEDConfig
def __init__(self, config: CaSEDConfig):
super().__init__(config)
# load CLIP
model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
self.vision_encoder = model.vision_model
self.vision_proj = model.visual_projection
self.language_encoder = model.text_model
self.language_proj = model.text_projection
self.logit_scale = model.logit_scale.exp()
self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
# load transforms
self.vocabulary_transforms = default_vocabulary_transforms()
# set hparams
self.hparams = {}
self.hparams["alpha"] = config.alpha
self.hparams["index_name"] = config.index_name
self.hparams["retrieval_num_results"] = config.retrieval_num_results
# set cache dir
self.hparams["cache_dir"] = Path(os.path.expanduser("~/.cache/cased"))
os.makedirs(self.hparams["cache_dir"], exist_ok=True)
# download databases
self.prepare_data()
# load faiss indices and metadata providers
self.resources = {}
for name, items in DATABASES.items():
database_path = self.hparams["cache_dir"] / "databases" / items["cache_subdir"]
text_index_fp = database_path / "text.index"
metadata_fp = database_path / "metadata/"
text_index = faiss.read_index(
str(text_index_fp), faiss.IO_FLAG_MMAP | faiss.IO_FLAG_READ_ONLY
)
metadata_provider = MetadataProvider(metadata_fp)
self.resources[name] = {
"device": self.device,
"model": "ViT-L-14",
"text_index": text_index,
"metadata_provider": metadata_provider,
}
def prepare_data(self):
"""Download data if needed."""
databases_path = Path(self.hparams["cache_dir"]) / "databases"
for name, items in DATABASES.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")
os.makedirs(target_path.parent, 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()
@torch.no_grad()
def query_index(self, sample_z: torch.Tensor) -> torch.Tensor:
"""Query the external database index.
Args:
sample_z (torch.Tensor): Sample to query the index.
"""
# get the index
resources = self.resources[self.hparams["index_name"]]
text_index = resources["text_index"]
metadata_provider = resources["metadata_provider"]
# query the index
sample_z = sample_z.squeeze(0)
sample_z = sample_z / sample_z.norm(dim=-1, keepdim=True)
query_input = sample_z.cpu().detach().numpy().tolist()
query = np.expand_dims(np.array(query_input).astype("float32"), 0)
distances, idxs, _ = text_index.search_and_reconstruct(
query, self.hparams["retrieval_num_results"]
)
results = idxs[0]
nb_results = np.where(results == -1)[0]
nb_results = nb_results[0] if len(nb_results) > 0 else len(results)
indices = results[:nb_results]
distances = distances[0][:nb_results]
if len(distances) == 0:
return []
# get the metadata
results = []
metadata = metadata_provider.get(indices[:20], ["caption"])
for key, (d, i) in enumerate(zip(distances, indices)):
output = {}
meta = None if key + 1 > len(metadata) else metadata[key]
if meta is not None:
output.update(meta)
output["id"] = i.item()
output["similarity"] = d.item()
results.append(output)
# get the captions only
vocabularies = [result["caption"] for result in results]
return vocabularies
@torch.no_grad()
def forward(self, images: dict, alpha: Optional[float] = None) -> torch.Tensor():
"""Forward pass.
Args:
images (dict): Dictionary with the images. The expected keys are:
- pixel_values (torch.Tensor): Pixel values of the images.
alpha (Optional[float]): Alpha value for the interpolation.
"""
# forward the images
images["pixel_values"] = images["pixel_values"].to(self.device)
images_z = self.vision_proj(self.vision_encoder(**images)[1])
vocabularies, samples_p = [], []
for image_z in images_z:
image_z = image_z.unsqueeze(0)
# generate a single text embedding from the unfiltered vocabulary
vocabulary = self.query_index(image_z)
text = self.processor(text=vocabulary, return_tensors="pt", padding=True)
text["input_ids"] = text["input_ids"][:, :77].to(self.device)
text["attention_mask"] = text["attention_mask"][:, :77].to(self.device)
text_z = self.language_encoder(**text)[1]
text_z = self.language_proj(text_z)
text_z = text_z / text_z.norm(dim=-1, keepdim=True)
text_z = text_z.mean(dim=0).unsqueeze(0)
text_z = text_z / text_z.norm(dim=-1, keepdim=True)
# filter the vocabulary, embed it, and get its mean embedding
vocabulary = self.vocabulary_transforms(vocabulary) or ["object"]
text = self.processor(text=vocabulary, return_tensors="pt", padding=True)
text = {k: v.to(self.device) for k, v in text.items()}
vocabulary_z = self.language_encoder(**text)[1]
vocabulary_z = self.language_proj(vocabulary_z)
vocabulary_z = vocabulary_z / vocabulary_z.norm(dim=-1, keepdim=True)
# get the image and text predictions
image_z = image_z / image_z.norm(dim=-1, keepdim=True)
text_z = text_z / text_z.norm(dim=-1, keepdim=True)
image_p = (self.logit_scale * image_z @ vocabulary_z.T).softmax(dim=-1)
text_p = (self.logit_scale * text_z @ vocabulary_z.T).softmax(dim=-1)
# average the image and text predictions
alpha = alpha or self.hparams["alpha"]
sample_p = alpha * image_p + (1 - alpha) * text_p
# save the results
samples_p.append(sample_p)
vocabularies.append(vocabulary)
# get the scores
samples_p = torch.stack(samples_p, dim=0)
scores = sample_p.cpu()
# define the results
results = {"vocabularies": vocabularies, "scores": scores}
return results
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