Spaces:
Runtime error
Runtime error
File size: 12,349 Bytes
a3ee979 07a2d78 a3ee979 07a2d78 a3ee979 07a2d78 a3ee979 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 |
import json
import tarfile
from pathlib import Path
from typing import Optional
import faiss
import gdown
import numpy as np
import open_clip
import torch
from open_clip.transformer import Transformer
from PIL import Image
from src.retrieval import ArrowMetadataProvider
from src.transforms import TextCompose, default_vocabulary_transforms
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
RETRIEVAL_DATABASES = {
"cc12m": "https://drive.google.com/uc?id=1HyM4mnKSxF0sqzAe-KZL8y-cQWRPiuXn&confirm=t",
}
class CaSED(torch.nn.Module):
"""Torch module for Category Search from External Databases (CaSED).
Args:
index_name (str): Name of the faiss index to use.
vocabulary_transforms (TextCompose): List of transforms to apply to the vocabulary.
model_name (str): Name of the CLIP model to use. Defaults to "ViT-L-14".
pretrained (str): Pretrained weights to use for the CLIP model. Defaults to "openai".
Extra hparams:
alpha (float): Weight for the average of the image and text predictions. Defaults to 0.5.
artifact_dir (str): Path to the directory where the databases are stored. Defaults to
"artifacts/".
retrieval_num_results (int): Number of results to return. Defaults to 10.
vocabulary_prompt (str): Prompt to use for the vocabulary. Defaults to "{}".
tau (float): Temperature to use for the classifier. Defaults to 1.0.
"""
def __init__(
self,
index_name: str = "ViT-L-14_CC12M",
vocabulary_transforms: TextCompose = default_vocabulary_transforms(),
model_name: str = "ViT-L-14",
pretrained: str = "openai",
vocabulary_prompt: str = "{}",
**kwargs,
):
super().__init__()
self._prev_vocab_words = None
self._prev_used_prompts = None
self._prev_vocab_words_z = None
model, _, preprocess = open_clip.create_model_and_transforms(
model_name, pretrained=pretrained, device="cpu"
)
tokenizer = open_clip.get_tokenizer(model_name)
self.tokenizer = tokenizer
self.preprocess = preprocess
kwargs["alpha"] = kwargs.get("alpha", 0.5)
kwargs["artifact_dir"] = kwargs.get("artifact_dir", "artifacts/")
kwargs["retrieval_num_results"] = kwargs.get("retrieval_num_results", 10)
vocabulary_prompt = kwargs.get("vocabulary_prompt", "{}")
kwargs["vocabulary_prompts"] = [vocabulary_prompt]
kwargs["tau"] = kwargs.get("tau", 1.0)
self.hparams = kwargs
language_encoder = LanguageTransformer(
model.transformer,
model.token_embedding,
model.positional_embedding,
model.ln_final,
model.text_projection,
model.attn_mask,
)
scale = model.logit_scale.exp().item()
classifier = NearestNeighboursClassifier(scale=scale, tau=self.hparams["tau"])
self.index_name = index_name
self.vocabulary_transforms = vocabulary_transforms
self.vision_encoder = model.visual
self.language_encoder = language_encoder
self.classifier = classifier
# download databases
self.prepare_data()
# load faiss indices
indices_list_dir = Path(self.hparams["artifact_dir"]) / "models" / "retrieval"
indices_fp = indices_list_dir / "indices.json"
self.indices = json.load(open(indices_fp))
# load faiss indices and metadata providers
self.resources = {}
for name, index_fp in self.indices.items():
text_index_fp = Path(index_fp) / "text.index"
metadata_fp = Path(index_fp) / "metadata/"
text_index = faiss.read_index(
str(text_index_fp), faiss.IO_FLAG_MMAP | faiss.IO_FLAG_READ_ONLY
)
metadata_provider = ArrowMetadataProvider(metadata_fp)
self.resources[name] = {
"device": DEVICE,
"model": model_name,
"text_index": text_index,
"metadata_provider": metadata_provider,
}
def prepare_data(self):
"""Download data if needed."""
databases_path = Path(self.hparams["artifact_dir"]) / "models" / "databases"
for name, url in RETRIEVAL_DATABASES.items():
database_path = Path(databases_path, name)
if database_path.exists():
continue
# download data
target_path = Path(databases_path, name + ".tar.gz")
try:
gdown.download(url, str(target_path), quiet=False)
tar = tarfile.open(target_path, "r:gz")
tar.extractall(target_path.parent)
tar.close()
target_path.unlink()
except FileNotFoundError:
print(f"Could not download {url}.")
print(f"Please download it manually and place it in {target_path.parent}.")
@torch.no_grad()
def query_index(self, sample_z: torch.Tensor) -> torch.Tensor:
# get the index
resources = self.resources[self.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 encode_vocabulary(self, vocabulary: list, use_prompts: bool = False) -> torch.Tensor:
"""Encode a vocabulary.
Args:
vocabulary (list): List of words.
"""
# check if vocabulary has changed
if vocabulary == self._prev_vocab_words and use_prompts == self._prev_used_prompts:
return self._prev_vocab_words_z
# tokenize vocabulary
classes = [c.replace("_", " ") for c in vocabulary]
prompts = self.hparams["vocabulary_prompts"] if use_prompts else ["{}"]
texts_views = [[p.format(c) for c in classes] for p in prompts]
tokenized_texts_views = [
torch.cat([self.tokenizer(prompt) for prompt in class_prompts])
for class_prompts in texts_views
]
tokenized_texts_views = torch.stack(tokenized_texts_views).to(DEVICE)
# encode vocabulary
T, C, _ = tokenized_texts_views.shape
texts_z_views = self.language_encoder(tokenized_texts_views.view(T * C, -1))
texts_z_views = texts_z_views.view(T, C, -1)
texts_z_views = texts_z_views / texts_z_views.norm(dim=-1, keepdim=True)
# cache vocabulary
self._prev_vocab_words = vocabulary
self._prev_used_prompts = use_prompts
self._prev_vocab_words_z = texts_z_views
return texts_z_views
@torch.no_grad()
def forward(self, image_fp: str, alpha: Optional[float] = None) -> torch.Tensor():
image = self.preprocess(Image.open(image_fp)).unsqueeze(0)
image_z = self.vision_encoder(image.to(DEVICE))
# get the vocabulary
vocabulary = self.query_index(image_z)
# generate a single text embedding from the unfiltered vocabulary
unfiltered_vocabulary_z = self.encode_vocabulary(vocabulary).squeeze(0)
text_z = unfiltered_vocabulary_z.mean(dim=0)
text_z = text_z / text_z.norm(dim=-1, keepdim=True)
text_z = text_z.unsqueeze(0)
# filter the vocabulary, embed it, and get its mean embedding
vocabulary = self.vocabulary_transforms(vocabulary) or ["object"]
vocabulary_z = self.encode_vocabulary(vocabulary, use_prompts=True)
mean_vocabulary_z = vocabulary_z.mean(dim=0)
mean_vocabulary_z = mean_vocabulary_z / mean_vocabulary_z.norm(dim=-1, keepdim=True)
# get the image and text predictions
image_p = self.classifier(image_z, vocabulary_z)
text_p = self.classifier(text_z, vocabulary_z)
# average the image and text predictions
alpha = alpha or self.hparams["alpha"]
sample_p = alpha * image_p + (1 - alpha) * text_p
# get the scores
sample_p = sample_p.cpu()
scores = sample_p[0].tolist()
del image_z, unfiltered_vocabulary_z, text_z, vocabulary_z, mean_vocabulary_z
del image_p, text_p, sample_p
return vocabulary, scores
class NearestNeighboursClassifier(torch.nn.Module):
"""Nearest neighbours classifier.
It computes the similarity between the query and the supports using the
cosine similarity and then applies a softmax to obtain the logits.
Args:
scale (float): Scale for the logits of the query. Defaults to 1.0.
tau (float): Temperature for the softmax. Defaults to 1.0.
"""
def __init__(self, scale: float = 1.0, tau: float = 1.0):
super().__init__()
self.scale = scale
self.tau = tau
def forward(self, query: torch.Tensor, supports: torch.Tensor):
query = query / query.norm(dim=-1, keepdim=True)
supports = supports / supports.norm(dim=-1, keepdim=True)
if supports.dim() == 2:
supports = supports.unsqueeze(0)
Q, _ = query.shape
N, C, _ = supports.shape
supports = supports.mean(dim=0)
supports = supports / supports.norm(dim=-1, keepdim=True)
similarity = self.scale * query @ supports.T
similarity = similarity / self.tau if self.tau != 1.0 else similarity
logits = similarity.softmax(dim=-1)
return logits
class LanguageTransformer(torch.nn.Module):
"""Language Transformer for CLIP.
Args:
transformer (Transformer): Transformer model.
token_embedding (torch.nn.Embedding): Token embedding.
positional_embedding (torch.nn.Parameter): Positional embedding.
ln_final (torch.nn.LayerNorm): Layer norm.
text_projection (torch.nn.Parameter): Text projection.
"""
def __init__(
self,
model: Transformer,
token_embedding: torch.nn.Embedding,
positional_embedding: torch.nn.Parameter,
ln_final: torch.nn.LayerNorm,
text_projection: torch.nn.Parameter,
attn_mask: torch.Tensor,
):
super().__init__()
self.transformer = model
self.token_embedding = token_embedding
self.positional_embedding = positional_embedding
self.ln_final = ln_final
self.text_projection = text_projection
self.register_buffer("attn_mask", attn_mask, persistent=False)
def forward(self, text: torch.Tensor) -> torch.Tensor:
cast_dtype = self.transformer.get_cast_dtype()
"""Forward pass for the text encoder."""
x = self.token_embedding(text).to(cast_dtype)
x = x + self.positional_embedding.to(cast_dtype)
x = x.permute(1, 0, 2)
x = self.transformer(x, attn_mask=self.attn_mask)
x = x.permute(1, 0, 2)
x = self.ln_final(x)
# x.shape = [batch_size, n_ctx, transformer.width]
# take features from the eot embedding (eot_token is the highest number in each sequence)
x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
return x
|