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synced 2025-10-30 21:46:04 +02:00
300 строки
11 KiB
Python
300 строки
11 KiB
Python
from misinformation.utils import AnalysisMethod
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import torch
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import torch.nn.functional as Func
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import requests
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import lavis
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from PIL import Image
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from IPython.display import display
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from lavis.models import load_model_and_preprocess
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class MultimodalSearch(AnalysisMethod):
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def __init__(self, subdict: dict) -> None:
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super().__init__(subdict)
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# self.subdict.update(self.set_keys())
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multimodal_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def load_feature_extractor_model(device, model_type):
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if model_type == "blip2":
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model, vis_processors, txt_processors = load_model_and_preprocess(
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name="blip2_feature_extractor",
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model_type="pretrain",
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is_eval=True,
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device=device,
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)
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elif model_type == "blip":
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model, vis_processors, txt_processors = load_model_and_preprocess(
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name="blip_feature_extractor",
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model_type="base",
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is_eval=True,
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device=device,
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)
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elif model_type == "albef":
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model, vis_processors, txt_processors = load_model_and_preprocess(
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name="albef_feature_extractor",
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model_type="base",
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is_eval=True,
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device=device,
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)
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elif model_type == "clip_base":
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model, vis_processors, txt_processors = load_model_and_preprocess(
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name="clip_feature_extractor",
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model_type="base",
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is_eval=True,
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device=device,
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)
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elif model_type == "clip_rn50":
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model, vis_processors, txt_processors = load_model_and_preprocess(
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name="clip_feature_extractor",
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model_type="RN50",
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is_eval=True,
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device=device,
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)
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elif model_type == "clip_vitl14":
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model, vis_processors, txt_processors = load_model_and_preprocess(
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name="clip_feature_extractor",
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model_type="ViT-L-14",
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is_eval=True,
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device=device,
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)
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else:
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print(
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"Please, use one of the following models: blip2, blip, albef, clip_base, clip_rn50, clip_vitl14"
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)
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return model, vis_processors, txt_processors
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def read_img(filepath):
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raw_image = Image.open(filepath).convert("RGB")
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return raw_image
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def read_and_process_images(image_paths, vis_processor):
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raw_images = [MultimodalSearch.read_img(path) for path in image_paths]
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images = [
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vis_processor["eval"](r_img)
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.unsqueeze(0)
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.to(MultimodalSearch.multimodal_device)
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for r_img in raw_images
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]
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images_tensors = torch.stack(images)
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return raw_images, images_tensors
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def extract_image_features(model, images_tensors, model_type):
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if model_type == "blip2":
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with torch.cuda.amp.autocast(
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enabled=(MultimodalSearch.multimodal_device != torch.device("cpu"))
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):
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features_image = [
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model.extract_features(
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{"image": ten, "text_input": ""}, mode="image"
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)
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for ten in images_tensors
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]
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features_image_stacked = torch.stack(
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[
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feat.image_embeds_proj[:, 0, :].squeeze(0)
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for feat in features_image
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]
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)
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elif model_type in ("clip_base", "clip_rn50", "clip_vitl14"):
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features_image = [
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model.extract_features({"image": ten}) for ten in images_tensors
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]
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features_image_stacked = torch.stack(
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[
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Func.normalize(feat.float(), dim=-1).squeeze(0)
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for feat in features_image
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]
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)
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else:
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features_image = [
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model.extract_features({"image": ten, "text_input": ""}, mode="image")
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for ten in images_tensors
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]
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features_image_stacked = torch.stack(
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[feat.image_embeds_proj[:, 0, :].squeeze(0) for feat in features_image]
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)
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return features_image_stacked
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def save_tensors(
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model_type, features_image_stacked, name="saved_features_image.pt"
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):
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with open(
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str(len(features_image_stacked)) + "_" + model_type + "_" + name, "wb"
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) as f:
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torch.save(features_image_stacked, f)
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return name
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def load_tensors(name="saved_features_image.pt"):
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features_image_stacked = torch.load(name)
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return features_image_stacked
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def extract_text_features(model, text_input):
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sample_text = {"text_input": [text_input]}
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features_text = model.extract_features(sample_text, mode="text")
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return features_text
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def parsing_images(self, model_type):
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if model_type in ("clip_base", "clip_rn50", "clip_vitl14"):
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path_to_lib = lavis.__file__[:-11] + "models/clip_models/"
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url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/lavis/models/clip_models/bpe_simple_vocab_16e6.txt.gz"
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r = requests.get(url, allow_redirects=False)
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open(path_to_lib + "bpe_simple_vocab_16e6.txt.gz", "wb").write(r.content)
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image_keys = sorted(self.keys())
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image_names = [self[k]["filename"] for k in image_keys]
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(
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model,
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vis_processors,
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txt_processors,
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) = MultimodalSearch.load_feature_extractor_model(
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MultimodalSearch.multimodal_device, model_type
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)
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raw_images, images_tensors = MultimodalSearch.read_and_process_images(
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image_names, vis_processors
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)
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features_image_stacked = MultimodalSearch.extract_image_features(
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model, images_tensors, model_type
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)
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MultimodalSearch.save_tensors(model_type, features_image_stacked)
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return (
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model,
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vis_processors,
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txt_processors,
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image_keys,
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image_names,
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features_image_stacked,
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)
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def querys_processing(
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self, search_query, model, txt_processors, vis_processors, model_type
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):
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for query in search_query:
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if not (len(query) == 1) and (query in ("image", "text_input")):
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raise SyntaxError(
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'Each querry must contain either an "image" or a "text_input"'
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)
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multi_sample = []
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for query in search_query:
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if "text_input" in query.keys():
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text_processing = txt_processors["eval"](query["text_input"])
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images_tensors = ""
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elif "image" in query.keys():
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_, images_tensors = MultimodalSearch.read_and_process_images(
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[query["image"]], vis_processors
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)
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text_processing = ""
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multi_sample.append(
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{"image": images_tensors, "text_input": text_processing}
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)
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if model_type in ("clip_base", "clip_rn50", "clip_vitl14"):
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multi_features_query = []
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for query in multi_sample:
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if query["image"] == "":
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features = model.extract_features(
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{"text_input": query["text_input"]}
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)
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features_squeeze = features.squeeze(0).to(
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MultimodalSearch.multimodal_device
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)
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multi_features_query.append(
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Func.normalize(features_squeeze, dim=-1)
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)
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if query["text_input"] == "":
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multi_features_query.append(
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MultimodalSearch.extract_image_features(
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model, query["image"], model_type
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)
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)
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else:
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multi_features_query = []
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for query in multi_sample:
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if query["image"] == "":
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features = model.extract_features(query, mode="text")
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features_squeeze = (
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features.text_embeds_proj[:, 0, :]
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.squeeze(0)
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.to(MultimodalSearch.multimodal_device)
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)
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multi_features_query.append(features_squeeze)
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if query["text_input"] == "":
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multi_features_query.append(
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MultimodalSearch.extract_image_features(
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model, query["image"], model_type
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)
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)
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multi_features_stacked = torch.stack(
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[query.squeeze(0) for query in multi_features_query]
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).to(MultimodalSearch.multimodal_device)
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return multi_features_stacked
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def multimodal_search(
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self,
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model,
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vis_processors,
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txt_processors,
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model_type,
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image_keys,
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features_image_stacked,
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search_query,
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):
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features_image_stacked.to(MultimodalSearch.multimodal_device)
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multi_features_stacked = MultimodalSearch.querys_processing(
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self, search_query, model, txt_processors, vis_processors, model_type
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)
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similarity = features_image_stacked @ multi_features_stacked.t()
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sorted_lists = [
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sorted(range(len(similarity)), key=lambda k: similarity[k, i], reverse=True)
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for i in range(len(similarity[0]))
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]
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places = [[item.index(i) for i in range(len(item))] for item in sorted_lists]
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for q in range(len(search_query)):
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for i, key in zip(range(len(image_keys)), image_keys):
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self[key]["rank " + list(search_query[q].values())[0]] = places[q][i]
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self[key][list(search_query[q].values())[0]] = similarity[i][q].item()
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return similarity
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def show_results(self, query):
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if "image" in query.keys():
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pic = Image.open(query["image"]).convert("RGB")
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pic.thumbnail((400, 400))
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display(
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"Your search query: ",
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pic,
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"--------------------------------------------------",
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"Results:",
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)
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elif "text_input" in query.keys():
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display(
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"Your search query: " + query["text_input"],
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"--------------------------------------------------",
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"Results:",
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)
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for s in sorted(
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self.items(), key=lambda t: t[1][list(query.values())[0]], reverse=True
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):
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p1 = Image.open(s[1]["filename"]).convert("RGB")
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p1.thumbnail((400, 400))
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display(
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p1,
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"Rank: "
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+ str(s[1]["rank " + list(query.values())[0]])
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+ " Val: "
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+ str(s[1][list(query.values())[0]]),
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)
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