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672 строки
25 KiB
Python
672 строки
25 KiB
Python
from ammico.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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import os
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import numpy as np
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from PIL import Image
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from skimage import transform as skimage_transform
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from scipy.ndimage import filters
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from matplotlib import pyplot as plt
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from IPython.display import display
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from lavis.models import load_model_and_preprocess, load_model, BlipBase
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from lavis.processors import load_processor
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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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multimodal_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def load_feature_extractor_model_blip2(self, device):
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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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return model, vis_processors, txt_processors
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def load_feature_extractor_model_blip(self, device):
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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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return model, vis_processors, txt_processors
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def load_feature_extractor_model_albef(self, device):
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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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return model, vis_processors, txt_processors
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def load_feature_extractor_model_clip_base(self, device):
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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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return model, vis_processors, txt_processors
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def load_feature_extractor_model_clip_vitl14(self, device):
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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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return model, vis_processors, txt_processors
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def load_feature_extractor_model_clip_vitl14_336(self, device):
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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-14-336",
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is_eval=True,
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device=device,
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)
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return model, vis_processors, txt_processors
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def read_img(self, 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(self, image_paths, vis_processor):
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raw_images = [MultimodalSearch.read_img(self, 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_blip2(self, model, images_tensors):
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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({"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 extract_image_features_clip(self, model, images_tensors):
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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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[Func.normalize(feat.float(), dim=-1).squeeze(0) for feat in features_image]
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)
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return features_image_stacked
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def extract_image_features_basic(self, model, images_tensors):
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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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self,
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model_type,
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features_image_stacked,
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name="saved_features_image.pt",
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path="./saved_tensors/",
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):
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if not os.path.exists(path):
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os.makedirs(path)
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with open(
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str(path)
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+ str(len(features_image_stacked))
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+ "_"
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+ model_type
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+ "_"
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+ name,
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"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(self, name):
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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(self, 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(
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self,
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model_type,
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path_to_saved_tensors="./saved_tensors/",
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path_to_load_tensors=None,
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):
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if model_type in ("clip_base", "clip_vitl14_336", "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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select_model = {
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"blip2": MultimodalSearch.load_feature_extractor_model_blip2,
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"blip": MultimodalSearch.load_feature_extractor_model_blip,
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"albef": MultimodalSearch.load_feature_extractor_model_albef,
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"clip_base": MultimodalSearch.load_feature_extractor_model_clip_base,
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"clip_vitl14": MultimodalSearch.load_feature_extractor_model_clip_vitl14,
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"clip_vitl14_336": MultimodalSearch.load_feature_extractor_model_clip_vitl14_336,
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}
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select_extract_image_features = {
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"blip2": MultimodalSearch.extract_image_features_blip2,
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"blip": MultimodalSearch.extract_image_features_basic,
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"albef": MultimodalSearch.extract_image_features_basic,
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"clip_base": MultimodalSearch.extract_image_features_clip,
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"clip_vitl14": MultimodalSearch.extract_image_features_clip,
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"clip_vitl14_336": MultimodalSearch.extract_image_features_clip,
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}
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if model_type in select_model.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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) = select_model[
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model_type
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](self, MultimodalSearch.multimodal_device)
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else:
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raise SyntaxError(
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"Please, use one of the following models: blip2, blip, albef, clip_base, clip_vitl14, clip_vitl14_336"
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)
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_, images_tensors = MultimodalSearch.read_and_process_images(
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self, image_names, vis_processors
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)
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if path_to_load_tensors is None:
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with torch.no_grad():
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features_image_stacked = select_extract_image_features[model_type](
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self, model, images_tensors
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)
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MultimodalSearch.save_tensors(
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self, model_type, features_image_stacked, path=path_to_saved_tensors
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)
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else:
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features_image_stacked = MultimodalSearch.load_tensors(
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self, str(path_to_load_tensors)
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)
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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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select_extract_image_features = {
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"blip2": MultimodalSearch.extract_image_features_blip2,
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"blip": MultimodalSearch.extract_image_features_basic,
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"albef": MultimodalSearch.extract_image_features_basic,
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"clip_base": MultimodalSearch.extract_image_features_clip,
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"clip_vitl14": MultimodalSearch.extract_image_features_clip,
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"clip_vitl14_336": MultimodalSearch.extract_image_features_clip,
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}
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for query in search_query:
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if len(query) != 1 and (query in ("image", "text_input")):
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raise SyntaxError(
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'Each query 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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self, [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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multi_features_query = []
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for query in multi_sample:
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if query["image"] == "":
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if model_type in ("clip_base", "clip_vitl14_336", "clip_vitl14"):
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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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else:
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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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select_extract_image_features[model_type](
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self, model, query["image"]
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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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filter_number_of_images=None,
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filter_val_limit=None,
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filter_rel_error=None,
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):
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if filter_number_of_images is None:
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filter_number_of_images = len(self)
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if filter_val_limit is None:
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filter_val_limit = 0
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if filter_rel_error is None:
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filter_rel_error = 1e10
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features_image_stacked.to(MultimodalSearch.multimodal_device)
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with torch.no_grad():
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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 = torch.argsort(similarity, dim=0, descending=True).T.tolist()
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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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max_val = similarity[sorted_lists[q][0]][q].item()
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for i, key in zip(range(len(image_keys)), sorted_lists[q]):
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if (
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i < filter_number_of_images
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and similarity[key][q].item() > filter_val_limit
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and 100 * abs(max_val - similarity[key][q].item()) / max_val
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< filter_rel_error
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):
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self[image_keys[key]][
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"rank " + list(search_query[q].values())[0]
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] = places[q][key]
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self[image_keys[key]][
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list(search_query[q].values())[0]
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] = similarity[key][q].item()
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else:
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self[image_keys[key]][
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"rank " + list(search_query[q].values())[0]
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] = None
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self[image_keys[key]][list(search_query[q].values())[0]] = 0
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return similarity, sorted_lists
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def itm_text_precessing(self, search_query):
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for query in search_query:
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if (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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text_query_index = []
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for i, query in zip(range(len(search_query)), search_query):
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if "text_input" in query.keys():
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text_query_index.append(i)
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return text_query_index
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def get_pathes_from_query(self, query):
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paths = []
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image_names = []
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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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if s[1]["rank " + list(query.values())[0]] is None:
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break
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paths.append(s[1]["filename"])
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image_names.append(s[0])
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return paths, image_names
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def read_and_process_images_itm(self, image_paths, vis_processor):
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raw_images = [MultimodalSearch.read_img(self, path) for path in image_paths]
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images = [vis_processor(r_img) for r_img in raw_images]
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images_tensors = torch.stack(images).to(MultimodalSearch.multimodal_device)
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return raw_images, images_tensors
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def compute_gradcam_batch(
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self,
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model,
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visual_input,
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text_input,
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tokenized_text,
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block_num=6,
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):
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model.text_encoder.base_model.base_model.encoder.layer[
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block_num
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].crossattention.self.save_attention = True
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output = model(
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{"image": visual_input, "text_input": text_input}, match_head="itm"
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)
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loss = output[:, 1].sum()
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model.zero_grad()
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loss.backward()
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with torch.no_grad():
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mask = tokenized_text.attention_mask.view(
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tokenized_text.attention_mask.size(0), 1, -1, 1, 1
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) # (bsz,1,token_len, 1,1)
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token_length = mask.sum() - 2
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token_length = token_length.cpu()
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# grads and cams [bsz, num_head, seq_len, image_patch]
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grads = model.text_encoder.base_model.base_model.encoder.layer[
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block_num
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].crossattention.self.get_attn_gradients()
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cams = model.text_encoder.base_model.base_model.encoder.layer[
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block_num
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].crossattention.self.get_attention_map()
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# assume using vit large with 576 num image patch
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cams = (
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cams[:, :, :, 1:].reshape(visual_input.size(0), 12, -1, 24, 24) * mask
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)
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grads = (
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grads[:, :, :, 1:]
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.clamp(0)
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.reshape(visual_input.size(0), 12, -1, 24, 24)
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* mask
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)
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gradcam = cams * grads
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# [enc token gradcam, average gradcam across token, gradcam for individual token]
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# gradcam = torch.cat((gradcam[0:1,:], gradcam[1:token_length+1, :].sum(dim=0, keepdim=True)/token_length, gradcam[1:, :]))
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gradcam = gradcam.mean(1).cpu().detach()
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gradcam = (
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gradcam[:, 1 : token_length + 1, :].sum(dim=1, keepdim=True)
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/ token_length
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)
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return gradcam, output
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def resize_img(self, raw_img):
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w, h = raw_img.size
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scaling_factor = 240 / w
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resized_image = raw_img.resize(
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(int(w * scaling_factor), int(h * scaling_factor))
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)
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return resized_image
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def get_att_map(self, img, att_map, blur=True, overlap=True):
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att_map -= att_map.min()
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if att_map.max() > 0:
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att_map /= att_map.max()
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att_map = skimage_transform.resize(
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att_map, (img.shape[:2]), order=3, mode="constant"
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)
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if blur:
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att_map = filters.gaussian_filter(att_map, 0.02 * max(img.shape[:2]))
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att_map -= att_map.min()
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att_map /= att_map.max()
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cmap = plt.get_cmap("jet")
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att_mapv = cmap(att_map)
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att_mapv = np.delete(att_mapv, 3, 2)
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if overlap:
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att_map = (
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1 * (1 - att_map**0.7).reshape(att_map.shape + (1,)) * img
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+ (att_map**0.7).reshape(att_map.shape + (1,)) * att_mapv
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)
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return att_map
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def upload_model_blip2_coco(self):
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itm_model = load_model(
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"blip2_image_text_matching",
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"coco",
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is_eval=True,
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device=MultimodalSearch.multimodal_device,
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)
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vis_processor = load_processor("blip_image_eval").build(image_size=364)
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return itm_model, vis_processor
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def upload_model_blip_base(self):
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itm_model = load_model(
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"blip_image_text_matching",
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"base",
|
|
is_eval=True,
|
|
device=MultimodalSearch.multimodal_device,
|
|
)
|
|
vis_processor = load_processor("blip_image_eval").build(image_size=384)
|
|
return itm_model, vis_processor
|
|
|
|
def upload_model_blip_large(self):
|
|
itm_model = load_model(
|
|
"blip_image_text_matching",
|
|
"large",
|
|
is_eval=True,
|
|
device=MultimodalSearch.multimodal_device,
|
|
)
|
|
vis_processor = load_processor("blip_image_eval").build(image_size=384)
|
|
return itm_model, vis_processor
|
|
|
|
def image_text_match_reordering(
|
|
self,
|
|
search_query,
|
|
itm_model_type,
|
|
image_keys,
|
|
sorted_lists,
|
|
batch_size=1,
|
|
need_grad_cam=False,
|
|
):
|
|
if itm_model_type == "blip2_coco" and need_grad_cam is True:
|
|
raise SyntaxError(
|
|
"The blip2_coco model does not yet work with gradcam. Please set need_grad_cam to False"
|
|
)
|
|
|
|
choose_model = {
|
|
"blip_base": MultimodalSearch.upload_model_blip_base,
|
|
"blip_large": MultimodalSearch.upload_model_blip_large,
|
|
"blip2_coco": MultimodalSearch.upload_model_blip2_coco,
|
|
}
|
|
|
|
itm_model, vis_processor_itm = choose_model[itm_model_type](self)
|
|
text_processor = load_processor("blip_caption")
|
|
tokenizer = BlipBase.init_tokenizer()
|
|
|
|
if itm_model_type == "blip2_coco":
|
|
need_grad_cam = False
|
|
|
|
text_query_index = MultimodalSearch.itm_text_precessing(self, search_query)
|
|
|
|
avg_gradcams = []
|
|
itm_scores = []
|
|
itm_scores2 = []
|
|
image_gradcam_with_itm = {}
|
|
|
|
for index_text_query in text_query_index:
|
|
query = search_query[index_text_query]
|
|
pathes, image_names = MultimodalSearch.get_pathes_from_query(self, query)
|
|
num_batches = int(len(pathes) / batch_size)
|
|
num_batches_residue = len(pathes) % batch_size
|
|
|
|
local_itm_scores = []
|
|
local_avg_gradcams = []
|
|
|
|
if num_batches_residue != 0:
|
|
num_batches = num_batches + 1
|
|
for i in range(num_batches):
|
|
filenames_in_batch = pathes[i * batch_size : (i + 1) * batch_size]
|
|
current_len = len(filenames_in_batch)
|
|
raw_images, images = MultimodalSearch.read_and_process_images_itm(
|
|
self, filenames_in_batch, vis_processor_itm
|
|
)
|
|
queries_batch = [text_processor(query["text_input"])] * current_len
|
|
queries_tok_batch = tokenizer(queries_batch, return_tensors="pt").to(
|
|
MultimodalSearch.multimodal_device
|
|
)
|
|
|
|
if need_grad_cam:
|
|
gradcam, itm_output = MultimodalSearch.compute_gradcam_batch(
|
|
self,
|
|
itm_model,
|
|
images,
|
|
queries_batch,
|
|
queries_tok_batch,
|
|
)
|
|
norm_imgs = [np.float32(r_img) / 255 for r_img in raw_images]
|
|
|
|
for norm_img, grad_cam in zip(norm_imgs, gradcam):
|
|
avg_gradcam = MultimodalSearch.get_att_map(
|
|
self, norm_img, np.float32(grad_cam[0]), blur=True
|
|
)
|
|
local_avg_gradcams.append(avg_gradcam)
|
|
|
|
else:
|
|
itm_output = itm_model(
|
|
{"image": images, "text_input": queries_batch}, match_head="itm"
|
|
)
|
|
|
|
with torch.no_grad():
|
|
itm_score = torch.nn.functional.softmax(itm_output, dim=1)
|
|
|
|
local_itm_scores.append(itm_score)
|
|
|
|
local_itm_scores2 = torch.cat(local_itm_scores)[:, 1]
|
|
if need_grad_cam:
|
|
localimage_gradcam_with_itm = {
|
|
n: i * 255 for n, i in zip(image_names, local_avg_gradcams)
|
|
}
|
|
else:
|
|
localimage_gradcam_with_itm = ""
|
|
image_names_with_itm = {
|
|
n: i.item() for n, i in zip(image_names, local_itm_scores2)
|
|
}
|
|
itm_rank = torch.argsort(local_itm_scores2, descending=True)
|
|
image_names_with_new_rank = {
|
|
image_names[i.item()]: rank
|
|
for i, rank in zip(itm_rank, range(len(itm_rank)))
|
|
}
|
|
for i, key in zip(range(len(image_keys)), sorted_lists[index_text_query]):
|
|
if image_keys[key] in image_names:
|
|
self[image_keys[key]][
|
|
"itm " + list(search_query[index_text_query].values())[0]
|
|
] = image_names_with_itm[image_keys[key]]
|
|
self[image_keys[key]][
|
|
"itm_rank " + list(search_query[index_text_query].values())[0]
|
|
] = image_names_with_new_rank[image_keys[key]]
|
|
else:
|
|
self[image_keys[key]][
|
|
"itm " + list(search_query[index_text_query].values())[0]
|
|
] = 0
|
|
self[image_keys[key]][
|
|
"itm_rank " + list(search_query[index_text_query].values())[0]
|
|
] = None
|
|
|
|
avg_gradcams.append(local_avg_gradcams)
|
|
itm_scores.append(local_itm_scores)
|
|
itm_scores2.append(local_itm_scores2)
|
|
image_gradcam_with_itm[
|
|
list(search_query[index_text_query].values())[0]
|
|
] = localimage_gradcam_with_itm
|
|
del (
|
|
itm_model,
|
|
vis_processor_itm,
|
|
text_processor,
|
|
raw_images,
|
|
images,
|
|
tokenizer,
|
|
queries_batch,
|
|
queries_tok_batch,
|
|
itm_score,
|
|
)
|
|
if need_grad_cam:
|
|
del itm_output, gradcam, norm_img, grad_cam, avg_gradcam
|
|
torch.cuda.empty_cache()
|
|
return itm_scores2, image_gradcam_with_itm
|
|
|
|
def show_results(self, query, itm=False, image_gradcam_with_itm=False):
|
|
if "image" in query.keys():
|
|
pic = Image.open(query["image"]).convert("RGB")
|
|
pic.thumbnail((400, 400))
|
|
display(
|
|
"Your search query: ",
|
|
pic,
|
|
"--------------------------------------------------",
|
|
"Results:",
|
|
)
|
|
elif "text_input" in query.keys():
|
|
display(
|
|
"Your search query: " + query["text_input"],
|
|
"--------------------------------------------------",
|
|
"Results:",
|
|
)
|
|
if itm:
|
|
current_querry_val = "itm " + list(query.values())[0]
|
|
current_querry_rank = "itm_rank " + list(query.values())[0]
|
|
else:
|
|
current_querry_val = list(query.values())[0]
|
|
current_querry_rank = "rank " + list(query.values())[0]
|
|
|
|
for s in sorted(
|
|
self.items(), key=lambda t: t[1][current_querry_val], reverse=True
|
|
):
|
|
if s[1][current_querry_rank] is None:
|
|
break
|
|
if image_gradcam_with_itm is False:
|
|
p1 = Image.open(s[1]["filename"]).convert("RGB")
|
|
else:
|
|
image = image_gradcam_with_itm[list(query.values())[0]][s[0]]
|
|
p1 = Image.fromarray(image.astype("uint8"), "RGB")
|
|
p1.thumbnail((400, 400))
|
|
display(
|
|
"Rank: "
|
|
+ str(s[1][current_querry_rank])
|
|
+ " Val: "
|
|
+ str(s[1][current_querry_val]),
|
|
s[0],
|
|
p1,
|
|
)
|
|
display(
|
|
"--------------------------------------------------",
|
|
)
|