Delete utils.py

utils.py

@@ -1,200 +0,0 @@
-
-import os 
-import numpy as np
-import pickle
-import torch
-import transformers
-from PIL import Image
-from open_clip import create_model_from_pretrained, create_model_and_transforms
-import json 
-
-# XLM model functions 
-from multilingual_clip import pt_multilingual_clip
-
-from model_loading import load_model
-
-
-
-class CustomDataSet(torch.utils.data.Dataset):
-    def __init__(self, main_dir, compose, image_name_list):
-        self.main_dir = main_dir
-        self.transform = compose
-        self.total_imgs = image_name_list
-
-    def __len__(self):
-        return len(self.total_imgs)
-
-    def get_image_name(self, idx):
-
-        return self.total_imgs[idx]
-
-    def __getitem__(self, idx):
-        img_loc = os.path.join(self.main_dir, self.total_imgs[idx])
-        image = Image.open(img_loc)
-
-        return self.transform(image)
-
-
-def features_pickle(file_path=None):
-
-    with open(file_path, 'rb') as handle:
-        features_pickle = pickle.load(handle)
-
-    return features_pickle
-
-
-def dataset_loading():
-
-    with open("/home/think3/Desktop/2. tf_testing_araclip/XTD_dataset/en_ar_XTD10_edited_v2.jsonl") as filino:
-
-
-        data = [json.loads(file_i) for file_i in filino]
-
-    sorted_data = sorted(data, key=lambda x: x['id'])
-
-    image_name_list = [lin["image_name"] for lin in sorted_data]
-
-
-    return sorted_data, image_name_list 
-
-
-def text_encoder(language_model, text):
-    """Normalize the text embeddings"""
-    embedding = language_model(text)
-    norm_embedding = embedding / np.linalg.norm(embedding)
-
-    return embedding, norm_embedding
-
-
-def compare_embeddings(logit_scale, img_embs, txt_embs):
-  
-  image_features = img_embs / img_embs.norm(dim=-1, keepdim=True)
-
-  text_features = txt_embs / txt_embs.norm(dim=-1, keepdim=True)
-
-  logits_per_text = logit_scale * text_features @ image_features.t()
-
-  return logits_per_text
-
-# Done 
-def compare_embeddings_text(full_text_embds, txt_embs):
-  
-  full_text_embds_features = full_text_embds / full_text_embds.norm(dim=-1, keepdim=True)
-
-  text_features = txt_embs / txt_embs.norm(dim=-1, keepdim=True)
-
-  logits_per_text_full = text_features @ full_text_embds_features.t()
-
-  return logits_per_text_full
-
-
-
-def find_image(language_model,clip_model, text_query, dataset, image_features, text_features_new,sorted_data, num=1):
-
-    embedding, _  = text_encoder(language_model, text_query)
-
-    logit_scale = clip_model.logit_scale.exp().float().to('cpu')
-
-    language_logits, text_logits = {}, {}
-
-    language_logits["Arabic"] = compare_embeddings(logit_scale, torch.from_numpy(image_features), torch.from_numpy(embedding))
-
-    text_logits["Arabic_text"] = compare_embeddings_text(torch.from_numpy(text_features_new), torch.from_numpy(embedding))
-
-    
-    for _, txt_logits in language_logits.items():
-
-        probs = txt_logits.softmax(dim=-1).cpu().detach().numpy().T
-
-        file_paths = []
-        labels, json_data = {}, {}
-
-        for i in range(1, num+1):
-            idx = np.argsort(probs, axis=0)[-i, 0]
-            path = 'photos/XTD10_dataset/' + dataset.get_image_name(idx)
-                    
-            path_l = (path,f"{sorted_data[idx]['caption_ar']}")
-
-            labels[f" Image # {i}"] = probs[idx]
-            json_data[f" Image # {i}"] = sorted_data[idx]
-
-            file_paths.append(path_l)
-
-
-    json_text = {} 
-
-    for _, txt_logits_full in text_logits.items():
-
-        probs_text = txt_logits_full.softmax(dim=-1).cpu().detach().numpy().T
-
-        for j in range(1, num+1):
-
-            idx = np.argsort(probs_text, axis=0)[-j, 0]
-            json_text[f" Text # {j}"] = sorted_data[idx]
-
-    return file_paths, labels, json_data, json_text
-
-
-
-class AraClip():
-    def __init__(self):
-
-        self.text_model = load_model('bert-base-arabertv2-ViT-B-16-SigLIP-512-epoch-155-trained-2M', in_features= 768, out_features=768)
-        self.language_model = lambda queries: np.asarray(self.text_model(queries).detach().to('cpu')) 
-        self.clip_model, self.compose = create_model_from_pretrained('hf-hub:timm/ViT-B-16-SigLIP-512')
-        self.sorted_data, self.image_name_list = dataset_loading()
-
-    def load_images(self):
-        # Return the features of the text and images
-        image_features_new = features_pickle('testing_pickle_files_images_text/image_features_XTD_1000_images_arabert_siglib_best_model.pickle')
-        return image_features_new
-    
-    def load_text(self):
-        text_features_new = features_pickle('testing_pickle_files_images_text/text_features_XTD_1000_images_arabert_siglib_best_model.pickle')
-        return text_features_new
-    
-    def load_dataset(self):
-        dataset = CustomDataSet("photos/XTD10_dataset", self.compose, self.image_name_list)
-        return dataset
-
-
-araclip = AraClip()
-
-def predict(text, num):
-
-    image_paths, labels, json_data, json_text = find_image(araclip.language_model,araclip.clip_model, text, araclip.load_dataset(), araclip.load_images() , araclip.load_text(), araclip.sorted_data, num=int(num))
-
-    return image_paths, labels, json_data, json_text
-
-
-class Mclip():
-    def __init__(self) -> None:
-
-    
-        self.tokenizer_mclip = transformers.AutoTokenizer.from_pretrained('M-CLIP/XLM-Roberta-Large-Vit-B-16Plus')
-        self.text_model_mclip = pt_multilingual_clip.MultilingualCLIP.from_pretrained('M-CLIP/XLM-Roberta-Large-Vit-B-16Plus')
-        self.language_model_mclip = lambda queries:  np.asarray(self.text_model_mclip.forward(queries, self.tokenizer_mclip).detach().to('cpu'))  
-        self.clip_model_mclip, _, self.compose_mclip = create_model_and_transforms('ViT-B-16-plus-240', pretrained="laion400m_e32")
-        self.sorted_data, self.image_name_list = dataset_loading()
-
-    def load_images(self):
-        # Return the features of the text and images
-        image_features_mclip = features_pickle('Cach_embeddings/image_features_XTD_1000_images_XLM_Roberta_Large_Vit_B_16Plus_ar.pickle')
-        return image_features_mclip
-    
-    def load_text(self):
-        text_features_new_mclip = features_pickle('Cach_embeddings/text_features_XTD_1000_images_XLM_Roberta_Large_Vit_B_16Plus_ar.pickle')
-        return text_features_new_mclip
-    
-    def load_dataset(self):
-        dataset_mclip = CustomDataSet("photos/XTD10_dataset", self.compose_mclip, self.image_name_list)
-        return dataset_mclip
-
-
-mclip = Mclip()
-
-def predict_mclip(text, num):
-
-    image_paths, labels, json_data, json_text = find_image(mclip.language_model_mclip,mclip.clip_model_mclip, text, mclip.load_dataset() , mclip.load_text() , mclip.load_text() , mclip.sorted_data , num=int(num))
-
-    return image_paths, labels, json_data, json_text