Delete model_loading.py

model_loading.py

@@ -1,59 +0,0 @@
-
-import os 
-import numpy as np
-import pickle
-import torch
-import transformers
-import torch.nn.functional as F
-from PIL import Image
-from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8
-import json 
-import pickle
-import gradio as gr
-
-
-# XLM model functions 
-from multilingual_clip import pt_multilingual_clip
-import transformers
-
-
-# Our model definition 
-
-class MultilingualClipEdited(torch.nn.Module):
-    def __init__(self, model_name, tokenizer_name, head_name, weights_dir='data/weights/', cache_dir=None,in_features=None,out_features=None):
-        super().__init__()
-        self.model_name = model_name
-        self.tokenizer_name = tokenizer_name
-        self.head_path = weights_dir + head_name
-
-        self.tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer_name, cache_dir=cache_dir)
-        self.transformer = transformers.AutoModel.from_pretrained(model_name, cache_dir=cache_dir)
-        self.clip_head = torch.nn.Linear(in_features=in_features, out_features=out_features)
-        self._load_head()
-
-    def forward(self, txt):
-        txt_tok = self.tokenizer(txt, padding=True, return_tensors='pt')
-        embs = self.transformer(**txt_tok)[0]
-        att = txt_tok['attention_mask']
-        embs = (embs * att.unsqueeze(2)).sum(dim=1) / att.sum(dim=1)[:, None]
-        return self.clip_head(embs)
-
-    def _load_head(self):
-        with open(self.head_path, 'rb') as f:
-            lin_weights = pickle.loads(f.read())
-        self.clip_head.weight = torch.nn.Parameter(torch.tensor(lin_weights[0]).float().t())
-        self.clip_head.bias = torch.nn.Parameter(torch.tensor(lin_weights[1]).float())
-
-AVAILABLE_MODELS = {
-    'bert-base-arabertv2-ViT-B-16-SigLIP-512-epoch-155-trained-2M':{
-    'model_name': 'Arabic-Clip/bert-base-arabertv2-ViT-B-16-SigLIP-512-epoch-155-trained-2M',
-    'tokenizer_name': 'Arabic-Clip/bert-base-arabertv2-ViT-B-16-SigLIP-512-epoch-155-trained-2M',
-    'head_name': 'arabertv2-vit-B-16-siglibheads_of_the_model_arabertv2-ViT-B-16-SigLIP-512-155_.pickle'
-    },
-
-}
-
-
-def load_model(name, cache_dir=None,in_features=None,out_features=None):
-    config = AVAILABLE_MODELS[name]
-    return MultilingualClipEdited(**config, cache_dir=cache_dir, in_features= in_features, out_features=out_features)