| import pickle | |
| import torch | |
| import transformers | |
| class MultilingualClip(torch.nn.Module): | |
| def __init__(self, model_name, tokenizer_name, head_name, weights_dir='data/weights/', cache_dir=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=768, out_features=640) | |
| 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 = { | |
| 'M-BERT-Distil-40': { | |
| 'model_name': 'M-CLIP/M-BERT-Distil-40', | |
| 'tokenizer_name': 'M-CLIP/M-BERT-Distil-40', | |
| 'head_name': 'M-BERT Distil 40 Linear Weights.pkl' | |
| }, | |
| 'M-BERT-Base-69': { | |
| 'model_name': 'M-CLIP/M-BERT-Base-69', | |
| 'tokenizer_name': 'M-CLIP/M-BERT-Base-69', | |
| 'head_name': 'M-BERT-Base-69 Linear Weights.pkl' | |
| }, | |
| 'Swe-CLIP-500k': { | |
| 'model_name': 'M-CLIP/Swedish-500k', | |
| 'tokenizer_name': 'M-CLIP/Swedish-500k', | |
| 'head_name': 'Swedish-500k Linear Weights.pkl' | |
| }, | |
| 'Swe-CLIP-2M': { | |
| 'model_name': 'M-CLIP/Swedish-2M', | |
| 'tokenizer_name': 'M-CLIP/Swedish-2M', | |
| 'head_name': 'Swedish-2M Linear Weights.pkl' | |
| }, | |
| 'M-BERT-Base-ViT-B': { | |
| 'model_name': 'M-CLIP/M-BERT-Base-ViT-B', | |
| 'tokenizer_name': 'M-CLIP/M-BERT-Base-ViT-B', | |
| 'head_name': 'M-BERT-Base-69-ViT Linear Weights.pkl' | |
| }, | |
| } | |
| def load_model(name, cache_dir=None): | |
| config = AVAILABLE_MODELS[name] | |
| return MultilingualClip(**config, cache_dir=cache_dir) | |