Add get_logits method and NLLB tokenizer (#756)

* Get logits method and set_language for tokenizer.

src/open_clip/factory.py

@@ -20,7 +20,6 @@
 from .transform import image_transform_v2, AugmentationCfg, PreprocessCfg, merge_preprocess_dict, merge_preprocess_kwargs
 from .tokenizer import HFTokenizer, SimpleTokenizer, DEFAULT_CONTEXT_LENGTH
 
-
 HF_HUB_PREFIX = 'hf-hub:'
 _MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
 _MODEL_CONFIGS = {}  # directory (model_name: config) of model architecture configs

src/open_clip/model.py

@@ -285,6 +285,15 @@ def encode_text(self, text, normalize: bool = False):
 
         return F.normalize(x, dim=-1) if normalize else x
 
+    def get_logits(self, image, text):
+        image_features = self.encode_image(image, normalize=True)
+        text_features = self.encode_text(text, normalize=True)
+        image_logits = self.logit_scale.exp() * image_features @ text_features.T
+        if self.logit_bias is not None:
+            image_logits += self.logit_bias
+        text_logits = image_logits.T
+        return image_logits, text_logits
+
     def forward(
             self,
             image: Optional[torch.Tensor] = None,
@@ -354,6 +363,15 @@ def encode_text(self, text, normalize: bool = False):
         features = self.text(text)
         return F.normalize(features, dim=-1) if normalize else features
 
+    def get_logits(self, image, text):
+        image_features = self.encode_image(image, normalize=True)
+        text_features = self.encode_text(text, normalize=True)
+        image_logits = self.logit_scale.exp() * image_features @ text_features.T
+        if self.logit_bias is not None:
+            image_logits += self.logit_bias
+        text_logits = image_logits.T
+        return image_logits, text_logits
+
     def forward(
             self,
             image: Optional[torch.Tensor] = None,
@@ -603,4 +621,4 @@ def get_model_tokenize_cfg(model):
     vocab_size = getattr(module, 'vocab_size', None)
     if vocab_size is not None:
         cfg['vocab_size'] = vocab_size
-    return cfg
+    return cfg

src/open_clip/tokenizer.py

@@ -9,6 +9,7 @@
 import string
 from functools import lru_cache, partial
 from typing import Callable, List, Optional, Union
+import warnings
 
 import ftfy
 import numpy as np
@@ -402,9 +403,15 @@ def __init__(
             context_length: Optional[int] = DEFAULT_CONTEXT_LENGTH,
             clean: str = 'whitespace',
             strip_sep_token: bool = False,
+            language: Optional[str] = None,
     ):
         from transformers import AutoTokenizer
         self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+        set_lang_fn = getattr(self.tokenizer, 'set_src_lang_special_tokens', None)
+        if callable(set_lang_fn):
+            self.set_lang_fn = set_lang_fn
+        if language is not None:
+            self.set_language(language)
         self.context_length = context_length
         self.clean_fn = get_clean_fn(clean)
         self.strip_sep_token = strip_sep_token
@@ -438,6 +445,12 @@ def __call__(self, texts: Union[str, List[str]], context_length: Optional[int] =
             )
 
         return input_ids
+
+    def set_language(self, src_lang):
+        if hasattr(self, 'set_lang_fn'):
+            self.set_lang_fn(src_lang)
+        else:
+            warnings.warn('Cannot set language for the tokenizer.')
 
 
 class SigLipTokenizer: