057: Code Refactoring - Siamese Architectures

2 files changed, 105 insertions, 99 deletions

diff --git a/examples/sentence_level/wmt_2020/ro_en/siamesetransquest.py b/examples/sentence_level/wmt_2020/ro_en/siamesetransquest.py
index 74400fe..480f75b 100755
--- a/examples/sentence_level/wmt_2020/ro_en/siamesetransquest.py
+++ b/examples/sentence_level/wmt_2020/ro_en/siamesetransquest.py
@@ -80,15 +80,15 @@ if siamesetransquest_config["evaluate_during_training"]:
 
             train_df, eval_df = train_test_split(train, test_size=0.1, random_state=SEED * i)
 
-            word_embedding_model = models.Transformer(MODEL_NAME, max_seq_length=siamesetransquest_config[
-                'max_seq_length'])
-
-            pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
-                                    pooling_mode_mean_tokens=True,
-                                    pooling_mode_cls_token=False,
-                                    pooling_mode_max_tokens=False)
-
-            model = SiameseTransQuestModel(modules=[word_embedding_model, pooling_model])
+            # word_embedding_model = models.Transformer(MODEL_NAME, max_seq_length=siamesetransquest_config[
+            #     'max_seq_length'])
+            #
+            # pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
+            #                         pooling_mode_mean_tokens=True,
+            #                         pooling_mode_cls_token=False,
+            #                         pooling_mode_max_tokens=False)
+
+            model = SiameseTransQuestModel(MODEL_NAME)
 
             train_samples = []
             eval_samples = []
diff --git a/transquest/algo/sentence_level/siamesetransquest/run_model.py b/transquest/algo/sentence_level/siamesetransquest/run_model.py
index 2da72f2..66e7c3b 100644
--- a/transquest/algo/sentence_level/siamesetransquest/run_model.py
+++ b/transquest/algo/sentence_level/siamesetransquest/run_model.py
@@ -38,94 +38,100 @@ class SiameseTransQuestModel(nn.Sequential):
     :param modules: This parameter can be used to create custom SentenceTransformer models from scratch.
     :param device: Device (like 'cuda' / 'cpu') that should be used for computation. If None, checks if a GPU can be used.
     """
-    def __init__(self, model_name_or_path: str = None, modules: Iterable[nn.Module] = None, device: str = None):
+    def __init__(self, model_name_or_path: str = None, device: str = None):
         save_model_to = None
 
-        if model_name_or_path is not None and model_name_or_path != "":
-            logger.info("Load pretrained SentenceTransformer: {}".format(model_name_or_path))
-            model_path = model_name_or_path
-
-            if not os.path.isdir(model_path) and not model_path.startswith('http://') and not model_path.startswith('https://'):
-                logger.info("Did not find folder {}".format(model_path))
-
-                if '\\' in model_path or model_path.count('/') > 1:
-                    raise AttributeError("Path {} not found".format(model_path))
-
-                model_path = __DOWNLOAD_SERVER__ + model_path + '.zip'
-                logger.info("Search model on server: {}".format(model_path))
-
-            if model_path.startswith('http://') or model_path.startswith('https://'):
-                model_url = model_path
-                folder_name = model_url.replace("https://", "").replace("http://", "").replace("/", "_")[:250][0:-4] #remove .zip file end
-
-                cache_folder = os.getenv('SENTENCE_TRANSFORMERS_HOME')
-                if cache_folder is None:
-                    try:
-                        from torch.hub import _get_torch_home
-                        torch_cache_home = _get_torch_home()
-                    except ImportError:
-                        torch_cache_home = os.path.expanduser(os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')))
-
-                    cache_folder = os.path.join(torch_cache_home, 'sentence_transformers')
-
-                model_path = os.path.join(cache_folder, folder_name)
-
-                if not os.path.exists(model_path) or not os.listdir(model_path):
-                    if os.path.exists(model_path):
-                        os.remove(model_path)
-
-                    model_url = model_url.rstrip("/")
-                    logger.info("Downloading sentence transformer model from {} and saving it at {}".format(model_url, model_path))
-
-                    model_path_tmp = model_path.rstrip("/").rstrip("\\")+"_part"
-                    try:
-                        zip_save_path = os.path.join(model_path_tmp, 'model.zip')
-                        http_get(model_url, zip_save_path)
-                        with ZipFile(zip_save_path, 'r') as zip:
-                            zip.extractall(model_path_tmp)
-                        os.remove(zip_save_path)
-                        os.rename(model_path_tmp, model_path)
-                    except requests.exceptions.HTTPError as e:
-                        shutil.rmtree(model_path_tmp)
-                        if e.response.status_code == 429:
-                            raise Exception("Too many requests were detected from this IP for the model {}. Please contact info@nils-reimers.de for more information.".format(model_name_or_path))
-
-                        if e.response.status_code == 404:
-                            logger.warning('SentenceTransformer-Model {} not found. Try to create it from scratch'.format(model_url))
-                            logger.warning('Try to create Transformer Model {} with mean pooling'.format(model_name_or_path))
-
-                            save_model_to = model_path
-                            model_path = None
-                            transformer_model = Transformer(model_name_or_path)
-                            pooling_model = Pooling(transformer_model.get_word_embedding_dimension())
-                            modules = [transformer_model, pooling_model]
-                        else:
-                            raise e
-                    except Exception as e:
-                        shutil.rmtree(model_path)
-                        raise e
-
-
-            #### Load from disk
-            if model_path is not None:
-                logger.info("Load SentenceTransformer from folder: {}".format(model_path))
-
-                if os.path.exists(os.path.join(model_path, 'config.json')):
-                    with open(os.path.join(model_path, 'config.json')) as fIn:
-                        config = json.load(fIn)
-                        if config['__version__'] > __version__:
-                            logger.warning("You try to use a model that was created with version {}, however, your version is {}. This might cause unexpected behavior or errors. In that case, try to update to the latest version.\n\n\n".format(config['__version__'], __version__))
-
-                with open(os.path.join(model_path, 'modules.json')) as fIn:
-                    contained_modules = json.load(fIn)
-
-                modules = OrderedDict()
-                for module_config in contained_modules:
-                    module_class = import_from_string(module_config['type'])
-                    module = module_class.load(os.path.join(model_path, module_config['path']))
-                    modules[module_config['name']] = module
-
-
+        transformer_model = Transformer(model_name_or_path, max_seq_length=80)
+        pooling_model = Pooling(transformer_model.get_word_embedding_dimension(), pooling_mode_mean_tokens=True,
+                                pooling_mode_cls_token=False,
+                                pooling_mode_max_tokens=False)
+        modules = [transformer_model, pooling_model]
+
+        # if model_name_or_path is not None and model_name_or_path != "":
+        #     logger.info("Load pretrained SentenceTransformer: {}".format(model_name_or_path))
+        #     model_path = model_name_or_path
+        #
+        #     if not os.path.isdir(model_path) and not model_path.startswith('http://') and not model_path.startswith('https://'):
+        #         logger.info("Did not find folder {}".format(model_path))
+        #
+        #         if '\\' in model_path or model_path.count('/') > 1:
+        #             raise AttributeError("Path {} not found".format(model_path))
+        #
+        #         model_path = __DOWNLOAD_SERVER__ + model_path + '.zip'
+        #         logger.info("Search model on server: {}".format(model_path))
+        #
+        #     if model_path.startswith('http://') or model_path.startswith('https://'):
+        #         model_url = model_path
+        #         folder_name = model_url.replace("https://", "").replace("http://", "").replace("/", "_")[:250][0:-4] #remove .zip file end
+        #
+        #         cache_folder = os.getenv('SENTENCE_TRANSFORMERS_HOME')
+        #         if cache_folder is None:
+        #             try:
+        #                 from torch.hub import _get_torch_home
+        #                 torch_cache_home = _get_torch_home()
+        #             except ImportError:
+        #                 torch_cache_home = os.path.expanduser(os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')))
+        #
+        #             cache_folder = os.path.join(torch_cache_home, 'sentence_transformers')
+        #
+        #         model_path = os.path.join(cache_folder, folder_name)
+        #
+        #         if not os.path.exists(model_path) or not os.listdir(model_path):
+        #             if os.path.exists(model_path):
+        #                 os.remove(model_path)
+        #
+        #             model_url = model_url.rstrip("/")
+        #             logger.info("Downloading sentence transformer model from {} and saving it at {}".format(model_url, model_path))
+        #
+        #             model_path_tmp = model_path.rstrip("/").rstrip("\\")+"_part"
+        #             try:
+        #                 zip_save_path = os.path.join(model_path_tmp, 'model.zip')
+        #                 http_get(model_url, zip_save_path)
+        #                 with ZipFile(zip_save_path, 'r') as zip:
+        #                     zip.extractall(model_path_tmp)
+        #                 os.remove(zip_save_path)
+        #                 os.rename(model_path_tmp, model_path)
+        #             except requests.exceptions.HTTPError as e:
+        #                 shutil.rmtree(model_path_tmp)
+        #                 if e.response.status_code == 429:
+        #                     raise Exception("Too many requests were detected from this IP for the model {}. Please contact info@nils-reimers.de for more information.".format(model_name_or_path))
+        #
+        #                 if e.response.status_code == 404:
+        #                     logger.warning('SentenceTransformer-Model {} not found. Try to create it from scratch'.format(model_url))
+        #                     logger.warning('Try to create Transformer Model {} with mean pooling'.format(model_name_or_path))
+        #
+        #                     save_model_to = model_path
+        #                     model_path = None
+        #                     transformer_model = Transformer(model_name_or_path)
+        #                     pooling_model = Pooling(transformer_model.get_word_embedding_dimension())
+        #                     modules = [transformer_model, pooling_model]
+        #                 else:
+        #                     raise e
+        #             except Exception as e:
+        #                 shutil.rmtree(model_path)
+        #                 raise e
+        #
+        #
+        # #     #### Load from disk
+        #     if model_path is not None:
+        #         logger.info("Load SentenceTransformer from folder: {}".format(model_path))
+        #
+        #         if os.path.exists(os.path.join(model_path, 'config.json')):
+        #             with open(os.path.join(model_path, 'config.json')) as fIn:
+        #                 config = json.load(fIn)
+        #                 if config['__version__'] > __version__:
+        #                     logger.warning("You try to use a model that was created with version {}, however, your version is {}. This might cause unexpected behavior or errors. In that case, try to update to the latest version.\n\n\n".format(config['__version__'], __version__))
+        #
+        #         with open(os.path.join(model_path, 'modules.json')) as fIn:
+        #             contained_modules = json.load(fIn)
+        #
+        #         modules = OrderedDict()
+        #         for module_config in contained_modules:
+        #             module_class = import_from_string(module_config['type'])
+        #             module = module_class.load(os.path.join(model_path, module_config['path']))
+        #             modules[module_config['name']] = module
+        #
+        #
         if modules is not None and not isinstance(modules, OrderedDict):
             modules = OrderedDict([(str(idx), module) for idx, module in enumerate(modules)])
 
@@ -387,10 +393,10 @@ class SiameseTransQuestModel(nn.Sequential):
 
         for idx, name in enumerate(self._modules):
             module = self._modules[name]
-            model_path = os.path.join(path, str(idx)+"_"+type(module).__name__)
-            os.makedirs(model_path, exist_ok=True)
-            module.save(model_path)
-            contained_modules.append({'idx': idx, 'name': name, 'path': os.path.basename(model_path), 'type': type(module).__module__})
+            # model_path = os.path.join(path, str(idx)+"_"+type(module).__name__)
+            os.makedirs(path, exist_ok=True)
+            module.save(path)
+            contained_modules.append({'idx': idx, 'name': name, 'path': os.path.basename(path), 'type': type(module).__module__})
 
         with open(os.path.join(path, 'modules.json'), 'w') as fOut:
             json.dump(contained_modules, fOut, indent=2)