import torch from data.config import Config from data.weibo.data_loader import WeiboDatasetLoader class WeiboConfig(Config): name = 'weibo' DatasetLoader = WeiboDatasetLoader data_path = '../../../../../media/external_3TB/3TB/ghorbanpoor/weibo/' # data_path = '/home/faeze/PycharmProjects/fake_news_detection/data/weibo/' output_path = '../../../../../media/external_10TB/10TB/ghorbanpoor/' # output_path = '' rumor_image_path = data_path + 'rumor_images/' nonrumor_image_path = data_path + 'nonrumor_images/' train_text_path = data_path + 'weibo_train.csv' validation_text_path = data_path + 'weibo_train.csv' test_text_path = data_path + 'weibo_test.csv' batch_size = 100 epochs = 100 num_workers = 1 head_lr = 0.0085 image_encoder_lr = 1.0e-05 text_encoder_lr = 0.00016 weight_decay = 0.00015 classification_lr = 0.00149 hidden_size = 128 projection_size = 64 dropout = 0.5 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") image_model_name = '../../../../../media/external_10TB/10TB/ghorbanpoor/huggingface/vit-base-patch16-224' image_embedding = 768 text_encoder_model = "../../../../../media/external_10TB/10TB/ghorbanpoor/huggingface/bert-base-chinese" # text_encoder_model = "/home/faeze/PycharmProjects/new_fake_news_detectioin/bert/bert-base-uncased" text_tokenizer = "../../../../../media/external_10TB/10TB/ghorbanpoor/huggingface/bert-base-chinese" # text_tokenizer = "/home/faeze/PycharmProjects/new_fake_news_detectioin/bert/bert-base-uncased" text_embedding = 768 max_length = 200 pretrained = True trainable = False temperature = 1.0 labels = ['real', 'fake'] wanted_accuracy = 0.85 def optuna(self, trial): self.head_lr = trial.suggest_loguniform('head_lr', 1e-5, 1e-1) self.image_encoder_lr = trial.suggest_loguniform('image_encoder_lr', 1e-6, 1e-3) self.text_encoder_lr = trial.suggest_loguniform('text_encoder_lr', 1e-6, 1e-3) self.classification_lr = trial.suggest_loguniform('classification_lr', 1e-5, 1e-1) self.head_weight_decay = trial.suggest_loguniform('head_weight_decay', 1e-5, 1e-1) # self.classification_weight_decay = trial.suggest_loguniform('classification_weight_decay', 1e-5, 1e-1) self.projection_size = trial.suggest_categorical('projection_size', [256, 128, 64]) # self.hidden_size = trial.suggest_categorical('hidden_size', [256, 128, 64, ]) self.dropout = trial.suggest_categorical('drop_out', [0.1, 0.3, 0.5, ])