def knowledge_celebrity_test(context_text_encoder: TextEncoder, context_image_encoder: ImageEncoder, context_encoder: ContextEncoder, to_hidden: ToHidden, celebrity_memory: Memory, text_decoder: TextDecoder, test_dataset: Dataset, celebrity_scores, text_length: int, vocab: Dict[str, int]): """Knowledge celebrity test. Args: context_text_encoder (TextEncoder): Context text encoder. context_image_encoder (ImageEncoder): Context image encoder. context_encoder (ContextEncoder): Context encoder. to_hidden (ToHidden): Context to hidden. celebrity_memory (Memory): Celebrity Memory. text_decoder (TextDecoder): Text decoder. test_dataset (Dataset): Valid dataset. celebrity_scores: Celebrity scores. text_length (int): Text length. vocab (Dict[str, int]): Vocabulary. """ id2word: List[str] = [None] * len(vocab) for word, wid in vocab.items(): id2word[wid] = word # Test dataset loader. test_data_loader = DataLoader( test_dataset, batch_size=KnowledgeCelebrityTestConfig.batch_size, num_workers=KnowledgeCelebrityTestConfig.num_data_loader_workers) sum_loss = 0 # Switch to eval mode. context_text_encoder.eval() context_image_encoder.eval() context_encoder.eval() to_hidden.eval() celebrity_memory.eval() text_decoder.eval() output_file = open('knowledge_celebrity.out', 'w') with torch.no_grad(): for batch_id, test_data in enumerate(test_data_loader): texts, text_lengths, images, utter_types = test_data # Sizes: # texts: (batch_size, dialog_context_size + 1, dialog_text_max_len) # text_lengths: (batch_size, dialog_context_size + 1) # images: (batch_size, dialog_context_size + 1, # pos_images_max_num, 3, image_size, image_size) # utter_types: (batch_size, ) # To device. texts = texts.to(GlobalConfig.device) text_lengths = text_lengths.to(GlobalConfig.device) images = images.to(GlobalConfig.device) texts.transpose_(0, 1) # (dialog_context_size + 1, batch_size, dialog_text_max_len) text_lengths.transpose_(0, 1) # (dialog_context_size + 1, batch_size) images.transpose_(0, 1) images.transpose_(1, 2) # (dialog_context_size + 1, pos_images_max_num, batch_size, 3, # image_size, image_size) # Encode context. context, hiddens = encode_context(context_text_encoder, context_image_encoder, context_encoder, texts, text_lengths, images) # (batch_size, context_vector_size) knowledge_entry = celebrity_scores encode_knowledge_func = partial(celebrity_memory, knowledge_entry) text_eval(to_hidden, text_decoder, text_length, id2word, context, texts[-1], hiddens, encode_knowledge_func, output_file=output_file) output_file.close()
def knowledge_celebrity_valid( context_text_encoder: TextEncoder, context_image_encoder: ImageEncoder, context_encoder: ContextEncoder, to_hidden: ToHidden, celebrity_memory: Memory, text_decoder: TextDecoder, valid_dataset: Dataset, celebrity_scores, text_length: int): """Knowledge celebrity valid. Args: context_text_encoder (TextEncoder): Context text encoder. context_image_encoder (ImageEncoder): Context image encoder. context_encoder (ContextEncoder): Context encoder. to_hidden (ToHidden): Context to hidden. celebrity_memory (Memory): Celebrity Memory. text_decoder (TextDecoder): Text decoder. valid_dataset (Dataset): Valid dataset. celebrity_scores: Celebrity scores. text_length (int): Text length. """ # Valid dataset loader. valid_data_loader = DataLoader( valid_dataset, batch_size=KnowledgeCelebrityValidConfig.batch_size, shuffle=True, num_workers=KnowledgeCelebrityValidConfig.num_data_loader_workers) sum_loss = 0 num_batches = 0 # Switch to eval mode. context_text_encoder.eval() context_image_encoder.eval() context_encoder.eval() to_hidden.eval() celebrity_memory.eval() text_decoder.eval() with torch.no_grad(): for batch_id, valid_data in enumerate(valid_data_loader): # Only valid `ValidConfig.num_batches` batches. if batch_id >= KnowledgeCelebrityValidConfig.num_batches: break num_batches += 1 texts, text_lengths, images, utter_types = valid_data # Sizes: # texts: (batch_size, dialog_context_size + 1, dialog_text_max_len) # text_lengths: (batch_size, dialog_context_size + 1) # images: (batch_size, dialog_context_size + 1, # pos_images_max_num, 3, image_size, image_size) # utter_types: (batch_size, ) # To device. texts = texts.to(GlobalConfig.device) text_lengths = text_lengths.to(GlobalConfig.device) images = images.to(GlobalConfig.device) # utter_types = utter_types.to(GlobalConfig.device) texts.transpose_(0, 1) # (dialog_context_size + 1, batch_size, dialog_text_max_len) text_lengths.transpose_(0, 1) # (dialog_context_size + 1, batch_size) images.transpose_(0, 1) images.transpose_(1, 2) # (dialog_context_size + 1, pos_images_max_num, batch_size, 3, # image_size, image_size) # Encode context. context, hiddens = encode_context(context_text_encoder, context_image_encoder, context_encoder, texts, text_lengths, images) # (batch_size, context_vector_size) knowledge_entry = celebrity_scores encode_knowledge_func = partial(celebrity_memory, knowledge_entry) loss, n_totals = text_loss(to_hidden, text_decoder, text_length, context, texts[-1], text_lengths[-1], hiddens, encode_knowledge_func) sum_loss += loss / text_length # Switch to train mode. context_text_encoder.train() context_image_encoder.train() context_encoder.train() to_hidden.train() celebrity_memory.train() text_decoder.train() return sum_loss / num_batches