A little bit of context
dlg_turnwise_embeddings = np.random.random((batch_size, 20, 256)) dlg_turnwise_embeddings = torch.from_numpy(dlg_turnwise_embeddings) if legacy: hidden = rnn_model.init_hidden(batch_size) # words_emb: batch_size x nef x seq_len # sent_emb: batch_size x nef words_emb, sent_emb = rnn_model(captions, cap_lens, hidden) else: words_emb = word_reducer(data_caption_word_embeddings) print(data_caption_word_embeddings) sent_emb = sent_reducer(data_caption_embeddings) # the input to the model is here # data caption word embedding # this would be changed to data_dialogue_word_embedding # words_emb torch.Size([48, 256, 14]) # we want the same size for the word and sentence embedding # sent_emb torch.Size([48, 256]) # TODO: write an LSTM model that outputs the following # with size ((batch_size, 256)) hidden = dlg_encoder.init_hidden(batch_size) dlg_entire_embeddings = dlg_encoder(dlg_turnwise_embeddings, dlg_turnwise_embeddings.shape, hidden)
Seems to happen when I do
dlg_turnwise_embeddings = torch.from_numpy(dlg_turnwise_embeddings)? Is it as simple as casting it to Long from Double values inside the tensor?