def run_evaluation(corpus_dir, save_dir, datafile, config_file):
config = Config.from_json_file(config_file)
vocab = Vocabulary("words")
# set checkpoint to load from; set to None if starting from scratch
load_filename = os.path.join(
save_dir, config.model_name, config.corpus_name,
'{}-{}_{}'.format(config.encoder_n_layers, config.decoder_n_layers,
config.hidden_size), 'last_checkpoint.tar')
# if loading on the same machine the model trained on
checkpoint = torch.load(load_filename)
# if loading a model trained on gpu to cpu
# checkpoint = torch.load(load_filename, map_location=torch.device('cpu'))
encoder_sd = checkpoint["en"]
decoder_sd = checkpoint["de"]
encoder_optimizer_sd = checkpoint["en_opt"]
decoder_optimizer_sd = checkpoint["de_opt"]
embedding_sd = checkpoint["embedding"]
vocab.__dict__ = checkpoint["voc_dict"]
print("Building encoder and decoder ...")
# initialize word embeddings
embedding = nn.Embedding(vocab.num_words, config.hidden_size)
embedding.load_state_dict(embedding_sd)
# initialize encoder and decoder models
encoder = EncoderRNN(config.hidden_size, embedding,
config.encoder_n_layers, config.dropout)
decoder = LuongAttnDecoderRNN(config.attn_model, embedding,
config.hidden_size, vocab.num_words,
config.decoder_n_layers, config.dropout)
encoder.load_state_dict(encoder_sd)
decoder.load_state_dict(decoder_sd)
# Set dropout layers to eval mode
encoder.eval()
decoder.eval()
# Initialize search module
searcher = GreedySearchDecoder(encoder, decoder)
# Begin chatting (uncomment and run the following line to begin)
evaluate_input(encoder, decoder, searcher, vocab)
i = i.strip('_checkpoint.tar')
if int(i) > int(recent):
recent = i
LOAD_MODEL_PATH = LOAD_MODEL_PATH + recent + '_checkpoint.tar'
#checkpoint = torch.load(LOAD_MODEL_PATH)
checkpoint = torch.load(LOAD_MODEL_PATH, map_location=torch.device('cpu'))
encoder_sd = checkpoint['en']
decoder_sd = checkpoint['de']
encoder_optimizer_sd = checkpoint['en_opt']
decoder_optimizer_sd = checkpoint['de_opt']
embedding_sd = checkpoint['embedding']
voc = Vocabulary()
voc.__dict__ = checkpoint['voc_dict']
#do not edit these parameters
attn_model = 'dot'
hidden_size = 500
encoder_n_layers = 2
decoder_n_layers = 2
dropout = 0.1
embedding = nn.Embedding(voc.num_of_words, hidden_size)
embedding.load_state_dict(embedding_sd)
encoder = Encoder(hidden_size, embedding, encoder_n_layers, dropout)
encoder.load_state_dict(encoder_sd)
decoder = Attention_Decoder(attn_model, embedding, hidden_size, hidden_size,
voc.num_of_words, decoder_n_layers, dropout)
decoder.load_state_dict(decoder_sd)
