def run_evaluation1(corpus_dir, save_dir, datafile, config_file):
config = Config.from_json_file(config_file)
save_dir = config.save_dir
# 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,
map_location=lambda storage, loc: storage)
# 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 = BPEVocab.from_files(config.bpe_vocab_path, config.bpe_codes_path)
print("Building encoder and decoder ...")
# initialize word embeddings
embedding = nn.Embedding(len(vocab), 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, len(vocab),
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_input1(encoder, decoder, searcher, vocab)
def run_training1(config_file, load_filename=""):
# read data
config = Config.from_json_file(config_file)
save_dir = config.save_dir
datafile = config.datafile
vocab = BPEVocab.from_files(config.bpe_vocab_path, config.bpe_codes_path)
if load_filename:
# 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"]
dataset = CornellMovieDialogDataset(config, paths=[datafile], vocab=vocab)
#todo try shuffle=False
#todo the batched input tensor to the model is transosed! is there anything wrong?
data_loader = DataLoader(dataset,
batch_size=config.batch_size,
shuffle=True,
collate_fn=dataset.collate_func,
drop_last=True)
num_tokens = len(vocab)
print("Building encoder and decoder ...")
# initialize word embeddings
embedding = nn.Embedding(num_tokens, config.hidden_size).to(config.device)
if load_filename:
embedding.load_state_dict(embedding_sd)
# initialize encoder and decoder models
encoder = EncoderRNN(config.hidden_size, embedding,
config.encoder_n_layers,
config.dropout).to(config.device)
decoder = LuongAttnDecoderRNN(config.attn_model, embedding,
config.hidden_size, num_tokens,
config.decoder_n_layers,
config.dropout).to(config.device)
if load_filename:
encoder.load_state_dict(encoder_sd)
decoder.load_state_dict(decoder_sd)
print("Models built and ready to go.")
#####################################
# ensure dropout layers are in train mode
encoder.train()
decoder.train()
# initilize optimizers
print("building optimizers")
encoder_optimizer = optim.Adam(encoder.parameters(),
lr=config.learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(),
lr=config.learning_rate *
config.decoder_learning_ratio)
if load_filename:
encoder_optimizer.load_state_dict(encoder_optimizer_sd)
decoder_optimizer.load_state_dict(decoder_optimizer_sd)
#for epoch in range(config.n_epochs):
training_iters1(data_loader,
config,
vocab,
encoder,
decoder,
encoder_optimizer,
decoder_optimizer,
embedding,
save_dir,
load_filename,
epoch=1)
def run_training(config_file, load_filename=""):
# read data
config = Config.from_json_file(config_file)
save_dir = config.save_dir
datafile = config.datafile
corpus_dir = config.corpus_dir
prepare_data = PrepareData(min_count=config.MIN_COUNT,
max_length=config.MAX_LENGTH)
vocab, pairs = prepare_data.load_prepare_data(corpus_dir, datafile,
save_dir)
# set checkpoint to load from; set to None if starting from scratch
# load_filename = os.path.join(save_dir, model_name, corpus_name, '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size),
# '{}_checkpoint.tar'.format(checkpoint_iter))
if load_filename:
# 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).to(config.device)
if load_filename:
embedding.load_state_dict(embedding_sd)
# initialize encoder and decoder models
encoder = EncoderRNN(config.hidden_size, embedding,
config.encoder_n_layers,
config.dropout).to(config.device)
decoder = LuongAttnDecoderRNN(config.attn_model, embedding,
config.hidden_size, vocab.num_words,
config.decoder_n_layers,
config.dropout).to(config.device)
if load_filename:
encoder.load_state_dict(encoder_sd)
decoder.load_state_dict(decoder_sd)
print("Models built and ready to go.")
#####################################
# ensure dropout layers are in train mode
encoder.train()
decoder.train()
# initilize optimizers
print("building optimizers")
encoder_optimizer = optim.Adam(encoder.parameters(),
lr=config.learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(),
lr=config.learning_rate *
config.decoder_learning_ratio)
if load_filename:
encoder_optimizer.load_state_dict(encoder_optimizer_sd)
decoder_optimizer.load_state_dict(decoder_optimizer_sd)
# run training iterations
training_iters(config, vocab, pairs, encoder, decoder, encoder_optimizer,
decoder_optimizer, embedding, save_dir, load_filename)