def load_data(dataset, opt):
if dataset == "atomic":
data_loader = load_atomic_data(opt)
elif dataset == "conceptnet":
data_loader = load_conceptnet_data(opt)
# Initialize TextEncoder
encoder_path = "model/encoder_bpe_40000.json"
bpe_path = "model/vocab_40000.bpe"
text_encoder = TextEncoder(encoder_path, bpe_path)
text_encoder.encoder = data_loader.vocab_encoder
text_encoder.decoder = data_loader.vocab_decoder
return data_loader, text_encoder
def main(num):
# Generate configuration files depending on experiment being run
utils.generate_config_files("atomic", num)
# Loads the correct configuration file
config_file = "config/atomic/config_{}.json".format(num)
print(config_file)
# Read config file to option
config = cfg.read_config(cfg.load_config(config_file))
opt, meta = cfg.get_parameters(config)
# Set the random seeds
torch.manual_seed(opt.train.static.seed)
random.seed(opt.train.static.seed)
if config.gpu_mode:
torch.cuda.manual_seed_all(opt.train.static.seed)
# Where to find the data
splits = ["train", "dev", "test"]
opt.train.dynamic.epoch = 0
print("Loading Data")
categories = opt.data.categories
path = "data/atomic/processed/{}/{}.pickle".format(
opt.exp, utils.make_name_string(opt.data))
data_loader = data.make_data_loader(opt, categories)
loaded = data_loader.load_data(path)
print(data_loader.sequences["train"]["total"].size(0))
data_loader.opt = opt
data_loader.batch_size = opt.train.dynamic.bs
print("Done.")
# Initialize text_encoder
text_encoder = TextEncoder(config.encoder_path, config.bpe_path)
special = [data.start_token, data.end_token]
special += ["<{}>".format(cat) for cat in categories]
special += [data.blank_token]
text_encoder.encoder = data_loader.vocab_encoder
text_encoder.decoder = data_loader.vocab_decoder
opt.data.maxe1 = data_loader.max_event
opt.data.maxe2 = data_loader.max_effect
opt.data.maxr = data.atomic_data.num_delimiter_tokens["category"]
n_special = len(special)
n_ctx = opt.data.maxe1 + opt.data.maxe2
n_vocab = len(text_encoder.encoder) + n_ctx
print(data_loader.__dict__.keys())
opt.net.vSize = n_vocab
print("Building Model")
model = models.make_model(opt,
n_vocab,
n_ctx,
n_special,
load=(opt.net.init == "pt"))
print("Done.")
print("Files will be logged at: {}".format(
utils.make_name(opt, prefix="results/losses/", is_dir=True,
eval_=True)))
data_loader.reset_offsets("train")
# Get number of examples
data.set_max_sizes(data_loader)
if config.gpu_mode:
print("Pushing to GPU: {}".format(config.gpu_index))
cfg.device = config.gpu_index
cfg.do_gpu = True
torch.cuda.set_device(cfg.device)
if config.multigpu:
model = models.multi_gpu(model, config.gpu_indices).cuda()
else:
model.cuda(cfg.device)
print("Done.")
print("Training")
optimizer = OpenAIAdam(model.parameters(),
lr=opt.train.dynamic.lr,
schedule=opt.train.static.lrsched,
warmup=opt.train.static.lrwarm,
t_total=meta.iterations,
b1=opt.train.static.b1,
b2=opt.train.static.b2,
e=opt.train.static.e,
l2=opt.train.static.l2,
vector_l2=opt.train.static.vl2,
max_grad_norm=opt.train.static.clip)
scorers = ["bleu", "rouge", "cider"]
trainer = train.make_trainer(opt, meta, data_loader, model, optimizer)
trainer.set_evaluator(opt, model, data_loader)
trainer.run()
print("Loading Data")
categories = opt.data.categories
path = "data/atomic/processed/generation/{}.pickle".format(
utils.make_name_string(opt.data))
data_loader = data.make_data_loader(opt, categories)
loaded = data_loader.load_data(path)
data_loader.batch_size = opt.train.dynamic.bs
print("Done.")
text_encoder = TextEncoder(config.encoder_path, config.bpe_path)
special = [data.start_token, data.end_token]
special += ["<{}>".format(cat) for cat in categories]
special += [data.blank_token]
text_encoder.encoder = data_loader.vocab_encoder
text_encoder.decoder = data_loader.vocab_decoder
context_size_event = data_loader.max_event
context_size_effect = data_loader.max_effect
n_special = len(special)
n_ctx = context_size_event + context_size_effect
n_vocab = len(text_encoder.encoder) + n_ctx
categories += ["xEffect"]
categories += ["xIntent"]
categories += ["xNeed"]
categories += ["xReact"]
categories += ["xWant"]
opt = DD()
opt.dataset = "atomic"
opt.exp = "generation"
opt.data = DD()
opt.data.categories = sorted(categories)
encoder_path = "model/encoder_bpe_40000.json"
bpe_path = "model/vocab_40000.bpe"
text_encoder = TextEncoder(encoder_path, bpe_path)
encoder = text_encoder.encoder
n_vocab = len(text_encoder.encoder)
special = [data.start_token, data.end_token]
special += ["<{}>".format(cat) for cat in categories]
special += [data.blank_token]
for special_token in special:
text_encoder.decoder[len(encoder)] = special_token
encoder[special_token] = len(encoder)
save_path = "data/atomic/processed/{}".format(opt.exp)
utils.mkpath(save_path)
'DefinedAs', 'DesireOf', 'Desires', 'HasA', 'HasFirstSubevent',
'HasLastSubevent', 'HasPainCharacter', 'HasPainIntensity',
'HasPrerequisite', 'HasProperty', 'HasSubevent', 'InheritsFrom',
'InstanceOf', 'IsA', 'LocatedNear', 'LocationOfAction', 'MadeOf',
'MotivatedByGoal', 'NotCapableOf', 'NotDesires', 'NotHasA',
'NotHasProperty', 'NotIsA', 'NotMadeOf', 'PartOf', 'ReceivesAction',
'RelatedTo', 'SymbolOf', 'UsedFor'
]
special = [data.start_token, data.end_token]
special += ["<{}>".format(relation) for relation in relations]
encoder_path = "model/encoder_bpe_40000.json"
bpe_path = "model/vocab_40000.bpe"
text_encoder = TextEncoder(encoder_path, bpe_path)
for special_token in special:
text_encoder.decoder[len(text_encoder.encoder)] = special_token
text_encoder.encoder[special_token] = len(text_encoder.encoder)
data_loader = cdata.GenerationDataLoader(opt)
data_loader.load_data("data/conceptnet/")
data_loader.make_tensors(text_encoder, special, test=False)
opt.data.maxr = data_loader.max_r
save_path = "data/conceptnet/processed/generation"
save_name = os.path.join(save_path,
"{}.pickle".format(utils.make_name_string(opt.data)))