def read_config(file_):
config = DD()
print(file_)
for k, v in file_.items():
if v == "True" or v == "T" or v == "true":
config[k] = True
elif v == "False" or v == "F" or v == "false":
config[k] = False
elif type(v) == dict:
config[k] = read_config(v)
else:
config[k] = v
return config
def get_data_parameters(opt, experiment, dataset):
data = DD()
if dataset == "atomic":
data.categories = sorted(opt.categories)
# hard-coded
data.maxe1 = 17
data.maxe2 = 35
data.maxr = 1
elif dataset == "conceptnet":
data.rel = opt.relation_format
data.trainsize = opt.training_set_size
data.devversion = opt.development_set_versions_to_use
data.maxe1 = opt.max_event_1_size
data.maxe2 = opt.max_event_2_size
if data.rel == "language":
# hard-coded
data.maxr = 5
else:
# hard-coded
data.maxr = 1
return data
def get_eval_parameters(opt, force_categories=None):
evaluate = DD()
if opt.eval_sampler == "beam":
evaluate.bs = opt.beam_size
elif opt.eval_sampler == "greedy":
evaluate.bs = 1
elif opt.eval_sampler == "topk":
evaluate.k = opt.topk_size
evaluate.smax = opt.gen_seqlength
evaluate.sample = opt.eval_sampler
evaluate.numseq = opt.num_sequences
evaluate.gs = opt.generate_sequences
evaluate.es = opt.evaluate_sequences
if opt.dataset == "atomic":
if "eval_categories" in opt and force_categories is None:
evaluate.categories = opt.eval_categories
else:
evaluate.categories = force_categories
return evaluate
def get_net_parameters(opt):
net = DD()
net.model = opt.model
net.nL = opt.num_layers
net.nH = opt.num_heads
net.hSize = opt.hidden_dim
net.edpt = opt.embedding_dropout
net.adpt = opt.attention_dropout
net.rdpt = opt.residual_dropout
net.odpt = opt.output_dropout
net.pt = opt.pretrain
net.afn = opt.activation
# how to intialize parameters
# format is gauss+{}+{}.format(mean, std)
# n = the default initialization pytorch
net.init = opt.init
return net
def get_training_parameters(opt):
train = DD()
static = DD()
static.exp = opt.exp
static.seed = opt.random_seed
# weight decay
static.l2 = opt.l2
static.vl2 = True
static.lrsched = opt.learning_rate_schedule # 'warmup_linear'
static.lrwarm = opt.learning_rate_warmup # 0.002
# gradient clipping
static.clip = opt.clip
# what loss function to use
static.loss = opt.loss
dynamic = DD()
dynamic.lr = opt.learning_rate # learning rate
dynamic.bs = opt.batch_size # batch size
# optimizer to use {adam, rmsprop, etc.}
dynamic.optim = opt.optimizer
# rmsprop
# alpha is interpolation average
static.update(opt[dynamic.optim])
train.static = static
train.dynamic = dynamic
return train
def get_parameters(opt, exp_type="model"):
params = DD()
params.net = DD()
params.mle = 0
params.dataset = opt.dataset
params.net = get_net_parameters(opt)
params.train = get_training_parameters(opt)
params.model = params.net.model
params.exp = opt.exp
params.data = get_data_parameters(opt, params.exp, params.dataset)
params.eval = get_eval_parameters(opt, params.data.get("categories", None))
meta = DD()
params.trainer = opt.trainer
meta.iterations = int(opt.iterations)
meta.cycle = opt.cycle
params.cycle = opt.cycle
params.iters = int(opt.iterations)
global toy
toy = opt.toy
global do_gen
do_gen = opt.do_gen
global save
save = opt.save
global test_save
test_save = opt.test_save
global save_strategy
save_strategy = opt.save_strategy
print(params)
return params, meta
import os import sys sys.path.append(os.getcwd()) import torch import src.data.conceptnet as cdata import src.data.data as data from utils.utils import DD import utils.utils as utils import random from src.data.utils import TextEncoder from tqdm import tqdm opt = DD() opt.dataset = "conceptnet" opt.exp = "generation" opt.data = DD() # Use relation embeddings rather than # splitting relations into its component words # Set to "language" for using component words # Set to "relation" to use unlearned relation embeddings opt.data.rel = "language" # Use 100k training set opt.data.trainsize = 100 # Use both dev sets (v1 an v2)
data_params[case.split("_")[0]] = case.split("_")[1].split("#")
else:
data_params[case.split("_")[0]] = case.split("_")[1]
return data_params
gens_file = args.gens_file
split = gens_file.split("/")[-1].split(".")[0]
n = args.n
def flatten(outer):
return [el for key in outer for el in key]
opt = DD()
opt.data = DD()
opt.dataset = "atomic"
opt.exp = "generation"
data_params = get_data_params(gens_file)
categories = data_params[
"categories"] #sorted(["oReact", "oEffect", "oWant", "xAttr", "xEffect", "xIntent", "xNeed", "xReact", "xWant"])
opt.data.categories = data_params["categories"]
if "maxe1" in data_params:
opt.data.maxe1 = data_params["maxe1"]
opt.data.maxe2 = data_params["maxe2"]
opt.data.maxr = data_params["maxr"]
#parser.add_argument("--n_per_node", type=int, default=3)
parser.add_argument("--n_per_node", type=int, nargs=3, default=[1, 1, 1], help='n per node for train, dev, test')
parser.add_argument("--comet", action='store_true', default=False, help='create data of the form s,r -> o, where sro are triples from ATOMIC')
parser.add_argument("--pathcomet", action='store_true', default=False, help='create data of the form path,s,r -> o, where sro are triples from ATOMIC')
#parser.add_argument("--add_orig", action='store_true', default=False, help='when generating pathcomet, add the original comet triples as well (train)')
parser.add_argument("--add_orig", type=int, nargs=3, default=[0, 0, 0], help='when generating pathcomet, add the original comet triples as well (train)')
args = parser.parse_args()
print("\n### ATOMIC Data Generation ###")
pprint.pprint(vars(args))
print('\n')
assert not (args.comet and args.pathcomet)
opt = DD()
opt.dataset = "atomic"
opt.exp = "generation"
opt.data = DD()
opt.data.categories = sorted(categories)
# Additionally add our options HERE
opt.pickled_data = args.pickled_data
opt.n_train = args.n_train
opt.n_dev = args.n_dev
opt.n_test = args.n_test
opt.max_path_len = args.max_path_len
opt.n_per_node = args.n_per_node
opt.comet = args.comet
opt.pathcomet = args.pathcomet
opt.add_orig = args.add_orig
import src.data.data as data
from utils.utils import DD
import utils.utils as utils
import random
from src.data.utils import TextEncoder
from tqdm import tqdm
import torch
# Manually change the set of categories you don't want to include
# if you want to be able to train on a separate set of categories
categories = []
categories += ["Intent"]
opt = DD()
opt.dataset = "motiv_sent"
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]
def get_data_parameters(opt, experiment, dataset):#####opt.exp & opt.data used for data loader path
data = DD() ##################
if dataset == "atomic":
data.categories = sorted(opt.categories)
#ORIGINAL CODE
# hard-coded
data.maxe1 = 17
data.maxe2 = 35
data.maxr = 1
#ADRIAN ADDED ################THIS IS WHERE YOU CHANGE PARAMETERS FOR NAME OF LOADER YOU'RE USING
##############MAXE1 == MAX EVENT
############MAXE2 == MAX EFFECT
data.maxe1 = 50
data.maxe2 = 35
data.maxr = 1
###
elif dataset == "conceptnet":
data.rel = opt.relation_format
data.trainsize = opt.training_set_size
data.devversion = opt.development_set_versions_to_use
data.maxe1 = opt.max_event_1_size
data.maxe2 = opt.max_event_2_size
if data.rel == "language":
# hard-coded
data.maxr = 5
else:
# hard-coded
data.maxr = 1
return data############
def load_default():
with open(abs_path("config_default.json"), 'r') as f:
config = json.load(f)
config = DD(config)
config.net = DD(config.net)
config.train = DD(config.train)
config.train.static = DD(config.train.static)
config.train.dynamic = DD(config.train.dynamic)
config.data = DD(config.data)
config.eval = DD(config.eval)
config.train.dynamic.epoch = 0
return DD(config)
def get_meta(config):
meta = DD()
meta.iterations = int(config.iters)
meta.cycle = config.cycle
return meta
