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import comet.src.data.data as data
import comet.src.data.config as cfg
import comet.src.evaluate.sampler as sampling
def do_gen_run(opt, generator, l, split="dev", scores={}):
# Generate sequences for examples in evaluation set using
# current trained model
if opt.eval.gs == "full":
sequences, avg_scores, indiv_scores = generator.generate(split)
else:
sequences, avg_scores, indiv_scores = generator.generate_some(split)
if avg_scores is not None:
# Record scores from generated sequences
for score_name, score_val in avg_scores.items():
scores.setdefault(score_name, {})
scores[score_name].setdefault(l, [])
scores[score_name][l] += [score_val]
# Save generated sequences
save_sequences(opt, sequences, avg_scores, indiv_scores,
l, split, opt.eval.gs == "full",
generator.data_loader)
def save_sequences(opt, sequences, avg_scores, indiv_scores,
l, split, full, data_loader):
# This seems a bit roundabout since l = opt.train.dynamic in train.py
# But it's in case we start checkpointing outside of epoch boundaries
opt.train.dynamic.epoch = l
if cfg.save:
if full:
names = {"gens": "gens", "scores": "scores",
"indiv": "indiv.scores"}
else:
names = {"gens": "gens.small", "scores": "scores.small",
"indiv": "indiv.scores.small"}
# Save generated sequences
data.save_eval_file(opt, sequences, names["gens"], split)
if avg_scores is not None:
# Save average scores over evaluation set for generated sequences
# Scores computed are the ones the generator was initialized with
data.save_eval_file(opt, avg_scores, names["scores"], split)
if split == "dev":
# Save individual scores
data.save_eval_file(
opt, indiv_scores, names["indiv"], split)
class Generator(object):
def __init__(self, opt, model, data_loader, scorers, reward_function=None):
super(Generator, self).__init__()
self.opt = opt
self.model = model
self.data_loader = data_loader
self.sampler = sampling.make_sampler(
opt.eval.sample, opt, data_loader)
def generate(self, split="dev"):
pass
def generate_batch(self, sequences, split, verbose=False, bs=32):
pass
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