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import random
import comet.src.train.train as base_train
import comet.src.train.batch as batch
import comet.src.evaluate.atomic_evaluate as evaluate
# import comet.src.evaluate.atomic_generate as gen
def make_trainer(opt, *args):
return AtomicGenerationIteratorTrainer(opt, *args)
class AtomicGenerationIteratorTrainer(base_train.IteratorTrainer):
def __init__(self, opt, *args):
super(AtomicGenerationIteratorTrainer, self).__init__(opt, *args)
self.initialize_losses(opt.data.get("categories", []))
def set_evaluator(self, opt, model, data_loader):
self.evaluator = evaluate.make_evaluator(
opt, model, data_loader)
# def set_generator(self, opt, model, data_loader, scores, reward=None):
# self.generator = gen.make_generator(
# opt, model, data_loader, scores, reward)
def set_sampler(self, opt):
if opt.train.static.samp not in self.samplers:
self.samplers[opt.train.static.samp] = sampling.make_sampler(
opt.train.static.samp, opt, self.data_loader, batch_mode=True)
self.batch_variables["sampler"] = self.samplers
def batch(self, opt, *args):
outputs = batch.batch_atomic_generate(opt, *args)
token_loss = outputs["loss"]
nums = outputs["nums"]
reset = outputs["reset"]
return token_loss, nums, reset
def initialize_losses(self, categories):
self.losses["train"] = {
"total_micro": [0],
"total_macro": [0]
}
nums = {"total_micro": 0, "total_macro": 0}
for category in categories:
micro_name = "{}_micro".format(category)
macro_name = "{}_macro".format(category)
self.losses["train"][micro_name] = [0]
self.losses["train"][macro_name] = [0]
nums[micro_name] = 0
nums[macro_name] = 0
return nums
def update_top_score(self, opt):
print(self.top_score)
if self.top_score is None:
self.top_score = (self.opt.train.dynamic.epoch,
self.get_tracked_score())
elif self.get_tracked_score() < self.top_score[-1]:
self.top_score = (self.opt.train.dynamic.epoch,
self.get_tracked_score())
print(self.top_score)
def get_tracked_score(self):
return self.losses["dev"]["total_micro"][self.opt.train.dynamic.epoch]
def counter(self, nums):
return nums["total_macro"]
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