stats_str = [('DIS_COSTS', 'Discriminator loss')]
stats_log = ['%s: %.4f' % (v, np.mean(stats[k]))
for k, v in stats_str if len(stats[k]) > 0]
stats_log.append('%i samples/s' % int(n_words_proc / (time.time() - tic)))
logger.info(('%06i - ' % n_iter) + ' - '.join(stats_log))
# reset
tic = time.time()
n_words_proc = 0
for k, _ in stats_str:
del stats[k][:]
# embeddings / discriminator evaluation
to_log = OrderedDict({'n_epoch': n_epoch})
evaluator.all_eval(to_log)
evaluator.eval_dis(to_log)
# JSON log / save best model / end of epoch
logger.info("__log__:%s" % json.dumps(to_log))
trainer.save_best(to_log, VALIDATION_METRIC)
logger.info('End of epoch %i.\n\n' % n_epoch)
# update the learning rate (stop if too small)
trainer.update_lr(to_log, VALIDATION_METRIC)
if trainer.map_optimizer.param_groups[0]['lr'] < params.min_lr:
logger.info('Learning rate < 1e-6. BREAK.')
break
"""
Learning loop for Procrustes Iterative Refinement
if len(stats[k]) > 0
]
stats_log.append('%i samples/s' % int(n_words_proc /
(time.time() - tic)))
logger.info(('%06i - ' % n_iter) + ' - '.join(stats_log))
# reset
tic = time.time()
n_words_proc = 0
for k, _ in stats_str:
del stats[k][:]
# embeddings / discriminator evaluation
to_log = OrderedDict({'n_epoch': n_epoch})
evaluator.all_eval(to_log)
evaluator.eval_dis(to_log)
# JSON log / save best model / end of epoch
logger.info("__log__:%s" % json.dumps(to_log))
trainer.save_best(to_log, VALIDATION_METRIC)
logger.info('End of epoch %i.\n\n' % n_epoch)
# update the learning rate (stop if too small)
trainer.update_lr(to_log, VALIDATION_METRIC)
if trainer.map_optimizer.param_groups[0]['lr'] < params.min_lr:
logger.info('Learning rate < 1e-6. BREAK.')
break
"""
Learning loop for Procrustes Iterative Refinement
"""
if params.n_refinement > 0:
def learning(params, src_data, tgt_data, options):
VALIDATION_METRIC = 'mean_cosine-csls_knn_10-S2T-10000'
logger = logging.getLogger('{}Log'.format(src_data.dataname))
for i in range(10):
# tic = time.time()
if i == 0:
options.initialize = True
else:
options.initialize = False
logger.info("src_learning {}回目".format(i + 1))
src_data = RVSML_OT_Learning(src_data, options, params)
logger.info("tgt_learning {}回目".format(i + 1))
tgt_data = RVSML_OT_Learning(tgt_data, options, params)
# build model / trainer / evaluator
src_emb, tgt_emb, mapping, discriminator = build_model(
params, src_data, tgt_data, True)
trainer = Trainer(src_emb, tgt_emb, mapping, discriminator, params)
evaluator = Evaluator(trainer)
"""
Learning loop for Adversarial Training
"""
logger.info('----> ADVERSARIAL TRAINING <----\n\n')
# training loop
for n_epoch in range(params.n_epochs):
logger.info('Starting adversarial training epoch %i...' % n_epoch)
tic = time.time()
n_words_proc = 0
stats = {'DIS_COSTS': []}
for n_iter in range(0, params.epoch_size, params.batch_size):
# discriminator training
for _ in range(params.dis_steps):
trainer.dis_step(stats)
# mapping training (discriminator fooling)
n_words_proc += trainer.mapping_step(stats)
# log stats
if n_iter % 500 == 0:
stats_str = [('DIS_COSTS', 'Discriminator loss')]
stats_log = [
'%s: %.4f' % (v, np.mean(stats[k]))
for k, v in stats_str if len(stats[k]) > 0
]
stats_log.append('%i samples/s' % int(n_words_proc /
(time.time() - tic)))
logger.info(('%06i - ' % n_iter) + ' - '.join(stats_log))
# reset
tic = time.time()
n_words_proc = 0
for k, _ in stats_str:
del stats[k][:]
# embeddings / discriminator evaluation
to_log = OrderedDict({'n_epoch': n_epoch})
evaluator.all_eval(to_log)
evaluator.eval_dis(to_log)
# JSON log / save best model / end of epoch
logger.info("__log__:%s" % json.dumps(to_log))
trainer.save_best(to_log, VALIDATION_METRIC)
logger.info('End of epoch %i.\n\n' % n_epoch)
# update the learning rate (stop if too small)
trainer.update_lr(to_log, VALIDATION_METRIC)
if trainer.map_optimizer.param_groups[0]['lr'] < params.min_lr:
logger.info('Learning rate < 1e-6. BREAK.')
break
"""
Learning loop for Procrustes Iterative Refinement
"""
# if params.n_refinement > 0:
# Get the best mapping according to VALIDATION_METRIC
logger.info('----> ITERATIVE PROCRUSTES REFINEMENT <----\n\n')
trainer.reload_best()
# training loop
for n_iter in range(params.n_refinement):
logger.info('Starting refinement iteration %i...' % n_iter)
# build a dictionary from aligned embeddings
trainer.build_dictionary()
# apply the Procrustes solution
trainer.procrustes()
# embeddings evaluation
to_log = OrderedDict({'n_iter': n_iter})
evaluator.all_eval(to_log)
# JSON log / save best model / end of epoch
logger.info("__log__:%s" % json.dumps(to_log))
trainer.save_best(to_log, VALIDATION_METRIC)
logger.info('End of refinement iteration %i.\n\n' % n_iter)
src_data.trans_mat = torch.mm(src_data.trans_mat,
trainer.mapping.weight.data)
return src_data, tgt_data