def evaluate(args, model_vae, encoder_tokenizer, decoder_tokenizer, table_name, prefix="", subset="test"):
# Loop to handle MNLI double evaluation (matched, mis-matched)
eval_output_dir = args.output_dir
if subset == 'test':
eval_dataset = load_and_cache_examples(args, [encoder_tokenizer, decoder_tokenizer], evaluate=True)
elif subset == 'train':
eval_dataset = load_and_cache_examples(args, [encoder_tokenizer, decoder_tokenizer], evaluate=False)
logger.info("***** Running evaluation on {} dataset *****".format(subset))
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
os.makedirs(eval_output_dir)
args.per_gpu_eval_batch_size = 1
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
model_vae.eval()
model_vae = model_vae.module if hasattr(model_vae, 'module') else model_vae # Take care of distributed/parallel training
mi = calc_mi(model_vae, eval_dataloader, args)
au = calc_au(model_vae, eval_dataloader, delta=0.01, args=args)[0]
ppl, elbo, nll, kl = calc_iwnll(model_vae, eval_dataloader, args, ns=100)
result = {
"perplexity": ppl, "elbo": elbo, "kl": kl, "nll": nll, "au": au, "mi": mi
}
output_eval_file = os.path.join(eval_output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(prefix))
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
row = {
'PartitionKey': 'MILU_Rule_Rule_Template',
'RowKey': str(datetime.now()),
'ExpName' : args.ExpName,
'test_perplexity': str( ppl ),
'test_elbo': str( elbo ),
'test_nll': str(nll),
'test_au': str(au),
'test_mi': str(mi)
}
# pdb.set_trace()
ts.insert_entity(table_name, row)
return result
def test(model, test_data_batch, mode, args, verbose=True):
global logging
report_kl_loss = report_rec_loss = report_loss = 0
report_num_words = report_num_sents = 0
for i in np.random.permutation(len(test_data_batch)):
batch_data = test_data_batch[i]
batch_size, sent_len = batch_data.size()
# not predict start symbol
report_num_words += (sent_len - 1) * batch_size
report_num_sents += batch_size
#loss, loss_rc, loss_kl = model.loss(batch_data, args.beta, nsamples=args.nsamples)
if args.iw_train_nsamples < 0:
loss, loss_rc, loss_kl = model.loss(batch_data,
args.beta,
nsamples=args.nsamples)
else:
loss, loss_rc, loss_kl = model.loss_iw(
batch_data, args.beta, nsamples=args.iw_train_nsamples, ns=ns)
assert (not loss_rc.requires_grad)
loss_rc = loss_rc.sum()
loss_kl = loss_kl.sum()
loss = loss.sum()
report_rec_loss += loss_rc.item()
report_kl_loss += loss_kl.item()
report_loss += loss.item()
mutual_info = calc_mi(model, test_data_batch)
test_loss = report_loss / report_num_sents
nll = (report_kl_loss + report_rec_loss) / report_num_sents
kl = report_kl_loss / report_num_sents
ppl = np.exp(nll * report_num_sents / report_num_words)
if verbose:
logging('%s --- avg_loss: %.4f, kl: %.4f, mi: %.4f, recon: %.4f, nll: %.4f, ppl: %.4f' % \
(mode, test_loss, report_kl_loss / report_num_sents, mutual_info,
report_rec_loss / report_num_sents, nll, ppl))
#sys.stdout.flush()
return test_loss, nll, kl, ppl, mutual_info
