def _trim_and_decode(ids):
"""Trim EOS and PAD tokens from ids, and decode to return a string."""
subtokenizer = Subtokenizer(FLAGS.vocab_file)
try:
index = list(ids).index(tokenizer.EOS_ID)
return subtokenizer.decode(ids[:index])
except ValueError: # No EOS found in sequence
return subtokenizer.decode(ids)
def eval_func(infer_graph, iteration=-1):
if isinstance(infer_graph, tf.compat.v1.GraphDef):
graph = tf.Graph()
with graph.as_default():
tf.import_graph_def(infer_graph, name='')
infer_graph = graph
subtokenizer = Subtokenizer(FLAGS.vocab_file)
input_tensor = infer_graph.get_tensor_by_name('input_tensor:0')
output_tensor = infer_graph.get_tensor_by_name(\
'model/Transformer/strided_slice_19:0')
ds = Dataset(FLAGS.inputs_file, FLAGS.reference_file, FLAGS.vocab_file)
from lpot.data import DATALOADERS
dataloader = DATALOADERS['tensorflow'](ds,
batch_size=FLAGS.batch_size,
collate_fn=collate_fn)
config = tf.compat.v1.ConfigProto()
config.use_per_session_threads = 1
config.inter_op_parallelism_threads = 1
sess = tf.compat.v1.Session(graph=infer_graph, config=config)
time_list = []
bleu_eval = bleu()
predictions = []
labels = []
warmup = 10
if iteration != -1:
assert iteration >= warmup, 'iteration must be larger than warmup'
for idx, (input_data, label) in enumerate(dataloader):
if idx < iteration or iteration == -1:
time_start = time.time()
out = sess.run([output_tensor], {input_tensor: input_data})
duration = time.time() - time_start
time_list.append(duration)
predictions.append(out)
labels.extend(label)
else:
break
latency = np.array(time_list[warmup:]).mean() / FLAGS.batch_size
print('Batch size = {}'.format(FLAGS.batch_size))
print('Latency: {:.3f} ms'.format(latency * 1000))
print('Throughput: {:.3f} items/sec'.format(1. / latency))
# only calculate accuracy when running out all predictions
if iteration == -1:
decode = []
for i, tr in enumerate(predictions):
for j, itr in enumerate(tr):
for k, otr in enumerate(itr):
try:
index = list(otr).index(tokenizer.EOS_ID)
decode.append(subtokenizer.decode(otr[:index]))
except:
decode.append(subtokenizer.decode(otr))
bleu_eval.update(decode, labels)
print('Accuracy is {:.3f}'.format(bleu_eval.result()))
return bleu_eval.result()