def _test(mod, doc, encoding):
"""Perform encoding specific serialization tests."""
# Encode/decode and re-assert document.
as_repr = tu.encode(doc, encoding)
as_doc_1 = tu.decode(as_repr, encoding)
tu.assert_doc(mod, as_doc_1)
# Re-encode.
as_repr_1 = tu.encode(as_doc_1, encoding)
# TODO - explore why sometimes XML encoding is problematic
# although all good when reencoding to json.
if len(as_repr) != len(as_repr_1) and encoding == 'xml':
as_repr = tu.encode(tu.decode(as_repr, encoding), 'json')
as_repr_1 = tu.encode(tu.decode(as_repr_1, encoding), 'json')
# Verify encoding equivalence.
tu.assert_int(len(as_repr), len(as_repr_1), msg=str(type(doc)))
tu.assert_str(as_repr, as_repr_1)
def _test(mod, doc, encoding):
"""Perform encoding specific serialization tests."""
# Encode/decode and re-assert document.
as_repr = tu.encode(doc, encoding)
as_doc_1 = tu.decode(as_repr, encoding)
tu.assert_doc(mod, as_doc_1)
# Re-encode and assert encodings.
as_repr_1 = tu.encode(as_doc_1, encoding)
tu.assert_int(len(as_repr), len(as_repr_1))
tu.assert_str(as_repr, as_repr_1)
def _test(mod, doc, encoding):
"""Perform encoding specific serialization tests."""
# Encode/decode and re-assert document.
as_repr = tu.encode(doc, encoding)
as_doc_1 = tu.decode(as_repr, encoding)
tu.assert_doc(mod, as_doc_1)
# Re-encode and assert encodings.
as_repr_1 = tu.encode(as_doc_1, encoding)
tu.assert_int(len(as_repr), len(as_repr_1))
tu.assert_str(as_repr, as_repr_1)
def predict(inputs_list, problem, predict_fn):
"""Encodes inputs, makes request to deployed TF model, and decodes outputs."""
assert isinstance(inputs_list, list)
fname = "inputs" if problem.has_inputs else "targets"
input_encoder = problem.feature_info[fname].encoder
encode_start = time.time()
input_ids_list = [
encode(inputs, input_encoder, add_eos=problem.has_inputs)
for inputs in inputs_list
]
encode_end = time.time()
examples = [
make_example(input_ids, problem, fname).SerializeToString()
for input_ids in input_ids_list
]
examples = {'input': examples}
predict_start = time.time()
predictions = predict_fn(examples)
predict_end = time.time()
output_decoder = problem.feature_info["targets"].encoder
decode_start = time.time()
outputs = [
(decode(output, output_decoder), score)
for output, score in zip(predictions["outputs"], predictions["scores"])
]
decode_end = time.time()
encode_time = (encode_end - encode_start) * 1000
predict_time = (predict_end - predict_start) * 1000
decode_time = (decode_end - decode_start) * 1000
total_time = (decode_end - encode_start) * 1000
print_str = """
Batch:{batch:d} \t
Encode:{encode:.3f} \t
Prediction:{predict:.3f} \t
Decode:{decode:.3f} \t
Total:{total:.3f}
"""
print(
print_str.format(batch=len(outputs),
encode=encode_time,
predict=predict_time,
decode=decode_time,
total=total_time))
return outputs