wehub-resource-sync
100 lines
4.0 KiB
Python
100 lines
4.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import io
|
|
import sys
|
|
|
|
sys.path.append("../../")
|
|
|
|
import numpy as np # noqa: E402
|
|
import paddle # noqa: E402
|
|
from args import parse_args # noqa: E402
|
|
from data import create_infer_loader # noqa: E402
|
|
from predict import post_process_seq # noqa: E402
|
|
|
|
from paddlenlp.data import Vocab # noqa: E402
|
|
from paddlenlp.datasets import load_dataset # noqa: E402
|
|
from paddlenlp.metrics import BLEU # noqa: E402
|
|
|
|
|
|
class Predictor(object):
|
|
def __init__(self, predictor, input_handles, output_handles):
|
|
self.predictor = predictor
|
|
self.input_handles = input_handles
|
|
self.output_handles = output_handles
|
|
|
|
@classmethod
|
|
def create_predictor(cls, args):
|
|
config = paddle.inference.Config(args.export_path + ".pdmodel", args.export_path + ".pdiparams")
|
|
if args.device == "gpu":
|
|
# set GPU configs accordingly
|
|
config.enable_use_gpu(100, 0)
|
|
elif args.device == "cpu":
|
|
# set CPU configs accordingly,
|
|
# such as enable_mkldnn, set_cpu_math_library_num_threads
|
|
config.disable_gpu()
|
|
elif args.device == "xpu":
|
|
# set XPU configs accordingly
|
|
config.enable_xpu(100)
|
|
config.switch_use_feed_fetch_ops(False)
|
|
predictor = paddle.inference.create_predictor(config)
|
|
input_handles = [predictor.get_input_handle(name) for name in predictor.get_input_names()]
|
|
output_handles = [predictor.get_output_handle(name) for name in predictor.get_output_names()]
|
|
return cls(predictor, input_handles, output_handles)
|
|
|
|
def predict_batch(self, data):
|
|
for input_field, input_handle in zip(data, self.input_handles):
|
|
input_handle.copy_from_cpu(input_field.numpy() if isinstance(input_field, paddle.Tensor) else input_field)
|
|
self.predictor.run()
|
|
output = [output_handle.copy_to_cpu() for output_handle in self.output_handles]
|
|
return output
|
|
|
|
def predict(self, dataloader, infer_output_file, trg_idx2word, bos_id, eos_id):
|
|
cand_list = []
|
|
with io.open(infer_output_file, "w", encoding="utf-8") as f:
|
|
for data in dataloader():
|
|
finished_seq = self.predict_batch(data)[0]
|
|
finished_seq = finished_seq[:, :, np.newaxis] if len(finished_seq.shape) == 2 else finished_seq
|
|
finished_seq = np.transpose(finished_seq, [0, 2, 1])
|
|
for ins in finished_seq:
|
|
for beam_idx, beam in enumerate(ins):
|
|
id_list = post_process_seq(beam, bos_id, eos_id)
|
|
word_list = [trg_idx2word[id] for id in id_list]
|
|
sequence = " ".join(word_list) + "\n"
|
|
f.write(sequence)
|
|
cand_list.append(word_list)
|
|
break
|
|
|
|
test_ds = load_dataset("iwslt15", splits="test")
|
|
bleu = BLEU()
|
|
for i, data in enumerate(test_ds):
|
|
ref = data["vi"].split()
|
|
bleu.add_inst(cand_list[i], [ref])
|
|
print("BLEU score is %s." % bleu.score())
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
|
|
predictor = Predictor.create_predictor(args)
|
|
test_loader, src_vocab_size, tgt_vocab_size, bos_id, eos_id = create_infer_loader(args)
|
|
tgt_vocab = Vocab.load_vocabulary(**test_loader.dataset.vocab_info["vi"])
|
|
trg_idx2word = tgt_vocab.idx_to_token
|
|
|
|
predictor.predict(test_loader, args.infer_output_file, trg_idx2word, bos_id, eos_id)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|