You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
88 lines
3.9 KiB
88 lines
3.9 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
|
|
#
|
|
# 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.
|
|
# ============================================================================
|
|
"""Transformer evaluation script."""
|
|
import os
|
|
import argparse
|
|
import mindspore.common.dtype as mstype
|
|
from mindspore.common.tensor import Tensor
|
|
from mindspore.train.model import Model
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
|
from mindspore import context
|
|
from src.dataset import create_gru_dataset
|
|
from src.seq2seq import Seq2Seq
|
|
from src.gru_for_infer import GRUInferCell
|
|
from src.config import config
|
|
|
|
def run_gru_eval():
|
|
"""
|
|
Transformer evaluation.
|
|
"""
|
|
parser = argparse.ArgumentParser(description='GRU eval')
|
|
parser.add_argument("--device_target", type=str, default="Ascend",
|
|
help="device where the code will be implemented, default is Ascend")
|
|
parser.add_argument('--device_id', type=int, default=0, help='device id of GPU or Ascend, default is 0')
|
|
parser.add_argument('--device_num', type=int, default=1, help='Use device nums, default is 1')
|
|
parser.add_argument('--ckpt_file', type=str, default="", help='ckpt file path')
|
|
parser.add_argument("--dataset_path", type=str, default="",
|
|
help="Dataset path, default: f`sns.")
|
|
args = parser.parse_args()
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, reserve_class_name_in_scope=False, \
|
|
device_id=args.device_id, save_graphs=False)
|
|
mindrecord_file = args.dataset_path
|
|
if not os.path.exists(mindrecord_file):
|
|
print("dataset file {} not exists, please check!".format(mindrecord_file))
|
|
raise ValueError(mindrecord_file)
|
|
dataset = create_gru_dataset(epoch_count=config.num_epochs, batch_size=config.eval_batch_size, \
|
|
dataset_path=mindrecord_file, rank_size=args.device_num, rank_id=0, do_shuffle=False, is_training=False)
|
|
dataset_size = dataset.get_dataset_size()
|
|
print("dataset size is {}".format(dataset_size))
|
|
network = Seq2Seq(config, is_training=False)
|
|
network = GRUInferCell(network)
|
|
network.set_train(False)
|
|
if args.ckpt_file != "":
|
|
parameter_dict = load_checkpoint(args.ckpt_file)
|
|
load_param_into_net(network, parameter_dict)
|
|
model = Model(network)
|
|
|
|
predictions = []
|
|
source_sents = []
|
|
target_sents = []
|
|
eval_text_len = 0
|
|
for batch in dataset.create_dict_iterator(output_numpy=True, num_epochs=1):
|
|
source_sents.append(batch["source_ids"])
|
|
target_sents.append(batch["target_ids"])
|
|
source_ids = Tensor(batch["source_ids"], mstype.int32)
|
|
target_ids = Tensor(batch["target_ids"], mstype.int32)
|
|
predicted_ids = model.predict(source_ids, target_ids)
|
|
print("predicts is ", predicted_ids.asnumpy())
|
|
print("target_ids is ", target_ids)
|
|
predictions.append(predicted_ids.asnumpy())
|
|
eval_text_len = eval_text_len + 1
|
|
|
|
f_output = open(config.output_file, 'w')
|
|
f_target = open(config.target_file, "w")
|
|
for batch_out, true_sentence in zip(predictions, target_sents):
|
|
for i in range(config.eval_batch_size):
|
|
target_ids = [str(x) for x in true_sentence[i].tolist()]
|
|
f_target.write(" ".join(target_ids) + "\n")
|
|
token_ids = [str(x) for x in batch_out[i].tolist()]
|
|
f_output.write(" ".join(token_ids) + "\n")
|
|
f_output.close()
|
|
f_target.close()
|
|
|
|
if __name__ == "__main__":
|
|
run_gru_eval()
|