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- # 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 import log as logger
- 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)
- if args.device_target == "GPU":
- if config.compute_type != mstype.float32:
- logger.warning('GPU only support fp32 temporarily, run with fp32.')
- config.compute_type = mstype.float32
- 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()
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