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141 lines
5.6 KiB
141 lines
5.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""FastText for Evaluation"""
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import argparse
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import numpy as np
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import mindspore.nn as nn
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import mindspore.common.dtype as mstype
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import mindspore.ops.operations as P
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from mindspore.common.tensor import Tensor
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.c_transforms as deC
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from mindspore import context
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from src.fasttext_model import FastText
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parser = argparse.ArgumentParser(description='fasttext')
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parser.add_argument('--data_path', type=str, help='infer dataset path..')
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parser.add_argument('--data_name', type=str, required=True, default='ag',
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help='dataset name. eg. ag, dbpedia')
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parser.add_argument("--model_ckpt", type=str, required=True,
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help="existed checkpoint address.")
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args = parser.parse_args()
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if args.data_name == "ag":
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from src.config import config_ag as config
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target_label1 = ['0', '1', '2', '3']
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elif args.data_name == 'dbpedia':
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from src.config import config_db as config
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target_label1 = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13']
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elif args.data_name == 'yelp_p':
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from src.config import config_yelpp as config
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target_label1 = ['0', '1']
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context.set_context(
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mode=context.GRAPH_MODE,
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save_graphs=False,
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device_target="Ascend")
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class FastTextInferCell(nn.Cell):
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"""
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Encapsulation class of FastText network infer.
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Args:
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network (nn.Cell): FastText model.
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Returns:
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Tuple[Tensor, Tensor], predicted_ids
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"""
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def __init__(self, network):
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super(FastTextInferCell, self).__init__(auto_prefix=False)
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self.network = network
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self.argmax = P.ArgMaxWithValue(axis=1, keep_dims=True)
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self.log_softmax = nn.LogSoftmax(axis=1)
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def construct(self, src_tokens, src_tokens_lengths):
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"""construct fasttext infer cell"""
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prediction = self.network(src_tokens, src_tokens_lengths)
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predicted_idx = self.log_softmax(prediction)
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predicted_idx, _ = self.argmax(predicted_idx)
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return predicted_idx
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def load_infer_dataset(batch_size, datafile, bucket):
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"""data loader for infer"""
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def batch_per_bucket(bucket_length, input_file):
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input_file = input_file + '/test_dataset_bs_' + str(bucket_length) + '.mindrecord'
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if not input_file:
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raise FileNotFoundError("input file parameter must not be empty.")
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data_set = ds.MindDataset(input_file,
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columns_list=['src_tokens', 'src_tokens_length', 'label_idx'])
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type_cast_op = deC.TypeCast(mstype.int32)
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data_set = data_set.map(operations=type_cast_op, input_columns="src_tokens")
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data_set = data_set.map(operations=type_cast_op, input_columns="src_tokens_length")
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data_set = data_set.map(operations=type_cast_op, input_columns="label_idx")
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data_set = data_set.batch(batch_size, drop_remainder=False)
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return data_set
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for i, _ in enumerate(bucket):
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bucket_len = bucket[i]
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ds_per = batch_per_bucket(bucket_len, datafile)
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if i == 0:
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data_set = ds_per
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else:
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data_set = data_set + ds_per
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return data_set
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def run_fasttext_infer():
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"""run infer with FastText"""
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dataset = load_infer_dataset(batch_size=config.batch_size, datafile=args.data_path, bucket=config.test_buckets)
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fasttext_model = FastText(config.vocab_size, config.embedding_dims, config.num_class)
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parameter_dict = load_checkpoint(args.model_ckpt)
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load_param_into_net(fasttext_model, parameter_dict=parameter_dict)
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ft_infer = FastTextInferCell(fasttext_model)
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model = Model(ft_infer)
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predictions = []
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target_sens = []
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for batch in dataset.create_dict_iterator(output_numpy=True, num_epochs=1):
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target_sens.append(batch['label_idx'])
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src_tokens = Tensor(batch['src_tokens'], mstype.int32)
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src_tokens_length = Tensor(batch['src_tokens_length'], mstype.int32)
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predicted_idx = model.predict(src_tokens, src_tokens_length)
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predictions.append(predicted_idx.asnumpy())
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from sklearn.metrics import accuracy_score, classification_report
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target_sens = np.array(target_sens).flatten()
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merge_target_sens = []
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for target_sen in target_sens:
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merge_target_sens.extend(target_sen)
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target_sens = merge_target_sens
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predictions = np.array(predictions).flatten()
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merge_predictions = []
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for prediction in predictions:
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merge_predictions.extend(prediction)
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predictions = merge_predictions
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acc = accuracy_score(target_sens, predictions)
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result_report = classification_report(target_sens, predictions, target_names=target_label1)
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print("********Accuracy: ", acc)
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print(result_report)
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if __name__ == '__main__':
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run_fasttext_infer()
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