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101 lines
4.1 KiB
101 lines
4.1 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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"""export checkpoint file into models"""
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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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from mindspore.common.tensor import Tensor
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import mindspore.ops.operations as P
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from mindspore import context
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from mindspore.train.serialization import load_checkpoint, export, load_param_into_net
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from src.fasttext_model import FastText
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parser = argparse.ArgumentParser(description='fasttexts')
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parser.add_argument('--device_target', type=str, choices=["Ascend", "GPU", "CPU"],
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default='Ascend', help='Device target')
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parser.add_argument('--device_id', type=int, default=0, help='Device id')
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parser.add_argument('--ckpt_file', type=str, required=True, help='Checkpoint file path')
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parser.add_argument('--file_name', type=str, default='fasttexts', help='Output file name')
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parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR',
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help='Output file format')
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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, yelp_p')
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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 FastTextInferExportCell(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(FastTextInferExportCell, 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 run_fasttext_export():
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"""export function"""
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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.ckpt_file)
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load_param_into_net(fasttext_model, parameter_dict)
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ft_infer = FastTextInferExportCell(fasttext_model)
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if args.data_name == "ag":
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src_tokens_shape = [config.batch_size, 467]
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src_tokens_length_shape = [config.batch_size, 1]
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elif args.data_name == 'dbpedia':
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src_tokens_shape = [config.batch_size, 1120]
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src_tokens_length_shape = [config.batch_size, 1]
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elif args.data_name == 'yelp_p':
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src_tokens_shape = [config.batch_size, 2955]
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src_tokens_length_shape = [config.batch_size, 1]
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file_name = args.file_name + '_' + args.data_name
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src_tokens = Tensor(np.ones((src_tokens_shape)).astype(np.int32))
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src_tokens_length = Tensor(np.ones((src_tokens_length_shape)).astype(np.int32))
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export(ft_infer, src_tokens, src_tokens_length, file_name=file_name, file_format=args.file_format)
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if __name__ == '__main__':
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run_fasttext_export()
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