mindspore/model_zoo/official/nlp/fasttext/export.py

# Copyright 2020 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.
# ============================================================================
"""export checkpoint file into models"""

import argparse
import numpy as np
import mindspore.nn as nn
from mindspore.common.tensor import Tensor
import mindspore.ops.operations as P
from mindspore import context
from mindspore.train.serialization import load_checkpoint, export, load_param_into_net
from  src.fasttext_model import FastText

parser = argparse.ArgumentParser(description='fasttexts')
parser.add_argument('--device_target', type=str, choices=["Ascend", "GPU", "CPU"],
                    default='Ascend', help='Device target')
parser.add_argument('--device_id', type=int, default=0, help='Device id')
parser.add_argument('--ckpt_file', type=str, required=True, help='Checkpoint file path')
parser.add_argument('--file_name', type=str, default='fasttexts', help='Output file name')
parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR',
                    help='Output file format')
parser.add_argument('--data_name', type=str, required=True, default='ag',
                    help='Dataset name. eg. ag, dbpedia, yelp_p')
args = parser.parse_args()

if args.data_name == "ag":
    from src.config import config_ag as config
    from src.config import config_ag_gpu as config_gpu
    target_label1 = ['0', '1', '2', '3']
elif args.data_name == 'dbpedia':
    from src.config import config_db as config
    from src.config import config_db_gpu as config_gpu
    target_label1 = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13']
elif args.data_name == 'yelp_p':
    from  src.config import config_yelpp as config
    from src.config import config_yelpp_gpu as config_gpu
    target_label1 = ['0', '1']

context.set_context(
    mode=context.GRAPH_MODE,
    save_graphs=False,
    device_target=args.device_target)
config = config_ascend if args.device_target == 'Ascend' else config_gpu

class FastTextInferExportCell(nn.Cell):
    """
    Encapsulation class of FastText network infer.

    Args:
        network (nn.Cell): FastText model.

    Returns:
        Tuple[Tensor, Tensor], predicted_ids
    """
    def __init__(self, network):
        super(FastTextInferExportCell, self).__init__(auto_prefix=False)
        self.network = network
        self.argmax = P.ArgMaxWithValue(axis=1, keep_dims=True)
        self.log_softmax = nn.LogSoftmax(axis=1)

    def construct(self, src_tokens, src_tokens_lengths):
        """construct fasttext infer cell"""
        prediction = self.network(src_tokens, src_tokens_lengths)
        predicted_idx = self.log_softmax(prediction)
        predicted_idx, _ = self.argmax(predicted_idx)

        return predicted_idx

def run_fasttext_export():
    """export function"""
    fasttext_model = FastText(config.vocab_size, config.embedding_dims, config.num_class)
    parameter_dict = load_checkpoint(args.ckpt_file)
    load_param_into_net(fasttext_model, parameter_dict)
    ft_infer = FastTextInferExportCell(fasttext_model)
    batch_size = config.batch_size
    if args.device_target == 'GPU':
        batch_size = config.distribute_batch_size
    if args.data_name == "ag":
        src_tokens_shape = [batch_size, 467]
        src_tokens_length_shape = [batch_size, 1]
    elif args.data_name == 'dbpedia':
        src_tokens_shape = [batch_size, 1120]
        src_tokens_length_shape = [batch_size, 1]
    elif args.data_name == 'yelp_p':
        src_tokens_shape = [batch_size, 2955]
        src_tokens_length_shape = [batch_size, 1]

    file_name = args.file_name + '_' + args.data_name
    src_tokens = Tensor(np.ones((src_tokens_shape)).astype(np.int32))
    src_tokens_length = Tensor(np.ones((src_tokens_length_shape)).astype(np.int32))
    export(ft_infer, src_tokens, src_tokens_length, file_name=file_name, file_format=args.file_format)

if __name__ == '__main__':
    run_fasttext_export()