export_model.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

from functools import partial
import argparse
import os
import random
import time

import numpy as np
import paddle
import paddle.nn.functional as F

from paddlenlp.data import Stack, Tuple, Pad
import paddlenlp as ppnlp

# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument("--params_path", type=str, required=True, default='./checkpoint/model_900/model_state.pdparams', help="The path to model parameters to be loaded.")
parser.add_argument("--output_path", type=str, default='./static_graph_params', help="The path of model parameter in static graph to be saved.")
args = parser.parse_args()
# yapf: enable

if __name__ == "__main__":

    # ErnieTinyTokenizer is special for ernie-tiny pretained model.
    tokenizer = ppnlp.transformers.ErnieTinyTokenizer.from_pretrained(
        'ernie-tiny')

    # The number of labels should be in accordance with the training dataset.
    label_map = {0: 'negative', 1: 'positive'}
    model = ppnlp.transformers.ErnieForSequenceClassification.from_pretrained(
        "ernie-tiny", num_classes=len(label_map))

    if args.params_path and os.path.isfile(args.params_path):
        state_dict = paddle.load(args.params_path)
        model.set_dict(state_dict)
        print("Loaded parameters from %s" % args.params_path)
    model.eval()

    # Convert to static graph with specific input description
    model = paddle.jit.to_static(
        model,
        input_spec=[
            paddle.static.InputSpec(
                shape=[None, None], dtype="int64"),  # input_ids
            paddle.static.InputSpec(
                shape=[None, None], dtype="int64")  # segment_ids
        ])
    # Save in static graph model.
    paddle.jit.save(model, args.output_path)