predict.py

# Copyright (c) 2020 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.

import argparse
import os

import paddle
from model import SentenceTransformer

from paddlenlp.data import Pad, Tuple
from paddlenlp.transformers import AutoModel, AutoTokenizer

# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--params_path", type=str, default='./checkpoint/model_2700/model_state.pdparams', help="The path to model parameters to be loaded.")
parser.add_argument("--max_seq_length", default=50, type=int, help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
args = parser.parse_args()
# fmt: on


def convert_example(example, tokenizer, max_seq_length=512):
    """
    Builds model inputs from a sequence or a pair of sequence for sequence classification tasks
    by concatenating and adding special tokens. And creates a mask from the two sequences passed
    to be used in a sequence-pair classification task.

    A BERT sequence has the following format:

    - single sequence: ``[CLS] X [SEP]``
    - pair of sequences: ``[CLS] A [SEP] B [SEP]``

    A BERT sequence pair mask has the following format:
    ::
        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
        | first sequence    | second sequence |

    If only one sequence, only returns the first portion of the mask (0's).


    Args:
        example(obj:`list[str]`): List of input data, containing query, title and label if it have label.
        tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
            which contains most of the methods. Users should refer to the superclass for more information regarding methods.
        max_seq_len(obj:`int`): The maximum total input sequence length after tokenization.
            Sequences longer than this will be truncated, sequences shorter will be padded.

    Returns:
        query_input_ids(obj:`list[int]`): The list of query token ids.
        query_token_type_ids(obj: `list[int]`): List of query sequence pair mask.
        title_input_ids(obj:`list[int]`): The list of title token ids.
        title_token_type_ids(obj: `list[int]`): List of title sequence pair mask.
        label(obj:`numpy.array`, data type of int64, optional): The input label if not is_test.
    """
    query, title = example[0], example[1]

    query_encoded_inputs = tokenizer(text=query, max_seq_len=max_seq_length)
    query_input_ids = query_encoded_inputs["input_ids"]
    query_token_type_ids = query_encoded_inputs["token_type_ids"]

    title_encoded_inputs = tokenizer(text=title, max_seq_len=max_seq_length)
    title_input_ids = title_encoded_inputs["input_ids"]
    title_token_type_ids = title_encoded_inputs["token_type_ids"]

    return query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids


def predict(model, data, tokenizer, label_map, batch_size=1):
    """
    Predicts the data labels.

    Args:
        model (obj:`paddle.nn.Layer`): A model to classify texts.
        data (obj:`List(Example)`): The processed data whose each element is a Example (numedtuple) object.
            A Example object contains `text`(word_ids) and `se_len`(sequence length).
        tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
            which contains most of the methods. Users should refer to the superclass for more information regarding methods.
        label_map(obj:`dict`): The label id (key) to label str (value) map.
        batch_size(obj:`int`, defaults to 1): The number of batch.

    Returns:
        results(obj:`dict`): All the predictions labels.
    """
    examples = []
    for text_pair in data:
        query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids = convert_example(
            text_pair, tokenizer, max_seq_length=args.max_seq_length
        )
        examples.append((query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids))

    # Separates data into some batches.
    batches = [examples[idx : idx + batch_size] for idx in range(0, len(examples), batch_size)]
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # query_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # query_segment
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # title_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # title_segment
    ): [data for data in fn(samples)]

    results = []
    model.eval()
    for batch in batches:
        query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids = batchify_fn(batch)

        query_input_ids = paddle.to_tensor(query_input_ids)
        query_token_type_ids = paddle.to_tensor(query_token_type_ids)
        title_input_ids = paddle.to_tensor(title_input_ids)
        title_token_type_ids = paddle.to_tensor(title_token_type_ids)

        probs = model(
            query_input_ids,
            title_input_ids,
            query_token_type_ids=query_token_type_ids,
            title_token_type_ids=title_token_type_ids,
        )
        idx = paddle.argmax(probs, axis=1).numpy()
        idx = idx.tolist()
        labels = [label_map[i] for i in idx]
        results.extend(labels)
    return results


if __name__ == "__main__":
    paddle.set_device(args.device)

    # ErnieTinyTokenizer is special for ernie-tiny pretained model.
    tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh")

    data = [
        ["世界上什么东西最小", "世界上什么东西最小？"],
        ["光眼睛大就好看吗", "眼睛好看吗？"],
        ["小蝌蚪找妈妈怎么样", "小蝌蚪找妈妈是谁画的"],
    ]
    label_map = {0: "dissimilar", 1: "similar"}

    pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh")
    model = SentenceTransformer(pretrained_model)

    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)

    results = predict(model, data, tokenizer, label_map, batch_size=args.batch_size)
    for idx, text in enumerate(data):
        print("Data: {} \t Label: {}".format(text, results[idx]))