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predict.py
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# 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)
else:
raise ValueError("Please set --params_path with correct pretrained model file")
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]))