predict.py

#coding:utf-8
#   Copyright (c) 2019 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.
"""Finetuning on classification task """

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import ast
import numpy as np
import os
import time
import paddle
import paddle.fluid as fluid
import paddlehub as hub

# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--batch_size",     type=int,   default=1, help="Total examples' number in batch for training.")
parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_pyreader", type=ast.literal_eval, default=False, help="Whether use pyreader to feed data.")
parser.add_argument("--dataset", type=str, default="chnsenticorp", help="The choice of dataset")
parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
args = parser.parse_args()
# yapf: enable.

if __name__ == '__main__':
    dataset = None
    metrics_choices = []
    # Download dataset and use ClassifyReader to read dataset
    if args.dataset.lower() == "chnsenticorp":
        dataset = hub.dataset.ChnSentiCorp()
        module = hub.Module(name="ernie")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "nlpcc_dbqa":
        dataset = hub.dataset.NLPCC_DBQA()
        module = hub.Module(name="ernie")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "lcqmc":
        dataset = hub.dataset.LCQMC()
        module = hub.Module(name="ernie")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "mrpc":
        dataset = hub.dataset.GLUE("MRPC")
        module = hub.Module(name="ernie_v2_eng_base")
        metrics_choices = ["f1", "acc"]
    # The first metric will be choose to eval. Ref: task.py:799
    elif args.dataset.lower() == "qqp":
        dataset = hub.dataset.GLUE("QQP")
        module = hub.Module(name="ernie_v2_eng_base")
        metrics_choices = ["f1", "acc"]
    elif args.dataset.lower() == "sst-2":
        dataset = hub.dataset.GLUE("SST-2")
        module = hub.Module(name="ernie_v2_eng_base")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "cola":
        dataset = hub.dataset.GLUE("CoLA")
        module = hub.Module(name="ernie_v2_eng_base")
        metrics_choices = ["matthews", "acc"]
    elif args.dataset.lower() == "qnli":
        dataset = hub.dataset.GLUE("QNLI")
        module = hub.Module(name="ernie_v2_eng_base")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "rte":
        dataset = hub.dataset.GLUE("RTE")
        module = hub.Module(name="ernie_v2_eng_base")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "mnli" or args.dataset.lower() == "mnli_m":
        dataset = hub.dataset.GLUE("MNLI_m")
        module = hub.Module(name="ernie_v2_eng_base")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "mnli_mm":
        dataset = hub.dataset.GLUE("MNLI_mm")
        module = hub.Module(name="ernie_v2_eng_base")
        metrics_choices = ["acc"]
    elif args.dataset.lower().startswith("xnli"):
        dataset = hub.dataset.XNLI(language=args.dataset.lower()[-2:])
        module = hub.Module(name="bert_multi_cased_L-12_H-768_A-12")
        metrics_choices = ["acc"]
    else:
        raise ValueError("%s dataset is not defined" % args.dataset)

    support_metrics = ["acc", "f1", "matthews"]
    for metric in metrics_choices:
        if metric not in support_metrics:
            raise ValueError("\"%s\" metric is not defined" % metric)

    inputs, outputs, program = module.context(
        trainable=True, max_seq_len=args.max_seq_len)
    reader = hub.reader.ClassifyReader(
        dataset=dataset,
        vocab_path=module.get_vocab_path(),
        max_seq_len=args.max_seq_len)

    # Construct transfer learning network
    # Use "pooled_output" for classification tasks on an entire sentence.
    # Use "sequence_output" for token-level output.
    pooled_output = outputs["pooled_output"]

    # Setup feed list for data feeder
    # Must feed all the tensor of ERNIE's module need
    feed_list = [
        inputs["input_ids"].name,
        inputs["position_ids"].name,
        inputs["segment_ids"].name,
        inputs["input_mask"].name,
    ]

    # Setup runing config for PaddleHub Finetune API
    config = hub.RunConfig(
        use_data_parallel=False,
        use_pyreader=args.use_pyreader,
        use_cuda=args.use_gpu,
        batch_size=args.batch_size,
        enable_memory_optim=False,
        checkpoint_dir=args.checkpoint_dir,
        strategy=hub.finetune.strategy.DefaultFinetuneStrategy())

    # Define a classfication finetune task by PaddleHub's API
    cls_task = hub.TextClassifierTask(
        data_reader=reader,
        feature=pooled_output,
        feed_list=feed_list,
        num_classes=dataset.num_labels,
        config=config,
        metrics_choices=metrics_choices)

    # Data to be prdicted
    data = [[d.text_a, d.text_b] for d in dataset.get_dev_examples()[:3]]

    index = 0
    run_states = cls_task.predict(data=data)
    results = [run_state.run_results for run_state in run_states]
    for batch_result in results:
        # get predict index
        batch_result = np.argmax(batch_result, axis=2)[0]
        for result in batch_result:
            print("%s\tpredict=%s" % (data[index][0], result))
            index += 1