gistfile1.txt

from abc import ABC
import json
import logging
import os

import torch
from transformers import BertModel, BertTokenizer
from torch import nn

from ts.torch_handler.base_handler import BaseHandler

logger = logging.getLogger(__name__)

class OrderClassifier(nn.Module):
    def __init__(self, n_classes, path):
        super(OrderClassifier, self).__init__()

        self.bert = BertModel.from_pretrained(path)
        self.drop = nn.Dropout(p=0.3)
        self.out = nn.Linear(self.bert.config.hidden_size, n_classes)

    def forward(self, input_ids, attention_mask):
        _, pooled_output = self.bert(
            input_ids=input_ids,
            attention_mask=attention_mask
        )

        output = self.drop(pooled_output)
        return self.out(output)

class TransformersClassifierHandler(BaseHandler, ABC):
    """
    Transformers text classifier handler class. This handler takes a text (string) and
    as input and returns the classification text based on the serialized transformers checkpoint.
    """
    def __init__(self):
        super(TransformersClassifierHandler, self).__init__()
        self.initialized = False

    def initialize(self, ctx):
        self.manifest = ctx.manifest

        properties = ctx.system_properties
        model_dir = properties.get("model_dir")
        self.device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")

        # Read model serialize/pt file
        print("model: "+model_dir+"/best_model.bin")
        self.model = torch.load(model_dir+"/best_model.bin")
        #AutoModelForSequenceClassification.from_pretrained(model_dir)
        print("load model success")
        self.tokenizer = BertTokenizer.from_pretrained(model_dir)
        print("load tokenizer success")
        self.model.to(self.device)
        self.model.eval()
        print("eval success")

        logger.debug('Transformer model from path {0} loaded successfully'.format(model_dir))

        # Read the mapping file, index to object name
        mapping_file_path = os.path.join(model_dir, "index_to_name.json")

        if os.path.isfile(mapping_file_path):
            with open(mapping_file_path) as f:
                self.mapping = json.load(f)
        else:
            logger.warning('Missing the index_to_name.json file. Inference output will not include class name.')

        self.initialized = True

    def preprocess(self, data):
        """ Very basic preprocessing code - only tokenizes.
            Extend with your own preprocessing steps as needed.
        """
        text = data[0].get("data")
        if text is None:
            text = data[0].get("body")
        sentences = text.decode('utf-8')
        logger.info("Received text: '%s'", sentences)

        inputs = self.tokenizer.encode_plus(
            sentences,
            add_special_tokens=True,
            return_tensors="pt"
        )
        return inputs

    def inference(self, inputs):
        """
        Predict the class of a text using a trained transformer model.
        """
        # NOTE: This makes the assumption that your model expects text to be tokenized
        # with "input_ids" and "token_type_ids" - which is true for some popular transformer models, e.g. bert.
        # If your transformer model expects different tokenization, adapt this code to suit
        # its expected input format.
        prediction = self.model(
            inputs['input_ids'].to(self.device),
            token_type_ids=inputs['token_type_ids'].to(self.device)
        )[0].argmax().item()
        logger.info("Model predicted: '%s'", prediction)

        if self.mapping:
            prediction = self.mapping[str(prediction)]

        return [prediction]

    def postprocess(self, inference_output):
        # TODO: Add any needed post-processing of the model predictions here
        return inference_output


_service = TransformersClassifierHandler()


def handle(data, context):
    try:
        if not _service.initialized:
            _service.initialize(context)

        if data is None:
            return None

        data = _service.preprocess(data)
        data = _service.inference(data)
        data = _service.postprocess(data)

        return data
    except Exception as e:
        raise e