PaddleSpeech/paddlespeech/server/engine/cls/paddleinference/cls_engine.py

# Copyright (c) 2022 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 io
import os
import time
from collections import OrderedDict
from typing import Optional

import numpy as np
import paddle
import yaml

from .pretrained_models import pretrained_models
from paddlespeech.cli.cls.infer import CLSExecutor
from paddlespeech.cli.log import logger
from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.paddle_predictor import init_predictor
from paddlespeech.server.utils.paddle_predictor import run_model

__all__ = ['CLSEngine', 'PaddleCLSConnectionHandler']


class CLSServerExecutor(CLSExecutor):
    def __init__(self):
        super().__init__()
        self.pretrained_models = pretrained_models

    def _init_from_path(
            self,
            model_type: str='panns_cnn14',
            cfg_path: Optional[os.PathLike]=None,
            model_path: Optional[os.PathLike]=None,
            params_path: Optional[os.PathLike]=None,
            label_file: Optional[os.PathLike]=None,
            predictor_conf: dict=None, ):
        """
        Init model and other resources from a specific path.
        """

        if cfg_path is None or model_path is None or params_path is None or label_file is None:
            tag = model_type + '-' + '32k'
            self.res_path = self._get_pretrained_path(tag)
            self.cfg_path = os.path.join(
                self.res_path, self.pretrained_models[tag]['cfg_path'])
            self.model_path = os.path.join(
                self.res_path, self.pretrained_models[tag]['model_path'])
            self.params_path = os.path.join(
                self.res_path, self.pretrained_models[tag]['params_path'])
            self.label_file = os.path.join(
                self.res_path, self.pretrained_models[tag]['label_file'])
        else:
            self.cfg_path = os.path.abspath(cfg_path)
            self.model_path = os.path.abspath(model_path)
            self.params_path = os.path.abspath(params_path)
            self.label_file = os.path.abspath(label_file)

        logger.info(self.cfg_path)
        logger.info(self.model_path)
        logger.info(self.params_path)
        logger.info(self.label_file)

        # config
        with open(self.cfg_path, 'r') as f:
            self._conf = yaml.safe_load(f)
        logger.info("Read cfg file successfully.")

        # labels
        self._label_list = []
        with open(self.label_file, 'r') as f:
            for line in f:
                self._label_list.append(line.strip())
        logger.info("Read label file successfully.")

        # Create predictor
        self.predictor_conf = predictor_conf
        self.predictor = init_predictor(
            model_file=self.model_path,
            params_file=self.params_path,
            predictor_conf=self.predictor_conf)
        logger.info("Create predictor successfully.")

    @paddle.no_grad()
    def infer(self):
        """
        Model inference and result stored in self.output.
        """
        output = run_model(self.predictor, [self._inputs['feats'].numpy()])
        self._outputs['logits'] = output[0]


class CLSEngine(BaseEngine):
    """CLS server engine

    Args:
        metaclass: Defaults to Singleton.
    """

    def __init__(self):
        super(CLSEngine, self).__init__()

    def init(self, config: dict) -> bool:
        """init engine resource

        Args:
            config_file (str): config file

        Returns:
            bool: init failed or success
        """
        self.executor = CLSServerExecutor()
        self.config = config
        self.engine_type = "inference"

        try:
            if self.config.predictor_conf.device is not None:
                self.device = self.config.predictor_conf.device
            else:
                self.device = paddle.get_device()
            paddle.set_device(self.device)
        except Exception as e:
            logger.error(
                "Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
            )
            logger.error(e)
            return False

        try:
            self.executor._init_from_path(
                self.config.model_type, self.config.cfg_path,
                self.config.model_path, self.config.params_path,
                self.config.label_file, self.config.predictor_conf)

        except Exception as e:
            logger.error("Initialize CLS server engine Failed.")
            logger.error(e)
            return False

        logger.info("Initialize CLS server engine successfully.")
        return True


class PaddleCLSConnectionHandler(CLSServerExecutor):
    def __init__(self, cls_engine):
        """The PaddleSpeech CLS Server Connection Handler
           This connection process every cls server request
        Args:
            cls_engine (CLSEngine): The CLS engine
        """
        super().__init__()
        logger.info(
            "Create PaddleCLSConnectionHandler to process the cls request")

        self._inputs = OrderedDict()
        self._outputs = OrderedDict()
        self.cls_engine = cls_engine
        self.executor = self.cls_engine.executor
        self._conf = self.executor._conf
        self._label_list = self.executor._label_list
        self.predictor = self.executor.predictor

    def run(self, audio_data):
        """engine run 

        Args:
            audio_data (bytes): base64.b64decode
        """

        self.preprocess(io.BytesIO(audio_data))
        st = time.time()
        self.infer()
        infer_time = time.time() - st

        logger.info("inference time: {}".format(infer_time))
        logger.info("cls engine type: inference")

    def postprocess(self, topk: int):
        """postprocess
        """
        assert topk <= len(
            self._label_list), 'Value of topk is larger than number of labels.'

        result = np.squeeze(self._outputs['logits'], axis=0)
        topk_idx = (-result).argsort()[:topk]
        topk_results = []
        for idx in topk_idx:
            res = {}
            label, score = self._label_list[idx], result[idx]
            res['class_name'] = label
            res['prob'] = score
            topk_results.append(res)

        return topk_results