FastDeploy/python/fastdeploy/vision/classification/ppcls/__init__.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.

from __future__ import absolute_import
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C


class PaddleClasPreprocessor:
    def __init__(self, config_file):
        """Create a preprocessor for PaddleClasModel from configuration file

        :param config_file: (str)Path of configuration file, e.g resnet50/inference_cls.yaml
        """
        self._preprocessor = C.vision.classification.PaddleClasPreprocessor(
            config_file)

    def run(self, input_ims):
        """Preprocess input images for PaddleClasModel

        :param: input_ims: (list of numpy.ndarray)The input image
        :return: list of FDTensor
        """
        return self._preprocessor.run(input_ims)

    def use_cuda(self, enable_cv_cuda=False, gpu_id=-1):
        """Use CUDA preprocessors

        :param: enable_cv_cuda: Whether to enable CV-CUDA
        :param: gpu_id: GPU device id
        """
        return self._preprocessor.use_cuda(enable_cv_cuda, gpu_id)

    def disable_normalize(self):
        """
        This function will disable normalize in preprocessing step.
        """
        self._preprocessor.disable_normalize()

    def disable_permute(self):
        """
        This function will disable hwc2chw in preprocessing step.
        """
        self._preprocessor.disable_permute()


class PaddleClasPostprocessor:
    def __init__(self, topk=1):
        """Create a postprocessor for PaddleClasModel

        :param topk: (int)Filter the top k classify label
        """
        self._postprocessor = C.vision.classification.PaddleClasPostprocessor(
            topk)

    def run(self, runtime_results):
        """Postprocess the runtime results for PaddleClasModel

        :param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
        :return: list of ClassifyResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
        """
        return self._postprocessor.run(runtime_results)


class PaddleClasModel(FastDeployModel):
    def __init__(self,
                 model_file,
                 params_file,
                 config_file,
                 runtime_option=None,
                 model_format=ModelFormat.PADDLE):
        """Load a image classification model exported by PaddleClas.

        :param model_file: (str)Path of model file, e.g resnet50/inference.pdmodel
        :param params_file: (str)Path of parameters file, e.g resnet50/inference.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
        :param config_file: (str) Path of configuration file for deploy, e.g resnet50/inference_cls.yaml
        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
        """

        super(PaddleClasModel, self).__init__(runtime_option)
        self._model = C.vision.classification.PaddleClasModel(
            model_file, params_file, config_file, self._runtime_option,
            model_format)
        assert self.initialized, "PaddleClas model initialize failed."

    def clone(self):
        """Clone PaddleClasModel object

        :return: a new PaddleClasModel object
        """

        class PaddleClasCloneModel(PaddleClasModel):
            def __init__(self, model):
                self._model = model

        clone_model = PaddleClasCloneModel(self._model.clone())
        return clone_model

    def predict(self, im, topk=1):
        """Classify an input image

        :param im: (numpy.ndarray) The input image data, a 3-D array with layout HWC, BGR format
        :param topk: (int) Filter the topk classify result, default 1
        :return: ClassifyResult
        """

        self.postprocessor.topk = topk
        return self._model.predict(im)

    def batch_predict(self, images):
        """Classify a batch of input image

        :param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
        :return list of ClassifyResult
        """

        return self._model.batch_predict(images)

    @property
    def preprocessor(self):
        """Get PaddleClasPreprocessor object of the loaded model

        :return PaddleClasPreprocessor
        """
        return self._model.preprocessor

    @property
    def postprocessor(self):
        """Get PaddleClasPostprocessor object of the loaded model

        :return PaddleClasPostprocessor
        """
        return self._model.postprocessor