PaddleX/libs/ultra-infer/python/ultra_infer/vision/detection/contrib/yolov8.py

# Copyright (c) 2024 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

from .... import UltraInferModel, ModelFormat
from .... import c_lib_wrap as C


class YOLOv8Preprocessor:
    def __init__(self):
        """Create a preprocessor for YOLOv8"""
        self._preprocessor = C.vision.detection.YOLOv8Preprocessor()

    def run(self, input_ims):
        """Preprocess input images for YOLOv8

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

    @property
    def size(self):
        """
        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
        """
        return self._preprocessor.size

    @property
    def padding_value(self):
        """
        padding value for preprocessing, default [114.0, 114.0, 114.0]
        """
        #  padding value, size should be the same as channels
        return self._preprocessor.padding_value

    @property
    def is_scale_up(self):
        """
        is_scale_up for preprocessing, the input image only can be zoom out, the maximum resize scale cannot exceed 1.0, default true
        """
        return self._preprocessor.is_scale_up

    @property
    def is_mini_pad(self):
        """
        is_mini_pad for preprocessing, pad to the minimum rectangle which height and width is times of stride, default false
        """
        return self._preprocessor.is_mini_pad

    @property
    def stride(self):
        """
        stride for preprocessing, only for mini_pad mode, default 32
        """
        return self._preprocessor.stride

    @size.setter
    def size(self, wh):
        assert isinstance(
            wh, (list, tuple)
        ), "The value to set `size` must be type of tuple or list."
        assert (
            len(wh) == 2
        ), "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
            len(wh)
        )
        self._preprocessor.size = wh

    @padding_value.setter
    def padding_value(self, value):
        assert isinstance(
            value, list
        ), "The value to set `padding_value` must be type of list."
        self._preprocessor.padding_value = value

    @is_scale_up.setter
    def is_scale_up(self, value):
        assert isinstance(
            value, bool
        ), "The value to set `is_scale_up` must be type of bool."
        self._preprocessor.is_scale_up = value

    @is_mini_pad.setter
    def is_mini_pad(self, value):
        assert isinstance(
            value, bool
        ), "The value to set `is_mini_pad` must be type of bool."
        self._preprocessor.is_mini_pad = value

    @stride.setter
    def stride(self, value):
        assert isinstance(value, int), "The value to set `stride` must be type of int."
        self._preprocessor.stride = value


class YOLOv8Postprocessor:
    def __init__(self):
        """Create a postprocessor for YOLOv8"""
        self._postprocessor = C.vision.detection.YOLOv8Postprocessor()

    def run(self, runtime_results, ims_info):
        """Postprocess the runtime results for YOLOv8

        :param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
        :param: ims_info: (list of dict)Record input_shape and output_shape
        :return: list of DetectionResult(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, ims_info)

    @property
    def conf_threshold(self):
        """
        confidence threshold for postprocessing, default is 0.25
        """
        return self._postprocessor.conf_threshold

    @property
    def nms_threshold(self):
        """
        nms threshold for postprocessing, default is 0.5
        """
        return self._postprocessor.nms_threshold

    @property
    def multi_label(self):
        """
        multi_label for postprocessing, set true for eval, default is True
        """
        return self._postprocessor.multi_label

    @conf_threshold.setter
    def conf_threshold(self, conf_threshold):
        assert isinstance(
            conf_threshold, float
        ), "The value to set `conf_threshold` must be type of float."
        self._postprocessor.conf_threshold = conf_threshold

    @nms_threshold.setter
    def nms_threshold(self, nms_threshold):
        assert isinstance(
            nms_threshold, float
        ), "The value to set `nms_threshold` must be type of float."
        self._postprocessor.nms_threshold = nms_threshold

    @multi_label.setter
    def multi_label(self, value):
        assert isinstance(
            value, bool
        ), "The value to set `multi_label` must be type of bool."
        self._postprocessor.multi_label = value


class YOLOv8(UltraInferModel):
    def __init__(
        self,
        model_file,
        params_file="",
        runtime_option=None,
        model_format=ModelFormat.ONNX,
    ):
        """Load a YOLOv8 model exported by YOLOv8.

        :param model_file: (str)Path of model file, e.g ./yolov8s.onnx
        :param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
        :param runtime_option: (ultra_infer.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
        :param model_format: (ultra_infer.ModelForamt)Model format of the loaded model
        """
        super(YOLOv8, self).__init__(runtime_option)

        self._model = C.vision.detection.YOLOv8(
            model_file, params_file, self._runtime_option, model_format
        )
        assert self.initialized, "YOLOv8 initialize failed."

    def predict(self, input_image):
        """Detect an input image

        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
        :param conf_threshold: confidence threshold for postprocessing, default is 0.25
        :param nms_iou_threshold: iou threshold for NMS, default is 0.5
        :return: DetectionResult
        """

        return self._model.predict(input_image)

    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 DetectionResult
        """

        return self._model.batch_predict(images)

    @property
    def preprocessor(self):
        """Get YOLOv8Preprocessor object of the loaded model

        :return YOLOv8Preprocessor
        """
        return self._model.preprocessor

    @property
    def postprocessor(self):
        """Get YOLOv8Postprocessor object of the loaded model

        :return YOLOv8Postprocessor
        """
        return self._model.postprocessor