PaddleX/libs/ultra-infer/python/ultra_infer/vision/detection/contrib/yolox.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 YOLOX(UltraInferModel):
    def __init__(
        self,
        model_file,
        params_file="",
        runtime_option=None,
        model_format=ModelFormat.ONNX,
    ):
        """Load a YOLOX model exported by YOLOX.

        :param model_file: (str)Path of model file, e.g ./yolox.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
        """
        # 调用基函数进行backend_option的初始化
        # 初始化后的option保存在self._runtime_option
        super(YOLOX, self).__init__(runtime_option)

        self._model = C.vision.detection.YOLOX(
            model_file, params_file, self._runtime_option, model_format
        )
        # 通过self.initialized判断整个模型的初始化是否成功
        assert self.initialized, "YOLOX initialize failed."

    def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
        """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, conf_threshold, nms_iou_threshold)

    # 一些跟YOLOX模型有关的属性封装
    # 多数是预处理相关，可通过修改如model.size = [1280, 1280]改变预处理时resize的大小（前提是模型支持）
    @property
    def size(self):
        """
        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
        """
        return self._model.size

    @property
    def padding_value(self):
        #  padding value, size should be the same as channels
        return self._model.padding_value

    @property
    def is_decode_exported(self):
        """
        whether the model_file was exported with decode module.
        The official YOLOX/tools/export_onnx.py script will export ONNX file without decode module.
        Please set it 'true' manually if the model file was exported with decode module.
        Default False.
        """
        return self._model.is_decode_exported

    @property
    def downsample_strides(self):
        """
        downsample strides for YOLOX to generate anchors, will take (8,16,32) as default values, might have stride=64.
        """
        return self._model.downsample_strides

    @property
    def max_wh(self):
        # for offsetting the boxes by classes when using NMS
        return self._model.max_wh

    @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 contains 2 elements means [width, height], but now it contains {} elements.".format(
            len(wh)
        )
        self._model.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._model.padding_value = value

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

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

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