Source code for fastdeploy.vision.detection.contrib.yolov7

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


[docs]class YOLOv7Preprocessor: def __init__(self): """Create a preprocessor for YOLOv7 """ self._preprocessor = C.vision.detection.YOLOv7Preprocessor()
[docs] def run(self, input_ims): """Preprocess input images for YOLOv7 :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 @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
[docs]class YOLOv7Postprocessor: def __init__(self): """Create a postprocessor for YOLOv7 """ self._postprocessor = C.vision.detection.YOLOv7Postprocessor()
[docs] def run(self, runtime_results, ims_info): """Postprocess the runtime results for YOLOv7 :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 @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
[docs]class YOLOv7(FastDeployModel): def __init__(self, model_file, params_file="", runtime_option=None, model_format=ModelFormat.ONNX): """Load a YOLOv7 model exported by YOLOv7. :param model_file: (str)Path of model file, e.g ./yolov7.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: (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 """ # 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(YOLOv7, self).__init__(runtime_option) self._model = C.vision.detection.YOLOv7( 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, "YOLOv7 initialize failed."
[docs] 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 """ self.postprocessor.conf_threshold = conf_threshold self.postprocessor.nms_threshold = nms_iou_threshold return self._model.predict(input_image)
[docs] 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 YOLOv7Preprocessor object of the loaded model :return YOLOv7Preprocessor """ return self._model.preprocessor @property def postprocessor(self): """Get YOLOv7Postprocessor object of the loaded model :return YOLOv7Postprocessor """ return self._model.postprocessor