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#
# 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
[docs]class YOLOv5Preprocessor:
def __init__(self):
"""Create a preprocessor for YOLOv5
"""
self._preprocessor = C.vision.detection.YOLOv5Preprocessor()
[docs] def run(self, input_ims):
"""Preprocess input images for YOLOv5
: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 rectange 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(
stride, int), "The value to set `stride` must be type of int."
self._preprocessor.stride = value
[docs]class YOLOv5Postprocessor:
def __init__(self):
"""Create a postprocessor for YOLOv5
"""
self._postprocessor = C.vision.detection.YOLOv5Postprocessor()
[docs] def run(self, runtime_results, ims_info):
"""Postprocess the runtime results for YOLOv5
: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
[docs]class YOLOv5(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a YOLOv5 model exported by YOLOv5.
:param model_file: (str)Path of model file, e.g ./yolov5.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(YOLOv5, self).__init__(runtime_option)
self._model = C.vision.detection.YOLOv5(
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, "YOLOv5 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 YOLOv5Preprocessor object of the loaded model
:return YOLOv5Preprocessor
"""
return self._model.preprocessor
@property
def postprocessor(self):
"""Get YOLOv5Postprocessor object of the loaded model
:return YOLOv5Postprocessor
"""
return self._model.postprocessor