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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 YOLOv5Lite(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a YOLOv5Lite model exported by YOLOv5Lite.
:param model_file: (str)Path of model file, e.g ./yolov5lite.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(YOLOv5Lite, self).__init__(runtime_option)
self._model = C.vision.detection.YOLOv5Lite(
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, "YOLOv5Lite 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 threashold for postprocessing, default is 0.25
:param nms_iou_threshold: iou threashold for NMS, default is 0.5
:return: DetectionResult
"""
return self._model.predict(input_image, conf_threshold,
nms_iou_threshold)
# 一些跟YOLOv5Lite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_no_pad(self):
# while is_mini_pad = false and is_no_pad = true, will resize the image to the set size
return self._model.is_no_pad
@property
def is_mini_pad(self):
# only pad to the minimum rectange which height and width is times of stride
return self._model.is_mini_pad
@property
def is_scale_up(self):
# if is_scale_up is false, the input image only can be zoom out, the maximum resize scale cannot exceed 1.0
return self._model.is_scale_up
@property
def stride(self):
# padding stride, for is_mini_pad
return self._model.stride
@property
def max_wh(self):
# for offseting the boxes by classes when using NMS
return self._model.max_wh
@property
def is_decode_exported(self):
"""
whether the model_file was exported with decode module.
The official YOLOv5Lite/export.py script will export ONNX file without decode module.
Please set it 'true' manually if the model file was exported with decode module.
False : ONNX files without decode module. True : ONNX file with decode module.
default False
"""
return self._model.is_decode_exported
@property
def anchor_config(self):
return self._model.anchor_config
@property
def downsample_strides(self):
"""
downsample strides for YOLOv5Lite to generate anchors, will take (8,16,32) as default values, might have stride=64.
"""
return self._model.downsample_strides
@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._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_no_pad.setter
def is_no_pad(self, value):
assert isinstance(
value, bool), "The value to set `is_no_pad` must be type of bool."
self._model.is_no_pad = 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._model.is_mini_pad = 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._model.is_scale_up = value
@stride.setter
def stride(self, value):
assert isinstance(
value, int), "The value to set `stride` must be type of int."
self._model.stride = 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
@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
@anchor_config.setter
def anchor_config(self, anchor_config_val):
assert isinstance(anchor_config_val, list),\
"The value to set `anchor_config` must be type of tuple or list."
assert isinstance(anchor_config_val[0], list),\
"The value to set `anchor_config` must be 2-dimensions tuple or list"
self._model.anchor_config = anchor_config_val
@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