YOLOV5 model to onnx and reasoning
Model to onnx
Normal model to onnx
- Load the model, it needs to be the model saved by torch.save
- Specify the name of the input and output
- Specify the input size
- export static model
- Export a dynamic dimensional model
import torch
import torch.nn
#-------------------------------------------------------
# 加载模型，需要是torch.save保存的模型
#-------------------------------------------------------
model = torch.load('yolov5s.pt',map_location=torch.device('cpu'))
model.eval()
#-------------------------------------------------------
# 指定输入输出的名字
#-------------------------------------------------------
input_names = ['input']
output_names = ['output']
#-------------------------------------------------------
# 指定输入size
#-------------------------------------------------------
x = torch.randn(1,3,640,640,requires_grad=True)
#-------------------------------------------------------
# 导出静态模型
#-------------------------------------------------------
torch.onnx.export(model,
x,
"model.onnx",
export_params=True,
opset_version=10,
do_constant_folding=True,
input_names=input_names,
output_names=output_names )
#-------------------------------------------------------
# 导出动态维度模型
#-------------------------------------------------------
torch.onnx.export(model,
x,
"model2.onnx",
export_params=True,
opset_version=10,
do_constant_folding=True,
input_names=input_names,
output_names=input_names,
dynamic_axes= {
input_names: {
0: 'batch_size', 2 : 'in_width', 3: 'int_height'},
output_names: {
0: 'batch_size', 2: 'out_width', 3:'out_height'}})
yolov5 model to onnx
Since the model of yolov5 is related to the whole project, the conventional method cannot be used to transfer onnx, only the internal transfer method of onnx can be used.
1. Static model
python export.py --weights yolov5s.pt --include onnx
2. Dynamic model
python export.py --weights yolov5s.pt --include onnx --dynamic
The dynamic model is as shown in the figure below. The
batch, width, and height are dynamic
onnx reasoning
normal model
x = torch.randn(1,3,640,640,requires_grad=True)
onnx_model = onnxruntime.InferenceSession("model.onnx")
print(onnx_model.get_inputs()[0].name)
inputs = {onnx_model.get_inputs()[0].name: x.cpu().numpy()}
outs = onnx_model.run(None, inputs)
print(outs[0])
yolov5 model
1. Reasoning
1.cv2读取图像并resize
2.图像转BGR2RGB和HWC2CHW
3.图像归一化
4.图像增加维度
5.onnx_session 推理
class YOLOV5():
def __init__(self,onnxpath):
self.onnx_session=onnxruntime.InferenceSession(onnxpath)
self.input_name=self.get_input_name()
self.output_name=self.get_output_name()
#-------------------------------------------------------
# 获取输入输出的名字
#-------------------------------------------------------
def get_input_name(self):
input_name=[]
for node in self.onnx_session.get_inputs():
input_name.append(node.name)
return input_name
def get_output_name(self):
output_name=[]
for node in self.onnx_session.get_outputs():
output_name.append(node.name)
return output_name
#-------------------------------------------------------
# 输入图像
#-------------------------------------------------------
def get_input_feed(self,img_tensor):
input_feed={}
for name in self.input_name:
input_feed[name]=img_tensor
return input_feed
#-------------------------------------------------------
# 1.cv2读取图像并resize
# 2.图像转BGR2RGB和HWC2CHW
# 3.图像归一化
# 4.图像增加维度
# 5.onnx_session 推理
#-------------------------------------------------------
def inference(self,img_path):
img=cv2.imread(img_path)
or_img=cv2.resize(img,(640,640))
img=or_img[:,:,::-1].transpose(2,0,1) #BGR2RGB和HWC2CHW
img=img.astype(dtype=np.float32)
img/=255.0
img=np.expand_dims(img,axis=0)
input_feed=self.get_input_feed(img)
pred=self.onnx_session.run(None,input_feed)[0]
return pred,or_img
2. Coordinate transformation
Convert the coordinates of the center point to the coordinates of the upper left corner and lower right corner
def xywh2xyxy(x):
# [x, y, w, h] to [x1, y1, x2, y2]
y = np.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2
y[:, 1] = x[:, 1] - x[:, 3] / 2
y[:, 2] = x[:, 0] + x[:, 2] / 2
y[:, 3] = x[:, 1] + x[:, 3] / 2
return y
3. Non-maximum suppression
1. Calculate the area of the box
2. Calculate the intersection area (intersect, disjoint)
3. Calculate the IOU between the box and other boxes, and remove the duplicate boxes, that is, the box with a large IOU value
4. Boxes with IOU less than thresh are reserved
#dets: array [x,6] 6个值分别为x1,y1,x2,y2,score,class
#thresh: 阈值
def nms(dets, thresh):
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
#-------------------------------------------------------
# 计算框的面积
# 置信度从大到小排序
#-------------------------------------------------------
areas = (y2 - y1 + 1) * (x2 - x1 + 1)
scores = dets[:, 4]
keep = []
index = scores.argsort()[::-1]
while index.size > 0:
i = index[0]
keep.append(i)
#-------------------------------------------------------
# 计算相交面积
# 1.相交
# 2.不相交
#-------------------------------------------------------
x11 = np.maximum(x1[i], x1[index[1:]])
y11 = np.maximum(y1[i], y1[index[1:]])
x22 = np.minimum(x2[i], x2[index[1:]])
y22 = np.minimum(y2[i], y2[index[1:]])
w = np.maximum(0, x22 - x11 + 1)
h = np.maximum(0, y22 - y11 + 1)
overlaps = w * h
#-------------------------------------------------------
# 计算该框与其它框的IOU，去除掉重复的框，即IOU值大的框
# IOU小于thresh的框保留下来
#-------------------------------------------------------
ious = overlaps / (areas[i] + areas[index[1:]] - overlaps)
idx = np.where(ious <= thresh)[0]
index = index[idx + 1]
return keep
Fourth, filter useless frames based on confidence
1. Delete BOX with confidence less than conf_thres
2. Obtain the category with the highest confidence through argmax
3. Filter each category separately
def filter_box(org_box,conf_thres,iou_thres): #过滤掉无用的框
#-------------------------------------------------------
# 删除为1的维度
# 删除置信度小于conf_thres的BOX
#-------------------------------------------------------
org_box=np.squeeze(org_box)
conf = org_box[..., 4] > conf_thres
box = org_box[conf == True]
#-------------------------------------------------------
# 通过argmax获取置信度最大的类别
#-------------------------------------------------------
cls_cinf = box[..., 5:]
cls = []
for i in range(len(cls_cinf)):
cls.append(int(np.argmax(cls_cinf[i])))
all_cls = list(set(cls))
#-------------------------------------------------------
# 分别对每个类别进行过滤
# 1.将第6列元素替换为类别下标
# 2.xywh2xyxy 坐标转换
# 3.经过非极大抑制后输出的BOX下标
# 4.利用下标取出非极大抑制后的BOX
#-------------------------------------------------------
output = []
for i in range(len(all_cls)):
curr_cls = all_cls[i]
curr_cls_box = []
curr_out_box = []
for j in range(len(cls)):
if cls[j] == curr_cls:
box[j][5] = curr_cls
curr_cls_box.append(box[j][:6])
curr_cls_box = np.array(curr_cls_box)
# curr_cls_box_old = np.copy(curr_cls_box)
curr_cls_box = xywh2xyxy(curr_cls_box)
curr_out_box = pynms(curr_cls_box,iou_thres)
for k in curr_out_box:
output.append(curr_cls_box[k])
output = np.array(output)
return output
5. Drawing
def draw(image,box_data):
#-------------------------------------------------------
# 取整，方便画框
#-------------------------------------------------------
boxes=box_data[...,:4].astype(np.int32)
scores=box_data[...,4]
classes=box_data[...,5].astype(np.int32)
for box, score, cl in zip(boxes, scores, classes):
top, left, right, bottom = box
print('class: {}, score: {}'.format(CLASSES[cl], score))
print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))
cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
(top, left ),
cv2.FONT_HERSHEY_SIMPLEX,
0.6, (0, 0, 255), 2)
Six, the total code
import os
import cv2
import numpy as np
import onnxruntime
import time
CLASSES=['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush'] #coco80类别
class YOLOV5():
def __init__(self,onnxpath):
self.onnx_session=onnxruntime.InferenceSession(onnxpath)
self.input_name=self.get_input_name()
self.output_name=self.get_output_name()
#-------------------------------------------------------
# 获取输入输出的名字
#-------------------------------------------------------
def get_input_name(self):
input_name=[]
for node in self.onnx_session.get_inputs():
input_name.append(node.name)
return input_name
def get_output_name(self):
output_name=[]
for node in self.onnx_session.get_outputs():
output_name.append(node.name)
return output_name
#-------------------------------------------------------
# 输入图像
#-------------------------------------------------------
def get_input_feed(self,img_tensor):
input_feed={
}
for name in self.input_name:
input_feed[name]=img_tensor
return input_feed
#-------------------------------------------------------
# 1.cv2读取图像并resize
# 2.图像转BGR2RGB和HWC2CHW
# 3.图像归一化
# 4.图像增加维度
# 5.onnx_session 推理
#-------------------------------------------------------
def inference(self,img_path):
img=cv2.imread(img_path)
or_img=cv2.resize(img,(640,640))
img=or_img[:,:,::-1].transpose(2,0,1) #BGR2RGB和HWC2CHW
img=img.astype(dtype=np.float32)
img/=255.0
img=np.expand_dims(img,axis=0)
input_feed=self.get_input_feed(img)
pred=self.onnx_session.run(None,input_feed)[0]
return pred,or_img
#dets: array [x,6] 6个值分别为x1,y1,x2,y2,score,class
#thresh: 阈值
def nms(dets, thresh):
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
#-------------------------------------------------------
# 计算框的面积
# 置信度从大到小排序
#-------------------------------------------------------
areas = (y2 - y1 + 1) * (x2 - x1 + 1)
scores = dets[:, 4]
keep = []
index = scores.argsort()[::-1]
while index.size > 0:
i = index[0]
keep.append(i)
#-------------------------------------------------------
# 计算相交面积
# 1.相交
# 2.不相交
#-------------------------------------------------------
x11 = np.maximum(x1[i], x1[index[1:]])
y11 = np.maximum(y1[i], y1[index[1:]])
x22 = np.minimum(x2[i], x2[index[1:]])
y22 = np.minimum(y2[i], y2[index[1:]])
w = np.maximum(0, x22 - x11 + 1)
h = np.maximum(0, y22 - y11 + 1)
overlaps = w * h
#-------------------------------------------------------
# 计算该框与其它框的IOU，去除掉重复的框，即IOU值大的框
# IOU小于thresh的框保留下来
#-------------------------------------------------------
ious = overlaps / (areas[i] + areas[index[1:]] - overlaps)
idx = np.where(ious <= thresh)[0]
index = index[idx + 1]
return keep
def xywh2xyxy(x):
# [x, y, w, h] to [x1, y1, x2, y2]
y = np.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2
y[:, 1] = x[:, 1] - x[:, 3] / 2
y[:, 2] = x[:, 0] + x[:, 2] / 2
y[:, 3] = x[:, 1] + x[:, 3] / 2
return y
def filter_box(org_box,conf_thres,iou_thres): #过滤掉无用的框
#-------------------------------------------------------
# 删除为1的维度
# 删除置信度小于conf_thres的BOX
#-------------------------------------------------------
org_box=np.squeeze(org_box)
conf = org_box[..., 4] > conf_thres
box = org_box[conf == True]
#-------------------------------------------------------
# 通过argmax获取置信度最大的类别
#-------------------------------------------------------
cls_cinf = box[..., 5:]
cls = []
for i in range(len(cls_cinf)):
cls.append(int(np.argmax(cls_cinf[i])))
all_cls = list(set(cls))
#-------------------------------------------------------
# 分别对每个类别进行过滤
# 1.将第6列元素替换为类别下标
# 2.xywh2xyxy 坐标转换
# 3.经过非极大抑制后输出的BOX下标
# 4.利用下标取出非极大抑制后的BOX
#-------------------------------------------------------
output = []
for i in range(len(all_cls)):
curr_cls = all_cls[i]
curr_cls_box = []
curr_out_box = []
for j in range(len(cls)):
if cls[j] == curr_cls:
box[j][5] = curr_cls
curr_cls_box.append(box[j][:6])
curr_cls_box = np.array(curr_cls_box)
# curr_cls_box_old = np.copy(curr_cls_box)
curr_cls_box = xywh2xyxy(curr_cls_box)
curr_out_box = pynms(curr_cls_box,iou_thres)
for k in curr_out_box:
output.append(curr_cls_box[k])
output = np.array(output)
return output
def draw(image,box_data):
#-------------------------------------------------------
# 取整，方便画框
#-------------------------------------------------------
boxes=box_data[...,:4].astype(np.int32)
scores=box_data[...,4]
classes=box_data[...,5].astype(np.int32)
for box, score, cl in zip(boxes, scores, classes):
top, left, right, bottom = box
print('class: {}, score: {}'.format(CLASSES[cl], score))
print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))
cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
(top, left ),
cv2.FONT_HERSHEY_SIMPLEX,
0.6, (0, 0, 255), 2)
if __name__=="__main__":
onnx_path='yolov5s.onnx'
model=YOLOV5(onnx_path)
output,or_img=model.inference('bicycle_1_1.jpg')
outbox=filter_box(output,0.5,0.5)
draw(or_img,outbox)
cv2.imwrite('res.jpg',or_img)
The running result is shown in the figure below
