pytorch yolov5 onnx推理
这里写自定义目录标题
- 包的版本
-
- pt转onnx
- onnx 推理
- 检查是否用GPU推理：
包的版本
cuda 10.1
torch 1.6.0+cu101
torchvision 0.7.0+cu101
onnx 1.7.0
onnx-simplifier 0.3.6
onnxoptimizer 0.2.6
onnxruntime 1.8.0
onnxruntime-gpu 1.3.0
或者
cuda 10.2
onnx 1.8.0
onnx-simplifier 0.3.4
onnxoptimizer 0.2.5
onnxruntime 1.7.0
onnxruntime-gpu 1.5.1
torch 1.8.1
torch-tb-profiler 0.3.1
torchsummary 1.5.1
torchvision 0.9.1
能不能推理成功，跟各个包的版本很大关系，多试onnxruntime的版本吧！
pt转onnx
python export.py --weights exp8/weights/best.pt --img 640 --batch 1
python -m onnxsim best.onnx best-sm.onnx
生成best-sm.onnx模型
- 我首先用的是yolov5 v3.0的export.py，但是显然这个代码有问题，采用netron 看导出的模型,最后一层的输出维度是 1 * 3 * 20 * 20 * 9，
不能用； - 然后采用 yolov5 v6.0的export.py，采用netron 看导出的模型,最后一层的输出维度是 1 * 25200 * 9，能用；
onnx 推理
编写推理脚本，参考这里
# coding=utf-8
import cv2.cv2 as cv2
import numpy as np
import onnxruntime
import torch
import torchvision
import time
import random
from utils.general import non_max_suppression
class YOLOV5_ONNX(object):
def __init__(self,onnx_path):
'''初始化onnx'''
self.onnx_session=onnxruntime.InferenceSession(onnx_path)
print(onnxruntime.get_device())
self.input_name=self.get_input_name()
self.output_name=self.get_output_name()
self.classes=['person', 'car', 'special_person', 'truck']
def get_input_name(self):
'''获取输入节点名称'''
input_name=[]
for node in self.onnx_session.get_inputs():
input_name.append(node.name)
return input_name
def get_output_name(self):
'''获取输出节点名称'''
output_name=[]
for node in self.onnx_session.get_outputs():
output_name.append(node.name)
return output_name
def get_input_feed(self,image_tensor):
'''获取输入tensor'''
input_feed={}
for name in self.input_name:
input_feed[name]=image_tensor
return input_feed
def letterbox(self,img, new_shape=(640, 640), color=(114, 114, 114), auto=False, scaleFill=False, scaleup=True,
stride=32):
'''图片归一化'''
# Resize and pad image while meeting stride-multiple constraints
shape = img.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better test mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return img, ratio, (dw, dh)
def xywh2xyxy(self,x):
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y = np.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
return y
def nms(self,prediction, conf_thres=0.1, iou_thres=0.6, agnostic=False):
if prediction.dtype is torch.float16:
prediction = prediction.float() # to FP32
xc = prediction[..., 4] > conf_thres # candidates
min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
max_det = 300 # maximum number of detections per image
output = [None] * prediction.shape[0]
for xi, x in enumerate(prediction): # image index, image inference
x = x[xc[xi]] # confidence
if not x.shape[0]:
continue
x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
box = self.xywh2xyxy(x[:, :4])
conf, j = x[:, 5:].max(1, keepdim=True)
x = torch.cat((torch.tensor(box), conf, j.float()), 1)[conf.view(-1) > conf_thres]
n = x.shape[0] # number of boxes
if not n:
continue
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
if i.shape[0] > max_det: # limit detections
i = i[:max_det]
output[xi] = x[i]
return output
def clip_coords(self,boxes, img_shape):
'''查看是否越界'''
# Clip bounding xyxy bounding boxes to image shape (height, width)
boxes[:, 0].clamp_(0, img_shape[1]) # x1
boxes[:, 1].clamp_(0, img_shape[0]) # y1
boxes[:, 2].clamp_(0, img_shape[1]) # x2
boxes[:, 3].clamp_(0, img_shape[0]) # y2
def scale_coords(self,img1_shape, coords, img0_shape, ratio_pad=None):
'''
坐标对应到原始图像上，反操作：减去pad，除以最小缩放比例
:param img1_shape: 输入尺寸
:param coords: 输入坐标
:param img0_shape: 映射的尺寸
:param ratio_pad:
:return:
'''
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new,计算缩放比率
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (
img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding ，计算扩充的尺寸
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
coords[:, [0, 2]] -= pad[0] # x padding，减去x方向上的扩充
coords[:, [1, 3]] -= pad[1] # y padding，减去y方向上的扩充
coords[:, :4] /= gain # 将box坐标对应到原始图像上
self.clip_coords(coords, img0_shape) # 边界检查
return coords
def sigmoid(self,x):
return 1 / (1 + np.exp(-x))
def infer(self,img_path):
'''执行前向操作预测输出'''
# 超参数设置
img_size=(640,640) #图片缩放大小
# 读取图片
src_img=cv2.imread(img_path)
start=time.time()
src_size=src_img.shape[:2]
# 图片填充并归一化
img=self.letterbox(src_img,img_size,stride=32)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
# 归一化
img=img.astype(dtype=np.float32)
img/=255.0
# # BGR to RGB
# img = img[:, :, ::-1].transpose(2, 0, 1)
# img = np.ascontiguousarray(img)
# 维度扩张
img=np.expand_dims(img,axis=0)
print('img resuming: ',time.time()-start)
# 前向推理
# start=time.time()
input_feed=self.get_input_feed(img)
# ort_inputs = {self.onnx_session.get_inputs()[0].name: input_feed[None].numpy()}
pred = torch.tensor(self.onnx_session.run(None, input_feed)[0])
results = non_max_suppression(pred, 0.5,0.5)
print('onnx resuming: ',time.time()-start)
# pred=self.onnx_session.run(output_names=self.output_name,input_feed=input_feed)
#映射到原始图像
img_shape=img.shape[2:]
# print(img_size)
for det in results: # detections per image
if det is not None and len(det):
det[:, :4] = self.scale_coords(img_shape, det[:, :4],src_size).round()
print(time.time()-start)
if det is not None and len(det):
self.draw(src_img, det)
def plot_one_box(self,x, img, color=None, label=None, line_thickness=None):
# Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
if label:
tf = max(tl - 1, 1) # font thickness
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
def draw(self,img, boxinfo):
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(self.classes))]
for *xyxy, conf, cls in boxinfo:
label = '%s %.2f' % (self.classes[int(cls)], conf)
# print('xyxy: ', xyxy)
self.plot_one_box(xyxy, img, label=label, color=colors[int(cls)], line_thickness=1)
# cv2.namedWindow("dst",0)
# cv2.imshow("dst", img)
cv2.imwrite("res1.jpg",img)
# cv2.waitKey(0)
# cv2.imencode('.jpg', img)[1].tofile(os.path.join(dst, id + ".jpg"))
return 0
if __name__=="__main__":
model=YOLOV5_ONNX(onnx_path="best-sm.onnx")
model.infer(img_path="inference/test/2-720.jpg")
跑代码发现报错：
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Load model from /notebooks/liujiali/gaosudongwu/yolov5/coverruns/exp8/weights/best.onnx failed:/onnxruntime_src/onnxruntime/core/graph/model_load_utils.h:57 void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map<std::basic_string<char>, int>&, const onnxruntime::logging::Logger&, bool, const string&, int) ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset 13 is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ai.onnx is till opset 12.
发现是在export.py这里应该修改op为12：
然后重新导出onnx模型，再进行推理。
检查是否用GPU推理：
import onnxruntime as ort
print(ort.get_device())
打印出GPU 或者 CPU，很明显，跟你配置的onnx版本有关系，多试试吧！
太难了~~~~