YOLOv7-Pose尝鲜，基于YOLOv7的关键点模型测评

                if self.kpt_label:
                    #Direct kpt prediction
                    pkpt_x = ps[:, 6::3] * 2. - 0.5
                    pkpt_y = ps[:, 7::3] * 2. - 0.5
                    pkpt_score = ps[:, 8::3]
                    #mask
                    kpt_mask = (tkpt[i][:, 0::2] != 0)
                    lkptv += self.BCEcls(pkpt_score, kpt_mask.float()) 
                    #l2 distance based loss
                    #lkpt += (((pkpt-tkpt[i])*kpt_mask)**2).mean()  #Try to make this loss based on distance instead of ordinary difference
                    #oks based loss
                    d = (pkpt_x-tkpt[i][:,0::2])**2 + (pkpt_y-tkpt[i][:,1::2])**2
                    s = torch.prod(tbox[i][:,-2:], dim=1, keepdim=True)
                    kpt_loss_factor = (torch.sum(kpt_mask != 0) + torch.sum(kpt_mask == 0))/torch.sum(kpt_mask != 0)
                    lkpt += kpt_loss_factor*((1 - torch.exp(-d/(s*(4*sigmas**2)+1e-9)))*kpt_mask).mean()

% weigths = torch.load('weights/yolov7-w6-pose.pt')
% image = cv2.imread('sample/pose.jpeg')
!python pose.py 

    # 原代码:
    for k, m in model.named_modules():
        m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
        if isinstance(m, models.common.Conv):  # assign export-friendly activations
            if isinstance(m.act, nn.Hardswish):
                m.act = Hardswish()
            elif isinstance(m.act, nn.SiLU):
                m.act = SiLU()
     model.model[-1].export = not opt.grid  # set Detect() layer grid export

    # 修改代码:
    for k, m in model.named_modules():
        m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
        if isinstance(m, models.common.Conv):  # assign export-friendly activations
            if isinstance(m.act, nn.Hardswish):
                m.act = Hardswish()
            elif isinstance(m.act, nn.SiLU):
                m.act = SiLU()
        elif isinstance(m, models.yolo.IKeypoint):
            m.forward = m.forward_keypoint  # assign forward (optional)
            # 此处切换检测头
    model.model[-1].export = not opt.grid  # set Detect() layer grid export

python export.py --weights 'weights/yolov7-w6-pose.pt' --img-size 960 --simplify True

import onnxruntime
import matplotlib.pyplot as plt
import torch
import cv2
from torchvision import transforms
import numpy as np
from utils.datasets import letterbox
from utils.general import non_max_suppression_kpt
from utils.plots import output_to_keypoint, plot_skeleton_kpts

device = torch.device("cpu")

image = cv2.imread('sample/pose.jpeg')
image = letterbox(image, 960, stride=64, auto=True)[0]
image_ = image.copy()
image = transforms.ToTensor()(image)
image = torch.tensor(np.array([image.numpy()]))

print(image.shape)
sess = onnxruntime.InferenceSession('weights/yolov7-w6-pose.onnx')
out = sess.run(['output'], {'images': image.numpy()})[0]
out = torch.from_numpy(out)

output = non_max_suppression_kpt(out, 0.25, 0.65, nc=1, nkpt=17, kpt_label=True)
output = output_to_keypoint(output)
nimg = image[0].permute(1, 2, 0) * 255
nimg = nimg.cpu().numpy().astype(np.uint8)
nimg = cv2.cvtColor(nimg, cv2.COLOR_RGB2BGR)
for idx in range(output.shape[0]):
    plot_skeleton_kpts(nimg, output[idx, 7:].T, 3)

# matplotlib inline
plt.figure(figsize=(8, 8))
plt.axis('off')
plt.imshow(nimg)
plt.show()
plt.savefig("tmp")