zeroXscorpion7

I want to test the efficacy in real-time, how do I identify one picture at a time?

def main(): net = model.A2J_model(num_classes = keypointsNumber) net.load_state_dict(torch.load(model_dir)) net = net.cuda() net.eval() post_precess = anchor.post_process(shape=[cropHeight//16,cropWidth//16],stride=16,P_h=None, P_w=None) output = torch.FloatTensor() data4DTemp = scio.loadmat(testingImageDir + str(1) + '.mat')['DepthNormal'] depthTemp = data4DTemp[:,:,3] img=depthTemp...

I remove my_dataloader and enter depthTemp into dataPreprocess, then I use torch.from_numpy to make it to tensor def dataPreprocess(img, depth_thres=0.4): imageOutputs = np.ones((cropHeight, cropWidth, 1), dtype='float32') imCrop = img.copy()[:, :]...

If you don't to use bndbox, you may train the data by yourself, or modify the train code.

import cv2 import torch import torch.utils.data import torch.optim.lr_scheduler as lr_scheduler import numpy as np import scipy.io as scio import os from PIL import Image from torch.autograd import Variable import model...

Hello, I tried to implement the average drop and increase calculation by myself, but I found that the calculation result does not match with the paper, how can I fix...