chaoshenbo() ######################################################### def commands (cmd): print cmd if cmd == 'd': t_down() elif cmd == 's': t_stop() elif cmd == 'u': t_up() elif cmd == 'l': t_left() elif cmd == 'r': t_right() HOST=ip.getip()#the PORT of raspberry pi port PORT=Read.LoadData('/config.conf')#the HOST of raspberry pi ip s= socket(AF_INET, SOCK_STREAM) s.bind((HOST, PORT)) s.listen(5) print ('listening on',PORT) while 1: conn, addr = s.accept() print ('Connected ok! By ',addr) while 1: command= conn.recv(20).replace('\n','') if command == 'g': conn.close break elif not command:break commands(bytearray(command)) #command conn.close()
output = net(data) test_loss = criterion(output, target).data.item() pred = output.data.max(1, keepdim=True)[1] pred = pred.view_as(target) correct += torch.sum(pred.eq(target)) test_loss /= len(test_loader.dataset) print( '\nTest set: Average loss at epoch {}: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n' .format(epoch, test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) # return (correct / len(test_loader.dataset)) train_loader, test_loader = Read.LoadData(50) net = LeNet.LeNet() tqt.utils.make_net_quant_or_not(net, 'net', quant=False) tqt.threshold.add_hook(net, 'net', tqt.threshold.hook_handler, show=True) img, label = next(iter(test_loader)) net(img) net.load_state_dict(torch.load('quant9844.pth')) tqt.utils.make_net_quant_or_not(net, 'net', quant=True) # tqt.threshold.init_network(net, net, 'net', show=True) # torch.save(net.state_dict(), 'quant.pth') learning_rate = 0.001 criterion = nn.CrossEntropyLoss(reduction='sum') quant_param = [
pred = output.data.max(1, keepdim=True)[1] pred = pred.view_as(target) correct += torch.sum(pred.eq(target)) test_loss /= len(test_loader.dataset) print( '\nTest set: Average loss at epoch {}: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n' .format(epoch, test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) # return (correct / len(test_loader.dataset)) net = LeNet_q.LeNet() train_loader, test_loader = Read.LoadData() learning_rate = 0.001 criterion = nn.CrossEntropyLoss(reduction='sum') optimizer = torch.optim.Adam(net.parameters(), learning_rate, betas=(0.9, 0.99)) for i in range(1): train(net, criterion, optimizer, train_loader, i) test(net, criterion, optimizer, test_loader, i) tqt.threshold.add_hook(net, '', tqt.threshold.hook_handler) for idx, (data, target) in enumerate(train_loader): data, target = Variable(data), Variable(target) break