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