def main():
dataset = exoskeleton_dataset.ExoskeletonDataset(
file="data/nc_agger2/constraint_regression", root_dir="/")
train_dataset, val_dataset, test_dataset = dataset.GetDataset()
#train_separately(train_dataset, val_dataset, test_dataset)
train_decoder(train_dataset, val_dataset, test_dataset)
def main():
dataset = exoskeleton_dataset.ExoskeletonDataset(file="data/exo_data_3",
root_dir="/")
train_dataset, val_dataset, test_dataset = dataset.GetDataset()
train_separately(train_dataset, val_dataset, test_dataset)
print("Separate network train done! Begin whole network training...")
train_wholenet(train_dataset, val_dataset, test_dataset)
def main():
q = Queue()
dataset = exoskeleton_dataset.ExoskeletonDataset(file="data/exo_data_3",
root_dir="/")
train_dataset, val_dataset, test_dataset = dataset.GetDataset()
t_udp = threading.Thread(target=udprecv, args=(q, ))
t_udp.start()
encoder_arch = [[14, 28], [28, 56], [56, 56], [56, 4]]
decoder_arch = [[18, 36], [36, 72], [72, 72], [72, 7]]
model = network.Net(encoder_arch, decoder_arch)
#model.load_state_dict(torch.load('./model'))
model.decoder.load_state_dict(torch.load("decoder.model"))
model.encoder.load_state_dict(torch.load("encoder.model"))
realtime_loss = nn.MSELoss()
while True:
string = q.get()
tensor_data = string_to_tensor(string)
target = torch.Tensor()
constrains = torch.Tensor()
master = torch.Tensor()
slave = torch.Tensor()
target = tensor_data["target"].unsqueeze(dim=1)
constrains = tensor_data["constrains"].unsqueeze(dim=1)
master = tensor_data["master"].unsqueeze(dim=1)
slave = tensor_data["slave"].unsqueeze(dim=1)
if target.__len__() != 7:
continue
if constrains.__len__() != 4:
continue
if slave.__len__() != 7:
continue
tmp = torch.cat((slave, target), 0)
y = model.encoder.forward(torch.transpose(tmp, 0, 1))
rl = realtime_loss.forward(constrains, y)
jc = np.array(y.detach().numpy())
jc_c = np.array(constrains.detach().numpy())
data_str = "%f,%f,%f,%f" % (jc_c[0], jc_c[1], jc_c[2], jc_c[3])
print("calculated:{}".format(data_str))
data_str = "%f,%f,%f,%f" % (jc[0][0], jc[0][1], jc[0][2], jc[0][3])
print("predicted:{}".format(data_str))
print(rl.detach())
s.sendto(data_str.encode('utf-8'), send_addr)
def main():
dataset = exoskeleton_dataset.ExoskeletonDataset(
file="data/validationset/va_set1", root_dir="/")
train_dataset, val_dataset, test_dataset = dataset.GetDataset()
f = open("mse_va1.txt", "w+")
realtime_loss = nn.MSELoss()
dataset_size = train_dataset.size
for k in range(dataset_size):
test_data(train_dataset[k], f)
dataset = []
dataset = exoskeleton_dataset.ExoskeletonDataset(
file="data/validationset/va_set2", root_dir="/")
train_dataset, val_dataset, test_dataset = dataset.GetDataset()
f = open("mse_va2.txt", "w+")
dataset_size = train_dataset.size
for k in range(dataset_size):
test_data(train_dataset[k], f)
dataset = []
dataset = exoskeleton_dataset.ExoskeletonDataset(
file="data/validationset/va_set3", root_dir="/")
train_dataset, val_dataset, test_dataset = dataset.GetDataset()
f = open("mse_va3.txt", "w+")
dataset_size = train_dataset.size
for k in range(dataset_size):
test_data(train_dataset[k], f)
dataset = []
dataset = exoskeleton_dataset.ExoskeletonDataset(
file="data/validationset/va_set4", root_dir="/")
train_dataset, val_dataset, test_dataset = dataset.GetDataset()
f = open("mse_va4.txt", "w+")
dataset_size = train_dataset.size
for k in range(dataset_size):
test_data(train_dataset[k], f)
def main():
dataset = exoskeleton_dataset.ExoskeletonDataset(
file="data/exo_data_3", root_dir="/")
train_dataset, val_dataset, test_dataset = dataset.GetDataset()
encoder_arch = [[14,28],[28,56],[56,56],[56,4]]
decoder_arch = [[18,36],[36,72],[72,72],[72,7]]
model = network.Net(encoder_arch, decoder_arch)
model.load_state_dict(torch.load('./model'))
#model.decoder.load_state_dict(torch.load("decoder.model"))
#model.encoder.load_state_dict(torch.load("encoder.model"))
realtime_loss = nn.MSELoss()
dataset_size = train_dataset.size
for k in range(dataset_size):
test_data(model, train_dataset[k])