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])