encoder.load_state_dict(
torch.load('./result/saved_models/il-uncertainty-02/encoder_119000.pth'))
generator.load_state_dict(
torch.load('./result/saved_models/il-uncertainty-02/generator_119000.pth'))
criterion = torch.nn.MSELoss()
e_optimizer = torch.optim.Adam(encoder.parameters(),
lr=opt.lr,
weight_decay=opt.weight_decay)
g_optimizer = torch.optim.Adam(generator.parameters(),
lr=opt.lr,
weight_decay=opt.weight_decay)
train_loader = DataLoader(CostMapDataset(
data_index=[1, 2, 3, 4, 5, 6, 7, 8, 9],
opt=opt,
dataset_path='/media/wang/DATASET/CARLA_HUMAN/town01/'),
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.n_cpu)
test_loader = DataLoader(CostMapDataset(
data_index=[10],
opt=opt,
dataset_path='/media/wang/DATASET/CARLA_HUMAN/town01/'),
batch_size=1,
shuffle=False,
num_workers=1)
test_samples = iter(test_loader)
def test_traj_v(xs, ys, step):
start_point_criterion = torch.nn.MSELoss()
criterion = torch.nn.BCELoss() #.to(device)
trajectory_criterion = torch.nn.MSELoss()
# e_optimizer = torch.optim.RMSprop(encoder.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
g_optimizer = torch.optim.RMSprop(generator.parameters(),
lr=opt.lr,
weight_decay=opt.weight_decay)
#g_optimizer = torch.optim.Adam(generator.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
d_optimizer = torch.optim.RMSprop(discriminator.parameters(),
lr=opt.lr,
weight_decay=opt.weight_decay)
#d_optimizer = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
train_loader = DataLoader(CostMapDataset(
data_index=[item for item in range(1, 10)],
opt=opt,
dataset_path='/media/wang/DATASET/CARLA_HUMAN/town01/'),
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.n_cpu)
test_loader = DataLoader(CostMapDataset(
data_index=[10],
opt=opt,
dataset_path='/media/wang/DATASET/CARLA_HUMAN/town01/'),
batch_size=1,
shuffle=False,
num_workers=1)
# test_loader = DataLoader(CostMapDataset(data_index=[8], opt=opt, dataset_path='/media/wang/DATASET/CARLA/town01/'), batch_size=1, shuffle=False, num_workers=1)
test_samples = iter(test_loader)
os.makedirs('result/output2/%s' % opt.dataset_name, exist_ok=True)
if not opt.test_mode:
logger = SummaryWriter(log_dir=log_path)
write_params(log_path, parser, description)
generator = Generator(opt.vector_dim+2, output=2).to(device)
encoder = CNN(input_dim=1, out_dim=128).to(device)
criterion = torch.nn.MSELoss()
e_optimizer = torch.optim.Adam(encoder.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
g_optimizer = torch.optim.Adam(generator.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
train_loader = DataLoader(CostMapDataset(data_index=[1,2,3,4,5,6,7,8,9], opt=opt, dataset_path='/media/wang/DATASET/CARLA_HUMAN/town01/'), batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
test_loader = DataLoader(CostMapDataset(data_index=[10], opt=opt, dataset_path='/media/wang/DATASET/CARLA_HUMAN/town01/'), batch_size=1, shuffle=False, num_workers=1)
test_samples = iter(test_loader)
def test_traj_v(xs, ys, step):
fig = plt.figure(figsize=(7, 7))
ax1 = fig.add_subplot(111)
for i in range(len(xs)):
ax1.plot(xs[i], ys[i], label=str(round(0.8*i, 1)), linewidth=5)
ax1.set_xlabel('Forward/(m)')
ax1.set_ylabel('Sideways/(m)')
ax1.set_xlim([0., 40])
ax1.set_ylim([-20, 20])
#plt.legend(loc='lower right')
#plt.legend(loc='lower right', bbox_to_anchor=(1.0, 0.))
plt.legend(loc='center', bbox_to_anchor=(0.9, 0.5))