yanchor='bottom',
bordercolor='#444',
borderwidth=0)
# legend = dict(x = 0, xanchor = 'left', y =0, yanchor = 'bottom', bordercolor = '#444', borderwidth = 0)
)
data = [trace_points, trace_depo] + path_traces
fig = go.Figure(data=data, layout=layout)
fig.show()
if __name__ == '__main__':
args = test_parser()
t1 = time()
pretrained = load_model(args.path,
embed_dim=128,
n_customer=args.n_customer,
n_encode_layers=3)
print(f'model loading time:{time()-t1}s')
if args.txt is not None:
datatxt = data_from_txt(args.txt)
data = []
for i in range(3):
elem = [datatxt[i].squeeze(0) for j in range(args.batch)]
data.append(torch.stack(elem, 0))
else:
# data = generate_data(n_samples = 2, n_customer = args.n_customer, seed = args.seed)
data = []
for i in range(3):
elem = [
generate_data(1, args.n_customer, args.seed)[i]
for j in range(args.batch)
autosize = True,
template = "plotly_white",
legend = dict(x = 1.05, xanchor = 'left', y =0, yanchor = 'bottom', bordercolor = 'black', borderwidth = 1)
# legend = dict(x = 1, xanchor = 'right', y =0, yanchor = 'bottom', bordercolor = '#444', borderwidth = 0)
# legend = dict(x = 0, xanchor = 'left', y =0, yanchor = 'bottom', bordercolor = '#444', borderwidth = 0)
)
data = [trace_points, trace_depo] + path_traces
fig = go.Figure(data = data, layout = layout)
fig.show()
if __name__ == '__main__':
args = test_parser()
t1 = time()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
pretrained = load_model(device, args.path, embed_dim = 128, n_encode_layers = 3)
print(f'model loading time:{time()-t1}s')
t2 = time()
if args.txt is not None:
hoge = TorchJson(args.txt)
data = hoge.load_json(device)# return tensor on GPU
for k, v in data.items():
shape = (args.batch, ) + v.size()[1:]
data[k] = v.expand(*shape).clone()
# print('k, v', k, *v.size())
# print(*shape)
else:
data = {}
for k in ['depot_xy', 'customer_xy', 'demand', 'car_start_node', 'car_capacity']: