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']: