"edge_index": dp.edge_index.to(device),
"pos": dp.pos.to(device)
}
F.update_params(params_dict)
y0 = dp.x.to(device)
t = dp.t[0:ts].to(device)
y_pd = adj_integr.integrate(F,
y0,
t,
method=args.method,
rtol=args.rtol,
atol=args.atol)
print("Plotting...")
print(y_pd.cpu().detach().numpy().shape)
print(dp.y.numpy().shape)
print(dataset[0].t.shape)
print(dataset[0].pos.shape)
plt.figure(0)
utils.plot_fields(
t=dataset[0].t,
coords=dataset[0].pos,
fields={
"y_pd": y_pd.cpu().detach().numpy(),
"y_gt": dp.y.numpy(),
},
save_path="./tmp_figs_dec12_n3000/",
delay=0.0001,
)
plt.show()
print("epoch {:>5d} | train loss: {:>7.12f}".format(epoch, losses.mean()))
torch.save(F.state_dict(), args.model_path)
if losses.mean() <= 0.0001:
break
dp = dataset[0]
params_dict = {"edge_index": dp.edge_index.to(device), "pos": dp.pos.to(device)}
F.update_params(params_dict)
y0 = dp.x.to(device)
t = dp.t[0:ts].to(device)
y_pd = adj_integr.integrate(
F, y0, t, method=args.method, rtol=args.rtol, atol=args.atol)
print("Plotting...")
print(y_pd.cpu().detach().numpy().shape)
print(dp.y.numpy().shape)
print(dataset[0].t.shape)
print(dataset[0].pos.shape)
plt.figure(0)
utils.plot_fields(
t=dataset[0].t,
coords=dataset[0].pos,
fields={
"y_pd": y_pd.cpu().detach().numpy(),
"y_gt": dp.y.numpy(),
},
save_path="./tmp_covid_state_daily_norm_10k/",
delay=0.0001,
)
plt.show()
np.linalg.norm(u[i].reshape(-1) - u_pd[i].reshape(-1)) /
(np.linalg.norm(u[i].reshape(-1)) + eps) for i in range(len(u))
]
diffs_over_time.append(diffs)
print("test case {:>5d} | test loss: {:>7.12f}".format(i, losses[i]))
if i in inds_of_sims_to_show:
print("Plotting...")
# utils.plot_grid(dataset[i].pos.cpu().detach().numpy())
plt.figure(0)
utils.plot_fields(
t=dataset[i].t,
coords=dataset[i].pos,
fields={
"y_pd": u_pd,
"y_gt": u,
},
save_path="./tmp_figs/",
delay=0.0001,
)
plt.show()
# if i == 2:
# break
print("Plotting diffs...")
plt.figure(0)
t = dataset[0].t.numpy()
for diff in diffs_over_time:
plt.plot(t, diff, alpha=0.5)
eps = 1.0e-6
diffs = [np.linalg.norm(u[i].reshape(-1) - u_pd[i].reshape(-1)) / (np.linalg.norm(u[i].reshape(-1)) + eps) for i in range(len(u))]
diffs_over_time.append(diffs)
print("test case {:>5d} | test loss: {:>7.12f}".format(i, losses[i]))
if i in inds_of_sims_to_show:
print("Plotting...")
utils.plot_grid(dataset[i].pos.cpu().detach().numpy())
plt.figure(0)
utils.plot_fields(
t=dataset[i].t.cpu().detach().numpy(),
coords=dataset[i].pos.cpu().detach().numpy(),
fields={
"y_pd": u_pd,
"y_gt": u,
},
save_path="./tmp_figs/",
delay=0.0001,
)
plt.show()
# if i == 2: # 3 for grids, 2 for time points
# break
print("Plotting diffs...")
plt.figure(0)
t = dataset[0].t.numpy()
for diff in diffs_over_time:
plt.plot(t, diff, alpha=0.5)