plt.title("Log # pixels with negative Jacobian per epoch")
plt.plot(x[:, 3])
# random.seed(1)
plt.savefig(describe.run_dir + f"lossj.png")
plt.clf()
with open(describe.run_dir + "loss.pickle", "wb") as f:
pickle.dump(x, f)
# torch.manual_seed(1)
# torch.cuda.manual_seed(1)
# np.random.seed(1)
image_A, image_B = (x[0].cuda() for x in next(zip(d1_t, d2_t)))
for N in range(3):
visualize.visualizeRegistration(
net,
image_A,
image_B,
N,
describe.run_dir + f"epoch{_:03}" + "case" + str(N) + ".png",
)
random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
np.random.seed(1)
image_A, image_B = (x[0].cuda() for x in next(zip(d1_t, d2_t)))
os.mkdir(describe.run_dir + "final/")
for N in range(30):
visualize.visualizeRegistrationCompact(net, image_A, image_B, N)
plt.savefig(describe.run_dir + f"final/{N}.png")
plt.clf()
loss_history.append(
[
inverse_consistency_loss.item(),
similarity_loss.item(),
transform_magnitude.item(),
torch.log(torch.sum(dA < 0) + 0.1),
]
)
print("]")
return loss_history
losses = []
for _ in range(400):
x = np.array(train2d(net, optimizer, image_A, image_B, epochs=10))
losses.append(x)
# random.seed(1)
# torch.manual_seed(1)
# torch.cuda.manual_seed(1)
# np.random.seed(1)
visualize.visualizeRegistration(
net,
image_A,
image_B,
0,
describe.run_dir + f"epoch{_:03}.png",
)
torch.save(net.state_dict(), describe.run_dir + "network.trch")
torch.save(optimizer.state_dict(), describe.run_dir + "opt.trch")
