def test_2d_registration_train(self):
import icon_registration.data as data
import icon_registration.networks as networks
import icon_registration.network_wrappers as network_wrappers
import icon_registration.train as train
import icon_registration.inverseConsistentNet as inverseConsistentNet
import numpy as np
import torch
import random
import os
random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
np.random.seed(1)
batch_size = 128
d1, d2 = data.get_dataset_triangles("train",
data_size=50,
hollow=False,
batch_size=batch_size)
d1_t, d2_t = data.get_dataset_triangles("test",
data_size=50,
hollow=False,
batch_size=batch_size)
lmbda = 2048
print("=" * 50)
net = inverseConsistentNet.InverseConsistentNet(
network_wrappers.FunctionFromVectorField(
networks.tallUNet2(dimension=2)),
# Our image similarity metric. The last channel of x and y is whether the value is interpolated or extrapolated,
# which is used by some metrics but not this one
lambda x, y: torch.mean((x[:, :1] - y[:, :1])**2),
lmbda,
)
input_shape = next(iter(d1))[0].size()
network_wrappers.assignIdentityMap(net, input_shape)
net.cuda()
optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
net.train()
y = np.array(train.train2d(net, optimizer, d1, d2, epochs=50))
# Test that image similarity is good enough
self.assertLess(np.mean(y[-5:, 1]), 0.1)
# Test that folds are rare enough
self.assertLess(np.mean(np.exp(y[-5:, 3] - 0.1)), 2)
print(y)
lambda x, y: torch.mean((x - y)**2),
700,
)
input_shape = next(iter(d1))[0].size()
network_wrappers.assignIdentityMap(net, input_shape)
net.cuda()
import icon_registration.train as train
optim = torch.optim.Adam(net.parameters(), lr=0.0001)
net.train().cuda()
xs = []
for _ in range(240):
y = np.array(train.train2d(net, optim, d1, d2, epochs=50))
xs.append(y)
x = np.concatenate(xs)
plt.title("Loss curve for " + type(net.regis_net).__name__)
plt.plot(x[:, :3])
plt.savefig(footsteps.output_dir + f"loss.png")
plt.clf()
plt.title("Log # pixels with negative Jacobian per epoch")
plt.plot(x[:, 3])
# random.seed(1)
plt.savefig(footsteps.output_dir + f"lossj.png")
plt.clf()
with open(footsteps.output_dir + "loss.pickle", "wb") as f:
pickle.dump(x, f)
# torch.manual_seed(1)
# torch.cuda.manual_seed(1)