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)
import numpy as np
import icon_registration.networks as networks
import icon_registration.network_wrappers as network_wrappers
import icon_registration.visualize as visualize
import icon_registration.inverseConsistentNet as inverseConsistentNet
import icon_registration.data as data
import footsteps
import os
import matplotlib.pyplot as plt
import random
import pickle
batch_size = 128
data_size = 50
d1, d2 = data.get_dataset_triangles("train",
data_size=data_size,
hollow=True,
batch_size=batch_size)
d1_t, d2_t = data.get_dataset_triangles("test",
data_size=data_size,
hollow=True,
batch_size=batch_size)
image_A, image_B = (x[0].cuda() for x in next(zip(d1, d2)))
net = inverseConsistentNet.InverseConsistentNet(
network_wrappers.DoubleNet(
network_wrappers.FunctionFromVectorField(
networks.tallUNet2(dimension=2)),
network_wrappers.FunctionFromVectorField(
networks.tallUNet2(dimension=2)),
),
