import parent
import data
import networks
import visualize
import train
import inverseConsistentNet
import numpy as np
import torch
import matplotlib.pyplot as plt
import random
import os
import describe
d1_triangles, d2_triangles = data.get_dataset_triangles("train",
data_size=50,
hollow=True,
samples=128)
# d1_triangles_test, d2_triangles_test = data.get_dataset_triangles(
# "test", data_size=50, hollow=True, samples=256
# )
d1_triangles_test, d2_triangles_test = d1_triangles, d2_triangles
network = networks.tallUNet2
d1, d2, d1_t, d2_t = (d1_triangles, d2_triangles, d1_triangles_test,
d2_triangles_test)
lmbda = 2048
random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
np.random.seed(1)
import torch
import matplotlib.pyplot as plt
import random
import os
import pickle
import describe
import argparse
parser = argparse.ArgumentParser()
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
random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
np.random.seed(1)
print("=" * 50)
net = inverseConsistentNet.InverseConsistentNet(
network_wrappers.DoubleNet(
network_wrappers.RandomShift(0.25),
import inverseConsistentNet
import numpy as np
import torch
import matplotlib.pyplot as plt
import random
import os
import describe
random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
np.random.seed(1)
d1_triangles, d2_triangles = data.get_dataset_triangles(
"train", data_size=50, hollow=False
)
network = networks.FCNet
d1, d2 = (d1_triangles, d2_triangles)
lmbda = 2048
image_A, image_B = (x[0].cuda() for x in next(zip(d1, d2)))
image_A = image_A[:1].cuda()
image_B = image_B[:1].cuda()
print("=" * 50)
print(network, lmbda)
net = inverseConsistentNet.InverseConsistentNet(network(), lmbda, image_A.size())
net.cuda()
optimizer = torch.optim.Adam(net.parameters(), lr=0.00001)
net.train()
