Validation_loader = torch.utils.data.DataLoader(validation_data,
batch_size=batch_size,
shuffle=True)
###### define constant########
input_channels = 3
hidden_size = 64
max_epochs = 100
lr = 3e-4
beta = 0 #0.000000001#0.00000001#0.0001#0.0000001#0.000001#0.00001
#######network################
#epoch=39
#M='/big_disk/akrami/git_repos_new/lesion-detector/VAE_9.5.2019/Brats_results'
##########load low res net##########
G = VAE_Generator(input_channels, hidden_size).cuda()
#load_model(epoch,G.encoder, G.decoder,LM)
opt_enc = optim.Adam(G.parameters(), lr=lr)
fixed_noise = Variable(torch.randn(batch_size, hidden_size)).cuda()
data = next(iter(Validation_loader))
fixed_batch = Variable(data).cuda()
#######losss#################
def MSE_loss(Y, X):
msk = torch.tensor(X > 1e-6).float()
ret = ((X - Y)**2) * msk
ret = torch.sum(ret, 1)
return ret
############################################
########## intilaize parameters##########
# define constant
input_channels = 3
hidden_size = 64
max_epochs = 200
lr = 3e-4
beta = 0
device = 'cuda'
#########################################
epoch = 99
LM = '/big_disk/akrami/git_repos_new/ImagePTE/src/Lesion Detection/models/RVAE_final_1'
##########load low res net##########
G = VAE_Generator(input_channels, hidden_size).cuda()
load_model(epoch, G.encoder, G.decoder, LM)
##########define beta loss##########
def MSE_loss(Y, X):
msk = torch.tensor(X > 1e-6).float()
ret = ((X - Y)**2) * msk
return ret
def BMSE_loss(Y, X, beta, sigma, Dim):
term1 = -((1 + beta) / beta)
K1 = 1 / pow((2 * math.pi * (sigma**2)), (beta * Dim / 2))
term2 = MSE_loss(Y, X)