runname = 'synthetic_loaddot191_Adam1'
device = 1
cuda0 = torch.device(f'cuda:{device}')
batch_size = 4
epochs = 200
in_channels = 191
if in_channels == 2:
inputtype = 'V1_V4'
if in_channels == 191:
inputtype = 'all_channels'
# -----
# Model, loss, & optimizer
# -----
model = model_file.ResblocksDeconv(in_channels, (240, 240))
if __name__ == '__main__':
if device >= 0:
model.cuda(device)
# lossFunction = module.LossFunction(device)
lossFunction = module.VGGLoss(device)
optimizer = optim.Adam(model.parameters(), 0.1)
hori_means, verti_means, std_avg = RF.extract_means_std()
# -----
# Inputs:
# Will be dot number times the gaus
# ------
cuda0 = torch.device(f'cuda:{device}')
batch_size = 64
all_image_size = 96
num_epochs = 500
lr = 0.0002
# Beta1 hyperparam for Adam optimizers
beta1 = 0.8
vgg_beta = 2
# -----
# Models
# -----
in_channels=191
netG = model_file.ResblocksDeconv(in_channels, (all_image_size,all_image_size))
# netG.apply(module.weights_init)
netG.load_state_dict(torch.load(f'{old_runname}/netG_epochs_{epoch_loaded}.model'))
netD = module.Discriminator().to(device)
netD.load_state_dict(torch.load(f'{old_runname}/netD_epochs_{epoch_loaded}.model', map_location='cpu'))
# netD.apply(module.weights_init)
if __name__ == '__main__':
if device >= 0:
netG.cuda(device)
netD.cuda(device)