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param.py
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param.py
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import torch
# training
epochs = 200
batch_size = 64
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
TrainNew = True
load = False
modelPath = "checkpoint/checkpoint_027601_lossD_{lossD}_lossG_{lossG}" + ".pth"
# device = torch.device('cpu')
latent = False
latentPath = "AE_M/190.pth"
# noise generate
z_dim = 200
z_dis = "norm"
z_mean = 0.
z_std = 0.33
# G
g_lr = 5e-6
weightClip = 0.01
n_critic = 5
# D
d_lr = 1e-6
leak_value = 0.2
soft_label = False
adv_weight = 0
d_thresh = 0.8
beta = (0.5, 0.999)
# data & network
cube_len = 32
bias = False
# output
model_save_step = 1
data_dir = '../volumetric_data/'
model_dir = 'chair/' # change it to train on other data models
output_dir = '../outputs'
# images_dir = '../test_outputs'
def print_params():
l = 16
print(l * '*' + 'hyper-parameters' + l * '*')
print('epochs =', epochs)
print('batch_size =', batch_size)
print('soft_labels =', soft_label)
print('adv_weight =', adv_weight)
print('d_thresh =', d_thresh)
print('z_dim =', z_dim)
print('z_dis =', z_dis)
print('model_images_save_step =', model_save_step)
print('data =', model_dir)
print('device =', device)
print('g_lr =', g_lr)
print('d_lr =', d_lr)
print('cube_len =', cube_len)
print('leak_value =', leak_value)
print('bias =', bias)
print(l * '*' + 'hyper-parameters' + l * '*')