import os
import torch
import torch.nn as nn
import numpy as np
import utils.general as utils
import utils.cgan as cgan_utils
from dense_CGAN import Generator, Discriminator
import torchvision
if __name__ == '__main__':
epochs = 200
batch_size = 100
latent_dim = 100
dataloader = utils.get_dataloader(batch_size, pad=False)
device = utils.get_device()
step_per_epoch = np.ceil(dataloader.dataset.__len__() / batch_size)
sample_dir = './samples'
checkpoint_dir = './checkpoints'
utils.makedirs(sample_dir, checkpoint_dir)
G = Generator(latent_dim=latent_dim).to(device)
D = Discriminator().to(device)
g_optim = utils.get_optim(G, 0.0005)
d_optim = utils.get_optim(D, 0.0005)
g_log = []
d_log = []
"""
import os
import torch
import torch.nn as nn
import numpy as np
import utils.general as utils
from GAN import Generator, Discriminator
import torchvision
if __name__ == '__main__':
epochs = 200
batch_size = 100
latent_dim = 100
dataloader = utils.get_dataloader(batch_size)
device = utils.get_device()
step_per_epoch = np.ceil(dataloader.dataset.__len__() / batch_size)
sample_dir = './samples'
checkpoint_dir = './checkpoints'
utils.makedirs(sample_dir, checkpoint_dir)
G = Generator(latent_dim = latent_dim).to(device)
D = Discriminator().to(device)
g_optim = utils.get_optim(G, 0.0002)
d_optim = utils.get_optim(D, 0.0002)
g_log = []
d_log = []