parser.add_argument("--n_cpu", type=int, default=16, help="number of cpu threads to use during batch generation")
opt = parser.parse_args()
epochs = opt.epochs
batch_size = opt.batch_size
dataset_path = opt.dataset_path
train_ds = ImageFolderDataset(dataset_path, img_dim=64)
train_dataloader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=opt.n_cpu)
os.makedirs("checkpoints", exist_ok=True)
os.makedirs("output", exist_ok=True)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = NVAE(z_dim=512, img_dim=(64, 64), M_N=opt.batch_size / len(train_ds))
# apply Spectral Normalization
model.apply(add_sn)
model.to(device)
if opt.pretrained_weights:
model.load_state_dict(torch.load(opt.pretrained_weights, map_location=device), strict=False)
optimizer = torch.optim.Adamax(model.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=15)
for epoch in range(epochs):
model.train()
import torch
from nvae.dataset import ImageFolderDataset
from nvae.utils import add_sn
from nvae.vae_celeba import NVAE
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
train_ds = ImageFolderDataset("E:\data\img_align_celeba", img_dim=64)
device = "cpu"
model = NVAE(z_dim=512, img_dim=(64, 64))
model.apply(add_sn)
model.to(device)
model.load_state_dict(torch.load("../checkpoints/ae_ckpt_7_0.080791.pth",
map_location=device),
strict=False)
model.eval()
img = train_ds[34].unsqueeze(0).to(device)
ori_image = img.permute(0, 2, 3, 1)[0]
ori_image = (ori_image.numpy() + 1) / 2 * 255
plt.imshow(ori_image.astype(np.uint8))
plt.show()
with torch.no_grad():
gen_imgs, _ = model(img)
epochs = opt.epochs
batch_size = opt.batch_size
dataset_path = opt.dataset_path
train_ds = ImageFolderDataset(dataset_path, img_dim=64)
train_dataloader = DataLoader(train_ds,
batch_size=batch_size,
shuffle=True,
num_workers=opt.n_cpu)
os.makedirs("checkpoints", exist_ok=True)
os.makedirs("output", exist_ok=True)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = NVAE(z_dim=512, img_dim=(64, 64))
# apply Spectral Normalization
model.apply(add_sn)
model.to(device)
if opt.pretrained_weights:
model.load_state_dict(torch.load(opt.pretrained_weights,
map_location=device),
strict=False)
warmup_kl = WarmupKLLoss(init_weights=[1., 1. / 2, 1. / 8],
steps=[4500, 3000, 1500],
M_N=opt.batch_size / len(train_ds),
eta_M_N=5e-6,
import torch
from nvae.utils import add_sn
from nvae.vae_celeba import NVAE
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
device = "cpu"
model = NVAE(z_dim=512, img_dim=(64, 64))
model.apply(add_sn)
model.to(device)
model.load_state_dict(torch.load("checkpoints/ae_ckpt_103_0.074130.pth",
map_location=device),
strict=False)
model.eval()
with torch.no_grad():
z = torch.randn((25, 512)).to(device)
gen_imgs, _ = model.decoder(z)
gen_imgs = gen_imgs.permute(0, 2, 3, 1)
for gen_img in gen_imgs:
gen_img = gen_img.cpu().numpy() * 255
gen_img = gen_img.astype(np.uint8)
plt.imshow(gen_img)
# plt.savefig(f"output/ae_ckpt_%d_%.6f.png" % (epoch, total_loss))
plt.show()
import numpy as np
import torch
from nvae.utils import add_sn
from nvae.vae_celeba import NVAE
if __name__ == '__main__':
img_size = 64
z_dim = 512
cols, rows = 12, 12
width = cols * img_size
height = rows * img_size
device = "cpu"
model = NVAE(z_dim=z_dim, img_dim=img_size)
model.apply(add_sn)
model.to(device)
model.load_state_dict(torch.load("checkpoints/ae_ckpt_0_0.761000.pth",
map_location=device),
strict=False)
model.eval()
result = np.zeros((width, height, 3), dtype=np.uint8)
with torch.no_grad():
z = torch.randn((cols * rows, z_dim, 2, 2)).to(device)
gen_imgs, _ = model.decoder(z)
gen_imgs = gen_imgs.reshape(rows, cols, 3, img_size, img_size)