| import torch, os, gradio as gr, numpy as np |
| from torchvision import utils, transforms |
| from progan_modules import Generator |
|
|
| CHECKPOINT_DIR = "./model" |
| Z_DIM, CHANNEL_SIZE = 128, 128 |
| DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
| FIXED_STEP = 6 |
| FIXED_ALPHA = 0.0 |
|
|
| g_running = Generator(CHANNEL_SIZE, Z_DIM, pixel_norm=False, tanh=False).to(DEVICE) |
| g_running.load_state_dict(torch.load(os.path.join(CHECKPOINT_DIR, "g.model"), map_location=DEVICE)) |
| g_running.eval() |
|
|
| to_pil = transforms.ToPILImage() |
|
|
| @torch.inference_mode() |
| def sample_images(n_images: int = 50, seed: int | None = None): |
| if seed is not None and seed >= 0: |
| torch.manual_seed(seed); np.random.seed(seed) |
| else: |
| torch.seed() |
|
|
| z = torch.randn(n_images, Z_DIM, device=DEVICE) |
| imgs = g_running(z, step=FIXED_STEP, alpha=FIXED_ALPHA).cpu() |
|
|
| grid = utils.make_grid(imgs, nrow=10, normalize=True, value_range=(-1, 1)) |
| return to_pil(grid) |
|
|
| demo = gr.Interface( |
| fn=sample_images, |
| inputs=[ |
| gr.Slider(1, 200, value=50, step=10, label="Jumlah Gambar (kelipatan 10)"), |
| gr.Number(value=-1, precision=0, label="Seed (‑1 = acak)"), |
| ], |
| outputs=gr.Image(type="pil", label="Grid Hasil"), |
| title="Progressive Growing Generative Adversarial Network", |
| description="contoh implementasi PGGAN untuk dataset jerawat", |
| allow_flagging="never", |
| ) |
|
|
| if __name__ == "__main__": |
| demo.queue() |
| demo.launch(show_api=False, share=True) |
|
|
