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