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21e2ec3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | """ShadeNet-2 torch inference (standalone CLI).
Usage:
pip install -r requirements.txt
python inference.py input.jpg --output_dir ./output
python inference.py ./photos --checkpoint ./checkpoints/shadenet2.ckpt
"""
import argparse
import os
import torch
from PIL import Image
from model import load_shadenet2
from inference_utils import build_grid, pil_to_np, resize_pad
IMAGE_SIZE = 512
@torch.no_grad()
def run(model, img_rgb: Image.Image, device: str) -> "torch.Tensor":
x = torch.from_numpy(pil_to_np(img_rgb)).to(device)
return model(x).detach().cpu()
def main():
ap = argparse.ArgumentParser(description="ShadeNet-2 inference")
ap.add_argument("input", help="Image file or folder")
ap.add_argument("--checkpoint", default="./checkpoints/shadenet2.ckpt")
ap.add_argument("--output_dir", default="./output")
ap.add_argument("--image-size", type=int, default=IMAGE_SIZE)
ap.add_argument("--no-ema", action="store_true",
help="Use raw weights instead of EMA")
args = ap.parse_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
model = load_shadenet2(args.checkpoint, device, use_ema=not args.no_ema)
if os.path.isdir(args.input):
files = sorted(os.path.join(args.input, f) for f in os.listdir(args.input)
if f.lower().endswith((".png", ".jpg", ".jpeg", ".webp")))
else:
files = [args.input]
os.makedirs(args.output_dir, exist_ok=True)
for fp in files:
img_rgb = resize_pad(Image.open(fp), args.image_size)
out = run(model, img_rgb, device).numpy()
base = os.path.splitext(os.path.basename(fp))[0]
build_grid(img_rgb, out).save(
os.path.join(args.output_dir, f"{base}_result.png"))
print(f"Saved: {base}_result.png")
if __name__ == "__main__":
main()
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