File size: 2,432 Bytes
ba7940e | 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 57 58 59 60 61 62 63 64 65 66 67 68 69 | """Standalone inference for the Tiny Vial CNN Hugging Face release."""
from __future__ import annotations
import argparse
from pathlib import Path
import numpy as np
from PIL import Image, ImageDraw
import torch
from safetensors.torch import load_file
from torch import nn
IMAGE_SIZE = 64
class TinyVialDetector(nn.Module):
def __init__(self) -> None:
super().__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 12, 3, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(12, 24, 3, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(24, 32, 3, stride=2, padding=1), nn.ReLU(),
)
self.head = nn.Sequential(
nn.Flatten(),
nn.Linear(32 * 8 * 8, 64), nn.ReLU(),
nn.Linear(64, 4), nn.Sigmoid(),
)
def forward(self, image: torch.Tensor) -> torch.Tensor:
return self.head(self.features(image))
def predict(image_path: Path, weights_path: Path) -> tuple[int, int, int, int]:
model = TinyVialDetector()
model.load_state_dict(load_file(weights_path, device="cpu"))
model.eval()
with Image.open(image_path) as source:
width, height = source.size
resized = source.convert("RGB").resize((IMAGE_SIZE, IMAGE_SIZE), Image.Resampling.BILINEAR)
array = np.asarray(resized, dtype=np.float32).copy()
tensor = torch.from_numpy(array).permute(2, 0, 1).unsqueeze(0) / 255.0
with torch.inference_mode():
cx, cy, box_width, box_height = model(tensor)[0].tolist()
x0 = max(0, min(width - 1, round((cx - box_width / 2) * width)))
y0 = max(0, min(height - 1, round((cy - box_height / 2) * height)))
x1 = max(x0, min(width - 1, round((cx + box_width / 2) * width) - 1))
y1 = max(y0, min(height - 1, round((cy + box_height / 2) * height) - 1))
return x0, y0, x1, y1
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("image", type=Path)
parser.add_argument("--weights", type=Path, default=Path("model.safetensors"))
parser.add_argument("--output", type=Path, default=Path("prediction.png"))
args = parser.parse_args()
bbox = predict(args.image, args.weights)
image = Image.open(args.image).convert("RGB")
ImageDraw.Draw(image).rectangle(bbox, outline="#76b900", width=2)
image.save(args.output)
print({"bbox_2d_tight": list(bbox), "output": str(args.output)})
if __name__ == "__main__":
main()
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