File size: 2,869 Bytes
df336dd | 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 Tiny Vial CNN v2 inference using Safetensors."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
from PIL import Image, ImageDraw
from safetensors.torch import load_file
import torch
from torch import nn
class TinyVialDetectorV2(nn.Module):
def __init__(self):
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.shared = nn.Sequential(nn.Flatten(), nn.Linear(32 * 8 * 8, 64), nn.ReLU())
self.objectness = nn.Linear(64, 1)
self.box = nn.Sequential(nn.Linear(64, 4), nn.Sigmoid())
def forward(self, image):
hidden = self.shared(self.features(image))
return self.objectness(hidden), self.box(hidden)
def pixel_box(parameters, width, height):
cx, cy, box_width, box_height = (float(value) for value in parameters)
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():
parser = argparse.ArgumentParser()
parser.add_argument("image", type=Path)
parser.add_argument("--weights", type=Path, default=Path("model.safetensors"))
parser.add_argument("--config", type=Path, default=Path("config.json"))
parser.add_argument("--output", type=Path, default=Path("prediction.png"))
args = parser.parse_args()
config = json.loads(args.config.read_text())
model = TinyVialDetectorV2()
model.load_state_dict(load_file(args.weights))
model.eval()
image = Image.open(args.image).convert("RGB")
resized = image.resize((config["image_size"], config["image_size"]), Image.Resampling.BILINEAR)
tensor = torch.from_numpy(np.asarray(resized, dtype=np.float32).copy()).permute(2, 0, 1).unsqueeze(0) / 255.0
with torch.inference_mode():
logits, parameters = model(tensor)
score = float(torch.sigmoid(logits[0, 0]))
detected = score >= float(config["objectness_threshold"])
bbox = pixel_box(parameters[0], *image.size) if detected else None
if bbox:
ImageDraw.Draw(image).rectangle(bbox, outline=(118, 185, 0), width=max(1, image.width // 128))
args.output.parent.mkdir(parents=True, exist_ok=True)
image.save(args.output)
print(json.dumps({"detected": detected, "objectness_score": round(score, 6), "threshold": config["objectness_threshold"], "bbox": list(bbox) if bbox else None, "output": str(args.output)}, indent=2))
if __name__ == "__main__":
main()
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