demo/hf_demo.py

import sys
from pathlib import Path

import gradio
import torch
from omegaconf import OmegaConf

sys.path.append(str(Path(__file__).resolve().parent.parent))

from yolo import (
    AugmentationComposer,
    NMSConfig,
    PostProcess,
    create_converter,
    create_model,
    draw_bboxes,
)

DEFAULT_MODEL = "v9-c"
IMAGE_SIZE = (640, 640)


def load_model(model_name):
    model_cfg = OmegaConf.load(f"yolo/config/model/{model_name}.yaml")
    model_cfg.model.auxiliary = {}
    model = create_model(model_cfg, True)
    converter = create_converter(model_cfg.name, model, model_cfg.anchor, IMAGE_SIZE, device)
    model = model.to(device).eval()
    return model, converter


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model, converter = load_model(DEFAULT_MODEL)
class_list = OmegaConf.load("yolo/config/dataset/coco.yaml").class_list

transform = AugmentationComposer([])


def predict(model_name, image, nms_confidence, nms_iou, max_bbox):
    global DEFAULT_MODEL, model, device, converter, class_list, post_proccess
    if model_name != DEFAULT_MODEL:
        model, converter = load_model(model_name)
        DEFAULT_MODEL = model_name

    image_tensor, _, rev_tensor = transform(image)

    image_tensor = image_tensor.to(device)[None]
    rev_tensor = rev_tensor.to(device)[None]

    nms_config = NMSConfig(nms_confidence, nms_iou, max_bbox)
    post_proccess = PostProcess(converter, nms_config)

    with torch.no_grad():
        predict = model(image_tensor)
        pred_bbox = post_proccess(predict, rev_tensor)

    result_image = draw_bboxes(image, pred_bbox, idx2label=class_list)

    return result_image


interface = gradio.Interface(
    fn=predict,
    inputs=[
        gradio.components.Dropdown(choices=["v9-c", "v9-m", "v9-s"], value="v9-c", label="Model Name"),
        gradio.components.Image(type="pil", label="Input Image"),
        gradio.components.Slider(0, 1, step=0.01, value=0.5, label="NMS Confidence Threshold"),
        gradio.components.Slider(0, 1, step=0.01, value=0.5, label="NMS IoU Threshold"),
        gradio.components.Slider(0, 1000, step=10, value=400, label="Max Bounding Box Number"),
    ],
    outputs=gradio.components.Image(type="pil", label="Output Image"),
)

if __name__ == "__main__":
    interface.launch()