objectmodel_v1/postprocess.py

from __future__ import annotations

import torch
from torch import Tensor

from .boxes import box_cxcywh_to_xyxy


@torch.no_grad()
def decode_predictions(
    outputs: dict[str, Tensor],
    image_sizes: list[tuple[int, int]],
    confidence: float = 0.25,
    top_k: int = 300,
) -> list[dict[str, Tensor]]:
    logits = outputs["pred_logits"].sigmoid()
    boxes = box_cxcywh_to_xyxy(outputs["pred_boxes"]).clamp(0.0, 1.0)
    results = []
    for index, (height, width) in enumerate(image_sizes):
        scores, labels = logits[index].max(dim=-1)
        keep = scores >= confidence
        if keep.sum() > top_k:
            selected = scores.masked_fill(~keep, -1).topk(top_k).indices
        else:
            selected = torch.where(keep)[0]
        selected_boxes = boxes[index, selected].clone()
        selected_boxes[:, [0, 2]] *= width
        selected_boxes[:, [1, 3]] *= height
        results.append(
            {"scores": scores[selected], "labels": labels[selected], "boxes": selected_boxes}
        )
    return results