83dc25dbc8b4ed98b4cc952e3cb35668e5a65490,coco_eval.py,,evaluate_coco,#Any#Any#Any#,11

Before Change

    // write output
    json.dump(results, open("{}_bbox_results.json".format(dataset.set_name), "w"), indent=4)
    json.dump(image_ids, open("{}_processed_image_ids.json".format(dataset.set_name), "w"), indent=4)

    // load results in COCO evaluation tool
    coco_true = dataset.coco

After Change

    
    model.eval()
    
    with torch.no_grad():

        // start collecting results
        results = []
        image_ids = []

        for index in range(len(dataset)):
            data = dataset[index]
            scale = data["scale"]

            // run network
            scores, labels, boxes = model(data["img"].permute(2, 0, 1).cuda().float().unsqueeze(dim=0))
            scores = scores.cpu()
            labels = labels.cpu()
            boxes  = boxes.cpu()

            // correct boxes for image scale
            boxes /= scale

            // change to (x, y, w, h) (MS COCO standard)
            boxes[:, 2] -= boxes[:, 0]
            boxes[:, 3] -= boxes[:, 1]

            // compute predicted labels and scores
            //for box, score, label in zip(boxes[0], scores[0], labels[0]):
            for box_id in range(boxes.shape[0]):
                score = float(scores[box_id])
                label = int(labels[box_id])
                box = boxes[box_id, :]

                // scores are sorted, so we can break
                if score < threshold:
                    break

                // append detection for each positively labeled class
                image_result = {
                    "image_id"    : dataset.image_ids[index],
                    "category_id" : dataset.label_to_coco_label(label),
                    "score"       : float(score),
                    "bbox"        : box.tolist(),
                }

                // append detection to results
                results.append(image_result)

            // append image to list of processed images
            image_ids.append(dataset.image_ids[index])

            // print progress
            print("{}/{}".format(index, len(dataset)), end="\r")

        if not len(results):
            return

        // write output
        json.dump(results, open("{}_bbox_results.json".format(dataset.set_name), "w"), indent=4)

        // load results in COCO evaluation tool
        coco_true = dataset.coco
        coco_pred = coco_true.loadRes("{}_bbox_results.json".format(dataset.set_name))

        // run COCO evaluation
        coco_eval = COCOeval(coco_true, coco_pred, "bbox")
        coco_eval.params.imgIds = image_ids
        coco_eval.evaluate()
        coco_eval.accumulate()
        coco_eval.summarize()

        model.train()

        return

Instances