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| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| import torch | |
| from ultralytics.engine.results import Results | |
| from ultralytics.models.fastsam.utils import bbox_iou | |
| from ultralytics.models.yolo.detect.predict import DetectionPredictor | |
| from ultralytics.utils import DEFAULT_CFG, ops | |
| class FastSAMPredictor(DetectionPredictor): | |
| """ | |
| FastSAMPredictor is specialized for fast SAM (Segment Anything Model) segmentation prediction tasks in Ultralytics | |
| YOLO framework. | |
| This class extends the DetectionPredictor, customizing the prediction pipeline specifically for fast SAM. | |
| It adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing | |
| for single-class segmentation. | |
| Attributes: | |
| cfg (dict): Configuration parameters for prediction. | |
| overrides (dict, optional): Optional parameter overrides for custom behavior. | |
| _callbacks (dict, optional): Optional list of callback functions to be invoked during prediction. | |
| """ | |
| def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): | |
| """ | |
| Initializes the FastSAMPredictor class, inheriting from DetectionPredictor and setting the task to 'segment'. | |
| Args: | |
| cfg (dict): Configuration parameters for prediction. | |
| overrides (dict, optional): Optional parameter overrides for custom behavior. | |
| _callbacks (dict, optional): Optional list of callback functions to be invoked during prediction. | |
| """ | |
| super().__init__(cfg, overrides, _callbacks) | |
| self.args.task = "segment" | |
| def postprocess(self, preds, img, orig_imgs): | |
| """ | |
| Perform post-processing steps on predictions, including non-max suppression and scaling boxes to original image | |
| size, and returns the final results. | |
| Args: | |
| preds (list): The raw output predictions from the model. | |
| img (torch.Tensor): The processed image tensor. | |
| orig_imgs (list | torch.Tensor): The original image or list of images. | |
| Returns: | |
| (list): A list of Results objects, each containing processed boxes, masks, and other metadata. | |
| """ | |
| p = ops.non_max_suppression( | |
| preds[0], | |
| self.args.conf, | |
| self.args.iou, | |
| agnostic=self.args.agnostic_nms, | |
| max_det=self.args.max_det, | |
| nc=1, # set to 1 class since SAM has no class predictions | |
| classes=self.args.classes, | |
| ) | |
| full_box = torch.zeros(p[0].shape[1], device=p[0].device) | |
| full_box[2], full_box[3], full_box[4], full_box[6:] = img.shape[3], img.shape[2], 1.0, 1.0 | |
| full_box = full_box.view(1, -1) | |
| critical_iou_index = bbox_iou(full_box[0][:4], p[0][:, :4], iou_thres=0.9, image_shape=img.shape[2:]) | |
| if critical_iou_index.numel() != 0: | |
| full_box[0][4] = p[0][critical_iou_index][:, 4] | |
| full_box[0][6:] = p[0][critical_iou_index][:, 6:] | |
| p[0][critical_iou_index] = full_box | |
| if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list | |
| orig_imgs = ops.convert_torch2numpy_batch(orig_imgs) | |
| results = [] | |
| proto = preds[1][-1] if len(preds[1]) == 3 else preds[1] # second output is len 3 if pt, but only 1 if exported | |
| for i, pred in enumerate(p): | |
| orig_img = orig_imgs[i] | |
| img_path = self.batch[0][i] | |
| if not len(pred): # save empty boxes | |
| masks = None | |
| elif self.args.retina_masks: | |
| pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) | |
| masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2]) # HWC | |
| else: | |
| masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC | |
| pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) | |
| results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks)) | |
| return results | |