class FastSAMPredictor(DetectionPredictor):
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
super().__init__(cfg, overrides, _callbacks)
self.args.task = 'segment'
def postprocess(self, preds, img, orig_imgs):
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=len(self.model.names),
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