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Simplify postprocessing methods (#4497)

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Glenn Jocher 2023-08-22 19:01:33 +02:00 • committed by GitHub
parent 6da8f7f51e
commit b890e1c937
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8 changed files with 45 additions and 61 deletions
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19
ultralytics/models/fastsam/predict.py
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@ -15,7 +15,6 @@ class FastSAMPredictor(DetectionPredictor):
self.args.task = 'segment'
def postprocess(self, preds, img, orig_imgs):
"""TODO: filter by classes."""
p = ops.non_max_suppression(preds[0],
self.args.conf,
self.args.iou,
@ -32,22 +31,20 @@ class FastSAMPredictor(DetectionPredictor):
full_box[0][6:] = p[0][critical_iou_index][:, 6:]
p[0][critical_iou_index] = full_box
results = []
is_list = isinstance(orig_imgs, list) # input images are a list, not a torch.Tensor
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] if isinstance(orig_imgs, list) else orig_imgs
path = self.batch[0]
img_path = path[i] if isinstance(path, list) else path
orig_img = orig_imgs[i] if is_list else orig_imgs
img_path = self.batch[0][i]
if not len(pred): # save empty boxes
results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6]))
continue
if self.args.retina_masks:
if not isinstance(orig_imgs, torch.Tensor):
masks = None
elif self.args.retina_masks:
if is_list:
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
if not isinstance(orig_imgs, torch.Tensor):
if is_list:
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
results.append(
Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
return results