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# Ultralytics YOLO 🚀, AGPL-3.0 license
from ultralytics.engine.results import Results
from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import DEFAULT_CFG, ops
import torch
def print_tensor_shapes(item, index_path=None):
if index_path is None:
index_path = [] # Initialize the index path for the top-level call
if isinstance(item, torch.Tensor):
# Print the index path and shape if the item is a tensor
print(f"Tensor at Index Path {index_path}: Shape {item.shape}")
elif isinstance(item, (list, tuple)):
# Recursively call the function for nested lists and tuples
for i, sub_item in enumerate(item):
print_tensor_shapes(sub_item, index_path + [i])
else:
# Print the type of the item if it is not a tensor, list, or tuple
print(f"Item at Index Path {index_path} is not a tensor, list, or tuple. It is a {type(item)}.")
class SegmentationPredictor(DetectionPredictor):
"""
A class extending the DetectionPredictor class for prediction based on a segmentation model.
Example:
```python
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.segment import SegmentationPredictor
args = dict(model='yolov8n-seg.pt', source=ASSETS)
predictor = SegmentationPredictor(overrides=args)
predictor.predict_cli()
```
"""
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):
#print(preds[0].shape)
regression_preds = preds[1][-1]
p, final_reg = ops.non_max_suppression(prediction=preds[0],
mask_coef = preds[1][1],
proto = preds[1][-2],
img_shape = img.shape[2:],
conf_thres=self.args.conf,
iou_thres=self.args.iou,
agnostic=self.args.agnostic_nms,
max_det=self.args.max_det,
nc=len(self.model.names),
regression_var=regression_preds,
classes=self.args.classes)
#print(p[0].shape)
results = []
is_list = isinstance(orig_imgs, list) # input images are a list, not a torch.Tensor
if len(preds[1])==3:
proto = preds[1][-1]
elif len(preds[1])==4:
proto = preds[1][-2]
else:
proto = preds[1]
#print(regression_preds.shape)
for i, pred in enumerate(p):
orig_img = orig_imgs[i] if is_list else orig_imgs
img_path = self.batch[0][i]
if not len(pred): # save empty boxes
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 is_list:
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
#print(masks.shape)
#print(final_reg[i].shape)
results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks, regression_preds=final_reg[i]))
return results
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