New YOLOv8 Results() class for prediction outputs (#314)
Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Viet Nhat Thai <60825385+vietnhatthai@users.noreply.github.com> Co-authored-by: Paula Derrenger <107626595+pderrenger@users.noreply.github.com>
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Ayush Chaurasia
GitHub
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commit
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32 changed files with 813 additions and 259 deletions
284
ultralytics/yolo/engine/results.py
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284
ultralytics/yolo/engine/results.py
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from functools import lru_cache
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import numpy as np
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import torch
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from ultralytics.yolo.utils import LOGGER, ops
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class Results:
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"""
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A class for storing and manipulating inference results.
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Args:
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boxes (Boxes, optional): A Boxes object containing the detection bounding boxes.
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masks (Masks, optional): A Masks object containing the detection masks.
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probs (torch.Tensor, optional): A tensor containing the detection class probabilities.
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orig_shape (tuple, optional): Original image size.
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Attributes:
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boxes (Boxes, optional): A Boxes object containing the detection bounding boxes.
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masks (Masks, optional): A Masks object containing the detection masks.
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probs (torch.Tensor, optional): A tensor containing the detection class probabilities.
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orig_shape (tuple, optional): Original image size.
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"""
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def __init__(self, boxes=None, masks=None, probs=None, orig_shape=None) -> None:
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self.boxes = Boxes(boxes, orig_shape) if boxes is not None else None # native size boxes
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self.masks = Masks(masks, orig_shape) if masks is not None else None # native size or imgsz masks
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self.probs = probs.softmax(0) if probs is not None else None
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self.orig_shape = orig_shape
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self.comp = ["boxes", "masks", "probs"]
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def pandas(self):
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pass
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# TODO masks.pandas + boxes.pandas + cls.pandas
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def __getitem__(self, idx):
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r = Results(orig_shape=self.orig_shape)
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for item in self.comp:
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if getattr(self, item) is None:
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continue
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setattr(r, item, getattr(self, item)[idx])
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return r
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def cpu(self):
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r = Results(orig_shape=self.orig_shape)
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for item in self.comp:
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if getattr(self, item) is None:
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continue
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setattr(r, item, getattr(self, item).cpu())
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return r
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def numpy(self):
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r = Results(orig_shape=self.orig_shape)
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for item in self.comp:
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if getattr(self, item) is None:
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continue
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setattr(r, item, getattr(self, item).numpy())
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return r
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def cuda(self):
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r = Results(orig_shape=self.orig_shape)
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for item in self.comp:
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if getattr(self, item) is None:
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continue
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setattr(r, item, getattr(self, item).cuda())
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return r
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def to(self, *args, **kwargs):
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r = Results(orig_shape=self.orig_shape)
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for item in self.comp:
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if getattr(self, item) is None:
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continue
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setattr(r, item, getattr(self, item).to(*args, **kwargs))
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return r
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def __len__(self):
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for item in self.comp:
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if getattr(self, item) is None:
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continue
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return len(getattr(self, item))
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def __str__(self):
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return self.__repr__()
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def __repr__(self):
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s = f'Ultralytics YOLO {self.__class__} instance\n' # string
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if self.boxes:
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s = s + self.boxes.__repr__() + '\n'
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if self.masks:
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s = s + self.masks.__repr__() + '\n'
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if self.probs:
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s = s + self.probs.__repr__()
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s += f'original size: {self.orig_shape}\n'
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return s
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class Boxes:
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"""
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A class for storing and manipulating detection boxes.
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Args:
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boxes (torch.Tensor) or (numpy.ndarray): A tensor or numpy array containing the detection boxes,
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with shape (num_boxes, 6). The last two columns should contain confidence and class values.
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orig_shape (tuple): Original image size, in the format (height, width).
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Attributes:
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boxes (torch.Tensor) or (numpy.ndarray): A tensor or numpy array containing the detection boxes,
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with shape (num_boxes, 6).
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orig_shape (torch.Tensor) or (numpy.ndarray): Original image size, in the format (height, width).
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Properties:
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xyxy (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format.
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conf (torch.Tensor) or (numpy.ndarray): The confidence values of the boxes.
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cls (torch.Tensor) or (numpy.ndarray): The class values of the boxes.
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xywh (torch.Tensor) or (numpy.ndarray): The boxes in xywh format.
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xyxyn (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format normalized by original image size.
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xywhn (torch.Tensor) or (numpy.ndarray): The boxes in xywh format normalized by original image size.
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"""
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def __init__(self, boxes, orig_shape) -> None:
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if boxes.ndim == 1:
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boxes = boxes[None, :]
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assert boxes.shape[-1] == 6 # xyxy, conf, cls
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self.boxes = boxes
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self.orig_shape = torch.as_tensor(orig_shape, device=boxes.device) if isinstance(boxes, torch.Tensor) \
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else np.asarray(orig_shape)
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@property
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def xyxy(self):
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return self.boxes[:, :4]
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@property
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def conf(self):
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return self.boxes[:, -2]
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@property
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def cls(self):
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return self.boxes[:, -1]
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@property
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@lru_cache(maxsize=2) # maxsize 1 should suffice
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def xywh(self):
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return ops.xyxy2xywh(self.xyxy)
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@property
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@lru_cache(maxsize=2)
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def xyxyn(self):
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return self.xyxy / self.orig_shape[[1, 0, 1, 0]]
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@property
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@lru_cache(maxsize=2)
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def xywhn(self):
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return self.xywh / self.orig_shape[[1, 0, 1, 0]]
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def cpu(self):
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boxes = self.boxes.cpu()
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return Boxes(boxes, self.orig_shape)
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def numpy(self):
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boxes = self.boxes.numpy()
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return Boxes(boxes, self.orig_shape)
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def cuda(self):
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boxes = self.boxes.cuda()
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return Boxes(boxes, self.orig_shape)
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def to(self, *args, **kwargs):
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boxes = self.boxes.to(*args, **kwargs)
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return Boxes(boxes, self.orig_shape)
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def pandas(self):
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LOGGER.info('results.pandas() method not yet implemented')
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'''
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new = copy(self) # return copy
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ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
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cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
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for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
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a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
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setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
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return new
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'''
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@property
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def shape(self):
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return self.boxes.shape
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def __len__(self): # override len(results)
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return len(self.boxes)
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def __str__(self):
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return self.__repr__()
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def __repr__(self):
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return (f"Ultralytics YOLO {self.__class__} masks\n" + f"type: {type(self.boxes)}\n" +
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f"shape: {self.boxes.shape}\n" + f"dtype: {self.boxes.dtype}")
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def __getitem__(self, idx):
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boxes = self.boxes[idx]
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return Boxes(boxes, self.orig_shape)
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class Masks:
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"""
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A class for storing and manipulating detection masks.
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Args:
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masks (torch.Tensor): A tensor containing the detection masks, with shape (num_masks, height, width).
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orig_shape (tuple): Original image size, in the format (height, width).
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Attributes:
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masks (torch.Tensor): A tensor containing the detection masks, with shape (num_masks, height, width).
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orig_shape (tuple): Original image size, in the format (height, width).
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Properties:
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segments (list): A list of segments which includes x,y,w,h,label,confidence, and mask of each detection masks.
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"""
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def __init__(self, masks, orig_shape) -> None:
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self.masks = masks # N, h, w
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self.orig_shape = orig_shape
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@property
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@lru_cache(maxsize=1)
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def segments(self):
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return [
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ops.scale_segments(self.masks.shape[1:], x, self.orig_shape, normalize=True)
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for x in reversed(ops.masks2segments(self.masks))]
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@property
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def shape(self):
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return self.masks.shape
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def cpu(self):
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masks = self.masks.cpu()
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return Masks(masks, self.orig_shape)
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def numpy(self):
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masks = self.masks.numpy()
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return Masks(masks, self.orig_shape)
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def cuda(self):
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masks = self.masks.cuda()
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return Masks(masks, self.orig_shape)
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def to(self, *args, **kwargs):
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masks = self.masks.to(*args, **kwargs)
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return Masks(masks, self.orig_shape)
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def __len__(self): # override len(results)
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return len(self.masks)
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def __str__(self):
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return self.__repr__()
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def __repr__(self):
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return (f"Ultralytics YOLO {self.__class__} masks\n" + f"type: {type(self.masks)}\n" +
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f"shape: {self.masks.shape}\n" + f"dtype: {self.masks.dtype}")
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def __getitem__(self, idx):
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masks = self.masks[idx]
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return Masks(masks, self.im_shape, self.orig_shape)
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if __name__ == "__main__":
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# test examples
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results = Results(boxes=torch.randn((2, 6)), masks=torch.randn((2, 160, 160)), orig_shape=[640, 640])
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results = results.cuda()
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print("--cuda--pass--")
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results = results.cpu()
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print("--cpu--pass--")
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results = results.to("cuda:0")
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print("--to-cuda--pass--")
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results = results.to("cpu")
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print("--to-cpu--pass--")
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results = results.numpy()
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print("--numpy--pass--")
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# box = Boxes(boxes=torch.randn((2, 6)), orig_shape=[5, 5])
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# box = box.cuda()
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# box = box.cpu()
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# box = box.numpy()
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# for b in box:
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# print(b)
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