xjiaxuan
/
image_to_pixle_params_yoloSAM
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
image_to_pixle_params_yoloSAM/ultralytics-main/ultralytics/utils/metrics.py

1467 lines
61 KiB
Python
Raw Normal View History

first commit
2025-07-14 17:36:53 +08:00
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Model validation metrics."""
import math
import warnings
from pathlib import Path
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import torch
from ultralytics.utils import LOGGER, DataExportMixin, SimpleClass, TryExcept, checks, plt_settings
OKS_SIGMA = (
np.array([0.26, 0.25, 0.25, 0.35, 0.35, 0.79, 0.79, 0.72, 0.72, 0.62, 0.62, 1.07, 1.07, 0.87, 0.87, 0.89, 0.89])
/ 10.0
)
def bbox_ioa(box1: np.ndarray, box2: np.ndarray, iou: bool = False, eps: float = 1e-7) -> np.ndarray:
"""
Calculate the intersection over box2 area given box1 and box2.
Args:
box1 (np.ndarray): A numpy array of shape (N, 4) representing N bounding boxes in x1y1x2y2 format.
box2 (np.ndarray): A numpy array of shape (M, 4) representing M bounding boxes in x1y1x2y2 format.
iou (bool, optional): Calculate the standard IoU if True else return inter_area/box2_area.
eps (float, optional): A small value to avoid division by zero.
Returns:
(np.ndarray): A numpy array of shape (N, M) representing the intersection over box2 area.
"""
# Get the coordinates of bounding boxes
b1_x1, b1_y1, b1_x2, b1_y2 = box1.T
b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
# Intersection area
inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * (
np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)
).clip(0)
# Box2 area
area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
if iou:
box1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
area = area + box1_area[:, None] - inter_area
# Intersection over box2 area
return inter_area / (area + eps)
def box_iou(box1: torch.Tensor, box2: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
"""
Calculate intersection-over-union (IoU) of boxes.
Args:
box1 (torch.Tensor): A tensor of shape (N, 4) representing N bounding boxes in (x1, y1, x2, y2) format.
box2 (torch.Tensor): A tensor of shape (M, 4) representing M bounding boxes in (x1, y1, x2, y2) format.
eps (float, optional): A small value to avoid division by zero.
Returns:
(torch.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2.
References:
https://github.com/pytorch/vision/blob/main/torchvision/ops/boxes.py
"""
# NOTE: Need .float() to get accurate iou values
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
(a1, a2), (b1, b2) = box1.float().unsqueeze(1).chunk(2, 2), box2.float().unsqueeze(0).chunk(2, 2)
inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp_(0).prod(2)
# IoU = inter / (area1 + area2 - inter)
return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
def bbox_iou(
box1: torch.Tensor,
box2: torch.Tensor,
xywh: bool = True,
GIoU: bool = False,
DIoU: bool = False,
CIoU: bool = False,
eps: float = 1e-7,
) -> torch.Tensor:
"""
Calculate the Intersection over Union (IoU) between bounding boxes.
This function supports various shapes for `box1` and `box2` as long as the last dimension is 4.
For instance, you may pass tensors shaped like (4,), (N, 4), (B, N, 4), or (B, N, 1, 4).
Internally, the code will split the last dimension into (x, y, w, h) if `xywh=True`,
or (x1, y1, x2, y2) if `xywh=False`.
Args:
box1 (torch.Tensor): A tensor representing one or more bounding boxes, with the last dimension being 4.
box2 (torch.Tensor): A tensor representing one or more bounding boxes, with the last dimension being 4.
xywh (bool, optional): If True, input boxes are in (x, y, w, h) format. If False, input boxes are in
(x1, y1, x2, y2) format.
GIoU (bool, optional): If True, calculate Generalized IoU.
DIoU (bool, optional): If True, calculate Distance IoU.
CIoU (bool, optional): If True, calculate Complete IoU.
eps (float, optional): A small value to avoid division by zero.
Returns:
(torch.Tensor): IoU, GIoU, DIoU, or CIoU values depending on the specified flags.
"""
# Get the coordinates of bounding boxes
if xywh: # transform from xywh to xyxy
(x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
else: # x1, y1, x2, y2 = box1
b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
# Intersection area
inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp_(0) * (
b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)
).clamp_(0)
# Union Area
union = w1 * h1 + w2 * h2 - inter + eps
# IoU
iou = inter / union
if CIoU or DIoU or GIoU:
cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width
ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height
if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
c2 = cw.pow(2) + ch.pow(2) + eps # convex diagonal squared
rho2 = (
(b2_x1 + b2_x2 - b1_x1 - b1_x2).pow(2) + (b2_y1 + b2_y2 - b1_y1 - b1_y2).pow(2)
) / 4 # center dist**2
if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
v = (4 / math.pi**2) * ((w2 / h2).atan() - (w1 / h1).atan()).pow(2)
with torch.no_grad():
alpha = v / (v - iou + (1 + eps))
return iou - (rho2 / c2 + v * alpha) # CIoU
return iou - rho2 / c2 # DIoU
c_area = cw * ch + eps # convex area
return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf
return iou # IoU
def mask_iou(mask1: torch.Tensor, mask2: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
"""
Calculate masks IoU.
Args:
mask1 (torch.Tensor): A tensor of shape (N, n) where N is the number of ground truth objects and n is the
product of image width and height.
mask2 (torch.Tensor): A tensor of shape (M, n) where M is the number of predicted objects and n is the
product of image width and height.
eps (float, optional): A small value to avoid division by zero.
Returns:
(torch.Tensor): A tensor of shape (N, M) representing masks IoU.
"""
intersection = torch.matmul(mask1, mask2.T).clamp_(0)
union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection
return intersection / (union + eps)
def kpt_iou(
kpt1: torch.Tensor, kpt2: torch.Tensor, area: torch.Tensor, sigma: List[float], eps: float = 1e-7
) -> torch.Tensor:
"""
Calculate Object Keypoint Similarity (OKS).
Args:
kpt1 (torch.Tensor): A tensor of shape (N, 17, 3) representing ground truth keypoints.
kpt2 (torch.Tensor): A tensor of shape (M, 17, 3) representing predicted keypoints.
area (torch.Tensor): A tensor of shape (N,) representing areas from ground truth.
sigma (list): A list containing 17 values representing keypoint scales.
eps (float, optional): A small value to avoid division by zero.
Returns:
(torch.Tensor): A tensor of shape (N, M) representing keypoint similarities.
"""
d = (kpt1[:, None, :, 0] - kpt2[..., 0]).pow(2) + (kpt1[:, None, :, 1] - kpt2[..., 1]).pow(2) # (N, M, 17)
sigma = torch.tensor(sigma, device=kpt1.device, dtype=kpt1.dtype) # (17, )
kpt_mask = kpt1[..., 2] != 0 # (N, 17)
e = d / ((2 * sigma).pow(2) * (area[:, None, None] + eps) * 2) # from cocoeval
# e = d / ((area[None, :, None] + eps) * sigma) ** 2 / 2 # from formula
return ((-e).exp() * kpt_mask[:, None]).sum(-1) / (kpt_mask.sum(-1)[:, None] + eps)
def _get_covariance_matrix(boxes: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Generate covariance matrix from oriented bounding boxes.
Args:
boxes (torch.Tensor): A tensor of shape (N, 5) representing rotated bounding boxes, with xywhr format.
Returns:
(torch.Tensor): Covariance matrices corresponding to original rotated bounding boxes.
"""
# Gaussian bounding boxes, ignore the center points (the first two columns) because they are not needed here.
gbbs = torch.cat((boxes[:, 2:4].pow(2) / 12, boxes[:, 4:]), dim=-1)
a, b, c = gbbs.split(1, dim=-1)
cos = c.cos()
sin = c.sin()
cos2 = cos.pow(2)
sin2 = sin.pow(2)
return a * cos2 + b * sin2, a * sin2 + b * cos2, (a - b) * cos * sin
def probiou(obb1: torch.Tensor, obb2: torch.Tensor, CIoU: bool = False, eps: float = 1e-7) -> torch.Tensor:
"""
Calculate probabilistic IoU between oriented bounding boxes.
Args:
obb1 (torch.Tensor): Ground truth OBBs, shape (N, 5), format xywhr.
obb2 (torch.Tensor): Predicted OBBs, shape (N, 5), format xywhr.
CIoU (bool, optional): If True, calculate CIoU.
eps (float, optional): Small value to avoid division by zero.
Returns:
(torch.Tensor): OBB similarities, shape (N,).
Notes:
OBB format: [center_x, center_y, width, height, rotation_angle].
References:
https://arxiv.org/pdf/2106.06072v1.pdf
"""
x1, y1 = obb1[..., :2].split(1, dim=-1)
x2, y2 = obb2[..., :2].split(1, dim=-1)
a1, b1, c1 = _get_covariance_matrix(obb1)
a2, b2, c2 = _get_covariance_matrix(obb2)
t1 = (
((a1 + a2) * (y1 - y2).pow(2) + (b1 + b2) * (x1 - x2).pow(2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps)
) * 0.25
t2 = (((c1 + c2) * (x2 - x1) * (y1 - y2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps)) * 0.5
t3 = (
((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2))
/ (4 * ((a1 * b1 - c1.pow(2)).clamp_(0) * (a2 * b2 - c2.pow(2)).clamp_(0)).sqrt() + eps)
+ eps
).log() * 0.5
bd = (t1 + t2 + t3).clamp(eps, 100.0)
hd = (1.0 - (-bd).exp() + eps).sqrt()
iou = 1 - hd
if CIoU: # only include the wh aspect ratio part
w1, h1 = obb1[..., 2:4].split(1, dim=-1)
w2, h2 = obb2[..., 2:4].split(1, dim=-1)
v = (4 / math.pi**2) * ((w2 / h2).atan() - (w1 / h1).atan()).pow(2)
with torch.no_grad():
alpha = v / (v - iou + (1 + eps))
return iou - v * alpha # CIoU
return iou
def batch_probiou(
obb1: Union[torch.Tensor, np.ndarray], obb2: Union[torch.Tensor, np.ndarray], eps: float = 1e-7
) -> torch.Tensor:
"""
Calculate the probabilistic IoU between oriented bounding boxes.
Args:
obb1 (torch.Tensor | np.ndarray): A tensor of shape (N, 5) representing ground truth obbs, with xywhr format.
obb2 (torch.Tensor | np.ndarray): A tensor of shape (M, 5) representing predicted obbs, with xywhr format.
eps (float, optional): A small value to avoid division by zero.
Returns:
(torch.Tensor): A tensor of shape (N, M) representing obb similarities.
References:
https://arxiv.org/pdf/2106.06072v1.pdf
"""
obb1 = torch.from_numpy(obb1) if isinstance(obb1, np.ndarray) else obb1
obb2 = torch.from_numpy(obb2) if isinstance(obb2, np.ndarray) else obb2
x1, y1 = obb1[..., :2].split(1, dim=-1)
x2, y2 = (x.squeeze(-1)[None] for x in obb2[..., :2].split(1, dim=-1))
a1, b1, c1 = _get_covariance_matrix(obb1)
a2, b2, c2 = (x.squeeze(-1)[None] for x in _get_covariance_matrix(obb2))
t1 = (
((a1 + a2) * (y1 - y2).pow(2) + (b1 + b2) * (x1 - x2).pow(2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps)
) * 0.25
t2 = (((c1 + c2) * (x2 - x1) * (y1 - y2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps)) * 0.5
t3 = (
((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2))
/ (4 * ((a1 * b1 - c1.pow(2)).clamp_(0) * (a2 * b2 - c2.pow(2)).clamp_(0)).sqrt() + eps)
+ eps
).log() * 0.5
bd = (t1 + t2 + t3).clamp(eps, 100.0)
hd = (1.0 - (-bd).exp() + eps).sqrt()
return 1 - hd
def smooth_bce(eps: float = 0.1) -> Tuple[float, float]:
"""
Compute smoothed positive and negative Binary Cross-Entropy targets.
Args:
eps (float, optional): The epsilon value for label smoothing.
Returns:
pos (float): Positive label smoothing BCE target.
neg (float): Negative label smoothing BCE target.
References:
https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
"""
return 1.0 - 0.5 * eps, 0.5 * eps
class ConfusionMatrix(DataExportMixin):
"""
A class for calculating and updating a confusion matrix for object detection and classification tasks.
Attributes:
task (str): The type of task, either 'detect' or 'classify'.
matrix (np.ndarray): The confusion matrix, with dimensions depending on the task.
nc (int): The number of category.
names (List[str]): The names of the classes, used as labels on the plot.
"""
def __init__(self, names: List[str] = [], task: str = "detect"):
"""
Initialize a ConfusionMatrix instance.
Args:
names (List[str], optional): Names of classes, used as labels on the plot.
task (str, optional): Type of task, either 'detect' or 'classify'.
"""
self.task = task
self.nc = len(names) # number of classes
self.matrix = np.zeros((self.nc + 1, self.nc + 1)) if self.task == "detect" else np.zeros((self.nc, self.nc))
self.names = names # name of classes
def process_cls_preds(self, preds, targets):
"""
Update confusion matrix for classification task.
Args:
preds (Array[N, min(nc,5)]): Predicted class labels.
targets (Array[N, 1]): Ground truth class labels.
"""
preds, targets = torch.cat(preds)[:, 0], torch.cat(targets)
for p, t in zip(preds.cpu().numpy(), targets.cpu().numpy()):
self.matrix[p][t] += 1
def process_batch(
self, detections: Dict[str, torch.Tensor], batch: Dict[str, Any], conf: float = 0.25, iou_thres: float = 0.45
) -> None:
"""
Update confusion matrix for object detection task.
Args:
detections (Dict[str, torch.Tensor]): Dictionary containing detected bounding boxes and their associated information.
Should contain 'cls', 'conf', and 'bboxes' keys, where 'bboxes' can be
Array[N, 4] for regular boxes or Array[N, 5] for OBB with angle.
batch (Dict[str, Any]): Batch dictionary containing ground truth data with 'bboxes' (Array[M, 4]| Array[M, 5]) and
'cls' (Array[M]) keys, where M is the number of ground truth objects.
conf (float, optional): Confidence threshold for detections.
iou_thres (float, optional): IoU threshold for matching detections to ground truth.
"""
conf = 0.25 if conf in {None, 0.001} else conf # apply 0.25 if default val conf is passed
gt_cls, gt_bboxes = batch["cls"], batch["bboxes"]
no_pred = len(detections["cls"]) == 0
if gt_cls.shape[0] == 0: # Check if labels is empty
if not no_pred:
detections = {k: detections[k][detections["conf"] > conf] for k in {"cls", "bboxes"}}
detection_classes = detections["cls"].int().tolist()
for dc in detection_classes:
self.matrix[dc, self.nc] += 1 # false positives
return
if no_pred:
gt_classes = gt_cls.int().tolist()
for gc in gt_classes:
self.matrix[self.nc, gc] += 1 # background FN
return
detections = {k: detections[k][detections["conf"] > conf] for k in {"cls", "bboxes"}}
gt_classes = gt_cls.int().tolist()
detection_classes = detections["cls"].int().tolist()
bboxes = detections["bboxes"]
is_obb = bboxes.shape[1] == 5 # check if detections contains angle for OBB
iou = batch_probiou(gt_bboxes, bboxes) if is_obb else box_iou(gt_bboxes, bboxes)
x = torch.where(iou > iou_thres)
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
else:
matches = np.zeros((0, 3))
n = matches.shape[0] > 0
m0, m1, _ = matches.transpose().astype(int)
for i, gc in enumerate(gt_classes):
j = m0 == i
if n and sum(j) == 1:
self.matrix[detection_classes[m1[j].item()], gc] += 1 # correct
else:
self.matrix[self.nc, gc] += 1 # true background
for i, dc in enumerate(detection_classes):
if not any(m1 == i):
self.matrix[dc, self.nc] += 1 # predicted background
def matrix(self):
"""Return the confusion matrix."""
return self.matrix
def tp_fp(self) -> Tuple[np.ndarray, np.ndarray]:
"""
Return true positives and false positives.
Returns:
tp (np.ndarray): True positives.
fp (np.ndarray): False positives.
"""
tp = self.matrix.diagonal() # true positives
fp = self.matrix.sum(1) - tp # false positives
# fn = self.matrix.sum(0) - tp # false negatives (missed detections)
return (tp[:-1], fp[:-1]) if self.task == "detect" else (tp, fp) # remove background class if task=detect
@TryExcept(msg="ConfusionMatrix plot failure")
@plt_settings()
def plot(self, normalize: bool = True, save_dir: str = "", on_plot=None):
"""
Plot the confusion matrix using matplotlib and save it to a file.
Args:
normalize (bool, optional): Whether to normalize the confusion matrix.
save_dir (str, optional): Directory where the plot will be saved.
on_plot (callable, optional): An optional callback to pass plots path and data when they are rendered.
"""
import matplotlib.pyplot as plt # scope for faster 'import ultralytics'
array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1) # normalize columns
array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)
fig, ax = plt.subplots(1, 1, figsize=(12, 9))
if self.nc >= 100: # downsample for large class count
k = max(2, self.nc // 60) # step size for downsampling, always > 1
keep_idx = slice(None, None, k) # create slice instead of array
self.names = self.names[keep_idx] # slice class names
array = array[keep_idx, :][:, keep_idx] # slice matrix rows and cols
n = (self.nc + k - 1) // k # number of retained classes
nc = nn = n if self.task == "classify" else n + 1 # adjust for background if needed
else:
nc = nn = self.nc if self.task == "classify" else self.nc + 1
ticklabels = (self.names + ["background"]) if (0 < nn < 99) and (nn == nc) else "auto"
xy_ticks = np.arange(len(ticklabels))
tick_fontsize = max(6, 15 - 0.1 * nc) # Minimum size is 6
label_fontsize = max(6, 12 - 0.1 * nc)
title_fontsize = max(6, 12 - 0.1 * nc)
btm = max(0.1, 0.25 - 0.001 * nc) # Minimum value is 0.1
with warnings.catch_warnings():
warnings.simplefilter("ignore") # suppress empty matrix RuntimeWarning: All-NaN slice encountered
im = ax.imshow(array, cmap="Blues", vmin=0.0, interpolation="none")
ax.xaxis.set_label_position("bottom")
if nc < 30: # Add score for each cell of confusion matrix
color_threshold = 0.45 * (1 if normalize else np.nanmax(array)) # text color threshold
for i, row in enumerate(array[:nc]):
for j, val in enumerate(row[:nc]):
val = array[i, j]
if np.isnan(val):
continue
ax.text(
j,
i,
f"{val:.2f}" if normalize else f"{int(val)}",
ha="center",
va="center",
fontsize=10,
color="white" if val > color_threshold else "black",
)
cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.05)
title = "Confusion Matrix" + " Normalized" * normalize
ax.set_xlabel("True", fontsize=label_fontsize, labelpad=10)
ax.set_ylabel("Predicted", fontsize=label_fontsize, labelpad=10)
ax.set_title(title, fontsize=title_fontsize, pad=20)
ax.set_xticks(xy_ticks)
ax.set_yticks(xy_ticks)
ax.tick_params(axis="x", bottom=True, top=False, labelbottom=True, labeltop=False)
ax.tick_params(axis="y", left=True, right=False, labelleft=True, labelright=False)
if ticklabels != "auto":
ax.set_xticklabels(ticklabels, fontsize=tick_fontsize, rotation=90, ha="center")
ax.set_yticklabels(ticklabels, fontsize=tick_fontsize)
for s in {"left", "right", "bottom", "top", "outline"}:
if s != "outline":
ax.spines[s].set_visible(False) # Confusion matrix plot don't have outline
cbar.ax.spines[s].set_visible(False)
fig.subplots_adjust(left=0, right=0.84, top=0.94, bottom=btm) # Adjust layout to ensure equal margins
plot_fname = Path(save_dir) / f"{title.lower().replace(' ', '_')}.png"
fig.savefig(plot_fname, dpi=250)
plt.close(fig)
if on_plot:
on_plot(plot_fname)
def print(self):
"""Print the confusion matrix to the console."""
for i in range(self.matrix.shape[0]):
LOGGER.info(" ".join(map(str, self.matrix[i])))
def summary(self, normalize: bool = False, decimals: int = 5) -> List[Dict[str, float]]:
"""
Generate a summarized representation of the confusion matrix as a list of dictionaries, with optional
normalization. This is useful for exporting the matrix to various formats such as CSV, XML, HTML, JSON, or SQL.
Args:
normalize (bool): Whether to normalize the confusion matrix values.
decimals (int): Number of decimal places to round the output values to.
Returns:
(List[Dict[str, float]]): A list of dictionaries, each representing one predicted class with corresponding values for all actual classes.
Examples:
>>> results = model.val(data="coco8.yaml", plots=True)
>>> cm_dict = results.confusion_matrix.summary(normalize=True, decimals=5)
>>> print(cm_dict)
"""
import re
names = self.names if self.task == "classify" else self.names + ["background"]
clean_names, seen = [], set()
for name in names:
clean_name = re.sub(r"[^a-zA-Z0-9_]", "_", name)
original_clean = clean_name
counter = 1
while clean_name.lower() in seen:
clean_name = f"{original_clean}_{counter}"
counter += 1
seen.add(clean_name.lower())
clean_names.append(clean_name)
array = (self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1)).round(decimals)
return [
dict({"Predicted": clean_names[i]}, **{clean_names[j]: array[i, j] for j in range(len(clean_names))})
for i in range(len(clean_names))
]
def smooth(y: np.ndarray, f: float = 0.05) -> np.ndarray:
"""Box filter of fraction f."""
nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)
p = np.ones(nf // 2) # ones padding
yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded
return np.convolve(yp, np.ones(nf) / nf, mode="valid") # y-smoothed
@plt_settings()
def plot_pr_curve(
px: np.ndarray,
py: np.ndarray,
ap: np.ndarray,
save_dir: Path = Path("pr_curve.png"),
names: Dict[int, str] = {},
on_plot=None,
):
"""
Plot precision-recall curve.
Args:
px (np.ndarray): X values for the PR curve.
py (np.ndarray): Y values for the PR curve.
ap (np.ndarray): Average precision values.
save_dir (Path, optional): Path to save the plot.
names (Dict[int, str], optional): Dictionary mapping class indices to class names.
on_plot (callable, optional): Function to call after plot is saved.
"""
import matplotlib.pyplot as plt # scope for faster 'import ultralytics'
fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
py = np.stack(py, axis=1)
if 0 < len(names) < 21: # display per-class legend if < 21 classes
for i, y in enumerate(py.T):
ax.plot(px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}") # plot(recall, precision)
else:
ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision)
ax.plot(px, py.mean(1), linewidth=3, color="blue", label=f"all classes {ap[:, 0].mean():.3f} mAP@0.5")
ax.set_xlabel("Recall")
ax.set_ylabel("Precision")
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
ax.set_title("Precision-Recall Curve")
fig.savefig(save_dir, dpi=250)
plt.close(fig)
if on_plot:
on_plot(save_dir)
@plt_settings()
def plot_mc_curve(
px: np.ndarray,
py: np.ndarray,
save_dir: Path = Path("mc_curve.png"),
names: Dict[int, str] = {},
xlabel: str = "Confidence",
ylabel: str = "Metric",
on_plot=None,
):
"""
Plot metric-confidence curve.
Args:
px (np.ndarray): X values for the metric-confidence curve.
py (np.ndarray): Y values for the metric-confidence curve.
save_dir (Path, optional): Path to save the plot.
names (Dict[int, str], optional): Dictionary mapping class indices to class names.
xlabel (str, optional): X-axis label.
ylabel (str, optional): Y-axis label.
on_plot (callable, optional): Function to call after plot is saved.
"""
import matplotlib.pyplot as plt # scope for faster 'import ultralytics'
fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
if 0 < len(names) < 21: # display per-class legend if < 21 classes
for i, y in enumerate(py):
ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric)
else:
ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric)
y = smooth(py.mean(0), 0.1)
ax.plot(px, y, linewidth=3, color="blue", label=f"all classes {y.max():.2f} at {px[y.argmax()]:.3f}")
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
ax.set_title(f"{ylabel}-Confidence Curve")
fig.savefig(save_dir, dpi=250)
plt.close(fig)
if on_plot:
on_plot(save_dir)
def compute_ap(recall: List[float], precision: List[float]) -> Tuple[float, np.ndarray, np.ndarray]:
"""
Compute the average precision (AP) given the recall and precision curves.
Args:
recall (list): The recall curve.
precision (list): The precision curve.
Returns:
ap (float): Average precision.
mpre (np.ndarray): Precision envelope curve.
mrec (np.ndarray): Modified recall curve with sentinel values added at the beginning and end.
"""
# Append sentinel values to beginning and end
mrec = np.concatenate(([0.0], recall, [1.0]))
mpre = np.concatenate(([1.0], precision, [0.0]))
# Compute the precision envelope
mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
# Integrate area under curve
method = "interp" # methods: 'continuous', 'interp'
if method == "interp":
x = np.linspace(0, 1, 101) # 101-point interp (COCO)
func = np.trapezoid if checks.check_version(np.__version__, ">=2.0") else np.trapz # np.trapz deprecated
ap = func(np.interp(x, mrec, mpre), x) # integrate
else: # 'continuous'
i = np.where(mrec[1:] != mrec[:-1])[0] # points where x-axis (recall) changes
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve
return ap, mpre, mrec
def ap_per_class(
tp: np.ndarray,
conf: np.ndarray,
pred_cls: np.ndarray,
target_cls: np.ndarray,
plot: bool = False,
on_plot=None,
save_dir: Path = Path(),
names: Dict[int, str] = {},
eps: float = 1e-16,
prefix: str = "",
) -> Tuple:
"""
Compute the average precision per class for object detection evaluation.
Args:
tp (np.ndarray): Binary array indicating whether the detection is correct (True) or not (False).
conf (np.ndarray): Array of confidence scores of the detections.
pred_cls (np.ndarray): Array of predicted classes of the detections.
target_cls (np.ndarray): Array of true classes of the detections.
plot (bool, optional): Whether to plot PR curves or not.
on_plot (callable, optional): A callback to pass plots path and data when they are rendered.
save_dir (Path, optional): Directory to save the PR curves.
names (Dict[int, str], optional): Dictionary of class names to plot PR curves.
eps (float, optional): A small value to avoid division by zero.
prefix (str, optional): A prefix string for saving the plot files.
Returns:
tp (np.ndarray): True positive counts at threshold given by max F1 metric for each class.
fp (np.ndarray): False positive counts at threshold given by max F1 metric for each class.
p (np.ndarray): Precision values at threshold given by max F1 metric for each class.
r (np.ndarray): Recall values at threshold given by max F1 metric for each class.
f1 (np.ndarray): F1-score values at threshold given by max F1 metric for each class.
ap (np.ndarray): Average precision for each class at different IoU thresholds.
unique_classes (np.ndarray): An array of unique classes that have data.
p_curve (np.ndarray): Precision curves for each class.
r_curve (np.ndarray): Recall curves for each class.
f1_curve (np.ndarray): F1-score curves for each class.
x (np.ndarray): X-axis values for the curves.
prec_values (np.ndarray): Precision values at mAP@0.5 for each class.
"""
# Sort by objectness
i = np.argsort(-conf)
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
# Find unique classes
unique_classes, nt = np.unique(target_cls, return_counts=True)
nc = unique_classes.shape[0] # number of classes, number of detections
# Create Precision-Recall curve and compute AP for each class
x, prec_values = np.linspace(0, 1, 1000), []
# Average precision, precision and recall curves
ap, p_curve, r_curve = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
for ci, c in enumerate(unique_classes):
i = pred_cls == c
n_l = nt[ci] # number of labels
n_p = i.sum() # number of predictions
if n_p == 0 or n_l == 0:
continue
# Accumulate FPs and TPs
fpc = (1 - tp[i]).cumsum(0)
tpc = tp[i].cumsum(0)
# Recall
recall = tpc / (n_l + eps) # recall curve
r_curve[ci] = np.interp(-x, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases
# Precision
precision = tpc / (tpc + fpc) # precision curve
p_curve[ci] = np.interp(-x, -conf[i], precision[:, 0], left=1) # p at pr_score
# AP from recall-precision curve
for j in range(tp.shape[1]):
ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
if j == 0:
prec_values.append(np.interp(x, mrec, mpre)) # precision at mAP@0.5
prec_values = np.array(prec_values) if prec_values else np.zeros((1, 1000)) # (nc, 1000)
# Compute F1 (harmonic mean of precision and recall)
f1_curve = 2 * p_curve * r_curve / (p_curve + r_curve + eps)
names = {i: names[k] for i, k in enumerate(unique_classes) if k in names} # dict: only classes that have data
if plot:
plot_pr_curve(x, prec_values, ap, save_dir / f"{prefix}PR_curve.png", names, on_plot=on_plot)
plot_mc_curve(x, f1_curve, save_dir / f"{prefix}F1_curve.png", names, ylabel="F1", on_plot=on_plot)
plot_mc_curve(x, p_curve, save_dir / f"{prefix}P_curve.png", names, ylabel="Precision", on_plot=on_plot)
plot_mc_curve(x, r_curve, save_dir / f"{prefix}R_curve.png", names, ylabel="Recall", on_plot=on_plot)
i = smooth(f1_curve.mean(0), 0.1).argmax() # max F1 index
p, r, f1 = p_curve[:, i], r_curve[:, i], f1_curve[:, i] # max-F1 precision, recall, F1 values
tp = (r * nt).round() # true positives
fp = (tp / (p + eps) - tp).round() # false positives
return tp, fp, p, r, f1, ap, unique_classes.astype(int), p_curve, r_curve, f1_curve, x, prec_values
class Metric(SimpleClass):
"""
Class for computing evaluation metrics for Ultralytics YOLO models.
Attributes:
p (list): Precision for each class. Shape: (nc,).
r (list): Recall for each class. Shape: (nc,).
f1 (list): F1 score for each class. Shape: (nc,).
all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10).
ap_class_index (list): Index of class for each AP score. Shape: (nc,).
nc (int): Number of classes.
Methods:
ap50(): AP at IoU threshold of 0.5 for all classes. Returns: List of AP scores. Shape: (nc,) or [].
ap(): AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: List of AP scores. Shape: (nc,) or [].
mp(): Mean precision of all classes. Returns: Float.
mr(): Mean recall of all classes. Returns: Float.
map50(): Mean AP at IoU threshold of 0.5 for all classes. Returns: Float.
map75(): Mean AP at IoU threshold of 0.75 for all classes. Returns: Float.
map(): Mean AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: Float.
mean_results(): Mean of results, returns mp, mr, map50, map.
class_result(i): Class-aware result, returns p[i], r[i], ap50[i], ap[i].
maps(): mAP of each class. Returns: Array of mAP scores, shape: (nc,).
fitness(): Model fitness as a weighted combination of metrics. Returns: Float.
update(results): Update metric attributes with new evaluation results.
"""
def __init__(self) -> None:
"""Initialize a Metric instance for computing evaluation metrics for the YOLOv8 model."""
self.p = [] # (nc, )
self.r = [] # (nc, )
self.f1 = [] # (nc, )
self.all_ap = [] # (nc, 10)
self.ap_class_index = [] # (nc, )
self.nc = 0
@property
def ap50(self) -> Union[np.ndarray, List]:
"""
Return the Average Precision (AP) at an IoU threshold of 0.5 for all classes.
Returns:
(np.ndarray | list): Array of shape (nc,) with AP50 values per class, or an empty list if not available.
"""
return self.all_ap[:, 0] if len(self.all_ap) else []
@property
def ap(self) -> Union[np.ndarray, List]:
"""
Return the Average Precision (AP) at an IoU threshold of 0.5-0.95 for all classes.
Returns:
(np.ndarray | list): Array of shape (nc,) with AP50-95 values per class, or an empty list if not available.
"""
return self.all_ap.mean(1) if len(self.all_ap) else []
@property
def mp(self) -> float:
"""
Return the Mean Precision of all classes.
Returns:
(float): The mean precision of all classes.
"""
return self.p.mean() if len(self.p) else 0.0
@property
def mr(self) -> float:
"""
Return the Mean Recall of all classes.
Returns:
(float): The mean recall of all classes.
"""
return self.r.mean() if len(self.r) else 0.0
@property
def map50(self) -> float:
"""
Return the mean Average Precision (mAP) at an IoU threshold of 0.5.
Returns:
(float): The mAP at an IoU threshold of 0.5.
"""
return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0
@property
def map75(self) -> float:
"""
Return the mean Average Precision (mAP) at an IoU threshold of 0.75.
Returns:
(float): The mAP at an IoU threshold of 0.75.
"""
return self.all_ap[:, 5].mean() if len(self.all_ap) else 0.0
@property
def map(self) -> float:
"""
Return the mean Average Precision (mAP) over IoU thresholds of 0.5 - 0.95 in steps of 0.05.
Returns:
(float): The mAP over IoU thresholds of 0.5 - 0.95 in steps of 0.05.
"""
return self.all_ap.mean() if len(self.all_ap) else 0.0
def mean_results(self) -> List[float]:
"""Return mean of results, mp, mr, map50, map."""
return [self.mp, self.mr, self.map50, self.map]
def class_result(self, i: int) -> Tuple[float, float, float, float]:
"""Return class-aware result, p[i], r[i], ap50[i], ap[i]."""
return self.p[i], self.r[i], self.ap50[i], self.ap[i]
@property
def maps(self) -> np.ndarray:
"""Return mAP of each class."""
maps = np.zeros(self.nc) + self.map
for i, c in enumerate(self.ap_class_index):
maps[c] = self.ap[i]
return maps
def fitness(self) -> float:
"""Return model fitness as a weighted combination of metrics."""
w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
return (np.nan_to_num(np.array(self.mean_results())) * w).sum()
def update(self, results: tuple):
"""
Update the evaluation metrics with a new set of results.
Args:
results (tuple): A tuple containing evaluation metrics:
- p (list): Precision for each class.
- r (list): Recall for each class.
- f1 (list): F1 score for each class.
- all_ap (list): AP scores for all classes and all IoU thresholds.
- ap_class_index (list): Index of class for each AP score.
- p_curve (list): Precision curve for each class.
- r_curve (list): Recall curve for each class.
- f1_curve (list): F1 curve for each class.
- px (list): X values for the curves.
- prec_values (list): Precision values for each class.
"""
(
self.p,
self.r,
self.f1,
self.all_ap,
self.ap_class_index,
self.p_curve,
self.r_curve,
self.f1_curve,
self.px,
self.prec_values,
) = results
@property
def curves(self) -> List:
"""Return a list of curves for accessing specific metrics curves."""
return []
@property
def curves_results(self) -> List[List]:
"""Return a list of curves for accessing specific metrics curves."""
return [
[self.px, self.prec_values, "Recall", "Precision"],
[self.px, self.f1_curve, "Confidence", "F1"],
[self.px, self.p_curve, "Confidence", "Precision"],
[self.px, self.r_curve, "Confidence", "Recall"],
]
class DetMetrics(SimpleClass, DataExportMixin):
"""
Utility class for computing detection metrics such as precision, recall, and mean average precision (mAP).
Attributes:
names (Dict[int, str]): A dictionary of class names.
box (Metric): An instance of the Metric class for storing detection results.
speed (Dict[str, float]): A dictionary for storing execution times of different parts of the detection process.
task (str): The task type, set to 'detect'.
stats (Dict[str, List]): A dictionary containing lists for true positives, confidence scores, predicted classes, target classes, and target images.
nt_per_class: Number of targets per class.
nt_per_image: Number of targets per image.
"""
def __init__(self, names: Dict[int, str] = {}) -> None:
"""
Initialize a DetMetrics instance with a save directory, plot flag, and class names.
Args:
names (Dict[int, str], optional): Dictionary of class names.
"""
self.names = names
self.box = Metric()
self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
self.task = "detect"
self.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
self.nt_per_class = None
self.nt_per_image = None
def update_stats(self, stat: Dict[str, Any]) -> None:
"""
Update statistics by appending new values to existing stat collections.
Args:
stat (Dict[str, any]): Dictionary containing new statistical values to append.
Keys should match existing keys in self.stats.
"""
for k in self.stats.keys():
self.stats[k].append(stat[k])
def process(self, save_dir: Path = Path("."), plot: bool = False, on_plot=None) -> Dict[str, np.ndarray]:
"""
Process predicted results for object detection and update metrics.
Args:
save_dir (Path): Directory to save plots. Defaults to Path(".").
plot (bool): Whether to plot precision-recall curves. Defaults to False.
on_plot (callable, optional): Function to call after plots are generated. Defaults to None.
Returns:
(Dict[str, np.ndarray]): Dictionary containing concatenated statistics arrays.
"""
stats = {k: np.concatenate(v, 0) for k, v in self.stats.items()} # to numpy
if len(stats) == 0:
return stats
results = ap_per_class(
stats["tp"],
stats["conf"],
stats["pred_cls"],
stats["target_cls"],
plot=plot,
save_dir=save_dir,
names=self.names,
on_plot=on_plot,
prefix="Box",
)[2:]
self.box.nc = len(self.names)
self.box.update(results)
self.nt_per_class = np.bincount(stats["target_cls"].astype(int), minlength=len(self.names))
self.nt_per_image = np.bincount(stats["target_img"].astype(int), minlength=len(self.names))
return stats
def clear_stats(self):
"""Clear the stored statistics."""
for v in self.stats.values():
v.clear()
@property
def keys(self) -> List[str]:
"""Return a list of keys for accessing specific metrics."""
return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]
def mean_results(self) -> List[float]:
"""Calculate mean of detected objects & return precision, recall, mAP50, and mAP50-95."""
return self.box.mean_results()
def class_result(self, i: int) -> Tuple[float, float, float, float]:
"""Return the result of evaluating the performance of an object detection model on a specific class."""
return self.box.class_result(i)
@property
def maps(self) -> np.ndarray:
"""Return mean Average Precision (mAP) scores per class."""
return self.box.maps
@property
def fitness(self) -> float:
"""Return the fitness of box object."""
return self.box.fitness()
@property
def ap_class_index(self) -> List:
"""Return the average precision index per class."""
return self.box.ap_class_index
@property
def results_dict(self) -> Dict[str, float]:
"""Return dictionary of computed performance metrics and statistics."""
return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness]))
@property
def curves(self) -> List[str]:
"""Return a list of curves for accessing specific metrics curves."""
return ["Precision-Recall(B)", "F1-Confidence(B)", "Precision-Confidence(B)", "Recall-Confidence(B)"]
@property
def curves_results(self) -> List[List]:
"""Return dictionary of computed performance metrics and statistics."""
return self.box.curves_results
def summary(self, normalize: bool = True, decimals: int = 5) -> List[Dict[str, Any]]:
"""
Generate a summarized representation of per-class detection metrics as a list of dictionaries. Includes shared
scalar metrics (mAP, mAP50, mAP75) alongside precision, recall, and F1-score for each class.
Args:
normalize (bool): For Detect metrics, everything is normalized by default [0-1].
decimals (int): Number of decimal places to round the metrics values to.
Returns:
(List[Dict[str, Any]]): A list of dictionaries, each representing one class with corresponding metric values.
Examples:
>>> results = model.val(data="coco8.yaml")
>>> detection_summary = results.summary()
>>> print(detection_summary)
"""
per_class = {
"Box-P": self.box.p,
"Box-R": self.box.r,
"Box-F1": self.box.f1,
}
return [
{
"Class": self.names[self.ap_class_index[i]],
"Images": self.nt_per_image[self.ap_class_index[i]],
"Instances": self.nt_per_class[self.ap_class_index[i]],
**{k: round(v[i], decimals) for k, v in per_class.items()},
"mAP50": round(self.class_result(i)[2], decimals),
"mAP50-95": round(self.class_result(i)[3], decimals),
}
for i in range(len(per_class["Box-P"]))
]
class SegmentMetrics(DetMetrics):
"""
Calculate and aggregate detection and segmentation metrics over a given set of classes.
Attributes:
names (Dict[int, str]): Dictionary of class names.
box (Metric): An instance of the Metric class for storing detection results.
seg (Metric): An instance of the Metric class to calculate mask segmentation metrics.
speed (Dict[str, float]): A dictionary for storing execution times of different parts of the detection process.
task (str): The task type, set to 'segment'.
stats (Dict[str, List]): A dictionary containing lists for true positives, confidence scores, predicted classes, target classes, and target images.
nt_per_class: Number of targets per class.
nt_per_image: Number of targets per image.
"""
def __init__(self, names: Dict[int, str] = {}) -> None:
"""
Initialize a SegmentMetrics instance with a save directory, plot flag, and class names.
Args:
names (Dict[int, str], optional): Dictionary of class names.
"""
DetMetrics.__init__(self, names)
self.seg = Metric()
self.task = "segment"
self.stats["tp_m"] = [] # add additional stats for masks
def process(self, save_dir: Path = Path("."), plot: bool = False, on_plot=None) -> Dict[str, np.ndarray]:
"""
Process the detection and segmentation metrics over the given set of predictions.
Args:
save_dir (Path): Directory to save plots. Defaults to Path(".").
plot (bool): Whether to plot precision-recall curves. Defaults to False.
on_plot (callable, optional): Function to call after plots are generated. Defaults to None.
Returns:
(Dict[str, np.ndarray]): Dictionary containing concatenated statistics arrays.
"""
stats = DetMetrics.process(self, save_dir, plot, on_plot=on_plot) # process box stats
results_mask = ap_per_class(
stats["tp_m"],
stats["conf"],
stats["pred_cls"],
stats["target_cls"],
plot=plot,
on_plot=on_plot,
save_dir=save_dir,
names=self.names,
prefix="Mask",
)[2:]
self.seg.nc = len(self.names)
self.seg.update(results_mask)
return stats
@property
def keys(self) -> List[str]:
"""Return a list of keys for accessing metrics."""
return DetMetrics.keys.fget(self) + [
"metrics/precision(M)",
"metrics/recall(M)",
"metrics/mAP50(M)",
"metrics/mAP50-95(M)",
]
def mean_results(self) -> List[float]:
"""Return the mean metrics for bounding box and segmentation results."""
return DetMetrics.mean_results(self) + self.seg.mean_results()
def class_result(self, i: int) -> List[float]:
"""Return classification results for a specified class index."""
return DetMetrics.class_result(self, i) + self.seg.class_result(i)
@property
def maps(self) -> np.ndarray:
"""Return mAP scores for object detection and semantic segmentation models."""
return DetMetrics.maps.fget(self) + self.seg.maps
@property
def fitness(self) -> float:
"""Return the fitness score for both segmentation and bounding box models."""
return self.seg.fitness() + DetMetrics.fitness.fget(self)
@property
def curves(self) -> List[str]:
"""Return a list of curves for accessing specific metrics curves."""
return DetMetrics.curves.fget(self) + [
"Precision-Recall(M)",
"F1-Confidence(M)",
"Precision-Confidence(M)",
"Recall-Confidence(M)",
]
@property
def curves_results(self) -> List[List]:
"""Return dictionary of computed performance metrics and statistics."""
return DetMetrics.curves_results.fget(self) + self.seg.curves_results
def summary(self, normalize: bool = True, decimals: int = 5) -> List[Dict[str, Any]]:
"""
Generate a summarized representation of per-class segmentation metrics as a list of dictionaries. Includes both
box and mask scalar metrics (mAP, mAP50, mAP75) alongside precision, recall, and F1-score for each class.
Args:
normalize (bool): For Segment metrics, everything is normalized by default [0-1].
decimals (int): Number of decimal places to round the metrics values to.
Returns:
(List[Dict[str, Any]]): A list of dictionaries, each representing one class with corresponding metric values.
Examples:
>>> results = model.val(data="coco8-seg.yaml")
>>> seg_summary = results.summary(decimals=4)
>>> print(seg_summary)
"""
per_class = {
"Mask-P": self.seg.p,
"Mask-R": self.seg.r,
"Mask-F1": self.seg.f1,
}
summary = DetMetrics.summary(self, normalize, decimals) # get box summary
for i, s in enumerate(summary):
s.update({**{k: round(v[i], decimals) for k, v in per_class.items()}})
return summary
class PoseMetrics(DetMetrics):
"""
Calculate and aggregate detection and pose metrics over a given set of classes.
Attributes:
names (Dict[int, str]): Dictionary of class names.
pose (Metric): An instance of the Metric class to calculate pose metrics.
box (Metric): An instance of the Metric class for storing detection results.
speed (Dict[str, float]): A dictionary for storing execution times of different parts of the detection process.
task (str): The task type, set to 'pose'.
stats (Dict[str, List]): A dictionary containing lists for true positives, confidence scores, predicted classes, target classes, and target images.
nt_per_class: Number of targets per class.
nt_per_image: Number of targets per image.
Methods:
process(tp_m, tp_b, conf, pred_cls, target_cls): Process metrics over the given set of predictions.
mean_results(): Return the mean of the detection and segmentation metrics over all the classes.
class_result(i): Return the detection and segmentation metrics of class `i`.
maps: Return the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95.
fitness: Return the fitness scores, which are a single weighted combination of metrics.
ap_class_index: Return the list of indices of classes used to compute Average Precision (AP).
results_dict: Return the dictionary containing all the detection and segmentation metrics and fitness score.
"""
def __init__(self, names: Dict[int, str] = {}) -> None:
"""
Initialize the PoseMetrics class with directory path, class names, and plotting options.
Args:
names (Dict[int, str], optional): Dictionary of class names.
"""
super().__init__(names)
self.pose = Metric()
self.task = "pose"
self.stats["tp_p"] = [] # add additional stats for pose
def process(self, save_dir: Path = Path("."), plot: bool = False, on_plot=None) -> Dict[str, np.ndarray]:
"""
Process the detection and pose metrics over the given set of predictions.
Args:
save_dir (Path): Directory to save plots. Defaults to Path(".").
plot (bool): Whether to plot precision-recall curves. Defaults to False.
on_plot (callable, optional): Function to call after plots are generated.
Returns:
(Dict[str, np.ndarray]): Dictionary containing concatenated statistics arrays.
"""
stats = DetMetrics.process(self, save_dir, plot, on_plot=on_plot) # process box stats
results_pose = ap_per_class(
stats["tp_p"],
stats["conf"],
stats["pred_cls"],
stats["target_cls"],
plot=plot,
on_plot=on_plot,
save_dir=save_dir,
names=self.names,
prefix="Pose",
)[2:]
self.pose.nc = len(self.names)
self.pose.update(results_pose)
return stats
@property
def keys(self) -> List[str]:
"""Return list of evaluation metric keys."""
return DetMetrics.keys.fget(self) + [
"metrics/precision(P)",
"metrics/recall(P)",
"metrics/mAP50(P)",
"metrics/mAP50-95(P)",
]
def mean_results(self) -> List[float]:
"""Return the mean results of box and pose."""
return DetMetrics.mean_results(self) + self.pose.mean_results()
def class_result(self, i: int) -> List[float]:
"""Return the class-wise detection results for a specific class i."""
return DetMetrics.class_result(self, i) + self.pose.class_result(i)
@property
def maps(self) -> np.ndarray:
"""Return the mean average precision (mAP) per class for both box and pose detections."""
return DetMetrics.maps.fget(self) + self.pose.maps
@property
def fitness(self) -> float:
"""Return combined fitness score for pose and box detection."""
return self.pose.fitness() + DetMetrics.fitness.fget(self)
@property
def curves(self) -> List[str]:
"""Return a list of curves for accessing specific metrics curves."""
return DetMetrics.curves.fget(self) + [
"Precision-Recall(B)",
"F1-Confidence(B)",
"Precision-Confidence(B)",
"Recall-Confidence(B)",
"Precision-Recall(P)",
"F1-Confidence(P)",
"Precision-Confidence(P)",
"Recall-Confidence(P)",
]
@property
def curves_results(self) -> List[List]:
"""Return dictionary of computed performance metrics and statistics."""
return DetMetrics.curves_results.fget(self) + self.pose.curves_results
def summary(self, normalize: bool = True, decimals: int = 5) -> List[Dict[str, Any]]:
"""
Generate a summarized representation of per-class pose metrics as a list of dictionaries. Includes both box and
pose scalar metrics (mAP, mAP50, mAP75) alongside precision, recall, and F1-score for each class.
Args:
normalize (bool): For Pose metrics, everything is normalized by default [0-1].
decimals (int): Number of decimal places to round the metrics values to.
Returns:
(List[Dict[str, Any]]): A list of dictionaries, each representing one class with corresponding metric values.
Examples:
>>> results = model.val(data="coco8-pose.yaml")
>>> pose_summary = results.summary(decimals=4)
>>> print(pose_summary)
"""
per_class = {
"Pose-P": self.pose.p,
"Pose-R": self.pose.r,
"Pose-F1": self.pose.f1,
}
summary = DetMetrics.summary(self, normalize, decimals) # get box summary
for i, s in enumerate(summary):
s.update({**{k: round(v[i], decimals) for k, v in per_class.items()}})
return summary
class ClassifyMetrics(SimpleClass, DataExportMixin):
"""
Class for computing classification metrics including top-1 and top-5 accuracy.
Attributes:
top1 (float): The top-1 accuracy.
top5 (float): The top-5 accuracy.
speed (dict): A dictionary containing the time taken for each step in the pipeline.
task (str): The task type, set to 'classify'.
"""
def __init__(self) -> None:
"""Initialize a ClassifyMetrics instance."""
self.top1 = 0
self.top5 = 0
self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
self.task = "classify"
def process(self, targets: torch.Tensor, pred: torch.Tensor):
"""
Process target classes and predicted classes to compute metrics.
Args:
targets (torch.Tensor): Target classes.
pred (torch.Tensor): Predicted classes.
"""
pred, targets = torch.cat(pred), torch.cat(targets)
correct = (targets[:, None] == pred).float()
acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy
self.top1, self.top5 = acc.mean(0).tolist()
@property
def fitness(self) -> float:
"""Return mean of top-1 and top-5 accuracies as fitness score."""
return (self.top1 + self.top5) / 2
@property
def results_dict(self) -> Dict[str, float]:
"""Return a dictionary with model's performance metrics and fitness score."""
return dict(zip(self.keys + ["fitness"], [self.top1, self.top5, self.fitness]))
@property
def keys(self) -> List[str]:
"""Return a list of keys for the results_dict property."""
return ["metrics/accuracy_top1", "metrics/accuracy_top5"]
@property
def curves(self) -> List:
"""Return a list of curves for accessing specific metrics curves."""
return []
@property
def curves_results(self) -> List:
"""Return a list of curves for accessing specific metrics curves."""
return []
def summary(self, normalize: bool = True, decimals: int = 5) -> List[Dict[str, float]]:
"""
Generate a single-row summary of classification metrics (Top-1 and Top-5 accuracy).
Args:
normalize (bool): For Classify metrics, everything is normalized by default [0-1].
decimals (int): Number of decimal places to round the metrics values to.
Returns:
(List[Dict[str, float]]): A list with one dictionary containing Top-1 and Top-5 classification accuracy.
Examples:
>>> results = model.val(data="imagenet10")
>>> classify_summary = results.summary(decimals=4)
>>> print(classify_summary)
"""
return [{"top1_acc": round(self.top1, decimals), "top5_acc": round(self.top5, decimals)}]
class OBBMetrics(DetMetrics):
"""
Metrics for evaluating oriented bounding box (OBB) detection.
Attributes:
names (Dict[int, str]): Dictionary of class names.
box (Metric): An instance of the Metric class for storing detection results.
speed (Dict[str, float]): A dictionary for storing execution times of different parts of the detection process.
task (str): The task type, set to 'obb'.
stats (Dict[str, List]): A dictionary containing lists for true positives, confidence scores, predicted classes, target classes, and target images.
nt_per_class: Number of targets per class.
nt_per_image: Number of targets per image.
References:
https://arxiv.org/pdf/2106.06072.pdf
"""
def __init__(self, names: Dict[int, str] = {}) -> None:
"""
Initialize an OBBMetrics instance with directory, plotting, and class names.
Args:
names (Dict[int, str], optional): Dictionary of class names.
"""
DetMetrics.__init__(self, names)
# TODO: probably remove task as well
self.task = "obb"