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ultralytics 8.0.65 YOLOv8 Pose models (#1347)

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Ayush Chaurasia 2023-04-06 03:55:32 +05:30 • committed by GitHub
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124
ultralytics/yolo/utils/metrics.py
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@ -13,6 +13,8 @@ import torch.nn as nn
from ultralytics.yolo.utils import LOGGER, SimpleClass, TryExcept
OKS_SIGMA = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0
# boxes
def box_area(box):
@ -108,8 +110,8 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
def mask_iou(mask1, mask2, eps=1e-7):
"""
mask1: [N, n] m1 means number of predicted objects
mask2: [M, n] m2 means number of gt objects
mask1: [N, n] m1 means number of gt objects
mask2: [M, n] m2 means number of predicted objects
Note: n means image_w x image_h
Returns: masks iou, [N, M]
"""
@ -118,16 +120,18 @@ def mask_iou(mask1, mask2, eps=1e-7):
return intersection / (union + eps)
def masks_iou(mask1, mask2, eps=1e-7):
def kpt_iou(kpt1, kpt2, area, sigma, eps=1e-7):
"""OKS
kpt1: [N, 17, 3], gt
kpt2: [M, 17, 3], pred
area: [N], areas from gt
"""
mask1: [N, n] m1 means number of predicted objects
mask2: [N, n] m2 means number of gt objects
Note: n means image_w x image_h
Returns: masks iou, (N, )
"""
intersection = (mask1 * mask2).sum(1).clamp(0) # (N, )
union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection
return intersection / (union + eps)
d = (kpt1[:, None, :, 0] - kpt2[..., 0]) ** 2 + (kpt1[:, None, :, 1] - kpt2[..., 1]) ** 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) ** 2 / (area[:, None, None] + eps) / 2 # from cocoeval
# e = d / ((area[None, :, None] + eps) * sigma) ** 2 / 2 # from formula
return (torch.exp(-e) * kpt_mask[:, None]).sum(-1) / (kpt_mask.sum(-1)[:, None] + eps)
def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
@ -649,13 +653,13 @@ class SegmentMetrics(SimpleClass):
self.seg = Metric()
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
def process(self, tp_m, tp_b, conf, pred_cls, target_cls):
def process(self, tp_b, tp_m, conf, pred_cls, target_cls):
"""
Processes the detection and segmentation metrics over the given set of predictions.
Args:
tp_m (list): List of True Positive masks.
tp_b (list): List of True Positive boxes.
tp_m (list): List of True Positive masks.
conf (list): List of confidence scores.
pred_cls (list): List of predicted classes.
target_cls (list): List of target classes.
@ -712,6 +716,100 @@ class SegmentMetrics(SimpleClass):
return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
class PoseMetrics(SegmentMetrics):
"""
Calculates and aggregates detection and pose metrics over a given set of classes.
Args:
save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory.
plot (bool): Whether to save the detection and segmentation plots. Default is False.
names (list): List of class names. Default is an empty list.
Attributes:
save_dir (Path): Path to the directory where the output plots should be saved.
plot (bool): Whether to save the detection and segmentation plots.
names (list): List of class names.
box (Metric): An instance of the Metric class to calculate box detection metrics.
pose (Metric): An instance of the Metric class to calculate mask segmentation metrics.
speed (dict): Dictionary to store the time taken in different phases of inference.
Methods:
process(tp_m, tp_b, conf, pred_cls, target_cls): Processes metrics over the given set of predictions.
mean_results(): Returns the mean of the detection and segmentation metrics over all the classes.
class_result(i): Returns the detection and segmentation metrics of class `i`.
maps: Returns the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95.
fitness: Returns the fitness scores, which are a single weighted combination of metrics.
ap_class_index: Returns the list of indices of classes used to compute Average Precision (AP).
results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score.
"""
def __init__(self, save_dir=Path('.'), plot=False, names=()) -> None:
super().__init__(save_dir, plot, names)
self.save_dir = save_dir
self.plot = plot
self.names = names
self.box = Metric()
self.pose = Metric()
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
def __getattr__(self, attr):
name = self.__class__.__name__
raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
def process(self, tp_b, tp_p, conf, pred_cls, target_cls):
"""
Processes the detection and pose metrics over the given set of predictions.
Args:
tp_b (list): List of True Positive boxes.
tp_p (list): List of True Positive keypoints.
conf (list): List of confidence scores.
pred_cls (list): List of predicted classes.
target_cls (list): List of target classes.
"""
results_pose = ap_per_class(tp_p,
conf,
pred_cls,
target_cls,
plot=self.plot,
save_dir=self.save_dir,
names=self.names,
prefix='Pose')[2:]
self.pose.nc = len(self.names)
self.pose.update(results_pose)
results_box = ap_per_class(tp_b,
conf,
pred_cls,
target_cls,
plot=self.plot,
save_dir=self.save_dir,
names=self.names,
prefix='Box')[2:]
self.box.nc = len(self.names)
self.box.update(results_box)
@property
def keys(self):
return [
'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)',
'metrics/precision(P)', 'metrics/recall(P)', 'metrics/mAP50(P)', 'metrics/mAP50-95(P)']
def mean_results(self):
return self.box.mean_results() + self.pose.mean_results()
def class_result(self, i):
return self.box.class_result(i) + self.pose.class_result(i)
@property
def maps(self):
return self.box.maps + self.pose.maps
@property
def fitness(self):
return self.pose.fitness() + self.box.fitness()
class ClassifyMetrics(SimpleClass):
"""
Class for computing classification metrics including top-1 and top-5 accuracy.