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ultralytics 8.0.48 Edge TPU fix and Metrics updates (#1171)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: majid nasiri <majnasai@gmail.com>
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Glenn Jocher 2023-02-27 21:34:22 -08:00 • committed by GitHub
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23 changed files with 426 additions and 245 deletions
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ultralytics/yolo/utils/metrics.py
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@ -43,16 +43,18 @@ def bbox_ioa(box1, box2, eps=1e-7):
def box_iou(box1, box2, eps=1e-7):
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
"""
Return intersection-over-union (Jaccard index) of boxes.
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
Arguments:
box1 (Tensor[N, 4])
box2 (Tensor[M, 4])
eps
Returns:
iou (Tensor[N, M]): the NxM matrix containing the pairwise
IoU values for every element in boxes1 and boxes2
iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2
"""
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
@ -109,7 +111,7 @@ 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
Note: n means image_w x image_h
return: masks iou, [N, M]
Returns: masks iou, [N, M]
"""
intersection = torch.matmul(mask1, mask2.t()).clamp(0)
union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection
@ -121,7 +123,7 @@ def masks_iou(mask1, mask2, eps=1e-7):
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
return: masks iou, (N, )
Returns: masks iou, (N, )
"""
intersection = (mask1 * mask2).sum(1).clamp(0) # (N, )
union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection
@ -317,10 +319,10 @@ def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confi
def compute_ap(recall, precision):
""" Compute the average precision, given the recall and precision curves
# Arguments
Arguments:
recall: The recall curve (list)
precision: The precision curve (list)
# Returns
Returns:
Average precision, precision curve, recall curve
"""
@ -344,17 +346,30 @@ def compute_ap(recall, precision):
def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=Path(), names=(), eps=1e-16, prefix=''):
""" Compute the average precision, given the recall and precision curves.
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
# Arguments
tp: True positives (nparray, nx1 or nx10).
conf: Objectness value from 0-1 (nparray).
pred_cls: Predicted object classes (nparray).
target_cls: True object classes (nparray).
plot: Plot precision-recall curve at mAP@0.5
save_dir: Plot save directory
# Returns
The average precision as computed in py-faster-rcnn.
"""
Computes 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. Defaults to False.
save_dir (Path, optional): Directory to save the PR curves. Defaults to an empty path.
names (tuple, optional): Tuple of class names to plot PR curves. Defaults to an empty tuple.
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-16.
prefix (str, optional): A prefix string for saving the plot files. Defaults to an empty string.
Returns:
(tuple): A tuple of six arrays and one array of unique classes, where:
tp (np.ndarray): True positive counts for each class.
fp (np.ndarray): False positive counts for each class.
p (np.ndarray): Precision values at each confidence threshold.
r (np.ndarray): Recall values at each confidence threshold.
f1 (np.ndarray): F1-score values at each confidence threshold.
ap (np.ndarray): Average precision for each class at different IoU thresholds.
unique_classes (np.ndarray): An array of unique classes that have data.
"""
# Sort by objectness
@ -411,6 +426,32 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=Path(), na
class Metric:
"""
Class for computing evaluation metrics for YOLOv8 model.
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:
self.p = [] # (nc, )
@ -420,10 +461,14 @@ class Metric:
self.ap_class_index = [] # (nc, )
self.nc = 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__}")
@property
def ap50(self):
"""AP@0.5 of all classes.
Return:
Returns:
(nc, ) or [].
"""
return self.all_ap[:, 0] if len(self.all_ap) else []
@ -431,7 +476,7 @@ class Metric:
@property
def ap(self):
"""AP@0.5:0.95
Return:
Returns:
(nc, ) or [].
"""
return self.all_ap.mean(1) if len(self.all_ap) else []
@ -439,7 +484,7 @@ class Metric:
@property
def mp(self):
"""mean precision of all classes.
Return:
Returns:
float.
"""
return self.p.mean() if len(self.p) else 0.0
@ -447,7 +492,7 @@ class Metric:
@property
def mr(self):
"""mean recall of all classes.
Return:
Returns:
float.
"""
return self.r.mean() if len(self.r) else 0.0
@ -455,7 +500,7 @@ class Metric:
@property
def map50(self):
"""Mean AP@0.5 of all classes.
Return:
Returns:
float.
"""
return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0
@ -463,7 +508,7 @@ class Metric:
@property
def map75(self):
"""Mean AP@0.75 of all classes.
Return:
Returns:
float.
"""
return self.all_ap[:, 5].mean() if len(self.all_ap) else 0.0
@ -471,7 +516,7 @@ class Metric:
@property
def map(self):
"""Mean AP@0.5:0.95 of all classes.
Return:
Returns:
float.
"""
return self.all_ap.mean() if len(self.all_ap) else 0.0
@ -506,6 +551,32 @@ class Metric:
class DetMetrics:
"""
This class is a utility class for computing detection metrics such as precision, recall, and mean average precision
(mAP) of an object detection model.
Args:
save_dir (Path): A path to the directory where the output plots will be saved. Defaults to current directory.
plot (bool): A flag that indicates whether to plot precision-recall curves for each class. Defaults to False.
names (tuple of str): A tuple of strings that represents the names of the classes. Defaults to an empty tuple.
Attributes:
save_dir (Path): A path to the directory where the output plots will be saved.
plot (bool): A flag that indicates whether to plot the precision-recall curves for each class.
names (tuple of str): A tuple of strings that represents the names of the classes.
box (Metric): An instance of the Metric class for storing the results of the detection metrics.
speed (dict): A dictionary for storing the execution time of different parts of the detection process.
Methods:
process(tp, conf, pred_cls, target_cls): Updates the metric results with the latest batch of predictions.
keys: Returns a list of keys for accessing the computed detection metrics.
mean_results: Returns a list of mean values for the computed detection metrics.
class_result(i): Returns a list of values for the computed detection metrics for a specific class.
maps: Returns a dictionary of mean average precision (mAP) values for different IoU thresholds.
fitness: Computes the fitness score based on the computed detection metrics.
ap_class_index: Returns a list of class indices sorted by their average precision (AP) values.
results_dict: Returns a dictionary that maps detection metric keys to their computed values.
"""
def __init__(self, save_dir=Path('.'), plot=False, names=()) -> None:
self.save_dir = save_dir
@ -514,6 +585,10 @@ class DetMetrics:
self.box = 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, conf, pred_cls, target_cls):
results = ap_per_class(tp, conf, pred_cls, target_cls, plot=self.plot, save_dir=self.save_dir,
names=self.names)[2:]
@ -548,6 +623,31 @@ class DetMetrics:
class SegmentMetrics:
"""
Calculates and aggregates detection and segmentation 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.
seg (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:
self.save_dir = save_dir
@ -557,7 +657,22 @@ class SegmentMetrics:
self.seg = 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_m, tp_b, 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.
conf (list): List of confidence scores.
pred_cls (list): List of predicted classes.
target_cls (list): List of target classes.
"""
results_mask = ap_per_class(tp_m,
conf,
pred_cls,
@ -610,12 +725,32 @@ class SegmentMetrics:
class ClassifyMetrics:
"""
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[str, float]): A dictionary containing the time taken for each step in the pipeline.
Properties:
fitness (float): The fitness of the model, which is equal to top-5 accuracy.
results_dict (Dict[str, Union[float, str]]): A dictionary containing the classification metrics and fitness.
keys (List[str]): A list of keys for the results_dict.
Methods:
process(targets, pred): Processes the targets and predictions to compute classification metrics.
"""
def __init__(self) -> None:
self.top1 = 0
self.top5 = 0
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, targets, pred):
# target classes and predicted classes
pred, targets = torch.cat(pred), torch.cat(targets)