ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
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@ -11,7 +11,10 @@ import torch
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from ultralytics.utils import LOGGER, SimpleClass, TryExcept, plt_settings
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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
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OKS_SIGMA = (
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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])
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/ 10.0
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)
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def bbox_ioa(box1, box2, iou=False, eps=1e-7):
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@ -33,8 +36,9 @@ def bbox_ioa(box1, box2, iou=False, eps=1e-7):
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b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
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# Intersection area
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inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * \
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(np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)).clip(0)
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inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * (
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np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)
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).clip(0)
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# Box2 area
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area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
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@ -99,8 +103,9 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
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w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
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# Intersection area
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inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp_(0) * \
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(b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp_(0)
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inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp_(0) * (
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b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)
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).clamp_(0)
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# Union Area
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union = w1 * h1 + w2 * h2 - inter + eps
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@ -111,10 +116,10 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
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cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width
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ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height
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if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
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c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared
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c2 = cw**2 + ch**2 + eps # convex diagonal squared
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rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2
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if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
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v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
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v = (4 / math.pi**2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
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with torch.no_grad():
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alpha = v / (v - iou + (1 + eps))
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return iou - (rho2 / c2 + v * alpha) # CIoU
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@ -202,12 +207,19 @@ def probiou(obb1, obb2, CIoU=False, eps=1e-7):
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a1, b1, c1 = _get_covariance_matrix(obb1)
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a2, b2, c2 = _get_covariance_matrix(obb2)
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t1 = (((a1 + a2) * (torch.pow(y1 - y2, 2)) + (b1 + b2) * (torch.pow(x1 - x2, 2))) /
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((a1 + a2) * (b1 + b2) - (torch.pow(c1 + c2, 2)) + eps)) * 0.25
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t1 = (
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((a1 + a2) * (torch.pow(y1 - y2, 2)) + (b1 + b2) * (torch.pow(x1 - x2, 2)))
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/ ((a1 + a2) * (b1 + b2) - (torch.pow(c1 + c2, 2)) + eps)
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) * 0.25
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t2 = (((c1 + c2) * (x2 - x1) * (y1 - y2)) / ((a1 + a2) * (b1 + b2) - (torch.pow(c1 + c2, 2)) + eps)) * 0.5
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t3 = torch.log(((a1 + a2) * (b1 + b2) - (torch.pow(c1 + c2, 2))) /
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(4 * torch.sqrt((a1 * b1 - torch.pow(c1, 2)).clamp_(0) *
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(a2 * b2 - torch.pow(c2, 2)).clamp_(0)) + eps) + eps) * 0.5
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t3 = (
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torch.log(
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((a1 + a2) * (b1 + b2) - (torch.pow(c1 + c2, 2)))
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/ (4 * torch.sqrt((a1 * b1 - torch.pow(c1, 2)).clamp_(0) * (a2 * b2 - torch.pow(c2, 2)).clamp_(0)) + eps)
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+ eps
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)
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* 0.5
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)
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bd = t1 + t2 + t3
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bd = torch.clamp(bd, eps, 100.0)
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hd = torch.sqrt(1.0 - torch.exp(-bd) + eps)
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@ -215,7 +227,7 @@ def probiou(obb1, obb2, CIoU=False, eps=1e-7):
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if CIoU: # only include the wh aspect ratio part
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w1, h1 = obb1[..., 2:4].split(1, dim=-1)
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w2, h2 = obb2[..., 2:4].split(1, dim=-1)
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v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
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v = (4 / math.pi**2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
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with torch.no_grad():
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alpha = v / (v - iou + (1 + eps))
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return iou - v * alpha # CIoU
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@ -239,12 +251,19 @@ def batch_probiou(obb1, obb2, eps=1e-7):
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a1, b1, c1 = _get_covariance_matrix(obb1)
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a2, b2, c2 = (x.squeeze(-1)[None] for x in _get_covariance_matrix(obb2))
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t1 = (((a1 + a2) * (torch.pow(y1 - y2, 2)) + (b1 + b2) * (torch.pow(x1 - x2, 2))) /
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((a1 + a2) * (b1 + b2) - (torch.pow(c1 + c2, 2)) + eps)) * 0.25
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t1 = (
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((a1 + a2) * (torch.pow(y1 - y2, 2)) + (b1 + b2) * (torch.pow(x1 - x2, 2)))
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/ ((a1 + a2) * (b1 + b2) - (torch.pow(c1 + c2, 2)) + eps)
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) * 0.25
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t2 = (((c1 + c2) * (x2 - x1) * (y1 - y2)) / ((a1 + a2) * (b1 + b2) - (torch.pow(c1 + c2, 2)) + eps)) * 0.5
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t3 = torch.log(((a1 + a2) * (b1 + b2) - (torch.pow(c1 + c2, 2))) /
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(4 * torch.sqrt((a1 * b1 - torch.pow(c1, 2)).clamp_(0) *
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(a2 * b2 - torch.pow(c2, 2)).clamp_(0)) + eps) + eps) * 0.5
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t3 = (
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torch.log(
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((a1 + a2) * (b1 + b2) - (torch.pow(c1 + c2, 2)))
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/ (4 * torch.sqrt((a1 * b1 - torch.pow(c1, 2)).clamp_(0) * (a2 * b2 - torch.pow(c2, 2)).clamp_(0)) + eps)
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+ eps
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)
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* 0.5
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)
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bd = t1 + t2 + t3
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bd = torch.clamp(bd, eps, 100.0)
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hd = torch.sqrt(1.0 - torch.exp(-bd) + eps)
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@ -279,10 +298,10 @@ class ConfusionMatrix:
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iou_thres (float): The Intersection over Union threshold.
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"""
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def __init__(self, nc, conf=0.25, iou_thres=0.45, task='detect'):
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def __init__(self, nc, conf=0.25, iou_thres=0.45, task="detect"):
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"""Initialize attributes for the YOLO model."""
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self.task = task
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self.matrix = np.zeros((nc + 1, nc + 1)) if self.task == 'detect' else np.zeros((nc, nc))
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self.matrix = np.zeros((nc + 1, nc + 1)) if self.task == "detect" else np.zeros((nc, nc))
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self.nc = nc # number of classes
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self.conf = 0.25 if conf in (None, 0.001) else conf # apply 0.25 if default val conf is passed
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self.iou_thres = iou_thres
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@ -361,11 +380,11 @@ class ConfusionMatrix:
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tp = self.matrix.diagonal() # true positives
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fp = self.matrix.sum(1) - tp # false positives
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# fn = self.matrix.sum(0) - tp # false negatives (missed detections)
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return (tp[:-1], fp[:-1]) if self.task == 'detect' else (tp, fp) # remove background class if task=detect
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return (tp[:-1], fp[:-1]) if self.task == "detect" else (tp, fp) # remove background class if task=detect
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@TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
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@TryExcept("WARNING ⚠️ ConfusionMatrix plot failure")
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@plt_settings()
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def plot(self, normalize=True, save_dir='', names=(), on_plot=None):
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def plot(self, normalize=True, save_dir="", names=(), on_plot=None):
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"""
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Plot the confusion matrix using seaborn and save it to a file.
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@ -377,30 +396,31 @@ class ConfusionMatrix:
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"""
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import seaborn as sn
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array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns
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array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1) # normalize columns
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array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)
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fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
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nc, nn = self.nc, len(names) # number of classes, names
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sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size
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labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels
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ticklabels = (list(names) + ['background']) if labels else 'auto'
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ticklabels = (list(names) + ["background"]) if labels else "auto"
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with warnings.catch_warnings():
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warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered
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sn.heatmap(array,
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ax=ax,
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annot=nc < 30,
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annot_kws={
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'size': 8},
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cmap='Blues',
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fmt='.2f' if normalize else '.0f',
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square=True,
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vmin=0.0,
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xticklabels=ticklabels,
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yticklabels=ticklabels).set_facecolor((1, 1, 1))
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title = 'Confusion Matrix' + ' Normalized' * normalize
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ax.set_xlabel('True')
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ax.set_ylabel('Predicted')
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warnings.simplefilter("ignore") # suppress empty matrix RuntimeWarning: All-NaN slice encountered
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sn.heatmap(
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array,
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ax=ax,
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annot=nc < 30,
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annot_kws={"size": 8},
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cmap="Blues",
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fmt=".2f" if normalize else ".0f",
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square=True,
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vmin=0.0,
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xticklabels=ticklabels,
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yticklabels=ticklabels,
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).set_facecolor((1, 1, 1))
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title = "Confusion Matrix" + " Normalized" * normalize
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ax.set_xlabel("True")
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ax.set_ylabel("Predicted")
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ax.set_title(title)
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plot_fname = Path(save_dir) / f'{title.lower().replace(" ", "_")}.png'
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fig.savefig(plot_fname, dpi=250)
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@ -411,7 +431,7 @@ class ConfusionMatrix:
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def print(self):
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"""Print the confusion matrix to the console."""
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for i in range(self.nc + 1):
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LOGGER.info(' '.join(map(str, self.matrix[i])))
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LOGGER.info(" ".join(map(str, self.matrix[i])))
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def smooth(y, f=0.05):
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@ -419,28 +439,28 @@ def smooth(y, f=0.05):
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nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)
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p = np.ones(nf // 2) # ones padding
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yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded
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return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed
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return np.convolve(yp, np.ones(nf) / nf, mode="valid") # y-smoothed
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@plt_settings()
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def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=(), on_plot=None):
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def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=(), on_plot=None):
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"""Plots a precision-recall curve."""
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fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
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py = np.stack(py, axis=1)
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if 0 < len(names) < 21: # display per-class legend if < 21 classes
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for i, y in enumerate(py.T):
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ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision)
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ax.plot(px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}") # plot(recall, precision)
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else:
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ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision)
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ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision)
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ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
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ax.set_xlabel('Recall')
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ax.set_ylabel('Precision')
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ax.plot(px, py.mean(1), linewidth=3, color="blue", label="all classes %.3f mAP@0.5" % ap[:, 0].mean())
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ax.set_xlabel("Recall")
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ax.set_ylabel("Precision")
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left')
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ax.set_title('Precision-Recall Curve')
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ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
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ax.set_title("Precision-Recall Curve")
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fig.savefig(save_dir, dpi=250)
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plt.close(fig)
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if on_plot:
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@ -448,24 +468,24 @@ def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=(), on_plot=N
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@plt_settings()
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def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric', on_plot=None):
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def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabel="Confidence", ylabel="Metric", on_plot=None):
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"""Plots a metric-confidence curve."""
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fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
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if 0 < len(names) < 21: # display per-class legend if < 21 classes
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for i, y in enumerate(py):
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ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric)
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ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric)
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else:
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ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric)
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ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric)
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y = smooth(py.mean(0), 0.05)
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ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
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ax.plot(px, y, linewidth=3, color="blue", label=f"all classes {y.max():.2f} at {px[y.argmax()]:.3f}")
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ax.set_xlabel(xlabel)
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ax.set_ylabel(ylabel)
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left')
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ax.set_title(f'{ylabel}-Confidence Curve')
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ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
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ax.set_title(f"{ylabel}-Confidence Curve")
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fig.savefig(save_dir, dpi=250)
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plt.close(fig)
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if on_plot:
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@ -494,8 +514,8 @@ def compute_ap(recall, precision):
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mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
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# Integrate area under curve
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method = 'interp' # methods: 'continuous', 'interp'
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if method == 'interp':
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method = "interp" # methods: 'continuous', 'interp'
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if method == "interp":
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x = np.linspace(0, 1, 101) # 101-point interp (COCO)
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ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate
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else: # 'continuous'
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@ -505,16 +525,9 @@ def compute_ap(recall, precision):
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return ap, mpre, mrec
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def ap_per_class(tp,
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conf,
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pred_cls,
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target_cls,
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plot=False,
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on_plot=None,
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save_dir=Path(),
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names=(),
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eps=1e-16,
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prefix=''):
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def ap_per_class(
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tp, conf, pred_cls, target_cls, plot=False, on_plot=None, save_dir=Path(), names=(), eps=1e-16, prefix=""
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):
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"""
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Computes the average precision per class for object detection evaluation.
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@ -591,10 +604,10 @@ def ap_per_class(tp,
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names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data
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names = dict(enumerate(names)) # to dict
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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)
|
||||
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
|
||||
|
|
@ -746,8 +759,18 @@ class Metric(SimpleClass):
|
|||
Updates the class attributes `self.p`, `self.r`, `self.f1`, `self.all_ap`, and `self.ap_class_index` based
|
||||
on the values provided in the `results` tuple.
|
||||
"""
|
||||
(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
|
||||
(
|
||||
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):
|
||||
|
|
@ -757,8 +780,12 @@ class Metric(SimpleClass):
|
|||
@property
|
||||
def curves_results(self):
|
||||
"""Returns 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']]
|
||||
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):
|
||||
|
|
@ -793,33 +820,35 @@ class DetMetrics(SimpleClass):
|
|||
curves_results: TODO
|
||||
"""
|
||||
|
||||
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None:
|
||||
def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
|
||||
"""Initialize a DetMetrics instance with a save directory, plot flag, callback function, and class names."""
|
||||
self.save_dir = save_dir
|
||||
self.plot = plot
|
||||
self.on_plot = on_plot
|
||||
self.names = names
|
||||
self.box = Metric()
|
||||
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
|
||||
self.task = 'detect'
|
||||
self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
|
||||
self.task = "detect"
|
||||
|
||||
def process(self, tp, conf, pred_cls, target_cls):
|
||||
"""Process predicted results for object detection and update metrics."""
|
||||
results = ap_per_class(tp,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
on_plot=self.on_plot)[2:]
|
||||
results = ap_per_class(
|
||||
tp,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
on_plot=self.on_plot,
|
||||
)[2:]
|
||||
self.box.nc = len(self.names)
|
||||
self.box.update(results)
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
"""Returns a list of keys for accessing specific metrics."""
|
||||
return ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
|
||||
return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]
|
||||
|
||||
def mean_results(self):
|
||||
"""Calculate mean of detected objects & return precision, recall, mAP50, and mAP50-95."""
|
||||
|
|
@ -847,12 +876,12 @@ class DetMetrics(SimpleClass):
|
|||
@property
|
||||
def results_dict(self):
|
||||
"""Returns dictionary of computed performance metrics and statistics."""
|
||||
return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
|
||||
return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness]))
|
||||
|
||||
@property
|
||||
def curves(self):
|
||||
"""Returns a list of curves for accessing specific metrics curves."""
|
||||
return ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
|
||||
return ["Precision-Recall(B)", "F1-Confidence(B)", "Precision-Confidence(B)", "Recall-Confidence(B)"]
|
||||
|
||||
@property
|
||||
def curves_results(self):
|
||||
|
|
@ -889,7 +918,7 @@ class SegmentMetrics(SimpleClass):
|
|||
results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score.
|
||||
"""
|
||||
|
||||
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None:
|
||||
def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
|
||||
"""Initialize a SegmentMetrics instance with a save directory, plot flag, callback function, and class names."""
|
||||
self.save_dir = save_dir
|
||||
self.plot = plot
|
||||
|
|
@ -897,8 +926,8 @@ class SegmentMetrics(SimpleClass):
|
|||
self.names = names
|
||||
self.box = Metric()
|
||||
self.seg = Metric()
|
||||
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
|
||||
self.task = 'segment'
|
||||
self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
|
||||
self.task = "segment"
|
||||
|
||||
def process(self, tp, tp_m, conf, pred_cls, target_cls):
|
||||
"""
|
||||
|
|
@ -912,26 +941,30 @@ class SegmentMetrics(SimpleClass):
|
|||
target_cls (list): List of target classes.
|
||||
"""
|
||||
|
||||
results_mask = ap_per_class(tp_m,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
prefix='Mask')[2:]
|
||||
results_mask = ap_per_class(
|
||||
tp_m,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
prefix="Mask",
|
||||
)[2:]
|
||||
self.seg.nc = len(self.names)
|
||||
self.seg.update(results_mask)
|
||||
results_box = ap_per_class(tp,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
prefix='Box')[2:]
|
||||
results_box = ap_per_class(
|
||||
tp,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
prefix="Box",
|
||||
)[2:]
|
||||
self.box.nc = len(self.names)
|
||||
self.box.update(results_box)
|
||||
|
||||
|
|
@ -939,8 +972,15 @@ class SegmentMetrics(SimpleClass):
|
|||
def keys(self):
|
||||
"""Returns a list of keys for accessing metrics."""
|
||||
return [
|
||||
'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)',
|
||||
'metrics/precision(M)', 'metrics/recall(M)', 'metrics/mAP50(M)', 'metrics/mAP50-95(M)']
|
||||
"metrics/precision(B)",
|
||||
"metrics/recall(B)",
|
||||
"metrics/mAP50(B)",
|
||||
"metrics/mAP50-95(B)",
|
||||
"metrics/precision(M)",
|
||||
"metrics/recall(M)",
|
||||
"metrics/mAP50(M)",
|
||||
"metrics/mAP50-95(M)",
|
||||
]
|
||||
|
||||
def mean_results(self):
|
||||
"""Return the mean metrics for bounding box and segmentation results."""
|
||||
|
|
@ -968,14 +1008,21 @@ class SegmentMetrics(SimpleClass):
|
|||
@property
|
||||
def results_dict(self):
|
||||
"""Returns results of object detection model for evaluation."""
|
||||
return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
|
||||
return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness]))
|
||||
|
||||
@property
|
||||
def curves(self):
|
||||
"""Returns a list of curves for accessing specific metrics curves."""
|
||||
return [
|
||||
'Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)',
|
||||
'Precision-Recall(M)', 'F1-Confidence(M)', 'Precision-Confidence(M)', 'Recall-Confidence(M)']
|
||||
"Precision-Recall(B)",
|
||||
"F1-Confidence(B)",
|
||||
"Precision-Confidence(B)",
|
||||
"Recall-Confidence(B)",
|
||||
"Precision-Recall(M)",
|
||||
"F1-Confidence(M)",
|
||||
"Precision-Confidence(M)",
|
||||
"Recall-Confidence(M)",
|
||||
]
|
||||
|
||||
@property
|
||||
def curves_results(self):
|
||||
|
|
@ -1012,7 +1059,7 @@ class PoseMetrics(SegmentMetrics):
|
|||
results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score.
|
||||
"""
|
||||
|
||||
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None:
|
||||
def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
|
||||
"""Initialize the PoseMetrics class with directory path, class names, and plotting options."""
|
||||
super().__init__(save_dir, plot, names)
|
||||
self.save_dir = save_dir
|
||||
|
|
@ -1021,8 +1068,8 @@ class PoseMetrics(SegmentMetrics):
|
|||
self.names = names
|
||||
self.box = Metric()
|
||||
self.pose = Metric()
|
||||
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
|
||||
self.task = 'pose'
|
||||
self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
|
||||
self.task = "pose"
|
||||
|
||||
def process(self, tp, tp_p, conf, pred_cls, target_cls):
|
||||
"""
|
||||
|
|
@ -1036,26 +1083,30 @@ class PoseMetrics(SegmentMetrics):
|
|||
target_cls (list): List of target classes.
|
||||
"""
|
||||
|
||||
results_pose = ap_per_class(tp_p,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
prefix='Pose')[2:]
|
||||
results_pose = ap_per_class(
|
||||
tp_p,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_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,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
prefix='Box')[2:]
|
||||
results_box = ap_per_class(
|
||||
tp,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
prefix="Box",
|
||||
)[2:]
|
||||
self.box.nc = len(self.names)
|
||||
self.box.update(results_box)
|
||||
|
||||
|
|
@ -1063,8 +1114,15 @@ class PoseMetrics(SegmentMetrics):
|
|||
def keys(self):
|
||||
"""Returns list of evaluation metric keys."""
|
||||
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)']
|
||||
"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 the mean results of box and pose."""
|
||||
|
|
@ -1088,8 +1146,15 @@ class PoseMetrics(SegmentMetrics):
|
|||
def curves(self):
|
||||
"""Returns a list of curves for accessing specific metrics curves."""
|
||||
return [
|
||||
'Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)',
|
||||
'Precision-Recall(P)', 'F1-Confidence(P)', 'Precision-Confidence(P)', 'Recall-Confidence(P)']
|
||||
"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):
|
||||
|
|
@ -1119,8 +1184,8 @@ class ClassifyMetrics(SimpleClass):
|
|||
"""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'
|
||||
self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
|
||||
self.task = "classify"
|
||||
|
||||
def process(self, targets, pred):
|
||||
"""Target classes and predicted classes."""
|
||||
|
|
@ -1137,12 +1202,12 @@ class ClassifyMetrics(SimpleClass):
|
|||
@property
|
||||
def results_dict(self):
|
||||
"""Returns a dictionary with model's performance metrics and fitness score."""
|
||||
return dict(zip(self.keys + ['fitness'], [self.top1, self.top5, self.fitness]))
|
||||
return dict(zip(self.keys + ["fitness"], [self.top1, self.top5, self.fitness]))
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
"""Returns a list of keys for the results_dict property."""
|
||||
return ['metrics/accuracy_top1', 'metrics/accuracy_top5']
|
||||
return ["metrics/accuracy_top1", "metrics/accuracy_top5"]
|
||||
|
||||
@property
|
||||
def curves(self):
|
||||
|
|
@ -1156,32 +1221,33 @@ class ClassifyMetrics(SimpleClass):
|
|||
|
||||
|
||||
class OBBMetrics(SimpleClass):
|
||||
|
||||
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None:
|
||||
def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
|
||||
self.save_dir = save_dir
|
||||
self.plot = plot
|
||||
self.on_plot = on_plot
|
||||
self.names = names
|
||||
self.box = Metric()
|
||||
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
|
||||
self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
|
||||
|
||||
def process(self, tp, conf, pred_cls, target_cls):
|
||||
"""Process predicted results for object detection and update metrics."""
|
||||
results = ap_per_class(tp,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
on_plot=self.on_plot)[2:]
|
||||
results = ap_per_class(
|
||||
tp,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
on_plot=self.on_plot,
|
||||
)[2:]
|
||||
self.box.nc = len(self.names)
|
||||
self.box.update(results)
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
"""Returns a list of keys for accessing specific metrics."""
|
||||
return ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
|
||||
return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]
|
||||
|
||||
def mean_results(self):
|
||||
"""Calculate mean of detected objects & return precision, recall, mAP50, and mAP50-95."""
|
||||
|
|
@ -1209,7 +1275,7 @@ class OBBMetrics(SimpleClass):
|
|||
@property
|
||||
def results_dict(self):
|
||||
"""Returns dictionary of computed performance metrics and statistics."""
|
||||
return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
|
||||
return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness]))
|
||||
|
||||
@property
|
||||
def curves(self):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue