Update Actions with Lychee and GitHub Token (#12592)
* Add Ultralytics Actions * Auto-format by Ultralytics actions * Update format.yml Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@@ -1,7 +1,5 @@
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
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"""
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Model validation metrics
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"""
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"""Model validation metrics."""
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import math
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import warnings
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@@ -25,11 +23,13 @@ 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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def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=''):
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""" Compute the average precision, given the recall and precision curves.
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def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=".", names=(), eps=1e-16, prefix=""):
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"""
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Compute the average precision, given the recall and precision curves.
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Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
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# Arguments
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tp: True positives (nparray, nx1 or nx10).
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@@ -83,10 +83,10 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names
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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:
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plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names)
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plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1')
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plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision')
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plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall')
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plot_pr_curve(px, py, ap, Path(save_dir) / f"{prefix}PR_curve.png", names)
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plot_mc_curve(px, f1, Path(save_dir) / f"{prefix}F1_curve.png", names, ylabel="F1")
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plot_mc_curve(px, p, Path(save_dir) / f"{prefix}P_curve.png", names, ylabel="Precision")
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plot_mc_curve(px, r, Path(save_dir) / f"{prefix}R_curve.png", names, ylabel="Recall")
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i = smooth(f1.mean(0), 0.1).argmax() # max F1 index
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p, r, f1 = p[:, i], r[:, i], f1[:, i]
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@@ -96,7 +96,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names
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def compute_ap(recall, precision):
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""" Compute the average precision, given the recall and precision curves
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"""Compute the average precision, given the recall and precision curves
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# Arguments
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recall: The recall curve (list)
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precision: The precision curve (list)
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@@ -112,8 +112,8 @@ 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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@@ -134,6 +134,7 @@ class ConfusionMatrix:
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def process_batch(self, detections, labels):
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"""
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Return intersection-over-union (Jaccard index) of boxes.
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Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
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Arguments:
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detections (Array[N, 6]), x1, y1, x2, y2, conf, class
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@@ -183,40 +184,41 @@ class ConfusionMatrix:
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# fn = self.matrix.sum(0) - tp # false negatives (missed detections)
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return tp[:-1], fp[:-1] # remove background class
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@TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
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def plot(self, normalize=True, save_dir='', names=()):
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@TryExcept("WARNING ⚠️ ConfusionMatrix plot failure")
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def plot(self, normalize=True, save_dir="", names=()):
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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 = (names + ['background']) if labels else 'auto'
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ticklabels = (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',
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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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ax.set_xlabel('True')
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ax.set_ylabel('Predicted')
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ax.set_title('Confusion Matrix')
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fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
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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",
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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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ax.set_xlabel("True")
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ax.set_ylabel("Predicted")
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ax.set_title("Confusion Matrix")
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fig.savefig(Path(save_dir) / "confusion_matrix.png", dpi=250)
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plt.close(fig)
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def print(self):
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for i in range(self.nc + 1):
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print(' '.join(map(str, self.matrix[i])))
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print(" ".join(map(str, self.matrix[i])))
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def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
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@@ -235,8 +237,9 @@ 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).clamp(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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@@ -247,10 +250,10 @@ 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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@@ -264,6 +267,7 @@ def box_iou(box1, box2, eps=1e-7):
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# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
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"""
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Return intersection-over-union (Jaccard index) of boxes.
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Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
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Arguments:
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box1 (Tensor[N, 4])
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@@ -282,7 +286,10 @@ def box_iou(box1, box2, eps=1e-7):
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def bbox_ioa(box1, box2, eps=1e-7):
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""" Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2
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"""
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Returns the intersection over box2 area given box1, box2.
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Boxes are x1y1x2y2
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box1: np.array of shape(4)
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box2: np.array of shape(nx4)
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returns: np.array of shape(n)
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@@ -293,8 +300,9 @@ def bbox_ioa(box1, box2, 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, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
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(np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
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inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * (
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np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)
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).clip(0)
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# box2 area
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box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
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@@ -315,46 +323,46 @@ def wh_iou(wh1, wh2, eps=1e-7):
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@threaded
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def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
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def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()):
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# 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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@threaded
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def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
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def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabel="Confidence", ylabel="Metric"):
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# 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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