368 lines
14 KiB
Python
368 lines
14 KiB
Python
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Model validation metrics
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"""
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import math
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import warnings
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from app.yolov5.utils import TryExcept, threaded
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def fitness(x):
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# Model fitness as a weighted combination of metrics
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w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
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return (x[:, :4] * w).sum(1)
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def smooth(y, f=0.05):
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# Box filter of fraction f
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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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def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16):
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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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conf: Objectness value from 0-1 (nparray).
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pred_cls: Predicted object classes (nparray).
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target_cls: True object classes (nparray).
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plot: Plot precision-recall curve at mAP@0.5
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save_dir: Plot save directory
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# Returns
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The average precision as computed in py-faster-rcnn.
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"""
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# Sort by objectness
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i = np.argsort(-conf)
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tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
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# Find unique classes
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unique_classes, nt = np.unique(target_cls, return_counts=True)
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nc = unique_classes.shape[0] # number of classes, number of detections
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# Create Precision-Recall curve and compute AP for each class
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px, py = np.linspace(0, 1, 1000), [] # for plotting
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ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
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for ci, c in enumerate(unique_classes):
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i = pred_cls == c
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n_l = nt[ci] # number of labels
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n_p = i.sum() # number of predictions
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if n_p == 0 or n_l == 0:
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continue
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# Accumulate FPs and TPs
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fpc = (1 - tp[i]).cumsum(0)
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tpc = tp[i].cumsum(0)
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# Recall
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recall = tpc / (n_l + eps) # recall curve
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r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases
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# Precision
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precision = tpc / (tpc + fpc) # precision curve
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p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score
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# AP from recall-precision curve
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for j in range(tp.shape[1]):
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ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
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if plot and j == 0:
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py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5
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# Compute F1 (harmonic mean of precision and recall)
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f1 = 2 * p * r / (p + r + eps)
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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) / 'PR_curve.png', names)
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# plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
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# plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
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# plot_mc_curve(px, r, Path(save_dir) / '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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tp = (r * nt).round() # true positives
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fp = (tp / (p + eps) - tp).round() # false positives
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return tp, fp, p, r, f1, ap, unique_classes.astype(int)
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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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# Arguments
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recall: The recall curve (list)
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precision: The precision curve (list)
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# Returns
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Average precision, precision curve, recall curve
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"""
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# Append sentinel values to beginning and end
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mrec = np.concatenate(([0.0], recall, [1.0]))
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mpre = np.concatenate(([1.0], precision, [0.0]))
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# Compute the precision envelope
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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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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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i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes
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ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve
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return ap, mpre, mrec
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class ConfusionMatrix:
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# Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
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def __init__(self, nc, conf=0.25, iou_thres=0.45):
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self.matrix = np.zeros((nc + 1, nc + 1))
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self.nc = nc # number of classes
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self.conf = conf
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self.iou_thres = iou_thres
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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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labels (Array[M, 5]), class, x1, y1, x2, y2
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Returns:
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None, updates confusion matrix accordingly
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"""
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if detections is None:
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gt_classes = labels.int()
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for gc in gt_classes:
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self.matrix[self.nc, gc] += 1 # background FN
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return
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detections = detections[detections[:, 4] > self.conf]
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gt_classes = labels[:, 0].int()
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detection_classes = detections[:, 5].int()
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iou = box_iou(labels[:, 1:], detections[:, :4])
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x = torch.where(iou > self.iou_thres)
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if x[0].shape[0]:
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matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
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if x[0].shape[0] > 1:
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matches = matches[matches[:, 2].argsort()[::-1]]
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matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
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matches = matches[matches[:, 2].argsort()[::-1]]
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matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
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else:
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matches = np.zeros((0, 3))
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n = matches.shape[0] > 0
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m0, m1, _ = matches.transpose().astype(int)
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for i, gc in enumerate(gt_classes):
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j = m0 == i
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if n and sum(j) == 1:
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self.matrix[detection_classes[m1[j]], gc] += 1 # correct
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else:
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self.matrix[self.nc, gc] += 1 # background FP
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if n:
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for i, dc in enumerate(detection_classes):
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if not any(m1 == i):
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self.matrix[dc, self.nc] += 1 # background FN
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def matrix(self):
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return self.matrix
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def tp_fp(self):
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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] # 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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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[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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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=names + ['background FP'] if labels else "auto",
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yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))
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ax.set_ylabel('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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def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
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# Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4)
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# Get the coordinates of bounding boxes
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if xywh: # transform from xywh to xyxy
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(x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, 1), box2.chunk(4, 1)
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w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
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b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
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b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
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else: # x1, y1, x2, y2 = box1
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b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, 1)
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b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, 1)
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w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1
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w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1
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# Intersection area
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inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
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(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
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# Union Area
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union = w1 * h1 + w2 * h2 - inter + eps
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# IoU
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iou = inter / union
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if CIoU or DIoU or GIoU:
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cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width
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ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, 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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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.pow(torch.atan(w2 / (h2 + eps)) - torch.atan(w1 / (h1 + eps)), 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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return iou - rho2 / c2 # DIoU
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c_area = cw * ch + eps # convex area
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return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf
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return iou # IoU
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def box_area(box):
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# box = xyxy(4,n)
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return (box[2] - box[0]) * (box[3] - box[1])
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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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box2 (Tensor[M, 4])
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Returns:
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iou (Tensor[N, M]): the NxM matrix containing the pairwise
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IoU values for every element in boxes1 and boxes2
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"""
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# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
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(a1, a2), (b1, b2) = box1[:, None].chunk(2, 2), box2.chunk(2, 1)
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inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
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# IoU = inter / (area1 + area2 - inter)
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return inter / (box_area(box1.T)[:, None] + box_area(box2.T) - inter + eps)
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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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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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"""
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# Get the coordinates of bounding boxes
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b1_x1, b1_y1, b1_x2, b1_y2 = box1
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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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# box2 area
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box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
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# Intersection over box2 area
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return inter_area / box2_area
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def wh_iou(wh1, wh2, eps=1e-7):
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# Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
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wh1 = wh1[:, None] # [N,1,2]
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wh2 = wh2[None] # [1,M,2]
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inter = torch.min(wh1, wh2).prod(2) # [N,M]
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return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter)
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# Plots ----------------------------------------------------------------------------------------------------------------
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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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# 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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else:
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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.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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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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# 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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else:
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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.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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fig.savefig(save_dir, dpi=250)
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plt.close(fig)
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