do metric and plot curves

yolov6/core/evaler.py

@@ -94,6 +94,15 @@ def predict_model(self, model, dataloader, task):
         self.speed_result = torch.zeros(4, device=self.device)
         pred_results = []
         pbar = tqdm(dataloader, desc="Inferencing model in val datasets.", ncols=NCOLS)
+
+        # whether to compute metric and plot PR curve and P、R、F1 curve under iou50 match rule
+        metric_and_plot = True if task == "val" else False
+        if metric_and_plot:
+            stats, ap = [], []
+            seen = 0
+            iouv = torch.linspace(0.5, 0.95, 10)  # iou vector for [email protected]:0.95
+            niou = iouv.numel()
+
         for i, (imgs, targets, paths, shapes) in enumerate(pbar):
 
             # pre-process
@@ -114,6 +123,10 @@ def predict_model(self, model, dataloader, task):
             self.speed_result[3] += time_sync() - t3  # post-process time
             self.speed_result[0] += len(outputs)
 
+            if metric_and_plot:
+                import copy
+                eval_outputs = copy.deepcopy([x.detach().cpu() for x in outputs])
+
             # save result
             pred_results.extend(self.convert_to_coco_format(outputs, imgs, paths, shapes, self.ids))
 
@@ -122,6 +135,71 @@ def predict_model(self, model, dataloader, task):
                 vis_num = min(len(imgs), 8)
                 vis_outputs = outputs[:vis_num]
                 vis_paths = paths[:vis_num]
+
+            if not metric_and_plot:
+                continue
+
+            # Statistics per image 
+            # This code is based on 
+            # https://github.com/ultralytics/yolov5/blob/master/val.py
+            for si, pred in enumerate(eval_outputs):
+                labels = targets[targets[:, 0] == si, 1:]
+                nl = len(labels)
+                tcls = labels[:, 0].tolist() if nl else []  # target class
+                seen += 1
+
+                if len(pred) == 0:
+                    if nl:
+                        stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
+                    continue
+
+                # Predictions
+                predn = pred.clone()
+                self.scale_coords(imgs[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1])  # native-space pred
+
+                # Assign all predictions as incorrect
+                correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)
+                if nl:
+
+                    from yolov6.utils.nms import xywh2xyxy
+
+                    # target boxes
+                    tbox = xywh2xyxy(labels[:, 1:5])
+                    tbox[:, [0, 2]] *= imgs[si].shape[1:][0]
+                    tbox[:, [1, 3]] *= imgs[si].shape[1:][1]
+
+                    self.scale_coords(imgs[si].shape[1:], tbox, shapes[si][0], shapes[si][1])  # native-space labels
+
+                    labelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labels
+
+                    from yolov6.utils.metrics import process_batch
+
+                    correct = process_batch(predn, labelsn, iouv)
+
+                # Append statistics (correct, conf, pcls, tcls)
+                stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
+
+        if metric_and_plot:
+            # Compute statistics
+            stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
+            if len(stats) and stats[0].any():
+
+                from yolov6.utils.metrics import ap_per_class
+                p, r, ap, f1, ap_class = ap_per_class(*stats, plot=metric_and_plot, save_dir=self.save_dir, names=model.names)
+                AP50_F1_max_idx = f1.mean(0).argmax()
+                LOGGER.info(f"IOU 50 best mF1 thershold near {AP50_F1_max_idx/1000.0}.")
+                ap50, ap = ap[:, 0], ap.mean(1)  # [email protected], [email protected]:0.95
+                mp, mr, map50, map = p[:, AP50_F1_max_idx].mean(), r[:, AP50_F1_max_idx].mean(), ap50.mean(), ap.mean()
+                nt = np.bincount(stats[3].astype(np.int64), minlength=model.nc)  # number of targets per class
+            else:
+                nt = torch.zeros(1)
+
+            # Print results
+            s = ('%s' + '%12s' * 4) % ('Class', 'Images', 'Labels', '[email protected]', '[email protected]')
+            LOGGER.info(s)
+            pf = '%s' + '%12i' * 2 + '%12.3g' * 2  # print format
+            LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr))
+
         return pred_results, vis_outputs, vis_paths
 
 

yolov6/utils/general.py

@@ -55,4 +55,28 @@ def xywh2xyxy(bboxes):
     bboxes[..., 1] = bboxes[..., 1] - bboxes[..., 3] * 0.5
     bboxes[..., 2] = bboxes[..., 0] + bboxes[..., 2]
     bboxes[..., 3] = bboxes[..., 1] + bboxes[..., 3]
-    return bboxes
+    return bboxes
+
+def box_iou(box1, box2):
+    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+    """
+    Return intersection-over-union (Jaccard index) of boxes.
+    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+    Arguments:
+        box1 (Tensor[N, 4])
+        box2 (Tensor[M, 4])
+    Returns:
+        iou (Tensor[N, M]): the NxM matrix containing the pairwise
+            IoU values for every element in boxes1 and boxes2
+    """
+
+    def box_area(box):
+        # box = 4xn
+        return (box[2] - box[0]) * (box[3] - box[1])
+
+    area1 = box_area(box1.T)
+    area2 = box_area(box2.T)
+
+    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
+    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
+    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)

yolov6/utils/metric.py

@@ -0,0 +1,167 @@
+# Model validation metrics
+# This code is based on
+# https://github.com/ultralytics/yolov5/blob/master/utils/metrics.py
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+from . import general
+
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()):
+    """ Compute the average precision, given the recall and precision curves.
+    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
+    # Arguments
+        tp:  True positives (nparray, nx1 or nx10).
+        conf:  Objectness value from 0-1 (nparray).
+        pred_cls:  Predicted object classes (nparray).
+        target_cls:  True object classes (nparray).
+        plot:  Plot precision-recall curve at [email protected]
+        save_dir:  Plot save directory
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+
+    # Sort by objectness
+    i = np.argsort(-conf)
+    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
+
+    # Find unique classes
+    unique_classes = np.unique(target_cls)
+    nc = unique_classes.shape[0]  # number of classes, number of detections
+
+    # Create Precision-Recall curve and compute AP for each class
+    px, py = np.linspace(0, 1, 1000), []  # for plotting
+    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
+    for ci, c in enumerate(unique_classes):
+        i = pred_cls == c
+        n_l = (target_cls == c).sum()  # number of labels
+        n_p = i.sum()  # number of predictions
+
+        if n_p == 0 or n_l == 0:
+            continue
+        else:
+            # Accumulate FPs and TPs
+            fpc = (1 - tp[i]).cumsum(0)
+            tpc = tp[i].cumsum(0)
+
+            # Recall
+            recall = tpc / (n_l + 1e-16)  # recall curve
+            r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
+
+            # Precision
+            precision = tpc / (tpc + fpc)  # precision curve
+            p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
+
+            # AP from recall-precision curve
+            for j in range(tp.shape[1]):
+                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
+                if plot and j == 0:
+                    py.append(np.interp(px, mrec, mpre))  # precision at [email protected]
+
+    # Compute F1 (harmonic mean of precision and recall)
+    f1 = 2 * p * r / (p + r + 1e-16)
+    if plot:
+        plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
+        plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
+        plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
+        plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')
+
+    # i = f1.mean(0).argmax()  # max F1 index
+    # return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32')
+    return p, r, ap, f1, unique_classes.astype('int32')
+
+
+def compute_ap(recall, precision):
+    """ Compute the average precision, given the recall and precision curves
+    # Arguments
+        recall:    The recall curve (list)
+        precision: The precision curve (list)
+    # Returns
+        Average precision, precision curve, recall curve
+    """
+
+    # Append sentinel values to beginning and end
+    mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01]))
+    mpre = np.concatenate(([1.], precision, [0.]))
+
+    # Compute the precision envelope
+    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
+
+    # Integrate area under curve
+    method = 'interp'  # methods: 'continuous', 'interp'
+    if method == 'interp':
+        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
+        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
+    else:  # 'continuous'
+        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
+
+    return ap, mpre, mrec
+
+# Plots ----------------------------------------------------------------------------------------------------------------
+
+def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
+    # Precision-recall curve
+    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+    py = np.stack(py, axis=1)
+
+    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
+        for i, y in enumerate(py.T):
+            ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}')  # plot(recall, precision)
+    else:
+        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
+
+    ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f [email protected]' % ap[:, 0].mean())
+    ax.set_xlabel('Recall')
+    ax.set_ylabel('Precision')
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    fig.savefig(Path(save_dir), dpi=250)
+
+
+def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):
+    # Metric-confidence curve
+    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+
+    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
+        for i, y in enumerate(py):
+            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(confidence, metric)
+    else:
+        ax.plot(px, py.T, linewidth=1, color='grey')  # plot(confidence, metric)
+
+    y = py.mean(0)
+    ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    fig.savefig(Path(save_dir), dpi=250)
+
+def process_batch(detections, labels, iouv):
+    """
+    Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.
+    Arguments:
+        detections (Array[N, 6]), x1, y1, x2, y2, conf, class
+        labels (Array[M, 5]), class, x1, y1, x2, y2
+    Returns:
+        correct (Array[N, 10]), for 10 IoU levels
+    """
+    correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
+    iou = general.box_iou(labels[:, 1:], detections[:, :4])
+    correct_class = labels[:, 0:1] == detections[:, 5]
+    for i in range(len(iouv)):
+        x = torch.where((iou >= iouv[i]) & correct_class)  # IoU > threshold and classes match
+        if x[0].shape[0]:
+            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detect, iou]
+            if x[0].shape[0] > 1:
+                matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                # matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+            correct[matches[:, 1].astype(int), i] = True
+    return torch.tensor(correct, dtype=torch.bool, device=iouv.device)