Confusion matrix

requirements.txt

@@ -16,8 +16,9 @@ tqdm>=4.41.0
 # logging -------------------------------------
 # wandb
 
-# coco ----------------------------------------
-# pycocotools>=2.0
+# plotting ------------------------------------
+seaborn
+pandas
 
 # export --------------------------------------
 # coremltools==4.0
@@ -26,4 +27,4 @@ tqdm>=4.41.0
 
 # extras --------------------------------------
 # thop  # FLOPS computation
-# seaborn  # plotting
+# pycocotools>=2.0  # COCO mAP

test.py

@@ -14,7 +14,7 @@
 from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, box_iou, \
     non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path
 from utils.loss import compute_loss
-from utils.metrics import ap_per_class
+from utils.metrics import ap_per_class, ConfusionMatrix
 from utils.plots import plot_images, output_to_target
 from utils.torch_utils import select_device, time_synchronized
 
@@ -89,6 +89,7 @@ def test(data,
         dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True)[0]
 
     seen = 0
+    confusion_matrix = ConfusionMatrix(nc=nc)
     names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
     coco91class = coco80_to_coco91_class()
     s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
@@ -176,6 +177,8 @@ def test(data,
                 # target boxes
                 tbox = xywh2xyxy(labels[:, 1:5])
                 scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1])  # native-space labels
+                if plots:
+                    confusion_matrix.process_batch(pred, torch.cat((labels[:, 0:1], tbox), 1))
 
                 # Per target class
                 for cls in torch.unique(tcls_tensor):
@@ -218,10 +221,12 @@ def test(data,
     else:
         nt = torch.zeros(1)
 
-    # W&B logging
-    if plots and wandb and wandb.run:
-        wandb.log({"Images": wandb_images})
-        wandb.log({"Validation": [wandb.Image(str(x), caption=x.name) for x in sorted(save_dir.glob('test*.jpg'))]})
+    # Plots
+    if plots:
+        confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
+        if wandb and wandb.run:
+            wandb.log({"Images": wandb_images})
+            wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]})
 
     # Print results
     pf = '%20s' + '%12.3g' * 6  # print format

train.py

@@ -396,8 +396,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
         if plots:
             plot_results(save_dir=save_dir)  # save as results.png
             if wandb:
-                wandb.log({"Results": [wandb.Image(str(save_dir / x), caption=x) for x in
-                                       ['results.png', 'precision_recall_curve.png']]})
+                files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png']
+                wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files
+                                       if (save_dir / f).exists()]})
         logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
     else:
         dist.destroy_process_group()

utils/metrics.py

@@ -4,6 +4,9 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+import torch
+
+from . import general
 
 
 def fitness(x):
@@ -102,6 +105,84 @@ def compute_ap(recall, precision):
     return ap, mpre, mrec
 
 
+class ConfusionMatrix:
+    # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
+    def __init__(self, nc, conf=0.25, iou_thres=0.45):
+        self.matrix = np.zeros((nc + 1, nc + 1))
+        self.nc = nc  # number of classes
+        self.conf = conf
+        self.iou_thres = iou_thres
+
+    def process_batch(self, detections, labels):
+        """
+        Return intersection-over-union (Jaccard index) of boxes.
+        Both sets of boxes are expected to be 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:
+            None, updates confusion matrix accordingly
+        """
+        detections = detections[detections[:, 4] > self.conf]
+        gt_classes = labels[:, 0].int()
+        detection_classes = detections[:, 5].int()
+        iou = general.box_iou(labels[:, 1:], detections[:, :4])
+
+        x = torch.where(iou > self.iou_thres)
+        if x[0].shape[0]:
+            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
+            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]]
+        else:
+            matches = np.zeros((0, 3))
+
+        n = matches.shape[0] > 0
+        m0, m1, _ = matches.transpose().astype(np.int16)
+        for i, gc in enumerate(gt_classes):
+            j = m0 == i
+            if n and sum(j) == 1:
+                self.matrix[gc, detection_classes[m1[j]]] += 1  # correct
+            else:
+                self.matrix[gc, self.nc] += 1  # background FP
+
+        if n:
+            for i, dc in enumerate(detection_classes):
+                if not any(m1 == i):
+                    self.matrix[self.nc, dc] += 1  # background FN
+
+    def matrix(self):
+        return self.matrix
+
+    def plot(self, save_dir='', names=()):
+        try:
+            import seaborn as sn
+
+            array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6)  # normalize
+            array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)
+
+            fig = plt.figure(figsize=(12, 9))
+            sn.set(font_scale=1.0 if self.nc < 50 else 0.8)  # for label size
+            labels = (0 < len(names) < 99) and len(names) == self.nc  # apply names to ticklabels
+            sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True,
+                       xticklabels=names + ['background FN'] if labels else "auto",
+                       yticklabels=names + ['background FP'] if labels else "auto").set_facecolor((1, 1, 1))
+            fig.axes[0].set_xlabel('True')
+            fig.axes[0].set_ylabel('Predicted')
+            fig.tight_layout()
+            fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
+        except Exception as e:
+            pass
+
+    def print(self):
+        for i in range(self.nc + 1):
+            print(' '.join(map(str, self.matrix[i])))
+
+
+# Plots ----------------------------------------------------------------------------------------------------------------
+
 def plot_pr_curve(px, py, ap, save_dir='.', names=()):
     fig, ax = plt.subplots(1, 1, figsize=(9, 6))
     py = np.stack(py, axis=1)