Update inference default to multi_label=False

models/common.py

@@ -7,7 +7,7 @@
 import requests
 import torch
 import torch.nn as nn
-from PIL import Image, ImageDraw
+from PIL import Image
 
 from utils.datasets import letterbox
 from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh

test.py

@@ -106,7 +106,7 @@ def test(data,
         with torch.no_grad():
             # Run model
             t = time_synchronized()
-            inf_out, train_out = model(img, augment=augment)  # inference and training outputs
+            out, train_out = model(img, augment=augment)  # inference and training outputs
             t0 += time_synchronized() - t
 
             # Compute loss
@@ -117,11 +117,11 @@ def test(data,
             targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
             lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
             t = time_synchronized()
-            output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb)
+            out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True)
             t1 += time_synchronized() - t
 
         # Statistics per image
-        for si, pred in enumerate(output):
+        for si, pred in enumerate(out):
             labels = targets[targets[:, 0] == si, 1:]
             nl = len(labels)
             tcls = labels[:, 0].tolist() if nl else []  # target class
@@ -209,7 +209,7 @@ def test(data,
             f = save_dir / f'test_batch{batch_i}_labels.jpg'  # labels
             Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
             f = save_dir / f'test_batch{batch_i}_pred.jpg'  # predictions
-            Thread(target=plot_images, args=(img, output_to_target(output), paths, f, names), daemon=True).start()
+            Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
 
     # Compute statistics
     stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy

utils/general.py

@@ -390,11 +390,12 @@ def wh_iou(wh1, wh2):
     return inter / (wh1.prod(2) + wh2.prod(2) - inter)  # iou = inter / (area1 + area2 - inter)
 
 
-def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
-    """Performs Non-Maximum Suppression (NMS) on inference results
+def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
+                        labels=()):
+    """Runs Non-Maximum Suppression (NMS) on inference results
 
     Returns:
-         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
+         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
     """
 
     nc = prediction.shape[2] - 5  # number of classes
@@ -406,7 +407,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
     max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
     time_limit = 10.0  # seconds to quit after
     redundant = True  # require redundant detections
-    multi_label = nc > 1  # multiple labels per box (adds 0.5ms/img)
+    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
     merge = False  # use merge-NMS
 
     t = time.time()

utils/plots.py

@@ -54,7 +54,7 @@ def butter_lowpass(cutoff, fs, order):
     return filtfilt(b, a, data)  # forward-backward filter
 
 
-def plot_one_box(x, img, color=None, label=None, line_thickness=None):
+def plot_one_box(x, img, color=None, label=None, line_thickness=3):
     # Plots one bounding box on image img
     tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
     color = color or [random.randint(0, 255) for _ in range(3)]