Targets scaling bug fix (#1529)

test.py

@@ -15,7 +15,7 @@ from utils.general import coco80_to_coco91_class, check_dataset, check_file, che
     non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path
 from utils.loss import compute_loss
 from utils.metrics import ap_per_class, ConfusionMatrix
-from utils.plots import plot_images, output_to_target
+from utils.plots import plot_images, output_to_target, plot_study_txt
 from utils.torch_utils import select_device, time_synchronized
 
 
@@ -102,7 +102,6 @@ def test(data,
         img /= 255.0  # 0 - 255 to 0.0 - 1.0
         targets = targets.to(device)
         nb, _, height, width = img.shape  # batch size, channels, height, width
-        targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)
 
         with torch.no_grad():
             # Run model
@@ -115,8 +114,9 @@ def test(data,
                 loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # box, obj, cls
 
             # Run NMS
-            t = time_synchronized()
+            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_txt else []  # for autolabelling
+            t = time_synchronized()
             output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb)
             t1 += time_synchronized() - t
 
@@ -324,8 +324,9 @@ if __name__ == '__main__':
             y = []  # y axis
             for i in x:  # img-size
                 print('\nRunning %s point %s...' % (f, i))
-                r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json)
+                r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,
+                               plots=False)
                 y.append(r + t)  # results and times
             np.savetxt(f, y, fmt='%10.4g')  # save
         os.system('zip -r study.zip study_*.txt')
-        # utils.plots.plot_study_txt(f, x)  # plot
+        plot_study_txt(f, x)  # plot

utils/plots.py

@@ -140,7 +140,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max
             labels = image_targets.shape[1] == 6  # labels if no conf column
             conf = None if labels else image_targets[:, 6]  # check for confidence presence (label vs pred)
 
-            if boxes.max() <= 1:  # if normalized
+            if boxes.shape[1] and boxes.max() <= 1:  # if normalized
                 boxes[[0, 2]] *= w  # scale to pixels
                 boxes[[1, 3]] *= h
             boxes[[0, 2]] += block_x
@@ -224,7 +224,7 @@ def plot_study_txt(f='study.txt', x=None):  # from utils.plots import *; plot_st
     ax = ax.ravel()
 
     fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
-    for f in ['study/study_coco_yolov5%s.txt' % x for x in ['s', 'm', 'l', 'x']]:
+    for f in ['study/study_coco_%s.txt' % x for x in ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']]:
         y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
         x = np.arange(y.shape[1]) if x is None else np.array(x)
         s = ['P', 'R', '[email protected]', '[email protected]:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']
@@ -368,7 +368,7 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
                     y[y == 0] = np.nan  # don't show zero loss values
                     # y /= y[0]  # normalize
                 label = labels[fi] if len(labels) else f.stem
-                ax[i].plot(x, y, marker='.', label=label, linewidth=1, markersize=6)
+                ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8)
                 ax[i].set_title(s[i])
                 # if i in [5, 6, 7]:  # share train and val loss y axes
                 #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])