Rename test.py to val.py (#4000)

utils/augmentations.py

@@ -90,7 +90,7 @@ def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleF
 
     # Scale ratio (new / old)
     r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-    if not scaleup:  # only scale down, do not scale up (for better test mAP)
+    if not scaleup:  # only scale down, do not scale up (for better val mAP)
         r = min(r, 1.0)
 
     # Compute padding

utils/general.py

@@ -633,7 +633,7 @@ def apply_classifier(x, model, img, im0):
             for j, a in enumerate(d):  # per item
                 cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
                 im = cv2.resize(cutout, (224, 224))  # BGR
-                # cv2.imwrite('test%i.jpg' % j, cutout)
+                # cv2.imwrite('example%i.jpg' % j, cutout)
 
                 im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
                 im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32

utils/plots.py

@@ -219,9 +219,9 @@ def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
     plt.close()
 
 
-def plot_test_txt():  # from utils.plots import *; plot_test()
-    # Plot test.txt histograms
-    x = np.loadtxt('test.txt', dtype=np.float32)
+def plot_val_txt():  # from utils.plots import *; plot_val()
+    # Plot val.txt histograms
+    x = np.loadtxt('val.txt', dtype=np.float32)
     box = xyxy2xywh(x[:, :4])
     cx, cy = box[:, 0], box[:, 1]
 
@@ -250,7 +250,7 @@ def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()
 
 
 def plot_study_txt(path='', x=None):  # from utils.plots import *; plot_study_txt()
-    # Plot study.txt generated by test.py
+    # Plot study.txt generated by val.py
     plot2 = False  # plot additional results
     if plot2:
         ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()