revert scale range for training

cellpose/transforms.py

@@ -800,7 +800,7 @@ def resize_image(img0, Ly=None, Lx=None, rsz=None, interpolation=cv2.INTER_LINEA
             imgs = np.zeros((img0.shape[0], Ly, Lx, img0.shape[-1]), np.float32)
         for i, img in enumerate(img0):
             imgi = resize_safe(img, Ly, Lx, interpolation=interpolation)
-            imgs[i] = imgi if imgi.ndim > 2 else imgi[..., np.newaxis]
+            imgs[i] = imgi if imgi.ndim > 2 or no_channels else imgi[..., np.newaxis]
     else:
         imgs = resize_safe(img0, Ly, Lx, interpolation=interpolation)
     return imgs
@@ -912,7 +912,7 @@ def random_rotate_and_resize(X, Y=None, scale_range=1., xy=(224, 224), do_3D=Fal
             # generate random augmentation parameters
             flip = np.random.rand() > .5
             theta = np.random.rand() * np.pi * 2 if rotate else 0.
-            scale[n] = 2 ** (-2 + 5 * np.random.rand())#(1 - scale_range / 2) + scale_range * np.random.rand()
+            scale[n] =  (1 - scale_range / 2) + scale_range * np.random.rand()
             if rescale is not None:
                 scale[n] *= 1. / rescale[n]
             dxy = np.maximum(0, np.array([Lx * scale[n] - xy[1],