diff --git a/test.png b/test.png
new file mode 100644
index 0000000..ba037b8
Binary files /dev/null and b/test.png differ
diff --git a/tools/show.py b/tools/show.py
index 3117c82..3547252 100644
--- a/tools/show.py
+++ b/tools/show.py
@@ -3,21 +3,30 @@ import torch
 import torch.nn.functional as F
 import torchvision.transforms.functional as Vision
 import cv2
+import math
+import numpy as np
 
 
-def DumpTensorToImage(tensor, name, autoPad=True, scale=1.0):
-    if len(tensor.shape) != 2:
+def DumpTensorToImage(tensor, name, autoPad=True, scale=1.0, auto2d=True):
+    if len(tensor.shape) != 2 and len(tensor.shape) != 1:
         raise ("Error input dims")
+
     tensor = tensor.float()
     maxv = torch.max(tensor)
     minv = torch.min(tensor)
     tensor = (((tensor - minv) / (maxv - minv)) * 256).byte().cpu()
     img = tensor.numpy()
     srp = img.shape
+
+    if auto2d and len(srp) == 1:
+        ceiled = math.ceil((srp[0]) ** 0.5)
+        img = cv2.copyMakeBorder(img, 0, ceiled * ceiled - srp[0], 0, 0, 0)
+        img = img.reshape((ceiled, ceiled))
+        srp = img.shape
     if autoPad and (max(srp) / min(srp) > 16):
-        img = cv2.resize(img,[max(srp),max(srp)])
-    srp = img.shape
+        img = cv2.resize(img, [max(srp), max(srp)])
+        srp = img.shape
     if scale != 1.0:
         img = cv2.resize(img, [int(srp[0] * scale), int(srp[1] * scale)])
-    srp = img.shape
+        srp = img.shape
     cv2.imwrite(name, img)
diff --git a/tools/test.py b/tools/test.py
index ef65f14..858d675 100644
--- a/tools/test.py
+++ b/tools/test.py
@@ -2,5 +2,9 @@ import show
 import torch
 
 
-radata = torch.randn(8192, 128)
-show.DumpTensorToImage(radata, "test.png", autoPad=True,scale=0.2)
+# radata = torch.randn(8192, 128)
+# show.DumpTensorToImage(radata, "test.png", autoPad=True,scale=0.2)
+
+
+radata = torch.randn(127)
+show.DumpTensorToImage(radata, "test.png")