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diff --git a/util/util.py b/util/util.py
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+from __future__ import print_function
+import torch
+import numpy as np
+from PIL import Image
+import inspect, re
+import numpy as np
+import os
+import collections
+
+# Converts a Tensor into a Numpy array
+# |imtype|: the desired type of the converted numpy array
+def tensor2im(image_tensor, imtype=np.uint8):
+    image_numpy = image_tensor[0].cpu().float().numpy()
+    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
+    return image_numpy.astype(imtype)
+
+
+def diagnose_network(net, name='network'):
+    mean = 0.0
+    count = 0
+    for param in net.parameters():
+        if param.grad is not None:
+            mean += torch.mean(torch.abs(param.grad.data))
+            count += 1
+    if count > 0:
+        mean = mean / count
+    print(name)
+    print(mean)
+
+
+def save_image(image_numpy, image_path):
+    image_pil = Image.fromarray(image_numpy)
+    image_pil.save(image_path)
+
+def info(object, spacing=10, collapse=1):
+    """Print methods and doc strings.
+    Takes module, class, list, dictionary, or string."""
+    methodList = [e for e in dir(object) if isinstance(getattr(object, e), collections.Callable)]
+    processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)
+    print( "\n".join(["%s %s" %
+                     (method.ljust(spacing),
+                      processFunc(str(getattr(object, method).__doc__)))
+                     for method in methodList]) )
+
+def varname(p):
+    for line in inspect.getframeinfo(inspect.currentframe().f_back)[3]:
+        m = re.search(r'\bvarname\s*\(\s*([A-Za-z_][A-Za-z0-9_]*)\s*\)', line)
+        if m:
+            return m.group(1)
+
+def print_numpy(x, val=True, shp=False):
+    x = x.astype(np.float64)
+    if shp:
+        print('shape,', x.shape)
+    if val:
+        x = x.flatten()
+        print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
+            np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
+
+
+def mkdirs(paths):
+    if isinstance(paths, list) and not isinstance(paths, str):
+        for path in paths:
+            mkdir(path)
+    else:
+        mkdir(paths)
+
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)