diff --git a/mace/python/tools/BUILD b/mace/python/tools/BUILD
index 5a048c378e9937728d334545c2b444abc57fc3f9..dec4dbc9162c6ce346900112df0285ccf31aa50d 100644
--- a/mace/python/tools/BUILD
+++ b/mace/python/tools/BUILD
@@ -19,3 +19,12 @@ py_binary(
         "@six_archive//:six",
     ],
 )
+
+py_binary(
+    name = "tf_ops_stats",
+    srcs = ["tf_ops_stats.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "@six_archive//:six",
+    ],
+)
diff --git a/mace/python/tools/tf_ops_stats.py b/mace/python/tools/tf_ops_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdf47e0772ee81072d12842980317e54ecb9dbc8
--- /dev/null
+++ b/mace/python/tools/tf_ops_stats.py
@@ -0,0 +1,148 @@
+import argparse
+import sys
+import six
+import tensorflow as tf
+from tensorflow import gfile
+
+# ./bazel-bin/mace/python/tools/tf_ops_stats --input model.pb
+
+FLAGS = None
+
+def hist_inc(hist, key):
+  if key in hist:
+    hist[key] += 1
+  else:
+    hist[key] = 1
+
+def to_int_list(long_list):
+  int_list = []
+  for value in long_list:
+    int_list.append(int(value))
+  return int_list
+
+def main(unused_args):
+  if not FLAGS.input or not gfile.Exists(FLAGS.input):
+    print('Input graph file ' + FLAGS.input + ' does not exist!')
+    return -1
+
+  input_graph_def = tf.GraphDef()
+  with gfile.Open(FLAGS.input, 'rb') as f:
+    data = f.read()
+    input_graph_def.ParseFromString(data)
+
+  with tf.Session() as session:
+    with session.graph.as_default() as graph:
+      tf.import_graph_def(input_graph_def, name='')
+  
+    stats = {}
+    ops = graph.get_operations()
+    # extract kernel size for conv_2d
+    tensor_shapes = {}
+    tensor_values = {}
+    for op in ops:
+      if op.type == 'Const':
+        for output in op.outputs:
+          tensor_name = output.name
+          tensor = output.eval()
+          tensor_shape = list(tensor.shape)
+          tensor_shapes[tensor_name] = tensor_shape
+          if len(tensor_shape) == 1 and tensor_shape[0] < 10:
+            tensor_values[tensor_name] = list(tensor)
+
+    for op in ops:
+      if op.type in ['Conv2D']:
+        padding = op.get_attr('padding')
+        strides = to_int_list(op.get_attr('strides'))
+        data_format = op.get_attr('data_format')
+        ksize = 'Unknown'
+        for input in op.inputs:
+          input_name = input.name
+          if input_name.endswith('weights/read:0'):
+            ksize = input.shape.as_list()
+            break
+          if input_name.endswith('weights:0') and input_name in tensor_shapes:
+            ksize = tensor_shapes[input_name]
+            break
+        key = '%s(padding=%s, strides=%s, ksize=%s, format=%s)' % (op.type, padding, strides, ksize, data_format)
+        hist_inc(stats, key)
+      elif op.type in ['FusedResizeAndPadConv2D']:
+        padding = op.get_attr('padding')
+        strides = to_int_list(op.get_attr('strides'))
+        resize_align_corners = op.get_attr('resize_align_corners')
+        ksize = 'Unknown'
+        for input in op.inputs:
+          input_name = input.name
+          if input_name.endswith('weights:0') and input_name in tensor_shapes:
+            ksize = tensor_shapes[input_name]
+            break
+        key = '%s(padding=%s, strides=%s, ksize=%s, resize_align_corners=%s)' % (op.type, padding, strides, ksize, resize_align_corners)
+        hist_inc(stats, key)
+      elif op.type in ['ResizeBilinear']:
+        align_corners = op.get_attr('align_corners')
+        size = 'Unknown'
+        for input in op.inputs:
+          input_name = input.name
+          if input_name.endswith('size:0') and input_name in tensor_values:
+            size = tensor_values[input_name]
+            break
+        key = '%s(size=%s, align_corners=%s)' % (op.type, size, align_corners)
+        hist_inc(stats, key)
+      elif op.type in ['AvgPool', 'MaxPool']:
+        padding = op.get_attr('padding')
+        strides = to_int_list(op.get_attr('strides'))
+        ksize = to_int_list(op.get_attr('ksize'))
+        data_format = op.get_attr('data_format')
+        key = '%s(padding=%s, strides=%s, ksize=%s)' % (op.type, padding, strides, ksize)
+        hist_inc(stats, key)
+      elif op.type in ['SpaceToBatchND', 'BatchToSpaceND']:
+        block_shape = 'Unknown'
+        for input in op.inputs:
+          input_name = input.name
+          if input_name.endswith('block_shape:0') and input_name in tensor_values:
+            block_shape = tensor_values[input_name]
+            break
+        paddings = 'Unknown'
+        for input in op.inputs:
+          input_name = input.name
+          if input_name.endswith('paddings:0') and input_name in tensor_values:
+            paddings = tensor_values[input_name]
+            break
+        crops = 'Unknown'
+        for input in op.inputs:
+          input_name = input.name
+          if input_name.endswith('crops:0') and input_name in tensor_values:
+            paddings = tensor_values[input_name]
+            break
+        if op.type == 'SpaceToBatchND':
+          key = '%s(block_shape=%s, paddings=%s)' % (op.type, block_shape, paddings)
+        else:
+          key = '%s(block_shape=%s, crops=%s)' % (op.type, block_shape, crops)
+        hist_inc(stats, key)
+      elif op.type == 'Pad':
+        paddings = 'Unknown'
+        for input in op.inputs:
+          input_name = input.name
+          if input_name.endswith('paddings:0') and input_name in tensor_values:
+            paddings = tensor_values[input_name]
+            break
+        key = '%s(paddings=%s)' % (op.type, paddings)
+        hist_inc(stats, key)
+      else:
+        hist_inc(stats, op.type)
+  
+  for key, value in sorted(six.iteritems(stats)):
+    print('%s: %d' % (key, value))
+
+def parse_args():
+  '''Parses command line arguments.'''
+  parser = argparse.ArgumentParser()
+  parser.add_argument(
+    '--input',
+    type=str,
+    default='',
+    help='TensorFlow \'GraphDef\' file to load.')
+  return parser.parse_known_args()
+
+if __name__ == '__main__':
+  FLAGS, unparsed = parse_args()
+  main(unused_args=[sys.argv[0]] + unparsed)