fix unittest and pylint

Author: lucienwang1009

tests/test_cond.py

@@ -12,7 +12,7 @@
 import tensorflow as tf
 
 from backend_test_base import Tf2OnnxBackendTestBase
-from common import unittest_main, check_opset_min_version
+from common import unittest_main, check_opset_min_version, check_tf_min_version
 
 
 # pylint: disable=missing-docstring,invalid-name,unused-argument,using-constant-test
@@ -267,7 +267,8 @@ def case_graph():
         output_names_with_port = ["output:0"]
         self.run_test_case(feed_dict, input_names_with_port, output_names_with_port)
 
-    @check_opset_min_version(9, "")
+    @check_tf_min_version("1.8", "shape inference for Reshape op screws up")
+    @check_opset_min_version(9, "ConstantOfShape")
     def test_cond_with_different_output_shape(self):
         input_shape = (10, 5, 20)
         inputs = tf.placeholder(tf.float32, input_shape, name="input")
@@ -277,29 +278,29 @@ def test_cond_with_different_output_shape(self):
         inputs = tf.reshape(inputs, shape)
 
         def pad_tensor(t, length):
-          """Pads the input tensor with 0s along the first dimension up to the length.
-
-          Args:
-            t: the input tensor, assuming the rank is at least 1.
-            length: a tensor of shape [1]  or an integer, indicating the first dimension
-              of the input tensor t after padding, assuming length <= t.shape[0].
-
-          Returns:
-            padded_t: the padded tensor, whose first dimension is length. If the length
-              is an integer, the first dimension of padded_t is set to length
-              statically.
-          """
-          t_rank = tf.rank(t)
-          t_shape = tf.shape(t)
-          t_d0 = t_shape[0]
-          pad_d0 = tf.expand_dims(length - t_d0, 0)
-          pad_shape = tf.cond(
-              # shape is [3], depending on input shape
-              tf.greater(t_rank, 1), lambda: tf.concat([pad_d0, t_shape[1:]], 0),
-              # shape is always [1]
-              lambda: tf.expand_dims(length - t_d0, 0))
-          padded_t = tf.concat([t, tf.zeros(pad_shape, dtype=t.dtype)], 0)
-          return padded_t
+            """Pads the input tensor with 0s along the first dimension up to the length.
+
+            Args:
+              t: the input tensor, assuming the rank is at least 1.
+              length: a tensor of shape [1]  or an integer, indicating the first dimension
+                of the input tensor t after padding, assuming length <= t.shape[0].
+
+            Returns:
+              padded_t: the padded tensor, whose first dimension is length. If the length
+                is an integer, the first dimension of padded_t is set to length
+                statically.
+            """
+            t_rank = tf.rank(t)
+            t_shape = tf.shape(t)
+            t_d0 = t_shape[0]
+            pad_d0 = tf.expand_dims(length - t_d0, 0)
+            pad_shape = tf.cond(
+                # shape is [3], depending on input shape
+                tf.greater(t_rank, 1), lambda: tf.concat([pad_d0, t_shape[1:]], 0),
+                # shape is always [1]
+                lambda: tf.expand_dims(length - t_d0, 0))
+            padded_t = tf.concat([t, tf.zeros(pad_shape, dtype=t.dtype)], 0)
+            return padded_t
 
         output = pad_tensor(inputs, 20)
         _ = tf.identity(output, name="output")

tf2onnx/onnx_opset/generator.py

@@ -42,7 +42,9 @@ def version_7(cls, ctx, node, **kwargs):
         # T output = Fill(int32 dims, T value, @int32 index_type)
         # T outputs = Tile(T value, int64 repeats (e.g. dims))
         fill_shape = ctx.get_shape(node.input[0])
+        utils.make_sure(fill_shape is not None, "shape of {} is None".format(node.input[0]))
         fill_shape_dims = fill_shape[0]
+        utils.make_sure(fill_shape_dims > 0, "opset 7 requires fill shape length > 0, or please try opset > 7")
         val_dtype = ctx.get_dtype(node.input[1])
         val_shape = ctx.get_shape(node.input[1])
 

tf2onnx/rewriter/cond_rewriter.py

@@ -107,7 +107,7 @@ def _get_output_shape_dtype(self, cond_context):
             false_output = cond_context.false_branch_context.output[i]
             true_shape = self.g.get_shape(true_output)
             utils.make_sure(true_shape is not None, "Shape of {} is None".format(true_output))
-            true_rank  = len(true_shape)
+            true_rank = len(true_shape)
             true_dtype = self.g.get_dtype(true_output)
             false_shape = self.g.get_shape(false_output)
             utils.make_sure(false_shape is not None, "Shape of {} is None".format(false_output))