resolve pylint errors

tests/backend_test_base.py

@@ -21,7 +21,7 @@
 from tf2onnx import optimizer
 
 
-# pylint: disable=missing-docstring,invalid-name,unused-argument,using-constant-test
+# pylint: disable=missing-docstring,invalid-name,unused-argument,using-constant-test, import-outside-toplevel
 
 class Tf2OnnxBackendTestBase(unittest.TestCase):
     def setUp(self):

tests/common.py

@@ -15,6 +15,7 @@
 import tensorflow as tf
 from tf2onnx import constants, logging, utils
 
+# pylint: disable=import-outside-toplevel
 __all__ = [
     "TestConfig",
     "get_test_config",

tests/run_pretrained_models.py

@@ -30,7 +30,7 @@
 from tf2onnx import loader, logging, optimizer, utils
 from tf2onnx.tfonnx import process_tf_graph
 
-# pylint: disable=broad-except,logging-not-lazy,unused-argument,unnecessary-lambda
+# pylint: disable=broad-except,logging-not-lazy,unused-argument,unnecessary-lambda,import-outside-toplevel
 
 logger = logging.getLogger("run_pretrained")
 

tf2onnx/graph.py

@@ -40,8 +40,8 @@ def __init__(self, node, graph, skip_conversion=False):
         """
         self._op = node
         self.graph = graph
-        self._input = [i for i in node.input]
-        self._output = [i for i in node.output]
+        self._input = list(node.input)
+        self._output = list(node.output)
         self._attr = {}
 
         graph.set_node_by_name(self)
@@ -306,11 +306,11 @@ def set_body_graph_as_attr(self, attr_name, graph):
 
     def update_proto(self):
         """Update protobuf from internal structure."""
-        nodes = [n for n in self._op.input]
+        nodes = list(self._op.input)
         for node in nodes:
             self._op.input.remove(node)
         self._op.input.extend(self.input)
-        nodes = [n for n in self._op.output]
+        nodes = list(self._op.output)
         for node in nodes:
             self._op.output.remove(node)
         self._op.output.extend(self.output)
@@ -325,7 +325,7 @@ def update_proto(self):
                 graph_proto = sub_graph.make_graph("graph for " + self.name + " " + attr_name)
                 self.set_attr(attr_name, graph_proto)
 
-        attr = [a for a in self.attr_onnx.values()]
+        attr = list(self.attr_onnx.values())
         if attr:
             self._op.attribute.extend(attr)
 
@@ -772,10 +772,12 @@ def get_shape(self, name):
         if shape:
             for i, v in enumerate(shape):
                 if v is None:
+                    # pylint: disable=unsupported-assignment-operation
                     shape[i] = -1
             # hack to allow utils.ONNX_UNKNOWN_DIMENSION to override batchsize if needed.
             # default is -1.
             if shape[0] == -1:
+                # pylint: disable=unsupported-assignment-operation
                 shape[0] = utils.ONNX_UNKNOWN_DIMENSION
             return shape
         return shape
@@ -839,7 +841,7 @@ def _get_unvisited_child(g, node, not_visited):
         label = [-1 for _ in range(n)]
         stack = []
         in_stack = dict()
-        not_visited = dict.fromkeys([i for i in range(n)])
+        not_visited = dict.fromkeys(range(n))
         label_counter = n - 1
 
         while not_visited:
@@ -884,7 +886,7 @@ def make_graph(self, doc, graph_name="tf2onnx"):
             if op.is_const():
                 const_ops.append(op)
                 continue
-            elif op.is_graph_input():
+            if op.is_graph_input():
                 if op not in self._order_sensitive_inputs:
                     order_non_sensitive_placeholders.append(op)
                 continue

tf2onnx/optimizer/transpose_optimizer.py

@@ -227,8 +227,7 @@ def _get_input_index_for_trans(self, node, trans):
         for i in node.input:
             if i == trans.output[0]:
                 break
-            else:
-                input_index += 1
+            input_index += 1
         return input_index
 
     # the assumption is: both node and trans have only 1 output

tf2onnx/rewriter/transpose_rewriter.py

@@ -26,7 +26,7 @@ def rewrite_transpose(g, ops):
     for match in match_results:
         output = match.get_op('output')
         shape = g.get_shape(output.input[0])
-        dims = [i for i in range(len(shape) - 1, -1, -1)]
+        dims = range(len(shape) - 1, -1, -1)
         output.set_attr("perm", dims)
         g.remove_input(output, output.input[1])
         to_delete = [n for n in match.get_nodes() if n != output]

tf2onnx/schemas.py

@@ -136,7 +136,7 @@ def build_onnx_op(node):
                 copied_sub_graph = copy.deepcopy(sub_graph)
                 graph_proto = copied_sub_graph.make_graph("graph for " + node.name + " " + attr_name)
                 attr.append(helper.make_attribute(attr_name, graph_proto))
-        attr.extend([a for a in node.attr_onnx.values()])
+        attr.extend(node.attr_onnx.values())
         if attr:
             onnx_node.attribute.extend(attr)
         return onnx_node

tf2onnx/tfonnx.py

@@ -233,7 +233,7 @@ def rewrite_incomplete_type_support(g, ops, impacted_ops):
         "Where": [0],  # Where's first input is bool
     }
     new_ops = []
-    org_ops = [n for n in ops]
+    org_ops = list(ops)
     for op in org_ops:
         if op.type in impacted_ops:
             cast_inserted = []
@@ -312,7 +312,7 @@ def tensorflow_onnx_mapping(g, ops_mapping):
     unmapped_op = collections.Counter()
     exceptions = []
 
-    ops = [n for n in g.get_nodes()]
+    ops = list(g.get_nodes())
     for node in ops:
         logger.debug("Process node: %s\n%s", node.name, node.summary)
 

tools/save_pretrained_model.py

@@ -7,7 +7,8 @@
 import tensorflow as tf
 import numpy as np
 
-# pylint: disable=redefined-outer-name,reimported
+
+# pylint: disable=redefined-outer-name,reimported,import-outside-toplevel
 
 def save_pretrained_model(sess, outputs, feeds, out_dir, model_name="pretrained"):
     """Save pretrained model and config"""
@@ -54,6 +55,7 @@ def save_pretrained_model(sess, outputs, feeds, out_dir, model_name="pretrained"
 
         # save graph and weights
         from tensorflow.saved_model import simple_save
+        # pylint: disable=unnecessary-comprehension
         simple_save(sess, saved_model,
                     {n: i for n, i in zip(inputs.keys(), feeds.keys())},
                     {op.name: op for op in outputs})