try to enable flake8 checker. (#503)

.flake8

@@ -0,0 +1,4 @@
+[flake8]
+max-line-length = 120
+per-file-ignores =
+    __init__.py:F401

keras2onnx/_graph_cvt.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """Helpers to convert variables to constants in TensorFlow 2.0."""
-
+# flake8: noqa
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

keras2onnx/_parser_1x.py

@@ -100,7 +100,9 @@ def on_parsing_keras_layer(graph, node_list, layer, kenode, model, varset, prefi
     for n_, o_ in enumerate(outputs):
         oname = prefix + o_.name
         k2o_logger().debug('\toutput: ' + oname)
-        o1 = varset.get_local_variable_or_declare_one(oname, infer_variable_type(o_, varset.target_opset, kenode_output_shapes[n_]))
+        o1 = varset.get_local_variable_or_declare_one(oname,
+                                                      infer_variable_type(o_, varset.target_opset,
+                                                                          kenode_output_shapes[n_]))
         operator.add_output(o1)
 
     if hasattr(layer, 'output_mask') and layer.output_mask is not None:
@@ -122,8 +124,6 @@ def on_parsing_keras_layer(graph, node_list, layer, kenode, model, varset, prefi
 
 
 def build_opdict_from_keras(model):
-    # type: (keras.Model) -> {}
-
     output_dict = {}
     for l_ in model.layers:
         if hasattr(l_, 'layers'):

keras2onnx/common/data_types.py

@@ -4,4 +4,4 @@
 # license information.
 ###############################################################################
 
-from onnxconverter_common.data_types import *
+from onnxconverter_common.data_types import *  # noqa

keras2onnx/common/onnx_ops.py

@@ -4,6 +4,7 @@
 # license information.
 ###############################################################################
 import functools
+import numpy as np
 import onnxconverter_common
 from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
 from onnxconverter_common.onnx_ops import *  # noqa:

keras2onnx/parser.py

@@ -150,7 +150,7 @@ def _check_layer_converter_availability(sub_model):
         else:
             layer_type = type(l_)
             exist = get_converter(layer_type) or \
-                    layer_type in [keras.layers.InputLayer, keras.layers.wrappers.TimeDistributed]
+                layer_type in [keras.layers.InputLayer, keras.layers.wrappers.TimeDistributed]
 
         if not exist:
             k2o_logger().info("The layer {} doesn't have a specific converter, fall back.".format(str(l_)))

keras2onnx/proto/tfcompat.py

@@ -36,14 +36,12 @@ def dump_graph_into_tensorboard(tf_graph):
 if is_tf2:
     tensorflow = _tf.compat.v1
 
-
     def is_subclassed(layer):
         """Returns True if the object is a subclassed layer or subclassed model."""
         return (layer.__module__.find('keras.engine') == -1 and
                 layer.__module__.find('keras.layers') == -1)
 else:
     tensorflow = _tf
 
-
     def is_subclassed(layer):
         return False

keras2onnx/topology.py

@@ -137,7 +137,8 @@ def _check_structure(self):
                 # A operator has an output, so we remove the operator from the unused-operator list.
                 unused_operators.discard(operator.full_name)
             for variable in operator.input_masks + operator.output_masks:
-                if variable is None: continue
+                if variable is None:
+                    continue
                 # A variable is used by an operator, so we remove the variable from the unused-variable list.
                 unused_variables.discard(variable.full_name)
                 # A operator has an output, so we remove the operator from the unused-operator list.
@@ -236,7 +237,8 @@ def _remove_unused_nodes(nodes, inputs, outputs):
 def _build_extra_inputs(container):
     # When calling ModelComponentContainer's add_initializer(...), nothing is added into the input list.
     # However, In ONNX, for target opset < 9, initializers should also be model's (GraphProto) inputs.
-    # Thus, we create ValueInfoProto objects from initializers (type: TensorProto) directly and then add them into model's input list.
+    # Thus, we create ValueInfoProto objects from initializers (type: TensorProto) ...
+    # ... directly and then add them into model's input list.
     extra_inputs = []  # ValueInfoProto list of the initializers
     for tensor in container.initializers:
         # Sometimes (especially when creating optional input values such as RNN's initial hidden state), an initializer
@@ -351,7 +353,8 @@ def convert_topology(topology, model_name, doc_string, target_opset, channel_fir
                     '{} nchw_inputs does not make effect. Please set nchw_inputs to empty.'.format(onnx_not_imported))
             k2o_logger().warning('{} so the convertor optimizer is not enabled.'.format(onnx_not_imported))
         except Exception as e:  # noqa
-            # either optimizer issue or converter issue, we just let it go to diagnose the issue from the converted model.
+            # either optimizer issue or converter issue, we just let it go...
+            # ... so that we can diagnose the issue from the converted model.
             k2o_logger().warning(
                 'There is an error({}) happened during optimizing on the converted model!'.format(type(e)))
             k2o_logger().warning(str(e))