fix pylint

Author: guschmue

tests/make_models.py

@@ -8,19 +8,10 @@
 from __future__ import unicode_literals
 
 import os
-import unittest
-from collections import namedtuple
-
-import graphviz as gv
-from onnx import TensorProto
-from onnx import helper
 
+import numpy as np
 import tensorflow as tf
 from tensorflow.python.framework.graph_util import convert_variables_to_constants
-import numpy as np
-
-import os
-
 
 # pylint: disable=missing-docstring
 
@@ -29,12 +20,13 @@
 training_epochs = 100
 
 # Training Data
-train_X = np.array(
+_train_x = np.array(
     [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1])
-train_Y = np.array(
+_train_y = np.array(
     [1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3])
-test_X = np.array([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
-test_Y = np.array([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])
+_test_x = np.array([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
+_test_y = np.array([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])
+
 
 def freeze_session(sess, keep_var_names=None, output_names=None, clear_devices=True):
     """Freezes the state of a session into a pruned computation graph."""
@@ -52,20 +44,21 @@ def freeze_session(sess, keep_var_names=None, output_names=None, clear_devices=T
                                                       output_names, freeze_var_names)
         return frozen_graph
 
+
 def train(model_path):
-    n_samples = train_X.shape[0]
+    n_samples = _train_x.shape[0]
 
     # tf Graph Input
-    X = tf.placeholder(tf.float32, name="X")
-    Y = tf.placeholder(tf.float32, name="Y")
+    x = tf.placeholder(tf.float32, name="X")
+    y = tf.placeholder(tf.float32, name="Y")
 
     # Set model weights
-    W = tf.Variable(np.random.randn(), name="W")
+    w = tf.Variable(np.random.randn(), name="W")
     b = tf.Variable(np.random.randn(), name="b")
 
-    pred = tf.add(tf.multiply(X, W), b)
+    pred = tf.add(tf.multiply(x, w), b)
     pred = tf.identity(pred, name="pred")
-    cost = tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * n_samples)
+    cost = tf.reduce_sum(tf.pow(pred - y, 2)) / (2 * n_samples)
 
     optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
     saver = tf.train.Saver()
@@ -75,11 +68,11 @@ def train(model_path):
         sess.run(tf.global_variables_initializer())
 
         # Fit all training data
-        for epoch in range(training_epochs):
-            for (x, y) in zip(train_X, train_Y):
-                sess.run(optimizer, feed_dict={X: x, Y: y})
-        training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
-        testing_cost = sess.run(cost, feed_dict={X: test_X, Y: test_Y})
+        for _ in range(training_epochs):
+            for (ix, iy) in zip(_train_x, _train_y):
+                sess.run(optimizer, feed_dict={x: ix, y: iy})
+        training_cost = sess.run(cost, feed_dict={x: _train_x, y: _train_y})
+        testing_cost = sess.run(cost, feed_dict={x: _test_x, y: _test_y})
         print("train_cost={}, test_cost={}, diff={}"
               .format(training_cost, testing_cost, abs(training_cost - testing_cost)))
 
@@ -92,8 +85,7 @@ def train(model_path):
         tf.train.write_graph(frozen_graph, p, "frozen.pb", as_text=False)
 
         p = os.path.abspath(os.path.join(model_path, "saved_model"))
-        tf.saved_model.simple_save(sess, p, inputs={"X": X}, outputs={"pred": pred})
+        tf.saved_model.simple_save(sess, p, inputs={"X": x}, outputs={"pred": pred})
 
 
 train("models/regression")
-

tests/run_pretrained_models.py

@@ -25,7 +25,6 @@
 # otherwise tf runtime error will show up when the tf model is restored from pb file because of un-registered ops.
 import tensorflow.contrib.rnn  # pylint: disable=unused-import
 import yaml
-from tensorflow.core.framework import graph_pb2
 
 import tf2onnx
 from tf2onnx import loader
@@ -227,7 +226,7 @@ def run_test(self, name, backend="caffe2", debug=False, onnx_file=None, opset=No
             graph_def, inputs, outputs = loader.from_saved_model(model_path, inputs, outputs)
         else:
             graph_def, inputs, outputs = loader.from_graphdef(model_path, inputs, outputs)
-    
+
         # create the input data
         inputs = {}
         for k, v in self.input_names.items():

tf2onnx/convert.py

@@ -57,7 +57,7 @@ def get_args():
         if not args.input and not args.outputs:
             raise ValueError("graphdef and checkpoint models need to provide inputs and outputs")
     if not any([args.graphdef, args.checkpoint, args.saved_model]):
-            raise ValueError("need input as graphdef, checkpoint or saved_model")
+        raise ValueError("need input as graphdef, checkpoint or saved_model")
     if args.inputs:
         args.inputs, args.shape_override = utils.split_nodename_and_shape(args.inputs)
     if args.outputs: