Nb clean + cleaning python files in /example/ with pyflakes (#898)

* nb-clean some notebooks

* empty cells

* pyflakes cleanup for /example/

* formatting

* additional fix for 3dunet pyflakes warnings

* formatting
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

examples/export_frozen_graph_tf2/example.ipynb

@@ -148,13 +148,6 @@
     "\n",
     "print(process.stdout)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -172,8 +165,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,

examples/python_3dunet/visualization_utils.py

@@ -38,15 +38,13 @@ def show_n_images(imgs, titles=None, enlarge=20, cmap='jet'):
 
 
 #--------------------------------------------------------------
-from skimage import io, color, img_as_float
+from skimage import color, img_as_float
 from skimage.exposure import adjust_gamma
 
 
 # Creates an image of original brain with segmentation overlay
 def show_label_on_image(test_img, test_lbl):
 
-    modes = {'flair': 0, 't1': 1, 't1c': 2, 't2': 3}
-
     label_im = test_lbl
 
     ones = np.argwhere(label_im == 1)
@@ -61,7 +59,6 @@ def show_label_on_image(test_img, test_lbl):
     image = adjust_gamma(np.abs(color.gray2rgb(gray_img)), 0.45)
     #sliced_image = image.copy()
 
-    red_multiplier = [1, 0.2, 0.2]
     green_multiplier = [0.35, 0.75, 0.25]
     blue_multiplier = [0, 0.5, 1.]  #[0,0.25,0.9]
     yellow_multiplier = [1, 1, 0.25]
@@ -69,7 +66,7 @@ def show_label_on_image(test_img, test_lbl):
 
     # change colors of segmented classes
     for i in range(len(ones)):
-        image[ones[i][0]][ones[i][1]] = blue_multiplier  #red_multiplier
+        image[ones[i][0]][ones[i][1]] = blue_multiplier
     for i in range(len(twos)):
         image[twos[i][0]][twos[i][1]] = yellow_multiplier
     for i in range(len(threes)):
@@ -90,7 +87,6 @@ def show_label_on_image4(test_img, label_im):
 
     # Construct a colour image to superimpose
     color_mask = np.zeros((rows, cols, 3))
-    red_multiplier = [1, 0.2, 0.2]
     green_multiplier = [0.35, 0.75, 0.25]
     blue_multiplier = [0, 0.25, 0.9]
     yellow_multiplier = [1, 1, 0.25]

examples/python_api_inference/resnet50_inference.ipynb

@@ -339,8 +339,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,

examples/python_bert_squad_example/BERT-Squad.ipynb

@@ -273,8 +273,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,

examples/python_bert_squad_example/bert-squad-migraphx.py

 import numpy as np
 import json
-import time
 import os.path
-from os import path
-import sys
-
 import tokenizers
-from run_onnx_squad import *
-
+import collections
+from run_onnx_squad import read_squad_examples, write_predictions, convert_examples_to_features
 import migraphx
 
+RawResult = collections.namedtuple("RawResult",
+                                   ["unique_id", "start_logits", "end_logits"])
+
 #######################################
 input_file = 'inputs_amd.json'
 with open(input_file) as json_file:

examples/python_bert_squad_example/run_onnx_squad.py

@@ -29,7 +29,6 @@ python onnx_squad.py --model $SQUAD_MODEL/squad.onnx \
 import argparse
 import collections
 import json
-import logging
 import math
 import os
 import sys
@@ -145,8 +144,6 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
                 tok_to_orig_index.append(i)
                 all_doc_tokens.append(sub_token)
 
-        tok_start_position = None
-        tok_end_position = None
         # The -3 accounts for [CLS], [SEP] and [SEP]
         max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
 
@@ -567,7 +564,7 @@ def main():
         sess_options = onnxrt.SessionOptions()
         sess_options.session_log_verbosity_level = args.log
 
-    tokenizer = BertWordPieceTokenizer(vocab_file)
+    tokenizer = BertWordPieceTokenizer(args.vocab_file)
 
     eval_examples = read_squad_examples(input_file=args.predict_file)
     input_ids, input_mask, segment_ids, extra_data = \

examples/python_nfnet_inference/nfnet_inference.ipynb

@@ -250,8 +250,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,

examples/python_super_resolution/Run_Super_Resolution_Model.ipynb

@@ -210,8 +210,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,