Add whitespace error checking to GitHub Actions (#311)

* Fix whitespace errors

As detected with `git diff --check`.

* Add a job to check for whitespace errors

I called it "lint" so that other checks could be added to it later.

* Bump copyright years in changed files

.gitattributes

@@ -26,4 +26,3 @@ LICENSE         text
 *.gif           binary
 *.ttf           binary
 *.pdf           binary
-

.github/workflows/pr_checks.yml

@@ -27,3 +27,11 @@ jobs:
         run: |
           pytest -v
           datum -h
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: Checking whitespace
+        run: |
+          empty_tree="$(git hash-object -t tree --stdin < /dev/null)"
+          git diff --check "$empty_tree"

datumaro/components/__init__.py

@@ -1,5 +1,4 @@
 
-# Copyright (C) 2019-2020 Intel Corporation
+# Copyright (C) 2019-2021 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
-

datumaro/components/algorithms/__init__.py

@@ -1,5 +1,4 @@
 
-# Copyright (C) 2019-2020 Intel Corporation
+# Copyright (C) 2019-2021 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
-

datumaro/plugins/accuracy_checker_plugin/__init__.py

@@ -1,4 +1,3 @@
-# Copyright (C) 2020 Intel Corporation
+# Copyright (C) 2020-2021 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
-

datumaro/plugins/accuracy_checker_plugin/details/representation.py

@@ -56,7 +56,3 @@ def import_prediction(pred):
     #     -
     else:
         raise NotImplementedError("Can't convert %s" % type(pred))
-
-
-
-

datumaro/plugins/openvino_plugin/README.md

@@ -1,17 +1,17 @@
-# OpenVINO™ Inference Interpreter 
+# OpenVINO™ Inference Interpreter
 Interpreter samples to parse OpenVINO™ inference outputs.
 
 ## Models supported from interpreter samples
-There are detection and image classification examples. 
-    
+There are detection and image classification examples.
+
 - Detection (SSD-based)
-  - Intel Pre-trained Models > Object Detection  
+  - Intel Pre-trained Models > Object Detection
     - [face-detection-0200](https://docs.openvinotoolkit.org/latest/omz_models_intel_face_detection_0200_description_face_detection_0200.html)
     - [face-detection-0202](https://docs.openvinotoolkit.org/latest/omz_models_intel_face_detection_0202_description_face_detection_0202.html)
     - [face-detection-0204](https://docs.openvinotoolkit.org/latest/omz_models_intel_face_detection_0204_description_face_detection_0204.html)
     - [person-detection-0200](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_detection_0200_description_person_detection_0200.html)
     - [person-detection-0201](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_detection_0201_description_person_detection_0201.html)
-    - [person-detection-0202](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_detection_0202_description_person_detection_0202.html) 
+    - [person-detection-0202](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_detection_0202_description_person_detection_0202.html)
     - [person-vehicle-bike-detection-2000](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_vehicle_bike_detection_2000_description_person_vehicle_bike_detection_2000.html)
     - [person-vehicle-bike-detection-2001](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_vehicle_bike_detection_2001_description_person_vehicle_bike_detection_2001.html)
     - [person-vehicle-bike-detection-2002](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_vehicle_bike_detection_2002_description_person_vehicle_bike_detection_2002.html)
@@ -28,7 +28,7 @@ There are detection and image classification examples.
     - [mobilenet-v2-pytorch](https://docs.openvinotoolkit.org/latest/omz_models_public_mobilenet_v2_pytorch_mobilenet_v2_pytorch.html)
 
 You can find more OpenVINO™ Trained Models [here](https://docs.openvinotoolkit.org/latest/omz_models_intel_index.html)
-To run the inference with OpenVINO™, the model format should be Intermediate Representation(IR). 
+To run the inference with OpenVINO™, the model format should be Intermediate Representation(IR).
 For the Caffe/TensorFlow/MXNet/Kaldi/ONNX models, please see the [Model Conversion Instruction](https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model.html)
 
 You need to implement your own interpreter samples to support the other OpenVINO™ Trained Models.
@@ -49,12 +49,12 @@ You need to implement your own interpreter samples to support the other OpenVINO
   ```
 
 ## Model inference
-- Prerequisites: 
+- Prerequisites:
     - OpenVINO™ (To install OpenVINO™, please see the [OpenVINO™ Installation Instruction](https://docs.openvinotoolkit.org/latest/openvino_docs_install_guides_installing_openvino_linux.html))
     - Datumaro (To install Datumaro, please see the [User Manual](docs/user_manual.md))
     - OpenVINO™ models (To download OpenVINO™ models, please see the [Model Downloader Instruction](https://docs.openvinotoolkit.org/latest/omz_tools_downloader_README.html))
     - PASCAL VOC 2012 dataset (To download VOC 2012 dataset, please go [VOC2012 download](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit))
-    
+
 - To run the inference with OpenVINO™ models and the interpreter samples, please follow the instructions below.
 
   ```bash
@@ -76,7 +76,7 @@ You need to implement your own interpreter samples to support the other OpenVINO
       -i samples/ssd_mobilenet_coco_detection_interp.py
   datum add path -p proj_ssd_mobilenet_v2_coco_detection -f voc VOCdevkit/
   datum model run -p proj_ssd_mobilenet_v2_coco_detection -m model-0
-  
+
   # Classification> mobilenet-v2-pytorch
   source /opt/intel/openvino/bin/setupvars.sh
   cd datumaro/plugins/openvino_plugin

datumaro/plugins/openvino_plugin/launcher.py

@@ -1,5 +1,5 @@
 
-# Copyright (C) 2019-2020 Intel Corporation
+# Copyright (C) 2019-2021 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
 
@@ -195,4 +195,3 @@ def categories(self):
 
     def process_outputs(self, inputs, outputs):
         return self._interpreter.process_outputs(inputs, outputs)
-

datumaro/plugins/openvino_plugin/samples/ssd_face_detection_interp.py

@@ -65,10 +65,10 @@ def process_outputs(inputs, outputs):
             w = min(int(det[5] * input_width - x), input_width)
             h = min(int(det[6] * input_height - y), input_height)
 
-            image_results.append(Bbox(x, y, w, h, label=label, 
+            image_results.append(Bbox(x, y, w, h, label=label,
                 attributes={ 'score': conf, 'scores': list(map(float, det_confs)) }
             ))
-            
+
             results.append(image_results)
 
     return results

datumaro/plugins/openvino_plugin/samples/ssd_person_detection_interp.py

@@ -65,10 +65,10 @@ def process_outputs(inputs, outputs):
             w = min(int(det[5] * input_width - x), input_width)
             h = min(int(det[6] * input_height - y), input_height)
 
-            image_results.append(Bbox(x, y, w, h, label=label, 
+            image_results.append(Bbox(x, y, w, h, label=label,
                 attributes={ 'score': conf, 'scores': list(map(float, det_confs)) }
             ))
-            
+
             results.append(image_results)
 
     return results

datumaro/plugins/openvino_plugin/samples/ssd_person_vehicle_bike_detection_interp.py

@@ -65,10 +65,10 @@ def process_outputs(inputs, outputs):
             w = min(int(det[5] * input_width - x), input_width)
             h = min(int(det[6] * input_height - y), input_height)
 
-            image_results.append(Bbox(x, y, w, h, label=label, 
+            image_results.append(Bbox(x, y, w, h, label=label,
                 attributes={ 'score': conf, 'scores': list(map(float, det_confs)) }
             ))
-            
+
             results.append(image_results)
 
     return results

datumaro/plugins/openvino_plugin/samples/ssd_vehicle_detection_interp.py

@@ -65,10 +65,10 @@ def process_outputs(inputs, outputs):
             w = min(int(det[5] * input_width - x), input_width)
             h = min(int(det[6] * input_height - y), input_height)
 
-            image_results.append(Bbox(x, y, w, h, label=label, 
+            image_results.append(Bbox(x, y, w, h, label=label,
                 attributes={ 'score': conf, 'scores': list(map(float, det_confs)) }
             ))
-            
+
             results.append(image_results)
 
     return results

docs/formats/coco_user_manual.md

@@ -103,7 +103,7 @@ datum import --format coco_stuff --input-path <path/to/stuff.json>
 Datumaro supports the following COCO tasks:
 - [Image Captioning](https://cocodataset.org/#captions-2015) (`coco_caption`)
 - [Object Detection](https://cocodataset.org/#detection-2020) (`coco_instances`)
-- Image classification (our extension) (`coco_labels`) - a format like Object Detection, which uses 
+- Image classification (our extension) (`coco_labels`) - a format like Object Detection, which uses
   only `category_id` and `score` annotation fields
 - [Panoptic Segmentation](https://cocodataset.org/#panoptic-2020) (`coco_panoptic`)
 - [Keypoint Detection](https://cocodataset.org/#keypoints-2020) (`coco_person_keypoints`)

docs/formats/mnist_user_manual.md

@@ -64,9 +64,9 @@ MNIST dataset directory should have the following structure:
 ```
 └─ Dataset/
     ├── labels.txt # list of non-digit labels (optional)
-    ├── t10k-images-idx3-ubyte.gz  
-    ├── t10k-labels-idx1-ubyte.gz  
-    ├── train-images-idx3-ubyte.gz  
+    ├── t10k-images-idx3-ubyte.gz
+    ├── t10k-labels-idx1-ubyte.gz
+    ├── train-images-idx3-ubyte.gz
     └── train-labels-idx1-ubyte.gz
 ```
 MNIST in CSV dataset directory should have the following structure:
@@ -75,7 +75,7 @@ MNIST in CSV dataset directory should have the following structure:
 ```
 └─ Dataset/
     ├── labels.txt # list of non-digit labels (optional)
-    ├── mnist_test.csv  
+    ├── mnist_test.csv
     └── mnist_train.csv
 ```
 If the dataset needs non-digit labels, you need to add the labels.txt

tests/assets/lfw_dataset/test/pairs.txt

@@ -2,4 +2,3 @@
 name1	1 	2
 name0	1	name1	1
 name0	1	name1	2
-

tests/assets/tf_detection_api_dataset/label_map.pbtxt

@@ -1,50 +1,49 @@
 item {
-	id: 1
-	name: 'label_0'
+  id: 1
+  name: 'label_0'
 }
 
 item {
-	id: 2
-	name: 'label_1'
+  id: 2
+  name: 'label_1'
 }
 
 item {
-	id: 3
-	name: 'label_2'
+  id: 3
+  name: 'label_2'
 }
 
 item {
-	id: 4
-	name: 'label_3'
+  id: 4
+  name: 'label_3'
 }
 
 item {
-	id: 5
-	name: 'label_4'
+  id: 5
+  name: 'label_4'
 }
 
 item {
-	id: 6
-	name: 'label_5'
+  id: 6
+  name: 'label_5'
 }
 
 item {
-	id: 7
-	name: 'label_6'
+  id: 7
+  name: 'label_6'
 }
 
 item {
-	id: 8
-	name: 'label_7'
+  id: 8
+  name: 'label_7'
 }
 
 item {
-	id: 9
-	name: 'label_8'
+  id: 9
+  name: 'label_8'
 }
 
 item {
-	id: 10
-	name: 'label_9'
+  id: 10
+  name: 'label_9'
 }
-

tests/assets/widerface_dataset/wider_face_split/wider_face_train_bbx_gt.txt

@@ -1,7 +1,7 @@
 0--Parade/0_Parade_image_01.jpg
 1
-1 2 2 2 0 0 0 0 0 0 
+1 2 2 2 0 0 0 0 0 0
 1--Handshaking/1_Handshaking_image_02.jpg
 2
-1 1 2 2 0 0 1 0 0 0 
-5 1 2 2 0 0 1 0 0 0 
+1 1 2 2 0 0 1 0 0 0
+5 1 2 2 0 0 1 0 0 0

tests/assets/widerface_dataset/wider_face_split/wider_face_val_bbx_gt.txt

@@ -1,5 +1,5 @@
 0--Parade/0_Parade_image_03.jpg
 3
-0 0 1 1 2 0 0 0 2 0 
-3 2 1 2 0 0 0 1 0 0 
-5 6 1 1 2 0 0 0 2 0 
+0 0 1 1 2 0 0 0 2 0
+3 2 1 2 0 0 0 1 0 0
+5 6 1 1 2 0 0 0 2 0