├── .github └── workflows │ └── main.yaml ├── LICENSE ├── code ├── datasets │ ├── detection │ │ ├── classes.json │ │ ├── student-id │ │ │ ├── 10001.jpg │ │ │ ├── 10001.json │ │ │ ├── 10002.jpg │ │ │ ├── 10002.json │ │ │ ├── 10003.jpg │ │ │ ├── 10003.json │ │ │ ├── 10004.jpg │ │ │ ├── 10004.json │ │ │ ├── 10005.jpg │ │ │ ├── 10005.json │ │ │ ├── 10006.jpg │ │ │ ├── 10006.json │ │ │ ├── 10007.jpg │ │ │ ├── 10007.json │ │ │ ├── 10008.jpg │ │ │ ├── 10008.json │ │ │ ├── 10009.jpg │ │ │ ├── 10009.json │ │ │ ├── 10010.jpg │ │ │ ├── 10010.json │ │ │ ├── 10011.jpg │ │ │ ├── 10011.json │ │ │ ├── 10012.jpg │ │ │ ├── 10012.json │ │ │ ├── 10013.jpg │ │ │ ├── 10013.json │ │ │ ├── 10014.jpg │ │ │ ├── 10014.json │ │ │ ├── 10015.jpg │ │ │ ├── 10015.json │ │ │ ├── 10016.jpg │ │ │ ├── 10016.json │ │ │ ├── 10017.jpg │ │ │ ├── 10017.json │ │ │ ├── 10018.jpg │ │ │ ├── 10018.json │ │ │ ├── 10019.jpg │ │ │ ├── 10019.json │ │ │ ├── 10020.jpg │ │ │ ├── 10020.json │ │ │ ├── 10021.jpg │ │ │ ├── 10021.json │ │ │ ├── 10022.jpg │ │ │ ├── 10022.json │ │ │ ├── 10023.jpg │ │ │ ├── 10023.json │ │ │ ├── 10024.jpg │ │ │ ├── 10024.json │ │ │ ├── 10025.jpg │ │ │ ├── 10025.json │ │ │ ├── 10026.jpg │ │ │ ├── 10026.json │ │ │ ├── 10027.jpg │ │ │ ├── 10027.json │ │ │ ├── 10028.jpg │ │ │ ├── 10028.json │ │ │ ├── 10029.jpg │ │ │ ├── 10029.json │ │ │ ├── 10030.jpg │ │ │ ├── 10030.json │ │ │ ├── 10031.jpg │ │ │ ├── 10031.json │ │ │ ├── 10032.jpg │ │ │ ├── 10032.json │ │ │ ├── 10033.jpg │ │ │ ├── 10033.json │ │ │ ├── 10034.jpg │ │ │ ├── 10034.json │ │ │ ├── 10035.jpg │ │ │ ├── 10035.json │ │ │ ├── 10036.jpg │ │ │ ├── 10036.json │ │ │ ├── 10037.jpg │ │ │ ├── 10037.json │ │ │ ├── 10038.jpg │ │ │ ├── 10038.json │ │ │ ├── 10039.jpg │ │ │ ├── 10039.json │ │ │ ├── 10040.jpg │ │ │ ├── 10040.json │ │ │ ├── 10041.jpg │ │ │ ├── 10041.json │ │ │ ├── 10042.jpg │ │ │ ├── 10042.json │ │ │ ├── 10043.jpg │ │ │ ├── 10043.json │ │ │ ├── 10044.jpg │ │ │ ├── 10044.json │ │ │ ├── 10045.jpg │ │ │ ├── 10045.json │ │ │ ├── 10046.jpg │ │ │ ├── 10046.json │ │ │ ├── 10047.jpg │ │ │ ├── 10047.json │ │ │ ├── 10048.jpg │ │ │ ├── 10048.json │ │ │ ├── 10049.jpg │ │ │ ├── 10049.json │ │ │ ├── 10050.jpg │ │ │ ├── 10050.json │ │ │ ├── 10051.jpg │ │ │ ├── 10051.json │ │ │ ├── 10052.jpg │ │ │ ├── 10052.json │ │ │ ├── 10053.jpg │ │ │ ├── 10053.json │ │ │ ├── 10054.jpg │ │ │ ├── 10054.json │ │ │ ├── 10055.jpg │ │ │ ├── 10055.json │ │ │ ├── 10056.jpg │ │ │ ├── 10056.json │ │ │ ├── 10057.jpg │ │ │ ├── 10057.json │ │ │ ├── 10058.jpg │ │ │ ├── 10058.json │ │ │ ├── 10059.jpg │ │ │ ├── 10059.json │ │ │ ├── 10060.jpg │ │ │ ├── 10060.json │ │ │ ├── 10061.jpg │ │ │ ├── 10061.json │ │ │ ├── 10062.jpg │ │ │ ├── 10062.json │ │ │ ├── 10063.jpg │ │ │ ├── 10063.json │ │ │ ├── 10064.jpg │ │ │ ├── 10064.json │ │ │ ├── 10065.jpg │ │ │ ├── 10065.json │ │ │ ├── 10066.jpg │ │ │ ├── 10066.json │ │ │ ├── 10067.jpg │ │ │ ├── 10067.json │ │ │ ├── 10068.jpg │ │ │ ├── 10068.json │ │ │ ├── 10069.jpg │ │ │ ├── 10069.json │ │ │ ├── 10070.jpg │ │ │ ├── 10070.json │ │ │ ├── 10071.jpg │ │ │ ├── 10071.json │ │ │ ├── 10072.jpg │ │ │ ├── 10072.json │ │ │ ├── 10073.jpg │ │ │ ├── 10073.json │ │ │ ├── 10074.jpg │ │ │ ├── 10074.json │ │ │ ├── 10075.jpg │ │ │ ├── 10075.json │ │ │ ├── 10076.jpg │ │ │ ├── 10076.json │ │ │ ├── 10077.jpg │ │ │ ├── 10077.json │ │ │ ├── 10078.jpg │ │ │ ├── 10078.json │ │ │ ├── 10079.jpg │ │ │ ├── 10079.json │ │ │ ├── 10080.jpg │ │ │ ├── 10080.json │ │ │ ├── 10081.jpg │ │ │ ├── 10081.json │ │ │ ├── 10082.jpg │ │ │ ├── 10082.json │ │ │ ├── 10083.jpg │ │ │ ├── 10083.json │ │ │ ├── 10084.jpg │ │ │ ├── 10084.json │ │ │ ├── 10085.jpg │ │ │ ├── 10085.json │ │ │ ├── 10086.jpg │ │ │ ├── 10086.json │ │ │ ├── 10087.jpg │ │ │ ├── 10087.json │ │ │ ├── 10088.jpg │ │ │ ├── 10088.json │ │ │ ├── 10089.jpg │ │ │ ├── 10089.json │ │ │ ├── 10090.jpg │ │ │ ├── 10090.json │ │ │ ├── 10091.jpg │ │ │ ├── 10091.json │ │ │ ├── 10092.jpg │ │ │ ├── 10092.json │ │ │ ├── 10093.jpg │ │ │ ├── 10093.json │ │ │ ├── 10094.jpg │ │ │ ├── 10094.json │ │ │ ├── 10095.jpg │ │ │ ├── 10095.json │ │ │ ├── 10096.jpg │ │ │ ├── 10096.json │ │ │ ├── 10097.jpg │ │ │ ├── 10097.json │ │ │ ├── 10098.jpg │ │ │ ├── 10098.json │ │ │ ├── 10099.jpg │ │ │ ├── 10099.json │ │ │ ├── 10100.jpg │ │ │ ├── 10100.json │ │ │ ├── 10101.jpg │ │ │ ├── 10101.json │ │ │ ├── 10102.jpg │ │ │ ├── 10102.json │ │ │ ├── 10103.jpg │ │ │ ├── 10103.json │ │ │ ├── 10104.jpg │ │ │ ├── 10104.json │ │ │ ├── 10105.jpg │ │ │ ├── 10105.json │ │ │ ├── 10106.jpg │ │ │ ├── 10106.json │ │ │ ├── 10107.jpg │ │ │ ├── 10107.json │ │ │ ├── 10108.jpg │ │ │ ├── 10108.json │ │ │ ├── 10109.jpg │ │ │ ├── 10109.json │ │ │ ├── 10110.jpg │ │ │ ├── 10110.json │ │ │ ├── 10111.jpg │ │ │ ├── 10111.json │ │ │ ├── 10112.jpg │ │ │ ├── 10112.json │ │ │ ├── 10113.jpg │ │ │ ├── 10113.json │ │ │ ├── 10114.jpg │ │ │ ├── 10114.json │ │ │ ├── 10115.jpg │ │ │ ├── 10115.json │ │ │ ├── 10116.jpg │ │ │ ├── 10116.json │ │ │ ├── 10117.jpg │ │ │ ├── 10117.json │ │ │ ├── 10118.jpg │ │ │ ├── 10118.json │ │ │ ├── 10119.jpg │ │ │ ├── 10119.json │ │ │ ├── 10120.jpg │ │ │ ├── 10120.json │ │ │ ├── 10121.jpg │ │ │ ├── 10121.json │ │ │ ├── 10122.jpg │ │ │ ├── 10122.json │ │ │ ├── 10123.jpg │ │ │ ├── 10123.json │ │ │ ├── 10124.jpg │ │ │ ├── 10124.json │ │ │ ├── 10125.jpg │ │ │ ├── 10125.json │ │ │ ├── 10126.jpg │ │ │ ├── 10126.json │ │ │ ├── 10127.jpg │ │ │ ├── 10127.json │ │ │ ├── 10128.jpg │ │ │ ├── 10128.json │ │ │ ├── 10129.jpg │ │ │ ├── 10129.json │ │ │ ├── 10130.jpg │ │ │ ├── 10130.json │ │ │ ├── 10131.jpg │ │ │ ├── 10131.json │ │ │ ├── 10132.jpg │ │ │ ├── 10132.json │ │ │ ├── 10133.jpg │ │ │ ├── 10133.json │ │ │ ├── 10134.jpg │ │ │ ├── 10134.json │ │ │ ├── 10135.jpg │ │ │ ├── 10135.json │ │ │ ├── 10136.jpg │ │ │ ├── 10136.json │ │ │ ├── 10137.jpg │ │ │ ├── 10137.json │ │ │ ├── 10138.jpg │ │ │ ├── 10138.json │ │ │ ├── 10139.jpg │ │ │ ├── 10139.json │ │ │ ├── 10140.jpg │ │ │ ├── 10140.json │ │ │ ├── 10141.jpg │ │ │ ├── 10141.json │ │ │ ├── 10142.jpg │ │ │ ├── 10142.json │ │ │ ├── 10143.jpg │ │ │ ├── 10143.json │ │ │ ├── 10144.jpg │ │ │ ├── 10144.json │ │ │ ├── 10145.jpg │ │ │ ├── 10145.json │ │ │ ├── 10146.jpg │ │ │ ├── 10146.json │ │ │ ├── 10147.jpg │ │ │ ├── 10147.json │ │ │ ├── 10148.jpg │ │ │ ├── 10148.json │ │ │ ├── 10149.jpg │ │ │ ├── 10149.json │ │ │ ├── 10150.jpg │ │ │ └── 10150.json │ │ ├── test.json │ │ ├── train.json │ │ └── valid.json │ └── orientation │ │ ├── test │ │ ├── 180 │ │ │ ├── 0052.png │ │ │ ├── 0054.png │ │ │ ├── 0059.png │ │ │ ├── 0065.png │ │ │ ├── 0073.png │ │ │ ├── 0077.png │ │ │ └── 0083.png │ │ ├── 270 │ │ │ ├── 0018.png │ │ │ ├── 0024.png │ │ │ ├── 0026.png │ │ │ ├── 0034.png │ │ │ ├── 0035.png │ │ │ ├── 0044.png │ │ │ └── 0045.png │ │ ├── 360 │ │ │ ├── 0156.png │ │ │ ├── 0162.png │ │ │ ├── 0175.png │ │ │ ├── 0176.png │ │ │ ├── 0178.png │ │ │ ├── 0193.png │ │ │ └── 0197.png │ │ └── 090 │ │ │ ├── 0108.png │ │ │ ├── 0119.png │ │ │ ├── 0129.png │ │ │ ├── 0130.png │ │ │ ├── 0138.png │ │ │ ├── 0141.png │ │ │ └── 0147.png │ │ ├── train │ │ ├── 180 │ │ │ ├── 0051.png │ │ │ ├── 0053.png │ │ │ ├── 0056.png │ │ │ ├── 0057.png │ │ │ ├── 0058.png │ │ │ ├── 0061.png │ │ │ ├── 0063.png │ │ │ ├── 0064.png │ │ │ ├── 0066.png │ │ │ ├── 0067.png │ │ │ ├── 0068.png │ │ │ ├── 0069.png │ │ │ ├── 0070.png │ │ │ ├── 0071.png │ │ │ ├── 0072.png │ │ │ ├── 0074.png │ │ │ ├── 0075.png │ │ │ ├── 0076.png │ │ │ ├── 0079.png │ │ │ ├── 0080.png │ │ │ ├── 0081.png │ │ │ ├── 0084.png │ │ │ ├── 0085.png │ │ │ ├── 0086.png │ │ │ ├── 0087.png │ │ │ ├── 0088.png │ │ │ ├── 0089.png │ │ │ ├── 0090.png │ │ │ ├── 0091.png │ │ │ ├── 0092.png │ │ │ ├── 0093.png │ │ │ ├── 0094.png │ │ │ ├── 0095.png │ │ │ ├── 0096.png │ │ │ ├── 0097.png │ │ │ ├── 0098.png │ │ │ ├── 0099.png │ │ │ └── 0100.png │ │ ├── 270 │ │ │ ├── 0001.png │ │ │ ├── 0002.png │ │ │ ├── 0003.png │ │ │ ├── 0004.png │ │ │ ├── 0005.png │ │ │ ├── 0007.png │ │ │ ├── 0008.png │ │ │ ├── 0009.png │ │ │ ├── 0010.png │ │ │ ├── 0011.png │ │ │ ├── 0012.png │ │ │ ├── 0013.png │ │ │ ├── 0015.png │ │ │ ├── 0016.png │ │ │ ├── 0017.png │ │ │ ├── 0019.png │ │ │ ├── 0020.png │ │ │ ├── 0021.png │ │ │ ├── 0022.png │ │ │ ├── 0023.png │ │ │ ├── 0025.png │ │ │ ├── 0027.png │ │ │ ├── 0028.png │ │ │ ├── 0030.png │ │ │ ├── 0032.png │ │ │ ├── 0033.png │ │ │ ├── 0036.png │ │ │ ├── 0037.png │ │ │ ├── 0038.png │ │ │ ├── 0039.png │ │ │ ├── 0041.png │ │ │ ├── 0042.png │ │ │ ├── 0043.png │ │ │ ├── 0046.png │ │ │ ├── 0047.png │ │ │ ├── 0048.png │ │ │ ├── 0049.png │ │ │ └── 0050.png │ │ ├── 360 │ │ │ ├── 0151.png │ │ │ ├── 0152.png │ │ │ ├── 0153.png │ │ │ ├── 0154.png │ │ │ ├── 0155.png │ │ │ ├── 0157.png │ │ │ ├── 0158.png │ │ │ ├── 0159.png │ │ │ ├── 0161.png │ │ │ ├── 0163.png │ │ │ ├── 0164.png │ │ │ ├── 0165.png │ │ │ ├── 0166.png │ │ │ ├── 0167.png │ │ │ ├── 0168.png │ │ │ ├── 0169.png │ │ │ ├── 0171.png │ │ │ ├── 0172.png │ │ │ ├── 0173.png │ │ │ ├── 0174.png │ │ │ ├── 0177.png │ │ │ ├── 0179.png │ │ │ ├── 0180.png │ │ │ ├── 0181.png │ │ │ ├── 0182.png │ │ │ ├── 0183.png │ │ │ ├── 0184.png │ │ │ ├── 0185.png │ │ │ ├── 0186.png │ │ │ ├── 0187.png │ │ │ ├── 0188.png │ │ │ ├── 0189.png │ │ │ ├── 0190.png │ │ │ ├── 0191.png │ │ │ ├── 0192.png │ │ │ ├── 0196.png │ │ │ ├── 0198.png │ │ │ └── 0199.png │ │ └── 090 │ │ │ ├── 0101.png │ │ │ ├── 0102.png │ │ │ ├── 0103.png │ │ │ ├── 0104.png │ │ │ ├── 0105.png │ │ │ ├── 0107.png │ │ │ ├── 0109.png │ │ │ ├── 0110.png │ │ │ ├── 0111.png │ │ │ ├── 0112.png │ │ │ ├── 0114.png │ │ │ ├── 0115.png │ │ │ ├── 0116.png │ │ │ ├── 0117.png │ │ │ ├── 0118.png │ │ │ ├── 0120.png │ │ │ ├── 0121.png │ │ │ ├── 0122.png │ │ │ ├── 0123.png │ │ │ ├── 0124.png │ │ │ ├── 0125.png │ │ │ ├── 0126.png │ │ │ ├── 0127.png │ │ │ ├── 0128.png │ │ │ ├── 0132.png │ │ │ ├── 0133.png │ │ │ ├── 0134.png │ │ │ ├── 0135.png │ │ │ ├── 0136.png │ │ │ ├── 0140.png │ │ │ ├── 0142.png │ │ │ ├── 0143.png │ │ │ ├── 0144.png │ │ │ ├── 0145.png │ │ │ ├── 0146.png │ │ │ ├── 0148.png │ │ │ ├── 0149.png │ │ │ └── 0150.png │ │ └── valid │ │ ├── 180 │ │ ├── 0055.png │ │ ├── 0060.png │ │ ├── 0062.png │ │ ├── 0078.png │ │ └── 0082.png │ │ ├── 270 │ │ ├── 0006.png │ │ ├── 0014.png │ │ ├── 0029.png │ │ ├── 0031.png │ │ └── 0040.png │ │ ├── 360 │ │ ├── 0160.png │ │ ├── 0170.png │ │ ├── 0194.png │ │ ├── 0195.png │ │ └── 0200.png │ │ └── 090 │ │ ├── 0106.png │ │ ├── 0113.png │ │ ├── 0131.png │ │ ├── 0137.png │ │ └── 0139.png ├── field_coordinates.yaml ├── images │ ├── aligned_image.jpg │ └── student-ids │ │ ├── 10151.jpg │ │ ├── 10152.jpg │ │ ├── 10153.jpg │ │ ├── 10154.jpg │ │ ├── 10155.jpg │ │ ├── 10156.jpg │ │ ├── 10157.jpg │ │ ├── 10158.jpg │ │ ├── 10159.jpg │ │ ├── 10160.jpg │ │ ├── 10161.jpg │ │ ├── 10162.jpg │ │ ├── 10163.jpg │ │ ├── 10164.jpg │ │ └── 10165.jpg ├── modules │ └── detection │ │ └── scripts │ │ ├── coco_eval.py │ │ ├── coco_utils.py │ │ ├── engine.py │ │ ├── transforms.py │ │ └── utils.py ├── tutorial.ipynb └── utils.py └── tutorial ├── README.md ├── babel.config.js ├── docs ├── conclusion.md ├── detection.md ├── doc1.md ├── extraction.md ├── introduction.md ├── mdx.md ├── orientation.md ├── sidebars.js └── tutorial.md ├── docusaurus.config.js ├── package.json ├── src ├── css │ └── custom.css ├── pages │ ├── index.js │ └── styles.module.css └── utils │ ├── CodeBlock.js │ └── OutputBlock.js ├── static ├── .nojekyll ├── code-snippets │ ├── detection_checkpoint.py │ ├── detection_checkpoint_output.txt │ ├── detection_dataset.py │ ├── detection_dataset_classes.py │ ├── detection_dataset_classes_output.txt │ ├── detection_dataset_init.py │ ├── detection_dataset_loaders.py │ ├── detection_dataset_transforms.py │ ├── detection_dataset_visualize.py │ ├── detection_model_evaluate.py │ ├── detection_model_init_checkpoint_output.txt │ ├── detection_model_init_function.py │ ├── detection_model_predict.py │ ├── detection_model_predictions_raw.py │ ├── detection_model_predictions_raw_output.txt │ ├── detection_model_predictions_visualize.py │ ├── detection_model_train.py │ ├── detection_model_train_function.py │ ├── detection_model_train_output.txt │ ├── detection_model_train_resume.py │ ├── detection_model_train_resume_output.txt │ ├── detection_module_image_alignment.py │ ├── detection_module_image_alignment_save.py │ ├── detection_module_imports.py │ ├── detection_optimizer_init.py │ ├── detection_optimizer_init_checkpoint_output.txt │ ├── detection_optimizer_init_output.txt │ ├── extraction_coordinates_init.py │ ├── extraction_coordinates_init_output.txt │ ├── extraction_predictions.py │ ├── orientation_checkpoint.py │ ├── orientation_checkpoint_output.txt │ ├── orientation_dataset_classes.py │ ├── orientation_dataset_classes_output.txt │ ├── orientation_dataset_init.py │ ├── orientation_dataset_loaders.py │ ├── orientation_dataset_transforms.py │ ├── orientation_dataset_visualize.py │ ├── orientation_model.py │ ├── orientation_model_init.py │ ├── orientation_model_init_checkpoint_output.txt │ ├── orientation_model_init_function.py │ ├── orientation_model_prediction_visualize.py │ ├── orientation_model_test.py │ ├── orientation_model_test_function.py │ ├── orientation_model_test_output.txt │ ├── orientation_model_train.py │ ├── orientation_model_train_function.py │ ├── orientation_model_train_output.txt │ ├── orientation_model_train_resume.py │ ├── orientation_model_train_resume_output.txt │ ├── orientation_model_visualize.py │ ├── orientation_model_visualize_output.txt │ ├── orientation_module_imports.py │ ├── orientation_optimizer.py │ ├── orientation_optimizer_init_checkpoint_output.txt │ ├── orientation_optimizer_init_output.txt │ ├── output.txt │ └── project-structure.txt └── img │ ├── detection-datasets.svg │ ├── detection-module.svg │ ├── detection-prediction-1.svg │ ├── detection-prediction-2.svg │ ├── detection-prediction.svg │ ├── detection-sample.svg │ ├── extraction-module.svg │ ├── extraction-prediction.svg │ ├── favicon.ico │ ├── fields-coordinates-hypothesis.svg │ ├── fields-coordinates.svg │ ├── image-alignment.svg │ ├── logo.svg │ ├── orientation-datasets.svg │ ├── orientation-module.svg │ ├── orientation-prediction-1.svg │ ├── orientation-prediction-2.svg │ ├── orientation-prediction.svg │ ├── orientation-sample.svg │ ├── sample-student-ids.svg │ ├── student-id-01.svg │ ├── student-id-1.svg │ ├── undraw_docusaurus_mountain.svg │ ├── undraw_docusaurus_react.svg │ └── undraw_docusaurus_tree.svg └── yarn.lock /.github/workflows/main.yaml: -------------------------------------------------------------------------------- 1 | on: [push] 2 | 3 | jobs: 4 | build_docs_job: 5 | runs-on: ubuntu-latest 6 | strategy: 7 | matrix: 8 | python-version: [3.6] 9 | steps: 10 | - name: Checkout 11 | uses: actions/checkout@v2 12 | - name: Dependencies 13 | run: | 14 | echo `python3 --version` 15 | sudo apt-get install -y python-setuptools 16 | python3 -m pip install --upgrade pip 17 | python3 -m pip install setuptools 18 | sudo apt-get install -y yarn 19 | id: build 20 | - name: Build the Website 21 | run: | 22 | cd tutorial 23 | yarn 24 | npm run build 25 | - name: Get output time 26 | run: echo "The time was ${{ steps.build.outputs.time }}" 27 | - name: Deploy 28 | uses: JamesIves/github-pages-deploy-action@releases/v3 29 | with: 30 | ACCESS_TOKEN: ${{ secrets.GITHUB_TOKEN }} 31 | BRANCH: gh-pages # The branch the action should deploy to. 32 | FOLDER: tutorial/build # The folder the action should deploy. 33 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 MbassiJaphet 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /code/datasets/detection/classes.json: -------------------------------------------------------------------------------- 1 | { 2 | "num_classes": 1, 3 | "classes": [ 4 | "STUDENT_ID" 5 | ] 6 | } -------------------------------------------------------------------------------- /code/datasets/detection/student-id/10001.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MbassiJaphet/pytorch-for-information-extraction/4e8eea92140df806c2a82c8fa3db4afaf13e66db/code/datasets/detection/student-id/10001.jpg 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-------------------------------------------------------------------------------- 1 | import random 2 | 3 | from torchvision.transforms import functional as F 4 | 5 | 6 | def _flip_coco_person_keypoints(kps, width): 7 | flip_inds = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15] 8 | flipped_data = kps[:, flip_inds] 9 | flipped_data[..., 0] = width - flipped_data[..., 0] 10 | # Maintain COCO convention that if visibility == 0, then x, y = 0 11 | inds = flipped_data[..., 2] == 0 12 | flipped_data[inds] = 0 13 | return flipped_data 14 | 15 | 16 | class Compose(object): 17 | def __init__(self, transforms): 18 | self.transforms = transforms 19 | 20 | def __call__(self, image, target): 21 | for t in self.transforms: 22 | image, target = t(image, target) 23 | return image, target 24 | 25 | 26 | class RandomHorizontalFlip(object): 27 | def __init__(self, prob): 28 | self.prob = prob 29 | 30 | def __call__(self, image, target): 31 | if random.random() < self.prob: 32 | height, width = image.shape[-2:] 33 | image = image.flip(-1) 34 | bbox = target["boxes"] 35 | bbox[:, [0, 2]] = width - bbox[:, [2, 0]] 36 | target["boxes"] = bbox 37 | if "masks" in target: 38 | target["masks"] = target["masks"].flip(-1) 39 | if "keypoints" in target: 40 | keypoints = target["keypoints"] 41 | keypoints = _flip_coco_person_keypoints(keypoints, width) 42 | target["keypoints"] = keypoints 43 | return image, target 44 | 45 | 46 | class ToTensor(object): 47 | def __call__(self, image, target): 48 | image = F.to_tensor(image) 49 | return image, target 50 | -------------------------------------------------------------------------------- /tutorial/README.md: -------------------------------------------------------------------------------- 1 | # Website 2 | 3 | This website is built using [Docusaurus 2](https://v2.docusaurus.io/), a modern static website generator. 4 | 5 | ### Installation 6 | 7 | ``` 8 | $ yarn 9 | ``` 10 | 11 | ### Local Development 12 | 13 | ``` 14 | $ yarn start 15 | ``` 16 | 17 | This command starts a local development server and open up a browser window. Most changes are reflected live without having to restart the server. 18 | 19 | ### Build 20 | 21 | ``` 22 | $ yarn build 23 | ``` 24 | 25 | This command generates static content into the `build` directory and can be served using any static contents hosting service. 26 | 27 | ### Deployment 28 | 29 | ``` 30 | $ GIT_USER= USE_SSH=true yarn deploy 31 | ``` 32 | 33 | If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch. 34 | -------------------------------------------------------------------------------- /tutorial/babel.config.js: -------------------------------------------------------------------------------- 1 | module.exports = { 2 | presets: [require.resolve('@docusaurus/core/lib/babel/preset')], 3 | }; 4 | -------------------------------------------------------------------------------- /tutorial/docs/mdx.md: -------------------------------------------------------------------------------- 1 | --- 2 | id: mdx 3 | title: Powered by MDX 4 | --- 5 | 6 | You can write JSX and use React components within your Markdown thanks to [MDX](https://mdxjs.com/). 7 | 8 | export const Highlight = ({children, color}) => ( {children} ); 14 | 15 | Docusaurus green and Facebook blue are my favorite colors. 16 | 17 | I can write **Markdown** alongside my _JSX_! 18 | -------------------------------------------------------------------------------- /tutorial/docs/sidebars.js: -------------------------------------------------------------------------------- 1 | module.exports = { 2 | tutorial: [ 3 | { 4 | type: 'doc', 5 | id: 'introduction' 6 | }, 7 | { 8 | type: 'category', 9 | label: 'Modules', 10 | collapsed: false, 11 | items: [ 12 | { 13 | type: 'doc', 14 | id: 'detection' 15 | }, 16 | { 17 | type: 'doc', 18 | id: 'orientation' 19 | }, 20 | { 21 | type: 'doc', 22 | id: 'extraction' 23 | }, 24 | ] 25 | }, 26 | { 27 | type: 'doc', 28 | id: 'conclusion' 29 | }, 30 | ] 31 | }; 32 | -------------------------------------------------------------------------------- /tutorial/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "pytorch-for-information-extraction", 3 | "version": "0.0.0", 4 | "private": true, 5 | "scripts": { 6 | "docusaurus": "docusaurus", 7 | "start": "docusaurus start", 8 | "build": "docusaurus build", 9 | "swizzle": "docusaurus swizzle", 10 | "deploy": "docusaurus deploy", 11 | "serve": "docusaurus serve" 12 | }, 13 | "dependencies": { 14 | "@docusaurus/core": "^2.0.0-alpha.66", 15 | "@docusaurus/preset-classic": "2.0.0-alpha.66", 16 | "@mdx-js/react": "^1.5.8", 17 | "clsx": "^1.1.1", 18 | "fs": "^0.0.1-security", 19 | "react": "^16.8.4", 20 | "react-dom": "^16.8.4", 21 | "react-syntax-highlighter": "^15.2.1", 22 | "readline": "^1.3.0" 23 | }, 24 | "browserslist": { 25 | "production": [ 26 | ">0.2%", 27 | "not dead", 28 | "not op_mini all" 29 | ], 30 | "development": [ 31 | "last 1 chrome version", 32 | "last 1 firefox version", 33 | "last 1 safari version" 34 | ] 35 | } 36 | } 37 | -------------------------------------------------------------------------------- /tutorial/src/pages/styles.module.css: -------------------------------------------------------------------------------- 1 | /* stylelint-disable docusaurus/copyright-header */ 2 | 3 | /** 4 | * CSS files with the .module.css suffix will be treated as CSS modules 5 | * and scoped locally. 6 | */ 7 | 8 | .heroBanner { 9 | padding: 4rem 0; 10 | text-align: center; 11 | position: relative; 12 | overflow: hidden; 13 | } 14 | 15 | @media screen and (max-width: 966px) { 16 | .heroBanner { 17 | padding: 2rem; 18 | } 19 | } 20 | 21 | .buttons { 22 | display: flex; 23 | align-items: center; 24 | justify-content: center; 25 | } 26 | 27 | .features { 28 | display: flex; 29 | align-items: center; 30 | padding: 2rem 0; 31 | width: 100%; 32 | } 33 | 34 | .featureImage { 35 | height: 200px; 36 | width: 200px; 37 | } 38 | -------------------------------------------------------------------------------- /tutorial/src/utils/OutputBlock.js: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | 3 | class OutputBlock extends React.Component { 4 | constructor(props) { 5 | super(props); 6 | this._currentFile = null; 7 | this.state = { 8 | outputString: '', 9 | }; 10 | } 11 | 12 | static getDerivedStateFromProps(props, state) { 13 | if (props.id !== state.prevFile) { 14 | return { 15 | outputString: '', 16 | prevFile: props.file, 17 | }; 18 | } 19 | return null; 20 | } 21 | 22 | componentDidMount() { 23 | this._loadAsyncData(this.props.file); 24 | } 25 | 26 | componentDidUpdate(prevProps, prevState) { 27 | if (!this.state.outputString) { 28 | this._loadAsyncData(this.props.file); 29 | } 30 | } 31 | 32 | componentWillUnmount() { 33 | this._currentFile = null; 34 | } 35 | 36 | 37 | render() { 38 | return ( 39 | 
40 |                 {this.state.outputString}
41 |
42 | ); 43 | } 44 | 45 | _loadAsyncData(file) { 46 | // if (file === this._currentFile) { 47 | // // Data for this id is already loading 48 | // } 49 | 50 | this._currentFile = file; 51 | fetch(`/pytorch-for-information-extraction/code-snippets/${file}.txt`) 52 | .then(res => res.text()) 53 | .then(text => { 54 | if (file === this._currentFile) { 55 | this.setState({ outputString: text }); 56 | } 57 | }) 58 | .catch(e => { 59 | console.log(e) 60 | }) 61 | } 62 | } 63 | 64 | export default OutputBlock -------------------------------------------------------------------------------- /tutorial/static/.nojekyll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MbassiJaphet/pytorch-for-information-extraction/4e8eea92140df806c2a82c8fa3db4afaf13e66db/tutorial/static/.nojekyll -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_checkpoint.py: -------------------------------------------------------------------------------- 1 | # select hardware use for computations 2 | device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') 3 | 4 | load_detection_checkpoint = True 5 | save_detection_checkpoint = True 6 | 7 | detection_checkpoint_path = os.path.join('checkpoints', 'detection_mask_rcnn_resnet50.pth.tar') 8 | 9 | ''' 10 | Do not edit the lines below 11 | ''' 12 | 13 | if load_detection_checkpoint : 14 | detection_checkpoint = torch.load(detection_checkpoint_path, map_location=device) if os.path.exists(detection_checkpoint_path) else None 15 | detection_model_state_dict = detection_checkpoint['model_state_dict'] if not detection_checkpoint == None else None 16 | detection_optimizer_state_dict = detection_checkpoint['optimizer_state_dict'] if not detection_checkpoint == None else None 17 | else : 18 | detection_checkpoint, detection_model_state_dict, detection_optimizer_state_dict = None, None, None 19 | 20 | if not save_detection_checkpoint : detection_checkpoint_path = None 21 | # initialize detection model using the state dictionary from checkpoint 22 | detection_model = get_instance_segmentation_model(num_detection_classes, state_dict = detection_model_state_dict) 23 | 24 | if detection_checkpoint == None : print('No checkpoint loaded ! Loaded pre-trained model instead...') 25 | else : 26 | print('Loaded model from checkpoint...') 27 | utils.checkpoint_summary(detection_checkpoint) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_checkpoint_output.txt: -------------------------------------------------------------------------------- 1 | No checkpoint loaded ! Loaded pre-trained model instead... -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_dataset_classes.py: -------------------------------------------------------------------------------- 1 | print('Number of classes: {}\nClasses: {}'.format(num_detection_classes, detection_classes)) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_dataset_classes_output.txt: -------------------------------------------------------------------------------- 1 | Number of classes: 2 2 | Classes: ['BACKGROUND', 'STUDENT_ID'] -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_dataset_init.py: -------------------------------------------------------------------------------- 1 | detection_data_path = os.path.join('datasets', 'detection') 2 | # training dataset 3 | detection_train_set = DetectionDataset(detection_data_path, mode='train', transforms=get_transform(True)) 4 | # validation dataset 5 | detection_valid_set = DetectionDataset(detection_data_path, mode='valid', transforms=get_transform(True)) 6 | #testing dataset 7 | detection_test_set = DetectionDataset(detection_data_path, mode='test', transforms=get_transform(False)) 8 | 9 | detection_classes = detection_train_set.classes 10 | num_detection_classes = len(detection_classes) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_dataset_loaders.py: -------------------------------------------------------------------------------- 1 | import modules.detection.scripts.utils as script_utils 2 | 3 | # data loader for training 4 | detection_train_loader = torch.utils.data.DataLoader( 5 | detection_train_set, batch_size=6, shuffle=True, num_workers=4, 6 | collate_fn=script_utils.collate_fn) 7 | 8 | # data loader for validation 9 | detection_valid_loader = torch.utils.data.DataLoader( 10 | detection_valid_set, batch_size=2, shuffle=False, num_workers=2, 11 | collate_fn=script_utils.collate_fn) 12 | 13 | # data loader for testing 14 | detection_test_loader = torch.utils.data.DataLoader( 15 | detection_test_set, batch_size=2, shuffle=False, num_workers=0, 16 | collate_fn=script_utils.collate_fn) 17 | 18 | # defining orientation data loaders dictionary 19 | detection_loaders = { 20 | 'train' : detection_train_loader, 21 | 'valid' : detection_valid_loader, 22 | 'test' : detection_test_loader, 23 | } -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_dataset_transforms.py: -------------------------------------------------------------------------------- 1 | import modules.detection.scripts.transforms as detection_transforms 2 | 3 | def get_transform(train): 4 | transforms = [] 5 | # converts the image, a PIL image, into a PyTorch Tensor 6 | transforms.append(detection_transforms.ToTensor()) 7 | ### feel free to add additional transforms here below 8 | 9 | return detection_transforms.Compose(transforms) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_dataset_visualize.py: -------------------------------------------------------------------------------- 1 | id = 0 2 | obj_id = None 3 | obj_label = '' 4 | fig = plt.figure(figsize=(15, 15)) 5 | 6 | image_tensor, targets = detection_train_set[id] 7 | 8 | boxes = targets['boxes'] # retrieve bounding boxes 9 | image = utils.tensorToPIL(image_tensor) 10 | image_array = np.array(image) 11 | 12 | for box in boxes : 13 | cv2.rectangle(image_array, (box[0],box[1]), (box[2],box[3]), (255,0,0), 2) # draw bounding boxes 14 | 15 | ax = fig.add_subplot(1, 2, 1, xticks=[], yticks=[]) 16 | plt.imshow(Image.fromarray(image_array)) 17 | ax.set_title('Image') 18 | 19 | if obj_id is not None: 20 | mask_tensor = targets['masks'][obj_id] # retrieve bounding masks 21 | obj_label_idx = targets['labels'][[obj_id]].item() # retrieve bounding labels 22 | obj_label = ': ' + detection_classes[obj_label_idx] 23 | else : 24 | mask_tensor = torch.zeros_like(image_tensor) 25 | for _mask_tensor in targets['masks'] : mask_tensor += _mask_tensor # paste mask for every object 26 | 27 | ax1 = fig.add_subplot(1, 2, 2, xticks=[], yticks=[]) 28 | ax1.set_title('Segentation Mask' + obj_label) 29 | mask = utils.tensorToPIL(mask_tensor) 30 | plt.imshow(mask) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_model_evaluate.py: -------------------------------------------------------------------------------- 1 | # pick one image from the test set 2 | id = 7 3 | image = Image.open(detection_test_set.image_urls[id]) 4 | print('Image size: ', image.size) 5 | 6 | fig = plt.figure(figsize=(10, 10)) 7 | ax = fig.add_subplot(1, 1, 1, xticks=[], yticks=[]) 8 | plt.imshow(image) 9 | ax.set_title('Test Image') 10 | 11 | # put the model in evaluation mode 12 | detection_model.eval() 13 | with torch.no_grad(): 14 | # forward pass the test image to get detection predictions 15 | detection_predictions = detection_model(imgToTensor(image).unsqueeze(0).to(device)) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_model_init_checkpoint_output.txt: -------------------------------------------------------------------------------- 1 | Loaded model from checkpoint... 2 | Epochs: 20 | Best Loss: None -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_model_init_function.py: -------------------------------------------------------------------------------- 1 | from torchvision.models.detection.faster_rcnn import FastRCNNPredictor 2 | from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor 3 | 4 | def get_instance_segmentation_model(num_classes, state_dict=None): 5 | # load an instance segmentation model pre-trained on COCO 6 | detection_model = torchvision.models.detection.maskrcnn_resnet50_fpn(progress=True, pretrained=True) 7 | # get the number of input features for the classifier 8 | in_features = detection_model.roi_heads.box_predictor.cls_score.in_features 9 | ### FINETUNE pret-trained model 10 | # replace the pre-trained head with a new one 11 | detection_model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) 12 | # now get the number of input features for the mask classifier 13 | in_features_mask = detection_model.roi_heads.mask_predictor.conv5_mask.in_channels 14 | hidden_layer = 256 15 | # and replace the mask predictor with a new one 16 | detection_model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask, hidden_layer, num_classes) 17 | if state_dict is not None: detection_model.load_state_dict(state_dict) 18 | return detection_model -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_model_predict.py: -------------------------------------------------------------------------------- 1 | # pick one image from the test set 2 | id = 7 3 | image = Image.open(detection_test_set.image_urls[id]) 4 | image_tensor = imgToTensor(image) 5 | 6 | fig = plt.figure(figsize=(10, 10)) 7 | ax = fig.add_subplot(1, 1, 1, xticks=[], yticks=[]) 8 | ax.set_title('Test Image') 9 | plt.imshow(image) 10 | 11 | # put the model in evaluation mode 12 | detection_model.eval() 13 | with torch.no_grad(): 14 | # forward pass the test image to get detection predictions 15 | detection_predictions = detection_model(image_tensor.unsqueeze(0).to(device)) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_model_predictions_raw.py: -------------------------------------------------------------------------------- 1 | print(detection_predictions[0]['labels'].size()[0], 'objects detected !') 2 | detection_predictions[0] -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_model_predictions_raw_output.txt: -------------------------------------------------------------------------------- 1 | 1 objects detected ! 2 | {'boxes': tensor([[ 57.7098, 58.0544, 618.8469, 417.4893]]), 3 | 'labels': tensor([1]), 4 | 'scores': tensor([0.9999]), 5 | 'masks': tensor([[[[0., 0., 0., ..., 0., 0., 0.], 6 | [0., 0., 0., ..., 0., 0., 0.], 7 | [0., 0., 0., ..., 0., 0., 0.], 8 | ..., 9 | [0., 0., 0., ..., 0., 0., 0.], 10 | [0., 0., 0., ..., 0., 0., 0.], 11 | [0., 0., 0., ..., 0., 0., 0.]]]])} -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_model_predictions_visualize.py: -------------------------------------------------------------------------------- 1 | # Select an index for a detected/predicted object 2 | idx = 0 3 | 4 | image_array = np.array(image) 5 | # Retrieve predicted bounding box for detected object 6 | box_tensor = detection_predictions[0]['boxes'][idx] 7 | box = [int(cord) for cord in box_tensor.cpu().numpy()] 8 | # draw predicted bounding box on image 9 | cv2.rectangle(image_array, (box[0],box[1]), (box[2],box[3]), (255,0,0), 2) 10 | 11 | # Retrieve predicted mask for detected object 12 | mask_tensor = detection_predictions[0]['masks'][idx] 13 | mask_tensor = utils.denoise_mask_tensor(mask_tensor.cpu()) 14 | mask = tensorToPIL(mask_tensor) 15 | 16 | # Retrieve predictied label for detected object 17 | label_idx = detection_predictions[0]['labels'][idx].item() 18 | label = detection_classes[label_idx] 19 | 20 | # Retrieve predictied score for detected object 21 | score = detection_predictions[0]['scores'][idx].item() 22 | 23 | fig = plt.figure(figsize=(20, 15)) 24 | ax = fig.add_subplot(1, 2, 1, xticks=[], yticks=[]) 25 | plt.imshow(image_array) 26 | ax.set_title('Object Detected: {} | Score: {}'.format(label, score)) 27 | ax1 = fig.add_subplot(1, 2, 2, xticks=[], yticks=[]) 28 | plt.imshow(mask) 29 | ax1.set_title('Segentation Mask') -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_model_train.py: -------------------------------------------------------------------------------- 1 | # start training the detection model for 20 epochs 2 | detection_model = train_detection_model(detection_model, num_epochs= 20, loaders= detection_loaders, 3 | checkpoint= detection_checkpoint, checkpoint_path= detection_checkpoint_path, 4 | optimizer= detection_optimizer, lr_scheduler= detection_lr_scheduler, 5 | print_freq=10, device= device) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_model_train_function.py: -------------------------------------------------------------------------------- 1 | # import training and evaluation functions 2 | from modules.detection.scripts.engine import train_one_epoch, evaluate 3 | 4 | def train_detection_model(model, num_epochs=10, loaders=None, checkpoint=None, checkpoint_path=None, 5 | optimizer= None, lr_scheduler= None, print_freq=1, device=torch.device('cuda')): 6 | 7 | if checkpoint is None: start_epoch = 1 8 | else: 9 | print('Resuming training from checkpoint...') 10 | start_epoch = checkpoint['epoch'] + 1 11 | 12 | model.to(device) # Move model to cpu or cuda device 13 | 14 | time_train = time.time() 15 | for epoch in range(start_epoch, num_epochs + 1): 16 | time_epoch = time.time() 17 | # train for one epoch, printing every '{print_freq}' iterations 18 | train_one_epoch(model, optimizer, loaders['train'], device, epoch, print_freq=print_freq) 19 | # update the learning rate 20 | lr_scheduler.step() 21 | # evaluate model on the validation dataset 22 | evaluate(model, loaders['valid'], device=device) 23 | 24 | time_epoch_elapsed = time.time() - time_epoch 25 | time_train_elapsed = time.time() - time_train 26 | print('Epoch: {}\tEpoch Time: {:.0f}m {:.0f}s\tElapsed Time: {:.0f}m {:.0f}s'.format( 27 | epoch, time_epoch_elapsed // 60, time_epoch_elapsed % 60, 28 | time_train_elapsed // 60, time_train_elapsed % 60)) 29 | 30 | # Save checkpoint after every epoch if checkpoint_path is given 31 | if not checkpoint_path == None: 32 | utils.save_checkpoint(model.state_dict(), optimizer.state_dict(), epoch, checkpoint_path) 33 | 34 | return model # retun trained model -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_model_train_resume.py: -------------------------------------------------------------------------------- 1 | # resume training the detection model up to 30 epochs 2 | detection_model = train_detection_model(detection_model, num_epochs= 30, loaders= detection_loaders, 3 | checkpoint= detection_checkpoint, checkpoint_path= detection_checkpoint_path, 4 | optimizer= detection_optimizer, lr_scheduler= detection_lr_scheduler, 5 | print_freq=10, device= device) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_module_image_alignment.py: -------------------------------------------------------------------------------- 1 | segmented_image = utils.segment_image(image, mask) 2 | aligned_student_id = utils.align_student_id(image, mask) 3 | 4 | fig = plt.figure(figsize=(20, 15)) 5 | ax = fig.add_subplot(1, 2, 1, xticks=[], yticks=[]) 6 | ax.set_title('Segmented Image', fontdict={'fontsize': 22}) 7 | plt.imshow(segmented_image) 8 | 9 | ax1 = fig.add_subplot(1, 2, 2, xticks=[], yticks=[]) 10 | ax1.set_title('Aligned Student-Id', fontdict={'fontsize': 22}) 11 | plt.imshow(aligned_student_id) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_module_image_alignment_save.py: -------------------------------------------------------------------------------- 1 | aligned_student_id.save('images/aligned_image.jpg') -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_module_imports.py: -------------------------------------------------------------------------------- 1 | # Resolve imports for detection module 2 | import os 3 | import cv2 4 | import time 5 | import torch 6 | import torchvision 7 | 8 | import numpy as np 9 | 10 | import utils 11 | 12 | from PIL import Image, ImageDraw, ImageFilter 13 | #%matplotlib inline 14 | from matplotlib import pyplot as plt 15 | 16 | # define transforms to convert PIL image to torch.Tensor 17 | imgToTensor = torchvision.transforms.ToTensor() 18 | # define transforms to convert torch.Tensor to PIL image 19 | tensorToPIL = torchvision.transforms.ToPILImage() -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_optimizer_init.py: -------------------------------------------------------------------------------- 1 | # activate gradients calculation for unfreezed parameters 2 | detection_params = [p for p in detection_model.parameters() if p.requires_grad] 3 | # initialize training optimizer with learning rate, momentum and weight decay 4 | detection_optimizer = torch.optim.SGD(detection_params, lr=0.005, momentum=0.9, weight_decay=0.0005) 5 | # define learning rate schelduler to gradually decay learning rate 6 | detection_lr_scheduler = torch.optim.lr_scheduler.StepLR(detection_optimizer, step_size=10, gamma=0.95) 7 | 8 | # load optimizer state dictionary from checkpoint if available 9 | if detection_optimizer_state_dict is None: print('No checkpoint loaded ! Optimizer not loaded from checkpoint...') 10 | else: 11 | detection_optimizer.load_state_dict(detection_optimizer_state_dict) 12 | print('Loaded optizer from checkpoint...') -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_optimizer_init_checkpoint_output.txt: -------------------------------------------------------------------------------- 1 | Loaded optimizer from checkpoint... -------------------------------------------------------------------------------- /tutorial/static/code-snippets/detection_optimizer_init_output.txt: -------------------------------------------------------------------------------- 1 | No checkpoint loaded ! Optimizer not loaded from checkpoint... -------------------------------------------------------------------------------- /tutorial/static/code-snippets/extraction_coordinates_init.py: -------------------------------------------------------------------------------- 1 | import yaml 2 | 3 | field_coordinates_file = './field_coordinates.yaml' 4 | with open(field_coordinates_file) as f: 5 | field_coordinates = yaml.load(f, Loader=yaml.FullLoader) 6 | 7 | # print field coordinates 8 | field_coordinates -------------------------------------------------------------------------------- /tutorial/static/code-snippets/extraction_coordinates_init_output.txt: -------------------------------------------------------------------------------- 1 | {'image_width': 324, 2 | 'image_height': 204, 3 | 'firstname': {'width': 125, 'height': 25, 'left': 55, 'top': 40}, 4 | 'lastname': {'width': 125, 'height': 25, 'left': 55, 'top': 65}, 5 | 'gender': {'width': 45, 'height': 20, 'left': 55, 'top': 123}, 6 | 'height': {'width': 45, 'height': 20, 'left': 100, 'top': 123}, 7 | 'birth_date': {'width': 125, 'height': 20, 'left': 55, 'top': 90}, 8 | 'department': {'width': 125, 'height': 20, 'left': 55, 'top': 140}, 9 | 'expiry_date': {'width': 125, 'height': 20, 'left': 55, 'top': 105}, 10 | 'photo': {'width': 110, 'height': 110, 'left': 210, 'top': 25}, 11 | 'mini_photo': {'width': 35, 'height': 35, 'left': 12, 'top': 132.5}, 12 | 'signature': {'width': 125, 'height': 44, 'left': 55, 'top': 160}, 13 | 'id': {'width': 45, 'height': 20, 'left': 279, 'top': 170}} -------------------------------------------------------------------------------- /tutorial/static/code-snippets/extraction_predictions.py: -------------------------------------------------------------------------------- 1 | import math 2 | 3 | aligned_image = Image.open('images/aligned_image.jpg') 4 | # get size of aligned image 5 | image_width = aligned_image.width 6 | image_height = aligned_image.height 7 | 8 | im_width = field_coordinates['image_width'] 9 | im_height = field_coordinates['image_height'] 10 | 11 | image_array = np.array(aligned_image.resize((im_width, im_height))) 12 | 13 | num_col = 5 14 | fig = plt.figure(figsize=(20, 5)) 15 | for counter, field_id in enumerate(field_coordinates): 16 | if field_id in ['image_width', 'image_height'] : continue 17 | cords = field_coordinates[field_id] 18 | x1, y1, x2, y2 = cords['left'], cords['top'], cords['width']+cords['left'], \ 19 | cords['height']+cords['top'] 20 | crop_cords = x1, y1, x2, y2 21 | cropped_image = utils.crop_image(image_array, crop_cords) 22 | ax = fig.add_subplot(math.ceil(len(field_coordinates)/num_col), num_col, counter-1, xticks=[], yticks=[]) 23 | plt.imshow(cropped_image) 24 | ax.set_title('{}'.format(field_id)) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_checkpoint.py: -------------------------------------------------------------------------------- 1 | # select hardware type use for computations 2 | device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') 3 | 4 | load_orientation_checkpoint = True 5 | save_orientation_checkpoint = True 6 | 7 | orientation_checkpoint_path = os.path.join('checkpoints', 'orientation_model_scratch.pth.tar') 8 | 9 | ''' 10 | Do not edit the lines below 11 | ''' 12 | 13 | if load_orientation_checkpoint: 14 | orientation_checkpoint = torch.load(orientation_checkpoint_path) if os.path.exists(orientation_checkpoint_path) else None 15 | orientation_model_state_dict = orientation_checkpoint['model_state_dict'] if not orientation_checkpoint == None else None 16 | orientation_optimizer_state_dict = orientation_checkpoint['optimizer_state_dict'] if not orientation_checkpoint == None else None 17 | else: 18 | orientation_checkpoint, orientation_model_state_dict, orientation_optimizer_state_dict = None, None, None 19 | 20 | if not save_orientation_checkpoint : orientation_checkpoint_path = None 21 | # initialize orientation model using the state dictionary from checkpoint 22 | orientation_model = get_orientation_model(num_orientation_classes, model_state_dict = orientation_model_state_dict).to(device) 23 | 24 | if orientation_checkpoint is None : print('No checkpoint loaded ! Initialized model from scratch instead...') 25 | else : 26 | print('Loaded model from checkpoint...') 27 | utils.checkpoint_summary(orientation_checkpoint) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_checkpoint_output.txt: -------------------------------------------------------------------------------- 1 | No checkpoint loaded ! Initialized model from scratch instead... -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_dataset_classes.py: -------------------------------------------------------------------------------- 1 | print('Number of classes: {}\nClasses: {}'.format(num_orientation_classes, orientation_classes)) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_dataset_classes_output.txt: -------------------------------------------------------------------------------- 1 | Number of classes: 4 2 | Classes: ['090', '180', '270', '360'] -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_dataset_init.py: -------------------------------------------------------------------------------- 1 | from torchvision import datasets 2 | 3 | # define urls of our datasets folders 4 | orientation_data_path = os.path.join('datasets', 'orientation') 5 | orientation_train_images_path = os.path.join(orientation_data_path, 'train') 6 | orientation_valid_images_path = os.path.join(orientation_data_path, 'valid') 7 | orientation_test_images_path = os.path.join(orientation_data_path, 'test') 8 | 9 | # initialize training dataset for orientation module 10 | orientation_train_set = datasets.ImageFolder(orientation_train_images_path, orientation_transform_train) 11 | # initialize validation dataset for orientation module 12 | orientation_valid_set = datasets.ImageFolder(orientation_valid_images_path, orientation_transform_valid) 13 | # initialize testing dataset for orientation module 14 | orientation_test_set = datasets.ImageFolder(orientation_test_images_path, orientation_transform_test) 15 | # retrieve orientation classes 16 | orientation_classes = orientation_train_set.classes 17 | num_orientation_classes = len(orientation_classes) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_dataset_loaders.py: -------------------------------------------------------------------------------- 1 | num_workers = 0 2 | 3 | # data loader for training 4 | orientation_train_loader = torch.utils.data.DataLoader( 5 | orientation_train_set, batch_size=4, shuffle=True, num_workers=num_workers 6 | ) 7 | # data loader for validation 8 | orientation_valid_loader = torch.utils.data.DataLoader( 9 | orientation_valid_set, batch_size=4, shuffle=True, num_workers=num_workers 10 | ) 11 | # data loader for testing 12 | orientation_test_loader = torch.utils.data.DataLoader( 13 | orientation_test_set, batch_size=2, shuffle=False, num_workers=num_workers 14 | ) 15 | 16 | # defining orientation data loaders dictionary 17 | orientation_loaders = { 18 | 'train' : orientation_train_loader, 19 | 'valid' : orientation_valid_loader, 20 | 'test' : orientation_test_loader, 21 | } -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_dataset_transforms.py: -------------------------------------------------------------------------------- 1 | import torchvision.transforms as transforms 2 | 3 | # transforms for training set 4 | orientation_transform_train = transforms.Compose([ 5 | transforms.Resize(size=(224, 224)), 6 | transforms.RandomAffine(degrees=5, translate=(0.1, 0.1), scale=(1.0, 1.1), shear=5, resample=False, fillcolor=0), 7 | transforms.RandomApply([transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)], p=0.5), 8 | transforms.RandomGrayscale(p=0.2), 9 | transforms.ToTensor(), 10 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 11 | ]) 12 | 13 | # transforms for testing set 14 | orientation_transform_test = transforms.Compose([ 15 | transforms.Resize(size=(224, 224)), 16 | transforms.ToTensor(), 17 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 18 | ]) 19 | 20 | # transforms for validation set 21 | orientation_transform_valid = transforms.Compose([ 22 | transforms.Resize(size=(224, 224)), 23 | transforms.ToTensor(), 24 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 25 | ]) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_dataset_visualize.py: -------------------------------------------------------------------------------- 1 | import random 2 | 3 | # plot the images in the batch, along with the corresponding labels 4 | fig = plt.figure(figsize=(20, 40)) 5 | # visualize transformed data 6 | for counter in range(4): 7 | image_tensor, label_tensor = orientation_train_set[random.choice(range(0, len(orientation_train_set)))] 8 | image_tensor = image_tensor * 0.226 + 0.445 # denormalize tensor 9 | ax = fig.add_subplot(1, 4, counter+1, xticks=[], yticks=[]) 10 | plt.imshow(tensorToPIL(image_tensor)) 11 | ax.set_title('Class Index: {} | Class Name: {}'.format(label_tensor, orientation_classes[label_tensor])) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.nn.functional as F 3 | 4 | class OrientationModel(torch.nn.Module): 5 | def __init__(self, num_classes): 6 | super(OrientationModel, self).__init__() 7 | # Define convolutional layers 8 | self.layer_1_conv = nn.Conv2d(3, 32, (3, 3), stride=1, padding=1) # W = 112 9 | self.layer_2_conv = nn.Conv2d(32, 64, (3, 3), stride=1, padding=1) # W = 56 10 | self.layer_3_conv = nn.Conv2d(64, 128, (3, 3), stride=1, padding=1) # W = 28 11 | 12 | # Define maxpool and dropout layers 13 | self.pool = nn.MaxPool2d(2, 2) 14 | self.dropout = nn.Dropout(p=0.2) 15 | 16 | # Define the fully-connected or linear layers 17 | self.fc1 = nn.Linear(128 * 28 * 28, 256) 18 | self.fc2 = nn.Linear(256, 512) 19 | # The outputs of the last linear equals the number of orientation classes 20 | self.fc3 = nn.Linear(512, num_classes) 21 | 22 | def forward(self, x): 23 | ## Define forward behavior 24 | x = self.pool(F.relu(self.layer_1_conv(x))) 25 | x = self.pool(F.relu(self.layer_2_conv(x))) 26 | x = self.pool(F.relu(self.layer_3_conv(x))) 27 | 28 | x = x.view(-1, 128 * 28 * 28) 29 | # Insert an activation layer after every linear layer 30 | x = F.relu(self.fc1(self.dropout(x))) 31 | x = F.relu(self.fc2(self.dropout(x))) 32 | x = F.relu(self.fc3(self.dropout(x))) 33 | 34 | return x # return orientation prediction -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_init.py: -------------------------------------------------------------------------------- 1 | def get_orientation_model(num_classes, model_state_dict=None): 2 | orientation_model = OrientationModel(num_classes) 3 | # initialize model state dictionary if specified 4 | if not model_state_dict == None: orientation_model.load_state_dict(model_state_dict) 5 | 6 | return orientation_model -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_init_checkpoint_output.txt: -------------------------------------------------------------------------------- 1 | Loaded model from checkpoint... 2 | Epochs: 20 | Best Loss: 0.008811209350824356 -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_init_function.py: -------------------------------------------------------------------------------- 1 | def get_orientation_model(num_classes, model_state_dict=None): 2 | orientation_model = OrientationModel(num_classes) 3 | # initialize model state dictionary if specified 4 | if not model_state_dict == None: orientation_model.load_state_dict(model_state_dict) 5 | 6 | return orientation_model -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_prediction_visualize.py: -------------------------------------------------------------------------------- 1 | # define function to correct orientation 2 | def rectify_orientation(image, rotation_angle): 3 | if rotation_angle == '090': return image.transpose(Image.ROTATE_270) 4 | elif rotation_angle == '180': return image.transpose(Image.ROTATE_180) 5 | elif rotation_angle == '270': return image.transpose(Image.ROTATE_90) 6 | else: return image 7 | 8 | image = Image.open('images/aligned_image.jpg') 9 | image_tensor = orientation_transform_test(image) 10 | orientation_prediction = orientation_model(image_tensor.unsqueeze(0))[0] 11 | print('Raw predictions: ', orientation_prediction) 12 | 13 | # retrieve index for maximum score 14 | orientation_index = torch.argmax(orientation_prediction) 15 | # retrieve class of maximun score 16 | orientation_class = orientation_classes[orientation_index] 17 | 18 | fig = plt.figure(figsize=(25, 25)) 19 | ax = fig.add_subplot(1, 2, 1, xticks=[], yticks=[]) 20 | ax.set_title('Raw Outputs : {}\nPredicted Orientation : {}'.format(orientation_prediction, orientation_class)) 21 | plt.imshow(image) 22 | ax1 = fig.add_subplot(1, 2, 2, xticks=[], yticks=[]) 23 | ax1.set_title('Corrected Orientation') 24 | plt.imshow(rectify_orientation(image, orientation_class)) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_test.py: -------------------------------------------------------------------------------- 1 | # call test function 2 | test_orientation_model(orientation_model, orientation_criterion, orientation_loaders, use_cuda= False) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_test_function.py: -------------------------------------------------------------------------------- 1 | def test_orientation_model(model, criterion, loaders=None, use_cuda= False): 2 | # Specify computation hardware for testing 3 | device = torch.device('cuda') if use_cuda else torch.device('cpu') 4 | # move model to selected hardware 5 | model = model.to(device) 6 | 7 | # monitor test loss and accuracy 8 | test_loss = 0. 9 | correct = 0. 10 | total = 0. 11 | 12 | model.eval() 13 | with torch.no_grad(): 14 | for batch_idx, (data, target) in enumerate(loaders['test']): 15 | # move data and labels to selected hardware 16 | data, target = data.to(device), target.to(device) 17 | # forward pass: compute predicted outputs by passing inputs to the model 18 | output = model(data) 19 | # calculate the loss 20 | loss = criterion(output, target) 21 | # update average test loss 22 | test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss)) 23 | # convert output scores to predicted class 24 | pred = output.data.max(1, keepdim=True)[1] 25 | # compare predictions to true label 26 | correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy()) 27 | total += data.size(0) 28 | print('Test Loss: {:.6f}\n'.format(test_loss)) 29 | print('\nTest Accuracy: %2d%% (%2d/%2d)' % ( 30 | 100. * correct / total, correct, total)) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_test_output.txt: -------------------------------------------------------------------------------- 1 | Test Loss: 0.000719 2 | 3 | Test Accuracy: 100% (28/28) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_train.py: -------------------------------------------------------------------------------- 1 | # start training the orientation model for 20 epochs 2 | orientation_model = train_orientation_model(orientation_model, orientation_checkpoint, 20, 3 | orientation_optimizer, orientation_criterion, use_cuda = torch.cuda.is_available(), 4 | checkpoint_path = orientation_checkpoint_path, loaders = orientation_loaders) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_train_resume.py: -------------------------------------------------------------------------------- 1 | # resume training of orientation model up to 30 epochs 2 | orientation_model = train_orientation_model(orientation_model, orientation_checkpoint, 30, 3 | orientation_optimizer, orientation_criterion, use_cuda = torch.cuda.is_available(), 4 | checkpoint_path = orientation_checkpoint_path, loaders = orientation_loaders) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_train_resume_output.txt: -------------------------------------------------------------------------------- 1 | Resuming training from checkpoint... 2 | Epoch: 21 Training Loss: 0.176632 Validation Loss: 0.017247 Epoch Time: 0m 1s Elapsed Time: 0m 1s 3 | Epoch: 22 Training Loss: 0.116598 Validation Loss: 0.004525 Epoch Time: 0m 1s Elapsed Time: 0m 2s 4 | Validation loss decreased (0.008811 --> 0.004525). Saving best checkpoint ... 5 | Epoch: 23 Training Loss: 0.078832 Validation Loss: 0.001973 Epoch Time: 0m 1s Elapsed Time: 0m 4s 6 | Validation loss decreased (0.004525 --> 0.001973). Saving best checkpoint ... 7 | Epoch: 24 Training Loss: 0.079359 Validation Loss: 0.002303 Epoch Time: 0m 1s Elapsed Time: 0m 6s 8 | Epoch: 25 Training Loss: 0.123845 Validation Loss: 0.001522 Epoch Time: 0m 1s Elapsed Time: 0m 7s 9 | Validation loss decreased (0.001973 --> 0.001522). Saving best checkpoint ... 10 | Epoch: 26 Training Loss: 0.163277 Validation Loss: 0.002705 Epoch Time: 0m 1s Elapsed Time: 0m 8s 11 | Epoch: 27 Training Loss: 0.030068 Validation Loss: 0.003213 Epoch Time: 0m 1s Elapsed Time: 0m 9s 12 | Epoch: 28 Training Loss: 0.055341 Validation Loss: 0.001434 Epoch Time: 0m 1s Elapsed Time: 0m 11s 13 | Validation loss decreased (0.001522 --> 0.001434). Saving best checkpoint ... 14 | Epoch: 29 Training Loss: 0.177483 Validation Loss: 0.001641 Epoch Time: 0m 1s Elapsed Time: 0m 12s 15 | Epoch: 30 Training Loss: 0.044176 Validation Loss: 0.000645 Epoch Time: 0m 1s Elapsed Time: 0m 13s 16 | Validation loss decreased (0.001434 --> 0.000645). Saving best checkpoint ... 17 | Saving checkpoint ... 18 | Training Complete ... -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_visualize.py: -------------------------------------------------------------------------------- 1 | # let's print our detection model 2 | print(orientation_model) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_model_visualize_output.txt: -------------------------------------------------------------------------------- 1 | OrientationModel( 2 | (layer_1_conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) 3 | (layer_2_conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) 4 | (layer_3_conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) 5 | (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) 6 | (dropout): Dropout(p=0.2, inplace=False) 7 | (fc1): Linear(in_features=100352, out_features=256, bias=True) 8 | (fc2): Linear(in_features=256, out_features=512, bias=True) 9 | (fc3): Linear(in_features=512, out_features=4, bias=True) 10 | ) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_module_imports.py: -------------------------------------------------------------------------------- 1 | # resolve imports for orientation module 2 | import os 3 | import cv2 4 | import time 5 | import torch 6 | import torchvision 7 | 8 | import numpy as np 9 | 10 | import utils 11 | 12 | from PIL import Image, ImageDraw 13 | #%matplotlib inline 14 | from matplotlib import pyplot as plt 15 | 16 | # define transforms to convert PIL image to torch.Tensor 17 | imgToTensor = torchvision.transforms.ToTensor() 18 | # define transforms to convert torch.Tensor to PIL image 19 | tensorToPIL = torchvision.transforms.ToPILImage() -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_optimizer.py: -------------------------------------------------------------------------------- 1 | # Select loss criterion 2 | orientation_criterion = torch.nn.CrossEntropyLoss() 3 | # Select optimizer 4 | orientation_optimizer = torch.optim.SGD(orientation_model.parameters(), lr=0.001) 5 | # Regularization. Decay LR by a factor of 0.9 every 15 epochs 6 | orientation_lr_scheduler = torch.optim.lr_scheduler.StepLR(orientation_optimizer, step_size=15, gamma=0.9) 7 | 8 | # load optimizer state dictionary from checkpoint if available 9 | if orientation_optimizer_state_dict is None: print('No checkpoint loaded ! Optimizer not loaded from checkpoint...') 10 | else: 11 | orientation_optimizer.load_state_dict(orientation_optimizer_state_dict) 12 | print('Loaded optimizer from checkpoint...') -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_optimizer_init_checkpoint_output.txt: -------------------------------------------------------------------------------- 1 | Loaded optimizer from checkpoint... -------------------------------------------------------------------------------- /tutorial/static/code-snippets/orientation_optimizer_init_output.txt: -------------------------------------------------------------------------------- 1 | Loaded optimizer from checkpoint... -------------------------------------------------------------------------------- /tutorial/static/code-snippets/output.txt: -------------------------------------------------------------------------------- 1 | from torchvision import datasets 2 | 3 | # define urls of our datasets folders 4 | orientation_data_path = os.path.join('datasets', 'orientation') 5 | orientation_train_images_path = os.path.join(orientation_data_path, 'train') 6 | orientation_valid_images_path = os.path.join(orientation_data_path, 'valid') 7 | orientation_test_images_path = os.path.join(orientation_data_path, 'test') 8 | 9 | # initialize training dataset for orientation module 10 | orientation_train_set = datasets.ImageFolder(orientation_train_images_path, orientation_transform_train) 11 | # initialize validation dataset for orientation module 12 | orientation_valid_set = datasets.ImageFolder(orientation_valid_images_path, orientation_transform_valid) 13 | # initialize testing dataset for orientation module 14 | orientation_test_set = datasets.ImageFolder(orientation_test_images_path, orientation_transform_test) 15 | # retrieve orientation classes 16 | orientation_classes = orientation_train_set.classes 17 | num_orientation_classes = len(orientation_classes) -------------------------------------------------------------------------------- /tutorial/static/code-snippets/project-structure.txt: -------------------------------------------------------------------------------- 1 | Project Structure (root) 2 | | 3 | ├─────────📁tutorial/ (Docusaurus project) 4 | | 5 | |─────────📂code/ (Python project) 6 | |─────────📁checkpoints/ 7 | |─────────📂datasets/ 8 | | |─────────📁detection/ 9 | | |─────────📁orientation/ 10 | |─────────📂images/ 11 | | |─────────📁student-ids/ 12 | |─────────📂modules/ 13 | | |─────────📁detection/ 14 | | |─────────scripts/ 15 | |─────────📄field_coordinates.yaml 16 | |─────────📄tutorial.ipynb 17 | |─────────📄utils.py 18 | -------------------------------------------------------------------------------- /tutorial/static/img/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MbassiJaphet/pytorch-for-information-extraction/4e8eea92140df806c2a82c8fa3db4afaf13e66db/tutorial/static/img/favicon.ico --------------------------------------------------------------------------------