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GitHub - usnistgov/object-detection-yolov3: Tensorflow 2.x Yolo-v3 Object Detection codebase
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class=\"heading-element\" dir=\"auto\"\u003eObject Detection\u003c/h1\u003e\u003ca id=\"user-content-object-detection\" class=\"anchor\" aria-label=\"Permalink: Object Detection\" href=\"#object-detection\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eObject Detection Tensorflow model ready to run on Enki.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYolo: \u003ca href=\"https://arxiv.org/abs/1506.02640\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1506.02640\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eYolo 9000: \u003ca href=\"https://arxiv.org/abs/1612.08242\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1612.08242\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eYolov3: \u003ca href=\"https://arxiv.org/abs/1804.02767\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1804.02767\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eEnki AI Cluster page:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://aihpc.ipages.nist.gov/pages/\" rel=\"nofollow\"\u003ehttps://aihpc.ipages.nist.gov/pages/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gitlab.nist.gov/gitlab/aihpc/pages/wikis/home\" rel=\"nofollow\"\u003ehttps://gitlab.nist.gov/gitlab/aihpc/pages/wikis/home\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eThis codebase is designed to work with Python3 and Tensorflow 2.x\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInput Data Constraints\u003c/h1\u003e\u003ca id=\"user-content-input-data-constraints\" class=\"anchor\" aria-label=\"Permalink: Input Data Constraints\" href=\"#input-data-constraints\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThere is example input data included in the repo under the data folder\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eInput data assumptions:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eimage type: image with one of these pixel types: uint8, uint16, int32, float32\u003c/li\u003e\n\u003cli\u003ebounding box list: Nx4 array of type: int32, flaot32, flaot32 where N is the number of boxes present in the image (can be 0)\u003c/li\u003e\n\u003cli\u003eeach input image must have a corresponding bounding box list of length 0 or more\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eBefore training script can be launched, the input data needs to be converted into a memory mapped database (\u003ca href=\"https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database\" rel=\"nofollow\"\u003elmdb\u003c/a\u003e) to enable fast memory mapped file reading during training.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLMDB Construction\u003c/h1\u003e\u003ca id=\"user-content-lmdb-construction\" class=\"anchor\" aria-label=\"Permalink: LMDB Construction\" href=\"#lmdb-construction\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis training code uses lmdb databases to store the image and mask data to enable parallel memory-mapped file reader to keep the GPUs fed.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe input folder of images and masks needs to be split into train and test. Train to update the model parameters, and test to estimate the generalization accuracy of the resulting model. By default 80% of the data is used for training, 20% for test.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python build_lmdb.py -h\nusage: build_lmdb [-h] --image_folder IMAGE_FOLDER --csv_folder CSV_FOLDER\n --output_folder OUTPUT_FOLDER --dataset_name DATASET_NAME\n [--train_fraction TRAIN_FRACTION]\n [--image_format IMAGE_FORMAT]\n\nScript which converts two folders of images and masks into a pair of lmdb\ndatabases for training.\n\noptional arguments:\n -h, --help show this help message and exit\n --image_folder IMAGE_FOLDER\n filepath to the folder containing the images\n --csv_folder CSV_FOLDER\n filepath to the folder containing the bounding box csv\n files\n --output_folder OUTPUT_FOLDER\n filepath to the folder where the outputs will be\n placed\n --dataset_name DATASET_NAME\n name of the dataset to be used in creating the lmdb\n files\n --train_fraction TRAIN_FRACTION\n what fraction of the dataset to use for training (0.0,\n 1.0)\n --image_format IMAGE_FORMAT\n format (extension) of the input images. E.g {tif, jpg,\n png)\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython build_lmdb.py -h\nusage: build_lmdb [-h] --image_folder IMAGE_FOLDER --csv_folder CSV_FOLDER\n --output_folder OUTPUT_FOLDER --dataset_name DATASET_NAME\n [--train_fraction TRAIN_FRACTION]\n [--image_format IMAGE_FORMAT]\n\nScript which converts two folders of images and masks into a pair of lmdb\ndatabases for training.\n\noptional arguments:\n -h, --help show this help message and exit\n --image_folder IMAGE_FOLDER\n filepath to the folder containing the images\n --csv_folder CSV_FOLDER\n filepath to the folder containing the bounding box csv\n files\n --output_folder OUTPUT_FOLDER\n filepath to the folder where the outputs will be\n placed\n --dataset_name DATASET_NAME\n name of the dataset to be used in creating the lmdb\n files\n --train_fraction TRAIN_FRACTION\n what fraction of the dataset to use for training (0.0,\n 1.0)\n --image_format IMAGE_FORMAT\n format (extension) of the input images. E.g {tif, jpg,\n png)\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining\u003c/h1\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWith the lmdb build there are two methods for training a model.\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eCluster: Single Node Multi GPU\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ethe include shell script \u003ccode\u003elaunch_train_sbatch.sh\u003c/code\u003e is setup to all training on the Enki Cluster.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eLocal: Single Node Multi GPU\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIf you want to train the model on local hardware, avoid using \u003ccode\u003elaunch_train_sbatch.sh\u003c/code\u003e, use python and directly launch \u003ccode\u003etrain.py\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eWhether you launch the training locally or on Enki, the training script will query the system and determine how many GPUs are available. It will then build the network for training using data-parallelism. So when you define your batch size, it will actually be multiplied by the number of GPUs you are using to train the network. So a batch size of 8 training on 4 gpus, results in an actual batch size of 32. Each GPU computes its own forward pass on its own data, computes the gradient, and then all N gradients are averaged.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe full help for the training script is:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python train.py -h\nusage: train_yolo [-h] [--batch_size BATCH_SIZE]\n [--learning_rate LEARNING_RATE]\n [--test_every_n_steps TEST_EVERY_N_STEPS] --train_database\n TRAIN_DATABASE_FILEPATH --test_database\n TEST_DATABASE_FILEPATH --output_dir OUTPUT_FOLDER\n [--early_stopping TERMINATE_AFTER_NUM_EPOCHS_WITHOUT_TEST_LOSS_IMPROVEMENT]\n [--use_augmentation USE_AUGMENTATION]\n\nScript which trains a yolo_v3 model\n\noptional arguments:\n -h, --help show this help message and exit\n --batch_size BATCH_SIZE\n training batch size\n --learning_rate LEARNING_RATE\n --test_every_n_steps TEST_EVERY_N_STEPS\n number of gradient update steps to take between test\n runs\n --train_database TRAIN_DATABASE_FILEPATH\n lmdb database to use for (Required)\n --test_database TEST_DATABASE_FILEPATH\n lmdb database to use for testing (Required)\n --output_dir OUTPUT_FOLDER\n Folder where outputs will be saved (Required)\n --early_stopping TERMINATE_AFTER_NUM_EPOCHS_WITHOUT_TEST_LOSS_IMPROVEMENT\n Perform early stopping when the test loss does not\n improve for N epochs.\n --use_augmentation USE_AUGMENTATION\n whether to use data augmentation [0 = false, 1 = true]\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython train.py -h\nusage: train_yolo [-h] [--batch_size BATCH_SIZE]\n [--learning_rate LEARNING_RATE]\n [--test_every_n_steps TEST_EVERY_N_STEPS] --train_database\n TRAIN_DATABASE_FILEPATH --test_database\n TEST_DATABASE_FILEPATH --output_dir OUTPUT_FOLDER\n [--early_stopping TERMINATE_AFTER_NUM_EPOCHS_WITHOUT_TEST_LOSS_IMPROVEMENT]\n [--use_augmentation USE_AUGMENTATION]\n\nScript which trains a yolo_v3 model\n\noptional arguments:\n -h, --help show this help message and exit\n --batch_size BATCH_SIZE\n training batch size\n --learning_rate LEARNING_RATE\n --test_every_n_steps TEST_EVERY_N_STEPS\n number of gradient update steps to take between test\n runs\n --train_database TRAIN_DATABASE_FILEPATH\n lmdb database to use for (Required)\n --test_database TEST_DATABASE_FILEPATH\n lmdb database to use for testing (Required)\n --output_dir OUTPUT_FOLDER\n Folder where outputs will be saved (Required)\n --early_stopping TERMINATE_AFTER_NUM_EPOCHS_WITHOUT_TEST_LOSS_IMPROVEMENT\n Perform early stopping when the test loss does not\n improve for N epochs.\n --use_augmentation USE_AUGMENTATION\n whether to use data augmentation [0 = false, 1 = true]\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA few of the arguments require explanation.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003etest_every_n_steps\u003c/code\u003e: typically, you run test/validation every epoch. However, I am often building models with very small amounts of data (e.g. 500 images). With an actual batch size of 32, that allows me 15 gradient updates per epoch. The model does not change that fast, so I impose a fixed global step count between test so that I don't spend all of my GPU time running the test data. A good value for this is typically 1000.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eearly_stopping\u003c/code\u003e: this is an integer specifying the early stopping criteria. If the model test loss does not improve after this number of epochs (epoch defined as \u003ccode\u003etest_every_n_steps steps\u003c/code\u003e updates) training is terminated because we have moved into overfitting the training dataset.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eImage Readers\u003c/h1\u003e\u003ca id=\"user-content-image-readers\" class=\"anchor\" aria-label=\"Permalink: Image Readers\" href=\"#image-readers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOne of the defining features of this codebase is the parallel (python multiprocess) image reading from lightning memory mapped databases.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThere are typically 1 or more reader threads feeding each GPU.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eEach ImageReader class instance:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eselects the next image (potentially at random from the shuffled dataset)\u003c/li\u003e\n\u003cli\u003eloads images from a shared lmdb read-only reference\u003c/li\u003e\n\u003cli\u003edetermines the image augmentation parameters from by defining augmentation limits\u003c/li\u003e\n\u003cli\u003eapplies the augmentation transformation to the image\u003c/li\u003e\n\u003cli\u003eadd the augmented image to the batch that reader is building\u003c/li\u003e\n\u003cli\u003eonce a batch is constructed, the imagereader adds it to the output queue shared among all of the imagereaders\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThe training script setups of python generators which just get a reference to the output batch queue data and pass it into tensorflow. One of the largest bottlenecks in deep learning is keeping the GPUs fed. By performing the image reading and data augmentation asynchronously all the main python training thread has to do is get a reference to the next batch (which is waiting in memory) and pass it to tensorflow to be copied to the GPUs.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf the imagereaders do not have enough bandwidth to keep up with the GPUs you can increase the number of readers per gpu, though 1 or 2 readers per gpus is often enough.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou will know whether the image readers are keeping up with the GPUs. When the imagereader output queue is getting empty a warning is printed to the log:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"Input Queue Starvation !!!!\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eInput Queue Starvation !!!!\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ealong with the matching message letting you know when the imagereaders have caught back up:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"Input Queue Starvation Over\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eInput Queue Starvation Over\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eImage Augmentation\u003c/h1\u003e\u003ca id=\"user-content-image-augmentation\" class=\"anchor\" aria-label=\"Permalink: Image Augmentation\" href=\"#image-augmentation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor each image being read from the lmdb, a unique set of augmentation parameters are defined.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ethe \u003ccode\u003eaugment\u003c/code\u003e class supports:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eTransformation\u003c/th\u003e\n\u003cth\u003eParameterization\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ereflection (x, y)\u003c/td\u003e\n\u003ctd\u003eBernoulli\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erotation\u003c/td\u003e\n\u003ctd\u003eUniform\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ejitter (x, y)\u003c/td\u003e\n\u003ctd\u003ePercent of Image Size\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003escale (x,y)\u003c/td\u003e\n\u003ctd\u003ePercent Change\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003enoise\u003c/td\u003e\n\u003ctd\u003ePercent Change of Current Image Dynamic Range\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eblur\u003c/td\u003e\n\u003ctd\u003eUniform Selection of Kernel Size\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epixel intensity\u003c/td\u003e\n\u003ctd\u003ePercent Change of Current Image Dynamic Range\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eThese augmentation transformations are generally configured based on domain expertise and stay fixed per dataset.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCurrently the only method for modifying them is to open the \u003ccode\u003eimagereader.py\u003c/code\u003e file and edit the augmentation parameters contained within the code block within the imagereader \u003ccode\u003e__init__\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"# setup the image data augmentation parameters\nself._reflection_flag = True\nself._rotation_flag = True\nself._jitter_augmentation_severity = 0.1 # x% of a FOV\nself._noise_augmentation_severity = 0.02 # vary noise by x% of the dynamic range present in the image\nself._scale_augmentation_severity = 0.1 # vary size by x%\nself._blur_max_sigma = 2 # pixels\n# self._intensity_augmentation_severity = 0.05\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e# setup the image data augmentation parameters\nself._reflection_flag = True\nself._rotation_flag = True\nself._jitter_augmentation_severity = 0.1 # x% of a FOV\nself._noise_augmentation_severity = 0.02 # vary noise by x% of the dynamic range present in the image\nself._scale_augmentation_severity = 0.1 # vary size by x%\nself._blur_max_sigma = 2 # pixels\n# self._intensity_augmentation_severity = 0.05\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Object Detection","anchor":"object-detection","htmlText":"Object Detection"},{"level":1,"text":"Input Data Constraints","anchor":"input-data-constraints","htmlText":"Input Data Constraints"},{"level":1,"text":"LMDB Construction","anchor":"lmdb-construction","htmlText":"LMDB Construction"},{"level":1,"text":"Training","anchor":"training","htmlText":"Training"},{"level":1,"text":"Image Readers","anchor":"image-readers","htmlText":"Image Readers"},{"level":1,"text":"Image Augmentation","anchor":"image-augmentation","htmlText":"Image Augmentation"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fusnistgov%2Fobject-detection-yolov3"}},{"displayName":"LICENSE.txt","repoName":"object-detection-yolov3","refName":"master","path":"LICENSE.txt","preferredFileType":"license","tabName":"License","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fusnistgov%2Fobject-detection-yolov3"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"blob_edit_unsaved_changes_storage":true,"accessible_code_button":true}}}}</script> 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width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Object Detection</h1><a id="user-content-object-detection" class="anchor" aria-label="Permalink: Object Detection" href="#object-detection"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Object Detection Tensorflow model ready to run on Enki.</p> <ul dir="auto"> <li>Yolo: <a href="https://arxiv.org/abs/1506.02640" rel="nofollow">https://arxiv.org/abs/1506.02640</a></li> <li>Yolo 9000: <a href="https://arxiv.org/abs/1612.08242" rel="nofollow">https://arxiv.org/abs/1612.08242</a></li> <li>Yolov3: <a href="https://arxiv.org/abs/1804.02767" rel="nofollow">https://arxiv.org/abs/1804.02767</a></li> </ul> <p dir="auto">Enki AI Cluster page:</p> <ul dir="auto"> <li><a href="https://aihpc.ipages.nist.gov/pages/" rel="nofollow">https://aihpc.ipages.nist.gov/pages/</a></li> <li><a href="https://gitlab.nist.gov/gitlab/aihpc/pages/wikis/home" rel="nofollow">https://gitlab.nist.gov/gitlab/aihpc/pages/wikis/home</a></li> </ul> <p dir="auto"><em>This codebase is designed to work with Python3 and Tensorflow 2.x</em></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Input Data Constraints</h1><a id="user-content-input-data-constraints" class="anchor" aria-label="Permalink: Input Data Constraints" href="#input-data-constraints"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">There is example input data included in the repo under the data folder</p> <p dir="auto">Input data assumptions:</p> <ul dir="auto"> <li>image type: image with one of these pixel types: uint8, uint16, int32, float32</li> <li>bounding box list: Nx4 array of type: int32, flaot32, flaot32 where N is the number of boxes present in the image (can be 0)</li> <li>each input image must have a corresponding bounding box list of length 0 or more</li> </ul> <p dir="auto">Before training script can be launched, the input data needs to be converted into a memory mapped database (<a href="https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database" rel="nofollow">lmdb</a>) to enable fast memory mapped file reading during training.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">LMDB Construction</h1><a id="user-content-lmdb-construction" class="anchor" aria-label="Permalink: LMDB Construction" href="#lmdb-construction"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This training code uses lmdb databases to store the image and mask data to enable parallel memory-mapped file reader to keep the GPUs fed.</p> <p dir="auto">The input folder of images and masks needs to be split into train and test. Train to update the model parameters, and test to estimate the generalization accuracy of the resulting model. By default 80% of the data is used for training, 20% for test.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python build_lmdb.py -h usage: build_lmdb [-h] --image_folder IMAGE_FOLDER --csv_folder CSV_FOLDER --output_folder OUTPUT_FOLDER --dataset_name DATASET_NAME [--train_fraction TRAIN_FRACTION] [--image_format IMAGE_FORMAT] Script which converts two folders of images and masks into a pair of lmdb databases for training. optional arguments: -h, --help show this help message and exit --image_folder IMAGE_FOLDER filepath to the folder containing the images --csv_folder CSV_FOLDER filepath to the folder containing the bounding box csv files --output_folder OUTPUT_FOLDER filepath to the folder where the outputs will be placed --dataset_name DATASET_NAME name of the dataset to be used in creating the lmdb files --train_fraction TRAIN_FRACTION what fraction of the dataset to use for training (0.0, 1.0) --image_format IMAGE_FORMAT format (extension) of the input images. E.g {tif, jpg, png)"><pre class="notranslate"><code>python build_lmdb.py -h usage: build_lmdb [-h] --image_folder IMAGE_FOLDER --csv_folder CSV_FOLDER --output_folder OUTPUT_FOLDER --dataset_name DATASET_NAME [--train_fraction TRAIN_FRACTION] [--image_format IMAGE_FORMAT] Script which converts two folders of images and masks into a pair of lmdb databases for training. optional arguments: -h, --help show this help message and exit --image_folder IMAGE_FOLDER filepath to the folder containing the images --csv_folder CSV_FOLDER filepath to the folder containing the bounding box csv files --output_folder OUTPUT_FOLDER filepath to the folder where the outputs will be placed --dataset_name DATASET_NAME name of the dataset to be used in creating the lmdb files --train_fraction TRAIN_FRACTION what fraction of the dataset to use for training (0.0, 1.0) --image_format IMAGE_FORMAT format (extension) of the input images. E.g {tif, jpg, png) </code></pre></div> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Training</h1><a id="user-content-training" class="anchor" aria-label="Permalink: Training" href="#training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">With the lmdb build there are two methods for training a model.</p> <ol dir="auto"> <li>Cluster: Single Node Multi GPU <ul dir="auto"> <li>the include shell script <code>launch_train_sbatch.sh</code> is setup to all training on the Enki Cluster.</li> </ul> </li> <li>Local: Single Node Multi GPU <ul dir="auto"> <li>If you want to train the model on local hardware, avoid using <code>launch_train_sbatch.sh</code>, use python and directly launch <code>train.py</code>.</li> </ul> </li> </ol> <p dir="auto">Whether you launch the training locally or on Enki, the training script will query the system and determine how many GPUs are available. It will then build the network for training using data-parallelism. So when you define your batch size, it will actually be multiplied by the number of GPUs you are using to train the network. So a batch size of 8 training on 4 gpus, results in an actual batch size of 32. Each GPU computes its own forward pass on its own data, computes the gradient, and then all N gradients are averaged.</p> <p dir="auto">The full help for the training script is:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python train.py -h usage: train_yolo [-h] [--batch_size BATCH_SIZE] [--learning_rate LEARNING_RATE] [--test_every_n_steps TEST_EVERY_N_STEPS] --train_database TRAIN_DATABASE_FILEPATH --test_database TEST_DATABASE_FILEPATH --output_dir OUTPUT_FOLDER [--early_stopping TERMINATE_AFTER_NUM_EPOCHS_WITHOUT_TEST_LOSS_IMPROVEMENT] [--use_augmentation USE_AUGMENTATION] Script which trains a yolo_v3 model optional arguments: -h, --help show this help message and exit --batch_size BATCH_SIZE training batch size --learning_rate LEARNING_RATE --test_every_n_steps TEST_EVERY_N_STEPS number of gradient update steps to take between test runs --train_database TRAIN_DATABASE_FILEPATH lmdb database to use for (Required) --test_database TEST_DATABASE_FILEPATH lmdb database to use for testing (Required) --output_dir OUTPUT_FOLDER Folder where outputs will be saved (Required) --early_stopping TERMINATE_AFTER_NUM_EPOCHS_WITHOUT_TEST_LOSS_IMPROVEMENT Perform early stopping when the test loss does not improve for N epochs. --use_augmentation USE_AUGMENTATION whether to use data augmentation [0 = false, 1 = true]"><pre class="notranslate"><code>python train.py -h usage: train_yolo [-h] [--batch_size BATCH_SIZE] [--learning_rate LEARNING_RATE] [--test_every_n_steps TEST_EVERY_N_STEPS] --train_database TRAIN_DATABASE_FILEPATH --test_database TEST_DATABASE_FILEPATH --output_dir OUTPUT_FOLDER [--early_stopping TERMINATE_AFTER_NUM_EPOCHS_WITHOUT_TEST_LOSS_IMPROVEMENT] [--use_augmentation USE_AUGMENTATION] Script which trains a yolo_v3 model optional arguments: -h, --help show this help message and exit --batch_size BATCH_SIZE training batch size --learning_rate LEARNING_RATE --test_every_n_steps TEST_EVERY_N_STEPS number of gradient update steps to take between test runs --train_database TRAIN_DATABASE_FILEPATH lmdb database to use for (Required) --test_database TEST_DATABASE_FILEPATH lmdb database to use for testing (Required) --output_dir OUTPUT_FOLDER Folder where outputs will be saved (Required) --early_stopping TERMINATE_AFTER_NUM_EPOCHS_WITHOUT_TEST_LOSS_IMPROVEMENT Perform early stopping when the test loss does not improve for N epochs. --use_augmentation USE_AUGMENTATION whether to use data augmentation [0 = false, 1 = true] </code></pre></div> <p dir="auto">A few of the arguments require explanation.</p> <ul dir="auto"> <li><code>test_every_n_steps</code>: typically, you run test/validation every epoch. However, I am often building models with very small amounts of data (e.g. 500 images). With an actual batch size of 32, that allows me 15 gradient updates per epoch. The model does not change that fast, so I impose a fixed global step count between test so that I don't spend all of my GPU time running the test data. A good value for this is typically 1000.</li> <li><code>early_stopping</code>: this is an integer specifying the early stopping criteria. If the model test loss does not improve after this number of epochs (epoch defined as <code>test_every_n_steps steps</code> updates) training is terminated because we have moved into overfitting the training dataset.</li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Image Readers</h1><a id="user-content-image-readers" class="anchor" aria-label="Permalink: Image Readers" href="#image-readers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">One of the defining features of this codebase is the parallel (python multiprocess) image reading from lightning memory mapped databases.</p> <p dir="auto">There are typically 1 or more reader threads feeding each GPU.</p> <p dir="auto">Each ImageReader class instance:</p> <ul dir="auto"> <li>selects the next image (potentially at random from the shuffled dataset)</li> <li>loads images from a shared lmdb read-only reference</li> <li>determines the image augmentation parameters from by defining augmentation limits</li> <li>applies the augmentation transformation to the image</li> <li>add the augmented image to the batch that reader is building</li> <li>once a batch is constructed, the imagereader adds it to the output queue shared among all of the imagereaders</li> </ul> <p dir="auto">The training script setups of python generators which just get a reference to the output batch queue data and pass it into tensorflow. One of the largest bottlenecks in deep learning is keeping the GPUs fed. By performing the image reading and data augmentation asynchronously all the main python training thread has to do is get a reference to the next batch (which is waiting in memory) and pass it to tensorflow to be copied to the GPUs.</p> <p dir="auto">If the imagereaders do not have enough bandwidth to keep up with the GPUs you can increase the number of readers per gpu, though 1 or 2 readers per gpus is often enough.</p> <p dir="auto">You will know whether the image readers are keeping up with the GPUs. When the imagereader output queue is getting empty a warning is printed to the log:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="Input Queue Starvation !!!!"><pre class="notranslate"><code>Input Queue Starvation !!!! </code></pre></div> <p dir="auto">along with the matching message letting you know when the imagereaders have caught back up:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="Input Queue Starvation Over"><pre class="notranslate"><code>Input Queue Starvation Over </code></pre></div> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Image Augmentation</h1><a id="user-content-image-augmentation" class="anchor" aria-label="Permalink: Image Augmentation" href="#image-augmentation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">For each image being read from the lmdb, a unique set of augmentation parameters are defined.</p> <p dir="auto">the <code>augment</code> class supports:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Transformation</th> <th>Parameterization</th> </tr> </thead> <tbody> <tr> <td>reflection (x, y)</td> <td>Bernoulli</td> </tr> <tr> <td>rotation</td> <td>Uniform</td> </tr> <tr> <td>jitter (x, y)</td> <td>Percent of Image Size</td> </tr> <tr> <td>scale (x,y)</td> <td>Percent Change</td> </tr> <tr> <td>noise</td> <td>Percent Change of Current Image Dynamic Range</td> </tr> <tr> <td>blur</td> <td>Uniform Selection of Kernel Size</td> </tr> <tr> <td>pixel intensity</td> <td>Percent Change of Current Image Dynamic Range</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">These augmentation transformations are generally configured based on domain expertise and stay fixed per dataset.</p> <p dir="auto">Currently the only method for modifying them is to open the <code>imagereader.py</code> file and edit the augmentation parameters contained within the code block within the imagereader <code>__init__</code>:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="# setup the image data augmentation parameters self._reflection_flag = True self._rotation_flag = True self._jitter_augmentation_severity = 0.1 # x% of a FOV self._noise_augmentation_severity = 0.02 # vary noise by x% of the dynamic range present in the image self._scale_augmentation_severity = 0.1 # vary size by x% self._blur_max_sigma = 2 # pixels # self._intensity_augmentation_severity = 0.05"><pre class="notranslate"><code># setup the image data augmentation parameters self._reflection_flag = True self._rotation_flag = True self._jitter_augmentation_severity = 0.1 # x% of a FOV self._noise_augmentation_severity = 0.02 # vary noise by x% of the dynamic range present in the image self._scale_augmentation_severity = 0.1 # vary size by x% self._blur_max_sigma = 2 # pixels # self._intensity_augmentation_severity = 0.05 </code></pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" 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<ghcc-consent id="ghcc" class="position-fixed bottom-0 left-0" style="z-index: 999999" data-initial-cookie-consent-allowed="" data-cookie-consent-required="false"></ghcc-consent> <div id="ajax-error-message" class="ajax-error-message flash flash-error" hidden> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-alert"> <path d="M6.457 1.047c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0 1 14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>