Instance Segmentation model ================================ This tutorial reveals end-to-end solution from installation to model export and optimization for instance segmentation task on a specific example. On this page, we show how to train, validate, export and optimize Mask-RCNN model on a toy dataset. To learn more about Instance Segmentation task, refer to :doc:`../../../explanation/algorithms/segmentation/instance_segmentation`. .. note:: To learn deeper how to manage training process of the model including additional parameters and its modification. The process has been tested on the following configuration. - Ubuntu 20.04 - NVIDIA GeForce RTX 3090 - Intel(R) Core(TM) i9-11900 - CUDA Toolkit 11.8 ************************* Setup virtual environment ************************* 1. You can follow the installation process from a :doc:`quick start guide <../../../get_started/installation>` to create a universal virtual environment for OpenVINO™ Training Extensions. 2. Activate your virtual environment: .. code-block:: shell .otx/bin/activate # or by this line, if you created an environment, using tox . venv/otx/bin/activate *************************** Dataset preparation *************************** .. note:: Currently, we support the following instance segmentation dataset formats: - `COCO `_ 1. Clone a repository with `WGISD dataset `_. .. code-block:: mkdir data ; cd data git clone https://github.com/thsant/wgisd.git cd wgisd git checkout 6910edc5ae3aae8c20062941b1641821f0c30127 This dataset contains images of grapevines with the annotation for different varieties of grapes. - ``CDY`` - Chardonnay - ``CFR`` - Cabernet Franc - ``CSV`` - Cabernet Sauvignon - ``SVB`` - Sauvignon Blanc - ``SYH`` - Syrah | .. image:: ../../../../../utils/images/wgisd_dataset_sample.jpg :width: 600 :alt: this image uploaded from this `source `_ | 2. Check the file structure of downloaded dataset, we will need the following file structure: .. code-block:: shell wgisd ├── annotations/ ├── instances_train.json ├── instances_val.json └── instances_test.json ├──images/ ├── train ├── val └── test (There may be more extra unrelated folders) We can do that by running these commands: .. code-block:: shell # format images folder mv data images # format annotations folder mv coco_annotations annotations # rename annotations to meet *_train.json pattern mv annotations/train_polygons_instances.json annotations/instances_train.json mv annotations/test_polygons_instances.json annotations/instances_val.json cp annotations/instances_val.json annotations/instances_test.json cd ../.. .. note:: We can use this dataset in the detection tutorial. refer to :doc:`./detection`. ********* Training ********* 1. First of all, you need to choose which instance segmentation model you want to train. The list of supported recipes for instance segmentation is available with the command line below. .. note:: The characteristics and detailed comparison of the models could be found in :doc:`Explanation section <../../../explanation/algorithms/segmentation/instance_segmentation>`. .. tab-set:: .. tab-item:: CLI .. code-block:: shell (otx) ...$ otx find --task INSTANCE_SEGMENTATION ┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Task ┃ Model Name ┃ Recipe Path ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ INSTANCE_SEGMENTATION │ openvino_model │ src/otx/recipe/instance_segmentation/openvino_model.yaml │ │ INSTANCE_SEGMENTATION │ maskrcnn_r50 │ src/otx/recipe/instance_segmentation/maskrcnn_r50.yaml │ │ INSTANCE_SEGMENTATION │ maskrcnn_r50_tile │ src/otx/recipe/instance_segmentation/maskrcnn_r50_tile.yaml │ │ INSTANCE_SEGMENTATION │ maskrcnn_swint │ src/otx/recipe/instance_segmentation/maskrcnn_swint.yaml │ │ INSTANCE_SEGMENTATION │ maskrcnn_efficientnetb2b │ src/otx/recipe/instance_segmentation/maskrcnn_efficientnetb2b.yaml │ │ INSTANCE_SEGMENTATION │ rtmdet_inst_tiny │ src/otx/recipe/instance_segmentation/rtmdet_inst_tiny.yaml │ │ INSTANCE_SEGMENTATION │ maskrcnn_efficientnetb2b_tile │ src/otx/recipe/instance_segmentation/maskrcnn_efficientnetb2b_tile.yaml │ │ INSTANCE_SEGMENTATION │ maskrcnn_swint_tile │ src/otx/recipe/instance_segmentation/maskrcnn_swint_tile.yaml │ └───────────────────────┴───────────────────────────────┴────────────────────────────────────────────────────────────────────────────────────┘ .. tab-item:: API .. code-block:: python from otx.engine.utils.api import list_models model_lists = list_models(task="INSTANCE_SEGMENTATION") print(model_lists) ''' [ 'maskrcnn_swint', 'maskrcnn_r50', 'maskrcnn_r50_tile', 'rtmdet_inst_tiny', 'maskrcnn_swint_tile', 'maskrcnn_efficientnetb2b_tile', 'openvino_model', 'maskrcnn_efficientnetb2b', ] ''' 2. On this step we will configure configuration with: - all necessary configs for maskrcnn_r50 - train/validation sets, based on provided annotation. It may be counterintuitive, but for ``--data_root`` we need to pass the path to the dataset folder root (in our case it's ``data/wgisd``) instead of the folder with validation images. This is because the function automatically detects annotations and images according to the expected folder structure we achieved above. Let's check the object detection configuration running the following command: .. code-block:: shell # or its config path (otx) ...$ otx train --config src/otx/recipe/instance_segmentation/maskrcnn_r50.yaml \ --data_root data/wgisd \ --work_dir otx-workspace \ --print_config ... data_root: data/wgisd work_dir: otx-workspace callback_monitor: val/map_50 disable_infer_num_classes: false engine: task: INSTANCE_SEGMENTATION device: auto data: ... .. note:: If you want to get configuration as yaml file, please use ``--print_config`` parameter and ``> configs.yaml``. .. code-block:: shell (otx) ...$ otx train --config src/otx/recipe/instance_segmentation/maskrcnn_r50.yaml --data_root data/wgisd --print_config > configs.yaml # Update configs.yaml & Train configs.yaml (otx) ...$ otx train --config configs.yaml 3. To start training we need to call ``otx train`` Here are the main outputs can expect with CLI: - ``{work_dir}/{timestamp}/checkpoints/epoch_*.ckpt`` - a model checkpoint file. - ``{work_dir}/{timestamp}/configs.yaml`` - The configuration file used in the training can be reused to reproduce the training. - ``{work_dir}/.latest`` - The results of each of the most recently executed subcommands are soft-linked. This allows you to skip checkpoints and config file entry as a workspace. .. tab-set:: .. tab-item:: CLI (auto-config) .. code-block:: shell (otx) ...$ otx train --data_root data/wgisd --task INSTANCE_SEGMENTATION .. tab-item:: CLI (with config) .. code-block:: shell (otx) ...$ otx train --config src/otx/recipe/instance_segmentation/maskrcnn_r50.yaml --data_root data/wgisd .. tab-item:: API (from_config) .. code-block:: python from otx.engine import Engine data_root = "data/wgisd" recipe = "src/otx/recipe/instance_segmentation/maskrcnn_r50.yaml" engine = Engine.from_config( config_path=recipe, data_root=data_root, work_dir="otx-workspace", ) engine.train(...) .. tab-item:: API .. code-block:: python from otx.engine import Engine data_root = "data/wgisd" engine = Engine( model="maskrcnn_r50", task="INSTANCE_SEGMENTATION", data_root=data_root, work_dir="otx-workspace", ) engine.train(...) .. note:: Because the dataset structure is mostly the same as detection, INSTANCE_SEGMENTATION requires the task type to be specified to enable auto-configuration. The training time highly relies on the hardware characteristics, for example on 1 NVIDIA GeForce RTX 3090 the training took about 10 minutes with full dataset. 4. ``(Optional)`` Additionally, we can tune training parameters such as batch size, learning rate, patience epochs or warm-up iterations. Learn more about recipe-specific parameters using ``otx train params --help``. It can be done by manually updating parameters in the ``configs.yaml`` file in your workplace or via the command line. For example, to decrease the batch size to 4, fix the number of epochs to 100 and disable early stopping, extend the command line above with the following line. .. tab-set:: .. tab-item:: CLI .. code-block:: shell (otx) ...$ otx train ... --data.train_subset.batch_size 4 \ --max_epochs 100 .. tab-item:: API .. code-block:: python from otx.core.config.data import SubsetConfig from otx.core.data.module import OTXDataModule from otx.engine import Engine datamodule = OTXDataModule(..., train_subset=SubsetConfig(..., batch_size=4)) engine = Engine(..., datamodule=datamodule) engine.train(max_epochs=100) 5. The training result ``checkpoints/*.ckpt`` file is located in ``{work_dir}`` folder, while training logs can be found in the ``{work_dir}/{timestamp}`` dir. .. note:: We also can visualize the training using ``Tensorboard`` as these logs are located in ``{work_dir}/{timestamp}/tensorboard``. .. code-block:: otx-workspace ├── 20240403_134256/ | ├── csv/ | ├── checkpoints/ | | └── epoch_*.pth | ├── tensorboard/ | └── configs.yaml └── .latest └── train/ ... After that, we have the PyTorch instance segmentation model trained with OpenVINO™ Training Extensions, which we can use for evaluation, export, optimization and deployment. *********** Validation *********** 1. ``otx test`` runs evaluation of a trained model on a specific dataset. The test function receives test annotation information and model snapshot, trained in the previous step. ``otx test`` will output a mAP_50 for instance segmentation. 2. The command below will run validation on our dataset and save performance results in ``otx-workspace``: .. tab-set:: .. tab-item:: CLI (with work_dir) .. code-block:: shell (otx) ...$ otx test --work_dir otx-workspace ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Test metric ┃ DataLoader 0 ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ test/data_time │ 0.0007903117220848799 │ │ test/iter_time │ 0.062202490866184235 │ │ test/map │ 0.33679962158203125 │ │ test/map_50 │ 0.5482384562492371 │ │ test/map_75 │ 0.37118086218833923 │ └───────────────────────────┴───────────────────────────┘ .. tab-item:: CLI (with config) .. code-block:: shell (otx) ...$ otx test --config src/otx/recipe/instance_segmentation/maskrcnn_r50.yaml \ --data_root data/wgisd \ --checkpoint otx-workspace/20240312_051135/checkpoints/epoch_059.ckpt ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Test metric ┃ DataLoader 0 ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ test/data_time │ 0.0007903117220848799 │ │ test/iter_time │ 0.062202490866184235 │ │ test/map │ 0.33679962158203125 │ │ test/map_50 │ 0.5482384562492371 │ │ test/map_75 │ 0.37118086218833923 │ └───────────────────────────┴───────────────────────────┘ .. tab-item:: API .. code-block:: python engine.test() 3. The output of ``{work_dir}/{timestamp}/csv/version_0/metrics.csv`` consists of a dict with target metric name and its value. ********* Export ********* 1. ``otx export`` exports a trained Pytorch `.pth` model to the OpenVINO™ Intermediate Representation (IR) format. It allows running the model on the Intel hardware much more efficient, especially on the CPU. Also, the resulting IR model is required to run PTQ optimization. IR model consists of 2 files: ``exported_model.xml`` for weights and ``exported_model.bin`` for architecture. 2. We can run the below command line to export the trained model and save the exported model to the ``{work_dir}/{timestamp}/`` folder. .. tab-set:: .. tab-item:: CLI (with work_dir) .. code-block:: shell (otx) ...$ otx export --work_dir otx-workspace ... Elapsed time: 0:00:06.588245 .. tab-item:: CLI (with config) .. code-block:: shell (otx) ...$ otx export ... --checkpoint otx-workspace/20240312_051135/checkpoints/epoch_033.ckpt ... Elapsed time: 0:00:06.588245 .. tab-item:: API .. code-block:: python engine.export() ************* Optimization ************* 1. We can further optimize the model with ``otx optimize``. It uses NNCF or PTQ depending on the model and transforms it to ``INT8`` format. Please, refer to :doc:`optimization explanation <../../../explanation/additional_features/models_optimization>` section to get the intuition of what we use under the hood for optimization purposes. 2. Command example for optimizing OpenVINO™ model (.xml) with OpenVINO™ PTQ. .. tab-set:: .. tab-item:: CLI .. code-block:: shell (otx) ...$ otx optimize --work_dir otx-workspace \ --checkpoint otx-workspace/20240312_052847/exported_model.xml ... Statistics collection ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 30/30 • 0:00:14 • 0:00:00 Applying Fast Bias correction ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 58/58 • 0:00:02 • 0:00:00 Elapsed time: 0:00:24.958733 .. tab-item:: API .. code-block:: python ckpt_path = "otx-workspace/20240312_052847/exported_model.xml" engine.optimize(checkpoint=ckpt_path) Please note, that PTQ will take some time (generally less than NNCF optimization) without logging to optimize the model. .. note:: You can also pass `export_demo_package=True` parameter to obtain `exportable_code.zip` archive with packed optimized model and demo package. Please refer to :doc:`export tutorial <../export>`. 3. Finally, we can also evaluate the optimized model by passing it to the ``otx test`` function. .. tab-set:: .. tab-item:: CLI .. code-block:: shell (otx) ...$ otx test --work_dir otx-workspace \ --checkpoint otx-workspace/20240312_055042/optimized_model.xml \ --engine.device cpu ... ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Test metric ┃ DataLoader 0 ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ test/map_50 │ 0.5482384562492371 │ └───────────────────────────┴───────────────────────────┘ Elapsed time: 0:00:10.260521 .. tab-item:: API .. code-block:: python ckpt_path = "otx-workspace/20240312_055042/optimized_model.xml" engine.test(checkpoint=ckpt_path) 3. Now we have fully trained, optimized and exported an efficient model representation ready-to-use instance segmentation model.