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 Instance Segmentation.

Note

To learn deeper how to manage training process of the model including additional parameters and its modification.

To learn how to deploy the trained model, refer to: How to deploy the model and use demo in exportable code.

To learn how to run the demo and visualize results, refer to: How to run the demonstration mode with OpenVINO™ Training Extensions CLI.

The process has been tested on the following configuration.

Ubuntu 20.04
NVIDIA GeForce RTX 3090
Intel(R) Core(TM) i9-11900
CUDA Toolkit 11.7

Setup virtual environment#

1. You can follow the installation process from a quick start guide to create a universal virtual environment for OpenVINO™ Training Extensions.

2. Activate your virtual environment:

.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.

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

this image uploaded from this `source <https://github.com/thsant/wgisd/blob/master/data/CDY_2015.jpg>`_

2. Check the file structure of downloaded dataset, we will need the following file structure:

wgisd
├── annotations/
    ├── instances_train.json
    ├── instances_val.json
    (Optional)
    └── instances_test.json
├──images/
    (Optional)
    ├── train
    ├── val
    └── test
(There may be more extra unrelated folders)

We can do that by running these commands:

# format images folder
mv data images

# format annotations folder
mv coco_annotations annotations

# rename annotations to meet *_train.json pattern
mv annotations/train_bbox_instances.json annotations/instances_train.json
mv annotations/test_bbox_instances.json annotations/instances_val.json

cd ../..

Note

We can use this dataset in the detection tutorial. refer to Object Detection model.

Training#

1. First of all, you need to choose which instance segmentation model you want to train. The list of supported templates for instance segmentation is available with the command line below.

Note

The characteristics and detailed comparison of the models could be found in Explanation section.

To modify the architecture of supported models with various backbones, please refer to the advanced tutorial for backbone replacement.

(otx) ...$ otx find --template --task instance_segmentation

+-----------------------+----------------------------------------------------------------+--------------------------+---------------------------------------------------------------------------------------------------+
|          TASK         |                               ID                               |           NAME           |                                             BASE PATH                                             |
+-----------------------+----------------------------------------------------------------+--------------------------+---------------------------------------------------------------------------------------------------+
| INSTANCE_SEGMENTATION |    Custom_Counting_Instance_Segmentation_MaskRCNN_ResNet50     |    MaskRCNN-ResNet50     |     src/otx/algorithms/detection/configs/instance_segmentation/resnet50_maskrcnn/template.yaml    |
| INSTANCE_SEGMENTATION | Custom_Counting_Instance_Segmentation_MaskRCNN_EfficientNetB2B | MaskRCNN-EfficientNetB2B | src/otx/algorithms/detection/configs/instance_segmentation/efficientnetb2b_maskrcnn/template.yaml |
| INSTANCE_SEGMENTATION | Custom_Counting_Instance_Segmentation_MaskRCNN_ConvNeXt        | MaskRCNN-ConvNeXt        | src/otx/algorithms/detection/configs/instance_segmentation/convnext_maskrcnn/template.yaml        |
+-----------------------+----------------------------------------------------------------+--------------------------+---------------------------------------------------------------------------------------------------+

2. We need to create OpenVINO™ Training Extensions workspace first.

Let’s prepare an OpenVINO™ Training Extensions instance segmentation workspace running the following command:

(otx) ...$ otx build --task instance_segmentation

[*] Workspace Path: otx-workspace-INSTANCE_SEGMENTATION
[*] Load Model Template ID: Custom_Counting_Instance_Segmentation_MaskRCNN_ResNet50
[*] Load Model Name: MaskRCNN-ResNet50
[*]     - Updated: otx-workspace-INSTANCE_SEGMENTATION/model.py
[*]     - Updated: otx-workspace-INSTANCE_SEGMENTATION/data_pipeline.py
[*]     - Updated: otx-workspace-INSTANCE_SEGMENTATION/tile_pipeline.py
[*]     - Updated: otx-workspace-INSTANCE_SEGMENTATION/deployment.py
[*]     - Updated: otx-workspace-INSTANCE_SEGMENTATION/hpo_config.yaml
[*]     - Updated: otx-workspace-INSTANCE_SEGMENTATION/compression_config.json
[*] Update data configuration file to: otx-workspace-INSTANCE_SEGMENTATION/data.yaml

(otx) ...$ cd ./otx-workspace-INSTANCE_SEGMENTATION

Note

The default model for instance segmentation is MaskRCNN-ResNet50. If you want to use a different model, use the commands below.

(otx) ...$ otx build --task instance_segmentation --model <Model-Name>

It will create otx-workspace-INSTANCE_SEGMENTATION with all necessary configs for MaskRCNN-ResNet50, prepared data.yaml to simplify CLI commands launch and splitted dataset.

Note

Using otx train with TEMPLATE allows you to run the training directly without otx build.

However, this requires --train-data-roots and --val-data-roots in the command.

(otx) ...$ otx train Custom_Counting_Instance_Segmentation_MaskRCNN_ResNet50 \
                  --train-data-roots <data_root_path>/wgisd \
                  --val-data-roots <data_root_path>/wgisd \
                  params --learning_parameters.num_iters 8

The command above also creates an otx-workspace-INSTANCE_SEGMENTATION, just like running build. This also updates data.yaml with data-specific commands.

Warning

Note, that we can’t run CLI commands for instance segmentation via model name, since the same models are utilized for different algorithm and the behavior can be unpredictable. Please, use the template path or template ID instead.

To simplify the command line functions calling, we may create a data.yaml file with annotations info and pass it as a --data parameter. The content of the otx-workspace-INSTANCE_SEGMENTATION/data.yaml for dataset should have absolute paths and will be similar to that:

Check otx-workspace-INSTANCE_SEGMENTATION/data.yaml to ensure, which data subsets will be used for training and validation, and update it if necessary.

data:
train:
  ann-files: null
  data-roots: <data_root_path>/wgisd
val:
  ann-files: null
  data-roots: <data_root_path>/wgisd
test:
  ann-files: null
  data-roots: null
unlabeled:
  file-list: null
  data-roots: null

3. To start training we need to call otx train command in our workspace:

(otx) .../otx-workspace-INSTANCE_SEGMENTATION$ otx train

...
2023-04-26 10:55:29,312 | INFO : Update LrUpdaterHook patience: 3 -> 3
2023-04-26 10:55:29,312 | INFO : Update CheckpointHook interval: 1 -> 2
2023-04-26 10:55:29,312 | INFO : Update EvalHook interval: 1 -> 2
2023-04-26 10:55:29,312 | INFO : Update EarlyStoppingHook patience: 10 -> 5
2023-04-26 10:55:46,681 | INFO : Epoch [1][28/28] lr: 5.133e-04, eta: 2:54:03, time: 1.055, data_time: 0.658, memory: 7521, current_iters: 27, loss_rpn_cls: 0.2227, loss_rpn_bbox: 0.1252, loss_cls: 1.0220, acc: 77.4606, loss_bbox: 0.7682, loss_mask: 1.1534, loss: 3.2915, grad_norm: 14.0078

...
2023-04-26 11:32:36,162 | INFO : called evaluate()
2023-04-26 11:32:36,511 | INFO : F-measure after evaluation: 0.5576271186440678
2023-04-26 11:32:36,511 | INFO : Evaluation completed
Performance(score: 0.5576271186440678, dashboard: (1 metric groups))
otx train time elapsed:  0:20:23.541362

The training time highly relies on the hardware characteristics, for example on 1 NVIDIA GeForce RTX 3090 the training took about 20 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 template-specific parameters using otx train params --help.

It can be done by manually updating parameters in the template.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.

otx train params --learning_parameters.batch_size 4 \
        --learning_parameters.num_iters 100 \
        --learning_parameters.enable_early_stopping false

5. The training results are weights.pth and label_schema.json files located in outputs/**_train/models folder, while training logs can be found in the outputs/**_train/logs dir.

weights.pth - a model snapshot
label_schema.json - a label schema used in training, created from a dataset

These are needed as inputs for the further commands: export, eval, optimize, deploy and demo.

Note

We also can visualize the training using Tensorboard as these logs are located in outputs/**/logs/**/tf_logs.

otx-workspace-INSTANCE_SEGMENTATION
├── outputs/
    ├── 20230403_134256_train/
        ├── logs/
        ├── models/
            ├── weights.pth
            └── label_schema.json
        └── cli_report.log
    ├── latest_trained_model
        ├── logs/
        ├── models/
        └── cli_report.log
...

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 eval runs evaluation of a trained model on a specific dataset.

The eval function receives test annotation information and model snapshot, trained in the previous step. Please note, label_schema.json file contains meta information about the dataset and it should be located in the same folder as the model snapshot.

otx eval will output a F-measure for instance segmentation.

2. The command below will run validation on our dataset and save performance results in outputs/**_eval/performance.json file:

(otx) ...$ otx eval --test-data-roots <data_root_path>/wgisd

We will get a similar to this validation output:

...

2023-04-26 12:46:27,856 | INFO : Inference completed
2023-04-26 12:46:27,856 | INFO : called evaluate()
2023-04-26 12:46:28,453 | INFO : F-measure after evaluation: 0.5576271186440678
2023-04-26 12:46:28,453 | INFO : Evaluation completed
Performance(score: 0.5576271186440678, dashboard: (1 metric groups))

Note

You can omit --test-data-roots if you are currently inside a workspace and have test-data stuff written in data.yaml.

Also, if you’re inside a workspace and weights.pth exists in outputs/latest_train_model/models dir, you can omit --load-weights as well, assuming those weights are the default as latest_train_model/models/weights.pth.

The output of ./outputs/**_eval/performance.json consists of a dict with target metric name and its value.

{"f-measure": 0.5576271186440678}

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: openvino.xml for weights and openvino.bin for architecture.

2. We can run the below command line to export the trained model and save the exported model to the outputs/**_export/openvino folder.

Note

if you’re inside a workspace and weights.pth exists in outputs/latest_train_model/models dir, you can omit --load-weights as well, assuming those weights are the default as latest_train_model/models/weights.pth.

(otx) ...$ otx export

...
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: otx-workspace-INSTANCE_SEGMENTATION/outputs/20230426_124738_export/logs/model.xml
[ SUCCESS ] BIN file: otx-workspace-INSTANCE_SEGMENTATION/outputs/20230426_124738_export/logs/model.bin

2023-04-26 12:47:48,293 - mmdeploy - INFO - Successfully exported OpenVINO model: outputs/20230426_124738_export/logs/model_ready.xml
2023-04-26 12:47:48,670 | INFO : Exporting completed

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 optimization explanation section to get the intuition of what we use under the hood for optimization purposes.

2. Command example for optimizing a PyTorch model (.pth) with OpenVINO™ NNCF.

Note

if you’re inside a workspace and weights.pth exists in outputs/latest_train_model/models dir, you can omit --load-weights as well (nncf only), assuming those weights are the default as latest_train_model/models/weights.pth.

(otx) ...$ otx optimize

3. Command example for optimizing OpenVINO™ model (.xml) with OpenVINO™ PTQ.

(otx) ...$ otx optimize --load-weights openvino_model/openvino.xml

Please note, that PTQ will take some time (generally less than NNCF optimization) without logging to optimize the model.

4. Now we have fully trained, optimized and exported an efficient model representation ready-to-use instance segmentation model.

The following tutorials provide further steps on how to deploy and use your model in the demonstration mode and visualize results.