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.
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 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#
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- ChardonnayCFR- Cabernet FrancCSV- Cabernet SauvignonSVB- Sauvignon BlancSYH- Syrah
2. Check the file structure of downloaded dataset, we will need the following file structure:
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:
# 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 Object Detection model.
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 Explanation section.
(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 │
└───────────────────────┴───────────────────────────────┴────────────────────────────────────────────────────────────────────────────────────┘
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:
# 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.
(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
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.
(otx) ...$ otx train --data_root data/wgisd --task INSTANCE_SEGMENTATION
(otx) ...$ otx train --config src/otx/recipe/instance_segmentation/maskrcnn_r50.yaml --data_root data/wgisd
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(...)
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.
(otx) ...$ otx train ... --data.train_subset.batch_size 4 \
--max_epochs 100
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.
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:
(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 │
└───────────────────────────┴───────────────────────────┘
(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 │
└───────────────────────────┴───────────────────────────┘
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.
(otx) ...$ otx export --work_dir otx-workspace
...
Elapsed time: 0:00:06.588245
(otx) ...$ otx export ... --checkpoint otx-workspace/20240312_051135/checkpoints/epoch_033.ckpt
...
Elapsed time: 0:00:06.588245
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 optimization explanation 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.
(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
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 export tutorial.
3. Finally, we can also evaluate the optimized model by passing
it to the otx test function.
(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
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.