Model (context)#

Models#

Register model#

Datumaro can execute deep learning models in various frameworks. Check the plugins section for more info.

Supported frameworks:

OpenVINO
Custom models via custom launchers

Models need to be added to the Datumaro project first. It can be done with the datum model add command.

Usage:

datum model add [-h] [-n NAME] -l LAUNCHER [--copy] [--no-check]
  [-p PROJECT_DIR] [-- EXTRA_ARGS]

Parameters:

-l, --launcher (string) - Model launcher name
--copy - Copy model data into project. By default, only the link is saved.
--no-check - Don’t check the model can be loaded
-n, --name (string) - Name of the new model (default: generate automatically)
-p, --project (string) - Directory of the project to operate on (default: current directory).
-h, --help - Print the help message and exit.
<extra args> - Additional arguments for the model launcher (use -- -h for help). Must be specified after the main command arguments.

Example: register an OpenVINO model

A model consists of a graph description and weights. There is also a script used to convert model outputs to internal data structures.

datum create
datum model add \
  -n <model_name> -l openvino -- \
  -d <path_to_xml> -w <path_to_bin> -i <path_to_interpretation_script>

Interpretation script for an OpenVINO detection model (convert.py): You can find OpenVINO model interpreter samples in datumaro/plugins/openvino/samples (instruction).

import datumaro as dm

max_det = 10
conf_thresh = 0.1

def process_outputs(inputs, outputs):
    # inputs = model input, array or images, shape = (N, C, H, W)
    # outputs = model output, shape = (N, 1, K, 7)
    # results = conversion result, [ [ Annotation, ... ], ... ]
    results = []
    for input, output in zip(inputs, outputs):
        input_height, input_width = input.shape[:2]
        detections = output[0]
        image_results = []
        for det in detections:
            label = int(det[1])
            conf = float(det[2])
            if conf <= conf_thresh:
                continue

            x = max(int(det[3] * input_width), 0)
            y = max(int(det[4] * input_height), 0)
            w = min(int(det[5] * input_width - x), input_width)
            h = min(int(det[6] * input_height - y), input_height)
            image_results.append(dm.Bbox(x, y, w, h,
                label=label, attributes={'score': conf} ))

            results.append(image_results[:max_det])

    return results

def get_categories():
    # Optionally, provide output categories - label map etc.
    # Example:
    label_categories = dm.LabelCategories()
    label_categories.add('person')
    label_categories.add('car')
    return { dm.AnnotationType.label: label_categories }

Remove Models#

To remove a model from a project, use the datum model remove command.

Usage:

datum model remove [-h] [-p PROJECT_DIR] name

Parameters:

<name> (string) - The name of the model to be removed
-p, --project (string) - Directory of the project to operate on (default: current directory).
-h, --help - Print the help message and exit.

Example:

datum create
datum model add <...> -n model1
datum remove model1

Run Model#

This command applies model to dataset images and produces a new dataset.

Usage:

datum model run

Parameters:

<target> (string) - A project build target to be used. By default, uses the combined project target.
-m, --model (string) - Model name
-o, --output-dir (string) - Output directory. By default, results will be stored in an auto-generated directory in the current directory.
--overwrite - Allows to overwrite existing files in the output directory, when it is specified and is not empty.
-p, --project (string) - Directory of the project to operate on (default: current directory).
-h, --help - Print the help message and exit.

Example: launch inference on a dataset

datum create
datum import <...>
datum model add mymodel <...>
datum model run -m mymodel -o inference