Train other models

import argparse
import json
import os
import typing as ty

single_label = "MODEL_TYPE_SINGLE_LABEL_CLASSIFICATION"
multi_label = "MODEL_TYPE_MULTI_LABEL_CLASSIFICATION"
labels_filename = "labels.txt"
unknown_label = "UNKNOWN"

API_KEY = os.environ['API_KEY']
API_KEY_ID = os.environ['API_KEY_ID']


# This parses the required args for the training script.
# The model_dir variable will contain the output directory where
# the ML model that this script creates should be stored.
# The data_json variable will contain the metadata for the dataset
# that you should use to train the model.
def parse_args():
    """Returns dataset file, model output directory, and num_epochs if present.
    These must be parsed as command line arguments and then used as the model
    input and output, respectively. The number of epochs can be used to
    optionally override the default.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument("--dataset_file", dest="data_json", type=str)
    parser.add_argument("--model_output_directory", dest="model_dir", type=str)
    parser.add_argument("--num_epochs", dest="num_epochs", type=int)
    args = parser.parse_args()
    return args.data_json, args.model_dir, args.num_epochs


# This is used for parsing the dataset file (produced and stored in Viam),
# parse it to get the label annotations
# Used for training classifiction models
def parse_filenames_and_labels_from_json(
    filename: str, all_labels: ty.List[str], model_type: str
) -> ty.Tuple[ty.List[str], ty.List[str]]:
    """Load and parse JSON file to return image filenames and corresponding
    labels. The JSON file contains lines, where each line has the key
    "image_path" and "classification_annotations".
    Args:
        filename: JSONLines file containing filenames and labels
        all_labels: list of all N_LABELS
        model_type: string single_label or multi_label
    """
    image_filenames = []
    image_labels = []

    with open(filename, "rb") as f:
        for line in f:
            json_line = json.loads(line)
            image_filenames.append(json_line["image_path"])

            annotations = json_line["classification_annotations"]
            labels = [unknown_label]
            for annotation in annotations:
                if model_type == multi_label:
                    if annotation["annotation_label"] in all_labels:
                        labels.append(annotation["annotation_label"])
                # For single label model, we want at most one label.
                # If multiple valid labels are present, we arbitrarily select
                # the last one.
                if model_type == single_label:
                    if annotation["annotation_label"] in all_labels:
                        labels = [annotation["annotation_label"]]
            image_labels.append(labels)
    return image_filenames, image_labels


# Parse the dataset file (produced and stored in Viam) to get
# bounding box annotations
# Used for training object detection models
def parse_filenames_and_bboxes_from_json(
    filename: str,
    all_labels: ty.List[str],
) -> ty.Tuple[ty.List[str], ty.List[str], ty.List[ty.List[float]]]:
    """Load and parse JSON file to return image filenames
    and corresponding labels with bboxes.
    Args:
        filename: JSONLines file containing filenames and bboxes
        all_labels: list of all N_LABELS
    """
    image_filenames = []
    bbox_labels = []
    bbox_coords = []

    with open(filename, "rb") as f:
        for line in f:
            json_line = json.loads(line)
            image_filenames.append(json_line["image_path"])
            annotations = json_line["bounding_box_annotations"]
            labels = []
            coords = []
            for annotation in annotations:
                if annotation["annotation_label"] in all_labels:
                    labels.append(annotation["annotation_label"])
                    # Store coordinates in rel_yxyx format so that
                    # we can use the keras_cv function
                    coords.append(
                        [
                            annotation["y_min_normalized"],
                            annotation["x_min_normalized"],
                            annotation["y_max_normalized"],
                            annotation["x_max_normalized"],
                        ]
                    )
            bbox_labels.append(labels)
            bbox_coords.append(coords)
    return image_filenames, bbox_labels, bbox_coords


# Build the model
def build_and_compile_model(
    labels: ty.List[str], model_type: str, input_shape: ty.Tuple[int, int, int]
) -> Model:
    """Builds and compiles a model
    Args:
        labels: list of string lists, where each string list contains up to
        N_LABEL labels associated with an image
        model_type: string single_label or multi_label
        input_shape: 3D shape of input
    """

    # TODO: Add logic to build and compile model

    return model


def save_labels(labels: ty.List[str], model_dir: str) -> None:
    """Saves a label.txt of output labels to the specified model directory.
    Args:
        labels: list of string lists, where each string list contains up to
        N_LABEL labels associated with an image
        model_dir: output directory for model artifacts
    """
    filename = os.path.join(model_dir, labels_filename)
    with open(filename, "w") as f:
        for label in labels[:-1]:
            f.write(label + "\n")
        f.write(labels[-1])


def save_model(
    model: Model,
    model_dir: str,
    model_name: str,
) -> None:
    """Save model as a TFLite model.
    Args:
        model: trained model
        model_dir: output directory for model artifacts
        model_name: name of saved model
    """
    file_type = ""

    # Save the model to the output directory.
    filename = os.path.join(model_dir, f"{model_name}.{file_type}")
    with open(filename, "wb") as f:
        f.write(model)


if __name__ == "__main__":
    DATA_JSON, MODEL_DIR = parse_args()

    IMG_SIZE = (256, 256)

    # Read dataset file.
    # TODO: change labels to the desired model output.
    LABELS = ["orange_triangle", "blue_star"]

    # The model type can be changed based on whether you want the model to
    # output one label per image or multiple labels per image
    model_type = multi_label
    image_filenames, image_labels = parse_filenames_and_labels_from_json(
        DATA_JSON, LABELS, model_type)

    # Build and compile model on data
    model = build_and_compile_model()

    # Save labels.txt file
    save_labels(LABELS + [unknown_label], MODEL_DIR)
    # Convert the model to tflite
    save_model(
        model, MODEL_DIR, "classification_model", IMG_SIZE + (3,)
    )

The script you are creating must take the following command line inputs:

• dataset_file: a file containing the data and metadata for the training job
• model_output_directory: the location where the produced model artifacts are saved to

The parse_args() function in the template parses your arguments.

You can add additional custom command line inputs by adding them to the parse_args() function.

When you submit a training job to the Viam Cloud, Viam will pass a dataset_file to the training script when you train an ML model with it. The file contains metadata from the dataset used for the training, including the file path for each data point and any annotations associated with the data.

Dataset JSON files for image datasets with bounding box labels and classification labels are formatted as follows:

{
    "image_path": "/path/to/data/data1.jpeg",
    "bounding_box_annotations": [
        {
            "annotation_label": "blue_star",
            "x_min_normalized": 0.38175675675675674,
            "x_max_normalized": 0.5101351351351351,
            "y_min_normalized": 0.35585585585585583,
            "y_max_normalized": 0.527027027027027
        }
    ],
    "classification_annotations": [
        {
            "annotation_label": "blue_star"
        }
    ]
}
{
    "image_path": "/path/to/data/data2.jpeg",
    "bounding_box_annotations": [
        {
            "annotation_label": "blue_star",
            "x_min_normalized": 0.2939189189189189,
            "x_max_normalized": 0.4594594594594595,
            "y_min_normalized": 0.25225225225225223,
            "y_max_normalized": 0.5495495495495496
        }
    ],
    "classification_annotations": [
        {
            "annotation_label": "blue_star"
        }
    ]
}

{
    "image_path": "/path/to/data/data3.jpeg",
    "bounding_box_annotations": [
        {
            "annotation_label": "blue_star",
            "x_min_normalized": 0.03557312252964427,
            "x_max_normalized": 0.2015810276679842,
            "y_min_normalized": 0.30526315789473685,
            "y_max_normalized": 0.5368421052631579
        },
        {
            "annotation_label": "blue_square",
            "x_min_normalized": 0.039525691699604744,
            "x_max_normalized": 0.2015810276679842,
            "y_min_normalized": 0.2578947368421053,
            "y_max_normalized": 0.5473684210526316
        }
    ],
    "classification_annotations": [
        {
            "annotation_label": "blue_star"
        },
        {
            "annotation_label": "blue_square"
        }
    ]
}

In your training script, you must parse the dataset file for the classification or bounding box annotations from the dataset metadata. Depending on if you are training a classification or detection model, the template script contains the parse_filenames_and_labels_from_json() and the parse_filenames_and_bboxes_from_json() function.