Skip to content
sleepKIT
User guide
HELIA

Model Exporting

Introduction

Section titled “Introduction”

Export mode is used to convert the trained TensorFlow model into a format that can be used for deployment onto Ambiq’s family of SoCs. Currently, the command will convert the TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models’ outputs match. The activations and weights can be quantized by configuring the quantization section in the configuration file or by setting the quantization parameter in the code.

  1. Load the configuration data (e.g. configuration.json)
  2. Load the test data (e.g. test.tfds)
  3. Load the trained model (e.g. model.keras)
  4. Quantize the model (e.g. 16x8)
  5. Convert the model (e.g. TFL, TFLM)
  6. Verify the models’ outputs match
  7. Save artifacts (e.g. model.tflite)

Example configuration

modes-export-1.json93 lines · JSONDownload
Show full exampleCollapse example
modes-export-1.json
{
    "name": "sd-2-tcn-sm",
    "job_dir": "./results/sd-2-tcn-sm",
    "verbose": 2,


    "datasets": [{
        "name": "cmidss",
        "params": {
            "path": "./datasets/cmidss"
        }
    }],


    "feature": {
        "name": "FS-W-A-5",
        "sampling_rate": 0.2,
        "frame_size": 12,
        "loader": "hdf5",
        "feat_key": "features",
        "label_key": "detect_labels",
        "mask_key": "mask",
        "feat_cols": null,
        "save_path": "./datasets/store/fs-w-a-5-60",
        "params": {}
    },


    "sampling_rate": 0.0083333,
    "frame_size": 240,


    "num_classes": 2,
    "class_map": {
        "0": 0,
        "1": 1,
        "2": 1,
        "3": 1,
        "4": 1,
        "5": 1
    },
    "class_names": ["WAKE", "SLEEP"],


    "samples_per_subject": 100,
    "val_samples_per_subject": 100,
    "test_samples_per_subject": 50,


    "val_size": 4000,
    "test_size": 2500,


    "val_subjects": 0.20,
    "batch_size": 128,
    "buffer_size": 10000,
    "epochs": 200,
    "steps_per_epoch": 25,
    "val_steps_per_epoch": 25,
    "val_metric": "loss",
    "lr_rate": 1e-3,
    "lr_cycles": 1,
    "label_smoothing": 0,


    "test_metric": "f1",
    "test_metric_threshold": 0.02,
    "tflm_var_name": "sk_detect_flatbuffer",
    "tflm_file": "sk_detect_flatbuffer.h",


    "backend": "pc",
    "display_report": true,


    "quantization": {
        "qat": false,
        "mode": "INT8",
        "io_type": "int8",
        "concrete": true,
        "debug": false
    },


    "model_file": "model.keras",
    "use_logits": false,
    "architecture": {
        "name": "tcn",
        "params": {
            "input_kernel": [1, 5],
            "input_norm": "batch",
            "blocks": [
                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
            ],
            "output_kernel": [1, 5],
            "include_top": true,
            "use_logits": true,
            "model_name": "tcn"
        }
    }
}
{
    "name": "sd-2-tcn-sm",
    "job_dir": "./results/sd-2-tcn-sm",
    "verbose": 2,

    "datasets": [{
        "name": "cmidss",
        "params": {
… 85 more lines

Export

Load

configuration

TaskParams

Load test

data

Load trained

model

Quantize

model

Convert

model

Verify

outputs

Save

artifacts

Usage

Section titled “Usage”

CLI

Section titled “CLI”

The following command will export a detect model using the reference configuration.

Terminal
sleepkit --task detect --mode export --config ./configuration.json

Python

Section titled “Python”

The model can be exported using the following snippet:

Python example
from pathlib import Path
import sleepkit as sk


task = sk.TaskFactory.get("detect")


params = sk.TaskParams.model_validate_json(
    Path("configuration.json").read_text()
)


task.export(params)

Example configuration

modes-export-4.json93 lines · JSONDownload
Show full exampleCollapse example
modes-export-4.json
{
    "name": "sd-2-tcn-sm",
    "job_dir": "./results/sd-2-tcn-sm",
    "verbose": 2,


    "datasets": [{
        "name": "cmidss",
        "params": {
            "path": "./datasets/cmidss"
        }
    }],


    "feature": {
        "name": "FS-W-A-5",
        "sampling_rate": 0.2,
        "frame_size": 12,
        "loader": "hdf5",
        "feat_key": "features",
        "label_key": "detect_labels",
        "mask_key": "mask",
        "feat_cols": null,
        "save_path": "./datasets/store/fs-w-a-5-60",
        "params": {}
    },


    "sampling_rate": 0.0083333,
    "frame_size": 240,


    "num_classes": 2,
    "class_map": {
        "0": 0,
        "1": 1,
        "2": 1,
        "3": 1,
        "4": 1,
        "5": 1
    },
    "class_names": ["WAKE", "SLEEP"],


    "samples_per_subject": 100,
    "val_samples_per_subject": 100,
    "test_samples_per_subject": 50,


    "val_size": 4000,
    "test_size": 2500,


    "val_subjects": 0.20,
    "batch_size": 128,
    "buffer_size": 10000,
    "epochs": 200,
    "steps_per_epoch": 25,
    "val_steps_per_epoch": 25,
    "val_metric": "loss",
    "lr_rate": 1e-3,
    "lr_cycles": 1,
    "label_smoothing": 0,


    "test_metric": "f1",
    "test_metric_threshold": 0.02,
    "tflm_var_name": "sk_detect_flatbuffer",
    "tflm_file": "sk_detect_flatbuffer.h",


    "backend": "pc",
    "display_report": true,


    "quantization": {
        "qat": false,
        "mode": "INT8",
        "io_type": "int8",
        "concrete": true,
        "debug": false
    },


    "model_file": "model.keras",
    "use_logits": false,
    "architecture": {
        "name": "tcn",
        "params": {
            "input_kernel": [1, 5],
            "input_norm": "batch",
            "blocks": [
                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
            ],
            "output_kernel": [1, 5],
            "include_top": true,
            "use_logits": true,
            "model_name": "tcn"
        }
    }
}
{
    "name": "sd-2-tcn-sm",
    "job_dir": "./results/sd-2-tcn-sm",
    "verbose": 2,

    "datasets": [{
        "name": "cmidss",
        "params": {
… 85 more lines

Arguments

Section titled “Arguments”

Please refer to TaskParams for the list of arguments that can be used with the export command.