TFLite Tester

TFLite Tester provides a controlled environment to validate TensorFlow Lite models by executing inferences against defined inputs and comparing results to expected outputs. Primary users are ML engineers, mobile developers, and edge AI teams during model development, quantization, and pre-deployment checks. It helps verify input-output mappings, detect drift after optimization, and benchmark delegate performance in deterministic runs.

Conceptually, the tool loads a model, registers input tensors (name, shape, dtype, and sample values), runs inference with a chosen delegate (CPU, GPU, or NNAPI), and captures outputs along with latency. It supports multiple input configurations, batch testing, and tolerance-based checks to accommodate minor numerical differences across platforms. When expected_outputs are provided, it computes per-tensor errors and an overall match score.

Unique value lies in its ability to compare quantized and non-quantized variants, report actionable diagnostics, and export structured test results for CI pipelines. Core features include input definition, delegate selection, output validation, latency reporting, multi-run aggregation, and report export. Typical use cases include validating a new quantization scheme, ensuring consistency across mobile and edge devices, and benchmarking runtime performance before release.