test_brevitas_cnv.py

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import pkg_resources as pk

import pytest

import brevitas.onnx as bo
import numpy as np
import os
import torch
from brevitas.export.onnx.generic.manager import BrevitasONNXManager
from qonnx.core.modelwrapper import ModelWrapper
from qonnx.transformation.fold_constants import FoldConstants
from qonnx.transformation.general import GiveUniqueNodeNames, RemoveStaticGraphInputs
from qonnx.transformation.infer_shapes import InferShapes
from qonnx.util.cleanup import cleanup as qonnx_cleanup

import finn.core.onnx_exec as oxe
from finn.transformation.qonnx.convert_qonnx_to_finn import ConvertQONNXtoFINN
from finn.util.test import get_test_model_trained

export_onnx_path = "test_brevitas_cnv.onnx"


@pytest.mark.brevitas_export
@pytest.mark.parametrize("abits", [1, 2])
@pytest.mark.parametrize("wbits", [1, 2])
@pytest.mark.parametrize("QONNX_export", [False, True])
def test_brevitas_cnv_export_exec(wbits, abits, QONNX_export):
    if wbits > abits:
        pytest.skip("No wbits > abits cases at the moment")
    cnv = get_test_model_trained("CNV", wbits, abits)
    ishape = (1, 3, 32, 32)
    if QONNX_export:
        BrevitasONNXManager.export(cnv, ishape, export_onnx_path)
        qonnx_cleanup(export_onnx_path, out_file=export_onnx_path)
        model = ModelWrapper(export_onnx_path)
        model = model.transform(ConvertQONNXtoFINN())
        model.save(export_onnx_path)
    else:
        bo.export_finn_onnx(cnv, ishape, export_onnx_path)
    model = ModelWrapper(export_onnx_path)
    model = model.transform(GiveUniqueNodeNames())
    model = model.transform(InferShapes())
    model = model.transform(FoldConstants())
    model = model.transform(RemoveStaticGraphInputs())
    assert len(model.graph.input) == 1
    assert len(model.graph.output) == 1
    fn = pk.resource_filename("finn.qnn-data", "cifar10/cifar10-test-data-class3.npz")
    input_tensor = np.load(fn)["arr_0"].astype(np.float32)
    input_tensor = input_tensor / 255
    assert input_tensor.shape == (1, 3, 32, 32)
    # run using FINN-based execution
    input_dict = {model.graph.input[0].name: input_tensor}
    output_dict = oxe.execute_onnx(model, input_dict, True)
    produced = output_dict[model.graph.output[0].name]
    # do forward pass in PyTorch/Brevitas
    input_tensor = torch.from_numpy(input_tensor).float()
    expected = cnv.forward(input_tensor).detach().numpy()
    assert np.isclose(produced, expected, atol=1e-3).all()
    assert np.argmax(produced) == 3
    os.remove(export_onnx_path)