test_streamline_cnv.py

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import brevitas.onnx as bo
import numpy as np
import pytest
import pkg_resources as pk

import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
from finn.util.test import get_test_model_trained
from finn.util.basic import make_build_dir
from finn.transformation.double_to_single_float import DoubleToSingleFloat

export_onnx_path = make_build_dir("test_streamline_cnv_")

# act bits
@pytest.mark.parametrize("abits", [1])
# weight bits
@pytest.mark.parametrize("wbits", [1])
# network topology / size
@pytest.mark.parametrize("size", ["CNV"])
def test_streamline_cnv(size, wbits, abits):
    if wbits > abits:
        pytest.skip("No wbits > abits cases at the moment")
    nname = "%s_%dW%dA" % (size, wbits, abits)
    finn_onnx = export_onnx_path + "/%s.onnx" % nname
    fc = get_test_model_trained(size, wbits, abits)
    bo.export_finn_onnx(fc, (1, 3, 32, 32), finn_onnx)
    model = ModelWrapper(finn_onnx)
    model = model.transform(DoubleToSingleFloat())
    model = model.transform(InferShapes())
    model = model.transform(FoldConstants())
    model = model.transform(GiveUniqueNodeNames())
    model = model.transform(GiveReadableTensorNames())
    # load one of the test vectors
    fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
    input_tensor = np.load(fn)["arr_0"].astype(np.float32)
    assert input_tensor.shape == (1, 3, 32, 32)
    # run using FINN-based execution
    input_dict = {"global_in": input_tensor}
    expected_ctx = oxe.execute_onnx(model, input_dict, True)
    expected = expected_ctx[model.graph.output[0].name]
    model.save("orig_cnv.onnx")
    model = model.transform(Streamline())
    model.save("streamlined_cnv.onnx")
    produced_ctx = oxe.execute_onnx(model, input_dict, True)
    produced = produced_ctx[model.graph.output[0].name]
    assert np.isclose(expected, produced, atol=1e-3).all()
    assert model.graph.node[0].op_type == "MultiThreshold"