tests/python/contrib/test_onnx_model.py

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"""Relay to ONNX target test cases"""
import pytest

pytest.importorskip("onnx")
pytest.importorskip("onnxruntime")

from collections import OrderedDict
import numpy as np
import onnxruntime as rt
import tvm
from tvm import relay
from tvm.contrib.target.onnx import to_onnx
import tvm.relay.testing
from tvm.relay.op.annotation import compiler_begin, compiler_end
from tvm.ir import IRModule
from tvm.relay import transform


def func_to_onnx(mod, params, name):
    onnx_model = to_onnx(mod, params, name, path=None)
    return onnx_model.SerializeToString()


def run_onnx(mod, params, name, input_data):
    onnx_model = func_to_onnx(mod, params, name)
    sess = rt.InferenceSession(onnx_model)
    input_names = {}
    for input, data in zip(sess.get_inputs(), input_data):
        input_names[input.name] = data
    output_names = [output.name for output in sess.get_outputs()]
    res = sess.run(output_names, input_names)
    return res[0]


def get_data(in_data_shapes, dtype="float32"):
    in_data = OrderedDict()
    for name, shape in in_data_shapes.items():
        in_data[name] = np.random.uniform(size=shape).astype(dtype)
    return in_data


def run_relay(mod, params, in_data):
    target = "llvm"
    ctx = tvm.context("llvm", 0)
    intrp = relay.create_executor("graph", mod, ctx=ctx, target=target)
    in_data = [tvm.nd.array(value) for value in in_data.values()]
    return intrp.evaluate()(*in_data, **params).asnumpy()


def _verify_results(mod, params, in_data):
    a = run_relay(mod, params, in_data)
    b = run_onnx(mod, params, "test_resent", in_data.values())
    np.testing.assert_allclose(a, b, rtol=1e-7, atol=1e-7)


def test_resnet():
    num_class = 1000
    in_data_shapes = OrderedDict({"data": (1, 3, 224, 224)})
    in_data = get_data(in_data_shapes, dtype="float32")
    for n in [18, 34, 50, 101]:
        mod, params = tvm.relay.testing.resnet.get_workload(1, num_class, num_layers=n)
        _verify_results(mod, params, in_data)


def test_squeezenet():
    in_data_shapes = OrderedDict({"data": (1, 3, 224, 224)})
    in_data = get_data(in_data_shapes, dtype="float32")
    for version in ["1.0", "1.1"]:
        mod, params = tvm.relay.testing.squeezenet.get_workload(1, version=version)
        _verify_results(mod, params, in_data)


@pytest.mark.skip("USE_TARGET_ONNX should be ON")
def test_partition():
    in_1 = relay.var("in_1", shape=(10, 10), dtype="float32")
    in_2 = relay.var("in_2", shape=(10, 10), dtype="float32")
    in_3 = relay.var("in_3", shape=(10, 10), dtype="float32")
    in_4 = relay.var("in_4", shape=(10, 10), dtype="float32")
    in_5 = relay.var("in_5", shape=(10, 10), dtype="float32")
    in_6 = relay.var("in_6", shape=(10, 10), dtype="float32")
    in_7 = relay.var("in_7", shape=(10, 10), dtype="float32")
    in_8 = relay.var("in_8", shape=(10, 10), dtype="float32")
    in_9 = relay.var("in_9", shape=(10, 10), dtype="float32")
    in_10 = relay.var("in_10", shape=(10, 10), dtype="float32")

    begin0 = compiler_begin(in_1, "onnx")
    begin1 = compiler_begin(in_2, "onnx")
    begin2 = compiler_begin(in_3, "onnx")
    begin3 = compiler_begin(in_4, "onnx")
    node0 = relay.add(begin0, begin1)
    node1 = relay.add(begin2, begin3)
    end0 = compiler_end(node0, "onnx")
    end1 = compiler_end(node1, "onnx")
    begin4 = compiler_begin(end0, "onnx")
    begin5 = compiler_begin(end1, "onnx")
    node2 = relay.add(begin4, begin5)
    end2 = compiler_end(node2, "onnx")

    dbegin0 = compiler_begin(in_5, "default")
    dbegin1 = compiler_begin(in_6, "default")
    node3 = relay.subtract(dbegin0, dbegin1)
    dbegin2 = compiler_begin(in_7, "default")
    dend1 = compiler_end(node3, "default")
    dbegin3 = compiler_begin(dend1, "default")
    node4 = relay.subtract(dbegin2, dbegin3)
    dend2 = compiler_end(node4, "default")

    begin6 = compiler_begin(end2, "onnx")
    begin7 = compiler_begin(dend2, "onnx")
    node5 = relay.add(begin6, begin7)
    end3 = compiler_end(node5, "onnx")
    end4 = compiler_end(node5, "onnx")
    dbegin4 = compiler_begin(in_8, "default")
    dbegin5 = compiler_begin(end3, "default")
    node6 = relay.subtract(dbegin4, dbegin5)
    begin8 = compiler_begin(in_9, "onnx")
    begin9 = compiler_begin(end4, "onnx")
    node7 = relay.multiply(begin8, begin9)
    end5 = compiler_end(node7, "onnx")

    dend3 = compiler_end(node6, "default")
    begin10 = compiler_begin(dend3, "onnx")
    begin11 = compiler_begin(end5, "onnx")
    node8 = relay.add(begin10, begin11)
    end6 = compiler_end(node8, "onnx")
    begin12 = compiler_begin(in_10, "onnx")
    begin13 = compiler_begin(end6, "onnx")
    node9 = relay.add(begin12, begin13)
    end7 = compiler_end(node9, "onnx")

    func = relay.Function([in_1, in_2, in_3, in_4, in_5, in_6, in_7, in_8, in_9, in_10], end7)

    target = "llvm"
    mod = IRModule.from_expr(func)
    mod = transform.PartitionGraph()(mod)

    with tvm.transform.PassContext(opt_level=3, disabled_pass=["FuseOps"]):
        graph_json, mod1, params = relay.build(mod, target)

    assert mod1.type_key == "metadata"
    assert mod1.imported_modules[0].type_key == "llvm"
    assert mod1.imported_modules[0].get_source()
    assert mod1.imported_modules[1].type_key == "onnx"
    assert mod1.imported_modules[1].get_source()


if __name__ == "__main__":
    test_resnet()
    test_squeezenet()
    # test_partition needs USE_TARGET_ONNX to be ON
    test_partition()