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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" dev = tvm.device("llvm", 0) in_data = [tvm.nd.array(value) for value in in_data.values()] return ( relay.create_executor("graph", mod, device=dev, target=target) .evaluate()(*in_data, **params) .numpy() ) 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()