zhiics commented on a change in pull request #5052: URL: https://github.com/apache/incubator-tvm/pull/5052#discussion_r439506457
########## File path: tests/python/contrib/test_onnx_model.py ########## @@ -0,0 +1,167 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Relay to ONNX serialization 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) + + +def skipped_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() + expr = func + mod["main"] = expr Review comment: you can just do `mod = IRModule.from_expr(func)` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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