wehub-resource-sync
790 lines
39 KiB
Python
790 lines
39 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# ruff: noqa: F401, F841
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import copy
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import json
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import numpy as np
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import pytest
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pytest.importorskip("onnx")
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import onnx
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from utils import verify_results
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import tvm
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import tvm.testing
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from tvm import relax
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from tvm.relax.frontend.onnx import from_onnx
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from tvm.relax.transform.legalize_ops import adreno as legalize_adreno
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from tvm.script import ir as I
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from tvm.script import relax as R
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from tvm.script import tirx as T
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from tvm.script.ir_builder import IRBuilder
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from tvm.script.ir_builder import relax as relax_builder
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TARGETS = [tvm.target.Target("qcom/adreno-opencl-texture")]
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@pytest.mark.gpu
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_network_resnet():
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target = TARGETS[0]
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@I.ir_module
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class Resnet:
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@R.function
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def main(
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data: R.Tensor((1, 3, 224, 224), dtype="float32"),
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resnetv22_batchnorm0_gamma: R.Tensor((3,), dtype="float32"),
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resnetv22_batchnorm0_beta: R.Tensor((3,), dtype="float32"),
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resnetv22_batchnorm0_running_mea: R.Tensor((3,), dtype="float32"),
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resnetv22_batchnorm0_running_var: R.Tensor((3,), dtype="float32"),
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resnetv22_conv0_weight: R.Tensor((64, 3, 7, 7), dtype="float32"),
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resnetv22_batchnorm1_gamma: R.Tensor((64,), dtype="float32"),
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resnetv22_batchnorm1_beta: R.Tensor((64,), dtype="float32"),
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resnetv22_batchnorm1_running_mea: R.Tensor((64,), dtype="float32"),
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resnetv22_batchnorm1_running_var: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm0_gamma: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm0_beta: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm0_running_mea: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm0_running_var: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_conv0_weight: R.Tensor((64, 64, 3, 3), dtype="float32"),
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resnetv22_stage1_batchnorm1_gamma: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm1_beta: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm1_running_mea: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm1_running_var: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_conv1_weight: R.Tensor((64, 64, 3, 3), dtype="float32"),
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resnetv22_stage1_batchnorm2_gamma: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm2_beta: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm2_running_mea: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm2_running_var: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_conv2_weight: R.Tensor((64, 64, 3, 3), dtype="float32"),
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resnetv22_stage1_batchnorm3_gamma: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm3_beta: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm3_running_mea: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_batchnorm3_running_var: R.Tensor((64,), dtype="float32"),
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resnetv22_stage1_conv3_weight: R.Tensor((64, 64, 3, 3), dtype="float32"),
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resnetv22_stage2_batchnorm0_gamma: R.Tensor((64,), dtype="float32"),
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resnetv22_stage2_batchnorm0_beta: R.Tensor((64,), dtype="float32"),
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resnetv22_stage2_batchnorm0_running_mea: R.Tensor((64,), dtype="float32"),
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resnetv22_stage2_batchnorm0_running_var: R.Tensor((64,), dtype="float32"),
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resnetv22_stage2_conv0_weight: R.Tensor((128, 64, 3, 3), dtype="float32"),
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resnetv22_stage2_batchnorm1_gamma: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_batchnorm1_beta: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_batchnorm1_running_mea: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_batchnorm1_running_var: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_conv1_weight: R.Tensor((128, 128, 3, 3), dtype="float32"),
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resnetv22_stage2_conv2_weight: R.Tensor((128, 64, 1, 1), dtype="float32"),
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resnetv22_stage2_batchnorm2_gamma: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_batchnorm2_beta: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_batchnorm2_running_mea: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_batchnorm2_running_var: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_conv3_weight: R.Tensor((128, 128, 3, 3), dtype="float32"),
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resnetv22_stage2_batchnorm3_gamma: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_batchnorm3_beta: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_batchnorm3_running_mea: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_batchnorm3_running_var: R.Tensor((128,), dtype="float32"),
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resnetv22_stage2_conv4_weight: R.Tensor((128, 128, 3, 3), dtype="float32"),
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resnetv22_stage3_batchnorm0_gamma: R.Tensor((128,), dtype="float32"),
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resnetv22_stage3_batchnorm0_beta: R.Tensor((128,), dtype="float32"),
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resnetv22_stage3_batchnorm0_running_mea: R.Tensor((128,), dtype="float32"),
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resnetv22_stage3_batchnorm0_running_var: R.Tensor((128,), dtype="float32"),
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resnetv22_stage3_conv0_weight: R.Tensor((256, 128, 3, 3), dtype="float32"),
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resnetv22_stage3_batchnorm1_gamma: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_batchnorm1_beta: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_batchnorm1_running_mea: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_batchnorm1_running_var: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_conv1_weight: R.Tensor((256, 256, 3, 3), dtype="float32"),
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resnetv22_stage3_conv2_weight: R.Tensor((256, 128, 1, 1), dtype="float32"),
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resnetv22_stage3_batchnorm2_gamma: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_batchnorm2_beta: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_batchnorm2_running_mea: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_batchnorm2_running_var: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_conv3_weight: R.Tensor((256, 256, 3, 3), dtype="float32"),
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resnetv22_stage3_batchnorm3_gamma: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_batchnorm3_beta: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_batchnorm3_running_mea: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_batchnorm3_running_var: R.Tensor((256,), dtype="float32"),
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resnetv22_stage3_conv4_weight: R.Tensor((256, 256, 3, 3), dtype="float32"),
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resnetv22_stage4_batchnorm0_gamma: R.Tensor((256,), dtype="float32"),
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resnetv22_stage4_batchnorm0_beta: R.Tensor((256,), dtype="float32"),
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resnetv22_stage4_batchnorm0_running_mea: R.Tensor((256,), dtype="float32"),
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resnetv22_stage4_batchnorm0_running_var: R.Tensor((256,), dtype="float32"),
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resnetv22_stage4_conv0_weight: R.Tensor((512, 256, 3, 3), dtype="float32"),
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resnetv22_stage4_batchnorm1_gamma: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_batchnorm1_beta: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_batchnorm1_running_mea: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_batchnorm1_running_var: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_conv1_weight: R.Tensor((512, 512, 3, 3), dtype="float32"),
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resnetv22_stage4_conv2_weight: R.Tensor((512, 256, 1, 1), dtype="float32"),
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resnetv22_stage4_batchnorm2_gamma: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_batchnorm2_beta: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_batchnorm2_running_mea: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_batchnorm2_running_var: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_conv3_weight: R.Tensor((512, 512, 3, 3), dtype="float32"),
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resnetv22_stage4_batchnorm3_gamma: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_batchnorm3_beta: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_batchnorm3_running_mea: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_batchnorm3_running_var: R.Tensor((512,), dtype="float32"),
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resnetv22_stage4_conv4_weight: R.Tensor((512, 512, 3, 3), dtype="float32"),
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resnetv22_batchnorm2_gamma: R.Tensor((512,), dtype="float32"),
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resnetv22_batchnorm2_beta: R.Tensor((512,), dtype="float32"),
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resnetv22_batchnorm2_running_mea: R.Tensor((512,), dtype="float32"),
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resnetv22_batchnorm2_running_var: R.Tensor((512,), dtype="float32"),
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reshape_attr_tensor164: R.Tensor((2,), dtype="int64"),
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resnetv22_dense0_weight: R.Tensor((1000, 512), dtype="float32"),
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resnetv22_dense0_bias: R.Tensor((1000,), dtype="float32"),
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) -> R.Tensor((1, 1000), dtype="float32"):
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with R.dataflow():
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lv: R.Tuple(
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R.Tensor((1, 3, 224, 224), dtype="float32"),
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R.Tensor((3,), dtype="float32"),
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R.Tensor((3,), dtype="float32"),
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) = R.nn.batch_norm(
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data,
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resnetv22_batchnorm0_gamma,
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resnetv22_batchnorm0_beta,
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resnetv22_batchnorm0_running_mea,
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resnetv22_batchnorm0_running_var,
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axis=1,
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epsilon=9.9999997473787516e-06,
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center=True,
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scale=True,
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momentum=0.10000000000000001,
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)
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lv1: R.Tensor((1, 3, 224, 224), dtype="float32") = lv[0]
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lv2: R.Tensor((3,), dtype="float32") = lv[1]
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lv3: R.Tensor((3,), dtype="float32") = lv[2]
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lv4: R.Tensor((1, 64, 112, 112), dtype="float32") = R.nn.conv2d(
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lv1,
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resnetv22_conv0_weight,
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strides=[2, 2],
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padding=[3, 3, 3, 3],
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dilation=[1, 1],
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groups=1,
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data_layout="NCHW",
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kernel_layout="OIHW",
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out_layout="NCHW",
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)
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lv5: R.Tuple(
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R.Tensor((1, 64, 112, 112), dtype="float32"),
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R.Tensor((64,), dtype="float32"),
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R.Tensor((64,), dtype="float32"),
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) = R.nn.batch_norm(
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lv4,
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resnetv22_batchnorm1_gamma,
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resnetv22_batchnorm1_beta,
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resnetv22_batchnorm1_running_mea,
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resnetv22_batchnorm1_running_var,
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axis=1,
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epsilon=9.9999997473787516e-06,
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center=True,
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scale=True,
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momentum=0.10000000000000001,
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)
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lv6: R.Tensor((1, 64, 112, 112), dtype="float32") = lv5[0]
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lv7: R.Tensor((64,), dtype="float32") = lv5[1]
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lv8: R.Tensor((64,), dtype="float32") = lv5[2]
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lv9: R.Tensor((1, 64, 112, 112), dtype="float32") = R.nn.relu(lv6)
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lv10: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.max_pool2d(
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lv9,
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pool_size=[3, 3],
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strides=[2, 2],
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dilation=[1, 1],
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padding=[1, 1, 1, 1],
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ceil_mode=False,
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count_include_pad=False,
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layout="NCHW",
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out_layout="NCHW",
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)
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lv11: R.Tuple(
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R.Tensor((1, 64, 56, 56), dtype="float32"),
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R.Tensor((64,), dtype="float32"),
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R.Tensor((64,), dtype="float32"),
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) = R.nn.batch_norm(
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lv10,
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resnetv22_stage1_batchnorm0_gamma,
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resnetv22_stage1_batchnorm0_beta,
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resnetv22_stage1_batchnorm0_running_mea,
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resnetv22_stage1_batchnorm0_running_var,
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axis=1,
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epsilon=9.9999997473787516e-06,
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center=True,
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scale=True,
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momentum=0.10000000000000001,
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)
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lv12: R.Tensor((1, 64, 56, 56), dtype="float32") = lv11[0]
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lv13: R.Tensor((64,), dtype="float32") = lv11[1]
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lv14: R.Tensor((64,), dtype="float32") = lv11[2]
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lv15: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.relu(lv12)
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lv16: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.conv2d(
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lv15,
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resnetv22_stage1_conv0_weight,
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strides=[1, 1],
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padding=[1, 1, 1, 1],
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dilation=[1, 1],
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groups=1,
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data_layout="NCHW",
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kernel_layout="OIHW",
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out_layout="NCHW",
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)
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lv17: R.Tuple(
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R.Tensor((1, 64, 56, 56), dtype="float32"),
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R.Tensor((64,), dtype="float32"),
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R.Tensor((64,), dtype="float32"),
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) = R.nn.batch_norm(
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lv16,
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resnetv22_stage1_batchnorm1_gamma,
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resnetv22_stage1_batchnorm1_beta,
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resnetv22_stage1_batchnorm1_running_mea,
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resnetv22_stage1_batchnorm1_running_var,
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axis=1,
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epsilon=9.9999997473787516e-06,
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center=True,
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scale=True,
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momentum=0.10000000000000001,
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)
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lv18: R.Tensor((1, 64, 56, 56), dtype="float32") = lv17[0]
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lv19: R.Tensor((64,), dtype="float32") = lv17[1]
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lv20: R.Tensor((64,), dtype="float32") = lv17[2]
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lv21: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.relu(lv18)
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lv22: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.conv2d(
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lv21,
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resnetv22_stage1_conv1_weight,
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strides=[1, 1],
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padding=[1, 1, 1, 1],
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dilation=[1, 1],
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groups=1,
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data_layout="NCHW",
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kernel_layout="OIHW",
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out_layout="NCHW",
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)
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lv23: R.Tensor((1, 64, 56, 56), dtype="float32") = R.add(lv22, lv10)
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lv24: R.Tuple(
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R.Tensor((1, 64, 56, 56), dtype="float32"),
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R.Tensor((64,), dtype="float32"),
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R.Tensor((64,), dtype="float32"),
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) = R.nn.batch_norm(
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lv23,
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resnetv22_stage1_batchnorm2_gamma,
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resnetv22_stage1_batchnorm2_beta,
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resnetv22_stage1_batchnorm2_running_mea,
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resnetv22_stage1_batchnorm2_running_var,
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axis=1,
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epsilon=9.9999997473787516e-06,
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center=True,
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scale=True,
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momentum=0.10000000000000001,
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)
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lv25: R.Tensor((1, 64, 56, 56), dtype="float32") = lv24[0]
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lv26: R.Tensor((64,), dtype="float32") = lv24[1]
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lv27: R.Tensor((64,), dtype="float32") = lv24[2]
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lv28: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.relu(lv25)
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lv29: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.conv2d(
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lv28,
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resnetv22_stage1_conv2_weight,
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strides=[1, 1],
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padding=[1, 1, 1, 1],
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dilation=[1, 1],
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groups=1,
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data_layout="NCHW",
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kernel_layout="OIHW",
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out_layout="NCHW",
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)
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lv30: R.Tuple(
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R.Tensor((1, 64, 56, 56), dtype="float32"),
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R.Tensor((64,), dtype="float32"),
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R.Tensor((64,), dtype="float32"),
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) = R.nn.batch_norm(
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lv29,
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resnetv22_stage1_batchnorm3_gamma,
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resnetv22_stage1_batchnorm3_beta,
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resnetv22_stage1_batchnorm3_running_mea,
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resnetv22_stage1_batchnorm3_running_var,
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axis=1,
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epsilon=9.9999997473787516e-06,
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center=True,
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scale=True,
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momentum=0.10000000000000001,
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)
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lv31: R.Tensor((1, 64, 56, 56), dtype="float32") = lv30[0]
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lv32: R.Tensor((64,), dtype="float32") = lv30[1]
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lv33: R.Tensor((64,), dtype="float32") = lv30[2]
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lv34: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.relu(lv31)
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lv35: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.conv2d(
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lv34,
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resnetv22_stage1_conv3_weight,
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strides=[1, 1],
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padding=[1, 1, 1, 1],
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dilation=[1, 1],
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groups=1,
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data_layout="NCHW",
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kernel_layout="OIHW",
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out_layout="NCHW",
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)
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lv36: R.Tensor((1, 64, 56, 56), dtype="float32") = R.add(lv35, lv23)
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lv37: R.Tuple(
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R.Tensor((1, 64, 56, 56), dtype="float32"),
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R.Tensor((64,), dtype="float32"),
|
|
R.Tensor((64,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv36,
|
|
resnetv22_stage2_batchnorm0_gamma,
|
|
resnetv22_stage2_batchnorm0_beta,
|
|
resnetv22_stage2_batchnorm0_running_mea,
|
|
resnetv22_stage2_batchnorm0_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv38: R.Tensor((1, 64, 56, 56), dtype="float32") = lv37[0]
|
|
lv39: R.Tensor((64,), dtype="float32") = lv37[1]
|
|
lv40: R.Tensor((64,), dtype="float32") = lv37[2]
|
|
lv41: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.relu(lv38)
|
|
lv42: R.Tensor((1, 128, 28, 28), dtype="float32") = R.nn.conv2d(
|
|
lv41,
|
|
resnetv22_stage2_conv0_weight,
|
|
strides=[2, 2],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv43: R.Tuple(
|
|
R.Tensor((1, 128, 28, 28), dtype="float32"),
|
|
R.Tensor((128,), dtype="float32"),
|
|
R.Tensor((128,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv42,
|
|
resnetv22_stage2_batchnorm1_gamma,
|
|
resnetv22_stage2_batchnorm1_beta,
|
|
resnetv22_stage2_batchnorm1_running_mea,
|
|
resnetv22_stage2_batchnorm1_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv44: R.Tensor((1, 128, 28, 28), dtype="float32") = lv43[0]
|
|
lv45: R.Tensor((128,), dtype="float32") = lv43[1]
|
|
lv46: R.Tensor((128,), dtype="float32") = lv43[2]
|
|
lv47: R.Tensor((1, 128, 28, 28), dtype="float32") = R.nn.relu(lv44)
|
|
lv48: R.Tensor((1, 128, 28, 28), dtype="float32") = R.nn.conv2d(
|
|
lv47,
|
|
resnetv22_stage2_conv1_weight,
|
|
strides=[1, 1],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv49: R.Tensor((1, 128, 28, 28), dtype="float32") = R.nn.conv2d(
|
|
lv41,
|
|
resnetv22_stage2_conv2_weight,
|
|
strides=[2, 2],
|
|
padding=[0, 0, 0, 0],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv50: R.Tensor((1, 128, 28, 28), dtype="float32") = R.add(lv48, lv49)
|
|
lv51: R.Tuple(
|
|
R.Tensor((1, 128, 28, 28), dtype="float32"),
|
|
R.Tensor((128,), dtype="float32"),
|
|
R.Tensor((128,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv50,
|
|
resnetv22_stage2_batchnorm2_gamma,
|
|
resnetv22_stage2_batchnorm2_beta,
|
|
resnetv22_stage2_batchnorm2_running_mea,
|
|
resnetv22_stage2_batchnorm2_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv52: R.Tensor((1, 128, 28, 28), dtype="float32") = lv51[0]
|
|
lv53: R.Tensor((128,), dtype="float32") = lv51[1]
|
|
lv54: R.Tensor((128,), dtype="float32") = lv51[2]
|
|
lv55: R.Tensor((1, 128, 28, 28), dtype="float32") = R.nn.relu(lv52)
|
|
lv56: R.Tensor((1, 128, 28, 28), dtype="float32") = R.nn.conv2d(
|
|
lv55,
|
|
resnetv22_stage2_conv3_weight,
|
|
strides=[1, 1],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv57: R.Tuple(
|
|
R.Tensor((1, 128, 28, 28), dtype="float32"),
|
|
R.Tensor((128,), dtype="float32"),
|
|
R.Tensor((128,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv56,
|
|
resnetv22_stage2_batchnorm3_gamma,
|
|
resnetv22_stage2_batchnorm3_beta,
|
|
resnetv22_stage2_batchnorm3_running_mea,
|
|
resnetv22_stage2_batchnorm3_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv58: R.Tensor((1, 128, 28, 28), dtype="float32") = lv57[0]
|
|
lv59: R.Tensor((128,), dtype="float32") = lv57[1]
|
|
lv60: R.Tensor((128,), dtype="float32") = lv57[2]
|
|
lv61: R.Tensor((1, 128, 28, 28), dtype="float32") = R.nn.relu(lv58)
|
|
lv62: R.Tensor((1, 128, 28, 28), dtype="float32") = R.nn.conv2d(
|
|
lv61,
|
|
resnetv22_stage2_conv4_weight,
|
|
strides=[1, 1],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv63: R.Tensor((1, 128, 28, 28), dtype="float32") = R.add(lv62, lv50)
|
|
lv64: R.Tuple(
|
|
R.Tensor((1, 128, 28, 28), dtype="float32"),
|
|
R.Tensor((128,), dtype="float32"),
|
|
R.Tensor((128,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv63,
|
|
resnetv22_stage3_batchnorm0_gamma,
|
|
resnetv22_stage3_batchnorm0_beta,
|
|
resnetv22_stage3_batchnorm0_running_mea,
|
|
resnetv22_stage3_batchnorm0_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv65: R.Tensor((1, 128, 28, 28), dtype="float32") = lv64[0]
|
|
lv66: R.Tensor((128,), dtype="float32") = lv64[1]
|
|
lv67: R.Tensor((128,), dtype="float32") = lv64[2]
|
|
lv68: R.Tensor((1, 128, 28, 28), dtype="float32") = R.nn.relu(lv65)
|
|
lv69: R.Tensor((1, 256, 14, 14), dtype="float32") = R.nn.conv2d(
|
|
lv68,
|
|
resnetv22_stage3_conv0_weight,
|
|
strides=[2, 2],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv70: R.Tuple(
|
|
R.Tensor((1, 256, 14, 14), dtype="float32"),
|
|
R.Tensor((256,), dtype="float32"),
|
|
R.Tensor((256,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv69,
|
|
resnetv22_stage3_batchnorm1_gamma,
|
|
resnetv22_stage3_batchnorm1_beta,
|
|
resnetv22_stage3_batchnorm1_running_mea,
|
|
resnetv22_stage3_batchnorm1_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv71: R.Tensor((1, 256, 14, 14), dtype="float32") = lv70[0]
|
|
lv72: R.Tensor((256,), dtype="float32") = lv70[1]
|
|
lv73: R.Tensor((256,), dtype="float32") = lv70[2]
|
|
lv74: R.Tensor((1, 256, 14, 14), dtype="float32") = R.nn.relu(lv71)
|
|
lv75: R.Tensor((1, 256, 14, 14), dtype="float32") = R.nn.conv2d(
|
|
lv74,
|
|
resnetv22_stage3_conv1_weight,
|
|
strides=[1, 1],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv76: R.Tensor((1, 256, 14, 14), dtype="float32") = R.nn.conv2d(
|
|
lv68,
|
|
resnetv22_stage3_conv2_weight,
|
|
strides=[2, 2],
|
|
padding=[0, 0, 0, 0],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv77: R.Tensor((1, 256, 14, 14), dtype="float32") = R.add(lv75, lv76)
|
|
lv78: R.Tuple(
|
|
R.Tensor((1, 256, 14, 14), dtype="float32"),
|
|
R.Tensor((256,), dtype="float32"),
|
|
R.Tensor((256,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv77,
|
|
resnetv22_stage3_batchnorm2_gamma,
|
|
resnetv22_stage3_batchnorm2_beta,
|
|
resnetv22_stage3_batchnorm2_running_mea,
|
|
resnetv22_stage3_batchnorm2_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv79: R.Tensor((1, 256, 14, 14), dtype="float32") = lv78[0]
|
|
lv80: R.Tensor((256,), dtype="float32") = lv78[1]
|
|
lv81: R.Tensor((256,), dtype="float32") = lv78[2]
|
|
lv82: R.Tensor((1, 256, 14, 14), dtype="float32") = R.nn.relu(lv79)
|
|
lv83: R.Tensor((1, 256, 14, 14), dtype="float32") = R.nn.conv2d(
|
|
lv82,
|
|
resnetv22_stage3_conv3_weight,
|
|
strides=[1, 1],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv84: R.Tuple(
|
|
R.Tensor((1, 256, 14, 14), dtype="float32"),
|
|
R.Tensor((256,), dtype="float32"),
|
|
R.Tensor((256,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv83,
|
|
resnetv22_stage3_batchnorm3_gamma,
|
|
resnetv22_stage3_batchnorm3_beta,
|
|
resnetv22_stage3_batchnorm3_running_mea,
|
|
resnetv22_stage3_batchnorm3_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv85: R.Tensor((1, 256, 14, 14), dtype="float32") = lv84[0]
|
|
lv86: R.Tensor((256,), dtype="float32") = lv84[1]
|
|
lv87: R.Tensor((256,), dtype="float32") = lv84[2]
|
|
lv88: R.Tensor((1, 256, 14, 14), dtype="float32") = R.nn.relu(lv85)
|
|
lv89: R.Tensor((1, 256, 14, 14), dtype="float32") = R.nn.conv2d(
|
|
lv88,
|
|
resnetv22_stage3_conv4_weight,
|
|
strides=[1, 1],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv90: R.Tensor((1, 256, 14, 14), dtype="float32") = R.add(lv89, lv77)
|
|
lv91: R.Tuple(
|
|
R.Tensor((1, 256, 14, 14), dtype="float32"),
|
|
R.Tensor((256,), dtype="float32"),
|
|
R.Tensor((256,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv90,
|
|
resnetv22_stage4_batchnorm0_gamma,
|
|
resnetv22_stage4_batchnorm0_beta,
|
|
resnetv22_stage4_batchnorm0_running_mea,
|
|
resnetv22_stage4_batchnorm0_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv92: R.Tensor((1, 256, 14, 14), dtype="float32") = lv91[0]
|
|
lv93: R.Tensor((256,), dtype="float32") = lv91[1]
|
|
lv94: R.Tensor((256,), dtype="float32") = lv91[2]
|
|
lv95: R.Tensor((1, 256, 14, 14), dtype="float32") = R.nn.relu(lv92)
|
|
lv96: R.Tensor((1, 512, 7, 7), dtype="float32") = R.nn.conv2d(
|
|
lv95,
|
|
resnetv22_stage4_conv0_weight,
|
|
strides=[2, 2],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv97: R.Tuple(
|
|
R.Tensor((1, 512, 7, 7), dtype="float32"),
|
|
R.Tensor((512,), dtype="float32"),
|
|
R.Tensor((512,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv96,
|
|
resnetv22_stage4_batchnorm1_gamma,
|
|
resnetv22_stage4_batchnorm1_beta,
|
|
resnetv22_stage4_batchnorm1_running_mea,
|
|
resnetv22_stage4_batchnorm1_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv98: R.Tensor((1, 512, 7, 7), dtype="float32") = lv97[0]
|
|
lv99: R.Tensor((512,), dtype="float32") = lv97[1]
|
|
lv100: R.Tensor((512,), dtype="float32") = lv97[2]
|
|
lv101: R.Tensor((1, 512, 7, 7), dtype="float32") = R.nn.relu(lv98)
|
|
lv102: R.Tensor((1, 512, 7, 7), dtype="float32") = R.nn.conv2d(
|
|
lv101,
|
|
resnetv22_stage4_conv1_weight,
|
|
strides=[1, 1],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv103: R.Tensor((1, 512, 7, 7), dtype="float32") = R.nn.conv2d(
|
|
lv95,
|
|
resnetv22_stage4_conv2_weight,
|
|
strides=[2, 2],
|
|
padding=[0, 0, 0, 0],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv104: R.Tensor((1, 512, 7, 7), dtype="float32") = R.add(lv102, lv103)
|
|
lv105: R.Tuple(
|
|
R.Tensor((1, 512, 7, 7), dtype="float32"),
|
|
R.Tensor((512,), dtype="float32"),
|
|
R.Tensor((512,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv104,
|
|
resnetv22_stage4_batchnorm2_gamma,
|
|
resnetv22_stage4_batchnorm2_beta,
|
|
resnetv22_stage4_batchnorm2_running_mea,
|
|
resnetv22_stage4_batchnorm2_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv106: R.Tensor((1, 512, 7, 7), dtype="float32") = lv105[0]
|
|
lv107: R.Tensor((512,), dtype="float32") = lv105[1]
|
|
lv108: R.Tensor((512,), dtype="float32") = lv105[2]
|
|
lv109: R.Tensor((1, 512, 7, 7), dtype="float32") = R.nn.relu(lv106)
|
|
lv110: R.Tensor((1, 512, 7, 7), dtype="float32") = R.nn.conv2d(
|
|
lv109,
|
|
resnetv22_stage4_conv3_weight,
|
|
strides=[1, 1],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv111: R.Tuple(
|
|
R.Tensor((1, 512, 7, 7), dtype="float32"),
|
|
R.Tensor((512,), dtype="float32"),
|
|
R.Tensor((512,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv110,
|
|
resnetv22_stage4_batchnorm3_gamma,
|
|
resnetv22_stage4_batchnorm3_beta,
|
|
resnetv22_stage4_batchnorm3_running_mea,
|
|
resnetv22_stage4_batchnorm3_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv112: R.Tensor((1, 512, 7, 7), dtype="float32") = lv111[0]
|
|
lv113: R.Tensor((512,), dtype="float32") = lv111[1]
|
|
lv114: R.Tensor((512,), dtype="float32") = lv111[2]
|
|
lv115: R.Tensor((1, 512, 7, 7), dtype="float32") = R.nn.relu(lv112)
|
|
lv116: R.Tensor((1, 512, 7, 7), dtype="float32") = R.nn.conv2d(
|
|
lv115,
|
|
resnetv22_stage4_conv4_weight,
|
|
strides=[1, 1],
|
|
padding=[1, 1, 1, 1],
|
|
dilation=[1, 1],
|
|
groups=1,
|
|
data_layout="NCHW",
|
|
kernel_layout="OIHW",
|
|
out_layout="NCHW",
|
|
)
|
|
lv117: R.Tensor((1, 512, 7, 7), dtype="float32") = R.add(lv116, lv104)
|
|
lv118: R.Tuple(
|
|
R.Tensor((1, 512, 7, 7), dtype="float32"),
|
|
R.Tensor((512,), dtype="float32"),
|
|
R.Tensor((512,), dtype="float32"),
|
|
) = R.nn.batch_norm(
|
|
lv117,
|
|
resnetv22_batchnorm2_gamma,
|
|
resnetv22_batchnorm2_beta,
|
|
resnetv22_batchnorm2_running_mea,
|
|
resnetv22_batchnorm2_running_var,
|
|
axis=1,
|
|
epsilon=9.9999997473787516e-06,
|
|
center=True,
|
|
scale=True,
|
|
momentum=0.10000000000000001,
|
|
)
|
|
lv119: R.Tensor((1, 512, 7, 7), dtype="float32") = lv118[0]
|
|
lv120: R.Tensor((512,), dtype="float32") = lv118[1]
|
|
lv121: R.Tensor((512,), dtype="float32") = lv118[2]
|
|
lv122: R.Tensor((1, 512, 7, 7), dtype="float32") = R.nn.relu(lv119)
|
|
lv123: R.Tensor((1, 512, 1, 1), dtype="float32") = R.mean(
|
|
lv122, axis=[2, 3], keepdims=True
|
|
)
|
|
lv124: R.Tensor((1, 512), dtype="float32") = R.reshape(lv123, R.shape([1, 512]))
|
|
lv125: R.Tensor((512, 1000), dtype="float32") = R.permute_dims(
|
|
resnetv22_dense0_weight, axes=[1, 0]
|
|
)
|
|
lv126: R.Tensor((1, 1000), dtype="float32") = R.matmul(lv124, lv125)
|
|
gv: R.Tensor((1, 1000), dtype="float32") = R.add(lv126, resnetv22_dense0_bias)
|
|
R.output(gv)
|
|
return gv
|
|
|
|
verify_results(Resnet, target, tvm.target.Target("llvm"))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tvm.testing.main()
|