| # 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. |
| |
| import pytest |
| import numpy as np |
| import tvm |
| import tvm.testing |
| import onnx |
| import copy |
| |
| from tvm import relax |
| from tvm.script import relax as R |
| from tvm.script import ir as I |
| from tvm.script import tir as T |
| from adreno_utils import verify |
| from tvm.script.ir_builder import IRBuilder |
| from tvm.script.ir_builder import relax as relax_builder |
| from tvm.relax.frontend.onnx import from_onnx |
| from tvm.relax.transform.legalize_ops import adreno as legalize_adreno |
| |
| import pytest |
| import json |
| import copy |
| |
| |
| @tvm.testing.requires_opencl |
| @tvm.testing.parametrize_targets("opencl") |
| def test_network_resnet(target): |
| @I.ir_module |
| class Resnet: |
| @R.function |
| def main( |
| data: R.Tensor((1, 3, 224, 224), dtype="float32"), |
| resnetv22_batchnorm0_gamma: R.Tensor((3,), dtype="float32"), |
| resnetv22_batchnorm0_beta: R.Tensor((3,), dtype="float32"), |
| resnetv22_batchnorm0_running_mea: R.Tensor((3,), dtype="float32"), |
| resnetv22_batchnorm0_running_var: R.Tensor((3,), dtype="float32"), |
| resnetv22_conv0_weight: R.Tensor((64, 3, 7, 7), dtype="float32"), |
| resnetv22_batchnorm1_gamma: R.Tensor((64,), dtype="float32"), |
| resnetv22_batchnorm1_beta: R.Tensor((64,), dtype="float32"), |
| resnetv22_batchnorm1_running_mea: R.Tensor((64,), dtype="float32"), |
| resnetv22_batchnorm1_running_var: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm0_gamma: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm0_beta: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm0_running_mea: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm0_running_var: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_conv0_weight: R.Tensor((64, 64, 3, 3), dtype="float32"), |
| resnetv22_stage1_batchnorm1_gamma: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm1_beta: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm1_running_mea: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm1_running_var: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_conv1_weight: R.Tensor((64, 64, 3, 3), dtype="float32"), |
| resnetv22_stage1_batchnorm2_gamma: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm2_beta: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm2_running_mea: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm2_running_var: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_conv2_weight: R.Tensor((64, 64, 3, 3), dtype="float32"), |
| resnetv22_stage1_batchnorm3_gamma: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm3_beta: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm3_running_mea: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_batchnorm3_running_var: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage1_conv3_weight: R.Tensor((64, 64, 3, 3), dtype="float32"), |
| resnetv22_stage2_batchnorm0_gamma: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage2_batchnorm0_beta: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage2_batchnorm0_running_mea: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage2_batchnorm0_running_var: R.Tensor((64,), dtype="float32"), |
| resnetv22_stage2_conv0_weight: R.Tensor((128, 64, 3, 3), dtype="float32"), |
| resnetv22_stage2_batchnorm1_gamma: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_batchnorm1_beta: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_batchnorm1_running_mea: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_batchnorm1_running_var: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_conv1_weight: R.Tensor((128, 128, 3, 3), dtype="float32"), |
| resnetv22_stage2_conv2_weight: R.Tensor((128, 64, 1, 1), dtype="float32"), |
| resnetv22_stage2_batchnorm2_gamma: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_batchnorm2_beta: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_batchnorm2_running_mea: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_batchnorm2_running_var: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_conv3_weight: R.Tensor((128, 128, 3, 3), dtype="float32"), |
| resnetv22_stage2_batchnorm3_gamma: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_batchnorm3_beta: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_batchnorm3_running_mea: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_batchnorm3_running_var: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage2_conv4_weight: R.Tensor((128, 128, 3, 3), dtype="float32"), |
| resnetv22_stage3_batchnorm0_gamma: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage3_batchnorm0_beta: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage3_batchnorm0_running_mea: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage3_batchnorm0_running_var: R.Tensor((128,), dtype="float32"), |
| resnetv22_stage3_conv0_weight: R.Tensor((256, 128, 3, 3), dtype="float32"), |
| resnetv22_stage3_batchnorm1_gamma: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_batchnorm1_beta: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_batchnorm1_running_mea: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_batchnorm1_running_var: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_conv1_weight: R.Tensor((256, 256, 3, 3), dtype="float32"), |
| resnetv22_stage3_conv2_weight: R.Tensor((256, 128, 1, 1), dtype="float32"), |
| resnetv22_stage3_batchnorm2_gamma: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_batchnorm2_beta: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_batchnorm2_running_mea: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_batchnorm2_running_var: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_conv3_weight: R.Tensor((256, 256, 3, 3), dtype="float32"), |
| resnetv22_stage3_batchnorm3_gamma: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_batchnorm3_beta: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_batchnorm3_running_mea: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_batchnorm3_running_var: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage3_conv4_weight: R.Tensor((256, 256, 3, 3), dtype="float32"), |
| resnetv22_stage4_batchnorm0_gamma: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage4_batchnorm0_beta: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage4_batchnorm0_running_mea: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage4_batchnorm0_running_var: R.Tensor((256,), dtype="float32"), |
| resnetv22_stage4_conv0_weight: R.Tensor((512, 256, 3, 3), dtype="float32"), |
| resnetv22_stage4_batchnorm1_gamma: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_batchnorm1_beta: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_batchnorm1_running_mea: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_batchnorm1_running_var: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_conv1_weight: R.Tensor((512, 512, 3, 3), dtype="float32"), |
| resnetv22_stage4_conv2_weight: R.Tensor((512, 256, 1, 1), dtype="float32"), |
| resnetv22_stage4_batchnorm2_gamma: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_batchnorm2_beta: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_batchnorm2_running_mea: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_batchnorm2_running_var: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_conv3_weight: R.Tensor((512, 512, 3, 3), dtype="float32"), |
| resnetv22_stage4_batchnorm3_gamma: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_batchnorm3_beta: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_batchnorm3_running_mea: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_batchnorm3_running_var: R.Tensor((512,), dtype="float32"), |
| resnetv22_stage4_conv4_weight: R.Tensor((512, 512, 3, 3), dtype="float32"), |
| resnetv22_batchnorm2_gamma: R.Tensor((512,), dtype="float32"), |
| resnetv22_batchnorm2_beta: R.Tensor((512,), dtype="float32"), |
| resnetv22_batchnorm2_running_mea: R.Tensor((512,), dtype="float32"), |
| resnetv22_batchnorm2_running_var: R.Tensor((512,), dtype="float32"), |
| reshape_attr_tensor164: R.Tensor((2,), dtype="int64"), |
| resnetv22_dense0_weight: R.Tensor((1000, 512), dtype="float32"), |
| resnetv22_dense0_bias: R.Tensor((1000,), dtype="float32"), |
| ) -> R.Tensor((1, 1000), dtype="float32"): |
| with R.dataflow(): |
| lv: R.Tuple( |
| R.Tensor((1, 3, 224, 224), dtype="float32"), |
| R.Tensor((3,), dtype="float32"), |
| R.Tensor((3,), dtype="float32"), |
| ) = R.nn.batch_norm( |
| data, |
| resnetv22_batchnorm0_gamma, |
| resnetv22_batchnorm0_beta, |
| resnetv22_batchnorm0_running_mea, |
| resnetv22_batchnorm0_running_var, |
| axis=1, |
| epsilon=9.9999997473787516e-06, |
| center=True, |
| scale=True, |
| momentum=0.10000000000000001, |
| ) |
| lv1: R.Tensor((1, 3, 224, 224), dtype="float32") = lv[0] |
| lv2: R.Tensor((3,), dtype="float32") = lv[1] |
| lv3: R.Tensor((3,), dtype="float32") = lv[2] |
| lv4: R.Tensor((1, 64, 112, 112), dtype="float32") = R.nn.conv2d( |
| lv1, |
| resnetv22_conv0_weight, |
| strides=[2, 2], |
| padding=[3, 3, 3, 3], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NCHW", |
| kernel_layout="OIHW", |
| out_layout="NCHW", |
| out_dtype="void", |
| ) |
| lv5: R.Tuple( |
| R.Tensor((1, 64, 112, 112), dtype="float32"), |
| R.Tensor((64,), dtype="float32"), |
| R.Tensor((64,), dtype="float32"), |
| ) = R.nn.batch_norm( |
| lv4, |
| resnetv22_batchnorm1_gamma, |
| resnetv22_batchnorm1_beta, |
| resnetv22_batchnorm1_running_mea, |
| resnetv22_batchnorm1_running_var, |
| axis=1, |
| epsilon=9.9999997473787516e-06, |
| center=True, |
| scale=True, |
| momentum=0.10000000000000001, |
| ) |
| lv6: R.Tensor((1, 64, 112, 112), dtype="float32") = lv5[0] |
| lv7: R.Tensor((64,), dtype="float32") = lv5[1] |
| lv8: R.Tensor((64,), dtype="float32") = lv5[2] |
| lv9: R.Tensor((1, 64, 112, 112), dtype="float32") = R.nn.relu(lv6) |
| lv10: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.max_pool2d( |
| lv9, |
| pool_size=[3, 3], |
| strides=[2, 2], |
| dilation=[1, 1], |
| padding=[1, 1, 1, 1], |
| ceil_mode=False, |
| count_include_pad=False, |
| layout="NCHW", |
| out_layout="NCHW", |
| ) |
| lv11: R.Tuple( |
| R.Tensor((1, 64, 56, 56), dtype="float32"), |
| R.Tensor((64,), dtype="float32"), |
| R.Tensor((64,), dtype="float32"), |
| ) = R.nn.batch_norm( |
| lv10, |
| resnetv22_stage1_batchnorm0_gamma, |
| resnetv22_stage1_batchnorm0_beta, |
| resnetv22_stage1_batchnorm0_running_mea, |
| resnetv22_stage1_batchnorm0_running_var, |
| axis=1, |
| epsilon=9.9999997473787516e-06, |
| center=True, |
| scale=True, |
| momentum=0.10000000000000001, |
| ) |
| lv12: R.Tensor((1, 64, 56, 56), dtype="float32") = lv11[0] |
| lv13: R.Tensor((64,), dtype="float32") = lv11[1] |
| lv14: R.Tensor((64,), dtype="float32") = lv11[2] |
| lv15: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.relu(lv12) |
| lv16: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.conv2d( |
| lv15, |
| resnetv22_stage1_conv0_weight, |
| strides=[1, 1], |
| padding=[1, 1, 1, 1], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NCHW", |
| kernel_layout="OIHW", |
| out_layout="NCHW", |
| out_dtype="void", |
| ) |
| lv17: R.Tuple( |
| R.Tensor((1, 64, 56, 56), dtype="float32"), |
| R.Tensor((64,), dtype="float32"), |
| R.Tensor((64,), dtype="float32"), |
| ) = R.nn.batch_norm( |
| lv16, |
| resnetv22_stage1_batchnorm1_gamma, |
| resnetv22_stage1_batchnorm1_beta, |
| resnetv22_stage1_batchnorm1_running_mea, |
| resnetv22_stage1_batchnorm1_running_var, |
| axis=1, |
| epsilon=9.9999997473787516e-06, |
| center=True, |
| scale=True, |
| momentum=0.10000000000000001, |
| ) |
| lv18: R.Tensor((1, 64, 56, 56), dtype="float32") = lv17[0] |
| lv19: R.Tensor((64,), dtype="float32") = lv17[1] |
| lv20: R.Tensor((64,), dtype="float32") = lv17[2] |
| lv21: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.relu(lv18) |
| lv22: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.conv2d( |
| lv21, |
| resnetv22_stage1_conv1_weight, |
| strides=[1, 1], |
| padding=[1, 1, 1, 1], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NCHW", |
| kernel_layout="OIHW", |
| out_layout="NCHW", |
| out_dtype="void", |
| ) |
| lv23: R.Tensor((1, 64, 56, 56), dtype="float32") = R.add(lv22, lv10) |
| lv24: R.Tuple( |
| R.Tensor((1, 64, 56, 56), dtype="float32"), |
| R.Tensor((64,), dtype="float32"), |
| R.Tensor((64,), dtype="float32"), |
| ) = R.nn.batch_norm( |
| lv23, |
| resnetv22_stage1_batchnorm2_gamma, |
| resnetv22_stage1_batchnorm2_beta, |
| resnetv22_stage1_batchnorm2_running_mea, |
| resnetv22_stage1_batchnorm2_running_var, |
| axis=1, |
| epsilon=9.9999997473787516e-06, |
| center=True, |
| scale=True, |
| momentum=0.10000000000000001, |
| ) |
| lv25: R.Tensor((1, 64, 56, 56), dtype="float32") = lv24[0] |
| lv26: R.Tensor((64,), dtype="float32") = lv24[1] |
| lv27: R.Tensor((64,), dtype="float32") = lv24[2] |
| lv28: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.relu(lv25) |
| lv29: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.conv2d( |
| lv28, |
| resnetv22_stage1_conv2_weight, |
| strides=[1, 1], |
| padding=[1, 1, 1, 1], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NCHW", |
| kernel_layout="OIHW", |
| out_layout="NCHW", |
| out_dtype="void", |
| ) |
| lv30: R.Tuple( |
| R.Tensor((1, 64, 56, 56), dtype="float32"), |
| R.Tensor((64,), dtype="float32"), |
| R.Tensor((64,), dtype="float32"), |
| ) = R.nn.batch_norm( |
| lv29, |
| resnetv22_stage1_batchnorm3_gamma, |
| resnetv22_stage1_batchnorm3_beta, |
| resnetv22_stage1_batchnorm3_running_mea, |
| resnetv22_stage1_batchnorm3_running_var, |
| axis=1, |
| epsilon=9.9999997473787516e-06, |
| center=True, |
| scale=True, |
| momentum=0.10000000000000001, |
| ) |
| lv31: R.Tensor((1, 64, 56, 56), dtype="float32") = lv30[0] |
| lv32: R.Tensor((64,), dtype="float32") = lv30[1] |
| lv33: R.Tensor((64,), dtype="float32") = lv30[2] |
| lv34: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.relu(lv31) |
| lv35: R.Tensor((1, 64, 56, 56), dtype="float32") = R.nn.conv2d( |
| lv34, |
| resnetv22_stage1_conv3_weight, |
| strides=[1, 1], |
| padding=[1, 1, 1, 1], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NCHW", |
| kernel_layout="OIHW", |
| out_layout="NCHW", |
| out_dtype="void", |
| ) |
| lv36: R.Tensor((1, 64, 56, 56), dtype="float32") = R.add(lv35, lv23) |
| lv37: R.Tuple( |
| R.Tensor((1, 64, 56, 56), dtype="float32"), |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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", |
| out_dtype="void", |
| ) |
| 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, out_dtype="void" |
| ) |
| gv: R.Tensor((1, 1000), dtype="float32") = R.add(lv126, resnetv22_dense0_bias) |
| R.output(gv) |
| return gv |
| |
| verify(Resnet, target) |
| |
| |
| if __name__ == "__main__": |
| tvm.testing.main() |
