!9062 upsample fp32
From: @zhaozhenlong Reviewed-by: @zhanghaibo5,@zhang_xue_tong Signed-off-by:pull/9062/MERGE
commit
6cf308076d
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* 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, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_LITE_NNACL_UPSAMPLE_PARAMETER_H_
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#define MINDSPORE_LITE_NNACL_UPSAMPLE_PARAMETER_H_
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#include "nnacl/op_base.h"
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typedef struct {
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OpParameter op_parameter_;
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int method_; // 0 for bilinear; 1 for nearest
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} UpsampleParameter;
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#endif // MINDSPORE_LITE_NNACL_UPSAMPLE_PARAMETER_H_
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* 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, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "src/ops/upsample.h"
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#include "src/ops/primitive_c.h"
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#include "src/ops/populate/populate_register.h"
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#include "nnacl/upsample_parameter.h"
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namespace mindspore {
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namespace lite {
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OpParameter *PopulateUpsampleParameter(const mindspore::lite::PrimitiveC *primitive) {
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UpsampleParameter *upsample_parameter = reinterpret_cast<UpsampleParameter *>(malloc(sizeof(UpsampleParameter)));
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if (upsample_parameter == nullptr) {
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MS_LOG(ERROR) << "malloc Upsample Parameter failed.";
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return nullptr;
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}
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memset(upsample_parameter, 0, sizeof(UpsampleParameter));
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auto param = reinterpret_cast<mindspore::lite::Upsample *>(const_cast<mindspore::lite::PrimitiveC *>(primitive));
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upsample_parameter->op_parameter_.type_ = primitive->Type();
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auto method = param->GetMode();
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if (method == "linear") {
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upsample_parameter->method_ = 0;
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} else {
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upsample_parameter->method_ = 1;
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}
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return reinterpret_cast<OpParameter *>(upsample_parameter);
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}
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Registry UpsampleParemeterRegistry(schema::PrimitiveType_Upsample, PopulateUpsampleParameter);
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} // namespace lite
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} // namespace mindspore
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* 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, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "src/runtime/kernel/arm/fp32/upsample_fp32.h"
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#include <algorithm>
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#include "nnacl/fp32/resize_fp32.h"
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#include "src/kernel_registry.h"
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#include "include/errorcode.h"
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using mindspore::lite::KernelRegistrar;
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using mindspore::lite::RET_ERROR;
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using mindspore::lite::RET_OK;
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using mindspore::schema::PrimitiveType_Upsample;
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namespace mindspore::kernel {
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int UpsampleCPUKernel::Init() {
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param_ = reinterpret_cast<UpsampleParameter *>(op_parameter_);
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MS_ASSERT(param_);
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if (!InferShapeDone()) {
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return RET_OK;
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}
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return ReSize();
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}
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int UpsampleCPUKernel::ReSize() {
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auto ret = RET_OK;
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auto out_tensor = out_tensors_.at(0);
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MS_ASSERT(out_tensor);
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auto out_shape = out_tensor->shape();
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if (out_shape.size() != 4) {
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MS_LOG(ERROR) << "Upsample out tensor dim should be 4";
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return RET_ERROR;
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}
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new_height_ = out_shape[1];
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new_width_ = out_shape[2];
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if (param_->method_ == 0) { // bilinear
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FreeTmpBuffer();
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ret = MallocTmpBuffer();
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if (ret != RET_OK) {
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FreeTmpBuffer();
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return ret;
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}
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auto input = in_tensors_.at(0);
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MS_ASSERT(input);
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auto input_shape = input->shape();
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auto output = out_tensors().at(0);
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MS_ASSERT(output);
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auto output_shape = output->shape();
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ret = PrepareResizeBilinear(input_shape.data(), output_shape.data(), align_corners_, y_bottoms_, y_tops_, x_lefts_,
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x_rights_, y_bottom_weights_, x_left_weights_);
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if (ret != RET_OK) {
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FreeTmpBuffer();
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}
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}
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return ret;
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}
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int UpsampleImpl(void *cdata, int task_id) {
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auto upsample_kernel = reinterpret_cast<UpsampleCPUKernel *>(cdata);
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auto error_code = upsample_kernel->RunImpl(task_id);
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if (error_code != RET_OK) {
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MS_LOG(ERROR) << "Upsample Run error task_id[" << task_id << "] error_code[" << error_code << "]";
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return RET_ERROR;
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}
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return RET_OK;
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}
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int UpsampleCPUKernel::RunImpl(int task_id) {
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MS_ASSERT(in_tensors_.size() == 2);
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auto input = in_tensors_.at(0); // input to be upsampled(resized)
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auto input_data = reinterpret_cast<float *>(input->data_c());
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MS_ASSERT(input_data);
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auto out_tensor = out_tensors_.at(0);
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MS_ASSERT(out_tensor);
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auto output_data = reinterpret_cast<float *>(out_tensor->data_c());
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MS_ASSERT(output_data);
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auto input_shape = input->shape();
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int ret = 0;
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switch (param_->method_) {
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case static_cast<int>(schema::ResizeMethod_LINEAR): {
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int n_h_begin, n_h_end;
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int n = out_tensor->shape()[0];
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int h = new_height_;
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int unit = UP_DIV(n * h, context_->thread_num_);
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n_h_begin = unit * task_id;
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n_h_end = std::min(n_h_begin + unit, n * h);
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int c = in_tensors_.at(0)->shape()[3];
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float *line0 = line_buffer_ + new_width_ * c * 2 * task_id;
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float *line1 = line0 + new_width_ * c;
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ret =
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ResizeBilinear2(input_data, output_data, input_shape.data(), out_tensor->shape().data(), y_bottoms_, y_tops_,
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x_lefts_, x_rights_, y_bottom_weights_, x_left_weights_, line0, line1, n_h_begin, n_h_end);
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break;
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}
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case static_cast<int>(schema::ResizeMethod_NEAREST): {
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align_corners_ = false;
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ret = ResizeNearestNeighbor(input_data, output_data, input_shape.data(), out_tensor->shape().data(),
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align_corners_, task_id, context_->thread_num_);
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break;
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}
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default: {
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MS_LOG(ERROR) << "Upsample unknown method " << param_->method_;
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ret = RET_ERROR;
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}
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}
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return ret;
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}
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int UpsampleCPUKernel::Run() {
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int error_code = ParallelLaunch(this->context_->thread_pool_, UpsampleImpl, this, context_->thread_num_);
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if (error_code != RET_OK) {
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MS_LOG(ERROR) << "Upsample run error, error_code[" << error_code << "]";
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FreeTmpBuffer();
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return RET_ERROR;
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}
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return RET_OK;
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}
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kernel::LiteKernel *CpuUpsampleFp32KernelCreator(const std::vector<lite::Tensor *> &inputs,
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const std::vector<lite::Tensor *> &outputs, OpParameter *parameter,
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const lite::InnerContext *ctx, const KernelKey &desc,
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const mindspore::lite::PrimitiveC *primitive) {
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MS_ASSERT(parameter != nullptr);
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MS_ASSERT(desc.type == PrimitiveType_Upsample);
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auto *kernel = new (std::nothrow) UpsampleCPUKernel(parameter, inputs, outputs, ctx, primitive);
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if (kernel == nullptr) {
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MS_LOG(ERROR) << "Create kernel failed, name: " << parameter->name_;
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free(parameter);
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return nullptr;
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}
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auto ret = kernel->Init();
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "Init kernel failed, name: " << parameter->name_
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<< ", type: " << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(parameter->type_));
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delete kernel;
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return nullptr;
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}
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return kernel;
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}
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REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_Upsample, CpuUpsampleFp32KernelCreator)
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} // namespace mindspore::kernel
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* 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, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_UPSAMPLE_FP32_H_
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#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_UPSAMPLE_FP32_H_
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#include <vector>
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#include "src/lite_kernel.h"
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#include "nnacl/upsample_parameter.h"
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#include "src/runtime/kernel/arm/fp32/resize_fp32.h"
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namespace mindspore::kernel {
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class UpsampleCPUKernel : public ResizeCPUKernel {
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public:
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UpsampleCPUKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
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const std::vector<lite::Tensor *> &outputs, const lite::InnerContext *ctx,
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const mindspore::lite::PrimitiveC *primitive)
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: ResizeCPUKernel(parameter, inputs, outputs, ctx, primitive) {}
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~UpsampleCPUKernel() = default;
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int Init() override;
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int ReSize() override;
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int Run() override;
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int RunImpl(int task_id) override;
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private:
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UpsampleParameter *param_;
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};
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} // namespace mindspore::kernel
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#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_UPSAMPLE_FP32_H_
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* 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, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <vector>
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#include "common/common_test.h"
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#include "mindspore/lite/src/kernel_registry.h"
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#include "mindspore/lite/src/lite_kernel.h"
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#include "mindspore/lite/src/tensor.h"
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#include "nnacl/upsample_parameter.h"
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#include "schema/ops_generated.h"
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#include "src/ops/upsample.h"
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using mindspore::schema::Format_NHWC;
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namespace mindspore {
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class TestUpsampleFp32 : public mindspore::CommonTest {
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public:
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TestUpsampleFp32() = default;
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void Prepare(const std::vector<int> &input_shape, float *input_data, float *scale_data, float *output_data,
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schema::ResizeMethod method, const int thread_num);
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void TearDown() override;
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public:
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float err_tol = 1e-5;
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lite::Tensor in_tensor_;
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lite::Tensor scale_tensor_;
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lite::Tensor out_tensor_;
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std::vector<lite::Tensor *> inputs_{&in_tensor_, &scale_tensor_};
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std::vector<lite::Tensor *> outputs_{&out_tensor_};
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UpsampleParameter *param_ = nullptr;
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kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Upsample};
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lite::InnerContext ctx_ = lite::InnerContext();
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kernel::KernelCreator creator_ = nullptr;
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kernel::LiteKernel *kernel_ = nullptr;
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lite::Upsample *upsample_ = nullptr;
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};
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void TestUpsampleFp32::TearDown() {
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in_tensor_.set_data(nullptr);
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scale_tensor_.set_data(nullptr);
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out_tensor_.set_data(nullptr);
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delete upsample_;
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delete kernel_;
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}
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void TestUpsampleFp32::Prepare(const std::vector<int> &input_shape, float *input_data, float *scale_data,
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float *output_data, schema::ResizeMethod method, const int thread_num) {
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in_tensor_.set_data_type(kNumberTypeFloat32);
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in_tensor_.set_format(Format_NHWC);
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in_tensor_.set_shape(input_shape);
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in_tensor_.set_data(input_data);
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scale_tensor_.set_data_type(kNumberTypeFloat32);
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scale_tensor_.set_data(scale_data);
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scale_tensor_.set_shape({4});
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out_tensor_.set_data_type(kNumberTypeFloat32);
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out_tensor_.set_data(output_data);
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upsample_ = new (std::nothrow) lite::Upsample;
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upsample_->InferShape(inputs_, outputs_);
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param_ = reinterpret_cast<UpsampleParameter *>(malloc(sizeof(UpsampleParameter)));
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param_->method_ = static_cast<int>(method);
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desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Upsample};
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ctx_ = lite::InnerContext();
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ctx_.thread_num_ = thread_num;
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ASSERT_EQ(lite::RET_OK, ctx_.Init());
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creator_ = lite::KernelRegistry::GetInstance()->GetCreator(desc);
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ASSERT_NE(creator_, nullptr);
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kernel_ = creator_(inputs_, outputs_, reinterpret_cast<OpParameter *>(param_), &ctx_, desc, nullptr);
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ASSERT_NE(kernel_, nullptr);
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}
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// 2*2 -> 4*4 1thread
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TEST_F(TestUpsampleFp32, test1) {
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float input_data[] = {0.0, 1.0, 2.0, 3.0};
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float output_data[16] = {0.0f};
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std::vector<int> input_shape = {1, 2, 2, 1};
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float scale_data[] = {1.0f, 2.0f, 2.0f, 1.0f};
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std::vector<float> expect = {0.0, 0.5, 1.0, 1.0, 1.0, 1.5, 2.0, 2.0, 2.0, 2.5, 3.0, 3.0, 2.0, 2.5, 3.0, 3.0};
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Prepare(input_shape, input_data, scale_data, output_data, schema::ResizeMethod_LINEAR, 1);
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auto ret = kernel_->Run();
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EXPECT_EQ(0, ret);
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auto output_size = 16;
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ASSERT_EQ(0, CompareOutputData(output_data, expect.data(), output_size, err_tol));
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}
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// 2*2 -> 4*4 2thread
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TEST_F(TestUpsampleFp32, test2) {
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float input_data[] = {0.0, 1.0, 2.0, 3.0};
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float output_data[16] = {0.0f};
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std::vector<int> input_shape = {1, 2, 2, 1};
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float scale_data[] = {1.0f, 2.0f, 2.0f, 1.0f};
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std::vector<float> expect = {0.0, 0.5, 1.0, 1.0, 1.0, 1.5, 2.0, 2.0, 2.0, 2.5, 3.0, 3.0, 2.0, 2.5, 3.0, 3.0};
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Prepare(input_shape, input_data, scale_data, output_data, schema::ResizeMethod_LINEAR, 2);
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auto ret = kernel_->Run();
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EXPECT_EQ(0, ret);
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auto output_size = 16;
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ASSERT_EQ(0, CompareOutputData(output_data, expect.data(), output_size, err_tol));
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}
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// 2*2*2*5 -> 2*4*4*5 thread num 1
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TEST_F(TestUpsampleFp32, test3) {
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float input_data[] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
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14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0,
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28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0};
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float output_data[160] = {0};
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std::vector<int> input_shape = {2, 2, 2, 5};
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float scale_data[] = {1.0f, 2.0f, 2.0f, 1.0f};
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std::vector<float> expect = {
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0.0, 1.0, 2.0, 3.0, 4.0, 2.5, 3.5, 4.5, 5.5, 6.5, 5.0, 6.0, 7.0, 8.0, 9.0, 5.0, 6.0, 7.0,
|
||||
8.0, 9.0, 5.0, 6.0, 7.0, 8.0, 9.0, 7.5, 8.5, 9.5, 10.5, 11.5, 10.0, 11.0, 12.0, 13.0, 14.0, 10.0,
|
||||
11.0, 12.0, 13.0, 14.0, 10.0, 11.0, 12.0, 13.0, 14.0, 12.5, 13.5, 14.5, 15.5, 16.5, 15.0, 16.0, 17.0, 18.0,
|
||||
19.0, 15.0, 16.0, 17.0, 18.0, 19.0, 10.0, 11.0, 12.0, 13.0, 14.0, 12.5, 13.5, 14.5, 15.5, 16.5, 15.0, 16.0,
|
||||
17.0, 18.0, 19.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 22.5, 23.5, 24.5, 25.5, 26.5,
|
||||
25.0, 26.0, 27.0, 28.0, 29.0, 25.0, 26.0, 27.0, 28.0, 29.0, 25.0, 26.0, 27.0, 28.0, 29.0, 27.5, 28.5, 29.5,
|
||||
30.5, 31.5, 30.0, 31.0, 32.0, 33.0, 34.0, 30.0, 31.0, 32.0, 33.0, 34.0, 30.0, 31.0, 32.0, 33.0, 34.0, 32.5,
|
||||
33.5, 34.5, 35.5, 36.5, 35.0, 36.0, 37.0, 38.0, 39.0, 35.0, 36.0, 37.0, 38.0, 39.0, 30.0, 31.0, 32.0, 33.0,
|
||||
34.0, 32.5, 33.5, 34.5, 35.5, 36.5, 35.0, 36.0, 37.0, 38.0, 39.0, 35.0, 36.0, 37.0, 38.0, 39.0};
|
||||
auto output_size = 160;
|
||||
|
||||
Prepare(input_shape, input_data, scale_data, output_data, schema::ResizeMethod_LINEAR, 1);
|
||||
auto ret = kernel_->Run();
|
||||
EXPECT_EQ(0, ret);
|
||||
|
||||
ASSERT_EQ(0, CompareOutputData(output_data, expect.data(), output_size, err_tol));
|
||||
}
|
||||
|
||||
// 2*2*2*5 -> 2*4*4*5 thread_num 2
|
||||
TEST_F(TestUpsampleFp32, test4) {
|
||||
float input_data[] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
|
||||
14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0,
|
||||
28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0};
|
||||
float output_data[160] = {0};
|
||||
std::vector<int> input_shape = {2, 2, 2, 5};
|
||||
std::vector<float> expect = {
|
||||
0.0, 1.0, 2.0, 3.0, 4.0, 2.5, 3.5, 4.5, 5.5, 6.5, 5.0, 6.0, 7.0, 8.0, 9.0, 5.0, 6.0, 7.0,
|
||||
8.0, 9.0, 5.0, 6.0, 7.0, 8.0, 9.0, 7.5, 8.5, 9.5, 10.5, 11.5, 10.0, 11.0, 12.0, 13.0, 14.0, 10.0,
|
||||
11.0, 12.0, 13.0, 14.0, 10.0, 11.0, 12.0, 13.0, 14.0, 12.5, 13.5, 14.5, 15.5, 16.5, 15.0, 16.0, 17.0, 18.0,
|
||||
19.0, 15.0, 16.0, 17.0, 18.0, 19.0, 10.0, 11.0, 12.0, 13.0, 14.0, 12.5, 13.5, 14.5, 15.5, 16.5, 15.0, 16.0,
|
||||
17.0, 18.0, 19.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 22.5, 23.5, 24.5, 25.5, 26.5,
|
||||
25.0, 26.0, 27.0, 28.0, 29.0, 25.0, 26.0, 27.0, 28.0, 29.0, 25.0, 26.0, 27.0, 28.0, 29.0, 27.5, 28.5, 29.5,
|
||||
30.5, 31.5, 30.0, 31.0, 32.0, 33.0, 34.0, 30.0, 31.0, 32.0, 33.0, 34.0, 30.0, 31.0, 32.0, 33.0, 34.0, 32.5,
|
||||
33.5, 34.5, 35.5, 36.5, 35.0, 36.0, 37.0, 38.0, 39.0, 35.0, 36.0, 37.0, 38.0, 39.0, 30.0, 31.0, 32.0, 33.0,
|
||||
34.0, 32.5, 33.5, 34.5, 35.5, 36.5, 35.0, 36.0, 37.0, 38.0, 39.0, 35.0, 36.0, 37.0, 38.0, 39.0};
|
||||
float scale_data[] = {1.0f, 2.0f, 2.0f, 1.0f};
|
||||
auto output_size = 160;
|
||||
std::vector<float> output(output_size, 0.0);
|
||||
Prepare(input_shape, input_data, scale_data, output_data, schema::ResizeMethod_LINEAR, 2);
|
||||
auto ret = kernel_->Run();
|
||||
EXPECT_EQ(0, ret);
|
||||
|
||||
ASSERT_EQ(0, CompareOutputData(output_data, expect.data(), output_size, err_tol));
|
||||
}
|
||||
|
||||
// 1 5 5 5 -> 1 2 2 5 thread num 1
|
||||
TEST_F(TestUpsampleFp32, test5) {
|
||||
float input_data[] = {
|
||||
0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0,
|
||||
16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0,
|
||||
32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0,
|
||||
48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0,
|
||||
64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0,
|
||||
80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0,
|
||||
96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0, 111.0,
|
||||
112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, 120.0, 121.0, 122.0, 123.0, 124.0};
|
||||
float output_data[20] = {0};
|
||||
std::vector<int> input_shape = {1, 5, 5, 5};
|
||||
std::vector<float> expect = {0.0, 1.0, 2.0, 3.0, 4.0, 12.5, 13.5, 14.5, 15.5, 16.5,
|
||||
62.5, 63.5, 64.5, 65.5, 66.5, 75.0, 76.0, 77.0, 78.0, 79.0};
|
||||
float scale_data[] = {1.0f, 0.4f, 0.4f, 1.0f};
|
||||
auto output_size = 20;
|
||||
|
||||
Prepare(input_shape, input_data, scale_data, output_data, schema::ResizeMethod_LINEAR, 2);
|
||||
auto ret = kernel_->Run();
|
||||
EXPECT_EQ(0, ret);
|
||||
|
||||
ASSERT_EQ(0, CompareOutputData(output_data, expect.data(), output_size, err_tol));
|
||||
}
|
||||
|
||||
// 2 2 2 5 -> 2 4 4 5 thread num 1
|
||||
TEST_F(TestUpsampleFp32, test6) {
|
||||
float input_data[] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
|
||||
14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0,
|
||||
28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0};
|
||||
float output_data[160] = {0};
|
||||
std::vector<int> input_shape = {2, 2, 2, 5};
|
||||
std::vector<int> output_shape = {2, 4, 4, 5};
|
||||
std::vector<float> expect = {
|
||||
0.0, 1.0, 2.0, 3.0, 4.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 5.0, 6.0, 7.0,
|
||||
8.0, 9.0, 0.0, 1.0, 2.0, 3.0, 4.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 5.0,
|
||||
6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0,
|
||||
19.0, 15.0, 16.0, 17.0, 18.0, 19.0, 10.0, 11.0, 12.0, 13.0, 14.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
|
||||
17.0, 18.0, 19.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 20.0, 21.0, 22.0, 23.0, 24.0,
|
||||
25.0, 26.0, 27.0, 28.0, 29.0, 25.0, 26.0, 27.0, 28.0, 29.0, 20.0, 21.0, 22.0, 23.0, 24.0, 20.0, 21.0, 22.0,
|
||||
23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 30.0,
|
||||
31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 35.0, 36.0, 37.0, 38.0, 39.0, 30.0, 31.0, 32.0, 33.0,
|
||||
34.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 35.0, 36.0, 37.0, 38.0, 39.0};
|
||||
size_t output_size = 160;
|
||||
float scale_data[] = {1.0f, 2.0f, 2.0f, 1.0f};
|
||||
Prepare(input_shape, input_data, scale_data, output_data, schema::ResizeMethod_NEAREST, 1);
|
||||
auto ret = kernel_->Run();
|
||||
EXPECT_EQ(0, ret);
|
||||
|
||||
ASSERT_EQ(0, CompareOutputData(output_data, expect.data(), output_size, err_tol));
|
||||
}
|
||||
|
||||
// 2 2 2 5 -> 2 4 4 5 thread num 2
|
||||
TEST_F(TestUpsampleFp32, test7) {
|
||||
float input_data[] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
|
||||
14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0,
|
||||
28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0};
|
||||
float output_data[160] = {0};
|
||||
std::vector<int> input_shape = {2, 2, 2, 5};
|
||||
std::vector<int> output_shape = {2, 4, 4, 5};
|
||||
std::vector<float> expect = {
|
||||
0.0, 1.0, 2.0, 3.0, 4.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 5.0, 6.0, 7.0,
|
||||
8.0, 9.0, 0.0, 1.0, 2.0, 3.0, 4.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 5.0,
|
||||
6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0,
|
||||
19.0, 15.0, 16.0, 17.0, 18.0, 19.0, 10.0, 11.0, 12.0, 13.0, 14.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
|
||||
17.0, 18.0, 19.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 20.0, 21.0, 22.0, 23.0, 24.0,
|
||||
25.0, 26.0, 27.0, 28.0, 29.0, 25.0, 26.0, 27.0, 28.0, 29.0, 20.0, 21.0, 22.0, 23.0, 24.0, 20.0, 21.0, 22.0,
|
||||
23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 30.0,
|
||||
31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 35.0, 36.0, 37.0, 38.0, 39.0, 30.0, 31.0, 32.0, 33.0,
|
||||
34.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 35.0, 36.0, 37.0, 38.0, 39.0};
|
||||
size_t output_size = 160;
|
||||
float scale_data[] = {1.0f, 2.0f, 2.0f, 1.0f};
|
||||
Prepare(input_shape, input_data, scale_data, output_data, schema::ResizeMethod_NEAREST, 2);
|
||||
auto ret = kernel_->Run();
|
||||
EXPECT_EQ(0, ret);
|
||||
|
||||
ASSERT_EQ(0, CompareOutputData(output_data, expect.data(), output_size, err_tol));
|
||||
}
|
||||
} // namespace mindspore
|
Loading…
Reference in new issue