forked from huawei/mindspore2022
106 lines
3.5 KiB
C++
106 lines
3.5 KiB
C++
/**
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* Copyright 2019-2020 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/bias_grad.h"
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namespace mindspore {
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namespace lite {
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#ifdef PRIMITIVE_WRITEABLE
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std::vector<int> BiasGrad::GetAxis() const { return this->primitive_->value.AsBiasGrad()->axis; }
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void BiasGrad::SetAxis(const std::vector<int> &axis) { this->primitive_->value.AsBiasGrad()->axis = axis; }
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int BiasGrad::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &inputs) {
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if (this->primitive_ == nullptr) {
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this->primitive_ = new (std::nothrow) schema::PrimitiveT;
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if (this->primitive_ == nullptr) {
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MS_LOG(ERROR) << "new primitiveT failed";
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return RET_ERROR;
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}
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this->primitive_->value.type = schema::PrimitiveType_BiasGrad;
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}
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if (this->primitive_->value.type != schema::PrimitiveType_BiasGrad) {
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MS_LOG(ERROR) << "Primitive type is error :" << this->primitive_->value.type;
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return RET_ERROR;
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}
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if (this->primitive_->value.value == nullptr) {
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auto attr = new (std::nothrow) schema::BiasGradT();
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if (attr == nullptr) {
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MS_LOG(ERROR) << "new primitiveT value failed";
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return RET_ERROR;
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}
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attr->axis = {0}; // GetValue<std::vector<int>>(prim.GetAttr("axis"));
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this->primitive_->value.value = attr;
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if (this->primitive_->value.value == nullptr) {
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MS_LOG(ERROR) << "primitive value is nullptr";
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return RET_ERROR;
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}
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}
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return RET_OK;
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}
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#else
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int BiasGrad::UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) {
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MS_ASSERT(nullptr != primitive);
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MS_ASSERT(nullptr != fbb);
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auto attr = primitive->value_as_BiasGrad();
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if (attr == nullptr) {
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MS_LOG(ERROR) << "value_as_BiasGrad return nullptr";
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return RET_ERROR;
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}
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std::vector<int32_t> axis;
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if (attr->axis() != nullptr) {
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for (int i = 0; i < static_cast<int>(attr->axis()->size()); i++) {
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axis.push_back(attr->axis()->data()[i]);
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}
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}
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auto val_offset = schema::CreateBiasGradDirect(*fbb, &axis);
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auto prim_offset = schema::CreatePrimitive(*fbb, schema::PrimitiveType_BiasGrad, val_offset.o);
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fbb->Finish(prim_offset);
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return RET_OK;
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}
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std::vector<int> BiasGrad::GetAxis() const {
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auto fb_vector = this->primitive_->value_as_BiasGrad()->axis();
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return std::vector<int>(fb_vector->begin(), fb_vector->end());
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}
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#endif
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int BiasGrad::InferShape(std::vector<Tensor *> inputs, std::vector<Tensor *> outputs) {
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if (1 != inputs.size()) {
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MS_LOG(ERROR) << "BiasGrad should have one input";
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return RET_ERROR;
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}
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if (1 != outputs.size()) {
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MS_LOG(ERROR) << "BiasGrad should have one output";
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return RET_ERROR;
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}
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auto *in0 = inputs.front();
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auto *out = outputs.front();
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MS_ASSERT(in0 != nullptr);
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MS_ASSERT(out != nullptr);
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auto inshape = in0->shape();
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int ndim = inshape.size();
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for (int i = 0; i < ndim - 1; i++) {
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inshape[i] = 1;
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}
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out->set_shape(inshape);
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out->set_data_type(in0->data_type());
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out->SetFormat(in0->GetFormat());
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return RET_OK;
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}
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} // namespace lite
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} // namespace mindspore
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