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!15111 [MSLITE] refix train tanhgrad bug 

From: @zhengjun10
Reviewed-by: @HilbertDavid,@hangangqiang,@HilbertDavid
Signed-off-by: @HilbertDavid,@HilbertDavid
This commit is contained in:
mindspore-ci-bot 2021-04-22 16:06:11 +08:00 committed by Gitee
parent 3598f124ce a9e5529496
commit e30fe568e7
2 changed files with 7 additions and 6 deletions
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2
mindspore/lite/src/runtime/kernel/arm/fp32_grad/activation_grad.cc
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@ -66,7 +66,7 @@ int ActivationGradCPUKernel::DoActivation(int task_id) {
// Sigmoid gets the input tensors in reverse order! // Sigmoid gets the input tensors in reverse order!
error_code = SigmoidGrad(input_addr + start, yt_addr + start, count, output_addr + start); error_code = SigmoidGrad(input_addr + start, yt_addr + start, count, output_addr + start);
} else if (param_act_grad_->type_ == schema::ActivationType_TANH) { } else if (param_act_grad_->type_ == schema::ActivationType_TANH) {
error_code = TanhGrad(yt_addr + start, input_addr + start, count, output_addr + start); error_code = TanhGrad(input_addr + start, yt_addr + start, count, output_addr + start);
} else if (param_act_grad_->type_ == schema::ActivationType_HSWISH) { } else if (param_act_grad_->type_ == schema::ActivationType_HSWISH) {
error_code = HSwishGrad(yt_addr + start, input_addr + start, count, output_addr + start); error_code = HSwishGrad(yt_addr + start, input_addr + start, count, output_addr + start);
} else if (param_act_grad_->type_ == schema::ActivationType_HSIGMOID) { } else if (param_act_grad_->type_ == schema::ActivationType_HSIGMOID) {

11
mindspore/lite/tools/anf_exporter/anf_exporter.cc
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@ -856,11 +856,12 @@ int AnfExporter::ProcessValueSequence(const ValueNodePtr &value_node, std::uniqu
(*schema_tensor)->dims = {static_cast<int32_t>(shape.size())}; (*schema_tensor)->dims = {static_cast<int32_t>(shape.size())};
(*schema_tensor)->nodeType = NodeType_ValueNode; (*schema_tensor)->nodeType = NodeType_ValueNode;
(*schema_tensor)->data.resize(shape.size() * sizeof(int)); (*schema_tensor)->data.resize(shape.size() * sizeof(int));
ret = memcpy_s((*schema_tensor)->data.data(), shape.size() * sizeof(int32_t), shape.data(), if (!shape.empty()) {
shape.size() * sizeof(int32_t)); if (EOK != memcpy_s((*schema_tensor)->data.data(), shape.size() * sizeof(int32_t), shape.data(),
if (ret != RET_OK) { shape.size() * sizeof(int32_t))) {
MS_LOG(ERROR) << "memcpy_s data into schema_tensor failed."; MS_LOG(ERROR) << "memcpy_s data into schema_tensor failed.";
return RET_ERROR; return RET_MEMORY_FAILED;
}
} }
node_id_map_[value_node->fullname_with_scope()] = meta_graphT->allTensors.size(); node_id_map_[value_node->fullname_with_scope()] = meta_graphT->allTensors.size();
output_cnode->inputIndex.emplace_back(meta_graphT->allTensors.size()); output_cnode->inputIndex.emplace_back(meta_graphT->allTensors.size());