add runstep and featuremap api

include/api/cfg.h

@@ -22,6 +22,7 @@
 #include <memory>
 #include "include/api/data_type.h"
 #include "include/api/dual_abi_helper.h"
+#include "include/api/types.h"
 
 namespace mindspore {
 

include/api/model.h

@@ -83,6 +83,14 @@ class MS_API Model {
   Status Predict(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs,
                  const MSKernelCallBack &before = nullptr, const MSKernelCallBack &after = nullptr);
 
+  /// \brief Train model by step.
+  ///
+  /// \param[in] before CallBack before predict.
+  /// \param[in] after CallBack after predict.
+  ///
+  /// \return Status.
+  Status RunStep(const MSKernelCallBack &before = nullptr, const MSKernelCallBack &after = nullptr);
+
   /// \brief Inference model with preprocess in model.
   ///
   /// \param[in] inputs A vector where model inputs are arranged in sequence.
@@ -143,7 +151,18 @@ class MS_API Model {
   /// \return Status of operation
   Status ApplyGradients(const std::vector<MSTensor> &gradients);
 
-  /// \brief Obtains optimizer params tensors of the model.
+  /// \brief Obtains all weights tensors of the model.
+  ///
+  /// \return The vector that includes all gradient tensors.
+  std::vector<MSTensor> GetFeatureMaps() const;
+
+  /// \brief update weights tensors of the model.
+  ///
+  /// \param[in] inputs A vector new weights.
+  /// \return Status of operation
+  Status UpdateFeatureMaps(const std::vector<MSTensor> &new_weights);
+
+    /// \brief Obtains optimizer params tensors of the model.
   ///
   /// \return The vector that includes all params tensors.
   std::vector<MSTensor> GetOptimizerParams() const;

mindspore/lite/src/cxx_api/model/model.cc

@@ -19,14 +19,14 @@
 #include <cuda_runtime.h>
 #endif
 #include <mutex>
-#include "include/api/types.h"
-#include "include/api/context.h"
 #include "include/api/callback/callback.h"
+#include "include/api/context.h"
 #include "include/api/dual_abi_helper.h"
-#include "src/cxx_api/model/model_impl.h"
-#include "src/cxx_api/callback/callback_impl.h"
-#include "src/cxx_api/callback/callback_adapter.h"
+#include "include/api/types.h"
 #include "src/common/log_adapter.h"
+#include "src/cxx_api/callback/callback_adapter.h"
+#include "src/cxx_api/callback/callback_impl.h"
+#include "src/cxx_api/model/model_impl.h"
 
 namespace mindspore {
 std::mutex g_impl_init_lock;
@@ -113,6 +113,16 @@ Status Model::UpdateWeights(const std::vector<MSTensor> &new_weights) {
   return impl_->UpdateWeights(new_weights);
 }
 
+Status Model::RunStep(const MSKernelCallBack &before, const MSKernelCallBack &after) {
+  if (impl_ == nullptr) {
+    MS_LOG(ERROR) << "Model implement is null.";
+    return kLiteNullptr;
+  }
+  auto inputs = impl_->GetInputs();
+  auto outputs = impl_->GetOutputs();
+  return impl_->Predict(inputs, &outputs, before, after);
+}
+
 Status Model::Predict(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs,
                       const MSKernelCallBack &before, const MSKernelCallBack &after) {
   if (impl_ == nullptr) {
@@ -291,6 +301,23 @@ Status Model::ApplyGradients(const std::vector<MSTensor> &gradients) {
   return impl_->ApplyGradients(gradients);
 }
 
+std::vector<MSTensor> Model::GetFeatureMaps() const {
+  std::vector<MSTensor> empty;
+  if (impl_ == nullptr) {
+    MS_LOG(ERROR) << "Model implement is null.";
+    return empty;
+  }
+  return impl_->GetFeatureMaps();
+}
+
+Status Model::UpdateFeatureMaps(const std::vector<MSTensor> &new_weights) {
+  if ((impl_ == nullptr) || (impl_->session_ == nullptr)) {
+    MS_LOG(ERROR) << "Model is null.";
+    return kLiteUninitializedObj;
+  }
+  return impl_->UpdateFeatureMaps(new_weights);
+}
+
 std::vector<MSTensor> Model::GetOptimizerParams() const {
   std::vector<MSTensor> empty;
   if (impl_ == nullptr) {

mindspore/lite/src/cxx_api/model/model_impl.cc

@@ -410,6 +410,44 @@ Status ModelImpl::ApplyGradients(const std::vector<MSTensor> &gradients) {
   return static_cast<StatusCode>(ret);
 }
 
+std::vector<MSTensor> ModelImpl::GetFeatureMaps() const {
+  std::vector<MSTensor> empty;
+  if (session_ == nullptr) {
+    MS_LOG(ERROR) << "Session is null.";
+    return empty;
+  }
+  auto params = session_->GetFeatureMaps();
+  if (params.empty()) {
+    MS_LOG(ERROR) << "No optimizer parameters avelibale.";
+    return empty;
+  }
+  std::vector<MSTensor> res = LiteTensorsToMSTensors(params, false);
+  return res;
+}
+
+Status ModelImpl::UpdateFeatureMaps(const std::vector<MSTensor> &new_weights) {
+  if (session_ == nullptr) {
+    MS_LOG(ERROR) << "Session is null.";
+    return kLiteNullptr;
+  }
+  if (new_weights.empty()) {
+    MS_LOG(ERROR) << "gradients is null.";
+    return kLiteInputParamInvalid;
+  }
+  std::vector<tensor::MSTensor *> inner_weights;
+  inner_weights.resize(new_weights.size());
+  for (size_t i = 0; i < new_weights.size(); i++) {
+    auto new_weight = new_weights[i];
+    if (new_weight.impl_ == nullptr || new_weight.impl_->lite_tensor() == nullptr) {
+      MS_LOG(ERROR) << "gradient tensor " << new_weight.Name() << " is null.";
+      return kLiteInputTensorError;
+    }
+    inner_weights[i] = new_weight.impl_->lite_tensor();
+  }
+  auto ret = session_->UpdateFeatureMaps(inner_weights);
+  return static_cast<StatusCode>(ret);
+}
+
 std::vector<MSTensor> ModelImpl::GetOptimizerParams() const {
   std::vector<MSTensor> empty;
   if (session_ == nullptr) {

mindspore/lite/src/cxx_api/model/model_impl.h

@@ -77,6 +77,8 @@ class ModelImpl {
   std::vector<MSTensor> GetOutputs();
   std::vector<MSTensor> GetGradients() const;
   Status ApplyGradients(const std::vector<MSTensor> &gradients);
+  std::vector<MSTensor> GetFeatureMaps() const;
+  Status UpdateFeatureMaps(const std::vector<MSTensor> &new_weights);
   std::vector<MSTensor> GetOptimizerParams() const;
   Status SetOptimizerParams(const std::vector<MSTensor> &params);
   MSTensor GetInputByTensorName(const std::string &name);

mindspore/lite/test/config/cropped_size.cfg

@@ -1 +1 @@
-790864
+839924