From 31008247034dcebe5a9297678a1654e68c34a1ab Mon Sep 17 00:00:00 2001
From: hexia <hexia15@huawei.com>
Date: Wed, 29 Jul 2020 18:13:03 +0800
Subject: [PATCH] fix input

---
 .../session/ascend_inference_session.cc       | 59 +++++++++++++------
 .../session/ascend_inference_session.h        |  4 +-
 2 files changed, 43 insertions(+), 20 deletions(-)

diff --git a/mindspore/ccsrc/backend/session/ascend_inference_session.cc b/mindspore/ccsrc/backend/session/ascend_inference_session.cc
index 69bb7de3cc..ee0816ee9e 100644
--- a/mindspore/ccsrc/backend/session/ascend_inference_session.cc
+++ b/mindspore/ccsrc/backend/session/ascend_inference_session.cc
@@ -94,25 +94,33 @@ bool AscendInferenceSession::CheckModelInputs(uint32_t graph_id, const std::vect
   MS_EXCEPTION_IF_NULL(kernel_graph);
   auto kernel_graph_inputs = kernel_graph->inputs();
   size_t no_weight_input = 0;
+  vector<ParameterPtr> paras;
+  // find parameters of graph inputs
   for (size_t i = 0; i < kernel_graph_inputs.size(); ++i) {
-    tensor::TensorPtr tensor = nullptr;
     if (!kernel_graph_inputs[i]->isa<Parameter>()) {
       MS_LOG(ERROR) << "Kernel graph inputs have anfnode which is not Parameter.";
       continue;
     }
     auto parameter = kernel_graph_inputs[i]->cast<ParameterPtr>();
     if (!AnfAlgo::IsParameterWeight(parameter)) {
-      // compare input number
-      if (no_weight_input >= inputs.size()) {
-        MS_LOG(ERROR) << "Input number is inconsistent. The actual input number [" << inputs.size()
-                      << "] less than that of graph.";
-        return false;
-      }
-      auto input = inputs[no_weight_input++];
-      if (!CompareInput(input, parameter)) {
-        MS_LOG(ERROR) << "Please check the input information.";
-        return false;
-      }
+      paras.push_back(parameter);
+    }
+  }
+
+  // check inputs
+  for (size_t i = 0; i < paras.size(); ++i) {
+    // compare input number
+    if (paras.size() != inputs.size()) {
+      MS_LOG(ERROR) << "Input number is inconsistent. The actual input number [" << inputs.size()
+                    << "] but the graph input number is [" << paras.size() << "]";
+      MS_LOG(ERROR) << "InputsInfo --" << InputsInfo(paras, inputs);
+      return false;
+    }
+    auto input = inputs[no_weight_input++];
+    if (!CompareInput(input, paras[i])) {
+      MS_LOG(ERROR) << "Please check the input information.";
+      MS_LOG(ERROR) << "InputsInfo --" << InputsInfo(paras, inputs);
+      return false;
     }
   }
   return true;
@@ -123,12 +131,6 @@ bool AscendInferenceSession::CompareInput(const tensor::TensorPtr &input, const
   MS_EXCEPTION_IF_NULL(parameter);
   // compare dims
   auto parameter_shape = AnfAlgo::GetOutputDeviceShape(parameter, 0);
-  if (input->shape().size() != parameter_shape.size()) {
-    MS_LOG(ERROR) << "Input dim is inconsistent. The actual dim is " << input->shape().size()
-                  << ", but the parameter dim is " << parameter_shape.size()
-                  << ". parameter : " << parameter->DebugString();
-    return false;
-  }
 
   // compare shape
   auto input_shape = input->shape();
@@ -153,12 +155,31 @@ bool AscendInferenceSession::CompareInput(const tensor::TensorPtr &input, const
   return true;
 }
 
-std::string AscendInferenceSession::PrintInputShape(std::vector<size_t> shape) const {
+template <typename T>
+std::string AscendInferenceSession::PrintInputShape(std::vector<T> shape) const {
   string res = "[";
   for (auto dim : shape) {
     res += " " + std::to_string(dim);
   }
   return res + " ]";
 }
+
+std::string AscendInferenceSession::InputsInfo(const std::vector<ParameterPtr> &paras,
+                                               const std::vector<tensor::TensorPtr> &inputs) const {
+  std::string graph = "graph inputs:{ ";
+  for (size_t i = 0; i < paras.size(); ++i) {
+    graph += std::to_string(i) + ": dims " + std::to_string(AnfAlgo::GetOutputDeviceShape(paras[i], 0).size()) +
+             ", shape " + PrintInputShape(AnfAlgo::GetOutputDeviceShape(paras[i], 0)) + ", data type " +
+             std::to_string(AnfAlgo::GetSelectKernelBuildInfo(paras[i])->GetOutputDeviceType(0)) + " }";
+  }
+
+  std::string actual = "actual inputs:{ ";
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    actual += std::to_string(i) + ": dims " + std::to_string(inputs[i]->shape().size()) + ", shape " +
+              PrintInputShape(inputs[i]->shape()) + ", data type " + std::to_string(inputs[i]->data_type()) + " }";
+  }
+  return graph + "   " + actual;
+}
+
 }  // namespace session
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/backend/session/ascend_inference_session.h b/mindspore/ccsrc/backend/session/ascend_inference_session.h
index 7f4f478002..976ce7b63f 100644
--- a/mindspore/ccsrc/backend/session/ascend_inference_session.h
+++ b/mindspore/ccsrc/backend/session/ascend_inference_session.h
@@ -41,7 +41,9 @@ class AscendInferenceSession : public AscendSession {
   GraphId CompileGraph(NotNull<FuncGraphPtr> func_graph) override;
   bool CheckModelInputs(uint32_t graph_id, const std::vector<tensor::TensorPtr> &inputs) const override;
   bool CompareInput(const tensor::TensorPtr &input, const ParameterPtr &parameter) const;
-  std::string PrintInputShape(std::vector<size_t> shape) const;
+  template <typename T>
+  std::string PrintInputShape(std::vector<T> shape) const;
+  std::string InputsInfo(const std::vector<ParameterPtr> &paras, const std::vector<tensor::TensorPtr> &inputs) const;
 };
 MS_REG_SESSION(kDavinciInferenceDevice, AscendInferenceSession);
 }  // namespace session