optimize custom vector

mindspore/lite/internal/include/vector.h

@@ -17,26 +17,24 @@
 #define MINDSPORE_LITE_INTERNAL_INCLUDE_VECTOR_H
 
 #include <stdint.h>
+#include <stdlib.h>
+#include <string.h>
+#include <stddef.h>
 #include <initializer_list>
-#include "internal/include/string.h"
+#define DEFAULT_CAPACITY 4
-#define DEFAULT_CAPACITY 1
 
 struct MSTensor;
 struct Node;
 
 template <typename T>
 class Vector {
- private:
-  size_t size_;
-  size_t elem_size_;
-  size_t capacity_;
-  T *data_;
-
  public:
   Vector();
 
   explicit Vector(size_t size);
 
+  Vector(size_t size, const T &value);
+
   Vector(const Vector<T> &vector);
 
   ~Vector();
@@ -92,23 +90,29 @@ class Vector {
   void reserve(size_t capacity);
 
   Vector<T> &operator=(const Vector<T> &v);
+
+ private:
+  size_t size_;
+  size_t elem_size_;
+  size_t capacity_;
+  T *data_;
 };
 
 template <typename T>
 bool operator==(const Vector<T> &lhs, const Vector<T> &rhs) {
-    if (lhs.size() != rhs.size()) {
+  if (lhs.size() != rhs.size()) {
-        return false;
+    return false;
-    }
+  }
-    for (int i = 0; i < lhs.size(); ++i) {
+  for (int i = 0; i < lhs.size(); ++i) {
-        if (lhs[i] != rhs[i]) {
+    if (lhs[i] != rhs[i]) {
-            return false;
+      return false;
-        }
     }
-    return true;
+  }
+  return true;
 }
 
 template <typename T>
 bool operator!=(const Vector<T> &lhs, const Vector<T> &rhs) {
-    return !(lhs == rhs);
+  return !(lhs == rhs);
 }
 #endif  // MINDSPORE_LITE_INTERNAL_INCLUDE_VECTOR_H

mindspore/lite/internal/src/kernel/fp32/matmul.cc

@@ -85,6 +85,7 @@ int DoMatMulInferShape(const TensorPtrVector &in_tensors, const TensorPtrVector
   int *in_shape[2] = {input0->shape_.data(), input1->shape_.data()};
   int out_format;
   int out_datatype;
+  output->shape_.resize(input0->shape_.size());
   int ret = MatMulInferShape(in_shape, 2, dim_size, output->shape_.data(), in_format, &out_format, in_datatype,
                              &out_datatype, param);
   if (ret != NNACL_OK) {
@@ -134,16 +135,16 @@ int DoMatMul(const TensorPtrVector &in_tensors, const TensorPtrVector &out_tenso
     LITE_LOG_ERROR("Malloc MatMulCPUKernelData failed");
     return RET_MEMORY_FAILED;
   }
-  kernel_data->a_c12_ptr_
+  kernel_data->a_c12_ptr_ =
-    = reinterpret_cast<float *>(allocator->Malloc(params->batch * params->row_12_ * params->deep_ * sizeof(float)));
+    reinterpret_cast<float *>(allocator->Malloc(params->batch * params->row_12_ * params->deep_ * sizeof(float)));
   if (kernel_data->a_c12_ptr_ == NULL) {
     FreeMatMulKernelData(kernel_data, allocator);
     return RET_MEMORY_FAILED;
   }
   memset(kernel_data->a_c12_ptr_, 0, params->row_12_ * params->deep_ * sizeof(float));
 
-  kernel_data->b_r8_ptr_
+  kernel_data->b_r8_ptr_ =
-    = reinterpret_cast<float *>(allocator->Malloc(params->batch * params->col_8_ * params->deep_ * sizeof(float)));
+    reinterpret_cast<float *>(allocator->Malloc(params->batch * params->col_8_ * params->deep_ * sizeof(float)));
   if (kernel_data->b_r8_ptr_ == NULL) {
     FreeMatMulKernelData(kernel_data, allocator);
     return RET_MEMORY_FAILED;
@@ -173,4 +174,3 @@ int DoMatMul(const TensorPtrVector &in_tensors, const TensorPtrVector &out_tenso
 
   return RET_OK;
 }
-

mindspore/lite/internal/src/lite_log.h

@@ -23,13 +23,12 @@
 #include <assert.h>
 #endif
 
-#ifndef Release
+#ifdef Debug
 #define LITE_DEBUG_LOG(format, ...) \
   printf("[DEBUG] [%s %s] [%s] [%d] " format "\n", __DATE__, __TIME__, __FILE__, __LINE__, __VA_ARGS__)
 #define LITE_INFO_LOG(format, ...) \
   printf("[INFO] [%s %s] [%s] [%d] " format "\n", __DATE__, __TIME__, __FILE__, __LINE__, __VA_ARGS__)
-#define LITE_LOG_INFO(...) \
+#define LITE_LOG_INFO(...) printf("[INFO] [%s %s] [%s] [%d] %s\n", __DATE__, __TIME__, __FILE__, __LINE__, __VA_ARGS__)
-  printf("[INFO] [%s %s] [%s] [%d] %s\n", __DATE__, __TIME__, __FILE__, __LINE__, __VA_ARGS__)
 #define LITE_WARNING_LOG(format, ...) \
   printf("[WARNING] [%s %s] [%s] [%d] " format "\n", __DATE__, __TIME__, __FILE__, __LINE__, __VA_ARGS__)
 #define LITE_ERROR_LOG(format, ...) \