!5198 [MS][LITE][Develop]support space_to_batch

Merge pull request !5198 from chenjianping/lite_dev

mindspore/lite/nnacl/fp32/space_to_batch.c

@@ -16,132 +16,79 @@
 #include "nnacl/fp32/space_to_batch.h"
 #include "nnacl/arithmetic_common.h"
 #include "nnacl/errorcode.h"
-#include "nnacl/fp32/concat.h"
 #include "nnacl/op_base.h"
 
-int EnumElement(int *shape, int n_dims) {
-  int total = 1;
-  for (int i = 0; i < n_dims; i++) {
-    total *= shape[i];
-  }
-  return total;
-}
-
-void TransposeForNHWC(const float *in_data, float *out_data, int *strides, int *out_strides, int *perm,
-                      int *output_shape, int h_start, int h_end) {
-  const int stride0 = strides[perm[0]];
-  const int stride1 = strides[perm[1]];
-  const int stride2 = strides[perm[2]];
-  const int stride3 = strides[perm[3]];
-  const int stride4 = strides[perm[4]];
-  const int out_stride0 = out_strides[0];
-  const int out_stride1 = out_strides[1];
-  const int out_stride2 = out_strides[2];
-  const int out_stride3 = out_strides[3];
-  const int out_stride4 = out_strides[4];
-  const int output0 = output_shape[0];
-  const int output2 = output_shape[2];
-  const int output3 = output_shape[3];
-  const int output4 = output_shape[4];
-
-  for (int i = 0; i < output0; ++i) {
-    int out_stride0_i = i * out_stride0;
-    int stride0_i = i * stride0;
-    for (int j = h_start; j < h_end; ++j) {
-      int out_stride1_j = j * out_stride1;
-      int stride1_j = j * stride1;
-      for (int k = 0; k < output2; ++k) {
-        int out_stride2_k = k * out_stride2;
-        int stride2_k = k * stride2;
-        for (int m = 0; m < output3; ++m) {
-          int out_stride3_m = m * out_stride3;
-          int stride3_m = m * stride3;
-          for (int n = 0; n < output4; ++n) {
-            int out_stride4_n = n * out_stride4;
-            int stride4_n = n * stride4;
-            memcpy(out_data + out_stride0_i + out_stride1_j + out_stride2_k + out_stride3_m + out_stride4_n,
-                   in_data + stride0_i + stride1_j + stride2_k + stride3_m + stride4_n, stride4 * sizeof(float));
-          }
-        }
+void DoSpaceToBatchNHWC(const float *input, float *output, SpaceToBatchParameter *param, int *in_shape,
+                        int *out_shape) {
+  int out_dim0 = out_shape[0];
+  int out_dim1 = out_shape[1];
+  int out_dim2 = out_shape[2];
+  int copy_num = out_shape[3];
+  int block_w = param->block_sizes_[1];
+  int block_h = param->block_sizes_[0];
+  int in_strides[4];
+  ComputeStrides(in_shape, in_strides, 4);
+  int out_strides[4];
+  ComputeStrides(out_shape, out_strides, 4);
+  size_t copy_size = copy_num * sizeof(float);
+  size_t out_offset = 0;
+  for (int n = 0; n < out_dim0; ++n) {
+    int in_n = n % in_shape[0];
+    int32_t stride_w = (n / in_shape[0]) % block_w;
+    int32_t stride_h = (n / in_shape[0]) / block_w;
+    size_t in_offset0 = in_n * in_strides[0];
+    for (int h = 0; h < out_dim1; ++h) {
+      size_t in_offset1 = in_offset0 + (h * block_h + stride_h) * in_strides[1];
+      for (int w = 0; w < out_dim2; ++w) {
+        size_t in_offset2 = in_offset1 + (w * block_w + stride_w) * in_strides[2];
+        memcpy(output + out_offset, input + in_offset2, copy_size);
+        out_offset += copy_num;
       }
     }
   }
 }
 
-int SpaceToBatchForNHWC(const float *input, float *output, int *in_shape, int shape_size, int *block_sizes, int h_start,
-                        int h_end) {
-  int trans_in_shape[6] = {in_shape[0],    in_shape[1] / block_sizes[0],
-                           block_sizes[0], in_shape[2] / block_sizes[1],
-                           block_sizes[1], in_shape[3]};
-  int trans_out_shape[6] = {
-    in_shape[0], block_sizes[0], block_sizes[1], in_shape[1] / block_sizes[0], in_shape[2] / block_sizes[1],
-    in_shape[3]};
-  int in_strides[C4NUM + 2];
-  ComputeStrides(trans_in_shape, in_strides, shape_size + 2);
-  int out_strides[C4NUM + 2];
-  ComputeStrides(trans_out_shape, out_strides, shape_size + 2);
-
-  int perm[6] = {0, 2, 4, 1, 3, 5};
-  TransposeForNHWC(input, output, in_strides, out_strides, perm, trans_out_shape, h_start, h_end);
-  return NNACL_OK;
-}
-
-void DoPadding(const float *input, float *padded_input, SpaceToBatchParameter param, float *tmp_space[]) {
-  float *tmp = padded_input;
-  (void)memcpy(tmp, input, param.num_elements_ * sizeof(float));
-  float *target = tmp_space[0];
-  float *tmp_zeros = tmp_space[1];
-  float *tmp2 = NULL;
-  int cur_shape[param.n_dims_], cur_start_shape[param.n_dims_], cur_end_shape[param.n_dims_],
-    cur_target_shape[param.n_dims_];
-  float *concat_inputs[3];
-  int *concat_shapes[4];
-
-  for (int i = 0; i < param.n_dims_; i++) {
-    cur_shape[i] = param.in_shape_[i];
-    cur_start_shape[i] = param.in_shape_[i];
-    cur_end_shape[i] = param.in_shape_[i];
-    cur_target_shape[i] = param.in_shape_[i];
-  }
-  for (int i = 0; i < param.n_space_dims_; ++i) {
-    if (param.padded_in_shape_[i + 1] > param.in_shape_[i + 1]) {
-      int concat_idx = 0;
-      cur_target_shape[i + 1] = 0;
-      if (param.paddings_[2 * i] != 0) {
-        cur_start_shape[i + 1] = param.paddings_[2 * i];
-        concat_inputs[concat_idx] = tmp_zeros;
-        concat_shapes[concat_idx++] = cur_start_shape;
-        cur_target_shape[i + 1] += cur_start_shape[i + 1];
+void DoSpaceToBatchPaddingNHWC(const float *input, float *output, int *in_shape, int *padding, int *out_shape,
+                              const float *pedding_h_data, const float *pedding_w_data) {
+  int in_h = in_shape[1];
+  int in_w = in_shape[2];
+  int in_c = in_shape[3];
+  int out_w = out_shape[2];
+  int out_c = out_shape[3];
+  size_t ped_h_num = out_w * out_c;
+  size_t ped_h_size = ped_h_num * sizeof(float);
+  size_t ped_w_size = out_c * sizeof(float);
+  size_t out_offset = 0;
+  int in_strides[4];
+  ComputeStrides(in_shape, in_strides, 4);
+  int out_strides[4];
+  ComputeStrides(out_shape, out_strides, 4);
+  size_t copy_size = in_c * sizeof(float);
+  for (int i = 0; i < in_shape[0]; ++i) {
+    size_t in_offset0 = i * in_strides[0];
+    for (int pad_h_top = 0; pad_h_top < padding[0]; ++pad_h_top) {
+        memcpy(output + out_offset, pedding_h_data, ped_h_size);
+        out_offset += ped_h_num;
+    }
+    for (int j = 0; j < in_h; ++j) {
+      size_t in_offset1 = in_offset0 + j * in_strides[1];
+      for (int pad_w_left = 0; pad_w_left < padding[2]; ++pad_w_left) {
+        memcpy(output + out_offset, pedding_w_data, ped_w_size);
+        out_offset += out_c;
       }
-
-      concat_inputs[concat_idx] = tmp;
-      concat_shapes[concat_idx++] = cur_shape;
-      cur_target_shape[i + 1] += cur_shape[i + 1];
-      if (param.paddings_[2 * i + 1] != 0) {
-        cur_end_shape[i + 1] = param.paddings_[2 * i + 1];
-        concat_inputs[concat_idx] = tmp_zeros;
-        concat_shapes[concat_idx++] = cur_end_shape;
-        cur_target_shape[i + 1] += cur_end_shape[i + 1];
+      for (int k = 0; k < in_w; ++k) {
+        size_t in_offset2 = in_offset1 + k * in_strides[2];
+        memcpy(output + out_offset, input + in_offset2, copy_size);
+        out_offset += in_c;
+      }
+      for (int pad_w_right = 0; pad_w_right < padding[3]; ++pad_w_right) {
+        memcpy(output + out_offset, pedding_w_data, ped_w_size);
+        out_offset += out_c;
       }
-      concat_shapes[concat_idx] = cur_target_shape;
-      Concat((void **)concat_inputs, concat_idx, i + 1, concat_shapes, param.n_dims_, target);
-
-      tmp2 = tmp;
-      tmp = target;
-      target = tmp2;
-      cur_start_shape[i + 1] = cur_end_shape[i + 1] = cur_shape[i + 1] = concat_shapes[concat_idx][i + 1];
+    }
+    for (int pad_h_bottom = 0; pad_h_bottom < padding[1]; ++pad_h_bottom) {
+      memcpy(output + out_offset, pedding_h_data, ped_h_size);
+      out_offset += ped_h_num;
     }
   }
-  if (padded_input != tmp) {
-    memcpy(padded_input, tmp, param.num_elements_padded_ * sizeof(float));
-  }
-}
-
-int SpaceToBatch(const float *input, float *output, SpaceToBatchParameter param, int h_start, int h_end) {
-  if (input == NULL || output == NULL) {
-    return NNACL_NULL_PTR;
-  }
-  int ret =
-    SpaceToBatchForNHWC(input, output, param.padded_in_shape_, param.n_dims_, param.block_sizes_, h_start, h_end);
-  return ret;
 }

mindspore/lite/nnacl/fp32/space_to_batch.h

@@ -22,26 +22,17 @@
 
 typedef struct SpaceToBatchParameter {
   OpParameter op_parameter_;
-  int block_sizes_[8];
-  int paddings_[8];
-  int n_dims_;
-  int num_elements_;
-  int num_elements_padded_;
-  int n_space_dims_;
-  int in_shape_[8];
-  int padded_in_shape_[8];
   bool need_paddings_;
+  int block_sizes_[4];
+  int paddings_[4];
 } SpaceToBatchParameter;
 #ifdef __cplusplus
 extern "C" {
 #endif
-int SpaceToBatch(const float *input, float *output, SpaceToBatchParameter param, int h_start, int h_end);
-int SpaceToBatchForNHWC(const float *input, float *output, int *in_shape, int shape_size, int *block_size, int h_start,
-                        int h_end);
-void TransposeForNHWC(const float *in_data, float *out_data, int *strides, int *out_strides, int *perm,
-                      int *output_shape, int h_start, int h_end);
-void DoPadding(const float *input, float *padded_input, SpaceToBatchParameter param, float *tmp_space[]);
-int EnumElement(int *shape, int n_dims);
+void DoSpaceToBatchNHWC(const float *input, float *output, SpaceToBatchParameter *param, int *in_shape,
+                        int *out_shape);
+void DoSpaceToBatchPaddingNHWC(const float *input, float *output, int *in_shape, int *padding, int *out_shape,
+                               const float *pedding_h_data, const float *pedding_w_data);
 #ifdef __cplusplus
 }
 #endif

mindspore/lite/src/lite_kernel.cc

@@ -39,6 +39,30 @@ int LiteKernel::DecOutTensorRefCount() {
   return 0;
 }
 
+int LiteKernel::Prepare() {
+  if (!InferShapeDone()) {
+    (const_cast<mindspore::lite::PrimitiveC *>(primitive_))->SetInferFlag(true);
+    auto ret = (const_cast<mindspore::lite::PrimitiveC *>(primitive_))->InferShape(in_tensors_, out_tensors_);
+    if (ret != 0) {
+      (const_cast<mindspore::lite::PrimitiveC *>(primitive_))->SetInferFlag(false);
+      MS_LOG(ERROR) << "InferShape fail!";
+      return ret;
+    }
+    ret = ReSize();
+    if (ret != 0) {
+      MS_LOG(ERROR) << "ReSize fail!ret: " << ret;
+      return ret;
+    }
+  }
+
+  auto &outputs = this->out_tensors();
+  for (auto *output : outputs) {
+    MS_ASSERT(output != nullptr);
+    output->MallocData();
+  }
+  return RET_OK;
+}
+
 std::vector<kernel::LiteKernel *> LiteKernelUtil::SubgraphInputKernels(
   const std::vector<kernel::LiteKernel *> &kernels) {
   std::vector<kernel::LiteKernel *> input_kernels;

mindspore/lite/src/lite_kernel.h

@@ -75,19 +75,7 @@ class LiteKernel {
 
   virtual ~LiteKernel() = default;
 
-  virtual int Prepare() {
-    if (!InferShapeDone()) {
-      (const_cast<mindspore::lite::PrimitiveC *>(primitive_))->InferShape(in_tensors_, out_tensors_);
-      ReSize();
-    }
-
-    auto &outputs = this->out_tensors();
-    for (auto *output : outputs) {
-      MS_ASSERT(output != nullptr);
-      output->MallocData();
-    }
-    return RET_OK;
-  }
+  virtual int Prepare();
 
   virtual int Init() { return -1; }
 

mindspore/lite/src/ops/concat.cc

@@ -96,17 +96,13 @@ int Concat::InferShape(std::vector<tensor::Tensor *> inputs_, std::vector<tensor
   auto input0_shape_without_axis = input0_shape;
   input0_shape_without_axis.erase(input0_shape_without_axis.begin() + axis);
   auto input0_data_type = inputs_.at(0)->data_type();
-  schema::Format input0_format = inputs_[0]->GetFormat();
   int output_axis_dim = input0_shape.at(axis);
   for (size_t i = 1; i < inputs_.size(); ++i) {
     if (inputs_.at(i)->data_type() != input0_data_type) {
       MS_LOG(ERROR) << "All inputs should have the same data type!";
       return RET_PARAM_INVALID;
     }
-    if (inputs_.at(i)->GetFormat() != input0_format) {
-      MS_LOG(ERROR) << "All input format should be the same!";
-      return RET_PARAM_INVALID;
-    }
+
     auto shape_tmp = inputs_.at(i)->shape();
     if (shape_tmp.size() != input0_shape.size()) {
       MS_LOG(ERROR) << "All inputs should have the same dim num!";

mindspore/lite/src/ops/primitive_c.cc

@@ -17,6 +17,7 @@
 #include "src/ops/primitive_c.h"
 #include <memory>
 #include "src/ops/space_to_batch.h"
+#include "src/ops/space_to_batch_nd.h"
 #include "src/ops/conv2d.h"
 #include "src/ops/roi_pooling.h"
 #include "src/ops/topk.h"
@@ -415,6 +416,8 @@ PrimitiveC *PrimitiveC::UnPackFromSchemaPrimitiveT(mindspore::schema::PrimitiveT
       return new BatchToSpace(primitive);
     case schema::PrimitiveType_SpaceToBatch:
       return new SpaceToBatch(primitive);
+    case schema::PrimitiveType_SpaceToBatchND:
+      return new SpaceToBatchND(primitive);
     case schema::PrimitiveType_BroadcastTo:
       return new BroadcastTo(primitive);
     case schema::PrimitiveType_DepthToSpace:
@@ -621,6 +624,8 @@ PrimitiveC *PrimitiveC::UnPackFromSchemaPrimitive(mindspore::schema::Primitive *
       return new BatchToSpace(const_cast<schema::Primitive *>(primitive));
     case schema::PrimitiveType_SpaceToBatch:
       return new SpaceToBatch(const_cast<schema::Primitive *>(primitive));
+    case schema::PrimitiveType_SpaceToBatchND:
+      return new SpaceToBatchND(const_cast<schema::Primitive *>(primitive));
     case schema::PrimitiveType_BroadcastTo:
       return new BroadcastTo(const_cast<schema::Primitive *>(primitive));
     case schema::PrimitiveType_DepthToSpace:

mindspore/lite/src/ops/space_to_batch.cc

@@ -105,8 +105,8 @@ int SpaceToBatch::InferShape(std::vector<lite::tensor::Tensor *> inputs, std::ve
 
   std::vector<int32_t> output_shape(input_shape.size());
   output_shape[NHWC_N] = input_shape[NHWC_N] * (block_sizes_[NHWC_N] * block_sizes_[NHWC_H]);
-  output_shape[NHWC_H] = input_shape[NHWC_H] / block_sizes_[NHWC_N];
-  output_shape[NHWC_W] = input_shape[NHWC_W] / block_sizes_[NHWC_H];
+  output_shape[NHWC_H] = (input_shape[NHWC_H] + paddings_[0] + paddings_[1]) / block_sizes_[NHWC_N];
+  output_shape[NHWC_W] = (input_shape[NHWC_W] + paddings_[2] + paddings_[3]) / block_sizes_[NHWC_H];
   output_shape[NHWC_C] = input_shape[NHWC_C];
   outputs[0]->set_shape(output_shape);
   return RET_OK;

mindspore/lite/src/ops/space_to_batch.h

@@ -36,7 +36,8 @@ class SpaceToBatch : public PrimitiveC {
 #else
   explicit SpaceToBatch(schema::Primitive *primitive) : PrimitiveC(primitive) {}
 #endif
-  int InferShape(std::vector<lite::tensor::Tensor *> inputs_, std::vector<lite::tensor::Tensor *> outputs_) override;
+  int InferShape(std::vector<lite::tensor::Tensor *> inputs, std::vector<lite::tensor::Tensor *> outputs) override;
+
   std::vector<int> GetBlockShape() const;
   std::vector<int> GetPaddings() const;
 

mindspore/lite/src/ops/space_to_batch_nd.cc

@@ -15,9 +15,17 @@
  */
 
 #include "src/ops/space_to_batch_nd.h"
+#include "src/common/common.h"
 
 namespace mindspore {
 namespace lite {
+namespace {
+constexpr int kSpaceToBatchNDOutputNum = 1;
+constexpr int kSpaceToBatchNDInputNum = 1;
+constexpr int kBlockSizesSize = 2;
+constexpr int kPaddingsSize = 4;
+}  // namespace
+
 #ifdef PRIMITIVE_WRITEABLE
 std::vector<int> SpaceToBatchND::GetBlockShape() const {
   return this->primitive_->value.AsSpaceToBatchND()->blockShape;
@@ -42,6 +50,48 @@ std::vector<int> SpaceToBatchND::GetPaddings() const {
   return std::vector<int>(fb_vector->begin(), fb_vector->end());
 }
 
-#endif
+#endif  // PRIMITIVE_WRITEABLE
+
+int SpaceToBatchND::InferShape(std::vector<lite::tensor::Tensor *> inputs,
+                               std::vector<lite::tensor::Tensor *> outputs) {
+  if (outputs.size() != kSpaceToBatchNDOutputNum || inputs.size() != kSpaceToBatchNDInputNum) {
+    MS_LOG(ERROR) << "Invalid output/input size! output size: " << outputs.size() << ",input size: " << inputs.size();
+    return 1;
+  }
+
+  auto input = inputs.at(0);
+  if (input->GetFormat() != schema::Format_NHWC) {
+    MS_LOG(ERROR) << "space_to_batch_nd only support NHWC now!";
+    return RET_ERROR;
+  }
+  outputs[0]->set_data_type(input->data_type());
+  outputs[0]->SetFormat(input->GetFormat());
+  if (!GetInferFlag()) {
+    return RET_OK;
+  }
+  auto input_shape = input->shape();
+  if (input_shape.size() != kDimension_4d) {
+    MS_LOG(ERROR) << "input shape dimension size only support " << kDimension_4d << " now!";
+    return RET_ERROR;
+  }
+  auto block_shape = GetBlockShape();
+  if (block_shape.size() != kBlockSizesSize) {
+    MS_LOG(ERROR) << "blockShape size != " << kBlockSizesSize;
+    return RET_ERROR;
+  }
+  auto pedding = GetPaddings();
+  if (pedding.size() != kPaddingsSize) {
+    MS_LOG(ERROR) << "pedding size should be " << kPaddingsSize;
+    return RET_ERROR;
+  }
+
+  std::vector<int32_t> output_shape(input_shape.size());
+  output_shape[NHWC_N] = input_shape[NHWC_N] * block_shape[0] * block_shape[1];
+  output_shape[NHWC_H] = (input_shape[NHWC_H] + pedding[0] + pedding[1]) / block_shape[0];
+  output_shape[NHWC_W] = (input_shape[NHWC_W] + pedding[2] + pedding[3]) / block_shape[1];
+  output_shape[NHWC_C] = input_shape[NHWC_C];
+  outputs[0]->set_shape(output_shape);
+  return RET_OK;
+}
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/src/ops/space_to_batch_nd.h

@@ -18,8 +18,6 @@
 #define LITE_MINDSPORE_LITE_C_OPS_SPACE_TO_BATCH_N_D_H_
 
 #include <vector>
-#include <set>
-#include <cmath>
 #include "ir/dtype/type_id.h"
 #include "src/ops/primitive_c.h"
 
@@ -38,6 +36,7 @@ class SpaceToBatchND : public PrimitiveC {
 #endif
   std::vector<int> GetBlockShape() const;
   std::vector<int> GetPaddings() const;
+  int InferShape(std::vector<lite::tensor::Tensor *> inputs, std::vector<lite::tensor::Tensor *> outputs) override;
 };
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/src/populate_parameter.cc

@@ -20,6 +20,7 @@
 #include "schema/ops_generated.h"
 #include "src/ops/constant_of_shape.h"
 #include "src/ops/space_to_batch.h"
+#include "src/ops/space_to_batch_nd.h"
 #include "src/ops/conv2d.h"
 #include "src/ops/roi_pooling.h"
 #include "src/ops/topk.h"
@@ -1217,10 +1218,22 @@ OpParameter *PopulateSpaceToBatchParameter(const mindspore::lite::PrimitiveC *pr
   (void)memcpy(space_batch_param->block_sizes_, (block_sizes.data()), block_sizes.size() * sizeof(int));
   auto paddings = ((mindspore::lite::SpaceToBatch *)primitive)->Paddings();
   (void)memcpy(space_batch_param->paddings_, (paddings.data()), paddings.size() * sizeof(int));
-  auto in_shape = ((mindspore::lite::SpaceToBatch *)primitive)->InShape();
-  (void)memcpy(space_batch_param->in_shape_, (in_shape.data()), in_shape.size() * sizeof(int));
-  auto padded_in_shape = ((mindspore::lite::SpaceToBatch *)primitive)->PaddedInShape();
-  (void)memcpy(space_batch_param->padded_in_shape_, (padded_in_shape.data()), padded_in_shape.size() * sizeof(int));
+  return reinterpret_cast<OpParameter *>(space_batch_param);
+}
+
+OpParameter *PopulateSpaceToBatchParameterND(const mindspore::lite::PrimitiveC *primitivec) {
+  auto *space_batch_param = new (std::nothrow) SpaceToBatchParameter();
+  if (space_batch_param == nullptr) {
+    MS_LOG(ERROR) << "new SpaceToBatchParameter failed.";
+    return nullptr;
+  }
+
+  mindspore::lite::SpaceToBatchND *primitive = (mindspore::lite::SpaceToBatchND *)primitivec;
+  space_batch_param->op_parameter_.type_ = primitive->Type();
+  auto block_sizes = primitive->GetBlockShape();
+  (void)memcpy(space_batch_param->block_sizes_, (block_sizes.data()), block_sizes.size() * sizeof(int));
+  auto paddings = primitive->GetPaddings();
+  (void)memcpy(space_batch_param->paddings_, (paddings.data()), paddings.size() * sizeof(int));
   return reinterpret_cast<OpParameter *>(space_batch_param);
 }
 
@@ -1573,6 +1586,7 @@ PopulateParameterRegistry::PopulateParameterRegistry() {
   populate_parameter_funcs_[schema::PrimitiveType_BatchToSpace] = PopulateBatchToSpaceParameter;
   populate_parameter_funcs_[schema::PrimitiveType_SpaceToDepth] = PopulateSpaceToDepthParameter;
   populate_parameter_funcs_[schema::PrimitiveType_SpaceToBatch] = PopulateSpaceToBatchParameter;
+  populate_parameter_funcs_[schema::PrimitiveType_SpaceToBatchND] = PopulateSpaceToBatchParameterND;
   populate_parameter_funcs_[schema::PrimitiveType_Crop] = PopulateCropParameter;
   populate_parameter_funcs_[schema::PrimitiveType_Unsqueeze] = PopulateUnsqueezeParameter;
   populate_parameter_funcs_[schema::PrimitiveType_Flatten] = PopulateFlattenParameter;

mindspore/lite/src/runtime/kernel/arm/fp32/space_to_batch.cc

@@ -29,8 +29,18 @@ using mindspore::lite::RET_FORMAT_ERR;
 using mindspore::lite::RET_OK;
 using mindspore::lite::RET_OP_EXECUTE_FAILURE;
 using mindspore::schema::PrimitiveType_SpaceToBatch;
+using mindspore::schema::PrimitiveType_SpaceToBatchND;
 
 namespace mindspore::kernel {
+namespace {
+size_t EnumElement(int *shape, int n_dims) {
+  size_t total = 1;
+  for (int i = 0; i < n_dims; i++) {
+    total *= shape[i];
+  }
+  return total;
+}
+}
 
 int SpaceToBatchCPUKernel::Init() {
   SpaceToBatchParameter *param = reinterpret_cast<SpaceToBatchParameter *>(this->op_parameter_);
@@ -40,37 +50,26 @@ int SpaceToBatchCPUKernel::Init() {
       break;
     }
   }
-  param->n_dims_ = DIMENSION_4D;
-  param->n_space_dims_ = SPACE_TO_BATCH_BLOCK_SIZES_SIZE;
+
   if (!InferShapeDone()) {
     return RET_OK;
   }
   return ReSize();
 }
 
-int SpaceToBatchCPUKernel::SpaceToBatchParallel(int task_id) {
-  int num_unit_thread = MSMIN(thread_h_stride_, num_unit_ - task_id * thread_h_stride_);
-  if (num_unit_thread <= 0) {
-    return RET_OK;
+void SpaceToBatchCPUKernel::FreeTmpBuffer() {
+  if (pedding_h_data_ != nullptr) {
+    context_->allocator->Free(pedding_h_data_);
+    pedding_h_data_ = nullptr;
   }
-  int thread_offset = task_id * thread_h_stride_;
-  SpaceToBatchParameter *param = reinterpret_cast<SpaceToBatchParameter *>(this->op_parameter_);
-  auto ret = SpaceToBatch(input_ptr_, output_ptr_, *param, thread_offset, thread_offset + num_unit_thread);
-  if (ret != RET_OK) {
-    MS_LOG(ERROR) << "SpaceToDepth error task_id[" << task_id << "] error_code[" << ret << "]";
-    return RET_ERROR;
+  if (pedding_w_data_ != nullptr) {
+    context_->allocator->Free(pedding_w_data_);
+    pedding_w_data_ = nullptr;
   }
-  return RET_OK;
-}
-
-int SpaceToBatchRun(int task_id, LiteParallelGroupEnv *penv, void *cdata) {
-  auto g_kernel = reinterpret_cast<SpaceToBatchCPUKernel *>(cdata);
-  auto ret = g_kernel->SpaceToBatchParallel(task_id);
-  if (ret != RET_OK) {
-    MS_LOG(ERROR) << "SpaceToBatchRun error task_id[" << task_id << "] error_code[" << ret << "]";
-    return RET_OP_EXECUTE_FAILURE;
+  if (pedding_input_ != nullptr) {
+    context_->allocator->Free(pedding_input_);
+    pedding_input_ = nullptr;
   }
-  return RET_OK;
 }
 
 int SpaceToBatchCPUKernel::ReSize() {
@@ -78,13 +77,39 @@ int SpaceToBatchCPUKernel::ReSize() {
     MS_LOG(ERROR) << "space_to_batch only support NHWC now!";
     return RET_FORMAT_ERR;
   }
+  FreeTmpBuffer();
   SpaceToBatchParameter *param = reinterpret_cast<SpaceToBatchParameter *>(this->op_parameter_);
-  param->num_elements_ = EnumElement(param->in_shape_, param->n_dims_);
-  param->num_elements_padded_ = EnumElement(param->padded_in_shape_, param->n_dims_);
-  num_unit_ = static_cast<int>(in_tensors_[kInputIndex]->shape().at(kNHWC_H));
-  num_unit_ /= param->block_sizes_[0];
-  thread_h_num_ = MSMIN(thread_num_, num_unit_);
-  thread_h_stride_ = UP_DIV(num_unit_, thread_h_num_);
+  if (!param->need_paddings_) {
+    return RET_OK;
+  }
+  auto input = in_tensors_[0];
+  auto in_shape = input->shape();
+  padded_in_shape_ = in_shape;
+  padded_in_shape_[1] = in_shape[1] + param->paddings_[0] + param->paddings_[1];
+  padded_in_shape_[2] = in_shape[2] + param->paddings_[2] + param->paddings_[3];
+  auto num_elements_padded = EnumElement(padded_in_shape_.data(), in_shape.size());
+  auto output_shape = out_tensors_[0]->shape();
+  auto pedding_h_size = output_shape[2] * output_shape[3] * sizeof(float);
+  pedding_h_data_ = reinterpret_cast<float *>(context_->allocator->Malloc(pedding_h_size));
+  if (pedding_h_data_ == nullptr) {
+    MS_LOG(ERROR) << "malloc pedding h data fail!";
+    return RET_ERROR;
+  }
+  auto pedding_w_size = output_shape[3] * sizeof(float);
+  pedding_w_data_ = reinterpret_cast<float *>(context_->allocator->Malloc(pedding_w_size));
+  if (pedding_w_data_ == nullptr) {
+    MS_LOG(ERROR) << "malloc pedding w data fail!";
+    FreeTmpBuffer();
+    return RET_ERROR;
+  }
+  pedding_input_ =
+      reinterpret_cast<float *>(context_->allocator->Malloc(num_elements_padded * sizeof(float)));
+  if (pedding_input_ == nullptr) {
+    MS_LOG(ERROR) << "malloc pedding buffer fail!";
+    return RET_ERROR;
+  }
+  memset(pedding_h_data_, 0, pedding_h_size);
+  memset(pedding_w_data_, 0, pedding_w_size);
   return RET_OK;
 }
 
@@ -96,54 +121,32 @@ int SpaceToBatchCPUKernel::Run() {
   }
   auto input = in_tensors_[0];
   auto output = out_tensors_[0];
-  input_ptr_ = reinterpret_cast<const float *>(input->Data());
-  output_ptr_ = reinterpret_cast<float *>(output->Data());
+  const float *input_ptr_ = reinterpret_cast<const float *>(input->Data());
+  float *output_ptr_ = reinterpret_cast<float *>(output->Data());
   SpaceToBatchParameter *param = reinterpret_cast<SpaceToBatchParameter *>(this->op_parameter_);
-
-  float *tmp_space[3] = {nullptr, nullptr, nullptr};
+  auto in_shape = input->shape();
+  auto out_shape = output->shape();
   if (param->need_paddings_) {
-    for (int i = 0; i < 3; ++i) {
-      tmp_space[i] =
-        reinterpret_cast<float *>(context_->allocator->Malloc(param->num_elements_padded_ * sizeof(float)));
-      (void)memset(tmp_space[i], 0, param->num_elements_padded_ * sizeof(float));
-      if (tmp_space[i] == nullptr) {
-        MS_LOG(ERROR) << "malloc tmp buffer fail!";
-        return RET_ERROR;
-      }
-    }
-    auto padded_input = tmp_space[0];
-    DoPadding(input_ptr_, padded_input, *param, tmp_space + 1);
-    input_ptr_ = padded_input;
-  }
-
-  if (input->GetFormat() == schema::Format_NHWC) {
-    ret = LiteBackendParallelLaunch(SpaceToBatchRun, this, thread_h_num_);
-    if (ret != RET_OK) {
-      MS_LOG(ERROR) << "SpaceToBatch error error_code[" << ret << "]";
-    }
+    DoSpaceToBatchPaddingNHWC(input_ptr_, pedding_input_, in_shape.data(), param->paddings_,
+                              padded_in_shape_.data(), pedding_h_data_, pedding_w_data_);
+    DoSpaceToBatchNHWC(pedding_input_, output_ptr_, param, padded_in_shape_.data(), out_shape.data());
+    return RET_OK;
   } else {
-    MS_LOG(ERROR) << "Only support NHWC now!";
-    ret = RET_FORMAT_ERR;
-  }
-  if (param->need_paddings_) {
-    for (int i = 0; i < 3; ++i) {
-      context_->allocator->Free(tmp_space[i]);
-    }
+    DoSpaceToBatchNHWC(input_ptr_, output_ptr_, param, in_shape.data(), out_shape.data());
+    return RET_OK;
   }
-
-  return ret;
 }  // namespace mindspore::kernel
 
 kernel::LiteKernel *CpuSpaceToBatchFp32KernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
                                                      const std::vector<lite::tensor::Tensor *> &outputs,
-                                                     OpParameter *opParameter, const lite::Context *ctx,
+                                                     OpParameter *param, const lite::Context *ctx,
                                                      const kernel::KernelKey &desc,
                                                      const mindspore::lite::PrimitiveC *primitive) {
-  if (opParameter == nullptr) {
-    MS_LOG(ERROR) << "Input opParameter is nullptr!";
+  if (param == nullptr) {
+    MS_LOG(ERROR) << "Input param is nullptr!";
     return nullptr;
   }
-  auto *kernel = new (std::nothrow) SpaceToBatchCPUKernel(opParameter, inputs, outputs, ctx, primitive);
+  auto *kernel = new (std::nothrow) SpaceToBatchCPUKernel(param, inputs, outputs, ctx, primitive);
   if (kernel == nullptr) {
     MS_LOG(ERROR) << "new SpaceToBatchCPUKernel fail!";
     return nullptr;
@@ -152,12 +155,13 @@ kernel::LiteKernel *CpuSpaceToBatchFp32KernelCreator(const std::vector<lite::ten
   auto ret = kernel->Init();
   if (ret != RET_OK) {
     delete kernel;
-    MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
-                  << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
+    MS_LOG(ERROR) << "Init kernel failed, name: " << param->name_ << ", type: "
+                  << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(param->type_));
     return nullptr;
   }
   return kernel;
 }
 
 REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_SpaceToBatch, CpuSpaceToBatchFp32KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_SpaceToBatchND, CpuSpaceToBatchFp32KernelCreator)
 }  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/fp32/space_to_batch.h

@@ -25,22 +25,20 @@ class SpaceToBatchCPUKernel : public LiteKernel {
   SpaceToBatchCPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
                         const std::vector<lite::tensor::Tensor *> &outputs, const lite::Context *ctx,
                         const mindspore::lite::PrimitiveC *primitive)
-      : LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_num_(ctx->thread_num_) {}
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive) {}
 
-  ~SpaceToBatchCPUKernel() = default;
+  ~SpaceToBatchCPUKernel() { FreeTmpBuffer(); }
 
   int Init() override;
   int ReSize() override;
   int Run() override;
-  int SpaceToBatchParallel(int task_id);
 
  private:
-  int thread_num_;
-  int thread_h_stride_;
-  int thread_h_num_;
-  int num_unit_;
-  const float *input_ptr_;
-  float *output_ptr_;
+  void FreeTmpBuffer();
+  float *pedding_input_ = nullptr;
+  float *pedding_h_data_ = nullptr;
+  float *pedding_w_data_ = nullptr;
+  std::vector<int> padded_in_shape_;
 };
 }  // namespace mindspore::kernel
 

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/space_to_batch_fp32_tests.cc

@@ -28,142 +28,175 @@ class SpaceToBatchTestFp32 : public mindspore::CommonTest {
   SpaceToBatchTestFp32() {}
 };
 
-void InitSpaceToBatchParameter(SpaceToBatchParameter *param) {
-  param->n_dims_ = 4;
-  param->n_space_dims_ = 2;
-
-  param->block_sizes_[0] = 2;
-  param->block_sizes_[1] = 2;
-
-  param->paddings_[0] = 2;
-  param->paddings_[1] = 0;
-  param->paddings_[2] = 2;
-  param->paddings_[3] = 2;
-
-  param->in_shape_[0] = 1;
-  param->in_shape_[1] = 4;
-  param->in_shape_[2] = 4;
-  param->in_shape_[3] = 1;
-
-  param->padded_in_shape_[0] = 1;
-  param->padded_in_shape_[1] = 6;
-  param->padded_in_shape_[2] = 8;
-  param->padded_in_shape_[3] = 1;
-
-  param->num_elements_ = 16;
-  param->num_elements_padded_ = 48;
-
-  param->need_paddings_ = true;
+TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest4) {
+  std::vector<float> input = {1,  2,  3,  4,  5,  6,  7,  8,
+                              9, 10, 11, 12, 13, 14, 15, 16};
+  const size_t kOutSize = 16;
+  std::vector<float> expect_out = {1,  2,  3,  4,  9, 10, 11, 12,
+                                   5,  6,  7,  8, 13, 14, 15, 16};
+  float out[kOutSize];
+  std::vector<int> in_shape = {1, 4, 4, 1};
+  std::vector<int> out_shape = {2, 2, 4, 1};
+  SpaceToBatchParameter param;
+  param.block_sizes_[0] = 2;
+  param.block_sizes_[1] = 1;
+  DoSpaceToBatchNHWC(input.data(), out, &param, in_shape.data(), out_shape.data());
+  for (int i = 0; i < kOutSize; ++i) {
+    std::cout << out[i] << " ";
+  }
+  std::cout << "\n";
+  CompareOutputData(out, expect_out.data(), kOutSize, 0.000001);
 }
 
-void InitSpaceToBatchParameter2(SpaceToBatchParameter *param) {
-  param->block_sizes_[0] = 2;
-  param->block_sizes_[1] = 2;
-
-  param->paddings_[0] = 2;
-  param->paddings_[1] = 0;
-  param->paddings_[2] = 2;
-  param->paddings_[3] = 2;
-
-  param->in_shape_[0] = 1;
-  param->in_shape_[1] = 4;
-  param->in_shape_[2] = 4;
-  param->in_shape_[3] = 1;
-
-  param->padded_in_shape_[0] = 1;
-  param->padded_in_shape_[1] = 6;
-  param->padded_in_shape_[2] = 8;
-  param->padded_in_shape_[3] = 1;
+TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest5) {
+  std::vector<float> input = {1,  2,  3,  4,  5,  6,  7,  8,
+                              9, 10, 11, 12, 13, 14, 15, 16};
+  size_t kOutSize = 16;
+  std::vector<float> expect_out = {1, 3, 5, 7, 9, 11, 13, 15,
+                                   2, 4, 6, 8, 10, 12, 14, 16};
+  float out[kOutSize];
+  std::vector<int> in_shape = {1, 4, 4, 1};
+  std::vector<int> out_shape = {2, 4, 2, 1};
+  SpaceToBatchParameter param;
+  param.block_sizes_[0] = 1;
+  param.block_sizes_[1] = 2;
+  DoSpaceToBatchNHWC(input.data(), out, &param, in_shape.data(), out_shape.data());
+  for (int i = 0; i < kOutSize; ++i) {
+    std::cout << out[i] << " ";
+  }
+  std::cout << "\n";
+  CompareOutputData(out, expect_out.data(), kOutSize, 0.000001);
 }
 
-TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest1) {
-  float input[16] = {1, 2, 5, 6, 10, 20, 3, 8, 18, 10, 3, 4, 11, 55, 15, 25};
-  const int out_size = 16;
-  float expect_out[16] = {1, 5, 18, 3, 2, 6, 10, 4, 10, 3, 11, 15, 20, 8, 55, 25};
-
-  float output[16];
-  int in_shape[4] = {1, 4, 4, 1};
-  int out_shape[4] = {4, 2, 2, 1};
-  int block_sizes[2] = {2, 2};
-  SpaceToBatchForNHWC((const float *)input, output, in_shape, 4, block_sizes, 0, 4 / 2);
-  for (int i = 0; i < out_size; ++i) {
-    std::cout << output[i] << " ";
+TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest6) {
+  std::vector<float> input = {1,  2,  3,  4,  5,  6,  7,  8,
+                              9, 10, 11, 12, 13, 14, 15, 16};
+  size_t kOutSize = 16;
+  std::vector<float> expect_out = {1, 3, 9, 11, 2, 4, 10, 12,
+                                   5, 7, 13, 15, 6, 8, 14, 16};
+  float out[kOutSize];
+  std::vector<int> in_shape = {1, 4, 4, 1};
+  std::vector<int> out_shape = {4, 2, 2, 1};
+  SpaceToBatchParameter param;
+  param.block_sizes_[0] = 2;
+  param.block_sizes_[1] = 2;
+  DoSpaceToBatchNHWC(input.data(), out, &param, in_shape.data(), out_shape.data());
+  for (int i = 0; i < kOutSize; ++i) {
+    std::cout << out[i] << " ";
   }
   std::cout << "\n";
-  CompareOutputData(output, expect_out, out_size, 0.000001);
+  CompareOutputData(out, expect_out.data(), kOutSize, 0.000001);
 }
 
-TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest2) {
+TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest7) {
+  std::vector<float> input = {1,  11,  2,  12,   3,  13,   4,  14,
+                              5,  15,  6,  16,   7,  17,   8,  18,
+                              9,  19, 10, 110,  11, 111,  12, 112,
+                             10,  11, 20,  12,  30,  13,  40,  14,
+                             50,  15, 60,  16,  70,  17,  80,  18,
+                             13, 113, 14, 114,  15, 115,  16, 116};
+  size_t kOutSize = 48;
+  std::vector<float> expect_out = {1,  11,   3,  13,  9,  19,  11, 111,
+                                  50,  15,  70,  17,  2,  12,   4,  14,
+                                  10, 110,  12, 112, 60,  16,  80,  18,
+                                   5,  15,   7,  17, 10,  11,  30,  13,
+                                  13, 113,  15, 115,  6,  16,   8,  18,
+                                  20,  12,  40,  14, 14, 114,  16, 116};
+  float out[kOutSize];
+  std::vector<int> in_shape = {1, 6, 4, 2};
+  std::vector<int> out_shape = {4, 3, 2, 2};
   SpaceToBatchParameter param;
-  InitSpaceToBatchParameter(&param);
-  float input[16] = {1, 2, 5, 6, 10, 20, 3, 8, 18, 10, 3, 4, 11, 55, 15, 25};
-  const int out_size = 48;
-  float expect_out[48] = {0, 0, 0, 0, 0, 1,  5, 0, 0, 18, 3,  0, 0, 0, 0, 0, 0, 2,  6, 0, 0, 10, 4,  0,
-                          0, 0, 0, 0, 0, 10, 3, 0, 0, 11, 15, 0, 0, 0, 0, 0, 0, 20, 8, 0, 0, 55, 25, 0};
-  float output[48];
-  int in_shape[4] = {1, 4, 4, 1};
-  int out_shape[4] = {4, 3, 4, 1};
-  int block_sizes[2] = {2, 2};
+  param.block_sizes_[0] = 2;
+  param.block_sizes_[1] = 2;
+  DoSpaceToBatchNHWC(input.data(), out, &param, in_shape.data(), out_shape.data());
+  for (int i = 0; i < kOutSize; ++i) {
+    std::cout << out[i] << " ";
+  }
+  std::cout << "\n";
+  CompareOutputData(out, expect_out.data(), kOutSize, 0.000001);
+}
 
-  float padded_input[48]{}, tmp[48]{}, tmp_zero[48]{};
-  float *tmp_space[3] = {padded_input, tmp, tmp_zero};
-  // DoPadding
-  DoPadding(input, padded_input, param, tmp_space + 1);
+TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest8) {
+  std::vector<float> input = {1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8,
+                              9, -9, 10, -10, 11, -11, 12, -12, 13, -13, 14, -14, 15, -15, 16, -16};
+  std::vector<float> expect_out = {1, -1, 2, -2, 3, -3, 4, -4, 0, 0, 5, -5, 6, -6, 7, -7, 8, -8, 0, 0,
+                                   9, -9, 10, -10, 11, -11, 12, -12, 0, 0, 13, -13, 14, -14, 15, -15, 16, -16, 0, 0,
+                                   0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  size_t kOutSize = 50;
+  float out[kOutSize];
+  std::vector<int> in_shape = {1, 4, 4, 2};
+  std::vector<int> out_shape = {1, 5, 5, 2};
+  std::vector<int> padding = {0, 1, 0, 1};
+  std::vector<float> pedding_h(10, 0);
+  std::vector<float> pedding_w(2, 0);
+  DoSpaceToBatchPaddingNHWC(input.data(), out, in_shape.data(), padding.data(), out_shape.data(), pedding_h.data(),
+                            pedding_w.data());
+  for (int i = 0; i < kOutSize; ++i) {
+    std::cout << out[i] << " ";
+  }
+  std::cout << "\n";
+  CompareOutputData(out, expect_out.data(), kOutSize, 0.000001);
+}
 
-  auto ret = SpaceToBatch((const float *)padded_input, output, param, 0, 4 / 2);
-  std::cout << "return " << ret << std::endl;
-  for (int i = 0; i < out_size; ++i) {
-    std::cout << output[i] << " ";
+TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest9) {
+  std::vector<float> input = {1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8,
+                              9, -9, 10, -10, 11, -11, 12, -12, 13, -13, 14, -14, 15, -15, 16, -16};
+  std::vector<float> expect_out = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                                   0, 0, 1, -1, 2, -2, 3, -3, 4, -4, 0, 0,
+                                   0, 0, 5, -5, 6, -6, 7, -7, 8, -8, 0, 0,
+                                   0, 0, 9, -9, 10, -10, 11, -11, 12, -12, 0, 0,
+                                   0, 0, 13, -13, 14, -14, 15, -15, 16, -16, 0, 0,
+                                   0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  size_t kOutSize = 72;
+  float out[kOutSize];
+  std::vector<int> in_shape = {1, 4, 4, 2};
+  std::vector<int> out_shape = {1, 6, 6, 2};
+  std::vector<int> padding = {1, 1, 1, 1};
+  std::vector<float> pedding_h(12, 0);
+  std::vector<float> pedding_w(2, 0);
+  DoSpaceToBatchPaddingNHWC(input.data(), out, in_shape.data(), padding.data(), out_shape.data(), pedding_h.data(),
+                            pedding_w.data());
+  for (int i = 0; i < kOutSize; ++i) {
+    std::cout << out[i] << " ";
   }
   std::cout << "\n";
-  CompareOutputData(output, expect_out, out_size, 0.000001);
+  CompareOutputData(out, expect_out.data(), kOutSize, 0.000001);
 }
 
-TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest3) {
+TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest10) {
+  std::vector<float> input = {1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8,
+                              9, -9, 10, -10, 11, -11, 12, -12, 13, -13, 14, -14, 15, -15, 16, -16};
+  std::vector<float> expect_out = {0, 0, 0, 0, 0, 0,
+                                   0, 0, 6, -6, 8, -8,
+                                   0, 0, 14, -14, 16, -16,
+                                   0, 0, 0, 0, 0, 0,
+                                   5, -5, 7, -7, 0, 0,
+                                   13, -13, 15, -15, 0, 0,
+                                   0, 0, 2, -2, 4, -4,
+                                   0, 0, 10, -10, 12, -12,
+                                   0, 0, 0, 0, 0, 0,
+                                   1, -1, 3, -3, 0, 0,
+                                   9, -9, 11, -11, 0, 0,
+                                   0, 0, 0, 0, 0, 0};
+  size_t kOutSize = 72;
+  float out[kOutSize];
+  float pedding_out[kOutSize];
+  std::vector<int> in_shape = {1, 4, 4, 2};
+  std::vector<int> pedding_out_shape = {1, 6, 6, 2};;
+  std::vector<int> out_shape = {4, 3, 3, 2};
+  std::vector<int> padding = {1, 1, 1, 1};
+  std::vector<float> pedding_h(12, 0);
+  std::vector<float> pedding_w(2, 0);
+  DoSpaceToBatchPaddingNHWC(input.data(), pedding_out, in_shape.data(), padding.data(), pedding_out_shape.data(),
+                            pedding_h.data(), pedding_w.data());
   SpaceToBatchParameter param;
-  InitSpaceToBatchParameter2(&param);
-  param.op_parameter_.type_ = schema::PrimitiveType_SpaceToBatch;
-
-  std::vector<float> input = {1, 2, 5, 6, 10, 20, 3, 8, 18, 10, 3, 4, 11, 55, 15, 25};
-  std::vector<int> in_shape = {1, 4, 4, 1};
-  lite::tensor::Tensor input_tensor;
-  input_tensor.SetData(input.data());
-  input_tensor.set_shape(in_shape);
-  input_tensor.SetFormat(schema::Format_NHWC);
-  input_tensor.set_data_type(kNumberTypeFloat32);
-  std::vector<lite::tensor::Tensor *> inputs_tensor;
-  inputs_tensor.emplace_back(&input_tensor);
-
-  const int out_size = 48;
-  float expect_out[48] = {0, 0, 0, 0, 0, 1,  5, 0, 0, 18, 3,  0, 0, 0, 0, 0, 0, 2,  6, 0, 0, 10, 4,  0,
-                          0, 0, 0, 0, 0, 10, 3, 0, 0, 11, 15, 0, 0, 0, 0, 0, 0, 20, 8, 0, 0, 55, 25, 0};
-  std::vector<float> output(48);
-  std::vector<int> out_shape = {4, 3, 4, 1};
-  lite::tensor::Tensor output_tensor;
-  output_tensor.SetData(output.data());
-  output_tensor.set_shape(out_shape);
-  output_tensor.SetFormat(schema::Format_NHWC);
-  output_tensor.set_data_type(kNumberTypeFloat32);
-  std::vector<lite::tensor::Tensor *> outputs_tensor;
-  outputs_tensor.emplace_back(&output_tensor);
-
-  lite::Context ctx;
-  ctx.thread_num_ = 2;
-  kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_SpaceToBatch};
-  auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
-  ASSERT_NE(creator, nullptr);
-  kernel::LiteKernel *kernel =
-    creator(inputs_tensor, outputs_tensor, reinterpret_cast<OpParameter *>(&param), &ctx, desc, nullptr);
-  ASSERT_NE(kernel, nullptr);
-  kernel->Run();
-
-  for (int i = 0; i < out_size; ++i) {
-    std::cout << output[i] << " ";
+  param.block_sizes_[0] = 2;
+  param.block_sizes_[1] = 2;
+  DoSpaceToBatchNHWC(pedding_out, out, &param, pedding_out_shape.data(), out_shape.data());
+  for (int i = 0; i < kOutSize; ++i) {
+    std::cout << out[i] << " ";
   }
   std::cout << "\n";
-  CompareOutputData(output.data(), expect_out, out_size, 0.000001);
-  input_tensor.SetData(nullptr);
-  output_tensor.SetData(nullptr);
+  CompareOutputData(out, expect_out.data(), kOutSize, 0.000001);
 }
-
 }  // namespace mindspore