Fix bug of resizeNearestNeighbor and expandDims operators.

mindspore/lite/nnacl/fp32/expandDims.c

@@ -18,7 +18,7 @@
 #include <string.h>
 #include "nnacl/errorcode.h"
 
-int ExpandDims(float *input_ptr, float *output_ptr, size_t data_size) {
+int ExpandDims(void *input_ptr, void *output_ptr, size_t data_size) {
   memcpy(output_ptr, input_ptr, data_size);
   return NNACL_OK;
 }

mindspore/lite/nnacl/fp32/expandDims.h

@@ -27,7 +27,7 @@ typedef struct ExpandDimsParameter {
 #ifdef __cplusplus
 extern "C" {
 #endif
-int ExpandDims(float *input_ptr, float *output_ptr, size_t data_size);
+int ExpandDims(void *input_ptr, void *output_ptr, size_t data_size);
 #ifdef __cplusplus
 }
 #endif

mindspore/lite/src/ops/resize.cc

@@ -97,31 +97,13 @@ int Resize::UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers:
 namespace {
 constexpr int kInputRank = 4;
 }  // namespace
-template <typename T>
-void CalShape(const T *data, const std::vector<Tensor *> &inputs, std::vector<int> *out_shape, int shape_size) {
-  int input_count = inputs[0]->ElementsNum();
-  int index = 0;
-  int size = 1;
-  for (int i = 0; i < shape_size; i++) {
-    if (static_cast<int>(data[i]) == -1) {
-      index = i;
-    } else {
-      size *= data[i];
-    }
-    out_shape->push_back(data[i]);
-  }
-  if (static_cast<int>(data[index]) == -1) {
-    (*out_shape)[index] = input_count / size;
-  }
-}
-
 int Resize::InferShape(std::vector<lite::Tensor *> inputs_, std::vector<lite::Tensor *> outputs_) {
   MS_ASSERT(this->primitive_ != nullptr);
   auto input = inputs_.front();
   if (input == nullptr) {
     return RET_ERROR;
   }
-  if (input->shape().size() != kInputRank) {
+  if (!input->shape().empty() && input->shape().size() != kInputRank) {
     MS_LOG(ERROR) << "Size of input shape is wrong.";
     return RET_ERROR;
   }
@@ -145,31 +127,9 @@ int Resize::InferShape(std::vector<lite::Tensor *> inputs_, std::vector<lite::Te
       return RET_INFER_INVALID;
     }
     size_t shape_size = shape_tensor->ElementsNum();
-    switch (shape_tensor->data_type()) {
-      case kNumberTypeInt8: {
-        auto data = reinterpret_cast<int8_t *>(shape_tensor->MutableData());
-        CalShape<int8_t>(data, inputs_, &output_shape, shape_size);
-      } break;
-      case kNumberTypeInt32: {
-        auto data = reinterpret_cast<int32_t *>(shape_tensor->MutableData());
-        CalShape<int32_t>(data, inputs_, &output_shape, shape_size);
-      } break;
-      case kNumberTypeInt64: {
-        auto data = reinterpret_cast<int64_t *>(shape_tensor->MutableData());
-        CalShape<int64_t>(data, inputs_, &output_shape, shape_size);
-      } break;
-      case kNumberTypeFloat: {
-        auto data = reinterpret_cast<float *>(shape_tensor->MutableData());
-        CalShape<float>(data, inputs_, &output_shape, shape_size);
-      } break;
-      case kNumberTypeUInt32: {
-        auto data = reinterpret_cast<uint32_t *>(shape_tensor->MutableData());
-        CalShape<uint32_t>(data, inputs_, &output_shape, shape_size);
-      } break;
-      default: {
-        MS_LOG(ERROR) << "Reshape weight tensor has unsupported dataType: " << shape_tensor->data_type();
-        return RET_INFER_ERR;
-      }
+    auto data = reinterpret_cast<int32_t *>(shape_tensor->data_c());
+    for (size_t i = 0; i < shape_size; i++) {
+      output_shape.push_back(data[i]);
     }
   } else if (inputs_.size() == kSingleNum) {
     auto new_height = GetNewHeight();

mindspore/lite/src/runtime/kernel/arm/base/resize_base.cc

@@ -29,7 +29,7 @@ using mindspore::lite::RET_OK;
 
 namespace mindspore::kernel {
 namespace {
-constexpr int kInputNum = 1;
+constexpr int kMaxInputNum = 2;
 constexpr int kOutputNum = 1;
 constexpr int kRank = 4;
 }  // namespace
@@ -46,15 +46,35 @@ int ResizeBaseCPUKernel::CheckParameters() {
     MS_LOG(ERROR) << "Resize method should be bilinear or nearest_neighbor, but got " << method_;
     return RET_INVALID_OP_ATTR;
   }
-  new_height_ = parameter->new_height_;
-  if (new_height_ < 1) {
-    MS_LOG(ERROR) << "Resize new_height should >= 1, but got " << new_height_;
-    return RET_INVALID_OP_ATTR;
-  }
-  new_width_ = parameter->new_width_;
-  if (new_width_ < 1) {
-    MS_LOG(ERROR) << "Resize new_width should >= 1, but got " << new_width_;
-    return RET_INVALID_OP_ATTR;
+  if (this->in_tensors_.size() == lite::kSingleNum) {
+    new_height_ = parameter->new_height_;
+    if (new_height_ < 1) {
+      MS_LOG(ERROR) << "Resize new_height should >= 1, but got " << new_height_;
+      return RET_INVALID_OP_ATTR;
+    }
+    new_width_ = parameter->new_width_;
+    if (new_width_ < 1) {
+      MS_LOG(ERROR) << "Resize new_width should >= 1, but got " << new_width_;
+      return RET_INVALID_OP_ATTR;
+    }
+  } else if (this->in_tensors_.size() == lite::kDoubleNum) {
+    auto out_shape = this->in_tensors_[1]->MutableData();
+    if (out_shape == nullptr) {
+      MS_LOG(INFO) << "Out shape is not assigned";
+      const_shape_ = false;
+    } else {
+      new_height_ = reinterpret_cast<int32_t *>(out_shape)[0];
+      if (new_height_ < 1) {
+        MS_LOG(ERROR) << "Resize new_height should >= 1, but got " << new_height_;
+        return RET_INVALID_OP_ATTR;
+      }
+      new_width_ = reinterpret_cast<int32_t *>(out_shape)[1];
+      if (new_width_ < 1) {
+        MS_LOG(ERROR) << "Resize new_width should >= 1, but got " << new_width_;
+        return RET_INVALID_OP_ATTR;
+      }
+      const_shape_ = true;
+    }
   }
   align_corners_ = parameter->align_corners_;
   preserve_aspect_ratio = parameter->preserve_aspect_ratio_;
@@ -66,8 +86,15 @@ int ResizeBaseCPUKernel::CheckParameters() {
 }
 
 int ResizeBaseCPUKernel::CheckInputsOuputs() {
-  if (in_tensors_.size() != kInputNum) {
-    MS_LOG(ERROR) << "Resize input num should be " << kInputNum << ", but got " << in_tensors_.size();
+  if (in_tensors_.size() <= lite::kDoubleNum) {
+    for (size_t i = 0; i < in_tensors_.size(); i++) {
+      auto input = in_tensors_.at(i);
+      if (input == nullptr) {
+        return RET_NULL_PTR;
+      }
+    }
+  } else {
+    MS_LOG(ERROR) << "Resize input num should be no more than" << kMaxInputNum << ", but got " << in_tensors_.size();
     return RET_ERROR;
   }
   auto input = in_tensors_.at(0);
@@ -97,7 +124,7 @@ int ResizeBaseCPUKernel::Init() {
 
   auto input = in_tensors_.at(0);
   auto input_shape = input->shape();
-  if (input_shape.size() != kRank) {
+  if (!input_shape.empty() && input_shape.size() != kRank) {
     MS_LOG(ERROR) << "Resize op support input rank 4, got " << input_shape.size();
     return RET_ERROR;
   }

mindspore/lite/src/runtime/kernel/arm/base/resize_base.h

@@ -42,6 +42,7 @@ class ResizeBaseCPUKernel : public LiteKernel {
   int64_t new_width_;
   bool align_corners_;
   bool preserve_aspect_ratio;
+  bool const_shape_;
 
  private:
   int CheckParameters();

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

@@ -48,10 +48,20 @@ int ExpandDimsCPUKernel::DoExpandDims(int task_id) {
     return RET_OK;
   }
   int offset = task_id * thread_sz_stride_;
-  int ret = ExpandDims(in_ptr_ + offset, out_ptr_ + offset, size * sizeof(float));
-  if (ret != RET_OK) {
-    MS_LOG(ERROR) << "ExpandDimsRun error task_id[" << task_id << "] error_code[" << ret << "]";
-    return ret;
+  if (this->in_tensors_[0]->data_type() == kNumberTypeFloat32) {
+    int ret = ExpandDims(reinterpret_cast<float *>(in_ptr_) + offset,
+                         reinterpret_cast<float *>(out_ptr_) + offset, size * sizeof(float));
+    if (ret != RET_OK) {
+      MS_LOG(ERROR) << "ExpandDimsRun error task_id[" << task_id << "] error_code[" << ret << "]";
+      return ret;
+    }
+  } else if (this->in_tensors_[0]->data_type() == kNumberTypeInt8) {
+    int ret = ExpandDims(reinterpret_cast<int8_t *>(in_ptr_) + offset,
+                         reinterpret_cast<int8_t *>(out_ptr_) + offset, size * sizeof(int8_t));
+    if (ret != RET_OK) {
+      MS_LOG(ERROR) << "ExpandDimsRun error task_id[" << task_id << "] error_code[" << ret << "]";
+      return ret;
+    }
   }
   return RET_OK;
 }
@@ -72,8 +82,8 @@ int ExpandDimsCPUKernel::Run() {
     MS_LOG(ERROR) << "Prepare fail!ret: " << prepare_ret;
     return prepare_ret;
   }
-  in_ptr_ = reinterpret_cast<float *>(in_tensors_.at(0)->MutableData());
-  out_ptr_ = reinterpret_cast<float *>(out_tensors_.at(0)->MutableData());
+  in_ptr_ = in_tensors_.at(0)->MutableData();
+  out_ptr_ = out_tensors_.at(0)->MutableData();
   auto ret = ParallelLaunch(this->context_->thread_pool_, ExpandDimsRun, this, thread_sz_count_);
   if (ret != RET_OK) {
     MS_LOG(ERROR) << "ExpandDimsRun error error_code[" << ret << "]";
@@ -105,4 +115,5 @@ kernel::LiteKernel *CpuExpandsDimsFp32KernelCreator(const std::vector<lite::Tens
 }
 
 REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_ExpandDims, CpuExpandsDimsFp32KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeInt8, PrimitiveType_ExpandDims, CpuExpandsDimsFp32KernelCreator)
 }  // namespace mindspore::kernel

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

@@ -44,8 +44,8 @@ class ExpandDimsCPUKernel : public LiteKernel {
   int thread_sz_count_;
   int thread_sz_stride_;
   size_t data_size_;
-  float *in_ptr_;
-  float *out_ptr_;
+  void *in_ptr_;
+  void *out_ptr_;
   int thread_count_;
 };
 }  // namespace mindspore::kernel

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

@@ -159,12 +159,10 @@ int PadCPUKernel::CheckPaddings(int *paddings, int length, int *input_shape, int
   for (auto i = 0; i < length; ++i) {
     int max_valid = input_shape[i] - offset;
     if (paddings[i * 2] > max_valid) {
-      MS_LOG(ERROR) << prefix << "paddings " << paddings[i * 2] << "should be less than " << max_valid + 1;
-      return RET_ERROR;
+      MS_LOG(WARNING) << prefix << "paddings " << paddings[i * 2] << "should be less than " << max_valid + 1;
     }
     if (paddings[i * 2 + 1] > max_valid) {
-      MS_LOG(ERROR) << prefix << "paddings " << paddings[i * 2 + 1] << "should be less than " << max_valid + 1;
-      return RET_ERROR;
+      MS_LOG(WARNING) << prefix << "paddings " << paddings[i * 2 + 1] << "should be less than " << max_valid + 1;
     }
   }
   return RET_OK;

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

@@ -179,6 +179,17 @@ int ResizeCPUKernel::RunImpl(int task_id) {
       break;
     }
     case static_cast<int>(schema::ResizeMethod_NEAREST_NEIGHBOR): {
+      if (in_tensors_.size() == lite::kDoubleNum && !const_shape_) {
+        auto out_shape = in_tensors_.at(1);
+        auto data = reinterpret_cast<int32_t *>(out_shape->MutableData());
+        if (data == nullptr) {
+          MS_LOG(ERROR) << "The out shape data is nullptr.";
+          return RET_NULL_PTR;
+        } else {
+          out_tensors_[0]->shape()[1] = static_cast<int64_t>(data[0]);
+          out_tensors_[0]->shape()[2] = static_cast<int64_t>(data[1]);
+        }
+      }
       ret = ResizeNearestNeighbor(input_data, output_data, input_shape.data(), out_tensors_[0]->shape().data(),
                                   align_corners_, task_id, context_->thread_num_);
       break;

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

@@ -81,4 +81,5 @@ kernel::LiteKernel *CpuShapeFp32KernelCreator(const std::vector<lite::Tensor *>
 }
 
 REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_Shape, CpuShapeFp32KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeInt8, PrimitiveType_Shape, CpuShapeFp32KernelCreator)
 }  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/int8/squeeze_int8.h

@@ -31,7 +31,9 @@ class SqueezeInt8CPUKernel : public SqueezeBaseCPUKernel {
   SqueezeInt8CPUKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
                        const std::vector<lite::Tensor *> &outputs, const InnerContext *ctx,
                        const mindspore::lite::PrimitiveC *primitive)
-      : SqueezeBaseCPUKernel(parameter, inputs, outputs, ctx, primitive) {}
+      : SqueezeBaseCPUKernel(parameter, inputs, outputs, ctx, primitive) {
+    para_ = reinterpret_cast<SqueezeParameter *>(parameter);
+  }
   ~SqueezeInt8CPUKernel() override { delete quant_Squeeze_parm_; }
 
   int Init() override;