!7466 [MSLITE] Fix bug of arithmetic operator.

Merge pull request !7466 from wangshaocong/bugfix_master_v2

mindspore/lite/nnacl/int8/scale_int8.c

@@ -18,7 +18,7 @@
 #include "nnacl/quantization/fixed_point.h"
 
 void ScaleInnerInt8(const int8_t *in_data, int8_t *out_data, const int8_t *scale, int outer_start, int outer_end,
-                    int axis_size, int inner_size, const ScaleParameter *scale_param) {
+                    int axis_size, int inner_size, const ScaleParameter *scale_param, int max, int min) {
   for (int out = outer_start; out < outer_end; out++) {
     int out_offset = out * axis_size * inner_size;
     for (int i = 0; i < axis_size; i++) {
@@ -34,8 +34,8 @@ void ScaleInnerInt8(const int8_t *in_data, int8_t *out_data, const int8_t *scale
                                 scale_param->scale_mul_arg_.multiplier_),
                               scale_param->scale_mul_arg_.right_shift_);
         int tmp = input_mul_scale + scale_param->output_zp_;
-        tmp = tmp > INT8_MAX ? INT8_MAX : tmp;
-        tmp = tmp < INT8_MIN ? INT8_MIN : tmp;
+        tmp = tmp > max ? max : tmp;
+        tmp = tmp < min ? min : tmp;
         out_data[in_offset] = tmp;
       }
     }
@@ -44,7 +44,7 @@ void ScaleInnerInt8(const int8_t *in_data, int8_t *out_data, const int8_t *scale
 
 void ScaleInnerWithBiasInt8(const int8_t *in_data, int8_t *out_data, const int8_t *scale, const int8_t *offset,
                             int outer_start, int outer_end, int axis_size, int inner_size,
-                            const ScaleParameter *scale_param) {
+                            const ScaleParameter *scale_param, int max, int min) {
   for (int out = outer_start; out < outer_end; out++) {
     int out_offset = out * axis_size * inner_size;
     for (int i = 0; i < axis_size; i++) {
@@ -63,10 +63,10 @@ void ScaleInnerWithBiasInt8(const int8_t *in_data, int8_t *out_data, const int8_
         int bias = RoundingDivideByPOT(
           SaturatingRoundingDoublingHighMul(tmp_bias * (1 << (unsigned int)scale_param->offset_mul_arg_.left_shift_),
                                             scale_param->offset_mul_arg_.multiplier_),
-          scale_param->scale_mul_arg_.right_shift_);
+          scale_param->offset_mul_arg_.right_shift_);
         int tmp = input_mul_scale + bias + scale_param->output_zp_;
-        tmp = tmp > INT8_MAX ? INT8_MAX : tmp;
-        tmp = tmp < INT8_MIN ? INT8_MIN : tmp;
+        tmp = tmp > max ? max : tmp;
+        tmp = tmp < min ? min : tmp;
         out_data[in_offset] = tmp;
       }
     }
@@ -74,21 +74,21 @@ void ScaleInnerWithBiasInt8(const int8_t *in_data, int8_t *out_data, const int8_
 }
 
 void DoScaleInt8(const int8_t *in_data, int8_t *out_data, const int8_t *scale, int task_id,
-                 const ScaleParameter *scale_param) {
+                 const ScaleParameter *scale_param, int max, int min) {
   int outer_step = UP_DIV(scale_param->outer_size_, scale_param->op_parameter_.thread_num_);
   int outer_start = task_id * outer_step;
   int outer_end = MSMIN(outer_start + outer_step, scale_param->outer_size_);
 
   ScaleInnerInt8(in_data, out_data, scale, outer_start, outer_end, scale_param->axis_size_, scale_param->inner_size_,
-                 scale_param);
+                 scale_param, max, min);
 }
 
 void DoScaleWithBiasInt8(const int8_t *in_data, int8_t *out_data, const int8_t *scale, const int8_t *offset,
-                         int task_id, const ScaleParameter *scale_param) {
+                         int task_id, const ScaleParameter *scale_param, int max, int min) {
   int outer_step = UP_DIV(scale_param->outer_size_, scale_param->op_parameter_.thread_num_);
   int outer_start = task_id * outer_step;
   int outer_end = MSMIN(outer_start + outer_step, scale_param->outer_size_);
 
   ScaleInnerWithBiasInt8(in_data, out_data, scale, offset, outer_start, outer_end, scale_param->axis_size_,
-                         scale_param->inner_size_, scale_param);
+                         scale_param->inner_size_, scale_param, max, min);
 }

mindspore/lite/nnacl/int8/scale_int8.h

@@ -23,9 +23,9 @@
 extern "C" {
 #endif
 void DoScaleInt8(const int8_t *in_data, int8_t *out_data, const int8_t *scale, int task_id,
-                 const ScaleParameter *scale_param);
+                 const ScaleParameter *scale_param, int max, int min);
 void DoScaleWithBiasInt8(const int8_t *in_data, int8_t *out_data, const int8_t *scale, const int8_t *offset,
-                         int task_id, const ScaleParameter *scale_param);
+                         int task_id, const ScaleParameter *scale_param, int max, int min);
 #ifdef __cplusplus
 }
 #endif

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

@@ -15,12 +15,13 @@
  */
 
 #include "src/runtime/kernel/arm/fp32/arithmetic.h"
-#include "src/runtime/kernel/arm/int8/add_int8.h"
-#include "src/runtime/kernel/arm/int8/mul_int8.h"
+
+#include "include/errorcode.h"
 #include "schema/model_generated.h"
 #include "src/kernel_registry.h"
+#include "src/runtime/kernel/arm/int8/add_int8.h"
+#include "src/runtime/kernel/arm/int8/mul_int8.h"
 #include "src/runtime/runtime_api.h"
-#include "include/errorcode.h"
 
 using mindspore::kernel::KERNEL_ARCH::kCPU;
 using mindspore::lite::KernelRegistrar;
@@ -48,6 +49,12 @@ int ArithmeticCPUKernel::ReSize() {
   arithmeticParameter_->in_elements_num0_ = in_tensors_[0]->ElementsNum();
   arithmeticParameter_->in_elements_num1_ = in_tensors_[1]->ElementsNum();
   arithmeticParameter_->out_elements_num_ = out_tensors_[0]->ElementsNum();
+  memcpy(arithmeticParameter_->in_shape0_, static_cast<void *>(in_tensors_[0]->shape().data()),
+         in_tensors_[0]->shape().size() * sizeof(int));
+  memcpy(arithmeticParameter_->in_shape1_, static_cast<void *>(in_tensors_[1]->shape().data()),
+         in_tensors_[1]->shape().size() * sizeof(int));
+  memcpy(arithmeticParameter_->out_shape_, static_cast<void *>(out_tensors_[0]->shape().data()),
+         out_tensors_[0]->shape().size() * sizeof(int));
 
   if (arithmeticParameter_->in_elements_num0_ == 1 || arithmeticParameter_->in_elements_num1_ == 1) {
     switch (arithmeticParameter_->op_parameter_.type_) {

mindspore/lite/src/runtime/kernel/arm/int8/scale_int8.cc

@@ -15,6 +15,7 @@
  */
 
 #include "src/runtime/kernel/arm/int8/scale_int8.h"
+
 #include <string.h>
 #include <vector>
 #include "nnacl/int8/scale_int8.h"
@@ -195,9 +196,35 @@ int ScaleInt8CPUKernel::ReSize() {
 
 int ScaleInt8CPUKernel::Scale(int task_id) {
   if (has_bias_) {
-    DoScaleWithBiasInt8(input_ptr_, output_ptr_, scale_, offset_, task_id, scale_param_);
+    switch (scale_param_->activation_type_) {
+      case schema::ActivationType_RELU:
+        DoScaleWithBiasInt8(input_ptr_, output_ptr_, scale_, offset_, task_id, scale_param_, INT8_MAX, 0);
+        break;
+      case schema::ActivationType_RELU6:
+        DoScaleWithBiasInt8(input_ptr_, output_ptr_, scale_, offset_, task_id, scale_param_, 6, 0);
+        break;
+      case schema::ActivationType_NO_ACTIVATION:
+        DoScaleWithBiasInt8(input_ptr_, output_ptr_, scale_, offset_, task_id, scale_param_, INT8_MAX, INT8_MIN);
+        break;
+      default:
+        MS_LOG(ERROR) << "Scale does not support activation type " << scale_param_->activation_type_;
+        return RET_ERROR;
+    }
   } else {
-    DoScaleInt8(input_ptr_, output_ptr_, scale_, task_id, scale_param_);
+    switch (scale_param_->activation_type_) {
+      case schema::ActivationType_RELU:
+        DoScaleInt8(input_ptr_, output_ptr_, scale_, task_id, scale_param_, INT8_MAX, 0);
+        break;
+      case schema::ActivationType_RELU6:
+        DoScaleInt8(input_ptr_, output_ptr_, scale_, task_id, scale_param_, 6, 0);
+        break;
+      case schema::ActivationType_NO_ACTIVATION:
+        DoScaleInt8(input_ptr_, output_ptr_, scale_, task_id, scale_param_, INT8_MAX, INT8_MIN);
+        break;
+      default:
+        MS_LOG(ERROR) << "Scale does not support activation type " << scale_param_->activation_type_;
+        return RET_ERROR;
+    }
   }
 
   return RET_OK;

mindspore/lite/test/models_onnx.cfg

@@ -5,3 +5,4 @@ ml_face_3d.onnx
 gts_version-RFB-320_simplified.onnx
 mnist-8.onnx
 crnn_lite_lstm_v2.onnx:32,32,32,1
+psenet_lite_mbv2.onnx:1,32,32,3

mindspore/lite/tools/converter/parser/onnx/onnx_pool_parser.cc

@@ -15,6 +15,7 @@
  */
 
 #include "tools/converter/parser/onnx/onnx_pool_parser.h"
+
 #include <memory>
 
 namespace mindspore {
@@ -77,7 +78,11 @@ STATUS OnnxPoolParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::Nod
       }
     }
     if (attribute_name == "auto_pad") {
-      MS_ASSERT(false);
+      if (onnx_node_attr.s() == "SAME_UPPER") {
+        attr->padMode = schema::PadMode_SAME_UPPER;
+      } else if (onnx_node_attr.s() == "SAME_LOWER") {
+        attr->padMode = schema::PadMode_SAME_LOWER;
+      }
     }
     if (attribute_name == "pads") {
       if (onnx_node_attr.ints_size() == 4) {

mindspore/lite/tools/converter/parser/onnx/onnx_relu_parser.cc

@@ -15,8 +15,9 @@
  */
 
 #include "tools/converter/parser/onnx/onnx_relu_parser.h"
-#include <vector>
+
 #include <memory>
+#include <vector>
 #include "securec/include/securec.h"
 
 namespace mindspore {
@@ -47,6 +48,12 @@ STATUS OnnxReluParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::Nod
     MS_LOG(DEBUG) << "onnx LeakyReluParser";
     attr->type = schema::ActivationType_LEAKY_RELU;
   }
+  for (const auto &onnx_node_attr : onnx_node.attribute()) {
+    const auto &attribute_name = onnx_node_attr.name();
+    if (attribute_name == "alpha") {
+      attr->alpha = onnx_node_attr.f();
+    }
+  }
 
   op->primitive->value.type = schema::PrimitiveType_Activation;
   op->primitive->value.value = attr.release();

mindspore/lite/tools/converter/parser/onnx/onnx_upsample_parser.cc

@@ -15,6 +15,7 @@
  */
 
 #include "tools/converter/parser/onnx/onnx_upsample_parser.h"
+
 #include <memory>
 
 namespace mindspore {
@@ -54,7 +55,7 @@ STATUS OnnxUpsampleParser::Parse(const onnx::GraphProto &onnx_graph, const onnx:
   attr->newWidth = 1;
   attr->newHeight = 1;
   attr->alignCorners = false;
-  op->primitive->value.type = schema::PrimitiveType_Upsample;
+  op->primitive->value.type = schema::PrimitiveType_Resize;
   op->primitive->value.value = attr.release();
   return RET_OK;
 }