1.Fix bugs of some InferShape. 2.Fix the bug of fc int8

mindspore/lite/src/ops/dedepthwise_conv2d.cc

@@ -141,8 +141,8 @@ int DeDepthwiseConv2D::InferShape(std::vector<lite::tensor::Tensor *> inputs_,
   pad_u_ = GetPadUp();
   pad_d_ = GetPadDown();
   pad_r_ = GetPadRight();
-  output_h = GetStrideH() * (input_h - 1) * GetKernelH() - pad_u_ - pad_d_;
-  output_w = GetStrideW() * (input_w - 1) * GetKernelW() - pad_l_ - pad_r_;
+  output_h = GetStrideH() * (input_h - 1) + GetKernelH() - pad_u_ - pad_d_;
+  output_w = GetStrideW() * (input_w - 1) + GetKernelW() - pad_l_ - pad_r_;
   if ((output_h + GetPadUp() + GetPadDown() - GetKernelH()) % GetStrideH() != 0) {
     output_h += (output_h + GetPadLeft() + GetPadRight() - GetKernelH()) % GetStrideH();
   }

mindspore/lite/src/ops/full_connection.cc

@@ -28,7 +28,7 @@ void FullConnection::SetHasBias(bool has_bias) { this->primitive->value.AsFullCo
 void FullConnection::SetAxis(int axis) { this->primitive->value.AsFullConnection()->axis = axis; }
 void FullConnection::SetUseAxis(bool use_axis) { this->primitive->value.AsFullConnection()->useAxis = use_axis; }
 void FullConnection::SetActivationType(int activationType) {
-  this->primitive->value.AsFullConnection()->activationType = (schema::ActivationType) activationType;
+  this->primitive->value.AsFullConnection()->activationType = (schema::ActivationType)activationType;
 }
 #else
 
@@ -47,43 +47,58 @@ int FullConnection::InferShape(std::vector<lite::tensor::Tensor *> inputs_,
   MS_ASSERT(this->primitive != nullptr);
   auto input0 = inputs_.front();
   MS_ASSERT(input0 != nullptr);
-  auto input1 = inputs_.at(1);
+  auto input1 = inputs_[1];
   MS_ASSERT(input1 != nullptr);
   auto output = outputs_.front();
   MS_ASSERT(output != nullptr);
-  output->set_data_type(input0->data_type());
-  output->SetFormat(input0->GetFormat());
   if (!GetInferFlag()) {
     return RET_OK;
   }
   if ((GetHasBias() && inputs_.size() != kMultiNum) || (!GetHasBias() && inputs_.size() != kDoubleNum)) {
     MS_LOG(ERROR) << "Input tensors num error";
-    return 1;
+    return RET_INPUT_TENSOR_ERROR;
   }
-  if (GetAxis() < 1 || GetAxis() > static_cast<int>(input0->shape().size())) {
+  if (GetUseAxis() && (GetAxis() < 1 || GetAxis() > static_cast<int>(input0->shape().size()))) {
     MS_LOG(ERROR) << "FullConnection axis invalid";
-    return 1;
+    return RET_ERROR;
   }
   int new_k = 1;
-  for (size_t i = GetAxis(); i < input0->shape().size(); ++i) {
-    new_k *= input0->shape().at(i);
+  if (GetUseAxis()) {
+    for (int i = GetAxis(); i < input0->shape().size(); ++i) {
+      new_k *= input0->shape()[i];
     }
-  if (new_k != input1->shape().at(1)) {
+    if (new_k != input1->shape()[1]) {
       MS_LOG(ERROR) << "Input1 size invalid";
-    return 1;
+      return RET_INPUT_TENSOR_ERROR;
+    }
+  } else {
+    new_k = input1->shape()[1];
   }
   if (GetHasBias()) {
-    if (inputs_.at(2)->shape()[0] != input1->shape()[0]) {
+    if (inputs_[2]->shape()[0] != input1->shape()[0]) {
       MS_LOG(ERROR) << "bias size invalid";
-      return 1;
+      return RET_INPUT_TENSOR_ERROR;
     }
   }
   std::vector<int> out_shape{inputs_[0]->shape()};
+  if (GetUseAxis()) {
     out_shape.resize(GetAxis() + 1);
     out_shape[GetAxis()] = input1->shape()[0];
+  } else {
+    int total = 1;
+    for (int i = 0; i < input0->shape().size(); ++i) {
+      total *= input0->shape()[i];
+    }
+    out_shape.resize(2);
+    auto batch_size = total / new_k;
+    out_shape[0] = batch_size;
+    out_shape[1] = input1->shape()[0];
+  }
   output->set_shape(out_shape);
+  output->set_data_type(input0->data_type());
+  output->SetFormat(input0->GetFormat());
 
-  return 0;
+  return RET_OK;
 }
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/src/runtime/kernel/arm/int8/fullconnection_int8.cc

@@ -91,8 +91,8 @@ int FullconnectionInt8CPUKernel::ReSize() {
   QuantizeRoundParameter(real_multiplier, &quant_params_.quant_multiplier, &quant_params_.left_shift,
                          &quant_params_.right_shift);
   CalculateActivationRangeQuantized(fc_param_->act_type_ == ActType_Relu, fc_param_->act_type_ == ActType_Relu6,
-                                    quant_params_.output.zp_, quant_params_.output.scale_, &quant_params_.out_act_max,
-                                    &quant_params_.out_act_min);
+                                    quant_params_.output.zp_, quant_params_.output.scale_, &quant_params_.out_act_min,
+                                    &quant_params_.out_act_max);
   return RET_OK;
 }