Add deconv+batchnorm fusion (#3318)

* add conv_transpose+bn fusion. test=develop

* delete note, test=develop

* fix format space, test=develop

* fix opt run error, test=develop

lite/core/mir/fusion/conv_bn_fuse_pass.cc

@@ -26,7 +26,8 @@ namespace mir {
 void ConvBNFusePass::Apply(const std::unique_ptr<SSAGraph>& graph) {
   // initialze fuser params
   std::vector<bool> conv_has_bias_cases{true, false};
-  std::vector<std::string> conv_type_cases{"conv2d", "depthwise_conv2d"};
+  std::vector<std::string> conv_type_cases{
+      "conv2d", "depthwise_conv2d", "conv2d_transpose"};
   // start fuse using params
   for (auto conv_has_bias : conv_has_bias_cases) {
     for (auto conv_type : conv_type_cases) {

lite/core/mir/fusion/conv_bn_fuser.cc

@@ -103,10 +103,17 @@ void ConvBNFuser::InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) {
   std::string conv_weight_name = matched.at("conv_weight")->arg()->name;
   auto conv_weight_t =
       scope->FindVar(conv_weight_name)->GetMutable<lite::Tensor>();
-  CHECK_EQ(static_cast<size_t>(bn_scale_t->data_size()),
-           static_cast<size_t>(conv_weight_t->dims()[0]))
-      << "The BN bias's size should be equal to the size of the first "
-      << "dim size of the conv weights";
+  if (conv_type_ == "conv2d_transpose") {
+    CHECK_EQ(static_cast<size_t>(bn_scale_t->data_size()),
+             static_cast<size_t>(conv_weight_t->dims()[1]))
+        << "The BN bias's size should be equal to the size of the first "
+        << "dim size of the conv weights";
+  } else {
+    CHECK_EQ(static_cast<size_t>(bn_scale_t->data_size()),
+             static_cast<size_t>(conv_weight_t->dims()[0]))
+        << "The BN bias's size should be equal to the size of the first "
+        << "dim size of the conv weights";
+  }
   size_t weight_num = conv_weight_t->data_size();
   bool enable_int8 = conv_op_desc->HasAttr("enable_int8") ? true : false;
   bool is_weight_quantization =
@@ -153,12 +160,29 @@ void ConvBNFuser::InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) {
     // compute new conv_weight for int8
     auto weight_scale =
         conv_op_desc->GetAttr<std::vector<float>>("weight_scale");
-    for (unsigned int i = 0; i < h; ++i) {
-      weight_scale[i] *= fabsf(alpha_data[i]);
-      if (alpha_data[i] < 0.f) {
-        auto ptr_row = conv_weight_d + i * w;
-        for (unsigned int j = 0; j < w; ++j) {
-          ptr_row[j] *= -1;
+    if (conv_type_ == "conv2d_transpose") {
+      int c_size = conv_weight_t->dims()[1] * conv_weight_t->dims()[2] *
+                   conv_weight_t->dims()[3];
+      int hw = conv_weight_t->dims()[2] * conv_weight_t->dims()[3];
+      for (unsigned int k = 0; k < conv_weight_t->dims()[0]; ++k) {
+        for (unsigned int i = 0; i < h; ++i) {
+          weight_scale[i] *= fabsf(alpha_data[i]);
+          if (alpha_data[i] < 0.f) {
+            auto ptr_row = conv_weight_d + k * c_size + i * hw;
+            for (unsigned int j = 0; j < hw; ++j) {
+              ptr_row[j] *= -1;
+            }
+          }
+        }
+      }
+    } else {
+      for (unsigned int i = 0; i < h; ++i) {
+        weight_scale[i] *= fabsf(alpha_data[i]);
+        if (alpha_data[i] < 0.f) {
+          auto ptr_row = conv_weight_d + i * w;
+          for (unsigned int j = 0; j < w; ++j) {
+            ptr_row[j] *= -1;
+          }
         }
       }
     }
@@ -176,9 +200,23 @@ void ConvBNFuser::InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) {
   } else {
     // compute new conv_weight
     auto conv_weight_d = conv_weight_t->mutable_data<float>();
-    for (unsigned int i = 0; i < h; ++i) {    // n: conv2d output channels
-      for (unsigned int j = 0; j < w; ++j) {  // w: conv2d input channels
-        conv_weight_d[i * w + j] *= alpha_data[i];
+    if (conv_type_ == "conv2d_transpose") {
+      int c_size = conv_weight_t->dims()[1] * conv_weight_t->dims()[2] *
+                   conv_weight_t->dims()[3];
+      int hw = conv_weight_t->dims()[2] * conv_weight_t->dims()[3];
+      for (unsigned int k = 0; k < conv_weight_t->dims()[0]; ++k) {
+        for (unsigned int i = 0; i < h; ++i) {
+          auto ptr_row = conv_weight_d + k * c_size + i * hw;
+          for (unsigned int j = 0; j < hw; ++j) {
+            ptr_row[j] *= alpha_data[i];
+          }
+        }
+      }
+    } else {
+      for (unsigned int i = 0; i < h; ++i) {    // n: conv2d output channels
+        for (unsigned int j = 0; j < w; ++j) {  // w: conv2d input channels
+          conv_weight_d[i * w + j] *= alpha_data[i];
+        }
       }
     }
   }