[onert/test] Update Pad test (Samsung#7638)

This commit changes Pad negative test to parameter test.

Signed-off-by: Hyeongseok Oh <[email protected]>

.ahub/tcchecker-tca/config.yaml

@@ -28,7 +28,6 @@ test:
       - excludes :
         - Fill.cc
         - If.cc
-        - Pad.cc
         - Reduce.cc
         - ResizeBilinear.c
         - While.cc

tests/nnfw_api/src/one_op_tests/Pad.cc

@@ -31,6 +31,21 @@ class PadVariation : public GenModelTest, public ::testing::WithParamInterface<P
 {
 };
 
+// Test with different value type
+INSTANTIATE_TEST_CASE_P(
+  GenModelTest, PadVariation,
+  ::testing::Values(
+    // float value
+    PadParam{uniformTCD<float>({{1, 2, 3, 4}}, {{0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 0, 0, 0, 0, 0}})},
+    // uint8 value
+    PadParam{
+      uniformTCD<uint8_t>({{1, 2, 3, 4}}, {{8, 8, 8, 8, 8, 1, 2, 8, 8, 3, 4, 8, 8, 8, 8, 8}}),
+      circle::TensorType::TensorType_UINT8, 1.0, 8},
+    // int8 value
+    PadParam{uniformTCD<int8_t>({{-2, -1, 1, 2}},
+                                {{-5, -5, -5, -5, -5, -2, -1, -5, -5, 1, 2, -5, -5, -5, -5, -5}}),
+             circle::TensorType::TensorType_INT8, 1.0, -5}));
+
 TEST_P(PadVariation, Test)
 {
   auto &param = GetParam();
@@ -51,29 +66,16 @@ TEST_P(PadVariation, Test)
   SUCCEED();
 }
 
-// Test with different value type
-INSTANTIATE_TEST_CASE_P(
-  GenModelTest, PadVariation,
-  ::testing::Values(
-    // float value
-    PadParam{uniformTCD<float>({{1, 2, 3, 4}}, {{0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 0, 0, 0, 0, 0}})},
-    // uint8 value
-    PadParam{
-      uniformTCD<uint8_t>({{1, 2, 3, 4}}, {{8, 8, 8, 8, 8, 1, 2, 8, 8, 3, 4, 8, 8, 8, 8, 8}}),
-      circle::TensorType::TensorType_UINT8, 1.0, 8},
-    // int8 value
-    PadParam{uniformTCD<int8_t>({{-2, -1, 1, 2}},
-                                {{-5, -5, -5, -5, -5, -2, -1, -5, -5, 1, 2, -5, -5, -5, -5, -5}}),
-             circle::TensorType::TensorType_INT8, 1.0, -5}));
-
-TEST_F(GenModelTest, neg_OneOp_Pad_InvalidPadRank)
+TEST_P(PadVariation, neg_InvalidPadRank)
 {
+  auto &param = GetParam();
+
   CircleGen cgen;
-  int in = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32});
+  int in = cgen.addTensor({{1, 2, 2, 1}, param.data_type}, param.scale, param.zero_point);
   std::vector<int32_t> padding_data{1, 1, 1, 1};
   uint32_t padding_buf = cgen.addBuffer(padding_data);
   int padding = cgen.addTensor({{4}, circle::TensorType::TensorType_INT32, padding_buf});
-  int out = cgen.addTensor({{1, 4, 4, 1}, circle::TensorType::TensorType_FLOAT32});
+  int out = cgen.addTensor({{1, 4, 4, 1}, param.data_type}, param.scale, param.zero_point);
 
   cgen.addOperatorPad({{in, padding}, {out}});
   cgen.setInputsAndOutputs({in}, {out});
@@ -85,14 +87,16 @@ TEST_F(GenModelTest, neg_OneOp_Pad_InvalidPadRank)
   SUCCEED();
 }
 
-TEST_F(GenModelTest, neg_OneOp_Pad_InvalidPadDim0)
+TEST_P(PadVariation, neg_InvalidPadDim0)
 {
+  auto &param = GetParam();
+
   CircleGen cgen;
-  int in = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32});
+  int in = cgen.addTensor({{1, 2, 2, 1}, param.data_type}, param.scale, param.zero_point);
   std::vector<int32_t> padding_data{1, 1, 1, 1};
   uint32_t padding_buf = cgen.addBuffer(padding_data);
   int padding = cgen.addTensor({{2, 2}, circle::TensorType::TensorType_INT32, padding_buf});
-  int out = cgen.addTensor({{1, 4, 4, 1}, circle::TensorType::TensorType_FLOAT32});
+  int out = cgen.addTensor({{1, 4, 4, 1}, param.data_type}, param.scale, param.zero_point);
 
   cgen.addOperatorPad({{in, padding}, {out}});
   cgen.setInputsAndOutputs({in}, {out});
@@ -104,14 +108,16 @@ TEST_F(GenModelTest, neg_OneOp_Pad_InvalidPadDim0)
   SUCCEED();
 }
 
-TEST_F(GenModelTest, neg_OneOp_Pad_InvalidPadDim1)
+TEST_P(PadVariation, neg_InvalidPadDim1)
 {
+  auto &param = GetParam();
+
   CircleGen cgen;
-  int in = cgen.addTensor({{1, 1, 1, 1}, circle::TensorType::TensorType_FLOAT32});
+  int in = cgen.addTensor({{1, 2, 2, 1}, param.data_type}, param.scale, param.zero_point);
   std::vector<int32_t> padding_data{1, 1, 1, 1};
   uint32_t padding_buf = cgen.addBuffer(padding_data);
   int padding = cgen.addTensor({{4, 1}, circle::TensorType::TensorType_INT32, padding_buf});
-  int out = cgen.addTensor({{2, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32});
+  int out = cgen.addTensor({{1, 4, 4, 1}, param.data_type}, param.scale, param.zero_point);
 
   cgen.addOperatorPad({{in, padding}, {out}});
   cgen.setInputsAndOutputs({in}, {out});
@@ -123,14 +129,20 @@ TEST_F(GenModelTest, neg_OneOp_Pad_InvalidPadDim1)
   SUCCEED();
 }
 
-TEST_F(GenModelTest, neg_OneOp_Pad_Type)
+TEST_P(PadVariation, neg_Type)
 {
+  auto &param = GetParam();
+
+  const circle::TensorType output_type = ((param.data_type == circle::TensorType::TensorType_UINT8)
+                                            ? circle::TensorType::TensorType_INT8
+                                            : circle::TensorType::TensorType_UINT8);
+
   CircleGen cgen;
-  int in = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32});
+  int in = cgen.addTensor({{1, 2, 2, 1}, param.data_type}, param.scale, param.zero_point);
   std::vector<int32_t> padding_data{0, 0, 1, 1, 1, 1, 0, 0};
   uint32_t padding_buf = cgen.addBuffer(padding_data);
   int padding = cgen.addTensor({{4, 2}, circle::TensorType::TensorType_INT32, padding_buf});
-  int out = cgen.addTensor({{1, 4, 4, 1}, circle::TensorType::TensorType_UINT8}, 1.0, 1);
+  int out = cgen.addTensor({{1, 4, 4, 1}, output_type}, 1.0, 0);
 
   cgen.addOperatorPad({{in, padding}, {out}});
   cgen.setInputsAndOutputs({in}, {out});