Fix some issues in official tf.nn.topk() in lite

tensorflow/contrib/lite/kernels/topk_v2.cc

@@ -25,8 +25,8 @@ namespace builtin {
 namespace topk_v2 {
 constexpr int kInputTensor = 0;
 constexpr int kInputTopK = 1;
-constexpr int kOutputIndexes = 0;
-constexpr int kOutputValues = 1;
+constexpr int kOutputValues = 0;
+constexpr int kOutputIndexes = 1;
 
 namespace {
 TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) {

tensorflow/contrib/lite/kernels/topk_v2_test.cc

@@ -31,8 +31,8 @@ class TopKV2OpModel : public SingleOpModel {
                 int top_k) {
     input_ = AddInput(input_type);
     top_k_ = AddInput(TensorType_INT32);
-    output_indexes_ = AddOutput(TensorType_INT32);
     output_values_ = AddOutput(input_type);
+    output_indexes_ = AddOutput(TensorType_INT32);
     SetBuiltinOp(BuiltinOperator_TOPK_V2, BuiltinOptions_TopKV2Options, 0);
     BuildInterpreter({input_shape, {1}});
     PopulateTensor<int32_t>(top_k_, {top_k});

tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc

@@ -124,6 +124,15 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) {
       SetDataTypeForAllOutputs(model, op, rand_op->dtype);
       break;
     }
+    case OperatorType::kTopK_V2: {
+      // topk(values: T, k: int32) -> values: T, indices: int32
+      CHECK_EQ(op->inputs.size(), 2);
+      CHECK_EQ(op->outputs.size(), 2);
+      CHECK(model->GetArray(op->inputs[1]).data_type == ArrayDataType::kInt32);
+      model->GetArray(op->outputs[0]).data_type = model->GetArray(op->inputs[0]).data_type;
+      model->GetArray(op->outputs[1]).data_type = ArrayDataType ::kInt32;
+      break;
+    }
     case OperatorType::kTensorFlowUnsupported: {
       auto* unsupported_op = static_cast<TensorFlowUnsupportedOperator*>(op);
       // Some output tensors from the op could be eliminated by optimization.

tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc

@@ -1110,8 +1110,8 @@ void ProcessGatherOperator(Model* model, GatherOperator* op) {
 void ProcessTopkV2Operator(Model* model, TopKV2Operator* op) {
   const auto& input_values = model->GetArray(op->inputs[0]);
   const auto& input_k = model->GetArray(op->inputs[1]);
-  auto& output_indexes = model->GetArray(op->outputs[0]);
-  auto& output_values = model->GetArray(op->outputs[1]);
+  auto& output_values = model->GetArray(op->outputs[0]);
+  auto& output_indexes = model->GetArray(op->outputs[1]);
 
   // Bail if we already know the output shape.
   if (output_indexes.has_shape()) {

tensorflow/contrib/lite/toco/import_tensorflow.cc

@@ -1970,7 +1970,7 @@ void ConvertTopKV2Operator(const NodeDef& node,
     op->inputs.push_back(node.input(1));
   }
   // The op has two outputs.
-  op->outputs.push_back(node.name() + ":0");
+  op->outputs.push_back(node.name());
   op->outputs.push_back(node.name() + ":1");
   model->operators.emplace_back(op.release());
 }

tensorflow/contrib/lite/toco/tooling_util.cc

@@ -825,11 +825,6 @@ void FixNoOrphanedArray(Model* model) {
 void CheckEachArray(const Model& model) {
   for (const auto& array_entry : model.GetArrayMap()) {
     const auto& array = array_entry.second;
-    if (array->has_shape()) {
-      for (int d : array->shape().dims()) {
-        CHECK_GE(d, 1);
-      }
-    }
     // It's OK to have a buffer or an alloc, but not both.
     // (Since allocs are for transient arrays without a buffer).
     CHECK(!array->buffer || !array->alloc);
@@ -839,6 +834,10 @@ void CheckEachArray(const Model& model) {
       // The presence of a fixed buffer should imply the presence of a fixed
       // shape.
       CHECK(array->has_shape());
+      // Constant buffer should has a valid shape.
+      for (int d : array->shape().dims()) {
+        CHECK_GE(d, 1);
+      }
       // The shape flat-size should agree with the buffer length.
       CHECK_EQ(array->buffer->Length(),
                RequiredBufferSizeForShape(array->shape()));