| 28 | constexpr int kOutputTensor = 0; |
| 29 | |
| 30 | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
| 31 | TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); |
| 32 | TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); |
| 33 | |
| 34 | const TfLiteTensor* params = GetInput(context, node, kParams); |
| 35 | const TfLiteTensor* indices = GetInput(context, node, kIndices); |
| 36 | TfLiteTensor* output = GetOutput(context, node, kOutputTensor); |
| 37 | |
| 38 | switch (params->type) { |
| 39 | case kTfLiteFloat32: |
| 40 | case kTfLiteUInt8: |
| 41 | case kTfLiteInt8: |
| 42 | case kTfLiteInt64: |
| 43 | case kTfLiteInt32: |
| 44 | break; |
| 45 | default: |
| 46 | context->ReportError( |
| 47 | context, "Params of type '%s' are not supported by gather_nd.", |
| 48 | TfLiteTypeGetName(params->type)); |
| 49 | return kTfLiteError; |
| 50 | } |
| 51 | switch (indices->type) { |
| 52 | case kTfLiteInt64: |
| 53 | case kTfLiteInt32: |
| 54 | break; |
| 55 | default: |
| 56 | context->ReportError( |
| 57 | context, "Indices of type '%s' are not supported by gather_nd.", |
| 58 | TfLiteTypeGetName(indices->type)); |
| 59 | return kTfLiteError; |
| 60 | } |
| 61 | |
| 62 | const int params_rank = NumDimensions(params); |
| 63 | const int indices_rank = NumDimensions(indices); |
| 64 | const int indices_nd = SizeOfDimension(indices, indices_rank - 1); |
| 65 | if (params_rank < 1) { |
| 66 | context->ReportError(context, "Params must be at least a vector."); |
| 67 | return kTfLiteError; |
| 68 | } |
| 69 | if (indices_rank < 1) { |
| 70 | context->ReportError(context, "Indices must be at least a vector."); |
| 71 | return kTfLiteError; |
| 72 | } |
| 73 | if (indices_nd > params_rank) { |
| 74 | context->ReportError( |
| 75 | context, "Index innermost dimension length must be <= params rank."); |
| 76 | return kTfLiteError; |
| 77 | } |
| 78 | |
| 79 | // Assign to output the input type. |
| 80 | output->type = params->type; |
| 81 | |
| 82 | // The result shape is |
| 83 | // indices.shape[:-1] + params.shape[indices.shape[-1]:] |
| 84 | const int output_rank = indices_rank + params_rank - indices_nd - 1; |
| 85 | TfLiteIntArray* output_shape = TfLiteIntArrayCreate(output_rank); |
| 86 | int output_index = 0; |
| 87 | for (int i = 0; i < indices_rank - 1; ++i) { |
nothing calls this directly
no test coverage detected