Add new clion rules, fix batch norml

libnd4j/blas/CMakeLists.txt

@@ -51,9 +51,9 @@ endif()
 
 if(WIN32 AND NOT ANDROID)
     get_property(dirs DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} PROPERTY INCLUDE_DIRECTORIES)
-	if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+    if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
-		set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wa,-mbig-obj")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wa,-mbig-obj")
-	endif()
+    endif()
     foreach(dir ${dirs})
         message(STATUS "dir='${dir}'")
     endforeach()
@@ -161,8 +161,8 @@ if(SD_CUDA)
     endif()
 
     if (CUDA_FOUND)
-		message("CUDA include directory: ${CUDA_INCLUDE_DIRS}")
+        message("CUDA include directory: ${CUDA_INCLUDE_DIRS}")
-		include_directories(${CUDA_INCLUDE_DIRS})
+        include_directories(${CUDA_INCLUDE_DIRS})
         message("CUDA found!")
         if ("${SD_EXPERIMENTAL}" STREQUAL "yes")
             message("Experimental mode ENABLED")
@@ -218,7 +218,7 @@ if(SD_CUDA)
 
 
         file(GLOB_RECURSE COMPILATION_UNITS false ../include/loops/cuda/compilation_units/*.cu.in
-          ../include/ops/impl/compilation_units/*.cpp.in)
+                ../include/ops/impl/compilation_units/*.cpp.in)
 
         foreach(FL_ITEM ${COMPILATION_UNITS})
             genCompilation(FL_ITEM)
@@ -229,12 +229,12 @@ if(SD_CUDA)
             file(GLOB_RECURSE CUSTOMOPS_CUDNN_SOURCES false ../include/ops/declarable/platform/cudnn/*.cu)
         endif()
 
-		add_library(samediff_obj OBJECT ${LOOPS_SOURCES_CUDA} ${LEGACY_SOURCES}
+        add_library(samediff_obj OBJECT ${LOOPS_SOURCES_CUDA} ${LEGACY_SOURCES}
                 ${CUSTOMOPS_HELPERS_SOURCES} ${HELPERS_SOURCES} ${EXEC_SOURCES}
                 ${LOOPS_SOURCES} ${ARRAY_SOURCES} ${TYPES_SOURCES}
                 ${MEMORY_SOURCES} ${GRAPH_SOURCES} ${CUSTOMOPS_SOURCES} ${INDEXING_SOURCES} ${EXCEPTIONS_SOURCES} ${OPS_SOURCES} ${PERF_SOURCES} ${CUSTOMOPS_CUDNN_SOURCES} ${CUSTOMOPS_MKLDNN_SOURCES}
-				${CUSTOMOPS_ARMCOMPUTE_SOURCES} ${CUSTOMOPS_GENERIC_SOURCES}
+                ${CUSTOMOPS_ARMCOMPUTE_SOURCES} ${CUSTOMOPS_GENERIC_SOURCES}
-				)
+                )
 
         if (WIN32)
             message("MSVC runtime for library: ${MSVC_RT_LIB}")
@@ -266,10 +266,10 @@ if(SD_CUDA)
             SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /EHsc /bigobj /std:c++14")
         endif()
 
-		target_link_libraries(${SD_LIBRARY_NAME} ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_cusolver_LIBRARY} ${CUDNN} ${MKLDNN})
+        target_link_libraries(${SD_LIBRARY_NAME} ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_cusolver_LIBRARY} ${CUDNN} ${MKLDNN})
-	    set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/cuda)
+        set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/cuda)
 
-		install(TARGETS ${SD_LIBRARY_NAME} DESTINATION .)
+        install(TARGETS ${SD_LIBRARY_NAME} DESTINATION .)
     endif(CUDA_FOUND)
 elseif(SD_CPU)
 
@@ -296,8 +296,8 @@ elseif(SD_CPU)
 
 
     file(GLOB_RECURSE COMPILATION_UNITS false ../include/ops/declarable/helpers/cpu/compilation_units/*.cpp.in
-    ../include/loops/cpu/compilation_units/*.cpp.in ../include/helpers/cpu/loops/*.cpp.in
+            ../include/loops/cpu/compilation_units/*.cpp.in ../include/helpers/cpu/loops/*.cpp.in
-    ../include/ops/impl/compilation_units/*.cpp.in)
+            ../include/ops/impl/compilation_units/*.cpp.in)
 
     foreach(FL_ITEM ${COMPILATION_UNITS})
         genCompilation(FL_ITEM)
@@ -312,29 +312,29 @@ elseif(SD_CPU)
 
 
     if(SD_CHECK_VECTORIZATION)
-       set(VECT_FILES cpu/NativeOps.cpp ${OPS_SOURCES} ${HELPERS_SOURCES} ${CUSTOMOPS_GENERIC_SOURCES} ${LOOPS_SOURCES})
+        set(VECT_FILES cpu/NativeOps.cpp ${OPS_SOURCES} ${HELPERS_SOURCES} ${CUSTOMOPS_GENERIC_SOURCES} ${LOOPS_SOURCES})
-       if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+        if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
 
-        if (CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 9.0)
+            if (CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 9.0)
-            set(CHECK_VECT_FLAGS "-ftree-vectorize -fsave-optimization-record")
+                set(CHECK_VECT_FLAGS "-ftree-vectorize -fsave-optimization-record")
-            #to process fsave-optimization-record we will need our cython version code
+                #to process fsave-optimization-record we will need our cython version code
-            message("Build Auto vectorization helpers")
+                message("Build Auto vectorization helpers")
-            execute_process(COMMAND "python3" "${CMAKE_CURRENT_SOURCE_DIR}/../auto_vectorization/cython_setup.py" "build_ext" "--inplace" WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/../auto_vectorization/" RESULT_VARIABLE ret)
+                execute_process(COMMAND "python3" "${CMAKE_CURRENT_SOURCE_DIR}/../auto_vectorization/cython_setup.py" "build_ext" "--inplace" WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/../auto_vectorization/" RESULT_VARIABLE ret)
-            message("build='${ret}'")
+                message("build='${ret}'")
 
-            #remove fail cases that gcc fails produce sometimes
+                #remove fail cases that gcc fails produce sometimes
-            file(GLOB_RECURSE FAILURE_CASES false ../include/loops/cpu/compilation_units/reduce3*.cpp)
+                file(GLOB_RECURSE FAILURE_CASES false ../include/loops/cpu/compilation_units/reduce3*.cpp)
-            #message("*****${FAILURE_CASES}")
+                #message("*****${FAILURE_CASES}")
-            foreach(FL_ITEM ${FAILURE_CASES})
+                foreach(FL_ITEM ${FAILURE_CASES})
-                message("Removing failure cases ${FL_ITEM}")
+                    message("Removing failure cases ${FL_ITEM}")
-                list(REMOVE_ITEM VECT_FILES ${FL_ITEM})
+                    list(REMOVE_ITEM VECT_FILES ${FL_ITEM})
-            endforeach() 
+                endforeach()
-        else()
+            else()
-            set(CHECK_VECT_FLAGS "-ftree-vectorize -fopt-info-vec-optimized-missed")
+                set(CHECK_VECT_FLAGS "-ftree-vectorize -fopt-info-vec-optimized-missed")
+            endif()
+            message("CHECK VECTORIZATION ${CHECK_VECT_FLAGS}")
+            set_source_files_properties( ${VECT_FILES}  PROPERTIES COMPILE_FLAGS "${CHECK_VECT_FLAGS}" )
         endif()
-        message("CHECK VECTORIZATION ${CHECK_VECT_FLAGS}")
-        set_source_files_properties( ${VECT_FILES}  PROPERTIES COMPILE_FLAGS "${CHECK_VECT_FLAGS}" )
-       endif()
     endif()
 
     message("CPU BLAS")
@@ -373,7 +373,11 @@ elseif(SD_CPU)
     foreach (_variableName ${_variableNames})
         message(STATUS "${_variableName}=${${_variableName}}")
     endforeach()
-    target_link_libraries(${SD_LIBRARY_NAME} ${MKLDNN}  ${MKLDNN_LIBRARIES} ${ARMCOMPUTE_LIBRARIES} ${OPENBLAS_LIBRARIES} ${BLAS_LIBRARIES} ${CPU_FEATURES})
+
+    #This breaks the build. Normally you want to run tests anyways.
+    if(NOT "$ENV{CLION_IDE}")
+        target_link_libraries(${SD_LIBRARY_NAME} ${MKLDNN}  ${MKLDNN_LIBRARIES} ${ARMCOMPUTE_LIBRARIES} ${OPENBLAS_LIBRARIES} ${BLAS_LIBRARIES} ${CPU_FEATURES})
+    endif()
 
     if ("${SD_ALL_OPS}" AND "${SD_BUILD_MINIFIER}")
         message(STATUS "Building minifier...")
@@ -382,7 +386,7 @@ elseif(SD_CPU)
     endif()
 
     if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU" AND "${CMAKE_CXX_COMPILER_VERSION}" VERSION_LESS 4.9)
-      message(FATAL_ERROR "You need at least GCC 4.9")
+        message(FATAL_ERROR "You need at least GCC 4.9")
     endif()
 
     # OpenMP works well pretty much only with GCC

libnd4j/include/ops/declarable/generic/nn/fusedBatchNorm.cpp

@@ -26,132 +26,138 @@
 #include <ops/declarable/CustomOperations.h>
 
 namespace sd {
-namespace ops {
+    namespace ops {
+
+        DECLARE_TYPES(fused_batch_norm) {
+            getOpDescriptor()
+                    ->setAllowedInputTypes(sd::DataType::ANY)
+                    ->setAllowedOutputTypes({ALL_FLOATS});
+        }
+
+        CUSTOM_OP_IMPL(fused_batch_norm, 3, 3, false, 0, 2) {
+            auto x      = INPUT_VARIABLE(0);                 // [bS,iH,iW,iD] (NHWC) or [bS,iD,iH,iW] (NCHW)
+            auto scale  = INPUT_VARIABLE(1);                 // [iD]
+            auto offset = INPUT_VARIABLE(2);                 // [iD]
+
+            auto y = OUTPUT_VARIABLE(0);                     // [bS,iH,iW,iD] (NHWC) or [bS,iD,iH,iW] (NCHW)
+            auto batchMean = OUTPUT_VARIABLE(1);             // [iD]
+            auto batchVar  = OUTPUT_VARIABLE(2);             // [iD]
+
+            const bool dataFormat = (bool)INT_ARG(0);               // 0->NHWC, 1->NCHW
+            const bool isTraining = (bool)INT_ARG(1);
+
+            REQUIRE_TRUE(x->rankOf() == 4, 0, "CUSTOM_OP fused_batch_norm: the rank of input x array must be equal to 4, but got %i instead !", x->rankOf());
+
+            int bS = x->sizeAt(0);              // batch size
+            int iH, iW, iD;                     // input height, input width, input depth(number of channels)
+            if(dataFormat) {
+                iD = x->sizeAt(1);
+                iH = x->sizeAt(2);
+                iW = x->sizeAt(3);
+            }
+            else {
+                iD = x->sizeAt(3);
+                iH = x->sizeAt(1);
+                iW = x->sizeAt(2);
+            }
+
+            auto xCast = x->cast(sd::DataType::FLOAT32);
+
+
+            REQUIRE_TRUE(scale->rankOf() == 1  && scale->sizeAt(0)  == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input scale array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(scale).c_str());
+            REQUIRE_TRUE(offset->rankOf() == 1 && offset->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input offset array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(offset).c_str());
+
+            NDArray *mean(nullptr), *variance(nullptr);
+            if(!isTraining) {
+                mean     = INPUT_VARIABLE(3);
+                variance = INPUT_VARIABLE(4);
+                REQUIRE_TRUE(mean->rankOf() == 1     && mean->sizeAt(0) == iD,     0, "CUSTOM_OP fused_batch_norm: wrong shape of input mean array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(mean).c_str());
+                REQUIRE_TRUE(variance->rankOf() == 1 && variance->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input variance array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(variance).c_str());
+            }
+            else {
+                //REQUIRE_TRUE(block.width() == 3, 0, "CUSTOM_OP fused_batch_norm: when isTraining=true then number of input arrays must be equal to 3, but got %i instead !", block.width());
+                std::vector<Nd4jLong> shape = {iD};
+                mean = NDArrayFactory::create_(scale->ordering(), shape, sd::DataType::FLOAT32, block.launchContext());
+                variance = NDArrayFactory::create_(scale->ordering(), shape, sd::DataType::FLOAT32, block.launchContext());
+            }
+
+
+            float epsilon;
+            if(block.getTArguments()->size() > 0) {
+                epsilon = (float) (T_ARG(0) > 1.001e-5 ? T_ARG(0) : 1.001e-5);
+            }
+            else {
+                epsilon = 0.001f;
+            }
+
+            const int restSize = x->lengthOf() / iD;
+
+            auto xAffected = NDArrayFactory::create(x->ordering(), {restSize, iD}, sd::DataType::FLOAT32, block.launchContext());
+            xAffected.assign(xCast);
+
+            const int restSizeMinusOne = (restSize > 1) ? (restSize - 1) : 1;
+            const float restSizeInv = 1.0f / restSize;
+            const float restSizeAdjust = (float)restSize / restSizeMinusOne;
+
+            if(isTraining) {
+                auto sum = xAffected.reduceAlongDimension(reduce::Sum, {0});
+                sum *=  restSizeInv;
+                mean->assign(sum);
+                *batchMean = *mean;
+            }
+            else
+                *batchMean = 0.;
+
+            auto xCentered = xAffected - *mean;
+            xAffected -= *mean;
+
+            if(isTraining) {
+                int power = 2;
+                xAffected.applyScalar(scalar::Pow, power, xAffected);
+                auto sum = xAffected.reduceAlongDimension(reduce::Sum, {0});
+                sum *=  restSizeInv;
+                variance->assign(sum);
+                auto varOutput   = (*variance) * restSizeAdjust;
+                batchVar->assign(varOutput);
+            }
+            else
+                *batchVar  = 0.;
+
+            auto scaledVariance =  ((*variance + epsilon).transform(transform::RSqrt) * (*scale)).cast(xAffected.dataType());
+            auto xScaled1 = xCentered * scaledVariance;
+            auto xShifted1 = xScaled1 + *offset;
+
+            y->assign(xShifted1);
+
+            if(isTraining) {
+                delete mean;
+                delete variance;
+            }
+
+            return Status::OK();
+        }
+
+
+
+        DECLARE_SHAPE_FN(fused_batch_norm) {
+            auto xShapeInfo     = inputShape->at(0);
+            auto scaleShapeInfo = inputShape->at(1);
+
+            const bool dataFormat = (bool)INT_ARG(0);               // 0->NHWC, 1->NCHW
+            const int iD = dataFormat ? xShapeInfo[2] : xShapeInfo[4];
+
+            REQUIRE_TRUE(scaleShapeInfo[0] == 1  && scaleShapeInfo[1] == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input scale array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(scaleShapeInfo).c_str());
+
+            Nd4jLong* outShapeInfo(nullptr), *batchMeanShapeInfo(nullptr), *batchVarShapeInfo(nullptr);
+
+            COPY_SHAPE(xShapeInfo, outShapeInfo);
+            COPY_SHAPE(scaleShapeInfo, batchMeanShapeInfo);
+            COPY_SHAPE(scaleShapeInfo, batchVarShapeInfo);
+
+            return SHAPELIST(CONSTANT(outShapeInfo), CONSTANT(batchMeanShapeInfo), CONSTANT(batchVarShapeInfo));
+        }
         
-    DECLARE_TYPES(fused_batch_norm) {
-        getOpDescriptor()
-                ->setAllowedInputTypes(sd::DataType::ANY)
-                ->setAllowedOutputTypes({ALL_FLOATS});
     }
-
-CUSTOM_OP_IMPL(fused_batch_norm, 3, 3, false, 0, 2) {
-    auto x      = INPUT_VARIABLE(0);                 // [bS,iH,iW,iD] (NHWC) or [bS,iD,iH,iW] (NCHW)
-    auto scale  = INPUT_VARIABLE(1);                 // [iD]
-    auto offset = INPUT_VARIABLE(2);                 // [iD]
-
-    auto y = OUTPUT_VARIABLE(0);                     // [bS,iH,iW,iD] (NHWC) or [bS,iD,iH,iW] (NCHW)
-    auto batchMean = OUTPUT_VARIABLE(1);             // [iD]
-    auto batchVar  = OUTPUT_VARIABLE(2);             // [iD]
-
-    const bool dataFormat = (bool)INT_ARG(0);               // 0->NHWC, 1->NCHW
-    const bool isTraining = (bool)INT_ARG(1);
-
-    REQUIRE_TRUE(x->rankOf() == 4, 0, "CUSTOM_OP fused_batch_norm: the rank of input x array must be equal to 4, but got %i instead !", x->rankOf());
-
-    int bS = x->sizeAt(0);              // batch size
-    int iH, iW, iD;                     // input height, input width, input depth(number of channels)
-    if(dataFormat) {
-        iD = x->sizeAt(1);
-        iH = x->sizeAt(2);
-        iW = x->sizeAt(3);
-    }
-    else {
-        iD = x->sizeAt(3);
-        iH = x->sizeAt(1);
-        iW = x->sizeAt(2);
-    }
-
-    REQUIRE_TRUE(scale->rankOf() == 1  && scale->sizeAt(0)  == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input scale array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(scale).c_str());
-    REQUIRE_TRUE(offset->rankOf() == 1 && offset->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input offset array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(offset).c_str());
-
-    NDArray *mean(nullptr), *variance(nullptr);
-    if(!isTraining){
-        mean     = INPUT_VARIABLE(3);
-        variance = INPUT_VARIABLE(4);
-        REQUIRE_TRUE(mean->rankOf() == 1     && mean->sizeAt(0) == iD,     0, "CUSTOM_OP fused_batch_norm: wrong shape of input mean array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(mean).c_str());
-        REQUIRE_TRUE(variance->rankOf() == 1 && variance->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input variance array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(variance).c_str());
-    }
-    else {
-        //REQUIRE_TRUE(block.width() == 3, 0, "CUSTOM_OP fused_batch_norm: when isTraining=true then number of input arrays must be equal to 3, but got %i instead !", block.width());
-        std::vector<Nd4jLong> shape = {iD};
-        mean = NDArrayFactory::create_(scale->ordering(), shape, scale->dataType(), block.launchContext());
-        variance = NDArrayFactory::create_(scale->ordering(), shape, scale->dataType(), block.launchContext());
-    }
-
-    // FIXME: double?
-    double epsilon;
-    if(block.getTArguments()->size() > 0)
-        epsilon = T_ARG(0) > 1.001e-5 ? T_ARG(0) : 1.001e-5;
-    else
-        epsilon = 0.001;
-
-    const int restSize = x->lengthOf() / iD;
-    auto xAffected = NDArrayFactory::create(x->ordering(), {restSize, iD}, mean->dataType(), block.launchContext());
-    xAffected.assign(x);
-
-    const int restSizeMinusOne = (restSize > 1) ? (restSize - 1) : 1;
-    // FIXME: float?
-    const double restSizeInv = 1.0 / restSize;
-    const double restSizeAdjust = (double)restSize / restSizeMinusOne;
-
-    if(isTraining) {
-        auto sum = xAffected.reduceAlongDimension(reduce::Sum, {0});
-        sum *= restSizeInv;
-        mean->assign(sum);
-        *batchMean = *mean;
-        //delete sum;
-    }
-    else
-        *batchMean = 0.;
-
-    xAffected -= *mean;
-
-    if(isTraining) {
-        int power = 2;
-        xAffected.applyScalar(scalar::Pow, power, xAffected);
-        auto sum = xAffected.reduceAlongDimension(reduce::Sum, {0});
-        sum *= restSizeInv;
-        variance->assign(sum);
-        *batchVar  = (*variance) * restSizeAdjust;
-        //delete sum;
-    }
-    else
-        *batchVar  = 0.;
-    xAffected *= (*variance + epsilon).transform(transform::RSqrt) * (*scale) + (*offset);
-    y->assign( xAffected );
-
-    if(isTraining) {
-        delete mean;
-        delete variance;
-    }
-
-    return Status::OK();
-}
-
-
-
-DECLARE_SHAPE_FN(fused_batch_norm) {
-    auto xShapeInfo     = inputShape->at(0);
-    auto scaleShapeInfo = inputShape->at(1);
-
-    const bool dataFormat = (bool)INT_ARG(0);               // 0->NHWC, 1->NCHW
-    const int iD = dataFormat ? xShapeInfo[2] : xShapeInfo[4];
-
-    REQUIRE_TRUE(scaleShapeInfo[0] == 1  && scaleShapeInfo[1] == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input scale array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(scaleShapeInfo).c_str());
-
-    Nd4jLong* outShapeInfo(nullptr), *batchMeanShapeInfo(nullptr), *batchVarShapeInfo(nullptr);
-
-    COPY_SHAPE(xShapeInfo, outShapeInfo);
-    COPY_SHAPE(scaleShapeInfo, batchMeanShapeInfo);
-    COPY_SHAPE(scaleShapeInfo, batchVarShapeInfo);
-
-    return SHAPELIST(CONSTANT(outShapeInfo), CONSTANT(batchMeanShapeInfo), CONSTANT(batchVarShapeInfo));
-}
-
-
-
-
-}
 }
 
 #endif

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/FusedBatchNorm.java

@@ -87,9 +87,12 @@ public class FusedBatchNorm extends DynamicCustomOp {
     }
 
     @Override
-    public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
+    public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes) {
         int n = args().length;
         Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
+        if(!dArguments.isEmpty()) {
+            return Arrays.asList(dArguments.get(0),dArguments.get(0),dArguments.get(0));
+        }
         return Arrays.asList(outputDataType == null ? DataType.FLOAT : outputDataType,
                 outputDataType == null ? DataType.FLOAT : outputDataType,
                 outputDataType == null ? DataType.FLOAT : outputDataType);

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java

@@ -69,10 +69,8 @@ public class TFGraphTestAllSameDiff {   //Note: Can't extend BaseNd4jTest here a
      * the status of the test failing. No tests will run.
      */
     public final static List<String> EXECUTE_ONLY_MODELS = Arrays.asList(
-               "max_pool_with_argmax/int32_int64_padding_SAME",
+                  "fused_batch_norm/float32_nhwc"
-            //      "fused_batch_norm/float32_nhwc",
+           // , "fused_batch_norm/float16_nhwc"
-                 "max_pool_with_argmax/int64_int64_padding_SAME"
-            //      "fused_batch_norm/float16_nhwc",
 
     );
 
@@ -86,9 +84,6 @@ public class TFGraphTestAllSameDiff {   //Note: Can't extend BaseNd4jTest here a
             // Still failing 2020/04/27 java.lang.IllegalStateException: Could not find class for TF Ops: TruncateMod
             "truncatemod/.*",
 
-            //Still failing as of 2019/09/11 - https://github.com/deeplearning4j/deeplearning4j/issues/6464 - not sure if related to: https://github.com/deeplearning4j/deeplearning4j/issues/6447
-            "cnn2d_nn/nhwc_b1_k12_s12_d12_SAME",
-
             //2019/09/11 - No tensorflow op found for SparseTensorDenseAdd
             // 2020/04/27 java.lang.IllegalStateException: Could not find class for TF Ops: SparseTensorDenseAdd
             "confusion/.*",

nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/definitions/TensorflowOpDeclarations.kt

@@ -958,7 +958,7 @@ val fusedBatchnormV1 = TensorflowMappingProcess(
                 "offset" to "offset","mean" to "mean","variance" to "variance"))),
         inputFrameworkOpName = "FusedBatchNorm",
         opMappingRegistry = tensorflowOpRegistry,
-        attributeMappingRules = listOf(valueMapping(mutableMapOf("epsilon" to "epsilon")),
+        attributeMappingRules = listOf(valueMapping(mutableMapOf("epsilon" to "epsilon","dtype" to "T")),
                 invertBooleanNumber(mutableMapOf("isTraining" to "is_training")),
                 stringEqualsRule(outputAttribute = "dataFormat",inputFrameworkAttributeName = "data_format",valueToTest = "NCHW",argumentIndex = 0))
 )
@@ -971,7 +971,7 @@ val fusedBatchnormV2 = TensorflowMappingProcess(
                 "offset" to "offset","mean" to "mean","variance" to "variance"))),
         inputFrameworkOpName = "FusedBatchNormV2",
         opMappingRegistry = tensorflowOpRegistry,
-        attributeMappingRules = listOf(valueMapping(mutableMapOf("epsilon" to "epsilon")),
+        attributeMappingRules = listOf(valueMapping(mutableMapOf("epsilon" to "epsilon","dtype" to "T")),
                 invertBooleanNumber(mutableMapOf("isTraining" to "is_training")),
                 stringEqualsRule(outputAttribute = "dataFormat",inputFrameworkAttributeName = "data_format",valueToTest = "NCHW",argumentIndex = 0))
 )
@@ -983,7 +983,7 @@ val fusedBatchnormV3 = TensorflowMappingProcess(
                 "offset" to "offset","mean" to "mean","variance" to "variance"))),
         inputFrameworkOpName = "FusedBatchNormV3",
         opMappingRegistry = tensorflowOpRegistry,
-        attributeMappingRules = listOf(valueMapping(mutableMapOf("epsilon" to "epsilon")),
+        attributeMappingRules = listOf(valueMapping(mutableMapOf("epsilon" to "epsilon","dtype" to "T")),
                 invertBooleanNumber(mutableMapOf("isTraining" to "is_training")),
                 stringEqualsRule(outputAttribute = "dataFormat",inputFrameworkAttributeName = "data_format",valueToTest = "NCHW",argumentIndex = 0))
 )

nd4j/samediff-import/samediff-import-tensorflow/src/main/resources/tensorflow-mapping-ruleset.pbtxt

@@ -8367,10 +8367,16 @@ mappings {
     functionName: "valuemapping"
     inputFloatName: "epsilon"
     outputDoubleName: "epsilon"
+    inputDataTypeName: "T"
+    outputDataTypeName: "dtype"
     inputToOutput {
       key: "epsilon"
       value: "epsilon"
     }
+    inputToOutput {
+      key: "dtype"
+      value: "T"
+    }
     ruleType: "attribute"
     inputFrameworkOpName: "FusedBatchNorm"
   }
@@ -12480,10 +12486,16 @@ mappings {
     functionName: "valuemapping"
     inputFloatName: "epsilon"
     outputDoubleName: "epsilon"
+    inputDataTypeName: "T"
+    outputDataTypeName: "dtype"
     inputToOutput {
       key: "epsilon"
       value: "epsilon"
     }
+    inputToOutput {
+      key: "dtype"
+      value: "T"
+    }
     ruleType: "attribute"
     inputFrameworkOpName: "FusedBatchNormV3"
   }
@@ -13056,10 +13068,16 @@ mappings {
     functionName: "valuemapping"
     inputFloatName: "epsilon"
     outputDoubleName: "epsilon"
+    inputDataTypeName: "T"
+    outputDataTypeName: "dtype"
     inputToOutput {
       key: "epsilon"
       value: "epsilon"
     }
+    inputToOutput {
+      key: "dtype"
+      value: "T"
+    }
     ruleType: "attribute"
     inputFrameworkOpName: "FusedBatchNormV2"
   }

nd4j/samediff-import/samediff-import-tensorflow/src/test/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TestTensorflowIR.kt

@@ -90,7 +90,9 @@ class TestTensorflowIR {
         //val inputMap = mapOf("image" to Nd4j.ones(1,128,128,4))
         val inputMap = emptyMap<String,INDArray>()
         val tensorflowIRGraph = TensorflowIRGraph(textGraph,tensorflowOps,tfImporter.registry)
-        val outputList = tensorflowIRGraph.nodeList().map { input -> input.nodeName() }.toSet()
+        val outputList = tensorflowIRGraph.nodeList().map { input -> input.nodeName() }.toMutableSet()
+        outputList.add("FusedBatchNormV3:1")
+        outputList.add("FusedBatchNormV3:2")
         val tfGraphRunner = TensorflowIRGraphRunner(tensorflowIRGraph, inputMap.keys.toList(), outputList.toList())
         val importedGraph = TFGraphMapper.importGraph(textGraph)
         val graph = tfImporter.importFromGraph(textGraph,inputMap)
@@ -104,7 +106,7 @@ class TestTensorflowIR {
         val names = tensorflowIRGraph.nodeList().map { input -> input.nodeName() }
         val skipValidation = setOf("parallel_stack/ExpandDims/dim")
         //assertEquals(output.keys,output2.keys)
-        val notEquals = HashSet<String>()
+    /*    val notEquals = HashSet<String>()
         names.forEach {
             val value = output[it]
             val value2 = output2[it]
@@ -115,9 +117,9 @@ class TestTensorflowIR {
                 val newVar = graph.variables[it]
                 notEquals.add(it)
             }
-        }
+        }*/
 
-        println(notEquals)
+        //println(notEquals)
 
         // assertEquals(output,output2)
         //assertEquals(tfOutput,output)

nd4j/samediff-import/samediff-import-tensorflow/tensorflow-processes.pbtxt

@@ -8367,10 +8367,16 @@ mappings {
     functionName: "valuemapping"
     inputFloatName: "epsilon"
     outputDoubleName: "epsilon"
+    inputDataTypeName: "T"
+    outputDataTypeName: "dtype"
     inputToOutput {
       key: "epsilon"
       value: "epsilon"
     }
+    inputToOutput {
+      key: "dtype"
+      value: "T"
+    }
     ruleType: "attribute"
     inputFrameworkOpName: "FusedBatchNorm"
   }
@@ -12480,10 +12486,16 @@ mappings {
     functionName: "valuemapping"
     inputFloatName: "epsilon"
     outputDoubleName: "epsilon"
+    inputDataTypeName: "T"
+    outputDataTypeName: "dtype"
     inputToOutput {
       key: "epsilon"
       value: "epsilon"
     }
+    inputToOutput {
+      key: "dtype"
+      value: "T"
+    }
     ruleType: "attribute"
     inputFrameworkOpName: "FusedBatchNormV3"
   }
@@ -13056,10 +13068,16 @@ mappings {
     functionName: "valuemapping"
     inputFloatName: "epsilon"
     outputDoubleName: "epsilon"
+    inputDataTypeName: "T"
+    outputDataTypeName: "dtype"
     inputToOutput {
       key: "epsilon"
       value: "epsilon"
     }
+    inputToOutput {
+      key: "dtype"
+      value: "T"
+    }
     ruleType: "attribute"
     inputFrameworkOpName: "FusedBatchNormV2"
   }