@tensorflow/tfjs-node - npm Package Compare versions

Comparing version 0.1.9 to 0.1.10

dist/nodejs_kernel_backend.d.ts

 import { BackendTimingInfo, DataType, KernelBackend, Rank, ShapeMap, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '@tensorflow/tfjs-core';
 import { Conv2DInfo } from '@tensorflow/tfjs-core/dist/ops/conv_util';
-import { TFJSBinding } from './tfjs_binding';
+import { TFEOpAttr, TFJSBinding } from './tfjs_binding';
 export declare class NodeJSKernelBackend implements KernelBackend {
-    private binding;
+    binding: TFJSBinding;
     private tensorMap;
     constructor(binding: TFJSBinding);
-    private getTFDType(dataType);
     private createOutputTensor(metadata);
     private getInputTensorIds(tensors);
     private createReductionOpAttrs(tensor);
-    private createTypeOpAttr(attrName, dtype);
     private executeSingleInput(name, input);
-    private executeSingleOutput(name, opAttrs, inputs);
-    private executeMultipleOutputs(name, opAttrs, inputs, numOutputs);
+    executeSingleOutput(name: string, opAttrs: TFEOpAttr[], inputs: Tensor[]): Tensor;
+    executeMultipleOutputs(name: string, opAttrs: TFEOpAttr[], inputs: Tensor[], numOutputs: number): Tensor[];
     dispose(): void;
@@ -29,2 +27,4 @@ read(dataId: object): Promise<Float32Array | Int32Array | Uint8Array>;
     add(a: Tensor, b: Tensor): Tensor;
+    select(condition: Tensor, a: Tensor, b: Tensor): Tensor;
+    addN<T extends Tensor>(tensors: T[]): T;
     subtract(a: Tensor, b: Tensor): Tensor;
@@ -48,5 +48,6 @@ multiply(a: Tensor, b: Tensor): Tensor;
     logicalOr(a: Tensor, b: Tensor): Tensor;
-    where(condition: Tensor, a: Tensor, b: Tensor, dtype: DataType): Tensor;
+    where(condition: Tensor): Tensor2D;
     topKValues<T extends Tensor>(x: T, k: number): Tensor1D;
     topKIndices(x: Tensor, k: number): Tensor1D;
+    topk<T extends Tensor>(x: T, k?: number, sorted?: boolean): [T, T];
     min(x: Tensor, axes: number[]): Tensor;
@@ -113,2 +114,4 @@ minimum(a: Tensor, b: Tensor): Tensor;
     gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T;
+    batchToSpaceND<T extends Tensor>(x: T, blockShape: number[], crops: number[][]): T;
+    spaceToBatchND<T extends Tensor>(x: T, blockShape: number[], paddings: number[][]): T;
     resizeBilinear(x: Tensor4D, newHeight: number, newWidth: number, alignCorners: boolean): Tensor4D;
@@ -120,5 +123,7 @@ resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D;
     localResponseNormalization4D(x: Tensor4D, radius: number, bias: number, alpha: number, beta: number): Tensor4D;
+    LRNGrad(dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, radius: number, bias: number, alpha: number, beta: number): Tensor4D;
     multinomial(logits: Tensor2D, normalized: boolean, numSamples: number, seed: number): Tensor2D;
     oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number): Tensor2D;
     cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean): Tensor;
+    nonMaxSuppression(boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number): Tensor1D;
     fromPixels(pixels: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement, numChannels: number): Tensor3D;
@@ -125,0 +130,0 @@ memory(): {

dist/nodejs_kernel_backend.js

@@ -40,2 +40,4 @@ "use strict";
 var types_1 = require("@tensorflow/tfjs-core/dist/types");
+var util_1 = require("util");
+var op_utils_1 = require("./ops/op_utils");
 var NodeJSKernelBackend = (function () {
@@ -46,14 +48,2 @@ function NodeJSKernelBackend(binding) {
     }
-    NodeJSKernelBackend.prototype.getTFDType = function (dataType) {
-        switch (dataType) {
-            case 'float32':
-                return this.binding.TF_FLOAT;
-            case 'int32':
-                return this.binding.TF_INT32;
-            case 'bool':
-                return this.binding.TF_BOOL;
-            default:
-                throw new Error('Unknown dtype `${dtype}`');
-        }
-    };
     NodeJSKernelBackend.prototype.createOutputTensor = function (metadata) {
@@ -100,15 +90,7 @@ var newId = {};
             { name: 'keep_dims', type: this.binding.TF_ATTR_BOOL, value: false },
-            this.createTypeOpAttr('T', tensor.dtype),
-            this.createTypeOpAttr('Tidx', 'int32')
+            op_utils_1.createTypeOpAttr('T', tensor.dtype), op_utils_1.createTypeOpAttr('Tidx', 'int32')
         ];
     };
-    NodeJSKernelBackend.prototype.createTypeOpAttr = function (attrName, dtype) {
-        return {
-            name: attrName,
-            type: this.binding.TF_ATTR_TYPE,
-            value: this.getTFDType(dtype)
-        };
-    };
     NodeJSKernelBackend.prototype.executeSingleInput = function (name, input) {
-        var opAttrs = [this.createTypeOpAttr('T', input.dtype)];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', input.dtype)];
         return this.executeSingleOutput(name, opAttrs, [input]);
@@ -162,3 +144,3 @@ };
         if (!this.tensorMap.has(dataId)) {
-            this.tensorMap.set(dataId, { shape: shape, dtype: this.getTFDType(dtype), values: null, id: -1 });
+            this.tensorMap.set(dataId, { shape: shape, dtype: op_utils_1.getTFDType(dtype), values: null, id: -1 });
         }
@@ -170,3 +152,3 @@ };
             { name: 'transpose_b', type: this.binding.TF_ATTR_BOOL, value: transposeB },
-            this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))
+            op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))
         ];
@@ -180,4 +162,3 @@ return this.executeSingleOutput('MatMul', opAttrs, [a, b]);
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
-            this.createTypeOpAttr('Index', 'int32'),
+            op_utils_1.createTypeOpAttr('T', x.dtype), op_utils_1.createTypeOpAttr('Index', 'int32'),
             { name: 'begin_mask', type: this.binding.TF_ATTR_INT, value: beginMask },
@@ -192,6 +173,3 @@ { name: 'end_mask', type: this.binding.TF_ATTR_INT, value: endMask },
     NodeJSKernelBackend.prototype.slice = function (x, begin, size) {
-        var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
-            this.createTypeOpAttr('Index', 'int32')
-        ];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', x.dtype), op_utils_1.createTypeOpAttr('Index', 'int32')];
         var beginTensor = tfjs_core_1.tensor1d(begin, 'int32');
@@ -202,6 +180,3 @@ var sizeTensor = tfjs_core_1.tensor1d(size, 'int32');
     NodeJSKernelBackend.prototype.reverse = function (a, axis) {
-        var opAttrs = [
-            this.createTypeOpAttr('Tidx', 'int32'),
-            this.createTypeOpAttr('T', a.dtype)
-        ];
+        var opAttrs = [op_utils_1.createTypeOpAttr('Tidx', 'int32'), op_utils_1.createTypeOpAttr('T', a.dtype)];
         var axisTensor = tfjs_core_1.tensor1d(axis, 'int32');
@@ -213,4 +188,3 @@ return this.executeSingleOutput('ReverseV2', opAttrs, [a, axisTensor]);
             { name: 'N', type: this.binding.TF_ATTR_INT, value: 2 },
-            this.createTypeOpAttr('Tidx', 'int32'),
-            this.createTypeOpAttr('T', a.dtype)
+            op_utils_1.createTypeOpAttr('Tidx', 'int32'), op_utils_1.createTypeOpAttr('T', a.dtype)
         ];
@@ -224,23 +198,34 @@ var axisTensor = tfjs_core_1.scalar(1, 'int32');
     NodeJSKernelBackend.prototype.add = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('Add', opAttrs, [a, b]);
     };
+    NodeJSKernelBackend.prototype.select = function (condition, a, b) {
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        return this.executeSingleOutput('Select', opAttrs, [condition, a, b]);
+    };
+    NodeJSKernelBackend.prototype.addN = function (tensors) {
+        var opAttrs = [
+            op_utils_1.createTypeOpAttr('T', tensors[0].dtype),
+            { name: 'N', type: this.binding.TF_ATTR_INT, value: tensors.length }
+        ];
+        return this.executeSingleOutput('AddN', opAttrs, tensors);
+    };
     NodeJSKernelBackend.prototype.subtract = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('Sub', opAttrs, [a, b]);
     };
     NodeJSKernelBackend.prototype.multiply = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('Mul', opAttrs, [a, b]);
     };
     NodeJSKernelBackend.prototype.realDivide = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('RealDiv', opAttrs, [a, b]);
     };
     NodeJSKernelBackend.prototype.floorDiv = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('FloorDiv', opAttrs, [a, b]);
     };
     NodeJSKernelBackend.prototype.divide = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('Div', opAttrs, [a, b]);
@@ -250,5 +235,4 @@ };
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
-            this.createTypeOpAttr('Tindices', 'int32'),
-            this.createTypeOpAttr('Tnumsegments', 'int32')
+            op_utils_1.createTypeOpAttr('T', x.dtype), op_utils_1.createTypeOpAttr('Tindices', 'int32'),
+            op_utils_1.createTypeOpAttr('Tnumsegments', 'int32')
         ];
@@ -264,5 +248,4 @@ return this.executeSingleOutput('UnsortedSegmentSum', opAttrs, [x, segmentIds, tfjs_core_1.scalar(numSegments, 'int32')]);
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
-            this.createTypeOpAttr('Tidx', 'int32'),
-            this.createTypeOpAttr('output_type', 'int32')
+            op_utils_1.createTypeOpAttr('T', x.dtype), op_utils_1.createTypeOpAttr('Tidx', 'int32'),
+            op_utils_1.createTypeOpAttr('output_type', 'int32')
         ];
@@ -274,5 +257,4 @@ return this.executeSingleOutput('ArgMin', opAttrs, [x, axisScalar]);
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
-            this.createTypeOpAttr('Tidx', 'int32'),
-            this.createTypeOpAttr('output_type', 'int32')
+            op_utils_1.createTypeOpAttr('T', x.dtype), op_utils_1.createTypeOpAttr('Tidx', 'int32'),
+            op_utils_1.createTypeOpAttr('output_type', 'int32')
         ];
@@ -282,23 +264,23 @@ return this.executeSingleOutput('ArgMax', opAttrs, [x, axisScalar]);
     NodeJSKernelBackend.prototype.equal = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('Equal', opAttrs, [a, b]);
     };
     NodeJSKernelBackend.prototype.notEqual = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('NotEqual', opAttrs, [a, b]);
     };
     NodeJSKernelBackend.prototype.less = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('Less', opAttrs, [a, b]);
     };
     NodeJSKernelBackend.prototype.lessEqual = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('LessEqual', opAttrs, [a, b]);
     };
     NodeJSKernelBackend.prototype.greater = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('Greater', opAttrs, [a, b]);
     };
     NodeJSKernelBackend.prototype.greaterEqual = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('GreaterEqual', opAttrs, [a, b]);
@@ -315,5 +297,4 @@ };
     };
-    NodeJSKernelBackend.prototype.where = function (condition, a, b, dtype) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
-        return this.executeSingleOutput('Select', opAttrs, [condition, a, b]);
+    NodeJSKernelBackend.prototype.where = function (condition) {
+        return this.executeSingleOutput('Where', [], [condition]);
     };
@@ -326,2 +307,12 @@ NodeJSKernelBackend.prototype.topKValues = function (x, k) {
     };
+    NodeJSKernelBackend.prototype.topk = function (x, k, sorted) {
+        var kCount = util_1.isNullOrUndefined(k) ? 1 : k;
+        var isSorted = util_1.isNullOrUndefined(sorted) ? true : sorted;
+        var opAttrs = [
+            { name: 'sorted', type: this.binding.TF_ATTR_BOOL, value: isSorted },
+            op_utils_1.createTypeOpAttr('T', x.dtype),
+        ];
+        var kTensor = tfjs_core_1.scalar(kCount, 'int32');
+        return this.executeMultipleOutputs('TopKV2', opAttrs, [x, kTensor], 2);
+    };
     NodeJSKernelBackend.prototype.min = function (x, axes) {
@@ -332,3 +323,3 @@ var axesTensor = tfjs_core_1.tensor1d(axes, 'int32');
     NodeJSKernelBackend.prototype.minimum = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('Minimum', opAttrs, [a, b]);
@@ -341,3 +332,3 @@ };
     NodeJSKernelBackend.prototype.maximum = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', types_1.upcastType(a.dtype, b.dtype))];
         return this.executeSingleOutput('Maximum', opAttrs, [a, b]);
@@ -348,3 +339,3 @@ };
             { name: 'keep_dims', type: this.binding.TF_ATTR_BOOL, value: false },
-            this.createTypeOpAttr('Tidx', 'int32')
+            op_utils_1.createTypeOpAttr('Tidx', 'int32')
         ];
@@ -357,3 +348,3 @@ var axesTensor = tfjs_core_1.tensor1d(axes, 'int32');
             { name: 'keep_dims', type: this.binding.TF_ATTR_BOOL, value: false },
-            this.createTypeOpAttr('Tidx', 'int32')
+            op_utils_1.createTypeOpAttr('Tidx', 'int32')
         ];
@@ -371,3 +362,3 @@ var axesTensor = tfjs_core_1.tensor1d(axes, 'int32');
         var dtype = types_1.upcastType(a.dtype, b.dtype);
-        var opAttrs = [this.createTypeOpAttr('T', dtype)];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', dtype)];
         return this.executeSingleOutput('Pow', opAttrs, [a.cast(dtype), b.cast(dtype)]);
@@ -398,3 +389,3 @@ };
     NodeJSKernelBackend.prototype.eluDer = function (dy, y) {
-        var opAttrs = [this.createTypeOpAttr('T', y.dtype)];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', y.dtype)];
         return this.executeSingleOutput('EluGrad', opAttrs, [dy, y]);
@@ -446,3 +437,3 @@ };
     NodeJSKernelBackend.prototype.mod = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', a.dtype)];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', a.dtype)];
         return this.executeSingleOutput('FloorMod', opAttrs, [a, b]);
@@ -475,3 +466,3 @@ };
     NodeJSKernelBackend.prototype.squaredDifference = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', a.dtype)];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', a.dtype)];
         return this.executeSingleOutput('SquaredDifference', opAttrs, [a, b]);
@@ -486,3 +477,3 @@ };
     NodeJSKernelBackend.prototype.atan2 = function (a, b) {
-        var opAttrs = [this.createTypeOpAttr('T', a.dtype)];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', a.dtype)];
         return this.executeSingleOutput('Atan2', opAttrs, [a, b]);
@@ -493,4 +484,4 @@ };
         var nans = this.isNaN(x);
-        var stepNoNans = this.where(this.greater(x, tfjs_core_1.scalar(0, dtype)), tfjs_core_1.ones(x.shape), tfjs_core_1.fill(x.shape, alpha, dtype), dtype);
-        return this.where(nans, x, stepNoNans, dtype);
+        var stepNoNans = this.select(this.greater(x, tfjs_core_1.scalar(0, dtype)), tfjs_core_1.ones(x.shape), tfjs_core_1.fill(x.shape, alpha, dtype));
+        return this.select(nans, x, stepNoNans);
     };
@@ -507,3 +498,3 @@ NodeJSKernelBackend.prototype.conv2d = function (x, filter, convInfo) {
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
+            op_utils_1.createTypeOpAttr('T', x.dtype),
             { name: 'strides', type: this.binding.TF_ATTR_INT, value: strides },
@@ -531,3 +522,3 @@ { name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding },
         var opAttrs = [
-            this.createTypeOpAttr('T', 'float32'),
+            op_utils_1.createTypeOpAttr('T', 'float32'),
             { name: 'strides', type: this.binding.TF_ATTR_INT, value: strides },
@@ -555,3 +546,3 @@ { name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding }, {
         var opAttrs = [
-            this.createTypeOpAttr('T', 'float32'),
+            op_utils_1.createTypeOpAttr('T', 'float32'),
             { name: 'strides', type: this.binding.TF_ATTR_INT, value: strides },
@@ -575,3 +566,3 @@ { name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding }, {
         var opAttrs = [
-            this.createTypeOpAttr('T', 'float32'),
+            op_utils_1.createTypeOpAttr('T', 'float32'),
             { name: 'strides', type: this.binding.TF_ATTR_INT, value: strides },
@@ -594,3 +585,3 @@ { name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding }, {
         var opAttrs = [
-            this.createTypeOpAttr('T', 'float32'),
+            op_utils_1.createTypeOpAttr('T', 'float32'),
             { name: 'strides', type: this.binding.TF_ATTR_INT, value: strides },
@@ -617,3 +608,3 @@ { name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding }, {
         var opAttrs = [
-            this.createTypeOpAttr('T', input.dtype),
+            op_utils_1.createTypeOpAttr('T', input.dtype),
             { name: 'strides', type: this.binding.TF_ATTR_INT, value: strides },
@@ -639,3 +630,3 @@ { name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding }, {
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
+            op_utils_1.createTypeOpAttr('T', x.dtype),
             { name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize },
@@ -661,3 +652,3 @@ { name: 'strides', type: this.binding.TF_ATTR_INT, value: strides },
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
+            op_utils_1.createTypeOpAttr('T', x.dtype),
             { name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize },
@@ -684,3 +675,3 @@ { name: 'strides', type: this.binding.TF_ATTR_INT, value: strides },
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
+            op_utils_1.createTypeOpAttr('T', x.dtype),
             { name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize },
@@ -707,3 +698,3 @@ { name: 'strides', type: this.binding.TF_ATTR_INT, value: strides },
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
+            op_utils_1.createTypeOpAttr('T', x.dtype),
             { name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize },
@@ -724,4 +715,4 @@ { name: 'strides', type: this.binding.TF_ATTR_INT, value: strides },
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
-            this.createTypeOpAttr('Tshape', shapeTensor.dtype)
+            op_utils_1.createTypeOpAttr('T', x.dtype),
+            op_utils_1.createTypeOpAttr('Tshape', shapeTensor.dtype)
         ];
@@ -731,6 +722,3 @@ return this.executeSingleOutput('Reshape', opAttrs, [x, shapeTensor]);
     NodeJSKernelBackend.prototype.cast = function (x, dtype) {
-        var opAttrs = [
-            this.createTypeOpAttr('SrcT', x.dtype),
-            this.createTypeOpAttr('DstT', dtype)
-        ];
+        var opAttrs = [op_utils_1.createTypeOpAttr('SrcT', x.dtype), op_utils_1.createTypeOpAttr('DstT', dtype)];
         return this.executeSingleOutput('Cast', opAttrs, [x]);
@@ -740,4 +728,3 @@ };
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
-            this.createTypeOpAttr('Tmultiples', 'int32')
+            op_utils_1.createTypeOpAttr('T', x.dtype), op_utils_1.createTypeOpAttr('Tmultiples', 'int32')
         ];
@@ -751,4 +738,4 @@ var multiples = tfjs_core_1.tensor1d(reps, 'int32');
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
-            this.createTypeOpAttr('Tpaddings', paddingsTensor.dtype)
+            op_utils_1.createTypeOpAttr('T', x.dtype),
+            op_utils_1.createTypeOpAttr('Tpaddings', paddingsTensor.dtype)
         ];
@@ -759,6 +746,3 @@ return this.executeSingleOutput('PadV2', opAttrs, [x, paddingsTensor, constantTensor]);
         var permTensor = tfjs_core_1.tensor1d(perm, 'int32');
-        var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
-            this.createTypeOpAttr('Tperm', 'int32')
-        ];
+        var opAttrs = [op_utils_1.createTypeOpAttr('T', x.dtype), op_utils_1.createTypeOpAttr('Tperm', 'int32')];
         return this.executeSingleOutput('Transpose', opAttrs, [x, permTensor]);
@@ -769,11 +753,29 @@ };
         var opAttrs = [
-            this.createTypeOpAttr('Tparams', x.dtype),
-            this.createTypeOpAttr('Tindices', indices.dtype),
-            this.createTypeOpAttr('Taxis', 'int32')
+            op_utils_1.createTypeOpAttr('Tparams', x.dtype),
+            op_utils_1.createTypeOpAttr('Tindices', indices.dtype),
+            op_utils_1.createTypeOpAttr('Taxis', 'int32')
         ];
         return this.executeSingleOutput('GatherV2', opAttrs, [x, indices, axisTensor]);
     };
+    NodeJSKernelBackend.prototype.batchToSpaceND = function (x, blockShape, crops) {
+        var blockShapeTensor = tfjs_core_1.tensor1d(blockShape, 'int32');
+        var cropsTensor = tfjs_core_1.tensor2d(crops, [crops.length, crops[0].length], 'int32');
+        var opAttrs = [
+            op_utils_1.createTypeOpAttr('T', x.dtype), op_utils_1.createTypeOpAttr('Tblock_shape', 'int32'),
+            op_utils_1.createTypeOpAttr('Tcrops', cropsTensor.dtype)
+        ];
+        return this.executeSingleOutput('BatchToSpaceND', opAttrs, [x, blockShapeTensor, cropsTensor]);
+    };
+    NodeJSKernelBackend.prototype.spaceToBatchND = function (x, blockShape, paddings) {
+        var blockShapeTensor = tfjs_core_1.tensor1d(blockShape, 'int32');
+        var paddingsTensor = tfjs_core_1.tensor2d(paddings, [paddings.length, paddings[0].length], 'int32');
+        var opAttrs = [
+            op_utils_1.createTypeOpAttr('T', x.dtype), op_utils_1.createTypeOpAttr('Tblock_shape', 'int32'),
+            op_utils_1.createTypeOpAttr('Tpaddings', paddingsTensor.dtype)
+        ];
+        return this.executeSingleOutput('SpaceToBatchND', opAttrs, [x, blockShapeTensor, paddingsTensor]);
+    };
     NodeJSKernelBackend.prototype.resizeBilinear = function (x, newHeight, newWidth, alignCorners) {
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
+            op_utils_1.createTypeOpAttr('T', x.dtype),
             {
@@ -790,3 +792,3 @@ name: 'align_corners',
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype), {
+            op_utils_1.createTypeOpAttr('T', x.dtype), {
                 name: 'align_corners',
@@ -801,3 +803,3 @@ type: this.binding.TF_ATTR_BOOL,
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
+            op_utils_1.createTypeOpAttr('T', x.dtype),
             {
@@ -814,3 +816,3 @@ name: 'align_corners',
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype), {
+            op_utils_1.createTypeOpAttr('T', x.dtype), {
                 name: 'align_corners',
@@ -837,3 +839,3 @@ type: this.binding.TF_ATTR_BOOL,
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
+            op_utils_1.createTypeOpAttr('T', x.dtype),
             {
@@ -862,3 +864,3 @@ name: 'epsilon',
         var opAttrs = [
-            this.createTypeOpAttr('T', x.dtype),
+            op_utils_1.createTypeOpAttr('T', x.dtype),
             { name: 'depth_radius', type: this.binding.TF_ATTR_INT, value: radius },
@@ -871,2 +873,12 @@ { name: 'bias', type: this.binding.TF_ATTR_FLOAT, value: bias },
     };
+    NodeJSKernelBackend.prototype.LRNGrad = function (dy, inputImage, outputImage, radius, bias, alpha, beta) {
+        var opAttrs = [
+            op_utils_1.createTypeOpAttr('T', dy.dtype),
+            { name: 'depth_radius', type: this.binding.TF_ATTR_INT, value: radius },
+            { name: 'bias', type: this.binding.TF_ATTR_FLOAT, value: bias },
+            { name: 'alpha', type: this.binding.TF_ATTR_FLOAT, value: alpha },
+            { name: 'beta', type: this.binding.TF_ATTR_FLOAT, value: beta },
+        ];
+        return this.executeSingleOutput('LRNGrad', opAttrs, [dy, inputImage, outputImage]);
+    };
     NodeJSKernelBackend.prototype.multinomial = function (logits, normalized, numSamples, seed) {
@@ -878,4 +890,4 @@ if (normalized) {
         var opAttrs = [
-            this.createTypeOpAttr('T', logits.dtype),
-            this.createTypeOpAttr('output_dtype', 'int32'),
+            op_utils_1.createTypeOpAttr('T', logits.dtype),
+            op_utils_1.createTypeOpAttr('output_dtype', 'int32'),
             { name: 'seed', type: this.binding.TF_ATTR_INT, value: seed },
@@ -892,4 +904,4 @@ { name: 'seed2', type: this.binding.TF_ATTR_INT, value: seed * seed },
             { name: 'axis', type: this.binding.TF_ATTR_INT, value: -1 },
-            this.createTypeOpAttr('T', indices.dtype),
-            this.createTypeOpAttr('TI', indices.dtype)
+            op_utils_1.createTypeOpAttr('T', indices.dtype),
+            op_utils_1.createTypeOpAttr('TI', indices.dtype)
         ];
@@ -905,7 +917,16 @@ return this.executeSingleOutput('OneHot', opAttrs, [
             { name: 'reverse', type: this.binding.TF_ATTR_BOOL, value: reverse },
-            this.createTypeOpAttr('T', x.dtype),
-            this.createTypeOpAttr('Tidx', 'int32')
+            op_utils_1.createTypeOpAttr('T', x.dtype), op_utils_1.createTypeOpAttr('Tidx', 'int32')
         ];
         return this.executeSingleOutput('Cumsum', opAttrs, [x, axisTensor]);
     };
+    NodeJSKernelBackend.prototype.nonMaxSuppression = function (boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        var opAttrs = [];
+        var maxOutputSizeTensor = tfjs_core_1.scalar(maxOutputSize, 'int32');
+        var iouThresholdTensor = tfjs_core_1.scalar(iouThreshold);
+        var scoreThresholdTensor = tfjs_core_1.scalar(scoreThreshold);
+        return this.executeSingleOutput('NonMaxSuppressionV3', opAttrs, [
+            boxes, scores, maxOutputSizeTensor, iouThresholdTensor,
+            scoreThresholdTensor
+        ]);
+    };
     NodeJSKernelBackend.prototype.fromPixels = function (pixels, numChannels) {
@@ -912,0 +933,0 @@ if (pixels == null) {

dist/run_tests.d.ts

@@ -1,1 +0,1 @@
-import './index';
+import '.';

dist/run_tests.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-require("./index");
+require(".");
 var jasmine_util = require("@tensorflow/tfjs-core/dist/jasmine_util");
 Error.stackTraceLimit = Infinity;
 var jasmineCtor = require('jasmine');
-var bindings = require("bindings");
-var nodejs_kernel_backend_1 = require("./nodejs_kernel_backend");
-process.on('unhandledRejection', function (e) { throw e; });
-jasmine_util.setTestEnvs([{
-        name: 'test-tensorflow',
-        factory: function () {
-            return new nodejs_kernel_backend_1.NodeJSKernelBackend(bindings('tfjs_binding.node'));
-        },
-        features: {}
-    }]);
+var op_utils_1 = require("./ops/op_utils");
+process.on('unhandledRejection', function (e) {
+    throw e;
+});
+jasmine_util.setTestEnvs([{ name: 'test-tensorflow', factory: function () { return op_utils_1.nodeBackend(); }, features: {} }]);
 var IGNORE_LIST = [
@@ -40,2 +35,3 @@ 'depthwiseConv2D',
 };
+console.log("Running tests against TensorFlow: " + op_utils_1.nodeBackend().binding.TF_Version);
 runner.execute();

dist/version.d.ts

@@ -1,2 +0,2 @@
-declare const version = "0.1.9";
+declare const version = "0.1.10";
 export { version };

dist/version.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-var version = '0.1.9';
+var version = '0.1.10';
 exports.version = version;

package.json

 {
   "name": "@tensorflow/tfjs-node",
-  "version": "0.1.9",
+  "version": "0.1.10",
   "main": "dist/index.js",
@@ -25,3 +25,3 @@ "types": "dist/index.d.ts",
   "devDependencies": {
-    "@tensorflow/tfjs-core": "~0.12.1",
+    "@tensorflow/tfjs-core": "~0.12.8",
     "@types/bindings": "~1.3.0",
@@ -43,4 +43,4 @@ "@types/jasmine": "~2.8.6",
   "peerDependencies": {
-    "@tensorflow/tfjs-core": "~0.12.1"
+    "@tensorflow/tfjs-core": "~0.12.8"
   }
 }

README.md

@@ -14,4 +14,4 @@ <a id="travis-badge" href="https://travis-ci.org/tensorflow/tfjs-node" alt="Build Status">
 - Mac OS X CPU (10.12.6 Siera or higher)
-- Linux CPU (Ubuntu 16.04 or higher)
-- Linux GPU (Ubuntu 16.04 or higher and Cuda 9.0 w/ CUDNN v7) ([see installation instructions](https://www.tensorflow.org/install/install_linux))
+- Linux CPU (Ubuntu 14.04 or higher)
+- Linux GPU (Ubuntu 14.04 or higher and Cuda 9.0 w/ CUDNN v7) ([see installation instructions](https://www.tensorflow.org/install/install_linux))
 
@@ -18,0 +18,0 @@ *Other Linux variants might also work but this project matches [core TensorFlow installation requirements](https://www.tensorflow.org/install/install_linux).*

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Improved metrics

Total package byte prevSize

247470

increased by5.93%

Number of package files

55

increased by12.24%

Lines of code

2236

increased by6.58%

No dependency changes