| 1 | {"version":3,"file":"tf-backend-cpu.fesm.min.js","sources":["../src/cpu_util.ts","../src/backend_cpu.ts","../src/kernels/Abs.ts","../src/utils/binary_impl.ts","../src/kernels/Complex.ts","../src/utils/zeros_impl.ts","../src/kernels/Identity.ts","../src/kernels/Real.ts","../src/kernels/Cast.ts","../src/utils/binary_utils.ts","../src/kernels/Add.ts","../src/kernels/Bincount_impl.ts","../src/utils/unary_impl.ts","../src/utils/unary_utils.ts","../src/kernels/Ceil.ts","../src/kernels/Concat_impl.ts","../src/kernels/Exp.ts","../src/kernels/Expm1.ts","../src/kernels/Floor.ts","../src/kernels/GatherV2_impl.ts","../src/kernels/Greater.ts","../src/kernels/Less.ts","../src/kernels/LinSpace_impl.ts","../src/kernels/Log.ts","../src/kernels/Max_impl.ts","../src/kernels/Maximum.ts","../src/kernels/Minimum.ts","../src/kernels/Multiply.ts","../src/kernels/Neg.ts","../src/kernels/NotEqual.ts","../src/kernels/Transpose_impl.ts","../src/kernels/Transpose.ts","../src/kernels/Prod.ts","../src/kernels/Range_impl.ts","../src/kernels/Rsqrt.ts","../src/kernels/Slice.ts","../src/kernels/SquaredDifference.ts","../src/kernels/StridedSlice_impl.ts","../src/kernels/Sub.ts","../src/kernels/Tile_impl.ts","../src/kernels/TopK_impl.ts","../src/kernels/Unique_impl.ts","../src/version.ts","../src/base.ts","../src/kernels/Elu.ts","../src/kernels/LeakyRelu.ts","../src/kernels/Prelu.ts","../src/kernels/Relu.ts","../src/kernels/Relu6.ts","../src/utils/fused_utils.ts","../src/kernels/Reshape.ts","../src/kernels/BatchMatMul.ts","../src/kernels/_FusedMatMul.ts","../src/kernels/Acos.ts","../src/kernels/Acosh.ts","../src/kernels/AddN.ts","../src/kernels/All.ts","../src/kernels/Any.ts","../src/kernels/ArgMax.ts","../src/kernels/ArgMin.ts","../src/kernels/Asin.ts","../src/kernels/Asinh.ts","../src/kernels/Atan.ts","../src/kernels/Atan2.ts","../src/kernels/Atanh.ts","../src/utils/pool_utils.ts","../src/kernels/AvgPool.ts","../src/kernels/AvgPool3D.ts","../src/kernels/AvgPool3DGrad.ts","../src/kernels/AvgPoolGrad.ts","../src/kernels/BatchNorm.ts","../src/kernels/BatchToSpaceND.ts","../src/kernels/Bincount.ts","../src/kernels/Clip.ts","../src/kernels/ComplexAbs.ts","../src/kernels/Imag.ts","../src/kernels/Concat.ts","../src/kernels/Conv2D.ts","../src/kernels/Conv2DBackpropFilter.ts","../src/kernels/Conv2DBackpropInput.ts","../src/kernels/Conv3D.ts","../src/kernels/Conv3DBackpropFilterV2.ts","../src/kernels/Conv3DBackpropInputV2.ts","../src/kernels/Cos.ts","../src/kernels/Cosh.ts","../src/kernels/CropAndResize.ts","../src/kernels/Cumsum.ts","../src/kernels/DenseBincount.ts","../src/kernels/DepthToSpace.ts","../src/kernels/DepthwiseConv2dNative.ts","../src/kernels/DepthwiseConv2dNativeBackpropFilter.ts","../src/kernels/DepthwiseConv2dNativeBackpropInput.ts","../src/kernels/Diag.ts","../src/kernels/Dilation2D.ts","../src/kernels/Dilation2DBackpropFilter.ts","../src/kernels/Dilation2DBackpropInput.ts","../src/kernels/EluGrad.ts","../src/kernels/Equal.ts","../src/kernels/Erf.ts","../src/kernels/ExpandDims.ts","../src/kernels/RealDiv.ts","../src/utils/fft_utils.ts","../src/kernels/FFT.ts","../src/kernels/Fill.ts","../src/kernels/FlipLeftRight.ts","../src/kernels/FloorDiv.ts","../src/kernels/FusedConv2D.ts","../src/kernels/FusedDepthwiseConv2D.ts","../src/kernels/GatherNd.ts","../src/kernels/GatherV2.ts","../src/kernels/GreaterEqual.ts","../src/kernels/IFFT.ts","../src/kernels/IsFinite.ts","../src/kernels/IsInf.ts","../src/kernels/IsNaN.ts","../src/kernels/LessEqual.ts","../src/kernels/LinSpace.ts","../src/kernels/Log1p.ts","../src/kernels/LogicalAnd.ts","../src/kernels/LogicalNot.ts","../src/kernels/LogicalOr.ts","../src/kernels/LRN.ts","../src/kernels/LRNGrad.ts","../src/kernels/Max.ts","../src/kernels/MaxPool.ts","../src/kernels/MaxPool3D.ts","../src/kernels/MaxPool3DGrad.ts","../src/kernels/MaxPoolGrad.ts","../src/kernels/MaxPoolWithArgmax.ts","../src/kernels/MaxPoolWithArgmax_impl.ts","../src/kernels/Sum.ts","../src/kernels/Mean.ts","../src/kernels/Min.ts","../src/kernels/MirrorPad.ts","../src/kernels/Mod.ts","../src/kernels/Softmax.ts","../src/kernels/Multinomial.ts","../src/kernels/NonMaxSuppressionV3.ts","../src/kernels/NonMaxSuppressionV4.ts","../src/kernels/NonMaxSuppressionV5.ts","../src/kernels/OneHot.ts","../src/kernels/ZerosLike.ts","../src/kernels/OnesLike.ts","../src/kernels/Pack.ts","../src/kernels/PadV2.ts","../src/kernels/Pow.ts","../src/kernels/Range.ts","../src/kernels/Reciprocal.ts","../src/kernels/ResizeBilinear.ts","../src/kernels/ResizeBilinearGrad.ts","../src/kernels/ResizeNearestNeighbor.ts","../src/kernels/ResizeNearestNeighborGrad.ts","../src/kernels/Reverse.ts","../src/kernels/RotateWithOffset.ts","../src/kernels/Round.ts","../src/kernels/Scatter_impl.ts","../src/kernels/ScatterNd.ts","../src/kernels/Select.ts","../src/kernels/Selu.ts","../src/kernels/Sigmoid.ts","../src/kernels/Sign.ts","../src/kernels/Sin.ts","../src/kernels/Sinh.ts","../src/kernels/Softplus.ts","../src/kernels/SpaceToBatchND.ts","../src/kernels/SparseToDense.ts","../src/kernels/SplitV.ts","../src/kernels/Sqrt.ts","../src/kernels/Square.ts","../src/kernels/Step.ts","../src/kernels/StridedSlice.ts","../src/kernels/Tan.ts","../src/kernels/Tanh.ts","../src/kernels/UnsortedSegmentSum.ts","../src/register_all_kernels.ts","../src/kernels/Tile.ts","../src/kernels/TopK.ts","../src/kernels/Unique.ts","../src/kernels/Unpack.ts"],"sourcesContent":["/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorInfo, util} from '@tensorflow/tfjs-core';\n\nexport function assertNotComplex(\n tensor: TensorInfo|TensorInfo[], opName: string): void {\n if (!Array.isArray(tensor)) {\n tensor = [tensor];\n }\n tensor.forEach(t => {\n if (t != null) {\n util.assert(\n t.dtype !== 'complex64',\n () => `${\n opName} does not support complex64 tensors in the CPU backend.`);\n }\n });\n}\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, BackendTimingInfo, buffer, DataStorage, DataType, DataValues, engine, env, kernel_impls, KernelBackend, Rank, ShapeMap, Tensor, Tensor2D, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nconst whereImpl = kernel_impls.whereImpl;\nimport {assertNotComplex} from './cpu_util';\n\ninterface DataId {}\n\nexport interface TensorData<D extends DataType> {\n values?: backend_util.BackendValues;\n dtype: D;\n // For complex numbers, the real and imaginary parts are stored as their own\n // individual tensors, with a parent joining the two with the\n // complexTensorInfos field.\n complexTensorInfos?: {real: TensorInfo, imag: TensorInfo};\n // refCount keeps track of how many tensors reference it. Used for memory\n // management.\n refCount: number;\n}\n\nexport class MathBackendCPU extends KernelBackend {\n public blockSize = 48;\n\n data: DataStorage<TensorData<DataType>>;\n private firstUse = true;\n private static nextDataId = 0;\n private nextDataId(): number {\n return MathBackendCPU.nextDataId++;\n }\n\n constructor() {\n super();\n this.data = new DataStorage(this, engine());\n }\n\n write(values: backend_util.BackendValues, shape: number[], dtype: DataType):\n DataId {\n if (this.firstUse) {\n this.firstUse = false;\n if (env().get('IS_NODE')) {\n backend_util.warn(\n '\\n============================\\n' +\n 'Hi there 👋. Looks like you are running TensorFlow.js in ' +\n 'Node.js. To speed things up dramatically, install our node ' +\n 'backend, which binds to TensorFlow C++, by running ' +\n 'npm i @tensorflow/tfjs-node, ' +\n 'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n 'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n 'suffix for CUDA) at the start of your program. ' +\n 'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n '\\n============================');\n }\n }\n const dataId = {id: this.nextDataId()};\n\n this.data.set(dataId, {values, dtype, refCount: 1});\n\n return dataId;\n }\n\n /**\n * Create a data bucket in cpu backend.\n * @param shape Shape of the `TensorInfo`.\n * @param dtype DType of the `TensorInfo`.\n * @param values The value of the `TensorInfo` stored as a flattened array.\n */\n makeTensorInfo(\n shape: number[], dtype: DataType,\n values?: backend_util.BackendValues|string[]): TensorInfo {\n let outId;\n if (dtype === 'string' && values != null && values.length > 0 &&\n util.isString(values[0])) {\n const encodedValues =\n (values as {} as string[]).map(d => util.encodeString(d));\n\n outId = this.write(encodedValues, shape, dtype);\n } else {\n outId = this.write(values as TypedArray, shape, dtype);\n }\n\n return {dataId: outId, shape, dtype};\n }\n\n /** Return refCount of a `TensorData`. */\n refCount(dataId: DataId): number {\n if (this.data.has(dataId)) {\n const tensorData = this.data.get(dataId);\n return tensorData.refCount;\n }\n return 0;\n }\n\n /** Increase refCount of a `TensorData`. */\n incRef(dataId: DataId): void {\n const tensorData = this.data.get(dataId);\n tensorData.refCount++;\n }\n\n /** Decrease refCount of a `TensorData`. */\n decRef(dataId: DataId): void {\n if (this.data.has(dataId)) {\n const tensorData = this.data.get(dataId);\n tensorData.refCount--;\n }\n }\n\n move(\n dataId: DataId, values: backend_util.BackendValues, shape: number[],\n dtype: DataType, refCount: number): void {\n this.data.set(dataId, {values, dtype, refCount});\n }\n\n numDataIds(): number {\n return this.data.numDataIds();\n }\n\n async read(dataId: DataId): Promise<backend_util.BackendValues> {\n return this.readSync(dataId);\n }\n readSync(dataId: DataId): backend_util.BackendValues {\n const {dtype, complexTensorInfos} = this.data.get(dataId);\n\n if (dtype === 'complex64') {\n const realValues =\n this.readSync(complexTensorInfos.real.dataId) as Float32Array;\n const imagValues =\n this.readSync(complexTensorInfos.imag.dataId) as Float32Array;\n return backend_util.mergeRealAndImagArrays(realValues, imagValues);\n }\n\n return this.data.get(dataId).values;\n }\n\n bufferSync<R extends Rank>(t: TensorInfo): TensorBuffer<R> {\n const data = this.readSync(t.dataId);\n let decodedData = data as DataValues;\n if (t.dtype === 'string') {\n try {\n // Decode the bytes into string.\n decodedData = (data as Uint8Array[]).map(d => util.decodeString(d));\n } catch {\n throw new Error('Failed to decode encoded string bytes into utf-8');\n }\n }\n return buffer(t.shape as ShapeMap[R], t.dtype, decodedData) as\n TensorBuffer<R>;\n }\n\n makeOutput<T extends Tensor>(\n values: backend_util.BackendValues, shape: number[], dtype: DataType): T {\n const dataId = this.write(values, shape, dtype);\n return engine().makeTensorFromDataId(dataId, shape, dtype, this) as T;\n }\n\n /**\n * Dispose the memory if the dataId has 0 refCount. Return true if the memory\n * is released or memory is not managed in this backend, false if memory is\n * not cleared.\n * @param dataId\n * @oaram force Optional, remove the data regardless of refCount\n */\n disposeData(dataId: DataId, force = false): boolean {\n if (this.data.has(dataId)) {\n this.data.get(dataId).refCount--;\n if (!force && this.data.get(dataId).refCount > 0) {\n return false;\n }\n\n const {complexTensorInfos} = this.data.get(dataId);\n\n if (complexTensorInfos != null) {\n this.disposeData(complexTensorInfos.real.dataId, true);\n this.disposeData(complexTensorInfos.imag.dataId, true);\n }\n\n this.data.delete(dataId);\n }\n return true;\n }\n\n disposeIntermediateTensorInfo(tensorInfo: TensorInfo): void {\n this.disposeData(tensorInfo.dataId);\n }\n\n async time(f: () => void): Promise<BackendTimingInfo> {\n const start = util.now();\n f();\n const kernelMs = util.now() - start;\n return {kernelMs};\n }\n\n memory() {\n return {\n // Unreliable due to automatic gc. The numbers above are cumulative.\n unreliable: true,\n reasons:\n ['The reported memory is an upper bound. Due to automatic garbage ' +\n 'collection, the true allocated memory may be less.']\n };\n }\n\n where(condition: Tensor): Tensor2D {\n assertNotComplex([condition], 'where');\n\n const condVals = this.readSync(condition.dataId) as TypedArray;\n return whereImpl(condition.shape, condVals);\n }\n\n dispose() {}\n\n floatPrecision(): 16|32 {\n return 32;\n }\n\n /** Returns the smallest representable number. */\n epsilon(): number {\n return super.epsilon();\n }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Abs, AbsInputs, KernelConfig, KernelFunc, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function simpleAbsImpl(vals: TypedArray): Float32Array {\n const resultValues = new Float32Array(vals.length);\n for (let i = 0; i < vals.length; ++i) {\n resultValues[i] = Math.abs(vals[i]);\n }\n return resultValues;\n}\n\nexport const abs = (args: {inputs: AbsInputs, backend: MathBackendCPU}) => {\n const {x} = args.inputs;\n const cpuBackend = args.backend;\n\n assertNotComplex(x, 'abs');\n\n let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n const values = cpuBackend.data.get(x.dataId).values as TypedArray;\n resultValues = simpleAbsImpl(values);\n\n return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n};\n\nexport const absConfig: KernelConfig = {\n kernelName: Abs,\n backendName: 'cpu',\n kernelFunc: abs as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DataType, NumericDataType, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {SimpleBinaryKernelImpl, SimpleBinaryOperation} from './binary_types';\n\n/**\n * Template that creates implementation for binary ops. Supports broadcast.\n */\nexport function createSimpleBinaryKernelImpl(op: SimpleBinaryOperation):\n SimpleBinaryKernelImpl {\n return (aShape: number[], bShape: number[], aVals: TypedArray,\n bVals: TypedArray, dtype: DataType): [TypedArray, number[]] => {\n const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n\n const resultRank = newShape.length;\n const resultStrides = util.computeStrides(newShape);\n const resultSize = util.sizeFromShape(newShape);\n\n const result =\n util.getTypedArrayFromDType(dtype as NumericDataType, resultSize);\n\n const aRank = aShape.length;\n const bRank = bShape.length;\n\n const aStrides = util.computeStrides(aShape);\n const bStrides = util.computeStrides(bShape);\n\n const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);\n const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);\n\n if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n for (let i = 0; i < result.length; ++i) {\n result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n }\n } else {\n for (let i = 0; i < result.length; ++i) {\n const loc = util.indexToLoc(i, resultRank, resultStrides);\n\n const aLoc = loc.slice(-aRank);\n aBroadcastDims.forEach(d => aLoc[d] = 0);\n const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n\n const bLoc = loc.slice(-bRank);\n bBroadcastDims.forEach(d => bLoc[d] = 0);\n const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n\n result[i] = op(aVals[aIndex], bVals[bIndex]);\n }\n }\n\n return [result, newShape];\n };\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Complex, ComplexInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport function complex(args: {inputs: ComplexInputs, backend: MathBackendCPU}):\n TensorInfo {\n const {inputs, backend} = args;\n const {real, imag} = inputs;\n\n const realVals = backend.data.get(real.dataId).values as TypedArray;\n const imagVals = backend.data.get(imag.dataId).values as TypedArray;\n\n const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n\n const complex = backend.data.get(complexInfo.dataId);\n\n // The complex tensor owns the underlying real and imag tensorInfos, only the\n // complex tensor tracks refCount, when complexData is disposed the\n // underlying tensorData will be disposed.\n complex.complexTensorInfos = {\n real: backend.makeTensorInfo(real.shape, 'float32', realVals),\n imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)\n };\n\n return complexInfo;\n}\n\nexport const complexConfig: KernelConfig = {\n kernelName: Complex,\n backendName: 'cpu',\n kernelFunc: complex as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, TensorInfo, util} from '@tensorflow/tfjs-core';\nimport {MathBackendCPU} from '../backend_cpu';\nimport {complex} from '../kernels/Complex';\n\n/**\n * Generates a tensorInfo with all zeros value.\n * @param backend cpu backend.\n * @param shape Shape for the zeros tensor.\n * @param dtype Optional. If set, the result has this dtype.\n */\nexport function zeros(\n backend: MathBackendCPU, shape: number[],\n dtype: DataType = 'float32'): TensorInfo {\n if (dtype === 'complex64') {\n const real = zeros(backend, shape, 'float32');\n const imag = zeros(backend, shape, 'float32');\n\n return complex({inputs: {real, imag}, backend});\n }\n\n const values = util.makeZerosTypedArray(util.sizeFromShape(shape), dtype);\n\n return backend.makeTensorInfo(shape, dtype, values);\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Identity, IdentityInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport function identity(\n args: {inputs: IdentityInputs, backend: MathBackendCPU}): TensorInfo {\n const {inputs, backend} = args;\n const {x} = inputs;\n\n backend.incRef(x.dataId);\n\n return {dataId: x.dataId, shape: x.shape, dtype: x.dtype};\n}\n\nexport const identityConfig: KernelConfig = {\n kernelName: Identity,\n backendName: 'cpu',\n kernelFunc: identity as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Real, RealInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport function real(args: {inputs: RealInputs, backend: MathBackendCPU}):\n TensorInfo {\n const {inputs, backend} = args;\n const {input} = inputs;\n\n const real = backend.data.get(input.dataId).complexTensorInfos.real;\n const realVal = backend.data.get(real.dataId).values;\n\n // When complex tensor is disposed, its underlying parts will be disposed too.\n // Make new tensor out of the real value of the complex. This makes sure the\n // value is still accessible even if complex tensor is disposed.\n return backend.makeTensorInfo(real.shape, real.dtype, realVal);\n}\n\nexport const realConfig: KernelConfig = {\n kernelName: Real,\n backendName: 'cpu',\n kernelFunc: real as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Cast, CastAttrs, CastInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {zeros} from '../utils/zeros_impl';\n\nimport {complex} from './Complex';\nimport {identity} from './Identity';\nimport {real} from './Real';\n\nexport function cast(\n args: {inputs: CastInputs, backend: MathBackendCPU, attrs: CastAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {dtype} = attrs;\n\n // Casting to complex64.\n if (dtype === 'complex64') {\n if (x.dtype === 'complex64') {\n return identity({inputs: {x}, backend});\n }\n\n const zerosTensorInfo = zeros(backend, x.shape, x.dtype);\n const floatX = cast({inputs: {x}, backend, attrs: {dtype: 'float32'}});\n\n const result =\n complex({inputs: {real: floatX, imag: zerosTensorInfo}, backend});\n\n backend.disposeIntermediateTensorInfo(zerosTensorInfo);\n backend.disposeIntermediateTensorInfo(floatX);\n\n return result;\n }\n\n // Casting from complex64\n if (x.dtype === 'complex64') {\n const realPart = real({inputs: {input: x}, backend});\n const result = cast({inputs: {x: realPart}, backend, attrs: {dtype}});\n\n backend.disposeIntermediateTensorInfo(realPart);\n\n return result;\n }\n\n if (!util.hasEncodingLoss(x.dtype, dtype)) {\n // We don't change the underlying data, since we cast to higher\n // precision.\n const result = identity({inputs: {x}, backend});\n return {dataId: result.dataId, shape: result.shape, dtype};\n }\n\n if (dtype === 'int32') {\n const values = backend.data.get(x.dataId).values as TypedArray;\n const resultValues = Int32Array.from(values);\n return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n }\n\n if (dtype === 'bool') {\n // This is essentially the result of notEqual(x, 0). We avoid using\n // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n // cast -> notEqual -> binary_utils.\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const zero = util.toTypedArray([0], x.dtype);\n\n const [resultData, resultShape] = createSimpleBinaryKernelImpl(\n (a, b) => (a !== b) ? 1 : 0)(x.shape, [], xVals, zero, 'bool');\n\n return backend.makeTensorInfo(resultShape, 'bool', resultData);\n }\n\n throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\n\nexport const castConfig: KernelConfig = {\n kernelName: Cast,\n backendName: 'cpu',\n kernelFunc: cast as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, BinaryInputs, DataType, KernelFunc, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {cast} from '../kernels/Cast';\nimport {complex} from '../kernels/Complex';\n\nimport {ComplexBinaryKernelImpl, ComplexBinaryOperation, SimpleBinaryKernelImpl} from './binary_types';\n\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param name Kernel name.\n * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.\n * @param binaryKernelComplexImpl Optional. If exists, represents a\n * `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype\n * is `complex64`.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n * result has the same dtype as the first input. This is mainly used in\n * comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(\n name: string, simpleImpl: SimpleBinaryKernelImpl,\n complexImpl?: ComplexBinaryKernelImpl, dtype?: DataType): KernelFunc {\n if (complexImpl == null) {\n return ({inputs, backend}) => {\n const {a, b} = inputs as BinaryInputs;\n const cpuBackend = backend as MathBackendCPU;\n\n assertNotComplex([a, b], name);\n\n const aVals = cpuBackend.data.get(a.dataId).values as TypedArray;\n const bVals = cpuBackend.data.get(b.dataId).values as TypedArray;\n\n const $dtype = dtype || a.dtype;\n\n const [resultData, resultShape] =\n simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n\n return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n };\n }\n\n return ({inputs, backend}) => {\n const {a, b} = inputs as BinaryInputs;\n const cpuBackend = backend as MathBackendCPU;\n\n if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n const $aComplex = cast(\n {inputs: {x: a}, backend: cpuBackend, attrs: {dtype: 'complex64'}});\n\n const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n\n const aReal = $aComplexVals.complexTensorInfos.real;\n const aImag = $aComplexVals.complexTensorInfos.imag;\n\n const aRealVals =\n cpuBackend.data.get(aReal.dataId).values as Float32Array;\n const aImagVals =\n cpuBackend.data.get(aImag.dataId).values as Float32Array;\n\n const $bComplex = cast(\n {inputs: {x: b}, backend: cpuBackend, attrs: {dtype: 'complex64'}});\n\n const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n\n const bReal = $bComplexVals.complexTensorInfos.real;\n const bImag = $bComplexVals.complexTensorInfos.imag;\n\n const bRealVals =\n cpuBackend.data.get(bReal.dataId).values as Float32Array;\n const bImagVals =\n cpuBackend.data.get(bImag.dataId).values as Float32Array;\n\n const [resultRealData, resultImagData, resultShape] = complexImpl(\n a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n\n const resultReal =\n cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);\n\n const resultImag =\n cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);\n\n const result = complex(\n {inputs: {real: resultReal, imag: resultImag}, backend: cpuBackend});\n\n cpuBackend.disposeIntermediateTensorInfo($aComplex);\n cpuBackend.disposeIntermediateTensorInfo($bComplex);\n cpuBackend.disposeIntermediateTensorInfo(resultReal);\n cpuBackend.disposeIntermediateTensorInfo(resultImag);\n\n return result;\n } else {\n const aVals = cpuBackend.data.get(a.dataId).values as TypedArray;\n const bVals = cpuBackend.data.get(b.dataId).values as TypedArray;\n\n const $dtype = dtype || a.dtype;\n\n const [resultData, resultShape] =\n simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n\n return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n }\n };\n}\n\n/**\n * Template that creates the complex type implementation for binary ops.\n * Supports broadcast.\n */\nexport function createComplexBinaryKernelImpl(op: ComplexBinaryOperation):\n ComplexBinaryKernelImpl {\n return (aShape: number[], bShape: number[], aRealVals: Float32Array,\n aImagVals: Float32Array, bRealVals: Float32Array,\n bImagVals: Float32Array): [TypedArray, TypedArray, number[]] => {\n const resultShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n const resultSize = util.sizeFromShape(resultShape);\n const resultRank = resultShape.length;\n const resultStrides = util.computeStrides(resultShape);\n\n const resultRealVals = util.getTypedArrayFromDType('float32', resultSize);\n const resultImagVals = util.getTypedArrayFromDType('float32', resultSize);\n\n const aBroadcastDims = backend_util.getBroadcastDims(aShape, resultShape);\n const bBroadcastDims = backend_util.getBroadcastDims(bShape, resultShape);\n\n const aVals = backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);\n const bVals = backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);\n\n const aRank = aShape.length;\n const aStrides = util.computeStrides(aShape);\n\n const bRank = bShape.length;\n const bStrides = util.computeStrides(bShape);\n\n if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n for (let i = 0; i < resultRealVals.length; i++) {\n const aIdx = i % aVals.length;\n const bIdx = i % bVals.length;\n\n const result =\n op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2],\n bVals[bIdx * 2 + 1]);\n\n resultRealVals[i] = result.real;\n resultImagVals[i] = result.imag;\n }\n } else {\n for (let i = 0; i < resultRealVals.length; i++) {\n const loc = util.indexToLoc(i, resultRank, resultStrides);\n\n const aLoc = loc.slice(-aRank);\n aBroadcastDims.forEach(d => aLoc[d] = 0);\n const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n\n const bLoc = loc.slice(-bRank);\n bBroadcastDims.forEach(d => bLoc[d] = 0);\n const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n\n const opResult =\n op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2],\n bVals[bIndex * 2 + 1]);\n\n resultRealVals[i] = opResult.real;\n resultImagVals[i] = opResult.imag;\n }\n }\n return [resultRealVals, resultImagVals, resultShape];\n };\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Add, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc, createComplexBinaryKernelImpl} from '../utils/binary_utils';\n\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl =\n createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n return {real: aReal + bReal, imag: aImag + bImag};\n }));\n\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\n\nexport const addConfig: KernelConfig = {\n kernelName: Add,\n backendName: 'cpu',\n kernelFunc: add\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {buffer, DataType, Rank, TensorBuffer, TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function bincountImpl(\n xVals: TypedArray, weightsVals: TypedArray, weightsDtype: DataType,\n weightsShape: number[], size: number): TypedArray {\n const weightsSize = util.sizeFromShape(weightsShape);\n const outVals = util.makeZerosTypedArray(size, weightsDtype) as TypedArray;\n\n for (let i = 0; i < xVals.length; i++) {\n const value = xVals[i];\n if (value < 0) {\n throw new Error('Input x must be non-negative!');\n }\n\n if (value >= size) {\n continue;\n }\n\n if (weightsSize > 0) {\n outVals[value] += weightsVals[i];\n } else {\n outVals[value] += 1;\n }\n }\n\n return outVals;\n}\n\nexport function bincountReduceImpl<R extends Rank>(\n xBuf: TensorBuffer<R>, weightsBuf: TensorBuffer<R>, size: number,\n binaryOutput = false): TensorBuffer<R> {\n const numRows = xBuf.shape[0];\n const numCols = xBuf.shape[1];\n\n const outBuf = buffer([numRows, size], weightsBuf.dtype);\n\n for (let i = 0; i < numRows; i++) {\n for (let j = 0; j < numCols; j++) {\n const value = xBuf.get(i, j);\n if (value < 0) {\n throw new Error('Input x must be non-negative!');\n }\n\n if (value >= size) {\n continue;\n }\n\n if (binaryOutput) {\n outBuf.set(1, i, value);\n } else {\n if (weightsBuf.size > 0) {\n outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);\n } else {\n outBuf.set(outBuf.get(i, value) + 1, i, value);\n }\n }\n }\n }\n\n return outBuf as TensorBuffer<R>;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {NumericDataType, util} from '@tensorflow/tfjs-core';\n\nimport {SimpleUnaryImpl, SimpleUnaryOperation} from './unary_types';\n\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op: SimpleUnaryOperation):\n SimpleUnaryImpl {\n return (values, dtype, attrs) => {\n const newValues =\n util.getTypedArrayFromDType(dtype as NumericDataType, values.length);\n for (let i = 0; i < values.length; ++i) {\n newValues[i] = op(values[i], attrs);\n }\n return newValues;\n };\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, KernelFunc, TypedArray, UnaryInputs, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nimport {SimpleUnaryImpl, SimpleUnaryOperation} from './unary_types';\n\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param name Kernel name.\n * @param op A `SimpleUnaryOperation` for the kernel.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n * result has the same dtype as the input. This is mainly used in certain\n * kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFunc(\n name: string, op: SimpleUnaryOperation, dtype?: DataType): KernelFunc {\n return ({inputs, attrs, backend}) => {\n const {x} = inputs as UnaryInputs;\n assertNotComplex(x, name);\n if (x.dtype === 'string' || dtype === 'string') {\n throw new Error('unaryKernelFunc does not support string input/output');\n }\n\n const cpuBackend = backend as MathBackendCPU;\n const values = cpuBackend.data.get(x.dataId).values as TypedArray;\n const xSize = util.sizeFromShape(x.shape);\n const $dtype = dtype || x.dtype;\n const newValues = util.getArrayFromDType($dtype, xSize);\n for (let i = 0; i < xSize; ++i) {\n newValues[i] = op(values[i], attrs);\n }\n return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n };\n}\n\n/**\n * Template that creates a `KernelFunc` for unary ops from the given\n * `SimpleUnaryImpl`..\n * @param name Kernel name.\n * @param unaryImpl A `SimpleUnaryImpl` that implements the op.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n * result has the same dtype as the input. This is mainly used in certain\n * kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFuncFromImpl(\n name: string, unaryImpl: SimpleUnaryImpl, dtype?: DataType): KernelFunc {\n return ({inputs, attrs, backend}) => {\n const {x} = inputs as UnaryInputs;\n assertNotComplex(x, name);\n if (x.dtype === 'string' || dtype === 'string') {\n throw new Error('unaryKernelFunc does not support string input/output');\n }\n\n const cpuBackend = backend as MathBackendCPU;\n const values = cpuBackend.data.get(x.dataId).values as TypedArray;\n const $dtype = dtype || x.dtype;\n const newValues = unaryImpl(values, $dtype, attrs);\n return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n };\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Ceil, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {createSimpleUnaryImpl} from '../utils/unary_impl';\nimport {unaryKernelFuncFromImpl} from '../utils/unary_utils';\n\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceil = unaryKernelFuncFromImpl(Ceil, ceilImpl);\n\nexport const ceilConfig: KernelConfig = {\n kernelName: Ceil,\n backendName: 'cpu',\n kernelFunc: ceil,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, BackendValues, DataType, TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function concatImpl(\n inputs: Array<{vals: BackendValues, shape: number[]}>, outShape: number[],\n dtype: DataType, simplyConcat: boolean): TypedArray|string[] {\n const outVals = util.getArrayFromDType(dtype, util.sizeFromShape(outShape));\n\n if (simplyConcat && dtype !== 'string') {\n // Use built-in TypedArray.set() method for speed.\n let offset = 0;\n inputs.forEach(input => {\n const size = util.sizeFromShape(input.shape);\n\n (outVals as TypedArray).set(input.vals as TypedArray, offset);\n offset += size;\n });\n } else {\n let colOffset = 0;\n\n inputs.forEach(input => {\n const decodedData = dtype === 'string' ?\n backend_util.fromUint8ToStringArray(input.vals as Uint8Array[]) :\n input.vals as TypedArray;\n\n let tIdx = 0;\n\n for (let row = 0; row < input.shape[0]; ++row) {\n const resIdx = row * outShape[1] + colOffset;\n for (let col = 0; col < input.shape[1]; ++col) {\n outVals[resIdx + col] = decodedData[tIdx++];\n }\n }\n\n colOffset += input.shape[1];\n });\n }\n\n return outVals;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Exp, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {createSimpleUnaryImpl} from '../utils/unary_impl';\nimport {unaryKernelFuncFromImpl} from '../utils/unary_utils';\n\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const exp = unaryKernelFuncFromImpl(Exp, expImpl);\n\nexport const expConfig: KernelConfig = {\n kernelName: Exp,\n backendName: 'cpu',\n kernelFunc: exp,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Expm1, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {createSimpleUnaryImpl} from '../utils/unary_impl';\nimport {unaryKernelFuncFromImpl} from '../utils/unary_utils';\n\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1 = unaryKernelFuncFromImpl(Expm1, expm1Impl);\n\nexport const expm1Config: KernelConfig = {\n kernelName: Expm1,\n backendName: 'cpu',\n kernelFunc: expm1,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Floor, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {createSimpleUnaryImpl} from '../utils/unary_impl';\nimport {unaryKernelFuncFromImpl} from '../utils/unary_utils';\n\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floor = unaryKernelFuncFromImpl(Floor, floorImpl);\n\nexport const floorConfig: KernelConfig = {\n kernelName: Floor,\n backendName: 'cpu',\n kernelFunc: floor,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {buffer, DataType, Rank, TensorBuffer} from '@tensorflow/tfjs-core';\n\nexport function gatherV2Impl<R extends Rank, D extends DataType>(\n xBuf: TensorBuffer<R, D>, indicesBuf: TensorBuffer<R, D>,\n flattenOutputShape: number[]): TensorBuffer<R, D> {\n const outBuf = buffer(flattenOutputShape, xBuf.dtype);\n for (let i = 0; i < outBuf.size; ++i) {\n const newLoc = outBuf.indexToLoc(i);\n\n const originalLoc: number[] = newLoc.slice();\n const batchIdx = originalLoc[0];\n const indicesIdx = originalLoc[2];\n const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);\n originalLoc[2] = indicesBuf.values[indicesIndex] as number;\n\n const originalIndex = xBuf.locToIndex(originalLoc);\n outBuf.values[i] = xBuf.values[originalIndex];\n }\n\n return outBuf as TensorBuffer<R, D>;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Greater, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const greaterImpl =\n createSimpleBinaryKernelImpl((a: number, b: number) => (a > b) ? 1 : 0);\nexport const greater =\n binaryKernelFunc(Greater, greaterImpl, null /* complexImpl */, 'bool');\n\nexport const greaterConfig: KernelConfig = {\n kernelName: Greater,\n backendName: 'cpu',\n kernelFunc: greater\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Less} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const lessImpl =\n createSimpleBinaryKernelImpl((a: number, b: number) => (a < b) ? 1 : 0);\nexport const less =\n binaryKernelFunc(Less, lessImpl, null /* complexImpl */, 'bool');\n\nexport const lessConfig: KernelConfig = {\n kernelName: Less,\n backendName: 'cpu',\n kernelFunc: less\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function linSpaceImpl(\n start: number, stop: number, num: number): TypedArray {\n const step = (stop - start) / (num - 1);\n\n const values = util.makeZerosTypedArray(num, 'float32');\n values[0] = start;\n for (let i = 1; i < values.length; i++) {\n values[i] = values[i - 1] + step;\n }\n\n return values;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Log} from '@tensorflow/tfjs-core';\n\nimport {createSimpleUnaryImpl} from '../utils/unary_impl';\nimport {unaryKernelFuncFromImpl} from '../utils/unary_utils';\n\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const log = unaryKernelFuncFromImpl(Log, logImpl);\n\nexport const logConfig: KernelConfig = {\n kernelName: Log,\n backendName: 'cpu',\n kernelFunc: log,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, NumericDataType, TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function maxImpl(\n aVals: TypedArray, reduceSize: number, outShape: number[],\n dtype: DataType): TypedArray {\n const vals = util.getTypedArrayFromDType(\n dtype as NumericDataType, util.sizeFromShape(outShape));\n\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let max = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (value > max) {\n max = value;\n }\n }\n vals[i] = max;\n }\n return vals;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Maximum} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const maximumImpl = createSimpleBinaryKernelImpl(\n ((aValue, bValue) => Math.max(aValue, bValue)));\nexport const maximum = binaryKernelFunc(Maximum, maximumImpl);\n\nexport const maximumConfig: KernelConfig = {\n kernelName: Maximum,\n backendName: 'cpu',\n kernelFunc: maximum\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Minimum} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const minimumImpl = createSimpleBinaryKernelImpl(\n ((aValue, bValue) => Math.min(aValue, bValue)));\nexport const minimum = binaryKernelFunc(Minimum, minimumImpl);\n\nexport const minimumConfig: KernelConfig = {\n kernelName: Minimum,\n backendName: 'cpu',\n kernelFunc: minimum\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Multiply} from '@tensorflow/tfjs-core';\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc, createComplexBinaryKernelImpl} from '../utils/binary_utils';\n\nexport const multiplyImpl =\n createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl =\n createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n return {\n real: aReal * bReal - aImag * bImag,\n imag: aReal * bImag + aImag * bReal\n };\n }));\n\nexport const multiply =\n binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\n\nexport const multiplyConfig: KernelConfig = {\n kernelName: Multiply,\n backendName: 'cpu',\n kernelFunc: multiply\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, KernelConfig, KernelFunc, Neg, TensorInfo, TypedArray, UnaryInputs, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {multiplyImpl} from './Multiply';\n\nexport function negImpl(xVals: TypedArray, xShape: number[], xDtype: DataType):\n [TypedArray, number[]] {\n const minusOne =\n util.createScalarValue(-1 as {} as 'float32', xDtype) as TypedArray;\n return multiplyImpl([], xShape, minusOne, xVals, xDtype);\n}\n\nexport function neg(args: {inputs: UnaryInputs, backend: MathBackendCPU}):\n TensorInfo {\n const {inputs, backend} = args;\n const {x} = inputs;\n\n assertNotComplex(x, 'neg');\n\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const [res, newShape] = negImpl(xVals, x.shape, x.dtype);\n\n return backend.makeTensorInfo(newShape, x.dtype, res);\n}\n\nexport const negConfig: KernelConfig = {\n kernelName: Neg,\n backendName: 'cpu',\n kernelFunc: neg as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, NotEqual} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const notEqualImpl =\n createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual =\n binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\n\nexport const notEqualConfig: KernelConfig = {\n kernelName: NotEqual,\n backendName: 'cpu',\n kernelFunc: notEqual\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, NumericDataType, TypedArray} from '@tensorflow/tfjs-core';\nimport {util} from '@tensorflow/tfjs-core';\n\nexport function transposeImpl(\n xVals: TypedArray, xShape: number[], dtype: DataType, perm: number[],\n newShape: number[]): TypedArray {\n const xRank = xShape.length;\n const xSize = util.sizeFromShape(xShape);\n const xStrides = util.computeStrides(xShape);\n const newStrides = util.computeStrides(newShape);\n\n const result = util.getTypedArrayFromDType(\n dtype as NumericDataType, util.sizeFromShape(newShape));\n\n for (let i = 0; i < xSize; ++i) {\n const loc = util.indexToLoc(i, xRank, xStrides);\n\n // Permute location.\n const newLoc: number[] = new Array(loc.length);\n for (let i = 0; i < newLoc.length; i++) {\n newLoc[i] = loc[perm[i]];\n }\n\n const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n result[newIndex] = xVals[i];\n }\n return result;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, TensorInfo, Transpose, TransposeAttrs, TransposeInputs, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nimport {transposeImpl} from './Transpose_impl';\n\nexport function transpose(args: {\n inputs: TransposeInputs,\n attrs: TransposeAttrs,\n backend: MathBackendCPU\n}): TensorInfo {\n const {inputs, attrs, backend} = args;\n const {x} = inputs;\n const {perm} = attrs;\n\n assertNotComplex(x, 'transpose');\n\n const xRank = x.shape.length;\n\n const newShape: number[] = new Array(xRank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = x.shape[perm[i]];\n }\n\n const values = backend.data.get(x.dataId).values as TypedArray;\n const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n\n const dataId = backend.write(result, newShape, x.dtype);\n return {dataId, shape: newShape, dtype: x.dtype};\n}\n\nexport const transposeConfig: KernelConfig = {\n kernelName: Transpose,\n backendName: 'cpu',\n kernelFunc: transpose as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DataType, KernelConfig, KernelFunc, Prod, ProdAttrs, ProdInputs, TensorInfo, TypedArray, upcastType, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {transpose} from './Transpose';\n\nexport function prodImpl(\n xShape: number[], xDtype: DataType, xVals: TypedArray,\n reductionAxes: number[]):\n {outVals: TypedArray, outShape: number[], outDtype: DataType} {\n const [outShape, reduceShape] =\n backend_util.computeOutAndReduceShapes(xShape, reductionAxes);\n const outDtype = upcastType(xDtype, 'int32');\n const outVals = util.makeZerosTypedArray(\n util.sizeFromShape(outShape), outDtype) as TypedArray;\n const reduceSize = util.sizeFromShape(reduceShape);\n\n for (let i = 0; i < outVals.length; ++i) {\n const offset = i * reduceSize;\n let prod = 1;\n for (let j = 0; j < reduceSize; ++j) {\n prod *= xVals[offset + j];\n }\n outVals[i] = prod;\n }\n\n return {outVals, outShape, outDtype};\n}\n\nexport function prod(\n args: {inputs: ProdInputs, backend: MathBackendCPU, attrs: ProdAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {axis, keepDims} = attrs;\n\n assertNotComplex(x, 'prod');\n\n const xRank = x.shape.length;\n const axes = util.parseAxisParam(axis, x.shape);\n\n const permutation = backend_util.getAxesPermutation(axes, xRank);\n let reductionAxes = axes;\n let permutedX = x;\n const intermediateTensorInfos = [];\n if (permutation != null) {\n permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutation}});\n intermediateTensorInfos.push(permutedX);\n reductionAxes = backend_util.getInnerMostAxes(reductionAxes.length, xRank);\n }\n\n const xVals = backend.data.get(permutedX.dataId).values as TypedArray;\n const {outVals, outShape, outDtype} =\n prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);\n\n let resultShape = outShape;\n if (keepDims) {\n resultShape = backend_util.expandShapeToKeepDim(outShape, axes);\n }\n\n intermediateTensorInfos.forEach(\n t => backend.disposeIntermediateTensorInfo(t));\n\n return backend.makeTensorInfo(resultShape, outDtype, outVals);\n}\n\nexport const prodConfig: KernelConfig = {\n kernelName: Prod,\n backendName: 'cpu',\n kernelFunc: prod as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataTypeMap, util} from '@tensorflow/tfjs-core';\n\nexport function rangeImpl(\n start: number, stop: number, step: number,\n dtype: 'float32'|'int32'): DataTypeMap['float32' | 'int32'] {\n const sameStartStop = start === stop;\n const increasingRangeNegativeStep = start < stop && step < 0;\n const decreasingRangePositiveStep = stop < start && step > 1;\n\n if (sameStartStop || increasingRangeNegativeStep ||\n decreasingRangePositiveStep) {\n return util.makeZerosTypedArray(0, dtype);\n }\n\n const numElements = Math.abs(Math.ceil((stop - start) / step));\n const values = util.makeZerosTypedArray(numElements, dtype);\n\n if (stop < start && step === 1) {\n // Auto adjust the step's sign if it hasn't been set\n // (or was set to 1)\n step = -1;\n }\n\n values[0] = start;\n for (let i = 1; i < values.length; i++) {\n values[i] = values[i - 1] + step;\n }\n return values;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Rsqrt} from '@tensorflow/tfjs-core';\n\nimport {createSimpleUnaryImpl} from '../utils/unary_impl';\nimport {unaryKernelFuncFromImpl} from '../utils/unary_utils';\n\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrt = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\n\nexport const rsqrtConfig: KernelConfig = {\n kernelName: Rsqrt,\n backendName: 'cpu',\n kernelFunc: rsqrt,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, BackendValues, buffer, DataType, KernelConfig, KernelFunc, Slice, slice_util, SliceAttrs, SliceInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function sliceImpl(\n vals: BackendValues, begin: number[], size: number[], shape: number[],\n dtype: DataType): BackendValues {\n const isContinous = slice_util.isSliceContinous(shape, begin, size);\n const length = util.sizeFromShape(size);\n const xStrides = util.computeStrides(shape);\n\n if (isContinous) {\n const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n\n if (dtype === 'string') {\n return (vals as Uint8Array[]).slice(flatOffset, flatOffset + length);\n }\n\n return (vals as TypedArray).subarray(flatOffset, flatOffset + length);\n }\n\n const decodedData = dtype === 'string' ?\n backend_util.fromUint8ToStringArray(vals as Uint8Array[]) :\n vals as TypedArray;\n\n const inBuf = buffer(shape, dtype, decodedData);\n const outBuf = buffer(size, dtype);\n for (let i = 0; i < outBuf.size; ++i) {\n const outLoc = outBuf.indexToLoc(i);\n const inLoc = outLoc.map((idx: number, j) => idx + begin[j]);\n outBuf.set(inBuf.get(...inLoc), ...outLoc);\n }\n\n if (dtype === 'string') {\n return backend_util.fromStringArrayToUint8(outBuf.values as string[]);\n }\n return outBuf.values as TypedArray;\n}\n\nexport function slice(\n args: {inputs: SliceInputs, backend: MathBackendCPU, attrs: SliceAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {begin, size} = attrs;\n\n assertNotComplex(x, 'slice');\n\n const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n slice_util.assertParamsValid(x, $begin, $size);\n\n const vals = backend.data.get(x.dataId).values;\n const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n return backend.makeTensorInfo($size, x.dtype, outVals);\n}\n\nexport const sliceConfig: KernelConfig = {\n kernelName: Slice,\n backendName: 'cpu',\n kernelFunc: slice as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, SquaredDifference} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const squaredDifferenceImpl = createSimpleBinaryKernelImpl(((a, b) => {\n const diff = a - b;\n return diff * diff;\n}));\nexport const squaredDifference =\n binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\n\nexport const squaredDifferenceConfig: KernelConfig = {\n kernelName: SquaredDifference,\n backendName: 'cpu',\n kernelFunc: squaredDifference\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {buffer, Rank, TensorBuffer} from '@tensorflow/tfjs-core';\n\nexport function stridedSliceImpl<R extends Rank>(\n outShape: number[], xBuf: TensorBuffer<R>, strides: number[],\n begin: number[]): TensorBuffer<R> {\n const outBuf = buffer(outShape, xBuf.dtype);\n\n for (let i = 0; i < outBuf.size; i++) {\n const loc = outBuf.indexToLoc(i);\n\n const newLoc: number[] = new Array(loc.length);\n for (let j = 0; j < newLoc.length; j++) {\n newLoc[j] = loc[j] * strides[j] + begin[j];\n }\n outBuf.set(xBuf.get(...newLoc), ...loc);\n }\n\n return outBuf as TensorBuffer<R>;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Sub} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc, createComplexBinaryKernelImpl} from '../utils/binary_utils';\n\nexport const subImpl =\n createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl =\n createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n return {real: aReal - bReal, imag: aImag - bImag};\n }));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\n\nexport const subConfig: KernelConfig = {\n kernelName: Sub,\n backendName: 'cpu',\n kernelFunc: sub\n};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {buffer, DataType, Rank, TensorBuffer} from '@tensorflow/tfjs-core';\n\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\n\nexport function tileImpl<R extends Rank>(\n xBuf: TensorBuffer<R, DataType>,\n reps: number[]): TensorBuffer<R, DataType> {\n const newShape: number[] = new Array(xBuf.rank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = xBuf.shape[i] * reps[i];\n }\n const result = buffer(newShape, xBuf.dtype);\n for (let i = 0; i < result.values.length; ++i) {\n const newLoc = result.indexToLoc(i);\n\n const originalLoc: number[] = new Array(xBuf.rank);\n for (let j = 0; j < originalLoc.length; j++) {\n originalLoc[j] = newLoc[j] % xBuf.shape[j];\n }\n\n const originalIndex = xBuf.locToIndex(originalLoc);\n\n result.values[i] = xBuf.values[originalIndex];\n }\n return result as TensorBuffer<R, DataType>;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the TopK kernel shared between webgl and cpu. */\n\nimport {buffer, NumericDataType, Rank, ShapeMap, Tensor, TensorBuffer, TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function topKImpl<T extends Tensor, R extends Rank>(\n x: TypedArray, xShape: number[], xDtype: NumericDataType, k: number,\n sorted: boolean):\n [TensorBuffer<R, NumericDataType>, TensorBuffer<R, 'int32'>] {\n // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n const lastDim = xShape[xShape.length - 1];\n const [batch, size] = [x.length / lastDim, lastDim];\n const allTopKVals = util.getTypedArrayFromDType(xDtype, batch * k);\n const allTopKIndices = util.getTypedArrayFromDType('int32', batch * k);\n\n for (let b = 0; b < batch; b++) {\n const offset = b * size;\n const vals = x.subarray(offset, offset + size);\n const valAndInd: Array<{value: number, index: number}> = [];\n for (let i = 0; i < vals.length; i++) {\n valAndInd.push({value: vals[i], index: i});\n }\n valAndInd.sort((a, b) => b.value - a.value);\n\n const outOffset = b * k;\n const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n for (let i = 0; i < k; i++) {\n topKVals[i] = valAndInd[i].value;\n topKIndices[i] = valAndInd[i].index;\n }\n }\n // Reshape back to the original input shape, except that the last\n // dimension is k.\n const outputShape = xShape.slice();\n outputShape[outputShape.length - 1] = k;\n\n return [\n buffer(outputShape as ShapeMap[R], xDtype, allTopKVals),\n buffer(outputShape as ShapeMap[R], 'int32', allTopKIndices)\n ];\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendValues, DataType, TensorBuffer, TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function uniqueImpl(\n values: BackendValues, axis: number, shape: number[], dtype: DataType): {\n outputValues: BackendValues,\n outputShape: number[],\n indices: BackendValues\n} {\n // Normalize and validate axis.\n const $axis = util.parseAxisParam(axis, shape)[0];\n\n // Calculate the new shape that is suitable for extracting data along the\n // given axis.\n //\n // The rank is 3.\n // The size of the 1st dimension is the size of all the axes < the given axis.\n // The size of the 2nd dimension is the same as the size of the given axis.\n // The size of the 3rd dimension is the size of all the axes > the given axis.\n //\n // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n // newShape would be: [2*3, 5, 4].\n //\n // Note that this is not the final output shape. This will be the shape for an\n // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n // values along the given axis. To demonstrate how it works, consider the\n // following example:\n //\n // Input: a 3D tensor, with shape [1, 2, 3]\n // [\n // [\n // [1,2,3],\n // [4,5,6]\n // ]\n // ]\n // Axis: 2 (the last axis).\n // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n //\n // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n // 1*2. The re-shaped data would look like:\n //\n // [\n // [\n // [1], [2], [3]\n // ],\n // [\n // [4], [5], [6]\n // ]\n // ]\n //\n // Then, we can construct a 3-level nested loop by the following dimension\n // order to extract the values along the axis (dimension1):\n // i: dimension1 // 0,1,2 (newShape[1])\n // m: dimension0 // 0,1 (newShape[0])\n // n: dimension2 // 0 (newShape[2])\n //\n // m, i, n\n // ---------\n // Iteration 0: data at [0, 0, 0] => \"1\"\n // Iteration 1: data at [1, 0, 0] => \"4\"\n // We got [1,4].\n // Iteration 2: data at [0, 1, 0] => \"2\"\n // Iteration 3: data at [1, 1, 0] => \"5\"\n // We got [2,5].\n // Iteration 4: data at [0, 2, 0] => \"3\"\n // Iteration 5: data at [1, 2, 0] => \"6\"\n // We got [3,6].\n const newShape = [1, shape[0], 1];\n for (let i = 0; i < $axis; i++) {\n newShape[0] *= shape[i];\n }\n newShape[1] = shape[$axis];\n for (let i = $axis + 1; i < shape.length; i++) {\n newShape[2] *= shape[i];\n }\n\n // A map from unique elements (their string representations) to their values\n // in \"indices\" (below).\n const uniqueElements: {[key: string]: number} = {};\n // The indices of each unique element in the original tensor along the given\n // axis. It is 1D and has the same size as the given axis.\n const indices = new Int32Array(shape[$axis]);\n // Create a buffer so we can easily extract value at a given location.\n const inputBuffer = new TensorBuffer(newShape, dtype, values as TypedArray);\n // The indices along the given axis that have unique elements. This is a\n // de-duped version of \"indices\" above.\n const uniqueIndices: number[] = [];\n const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n for (let i = 0; i < shape[$axis]; i++) {\n // Extract values along the axis.\n let element: string;\n if (is1DTensor) {\n // Fast path for 1D tensor input.\n element = values[i].toString();\n } else {\n const axisValues = [];\n for (let m = 0; m < newShape[0]; m++) {\n for (let n = 0; n < newShape[2]; n++) {\n axisValues.push(inputBuffer.get(m, i, n));\n }\n }\n element = axisValues.join(',');\n }\n\n // Dedup and update various indices.\n if (uniqueElements[element] !== undefined) {\n indices[i] = uniqueElements[element];\n } else {\n const uniqueIndex = Object.keys(uniqueElements).length;\n uniqueElements[element] = uniqueIndex;\n indices[i] = uniqueIndex;\n uniqueIndices.push(i);\n }\n }\n\n // Now we know where each of the unique elements are located along the axis\n // (uniqueIndices). Extract them from input buffer and store them in the\n // output buffer.\n const outputTmpShape = newShape.slice();\n outputTmpShape[1] = Object.keys(uniqueElements).length;\n const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n uniqueIndices.forEach((uniqueElementIndex, i) => {\n for (let m = 0; m < newShape[0]; m++) {\n for (let n = 0; n < newShape[2]; n++) {\n outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n }\n }\n });\n\n // The output shape can be calculated from the input shape with the size of\n // the given axis replaced by the number of unique elements along that axis.\n const outputShape = shape.slice();\n outputShape[$axis] = outputTmpShape[1];\n\n return {\n outputValues: outputBuffer.values as BackendValues,\n outputShape,\n indices,\n };\n}\n","/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '3.2.0';\nexport {version};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/*\n * base.ts contains all the exports from tfjs-backend-cpu\n * without auto-kernel registration\n */\nimport {registerBackend} from '@tensorflow/tfjs-core';\nimport {MathBackendCPU} from './backend_cpu';\nimport * as shared from './shared';\n\nexport {MathBackendCPU} from './backend_cpu';\nexport {version as version_cpu} from './version';\nexport {shared};\n\n// Side effects for default initialization of MathBackendCPU\nregisterBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Elu, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const elu =\n unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : (Math.exp(xi) - 1));\n\nexport const eluConfig: KernelConfig = {\n kernelName: Elu,\n backendName: 'cpu',\n kernelFunc: elu,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, LeakyRelu, LeakyReluAttrs, LeakyReluInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function leakyRelu(args: {\n inputs: LeakyReluInputs,\n backend: MathBackendCPU,\n attrs: LeakyReluAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {alpha} = attrs;\n\n assertNotComplex([x], 'leakyRelu');\n\n const xSize = util.sizeFromShape(x.shape);\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const outVals = util.getTypedArrayFromDType('float32', xSize);\n\n for (let i = 0; i < xVals.length; i++) {\n outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i];\n }\n\n return backend.makeTensorInfo(x.shape, 'float32', outVals);\n}\n\nexport const leakyReluConfig: KernelConfig = {\n kernelName: LeakyRelu,\n backendName: 'cpu',\n kernelFunc: leakyRelu as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Prelu, PreluInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\n\nconst preluImpl = createSimpleBinaryKernelImpl(\n (xValue: number, aValue: number) => xValue < 0 ? aValue * xValue : xValue);\n\nexport function prelu(args: {inputs: PreluInputs, backend: MathBackendCPU}):\n TensorInfo {\n const {inputs, backend} = args;\n const {x, alpha} = inputs;\n\n assertNotComplex([x, alpha], 'prelu');\n\n const aVals = backend.data.get(x.dataId).values as TypedArray;\n const bVals = backend.data.get(alpha.dataId).values as TypedArray;\n\n const [resultData, resultShape] =\n preluImpl(x.shape, alpha.shape, aVals, bVals, x.dtype);\n\n return backend.makeTensorInfo(resultShape, x.dtype, resultData);\n}\n\nexport const preluConfig: KernelConfig = {\n kernelName: Prelu,\n backendName: 'cpu',\n kernelFunc: prelu,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Relu} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const relu = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));\n\nexport const reluConfig: KernelConfig = {\n kernelName: Relu,\n backendName: 'cpu',\n kernelFunc: relu,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Relu6} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const relu6 =\n unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));\n\nexport const relu6Config: KernelConfig = {\n kernelName: Relu6,\n backendName: 'cpu',\n kernelFunc: relu6,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs, backend_util, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {elu} from '../kernels/Elu';\nimport {identity} from '../kernels/Identity';\nimport {leakyRelu} from '../kernels/LeakyRelu';\nimport {prelu} from '../kernels/Prelu';\nimport {relu} from '../kernels/Relu';\nimport {relu6} from '../kernels/Relu6';\n\nexport function applyActivation(\n backend: MathBackendCPU, x: TensorInfo, activation: backend_util.Activation,\n preluActivationWeights?: TensorInfo, leakyreluAlpha?: number): TensorInfo {\n if (activation === 'linear') {\n return identity({inputs: {x}, backend});\n } else if (activation === 'relu') {\n return relu({inputs: {x}, backend}) as TensorInfo;\n } else if (activation === 'elu') {\n return elu({inputs: {x}, backend}) as TensorInfo;\n } else if (activation === 'relu6') {\n return relu6({inputs: {x}, backend}) as TensorInfo;\n } else if (activation === 'prelu') {\n return prelu({inputs: {x, alpha: preluActivationWeights}, backend});\n } else if (activation === 'leakyrelu') {\n return leakyRelu({inputs: {x}, backend, attrs: {alpha: leakyreluAlpha}});\n }\n throw new Error(\n `Activation ${activation} has not been implemented for the CPU backend.`);\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Reshape, ReshapeAttrs, ReshapeInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport function reshape(\n args:\n {inputs: ReshapeInputs, backend: MathBackendCPU, attrs: ReshapeAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {shape} = attrs;\n\n const xSize = util.sizeFromShape(x.shape);\n const $shape = util.inferFromImplicitShape(shape, xSize);\n const $xSize = util.sizeFromShape($shape);\n\n util.assert(\n xSize === $xSize,\n () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n `shape must have the same number of elements.`);\n\n backend.incRef(x.dataId);\n\n const xData = backend.data.get(x.dataId);\n\n if (xData.complexTensorInfos != null) {\n const real = xData.complexTensorInfos.real;\n const imag = xData.complexTensorInfos.imag;\n\n real.shape = $shape;\n imag.shape = $shape;\n }\n\n return {dataId: x.dataId, shape: $shape, dtype: x.dtype};\n}\n\nexport const reshapeConfig: KernelConfig = {\n kernelName: Reshape,\n backendName: 'cpu',\n kernelFunc: reshape as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs, buffer, KernelConfig, KernelFunc, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nimport {reshape} from './Reshape';\n\nexport function batchMatMul(args: {\n inputs: BatchMatMulInputs,\n attrs: BatchMatMulAttrs,\n backend: MathBackendCPU\n}) {\n const {inputs, backend, attrs} = args;\n const {a, b} = inputs;\n const {transposeA, transposeB} = attrs;\n\n assertNotComplex([a, b], 'matMul');\n\n const aRank = a.shape.length;\n const bRank = b.shape.length;\n\n const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n\n const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n\n const outerDimsA = a.shape.slice(0, -2);\n const outerDimsB = b.shape.slice(0, -2);\n\n const batchDimA = util.sizeFromShape(outerDimsA);\n const batchDimB = util.sizeFromShape(outerDimsB);\n\n const batchDimsCompatible =\n batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;\n\n util.assert(\n aRank >= 2 && bRank >= 2 && batchDimsCompatible,\n () => `Error in matMul: the input batch dimensions must either be the ` +\n `same or at least one input batch dimension must be 1. Got input ` +\n `batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);\n\n const outShapeOuterDims =\n batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2);\n const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n\n util.assert(\n innerShapeA === innerShapeB,\n () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n `${innerShapeB}) of Tensors with shapes ${a.shape} and ` +\n `${b.shape} and transposeA=${transposeA}` +\n ` and transposeB=${transposeB} must match.`);\n\n const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] :\n [batchDimA, outerShapeA, innerShapeA];\n const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] :\n [batchDimB, innerShapeB, outerShapeB];\n\n // The rest of the implementation is designed to operate on rank-3 tensors\n const a3d = reshape({inputs: {x: a}, backend, attrs: {shape: a3dShape}});\n const b3d = reshape({inputs: {x: b}, backend, attrs: {shape: b3dShape}});\n\n const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n const batchDim = Math.max(batchDimA, batchDimB);\n\n const a3dValues = backend.data.get(a3d.dataId).values as TypedArray;\n const b3dValues = backend.data.get(b3d.dataId).values as TypedArray;\n\n const a3dStrides = util.computeStrides(a3d.shape);\n const b3dStrides = util.computeStrides(b3d.shape);\n\n const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n [a3dStrides[0], 1, a3dStrides[1]] :\n [a3dStrides[0], a3dStrides[1], 1];\n const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n [1, b3dStrides[1], b3dStrides[0]] :\n [b3dStrides[1], 1, b3dStrides[0]];\n\n const size = leftDim * rightDim;\n const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);\n\n const resVals = result.values as TypedArray;\n const blockSize = backend.blockSize;\n\n for (let bi = 0; bi < batchDim; bi++) {\n for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n // for when blockSize doesn't evenly divide the input\n const iBlock = Math.min(i0 + blockSize, leftDim);\n const jBlock = Math.min(j0 + blockSize, rightDim);\n const kBlock = Math.min(k0 + blockSize, sharedDim);\n\n for (let i = i0; i < iBlock; i++) {\n for (let j = j0; j < jBlock; j++) {\n let sum = 0.0;\n\n for (let k = k0; k < kBlock; k++) {\n const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;\n const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;\n const aVal =\n a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep];\n const bVal =\n b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];\n sum += aVal * bVal;\n }\n resVals[bi * size + (i * rightDim + j)] += sum;\n }\n }\n }\n }\n }\n }\n\n backend.disposeIntermediateTensorInfo(a3d);\n backend.disposeIntermediateTensorInfo(b3d);\n\n // set correct shape on output.\n return backend.makeTensorInfo(\n outShape, result.dtype, result.values as TypedArray);\n}\n\nexport const batchMatMulConfig: KernelConfig = {\n kernelName: BatchMatMul,\n backendName: 'cpu',\n kernelFunc: batchMatMul as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {applyActivation} from '../utils/fused_utils';\n\nimport {add} from './Add';\nimport {batchMatMul} from './BatchMatMul';\n\nexport function _fusedMatMul(args: {\n inputs: _FusedMatMulInputs,\n attrs: _FusedMatMulAttrs,\n backend: MathBackendCPU\n}) {\n const {inputs, backend, attrs} = args;\n const {a, b, bias, preluActivationWeights} = inputs;\n const {transposeA, transposeB, activation, leakyreluAlpha} = attrs;\n\n let current;\n let addRes;\n let activationRes;\n\n const intermediates: TensorInfo[] = [];\n\n const matMulRes =\n batchMatMul({inputs: {a, b}, attrs: {transposeA, transposeB}, backend});\n current = matMulRes;\n\n if (bias) {\n addRes = add({inputs: {a: current, b: bias}, backend}) as TensorInfo;\n intermediates.push(current);\n current = addRes;\n }\n if (activation) {\n activationRes = applyActivation(\n backend, current, activation, preluActivationWeights, leakyreluAlpha);\n intermediates.push(current);\n current = activationRes;\n }\n\n for (const i of intermediates) {\n backend.disposeIntermediateTensorInfo(i);\n }\n\n return current;\n}\n\nexport const _fusedMatMulConfig: KernelConfig = {\n kernelName: _FusedMatMul,\n backendName: 'cpu',\n kernelFunc: _fusedMatMul as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Acos, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const acos = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\n\nexport const acosConfig: KernelConfig = {\n kernelName: Acos,\n backendName: 'cpu',\n kernelFunc: acos,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Acosh, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const acosh = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\n\nexport const acoshConfig: KernelConfig = {\n kernelName: Acosh,\n backendName: 'cpu',\n kernelFunc: acosh,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AddN, AddNInputs, buffer, KernelConfig, KernelFunc, Tensor, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function addN(args: {inputs: AddNInputs, backend: MathBackendCPU}):\n TensorInfo {\n const {inputs, backend} = args;\n const tensors = inputs as Tensor[];\n\n assertNotComplex(inputs, 'addN');\n\n const vals =\n tensors.map(t => backend.data.get(t.dataId).values as TypedArray);\n const outBuf = buffer(tensors[0].shape, tensors[0].dtype as 'float32');\n const outVals = outBuf.values;\n for (let i = 0; i < tensors.length; i++) {\n const currVals = vals[i];\n for (let j = 0; j < outVals.length; j++) {\n outVals[j] += currVals[j];\n }\n }\n\n return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\n\nexport const addNConfig: KernelConfig = {\n kernelName: AddN,\n backendName: 'cpu',\n kernelFunc: addN as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {All, AllAttrs, AllInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {reshape} from './Reshape';\nimport {transpose} from './Transpose';\n\nexport function all(\n args: {inputs: AllInputs, backend: MathBackendCPU, attrs: AllAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {axis, keepDims} = attrs;\n\n assertNotComplex(x, 'all');\n\n const origAxes = util.parseAxisParam(axis, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n if (permutedAxes != null) {\n $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});\n axes = backend_util.getInnerMostAxes(axes.length, x.shape.length);\n }\n\n backend_util.assertAxesAreInnerMostDims('all', axes, $x.shape.length);\n const [outShape, reduceShape] =\n backend_util.computeOutAndReduceShapes($x.shape, axes);\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = util.makeZerosTypedArray(util.sizeFromShape(outShape), $x.dtype);\n\n const aVals = backend.data.get($x.dataId).values as TypedArray;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let all = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n all = all && value;\n }\n vals[i] = all;\n }\n\n if (permutedAxes != null) {\n backend.disposeIntermediateTensorInfo($x);\n }\n\n const result = backend.makeTensorInfo(outShape, $x.dtype, vals);\n\n if (keepDims) {\n const expandedShape = backend_util.expandShapeToKeepDim(outShape, origAxes);\n const reshapedResult =\n reshape({inputs: {x: result}, backend, attrs: {shape: expandedShape}});\n\n backend.disposeIntermediateTensorInfo(result);\n\n return reshapedResult;\n }\n\n return result;\n}\n\nexport const allConfig: KernelConfig = {\n kernelName: All,\n backendName: 'cpu',\n kernelFunc: all as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Any, AnyAttrs, AnyInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {reshape} from './Reshape';\nimport {transpose} from './Transpose';\n\nexport function any(\n args: {inputs: AnyInputs, backend: MathBackendCPU, attrs: AnyAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {axis, keepDims} = attrs;\n\n assertNotComplex(x, 'any');\n\n const origAxes = util.parseAxisParam(axis, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n if (permutedAxes != null) {\n $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});\n axes = backend_util.getInnerMostAxes(axes.length, x.shape.length);\n }\n\n backend_util.assertAxesAreInnerMostDims('any', axes, $x.shape.length);\n const [outShape, reduceShape] =\n backend_util.computeOutAndReduceShapes($x.shape, axes);\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = util.makeZerosTypedArray(util.sizeFromShape(outShape), $x.dtype);\n\n const aVals = backend.data.get($x.dataId).values as TypedArray;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let anyVal = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n anyVal = anyVal || value;\n }\n vals[i] = anyVal;\n }\n\n if (permutedAxes != null) {\n backend.disposeIntermediateTensorInfo($x);\n }\n\n const result = backend.makeTensorInfo(outShape, $x.dtype, vals);\n\n if (keepDims) {\n const expandedShape = backend_util.expandShapeToKeepDim(outShape, origAxes);\n const reshapedResult =\n reshape({inputs: {x: result}, backend, attrs: {shape: expandedShape}});\n\n backend.disposeIntermediateTensorInfo(result);\n\n return reshapedResult;\n }\n\n return result;\n}\n\nexport const anyConfig: KernelConfig = {\n kernelName: Any,\n backendName: 'cpu',\n kernelFunc: any as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ArgMax, ArgMaxAttrs, ArgMaxInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {transpose} from './Transpose';\n\nexport function argMax(\n args: {inputs: ArgMaxInputs, backend: MathBackendCPU, attrs: ArgMaxAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {axis} = attrs;\n\n assertNotComplex(x, 'argMax');\n\n let axes = util.parseAxisParam(axis, x.shape);\n const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n const intermediateTensorInfos = [];\n if (permutedAxes != null) {\n $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});\n intermediateTensorInfos.push($x);\n axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);\n }\n\n axes = [axes[0]];\n backend_util.assertAxesAreInnerMostDims('argMax', axes, $x.shape.length);\n const [outShape, reduceShape] =\n backend_util.computeOutAndReduceShapes($x.shape, axes);\n\n const outSize = util.sizeFromShape(outShape);\n const vals = util.makeZerosTypedArray(outSize, 'int32');\n const reduceSize = util.sizeFromShape(reduceShape);\n\n const aVals = backend.data.get($x.dataId).values as TypedArray;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let max = aVals[offset];\n let maxIndex = 0;\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (value > max) {\n max = value;\n maxIndex = j;\n }\n }\n vals[i] = maxIndex;\n }\n\n intermediateTensorInfos.forEach(\n t => backend.disposeIntermediateTensorInfo(t));\n\n return backend.makeTensorInfo(outShape, 'int32', vals);\n}\n\nexport const argMaxConfig: KernelConfig = {\n kernelName: ArgMax,\n backendName: 'cpu',\n kernelFunc: argMax as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ArgMin, ArgMinAttrs, ArgMinInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {transpose} from './Transpose';\n\nexport function argMin(\n args: {inputs: ArgMinInputs, backend: MathBackendCPU, attrs: ArgMinAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {axis} = attrs;\n\n assertNotComplex(x, 'argMin');\n\n let axes = util.parseAxisParam(axis, x.shape);\n const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n const intermediateTensorInfos = [];\n if (permutedAxes != null) {\n $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});\n intermediateTensorInfos.push($x);\n axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);\n }\n\n axes = [axes[0]];\n backend_util.assertAxesAreInnerMostDims('argMin', axes, $x.shape.length);\n const [outShape, reduceShape] =\n backend_util.computeOutAndReduceShapes($x.shape, axes);\n\n const outSize = util.sizeFromShape(outShape);\n const vals = util.makeZerosTypedArray(outSize, 'int32');\n const reduceSize = util.sizeFromShape(reduceShape);\n\n const aVals = backend.data.get($x.dataId).values as TypedArray;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let min = aVals[offset];\n let minIndex = 0;\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (value < min) {\n min = value;\n minIndex = j;\n }\n }\n vals[i] = minIndex;\n }\n\n intermediateTensorInfos.forEach(\n t => backend.disposeIntermediateTensorInfo(t));\n\n return backend.makeTensorInfo(outShape, 'int32', vals);\n}\n\nexport const argMinConfig: KernelConfig = {\n kernelName: ArgMin,\n backendName: 'cpu',\n kernelFunc: argMin as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Asin, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const asin = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\n\nexport const asinConfig: KernelConfig = {\n kernelName: Asin,\n backendName: 'cpu',\n kernelFunc: asin,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Asinh, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const asinh = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\n\nexport const asinhConfig: KernelConfig = {\n kernelName: Asinh,\n backendName: 'cpu',\n kernelFunc: asinh,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Atan, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const atan = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\n\nexport const atanConfig: KernelConfig = {\n kernelName: Atan,\n backendName: 'cpu',\n kernelFunc: atan,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Atan2, KernelConfig} from '@tensorflow/tfjs-core';\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const atan2Impl = createSimpleBinaryKernelImpl(\n (aValue, bValue) => Math.atan2(aValue, bValue));\n\nexport const atan2 = binaryKernelFunc(Atan2, atan2Impl);\n\nexport const atan2Config: KernelConfig = {\n kernelName: Atan2,\n backendName: 'cpu',\n kernelFunc: atan2,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Atanh, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const atanh = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\n\nexport const atanhConfig: KernelConfig = {\n kernelName: Atanh,\n backendName: 'cpu',\n kernelFunc: atanh,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, buffer, DataType, Rank, TensorBuffer, TypedArray} from '@tensorflow/tfjs-core';\n\nexport function pool(\n xValues: TypedArray, xShape: number[], dtype: DataType, strides: number[],\n convInfo: backend_util.Conv2DInfo,\n poolType: 'max'|'avg'): TensorBuffer<Rank, DataType> {\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n\n const initialValue =\n (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n Number.POSITIVE_INFINITY);\n\n const output = buffer(convInfo.outShape, dtype);\n const outputVals = output.values;\n\n const outputBatchStrides =\n convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n const outputColStrides = convInfo.outShape[3];\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const outputBatchOffset = b * outputBatchStrides;\n const inputBatchOffset = b * strides[0];\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const xRCorner = yR * strideHeight - padTop;\n const xRMin = Math.max(0, xRCorner);\n const xRMax =\n Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const xCCorner = yC * strideWidth - padLeft;\n const xCMin = Math.max(0, xCCorner);\n const xCMax =\n Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n let minMaxValue = initialValue;\n let avgValue = 0;\n let count = 0;\n for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n const xROffset = inputBatchOffset + xR * strides[1];\n for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n const xCOffset = xROffset + xC * strides[2];\n const pixel = xValues[xCOffset + d];\n if ((poolType === 'max' && pixel > minMaxValue)) {\n minMaxValue = pixel;\n } else if (poolType === 'avg') {\n avgValue += pixel;\n count++;\n }\n }\n if (isNaN(minMaxValue)) {\n break;\n }\n }\n const outputOffset = outputRowOffset + yC * outputColStrides + d;\n outputVals[outputOffset] =\n poolType === 'avg' ? avgValue / count : minMaxValue;\n }\n }\n }\n }\n return output;\n}\n\nexport function maxPoolPositions(\n xValues: TypedArray, xShape: number[], dtype: DataType,\n convInfo: backend_util.Conv2DInfo, flattenPositions = false,\n includeBatchInIndex = false): TensorBuffer<Rank, 'int32'> {\n const maxPositions = buffer(convInfo.outShape, 'int32');\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n\n const xBuf = buffer(xShape, dtype, xValues);\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const xRCorner = yR * strideHeight - padTop;\n let xRMin = xRCorner;\n while (xRMin < 0) {\n xRMin += dilationHeight;\n }\n // const xRMin = Math.max(0, xRCorner);\n const xRMax =\n Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const xCCorner = yC * strideWidth - padLeft;\n let xCMin = xCCorner;\n while (xCMin < 0) {\n xCMin += dilationWidth;\n }\n const xCMax =\n Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n let maxValue = Number.NEGATIVE_INFINITY;\n let maxPosition = -1;\n\n for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n const wR = xR - xRCorner;\n for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n const wC = xC - xCCorner;\n const pixel = xBuf.get(b, xR, xC, d);\n if (pixel > maxValue) {\n maxValue = pixel as number;\n if (flattenPositions) {\n maxPosition = includeBatchInIndex ?\n ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *\n convInfo.inChannels +\n d :\n (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n } else {\n maxPosition = wR * effectiveFilterWidth + wC;\n }\n }\n }\n }\n maxPositions.set(maxPosition, b, yR, yC, d);\n }\n }\n }\n }\n return maxPositions;\n}\n\nexport function pool3d(\n xValues: TypedArray, xShape: number[], dtype: DataType, strides: number[],\n convInfo: backend_util.Conv3DInfo,\n poolType: 'max'|'avg'): TensorBuffer<Rank, DataType> {\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padFront = convInfo.padInfo.front;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n\n const initialValue =\n (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n Number.POSITIVE_INFINITY);\n\n const output = buffer(convInfo.outShape, dtype);\n const outputVals = output.values;\n\n const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *\n convInfo.outShape[3] * convInfo.outShape[4];\n const outputDepthStrides =\n convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n const outputColStrides = convInfo.outShape[4];\n\n for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n const outputBatchOffset = batch * outputBatchStrides;\n const inputBatchOffset = batch * strides[0];\n for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n const xDepthCorner = yDepth * strideDepth - padFront;\n let xDepthMin = xDepthCorner;\n while (xDepthMin < 0) {\n xDepthMin += dilationDepth;\n }\n const xDepthMax =\n Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n const outputDepthOffset =\n outputBatchOffset + yDepth * outputDepthStrides;\n for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n const xRowCorner = yRow * strideHeight - padTop;\n let xRowMin = xRowCorner;\n while (xRowMin < 0) {\n xRowMin += dilationHeight;\n }\n const xRowMax =\n Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n const xColCorner = yCol * strideWidth - padLeft;\n let xColMin = xColCorner;\n while (xColMin < 0) {\n xColMin += dilationWidth;\n }\n const xColMax =\n Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n // Shader code begins\n const outputColOffset = outputRowOffset + yCol * outputColStrides;\n let minMaxValue = initialValue;\n let avgValue = 0;\n let count = 0;\n for (let xDepth = xDepthMin; xDepth < xDepthMax;\n xDepth += dilationDepth) {\n const xDepthOffset = inputBatchOffset + xDepth * strides[1];\n for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n const xRowOffset = xDepthOffset + xRow * strides[2];\n for (let xCol = xColMin; xCol < xColMax;\n xCol += dilationWidth) {\n const xColOffset = xRowOffset + xCol * strides[3];\n const pixel = xValues[xColOffset + channel];\n if ((poolType === 'max' && pixel > minMaxValue)) {\n minMaxValue = pixel;\n } else if (poolType === 'avg') {\n avgValue += pixel;\n count++;\n }\n if (isNaN(minMaxValue)) {\n break;\n }\n }\n if (isNaN(minMaxValue)) {\n break;\n }\n }\n if (isNaN(minMaxValue)) {\n break;\n }\n }\n const outputOffset = outputColOffset + channel;\n outputVals[outputOffset] =\n poolType === 'avg' ? avgValue / count : minMaxValue;\n }\n }\n }\n }\n }\n\n return output;\n}\n\nexport function maxPool3dPositions(\n xBuf: TensorBuffer<Rank, DataType>,\n convInfo: backend_util.Conv3DInfo): TensorBuffer<Rank, DataType> {\n const maxPositions = buffer(convInfo.outShape, 'int32');\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padFront = convInfo.padInfo.front;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n\n for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n const xDepthCorner = yDepth * strideDepth - padFront;\n let xDepthMin = xDepthCorner;\n while (xDepthMin < 0) {\n xDepthMin += dilationDepth;\n }\n const xDepthMax =\n Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n const xRowCorner = yRow * strideHeight - padTop;\n let xRowMin = xRowCorner;\n while (xRowMin < 0) {\n xRowMin += dilationHeight;\n }\n const xRowMax =\n Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n const xColCorner = yCol * strideWidth - padLeft;\n let xColMin = xColCorner;\n while (xColMin < 0) {\n xColMin += dilationWidth;\n }\n const xColMax =\n Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n\n // Shader code begins\n let maxValue = Number.NEGATIVE_INFINITY;\n let maxPosition = -1;\n\n for (let xDepth = xDepthMin; xDepth < xDepthMax;\n xDepth += dilationDepth) {\n const wDepth = xDepth - xDepthCorner;\n for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n const wRow = xRow - xRowCorner;\n for (let xCol = xColMin; xCol < xColMax;\n xCol += dilationWidth) {\n const wCol = xCol - xColCorner;\n const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n if (pixel >= maxValue) {\n maxValue = pixel as number;\n maxPosition =\n wDepth * effectiveFilterHeight * effectiveFilterWidth +\n wRow * effectiveFilterHeight + wCol;\n }\n }\n }\n }\n\n maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n }\n }\n }\n }\n }\n\n return maxPositions;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {AvgPool, AvgPoolAttrs, AvgPoolInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {pool} from '../utils/pool_utils';\nimport {identity} from './Identity';\n\nexport function avgPool(\n args:\n {inputs: AvgPoolInputs, backend: MathBackendCPU, attrs: AvgPoolAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n assertNotComplex(x, 'avgPool');\n const {filterSize, strides, pad, dimRoundingMode} = attrs;\n const dilations = 1;\n\n util.assert(\n backend_util.eitherStridesOrDilationsAreOne(strides, dilations),\n () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n\n const convInfo = backend_util.computePool2DInfo(\n x.shape as [number, number, number, number], filterSize, strides,\n dilations, pad, dimRoundingMode);\n let res: TensorInfo;\n\n if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n res = identity({inputs: {x}, backend});\n } else {\n const xValues = backend.data.get(x.dataId).values as TypedArray;\n const strides = util.computeStrides(x.shape);\n const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'avg');\n res = backend.makeTensorInfo(\n convInfo.outShape, x.dtype, buffer.values as TypedArray);\n }\n return res;\n}\n\nexport const avgPoolConfig: KernelConfig = {\n kernelName: AvgPool,\n backendName: 'cpu',\n kernelFunc: avgPool as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {pool3d} from '../utils/pool_utils';\n\nexport function avgPool3D(args: {\n inputs: AvgPool3DInputs,\n backend: MathBackendCPU,\n attrs: AvgPool3DAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {filterSize, strides, pad, dimRoundingMode, dataFormat} = attrs;\n\n assertNotComplex(x, 'avgPool3d');\n\n const convInfo = backend_util.computePool3DInfo(\n x.shape as [number, number, number, number, number], filterSize, strides,\n 1 /* dilations */, pad, dimRoundingMode, dataFormat);\n\n const xValues = backend.data.get(x.dataId).values as TypedArray;\n const outBuf = pool3d(\n xValues, x.shape, x.dtype, util.computeStrides(x.shape), convInfo, 'avg');\n\n return backend.makeTensorInfo(outBuf.shape, 'float32', outBuf.values);\n}\n\nexport const avgPool3DConfig: KernelConfig = {\n kernelName: AvgPool3D,\n backendName: 'cpu',\n kernelFunc: avgPool3D as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AvgPool3DGrad, AvgPool3DGradAttrs, AvgPool3DGradInputs, backend_util, buffer, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function avgPool3DGrad(args: {\n inputs: AvgPool3DGradInputs,\n backend: MathBackendCPU,\n attrs: AvgPool3DGradAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {dy, input} = inputs;\n const {filterSize, strides, pad, dimRoundingMode} = attrs;\n\n assertNotComplex([dy, input], 'avgPool3DGrad');\n\n const convInfo = backend_util.computePool3DInfo(\n input.shape as [number, number, number, number, number], filterSize,\n strides, 1 /* dilations */, pad, dimRoundingMode);\n\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const filterDepth = convInfo.filterDepth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const dx = buffer(input.shape, 'float32');\n\n const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n\n const dyBuf = backend.bufferSync(dy);\n\n for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n // Shader code begins.\n const dyDepthCorner = dxDepth - padFront;\n const dyRowCorner = dxRow - padTop;\n const dyColCorner = dxCol - padLeft;\n let dotProd = 0;\n for (let wDepth = 0; wDepth < effectiveFilterDepth;\n wDepth += dilationDepth) {\n const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n Math.floor(dyDepth) !== dyDepth) {\n continue;\n }\n for (let wRow = 0; wRow < effectiveFilterHeight;\n wRow += dilationHeight) {\n const dyRow = (dyRowCorner + wRow) / strideHeight;\n if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n Math.floor(dyRow) !== dyRow) {\n continue;\n }\n for (let wCol = 0; wCol < effectiveFilterWidth;\n wCol += dilationWidth) {\n const dyCol = (dyColCorner + wCol) / strideWidth;\n if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n Math.floor(dyCol) !== dyCol) {\n continue;\n }\n\n const pixel =\n dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n dotProd += pixel;\n }\n }\n }\n dx.set(\n dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n }\n }\n }\n }\n }\n\n return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\n\nexport const avgPool3DGradConfig: KernelConfig = {\n kernelName: AvgPool3DGrad,\n backendName: 'cpu',\n kernelFunc: avgPool3DGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {AvgPoolGrad, AvgPoolGradAttrs, AvgPoolGradInputs, backend_util, buffer, KernelConfig, KernelFunc, Rank, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function avgPoolGrad(args: {\n inputs: AvgPoolGradInputs,\n backend: MathBackendCPU,\n attrs: AvgPoolGradAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {dy, input} = inputs;\n const x = input;\n assertNotComplex([dy, input], 'avgPoolGrad');\n const {filterSize, strides, pad} = attrs;\n\n const convInfo = backend_util.computePool2DInfo(\n x.shape as [number, number, number, number], filterSize, strides,\n 1 /* dilations */, pad);\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const dx =\n buffer<Rank.R4>(x.shape as [number, number, number, number], 'float32');\n\n const avgMultiplier = 1 / (filterHeight * filterWidth);\n\n const dyData = backend.data.get(dy.dataId).values as Float32Array;\n const dyBuf = buffer<Rank.R4>(\n dy.shape as [number, number, number, number], 'float32', dyData);\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n // Shader code begins.\n const dyRCorner = dxR - padTop;\n const dyCCorner = dxC - padLeft;\n let dotProd = 0;\n for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n const dyR = (dyRCorner + wR) / strideHeight;\n if (dyR < 0 || dyR >= convInfo.outHeight ||\n Math.floor(dyR) !== dyR) {\n continue;\n }\n for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n const dyC = (dyCCorner + wC) / strideWidth;\n if (dyC < 0 || dyC >= convInfo.outWidth ||\n Math.floor(dyC) !== dyC) {\n continue;\n }\n\n const pixel = dyBuf.get(b, dyR, dyC, d);\n dotProd += pixel;\n }\n }\n dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n }\n }\n }\n }\n return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\n\nexport const avgPoolGradConfig: KernelConfig = {\n kernelName: AvgPoolGrad,\n backendName: 'cpu',\n kernelFunc: avgPoolGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {FusedBatchNorm, FusedBatchNormAttrs, FusedBatchNormInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function batchNorm(args: {\n inputs: FusedBatchNormInputs,\n backend: MathBackendCPU,\n attrs: FusedBatchNormAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, scale, offset, mean, variance} = inputs;\n\n util.assert(\n mean.shape.length === variance.shape.length,\n () => 'Batch normalization gradient requires mean and variance to have ' +\n 'equal ranks.');\n util.assert(\n offset == null || mean.shape.length === offset.shape.length,\n () => 'Batch normalization gradient requires mean and offset to have ' +\n 'equal ranks.');\n util.assert(\n scale == null || mean.shape.length === scale.shape.length,\n () => 'Batch normalization gradient requires mean and scale to have ' +\n 'equal ranks.');\n\n assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n\n let {varianceEpsilon} = attrs;\n if (varianceEpsilon == null) {\n varianceEpsilon = 0.001;\n }\n\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const mVals = backend.data.get(mean.dataId).values as TypedArray;\n const varVals = backend.data.get(variance.dataId).values as TypedArray;\n const sVals = scale ? backend.data.get(scale.dataId).values as TypedArray :\n new Float32Array([1]);\n const offVals = offset ?\n backend.data.get(offset.dataId).values as TypedArray :\n new Float32Array([0]);\n const outVals = new Float32Array(xVals.length);\n\n const offValsLength = offVals.length;\n const sValsLength = sVals.length;\n const varValsLength = varVals.length;\n const mValsLength = mVals.length;\n\n let offi = 0;\n let mi = 0;\n let si = 0;\n let vi = 0;\n for (let i = 0; i < xVals.length; ++i) {\n outVals[i] = offVals[offi++] +\n (xVals[i] - mVals[mi++]) * sVals[si++] /\n Math.sqrt(varVals[vi++] + varianceEpsilon);\n if (offi >= offValsLength) {\n offi = 0;\n }\n if (mi >= mValsLength) {\n mi = 0;\n }\n if (si >= sValsLength) {\n si = 0;\n }\n if (vi >= varValsLength) {\n vi = 0;\n }\n }\n return backend.makeTensorInfo(x.shape, x.dtype, outVals);\n}\n\nexport const batchNormConfig: KernelConfig = {\n kernelName: FusedBatchNorm,\n backendName: 'cpu',\n kernelFunc: batchNorm as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, BatchToSpaceND, BatchToSpaceNDAttrs, BatchToSpaceNDInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {reshape} from './Reshape';\nimport {slice} from './Slice';\nimport {transpose} from './Transpose';\n\nexport function batchToSpaceND(args: {\n inputs: BatchToSpaceNDInputs,\n backend: MathBackendCPU,\n attrs: BatchToSpaceNDAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {blockShape, crops} = attrs;\n\n assertNotComplex([x], 'batchToSpaceND');\n\n const prod = blockShape.reduce((a, b) => a * b);\n\n const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n const reshapedPermuted =\n backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n const sliceBeginCoords =\n backend_util.getSliceBeginCoords(crops, blockShape.length);\n const sliceSize =\n backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n\n const xReshaped = reshape({inputs: {x}, backend, attrs: {shape: reshaped}});\n const xTransposed =\n transpose({inputs: {x: xReshaped}, backend, attrs: {perm: permuted}});\n const xTransposedReshaped = reshape(\n {inputs: {x: xTransposed}, backend, attrs: {shape: reshapedPermuted}});\n const result = slice({\n inputs: {x: xTransposedReshaped},\n backend,\n attrs: {begin: sliceBeginCoords, size: sliceSize}\n });\n\n backend.disposeIntermediateTensorInfo(xReshaped);\n backend.disposeIntermediateTensorInfo(xTransposed);\n backend.disposeIntermediateTensorInfo(xTransposedReshaped);\n\n return result;\n}\n\nexport const batchToSpaceNDConfig: KernelConfig = {\n kernelName: BatchToSpaceND,\n backendName: 'cpu',\n kernelFunc: batchToSpaceND as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Bincount, BincountAttrs, BincountInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {bincountImpl} from './Bincount_impl';\n\nexport function bincount(args: {\n inputs: BincountInputs,\n backend: MathBackendCPU,\n attrs: BincountAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, weights} = inputs;\n const {size} = attrs;\n\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const weightsVals = backend.data.get(weights.dataId).values as TypedArray;\n\n const outVals =\n bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);\n\n return backend.makeTensorInfo([size], weights.dtype, outVals);\n}\n\nexport const bincountConfig: KernelConfig = {\n kernelName: Bincount,\n backendName: 'cpu',\n kernelFunc: bincount as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ClipByValue, ClipByValueAttrs, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const clip = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n const clipAttrs = attrs as {} as ClipByValueAttrs;\n if (xi > clipAttrs.clipValueMax) {\n return clipAttrs.clipValueMax;\n }\n return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\n\nexport const clipConfig: KernelConfig = {\n kernelName: ClipByValue,\n backendName: 'cpu',\n kernelFunc: clip,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ComplexAbs, ComplexAbsInputs, KernelConfig, KernelFunc, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport const complexAbs =\n (args: {inputs: ComplexAbsInputs, backend: MathBackendCPU}) => {\n const {x} = args.inputs;\n const cpuBackend = args.backend;\n const resultValues = new Float32Array(util.sizeFromShape(x.shape));\n const complexVals = cpuBackend.data.get(x.dataId);\n const real = complexVals.complexTensorInfos.real;\n const imag = complexVals.complexTensorInfos.imag;\n const realVals = cpuBackend.data.get(real.dataId).values as Float32Array;\n const imagVals = cpuBackend.data.get(imag.dataId).values as Float32Array;\n for (let i = 0; i < realVals.length; i++) {\n const real = realVals[i];\n const imag = imagVals[i];\n resultValues[i] = Math.hypot(real, imag);\n }\n\n return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n };\n\nexport const complexAbsConfig: KernelConfig = {\n kernelName: ComplexAbs,\n backendName: 'cpu',\n kernelFunc: complexAbs as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Imag, ImagInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport function imag(args: {inputs: ImagInputs, backend: MathBackendCPU}):\n TensorInfo {\n const {inputs, backend} = args;\n const {input} = inputs;\n\n const imag = backend.data.get(input.dataId).complexTensorInfos.imag;\n const imagVal = backend.data.get(imag.dataId).values;\n\n // When complex tensor is disposed, its underlying parts will be disposed too.\n // Make new tensor out of the imag value of the complex. This makes sure the\n // value is still accessible even if complex tensor is disposed.\n return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);\n}\n\nexport const imagConfig: KernelConfig = {\n kernelName: Imag,\n backendName: 'cpu',\n kernelFunc: imag as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Concat, ConcatAttrs, ConcatInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nimport {complex} from './Complex';\nimport {concatImpl} from './Concat_impl';\nimport {identity} from './Identity';\nimport {imag} from './Imag';\nimport {real} from './Real';\nimport {reshape} from './Reshape';\n\nexport function concat(\n args: {inputs: ConcatInputs, backend: MathBackendCPU, attrs: ConcatAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {axis} = attrs;\n\n const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n\n if (util.sizeFromShape(outShape) === 0) {\n return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n }\n\n // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n if ($inputs.length === 1) {\n return identity({inputs: {x: $inputs[0]}, backend});\n }\n\n const shapes = $inputs.map(t => t.shape);\n backend_util.assertParamsConsistent(shapes, $axis);\n\n if ($inputs[0].dtype === 'complex64') {\n const reals = $inputs.map((t) => real({inputs: {input: t}, backend}));\n const imags = $inputs.map((t) => imag({inputs: {input: t}, backend}));\n\n const realConcated = concat({inputs: reals, backend, attrs: {axis: $axis}});\n const imagConcated = concat({inputs: imags, backend, attrs: {axis: $axis}});\n\n const result =\n complex({inputs: {real: realConcated, imag: imagConcated}, backend});\n\n reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n backend.disposeIntermediateTensorInfo(realConcated);\n backend.disposeIntermediateTensorInfo(imagConcated);\n\n return result;\n }\n\n // Any concat of n-dimensional tensors across any axis can be reduced to\n // a concatenation of two-dimensional tensors across the axis 1 by first\n // partitioning the axes of the original tensors into those less than the\n // axis to be concatenated and the rest. Then reshape the tensors\n // into a two-dimensional tensor by collapsing these two sets of axes and\n // concatenate the resulting matrices across the axis 1, finally reshaping\n // the result to have the proper shape.\n const inputs2D = $inputs.map(t => {\n const innerSize = util.sizeFromShape(t.shape.slice($axis));\n const shape = [-1, innerSize];\n return reshape({inputs: {x: t}, backend, attrs: {shape}});\n });\n\n const inputsValShapes = inputs2D.map(t => {\n return {vals: backend.data.get(t.dataId).values, shape: t.shape};\n });\n\n // Concats 2d tensors along axis=1.\n outShape =\n backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);\n const simplyConcat = inputs2D[0].shape[0] === 1;\n const outVals =\n concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);\n\n const finalOutShape =\n backend_util.computeOutShape($inputs.map(t => t.shape), $axis);\n\n const outInfo =\n backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n\n inputs2D.forEach(t => backend.disposeIntermediateTensorInfo(t));\n\n return outInfo;\n}\n\nexport const concatConfig: KernelConfig = {\n kernelName: Concat,\n backendName: 'cpu',\n kernelFunc: concat as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv2D, Conv2DAttrs, Conv2DInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function conv2D(\n args: {inputs: Conv2DInputs, backend: MathBackendCPU, attrs: Conv2DAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, filter} = inputs;\n const {strides, pad, dataFormat, dilations, dimRoundingMode} = attrs;\n\n assertNotComplex([x, filter], 'conv2d');\n\n const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util.computeConv2DInfo(\n x.shape as [number, number, number, number],\n filter.shape as [number, number, number, number], strides, dilations, pad,\n dimRoundingMode, false /* depthwise */, $dataFormat);\n\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const padLeft = convInfo.padInfo.left;\n const padTop = convInfo.padInfo.top;\n const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n\n const y = new TensorBuffer(convInfo.outShape, x.dtype as 'float32');\n\n const xStrides = util.computeStrides(x.shape);\n const filterStrides = util.computeStrides(filter.shape);\n\n const xBatchStride = xStrides[0];\n const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];\n const xColStride = isChannelsLast ? xStrides[2] : 1;\n const xChannelStride = isChannelsLast ? 1 : xStrides[1];\n const yBatchStride = y.strides[0];\n const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n const yColStride = isChannelsLast ? y.strides[2] : 1;\n const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const wVals = backend.data.get(filter.dataId).values as TypedArray;\n const yVals = y.values;\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * xBatchStride;\n const yOffset1 = b * yBatchStride;\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const yOffset2 = yOffset1 + yR * yRowStride;\n const xRCorner = yR * convInfo.strideHeight - padTop;\n for (let wR = 0; wR < filterHeight; ++wR) {\n const xR = xRCorner + wR * dilationHeight;\n if (xR < 0 || xR >= convInfo.inHeight) {\n continue;\n }\n const wOffset1 = wR * filterStrides[0];\n const xOffset2 = xOffset1 + xR * xRowStride;\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const yOffset3 = yOffset2 + yC * yColStride;\n const xCCorner = yC * convInfo.strideWidth - padLeft;\n for (let wC = 0; wC < filterWidth; ++wC) {\n const xC = xCCorner + wC * dilationWidth;\n if (xC < 0 || xC >= convInfo.inWidth) {\n continue;\n }\n const wOffset2 = wOffset1 + wC * filterStrides[1];\n const xOffset3 = xOffset2 + xC * xColStride;\n let wOffset3 = wOffset2;\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const xVal = xVals[xOffset3 + d1 * xChannelStride];\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n yVals[yOffset3 + d2 * yChannelStride] +=\n xVal * wVals[wOffset3 + d2];\n }\n wOffset3 += convInfo.outChannels;\n }\n }\n }\n }\n }\n }\n\n return backend.makeTensorInfo(y.shape, y.dtype, yVals);\n}\n\nexport const conv2DConfig: KernelConfig = {\n kernelName: Conv2D,\n backendName: 'cpu',\n kernelFunc: conv2D as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv2DBackpropFilter, Conv2DBackpropFilterAttrs, Conv2DBackpropFilterInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function conv2DBackpropFilter(args: {\n inputs: Conv2DBackpropFilterInputs,\n backend: MathBackendCPU,\n attrs: Conv2DBackpropFilterAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, dy} = inputs;\n const {strides, pad, dataFormat, dimRoundingMode, filterShape} = attrs;\n\n assertNotComplex([x, dy], 'conv2dBackpropFilter');\n\n const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util.computeConv2DInfo(\n x.shape as [number, number, number, number], filterShape, strides,\n 1 /* dilations */, pad, dimRoundingMode, false /* depthwise */,\n $dataFormat);\n\n const {strideHeight, strideWidth, filterHeight, filterWidth} = convInfo;\n const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n const dW = new TensorBuffer(convInfo.filterShape, 'float32');\n\n const leftPad = convInfo.padInfo.left;\n const topPad = convInfo.padInfo.top;\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const dyVals = backend.data.get(dy.dataId).values as TypedArray;\n\n const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n\n for (let wR = 0; wR < filterHeight; ++wR) {\n const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n const yRMax = Math.min(\n convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n\n for (let wC = 0; wC < filterWidth; ++wC) {\n const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n const yCMax = Math.min(\n convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n let dotProd = 0;\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let yR = yRMin; yR < yRMax; ++yR) {\n const xR = wR + yR * strideHeight - topPad;\n for (let yC = yCMin; yC < yCMax; ++yC) {\n const xC = wC + yC * strideWidth - leftPad;\n if (isChannelsLast) {\n dotProd += (xBuf.get(b, xR, xC, d1) as number) *\n (dyBuf.get(b, yR, yC, d2) as number);\n } else {\n dotProd += (xBuf.get(b, d1, xR, xC) as number) *\n (dyBuf.get(b, d2, yR, yC) as number);\n }\n }\n }\n }\n dW.set(dotProd, wR, wC, d1, d2);\n }\n }\n }\n }\n\n return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\n\nexport const conv2DBackpropFilterConfig: KernelConfig = {\n kernelName: Conv2DBackpropFilter,\n backendName: 'cpu',\n kernelFunc: conv2DBackpropFilter as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function conv2DBackpropInput(args: {\n inputs: Conv2DBackpropInputInputs,\n backend: MathBackendCPU,\n attrs: Conv2DBackpropInputAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {dy, filter} = inputs;\n const {inputShape, strides, pad, dataFormat, dimRoundingMode} = attrs;\n\n assertNotComplex([dy, filter], 'conv2dBackpropInput');\n\n const filterStrides = util.computeStrides(filter.shape);\n const dyStrides = util.computeStrides(dy.shape);\n\n let $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util.computeConv2DInfo(\n inputShape, filter.shape as [number, number, number, number], strides,\n 1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);\n\n const dx = new TensorBuffer(convInfo.inShape, 'float32');\n const dxValues = dx.values;\n const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n const fltValues = backend.data.get(filter.dataId).values as TypedArray;\n const [fltS0, fltS1, fltS2] = filterStrides;\n const {\n batchSize,\n filterHeight,\n filterWidth,\n inChannels,\n inHeight,\n inWidth,\n outChannels,\n outHeight,\n outWidth,\n strideHeight,\n strideWidth\n } = convInfo;\n $dataFormat = convInfo.dataFormat;\n const topPad = filterHeight - 1 - convInfo.padInfo.top;\n const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n const isChannelsLast = $dataFormat === 'channelsLast';\n const xBatchStride = dx.strides[0];\n const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n const xColStride = isChannelsLast ? dx.strides[2] : 1;\n const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n const yBatchStride = dyStrides[0];\n const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n const yColStride = isChannelsLast ? dyStrides[2] : 1;\n const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n\n for (let b = 0; b < batchSize; ++b) {\n for (let d1 = 0; d1 < inChannels; ++d1) {\n for (let xR = 0; xR < inHeight; ++xR) {\n const xRCorner = xR - topPad;\n const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n const yRMax =\n Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n\n for (let xC = 0; xC < inWidth; ++xC) {\n const xCCorner = xC - leftPad;\n const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n const yCMax =\n Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n let dotProd = 0;\n for (let yR = xRMin; yR < yRMax; ++yR) {\n const wR = yR * strideHeight - xRCorner;\n\n for (let yC = xCMin; yC < yCMax; ++yC) {\n const wC = yC * strideWidth - xCCorner;\n const dyOffset =\n yBatchStride * b + yRowStride * yR + yColStride * yC;\n const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n\n for (let d2 = 0; d2 < outChannels; ++d2) {\n const pixel = dyValues[dyOffset + yChannelStride * d2];\n const weight = fltValues[fltOffset + d2];\n dotProd += pixel * weight;\n }\n }\n }\n const dxOffset = xBatchStride * b + xRowStride * xR +\n xColStride * xC + xChannelStride * d1;\n dxValues[dxOffset] = dotProd;\n }\n }\n }\n }\n\n return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\n\nexport const conv2DBackpropInputConfig: KernelConfig = {\n kernelName: Conv2DBackpropInput,\n backendName: 'cpu',\n kernelFunc: conv2DBackpropInput as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv3D, Conv3DAttrs, Conv3DInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function conv3D(\n args: {inputs: Conv3DInputs, backend: MathBackendCPU, attrs: Conv3DAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, filter} = inputs;\n const {strides, pad, dilations} = attrs;\n\n assertNotComplex([x, filter], 'conv3d');\n\n const convInfo = backend_util.computeConv3DInfo(\n x.shape as [number, number, number, number, number],\n filter.shape as [number, number, number, number, number], strides,\n dilations, pad);\n\n const {\n filterDepth,\n filterHeight,\n filterWidth,\n dilationDepth,\n dilationHeight,\n dilationWidth,\n padInfo\n } = convInfo;\n const padFront = padInfo.front;\n const padLeft = padInfo.left;\n const padTop = padInfo.top;\n const y = new TensorBuffer(convInfo.outShape, x.dtype as 'float32');\n\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const wVals = backend.data.get(filter.dataId).values as TypedArray;\n const yVals = y.values;\n\n const xStrides = util.computeStrides(x.shape);\n const filterStrides = util.computeStrides(filter.shape);\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * xStrides[0];\n const yOffset1 = b * y.strides[0];\n for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n const yOffset2 = yOffset1 + yF * y.strides[1];\n const xFCorner = yF * convInfo.strideDepth - padFront;\n for (let wF = 0; wF < filterDepth; ++wF) {\n const xF = xFCorner + wF * dilationDepth;\n if (xF < 0 || xF >= convInfo.inDepth) {\n continue;\n }\n const wOffset1 = wF * filterStrides[0];\n const xOffset2 = xOffset1 + xF * xStrides[1];\n\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const yOffset3 = yOffset2 + yR * y.strides[2];\n const xRCorner = yR * convInfo.strideHeight - padTop;\n for (let wR = 0; wR < filterHeight; ++wR) {\n const xR = xRCorner + wR * dilationHeight;\n if (xR < 0 || xR >= convInfo.inHeight) {\n continue;\n }\n const wOffset2 = wOffset1 + wR * filterStrides[1];\n const xOffset3 = xOffset2 + xR * xStrides[2];\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n const xCCorner = yC * convInfo.strideWidth - padLeft;\n for (let wC = 0; wC < filterWidth; ++wC) {\n const xC = xCCorner + wC * dilationWidth;\n if (xC < 0 || xC >= convInfo.inWidth) {\n continue;\n }\n const wOffset3 = wOffset2 + wC * filterStrides[2];\n const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n let wOffset4 = wOffset3;\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const xVal = xVals[xOffset4 + d1];\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n }\n wOffset4 += convInfo.outChannels;\n }\n }\n }\n }\n }\n }\n }\n }\n\n return backend.makeTensorInfo(y.shape, y.dtype, y.values);\n}\n\nexport const conv3DConfig: KernelConfig = {\n kernelName: Conv3D,\n backendName: 'cpu',\n kernelFunc: conv3D as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv3DBackpropFilterV2, Conv3DBackpropFilterV2Attrs, Conv3DBackpropFilterV2Inputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function conv3DBackpropFilterV2(args: {\n inputs: Conv3DBackpropFilterV2Inputs,\n backend: MathBackendCPU,\n attrs: Conv3DBackpropFilterV2Attrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, dy} = inputs;\n const {strides, pad, filterShape} = attrs;\n\n assertNotComplex([x, dy], 'conv3dBackpropFilterV2');\n\n const xStrides = util.computeStrides(x.shape);\n const dyStrides = util.computeStrides(dy.shape);\n\n const convInfo = backend_util.computeConv3DInfo(\n x.shape as [number, number, number, number, number], filterShape, strides,\n 1 /* dilations */, pad);\n\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const filterDepth = convInfo.filterDepth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n\n const dw = new TensorBuffer(convInfo.filterShape, 'float32');\n const dwValues = dw.values;\n const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n const xValues = backend.data.get(x.dataId).values as TypedArray;\n const [xS0, xS1, xS2, xS3] = xStrides;\n\n const frontPad = convInfo.padInfo.front;\n const leftPad = convInfo.padInfo.left;\n const topPad = convInfo.padInfo.top;\n\n for (let wF = 0; wF < filterDepth; ++wF) {\n const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n const yFMax = Math.min(\n convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n const wOffset1 = wF * dwS0;\n\n for (let wR = 0; wR < filterHeight; ++wR) {\n const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n const yRMax = Math.min(\n convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n const wOffset2 = wR * dwS1 + wOffset1;\n\n for (let wC = 0; wC < filterWidth; ++wC) {\n const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n const yCMax = Math.min(\n convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n const wOffset3 = wC * dwS2 + wOffset2;\n\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const wOffset4 = d1 * dwS3 + wOffset3;\n\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n let dotProd = 0;\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * xS0;\n const yOffset1 = b * dyS0;\n\n for (let yF = yFMin; yF < yFMax; ++yF) {\n const xF = wF + yF * strideDepth - frontPad;\n const xOffset2 = xF * xS1 + xOffset1;\n const yOffset2 = yF * dyS1 + yOffset1;\n\n for (let yR = yRMin; yR < yRMax; ++yR) {\n const xR = wR + yR * strideHeight - topPad;\n const xOffset3 = xR * xS2 + xOffset2;\n const yOffset3 = yR * dyS2 + yOffset2;\n\n for (let yC = yCMin; yC < yCMax; ++yC) {\n const xC = wC + yC * strideWidth - leftPad;\n const xOffset4 = xC * xS3 + xOffset3;\n const yOffset4 = yC * dyS3 + yOffset3;\n\n dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n }\n }\n }\n }\n dwValues[wOffset4 + d2] = dotProd;\n }\n }\n }\n }\n }\n\n return backend.makeTensorInfo(dw.shape, dw.dtype, dw.values);\n}\n\nexport const conv3DBackpropFilterV2Config: KernelConfig = {\n kernelName: Conv3DBackpropFilterV2,\n backendName: 'cpu',\n kernelFunc: conv3DBackpropFilterV2 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv3DBackpropInputV2, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function conv3DBackpropInputV2(args: {\n inputs: Conv3DBackpropInputV2Inputs,\n backend: MathBackendCPU,\n attrs: Conv3DBackpropInputV2Attrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {dy, filter} = inputs;\n const {pad, strides, inputShape} = attrs;\n\n assertNotComplex([dy], 'conv3dBackpropInputV2');\n\n const dyStrides = util.computeStrides(dy.shape);\n const filterStrides = util.computeStrides(filter.shape);\n\n const convInfo = backend_util.computeConv3DInfo(\n inputShape, filter.shape as [number, number, number, number, number],\n strides, 1 /* dilations */, pad);\n\n const dx = new TensorBuffer(convInfo.inShape, 'float32');\n const dxValues = dx.values;\n const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n const fltValues = backend.data.get(filter.dataId).values as TypedArray;\n const [fltS0, fltS1, fltS2, fltS3] = filterStrides;\n const {\n batchSize,\n filterDepth,\n filterHeight,\n filterWidth,\n inChannels,\n inDepth,\n inHeight,\n inWidth,\n outChannels,\n outDepth,\n outHeight,\n outWidth,\n strideDepth,\n strideHeight,\n strideWidth\n } = convInfo;\n const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n const topPad = filterHeight - 1 - convInfo.padInfo.top;\n const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n for (let b = 0; b < batchSize; ++b) {\n for (let d1 = 0; d1 < inChannels; ++d1) {\n // Frames of depth\n for (let xF = 0; xF < inDepth; ++xF) {\n const xFCorner = xF - frontPad;\n const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n const yFMax =\n Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n\n // Rows as per standard 2d matrix notation\n for (let xR = 0; xR < inHeight; ++xR) {\n const xRCorner = xR - topPad;\n const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n const yRMax =\n Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n // Columns as per standard 2d matrix notation\n for (let xC = 0; xC < inWidth; ++xC) {\n const xCCorner = xC - leftPad;\n const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n const yCMax =\n Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n let dotProd = 0;\n for (let yF = xFMin; yF < yFMax; ++yF) {\n const wF = yF * strideDepth - xFCorner;\n\n for (let yR = xRMin; yR < yRMax; ++yR) {\n const wR = yR * strideHeight - xRCorner;\n\n for (let yC = xCMin; yC < yCMax; ++yC) {\n const wC = yC * strideWidth - xCCorner;\n const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n fltS1 * (filterHeight - 1 - wR) +\n fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n\n for (let d2 = 0; d2 < outChannels; ++d2) {\n const pixel = dyValues[dyOffset + d2];\n const weight = fltValues[fltOffset + d2];\n dotProd += pixel * weight;\n }\n }\n }\n }\n dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n dotProd;\n }\n }\n }\n }\n }\n\n return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\n\nexport const conv3DBackpropInputV2Config: KernelConfig = {\n kernelName: Conv3DBackpropInputV2,\n backendName: 'cpu',\n kernelFunc: conv3DBackpropInputV2 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Cos, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const cos = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\n\nexport const cosConfig: KernelConfig = {\n kernelName: Cos,\n backendName: 'cpu',\n kernelFunc: cos,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Cosh, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const cosh = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\n\nexport const coshConfig: KernelConfig = {\n kernelName: Cosh,\n backendName: 'cpu',\n kernelFunc: cosh,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {buffer, CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport function cropAndResize(args: {\n inputs: CropAndResizeInputs,\n backend: MathBackendCPU,\n attrs: CropAndResizeAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {image, boxes, boxInd} = inputs;\n const {cropSize, method, extrapolationValue} = attrs;\n\n const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n const numBoxes = boxes.shape[0];\n\n const [cropHeight, cropWidth] = cropSize;\n const output =\n buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');\n\n const boxVals = backend.data.get(boxes.dataId).values as TypedArray;\n const boxIndVals = backend.data.get(boxInd.dataId).values as TypedArray;\n const imageVals = backend.data.get(image.dataId).values as TypedArray;\n\n const inStride =\n util.computeStrides(image.shape); // to calculate flat indexes into image\n const outStride = util.computeStrides(\n output.shape); // to calculate flat indexes into output\n\n // Reference implementation\n // tslint:disable-next-line:max-line-length\n // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n for (let b = 0; b < numBoxes; b++) {\n const startInd = b * 4;\n const y1 = boxVals[startInd];\n const x1 = boxVals[startInd + 1];\n const y2 = boxVals[startInd + 2];\n const x2 = boxVals[startInd + 3];\n\n const bInd: number = boxIndVals[b];\n if (bInd >= batch) {\n continue;\n }\n\n const heightScale =\n (cropHeight > 1) ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;\n const widthScale =\n (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n\n for (let y = 0; y < cropHeight; y++) {\n const yInd: number = (cropHeight > 1) ?\n y1 * (imageHeight - 1) + y * (heightScale) :\n 0.5 * (y1 + y2) * (imageHeight - 1);\n\n if (yInd < 0 || yInd > imageHeight - 1) {\n for (let x = 0; x < cropWidth; x++) {\n for (let c = 0; c < numChannels; c++) {\n const ind =\n c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = extrapolationValue;\n }\n }\n continue;\n }\n\n if (method === 'bilinear') {\n const topInd = Math.floor(yInd);\n const bottomInd = Math.ceil(yInd);\n const yLerp = yInd - topInd;\n\n for (let x = 0; x < cropWidth; x++) {\n const xInd = (cropWidth > 1) ?\n x1 * (imageWidth - 1) + x * widthScale :\n 0.5 * (x1 + x2) * (imageWidth - 1);\n\n if (xInd < 0 || xInd > imageWidth - 1) {\n for (let c = 0; c < numChannels; c++) {\n const ind =\n c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = extrapolationValue;\n }\n continue;\n }\n\n const leftInd = Math.floor(xInd);\n const rightInd = Math.ceil(xInd);\n const xLerp = xInd - leftInd;\n\n for (let c = 0; c < numChannels; c++) {\n let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n bInd * inStride[0];\n const topLeft = imageVals[ind];\n\n ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n bInd * inStride[0];\n const topRight = imageVals[ind];\n\n ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n bInd * inStride[0];\n const bottomLeft = imageVals[ind];\n\n ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n bInd * inStride[0];\n const bottomRight = imageVals[ind];\n\n const top = topLeft + (topRight - topLeft) * xLerp;\n const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n\n ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = top + ((bottom - top) * yLerp);\n }\n }\n } else { // method == \"nearest\"\n for (let x = 0; x < cropWidth; ++x) {\n const xInd = (cropWidth > 1) ?\n x1 * (imageWidth - 1) + x * widthScale :\n 0.5 * (x1 + x2) * (imageWidth - 1);\n\n if (xInd < 0 || xInd > imageWidth - 1) {\n for (let c = 0; c < numChannels; c++) {\n const ind =\n c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = extrapolationValue;\n }\n continue;\n }\n\n const closestX = Math.round(xInd);\n const closestY = Math.round(yInd);\n for (let c = 0; c < numChannels; c++) {\n const inInd = c + closestX * inStride[2] + closestY * inStride[1] +\n bInd * inStride[0];\n const outInd =\n c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[outInd] = imageVals[inInd];\n }\n }\n }\n }\n }\n\n return backend.makeTensorInfo(output.shape, output.dtype, output.values);\n}\n\nexport const cropAndResizeConfig: KernelConfig = {\n kernelName: CropAndResize,\n backendName: 'cpu',\n kernelFunc: cropAndResize as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Cumsum, CumsumAttrs, CumsumInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, upcastType, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {transpose} from './Transpose';\n\nexport function cumsum(\n args: {inputs: CumsumInputs, backend: MathBackendCPU, attrs: CumsumAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {axis, exclusive, reverse} = attrs;\n\n assertNotComplex(x, 'cumsum');\n\n const permutation = backend_util.getAxesPermutation([axis], x.shape.length);\n let $x = x;\n if (permutation != null) {\n $x = transpose({inputs: {x}, backend, attrs: {perm: permutation}});\n }\n const permutedAxis = backend_util.getInnerMostAxes(1, x.shape.length)[0];\n\n if (permutedAxis !== $x.shape.length - 1) {\n throw new Error(\n `backend.cumsum in CPU expects an inner-most ` +\n `axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);\n }\n\n const resultDtype = upcastType($x.dtype, 'int32');\n const vals = util.makeZerosTypedArray(\n util.sizeFromShape($x.shape), resultDtype) as TypedArray;\n\n const aVals = backend.data.get($x.dataId).values as TypedArray;\n const finalDim = $x.shape[$x.shape.length - 1];\n const indexAdjuster = reverse ?\n (i: number, j: number) => i + finalDim - j - 1 :\n (i: number, j: number) => i + j;\n for (let i = 0; i < aVals.length; i += finalDim) {\n for (let j = 0; j < finalDim; j++) {\n const idx = indexAdjuster(i, j);\n if (j === 0) {\n vals[idx] = exclusive ? 0 : aVals[idx];\n } else {\n const prevIdx = indexAdjuster(i, j - 1);\n vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n aVals[idx] + vals[prevIdx];\n }\n }\n }\n\n const result = backend.makeTensorInfo($x.shape, resultDtype, vals);\n\n if (permutation != null) {\n const reversePermutation = backend_util.getUndoAxesPermutation(permutation);\n const reverseTransposedResult = transpose(\n {inputs: {x: result}, backend, attrs: {perm: reversePermutation}});\n\n backend.disposeIntermediateTensorInfo(result);\n backend.disposeIntermediateTensorInfo($x);\n\n return reverseTransposedResult;\n }\n\n return result;\n}\n\nexport const cumsumConfig: KernelConfig = {\n kernelName: Cumsum,\n backendName: 'cpu',\n kernelFunc: cumsum as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DenseBincount, DenseBincountAttrs, DenseBincountInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {bincountImpl, bincountReduceImpl} from './Bincount_impl';\n\nexport function denseBincount(args: {\n inputs: DenseBincountInputs,\n backend: MathBackendCPU,\n attrs: DenseBincountAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, weights} = inputs;\n const {size, binaryOutput} = attrs;\n\n if (x.shape.length === 1) {\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const weightsVals = backend.data.get(weights.dataId).values as TypedArray;\n\n const outVals =\n bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);\n\n return backend.makeTensorInfo([size], weights.dtype, outVals);\n } else if (x.shape.length === 2) {\n const xBuf = backend.bufferSync(x);\n const weightsBuf = backend.bufferSync(weights);\n\n const outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput);\n\n return backend.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);\n }\n\n throw new Error(\n `Error in denseBincount: input must be at most rank 2, but got rank` +\n `${x.shape.length}.`);\n}\n\nexport const denseBincountConfig: KernelConfig = {\n kernelName: DenseBincount,\n backendName: 'cpu',\n kernelFunc: denseBincount as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DepthToSpace, DepthToSpaceAttrs, DepthToSpaceInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport function depthToSpace(args: {\n inputs: DepthToSpaceInputs,\n backend: MathBackendCPU,\n attrs: DepthToSpaceAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {blockSize, dataFormat} = attrs;\n\n util.assert(\n dataFormat === 'NHWC',\n () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${\n dataFormat}`);\n util.assert(\n blockSize > 1,\n () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n\n const batchSize = x.shape[0];\n const inputHeight = x.shape[1];\n const inputWidth = x.shape[2];\n const inputDepth = x.shape[3];\n\n const outputHeight = inputHeight * blockSize;\n const outputWidth = inputWidth * blockSize;\n const outputDepth = inputDepth / (blockSize * blockSize);\n\n const xValues = backend.data.get(x.dataId).values as TypedArray;\n const result =\n new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n\n let outputIdx = 0;\n for (let b = 0; b < batchSize; ++b) {\n for (let h = 0; h < outputHeight; ++h) {\n const inH = Math.floor(h / blockSize);\n const offsetH = (h % blockSize);\n for (let w = 0; w < outputWidth; ++w) {\n const inW = Math.floor(w / blockSize);\n const offsetW = (w % blockSize);\n const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n for (let d = 0; d < outputDepth; ++d) {\n const inD = d + offsetD;\n const inputIdx =\n inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n result[outputIdx++] = xValues[inputIdx];\n }\n }\n }\n }\n\n return backend.makeTensorInfo(\n [batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);\n}\n\nexport const depthToSpaceConfig: KernelConfig = {\n kernelName: DepthToSpace,\n backendName: 'cpu',\n kernelFunc: depthToSpace as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function depthwiseConv2dNative(args: {\n inputs: DepthwiseConv2dNativeInputs,\n backend: MathBackendCPU,\n attrs: DepthwiseConv2dNativeAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, filter} = inputs;\n const {strides, pad, dilations, dimRoundingMode} = attrs;\n\n assertNotComplex([x, filter], 'depthwiseConv2DNative');\n\n const xStrides = util.computeStrides(x.shape);\n const filterStrides = util.computeStrides(filter.shape);\n\n let $dilations = dilations;\n if ($dilations == null) {\n $dilations = [1, 1];\n }\n\n util.assert(\n backend_util.eitherStridesOrDilationsAreOne(strides, $dilations),\n () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n `1. Got strides ${strides} and dilations '${$dilations}'`);\n\n const convInfo = backend_util.computeConv2DInfo(\n x.shape as [number, number, number, number],\n filter.shape as [number, number, number, number], strides, $dilations,\n pad, dimRoundingMode, true /* depthwise */);\n\n const {filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo} =\n convInfo;\n const padLeft = padInfo.left;\n const padTop = padInfo.top;\n const chMul = convInfo.outChannels / convInfo.inChannels;\n const y = new TensorBuffer(convInfo.outShape, x.dtype as 'float32');\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const wVals = backend.data.get(filter.dataId).values as TypedArray;\n const yVals = y.values;\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * xStrides[0];\n const yOffset1 = b * y.strides[0];\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const yOffset2 = yOffset1 + yR * y.strides[1];\n const xRCorner = yR * convInfo.strideHeight - padLeft;\n for (let wR = 0; wR < filterHeight; ++wR) {\n const xR = xRCorner + wR * dilationHeight;\n if (xR < 0 || xR >= convInfo.inHeight) {\n continue;\n }\n const wOffset1 = wR * filterStrides[0];\n const xOffset2 = xOffset1 + xR * xStrides[1];\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const yOffset3 = yOffset2 + yC * y.strides[2];\n const xCCorner = yC * convInfo.strideWidth - padTop;\n for (let wC = 0; wC < filterWidth; ++wC) {\n const xC = xCCorner + wC * dilationWidth;\n if (xC < 0 || xC >= convInfo.inWidth) {\n continue;\n }\n const wOffset2 = wOffset1 + wC * filterStrides[1];\n const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n let yOffset4 = yOffset3;\n let wOffset3 = wOffset2;\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const xVal = xVals[xOffset3 + d1];\n for (let q = 0; q < chMul; ++q) {\n yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n }\n yOffset4 += chMul;\n wOffset3 += chMul;\n }\n }\n }\n }\n }\n }\n\n return backend.makeTensorInfo(y.shape, y.dtype, y.values);\n}\n\nexport const depthwiseConv2dNativeConfig: KernelConfig = {\n kernelName: DepthwiseConv2dNative,\n backendName: 'cpu',\n kernelFunc: depthwiseConv2dNative as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropFilterAttrs, DepthwiseConv2dNativeBackpropFilterInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function depthwiseConv2dNativeBackpropFilter(args: {\n inputs: DepthwiseConv2dNativeBackpropFilterInputs,\n backend: MathBackendCPU,\n attrs: DepthwiseConv2dNativeBackpropFilterAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, dy} = inputs;\n const {strides, dilations, pad, dimRoundingMode, filterShape} = attrs;\n\n assertNotComplex([x, dy], 'depthwiseConv2dNativeBackpropFilter');\n\n const convInfo = backend_util.computeConv2DInfo(\n x.shape as [number, number, number, number], filterShape, strides,\n dilations, pad, dimRoundingMode, true /* depthwise */);\n\n const {strideHeight, strideWidth, filterHeight, filterWidth} = convInfo;\n\n const dW = new TensorBuffer(convInfo.filterShape, 'float32');\n\n const leftPad = convInfo.padInfo.left;\n const topPad = convInfo.padInfo.top;\n const chMul = convInfo.outChannels / convInfo.inChannels;\n\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n const dyVals = backend.data.get(dy.dataId).values as TypedArray;\n const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n for (let wR = 0; wR < filterHeight; ++wR) {\n const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n const yRMax = Math.min(\n convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n\n for (let wC = 0; wC < filterWidth; ++wC) {\n const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n const yCMax = Math.min(\n convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n const d1 = Math.trunc(d2 / chMul);\n const dm = d2 % chMul;\n\n let dotProd = 0;\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let yR = yRMin; yR < yRMax; ++yR) {\n const xR = wR + yR * strideHeight - topPad;\n for (let yC = yCMin; yC < yCMax; ++yC) {\n const xC = wC + yC * strideWidth - leftPad;\n dotProd += (xBuf.get(b, xR, xC, d1) as number) *\n (dyBuf.get(b, yR, yC, d2) as number);\n }\n }\n }\n dW.set(dotProd, wR, wC, d1, dm);\n }\n }\n }\n\n return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\n\nexport const depthwiseConv2dNativeBackpropFilterConfig: KernelConfig = {\n kernelName: DepthwiseConv2dNativeBackpropFilter,\n backendName: 'cpu',\n kernelFunc: depthwiseConv2dNativeBackpropFilter as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DepthwiseConv2dNativeBackpropInput, DepthwiseConv2dNativeBackpropInputAttrs, DepthwiseConv2dNativeBackpropInputInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function depthwiseConv2dNativeBackpropInput(args: {\n inputs: DepthwiseConv2dNativeBackpropInputInputs,\n backend: MathBackendCPU,\n attrs: DepthwiseConv2dNativeBackpropInputAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {dy, filter} = inputs;\n const {strides, dilations, pad, dimRoundingMode, inputShape} = attrs;\n\n assertNotComplex([dy, filter], 'depthwiseConv2DNativeBackpropInput');\n\n const dyStrides = util.computeStrides(dy.shape);\n const filterStrides = util.computeStrides(filter.shape);\n\n const convInfo = backend_util.computeConv2DInfo(\n inputShape, filter.shape as [number, number, number, number], strides,\n dilations, pad, dimRoundingMode, true /* depthwise */);\n\n const dx = new TensorBuffer(convInfo.inShape, 'float32');\n const dxValues = dx.values;\n const [dxS0, dxS1, dxS2] = dx.strides;\n const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n const [dyS0, dyS1, dyS2] = dyStrides;\n const fltValues = backend.data.get(filter.dataId).values as TypedArray;\n const [fltS0, fltS1, fltS2] = filterStrides;\n const {\n batchSize,\n filterHeight,\n filterWidth,\n inChannels,\n inHeight,\n inWidth,\n outChannels,\n outHeight,\n outWidth,\n strideHeight,\n strideWidth\n } = convInfo;\n const topPad = filterHeight - 1 - convInfo.padInfo.top;\n const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n const chMul = outChannels / inChannels;\n\n for (let b = 0; b < batchSize; ++b) {\n for (let d1 = 0; d1 < inChannels; ++d1) {\n for (let xR = 0; xR < inHeight; ++xR) {\n const xRCorner = xR - topPad;\n const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n const yRMax =\n Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n\n for (let xC = 0; xC < inWidth; ++xC) {\n const xCCorner = xC - leftPad;\n const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n const yCMax =\n Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n let dotProd = 0;\n for (let yR = xRMin; yR < yRMax; ++yR) {\n const wR = yR * strideHeight - xRCorner;\n\n for (let yC = xCMin; yC < yCMax; ++yC) {\n const wC = yC * strideWidth - xCCorner;\n const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n\n for (let dm = 0; dm < chMul; ++dm) {\n const d2 = d1 * chMul + dm;\n const pixel = dyValues[dyOffset + d2];\n const weight = fltValues[fltOffset + dm];\n dotProd += pixel * weight;\n }\n }\n }\n dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n }\n }\n }\n }\n\n return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\n\nexport const depthwiseConv2dNativeBackpropInputConfig: KernelConfig = {\n kernelName: DepthwiseConv2dNativeBackpropInput,\n backendName: 'cpu',\n kernelFunc: depthwiseConv2dNativeBackpropInput as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {buffer, Diag, DiagInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport function diag(args: {inputs: DiagInputs, backend: MathBackendCPU}):\n TensorInfo {\n const {inputs, backend} = args;\n const {x} = inputs;\n\n const xSize = util.sizeFromShape(x.shape);\n\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const outBuf = buffer([xSize, xSize], x.dtype);\n const vals = outBuf.values;\n for (let i = 0; i < xVals.length; i++) {\n vals[i * xSize + i] = xVals[i];\n }\n\n const outShape = [...x.shape, ...x.shape];\n\n return backend.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);\n}\n\nexport const diagConfig: KernelConfig = {\n kernelName: Diag,\n backendName: 'cpu',\n kernelFunc: diag as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Dilation2D, Dilation2DAttrs, Dilation2DInputs, KernelConfig, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport const dilation2dConfig: KernelConfig = {\n kernelName: Dilation2D,\n backendName: 'cpu',\n kernelFunc: ({inputs, backend, attrs}) => {\n const {x, filter} = inputs as Dilation2DInputs;\n const {strides, pad, dilations} = attrs as {} as Dilation2DAttrs;\n const cpuBackend = backend as MathBackendCPU;\n\n const xVals = cpuBackend.data.get(x.dataId).values as TypedArray;\n const xRank = x.shape.length;\n\n const filterVals = cpuBackend.data.get(filter.dataId).values as TypedArray;\n const filterRank = filter.shape.length;\n\n const {\n batchSize,\n inHeight,\n inWidth,\n inChannels,\n outHeight,\n outWidth,\n padInfo,\n strideHeight,\n strideWidth,\n filterHeight,\n filterWidth,\n dilationHeight,\n dilationWidth,\n outShape\n } =\n backend_util.computeDilation2DInfo(\n x.shape as [number, number, number, number],\n filter.shape as [number, number, number], strides, pad,\n 'NHWC' /* dataFormat */, dilations);\n\n const outSize = util.sizeFromShape(outShape);\n const outRank = outShape.length;\n const outputVals = util.getArrayFromDType(x.dtype, outSize);\n\n // Upsampling the input by fill in `dilation size - 1` values between each\n // input value.\n // This implementation follows the TF c++ implementation:\n // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n for (let b = 0; b < batchSize; ++b) {\n for (let hOut = 0; hOut < outHeight; ++hOut) {\n const hBeg = hOut * strideHeight - padInfo.top;\n for (let wOut = 0; wOut < outWidth; ++wOut) {\n const wBeg = wOut * strideWidth - padInfo.left;\n for (let d = 0; d < inChannels; ++d) {\n let curVal = Number.MIN_SAFE_INTEGER;\n for (let h = 0; h < filterHeight; ++h) {\n const hIn = hBeg + h * dilationHeight;\n if (hIn >= 0 && hIn < inHeight) {\n for (let w = 0; w < filterWidth; ++w) {\n const wIn = wBeg + w * dilationWidth;\n if (wIn >= 0 && wIn < inWidth) {\n const xIndex = util.locToIndex(\n [b, hIn, wIn, d], xRank, util.computeStrides(x.shape));\n const filterIndex = util.locToIndex(\n [h, w, d], filterRank,\n util.computeStrides(filter.shape));\n const val = xVals[xIndex] + filterVals[filterIndex];\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n const outputIndex = util.locToIndex(\n [b, hOut, wOut, d], outRank, util.computeStrides(outShape));\n outputVals[outputIndex] = curVal;\n }\n }\n }\n }\n\n const dataId = cpuBackend.write(\n util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n\n return {dataId, shape: outShape, dtype: x.dtype};\n }\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Dilation2DAttrs, Dilation2DBackpropFilter, Tensor3D, Tensor4D, TypedArray, util} from '@tensorflow/tfjs-core';\nimport {KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport const dilation2dBackpropFilterConfig: KernelConfig = {\n kernelName: Dilation2DBackpropFilter,\n backendName: 'cpu',\n kernelFunc: ({inputs, backend, attrs}) => {\n const {x, filter, dy} =\n inputs as {x: Tensor4D, filter: Tensor3D, dy: Tensor4D};\n const {strides, pad, dilations} = attrs as {} as Dilation2DAttrs;\n const cpuBackend = backend as MathBackendCPU;\n\n const $x =\n util.toNestedArray(\n x.shape, cpuBackend.data.get(x.dataId).values as TypedArray) as\n number[][][][];\n\n const $filter = util.toNestedArray(\n filter.shape,\n cpuBackend.data.get(filter.dataId).values as\n TypedArray) as number[][][];\n\n const {\n batchSize,\n inHeight,\n inWidth,\n inChannels,\n outHeight,\n outWidth,\n padInfo,\n strideHeight,\n strideWidth,\n filterHeight,\n filterWidth,\n dilationHeight,\n dilationWidth,\n outShape\n } =\n backend_util.computeDilation2DInfo(\n x.shape as [number, number, number, number],\n filter.shape as [number, number, number], strides, pad,\n 'NHWC' /* dataFormat */, dilations);\n\n util.assert(\n dy.rank === outShape.length,\n () => `Error in ${Dilation2DBackpropFilter}, dy ` +\n `must have the same rank as output ${outShape.length}, but got ` +\n `${dy.rank}`);\n\n const $dy =\n util.toNestedArray(\n outShape, cpuBackend.data.get(dy.dataId).values as TypedArray) as\n number[][][][];\n\n // The computed filter gradients has the same dimensions as the filter:\n // [filterHeight, filterWidth, depth]\n const gradients = util.makeZerosNestedTypedArray(\n filter.shape, filter.dtype) as number[][][];\n\n // In the case of multiple argmax branches, we only back-propagate along the\n // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n // similarly to the max-pooling backward routines.\n // This implementation follows the TF c++ implementation:\n // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n for (let b = 0; b < batchSize; ++b) {\n for (let hOut = 0; hOut < outHeight; ++hOut) {\n const hBeg = hOut * strideHeight - padInfo.top;\n for (let wOut = 0; wOut < outWidth; ++wOut) {\n const wBeg = wOut * strideWidth - padInfo.left;\n for (let d = 0; d < inChannels; ++d) {\n let curVal = Number.MIN_SAFE_INTEGER;\n let hMax = 0;\n let wMax = 0;\n for (let h = 0; h < filterHeight; ++h) {\n const hIn = hBeg + h * dilationHeight;\n if (hIn >= 0 && hIn < inHeight) {\n for (let w = 0; w < filterWidth; ++w) {\n const wIn = wBeg + w * dilationWidth;\n if (wIn >= 0 && wIn < inWidth) {\n const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n if (val > curVal) {\n curVal = val;\n hMax = h;\n wMax = w;\n }\n }\n }\n }\n }\n gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n }\n }\n }\n }\n\n const dataId = cpuBackend.write(\n util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n\n return {dataId, shape: filter.shape, dtype: filter.dtype};\n }\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Dilation2DAttrs, Dilation2DBackpropInput, Tensor3D, Tensor4D, TypedArray, util} from '@tensorflow/tfjs-core';\nimport {KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport const dilation2dBackpropInputConfig: KernelConfig = {\n kernelName: Dilation2DBackpropInput,\n backendName: 'cpu',\n kernelFunc: ({inputs, backend, attrs}) => {\n const {x, filter, dy} =\n inputs as {x: Tensor4D, filter: Tensor3D, dy: Tensor4D};\n const {strides, pad, dilations} = attrs as {} as Dilation2DAttrs;\n const cpuBackend = backend as MathBackendCPU;\n\n const $x =\n util.toNestedArray(\n x.shape, cpuBackend.data.get(x.dataId).values as TypedArray) as\n number[][][][];\n\n const $filter = util.toNestedArray(\n filter.shape,\n cpuBackend.data.get(filter.dataId).values as\n TypedArray) as number[][][];\n\n const {\n batchSize,\n inHeight,\n inWidth,\n inChannels,\n outHeight,\n outWidth,\n padInfo,\n strideHeight,\n strideWidth,\n filterHeight,\n filterWidth,\n dilationHeight,\n dilationWidth,\n outShape\n } =\n backend_util.computeDilation2DInfo(\n x.shape as [number, number, number, number],\n filter.shape as [number, number, number], strides, pad,\n 'NHWC' /* dataFormat */, dilations);\n\n util.assert(\n dy.rank === outShape.length,\n () => `Error in ${Dilation2DBackpropInput}, dy ` +\n `must have the same rank as output ${outShape.length}, but got ` +\n `${dy.rank}`);\n\n const $dy =\n util.toNestedArray(\n outShape, cpuBackend.data.get(dy.dataId).values as TypedArray) as\n number[][][][];\n\n // The computed gradients has the same dimensions as the input:\n // [batch, inputHeight, inputCols, inChannel]\n const gradients =\n util.makeZerosNestedTypedArray(x.shape, x.dtype) as number[][][][];\n\n // In the case of multiple argmax branches, we only back-propagate along the\n // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n // similarly to the max-pooling backward routines.\n // This implementation follows the TF c++ implementation:\n // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n for (let b = 0; b < batchSize; ++b) {\n for (let hOut = 0; hOut < outHeight; ++hOut) {\n const hBeg = hOut * strideHeight - padInfo.top;\n for (let wOut = 0; wOut < outWidth; ++wOut) {\n const wBeg = wOut * strideWidth - padInfo.left;\n for (let d = 0; d < inChannels; ++d) {\n let curVal = Number.MIN_SAFE_INTEGER;\n let hInMax = (hBeg < 0) ? 0 : hBeg;\n let wInMax = (wBeg < 0) ? 0 : wBeg;\n for (let h = 0; h < filterHeight; ++h) {\n const hIn = hBeg + h * dilationHeight;\n if (hIn >= 0 && hIn < inHeight) {\n for (let w = 0; w < filterWidth; ++w) {\n const wIn = wBeg + w * dilationWidth;\n if (wIn >= 0 && wIn < inWidth) {\n const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n if (val > curVal) {\n curVal = val;\n hInMax = hIn;\n wInMax = wIn;\n }\n }\n }\n }\n }\n gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n }\n }\n }\n }\n\n const dataId = cpuBackend.write(\n util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n\n return {dataId, shape: x.shape, dtype: x.dtype};\n }\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {EluGrad, EluGradInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function eluGrad(args: {inputs: EluGradInputs, backend: MathBackendCPU}):\n TensorInfo {\n const {inputs, backend} = args;\n const {dy, y} = inputs;\n\n assertNotComplex([dy, y], 'eluGrad');\n\n const resultValues = new Float32Array(util.sizeFromShape(y.shape));\n const values = backend.data.get(y.dataId).values as TypedArray;\n const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n for (let i = 0; i < values.length; ++i) {\n const v = values[i];\n if (v >= 1) {\n resultValues[i] = dyValues[i];\n } else {\n resultValues[i] = dyValues[i] * (v + 1);\n }\n }\n\n return backend.makeTensorInfo(y.shape, 'float32', resultValues);\n}\n\nexport const eluGradConfig: KernelConfig = {\n kernelName: EluGrad,\n backendName: 'cpu',\n kernelFunc: eluGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Equal, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const equalImpl =\n createSimpleBinaryKernelImpl((a: number, b: number) => (a === b) ? 1 : 0);\nexport const equal =\n binaryKernelFunc(Equal, equalImpl, null /* complexImpl */, 'bool');\n\nexport const equalConfig: KernelConfig = {\n kernelName: Equal,\n backendName: 'cpu',\n kernelFunc: equal\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Erf, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nconst p = backend_util.ERF_P;\nconst a1 = backend_util.ERF_A1;\nconst a2 = backend_util.ERF_A2;\nconst a3 = backend_util.ERF_A3;\nconst a4 = backend_util.ERF_A4;\nconst a5 = backend_util.ERF_A5;\n\nexport const erf = unaryKernelFunc(\n Erf,\n (xi) => {\n const sign = Math.sign(xi);\n const v = Math.abs(xi);\n const t = 1.0 / (1.0 + p * v);\n return sign *\n (1.0 -\n (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n Math.exp(-v * v));\n },\n);\n\nexport const erfConfig: KernelConfig = {\n kernelName: Erf,\n backendName: 'cpu',\n kernelFunc: erf,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ExpandDims, ExpandDimsAttrs, ExpandDimsInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {reshape} from './Reshape';\n\nexport function expandDims(args: {\n inputs: ExpandDimsInputs,\n backend: MathBackendCPU,\n attrs: ExpandDimsAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {input} = inputs;\n const {dim} = attrs;\n\n const inputRank = input.shape.length;\n const newShape = input.shape.slice();\n let $dim = dim;\n if (dim < 0) {\n // Negative value is counted from the tail of rank.\n util.assert(\n -(inputRank + 1) <= dim,\n () => `Axis must be in the interval [${- (inputRank + 1)}, ${\n inputRank}]`);\n $dim = inputRank + dim + 1;\n }\n newShape.splice($dim, 0, 1);\n\n return reshape({inputs: {x: input}, backend, attrs: {shape: newShape}});\n}\n\nexport const expandDimsConfig: KernelConfig = {\n kernelName: ExpandDims,\n backendName: 'cpu',\n kernelFunc: expandDims as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, RealDiv} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const realDivImpl =\n createSimpleBinaryKernelImpl((a: number, b: number) => a / b);\nexport const div = binaryKernelFunc(RealDiv, realDivImpl);\n\nexport const realDivConfig: KernelConfig = {\n kernelName: RealDiv,\n backendName: 'cpu',\n kernelFunc: div\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Tensor, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {add} from '../kernels/Add';\nimport {complex} from '../kernels/Complex';\nimport {concat} from '../kernels/Concat';\nimport {identity} from '../kernels/Identity';\nimport {imag} from '../kernels/Imag';\nimport {multiply} from '../kernels/Multiply';\nimport {real} from '../kernels/Real';\nimport {realDivConfig} from '../kernels/RealDiv';\nimport {slice} from '../kernels/Slice';\nimport {sub} from '../kernels/Sub';\n\n/**\n * Calculate FFT of inner most elements of batch tensor.\n */\nexport function fftBatch(\n input: TensorInfo, inverse: boolean,\n cpuBackend: MathBackendCPU): TensorInfo {\n const inputShape = input.shape;\n const batch = inputShape[0];\n const innerDim = inputShape[1];\n\n const inputVals = cpuBackend.data.get(input.dataId);\n\n const real2D = inputVals.complexTensorInfos.real;\n const imag2D = inputVals.complexTensorInfos.imag;\n\n // Collects real and imaginary values separately.\n const resultShape = [batch, innerDim];\n const resultSize = util.sizeFromShape(resultShape);\n const resultReal = util.getTypedArrayFromDType('float32', resultSize);\n const resultImag = util.getTypedArrayFromDType('float32', resultSize);\n\n for (let b = 0; b < batch; b++) {\n // TODO: Support slice ops for complex type.\n const r = slice({\n inputs: {x: real2D},\n backend: cpuBackend,\n attrs: {begin: [b, 0], size: [1, innerDim]}\n });\n const i = slice({\n inputs: {x: imag2D},\n backend: cpuBackend,\n attrs: {begin: [b, 0], size: [1, innerDim]}\n });\n\n const input = complex({inputs: {real: r, imag: i}, backend: cpuBackend});\n\n // Run FFT by batch element.\n const {real, imag} = fftImpl(input, inverse, cpuBackend);\n const res = backend_util.mergeRealAndImagArrays(real, imag);\n\n for (let d = 0; d < innerDim; d++) {\n const c = backend_util.getComplexWithIndex(res, d);\n resultReal[b * innerDim + d] = c.real;\n resultImag[b * innerDim + d] = c.imag;\n }\n\n cpuBackend.disposeIntermediateTensorInfo(r);\n cpuBackend.disposeIntermediateTensorInfo(i);\n cpuBackend.disposeIntermediateTensorInfo(input);\n }\n\n const $realInfo: TensorInfo =\n cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);\n const $imagInfo: TensorInfo =\n cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);\n\n const result = complex(\n {inputs: {real: $realInfo, imag: $imagInfo}, backend: cpuBackend});\n\n cpuBackend.disposeIntermediateTensorInfo($realInfo);\n cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n\n return result;\n}\n\nexport function fftImpl(\n input: TensorInfo, inverse: boolean,\n cpuBackend: MathBackendCPU): {real: Float32Array, imag: Float32Array} {\n const inputSize = util.sizeFromShape(input.shape);\n\n const inputVals = cpuBackend.data.get(input.dataId);\n\n const realVals =\n cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values as\n Float32Array;\n\n const imagVals =\n cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values as\n Float32Array;\n\n if (isExponentOf2(inputSize)) {\n const result =\n fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n\n const resultShape = [input.shape[0], input.shape[1]];\n\n if (inverse) {\n const realInfo: TensorInfo =\n cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);\n const imagInfo: TensorInfo =\n cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);\n\n const sizeInfo: TensorInfo = cpuBackend.makeTensorInfo(\n [], 'float32',\n util.createScalarValue(inputSize as {} as 'float32', 'float32'));\n const sizeInfoCopy =\n identity({inputs: {x: sizeInfo}, backend: cpuBackend});\n\n const divRealInfo =\n realDivConfig.kernelFunc(\n {inputs: {a: realInfo, b: sizeInfo}, backend: cpuBackend}) as\n TensorInfo;\n const divImagInfo =\n realDivConfig.kernelFunc(\n {inputs: {a: imagInfo, b: sizeInfoCopy}, backend: cpuBackend}) as\n TensorInfo;\n\n const divRealVals =\n cpuBackend.data.get(divRealInfo.dataId).values as Float32Array;\n const divImagVals =\n cpuBackend.data.get(divImagInfo.dataId).values as Float32Array;\n\n cpuBackend.disposeIntermediateTensorInfo(realInfo);\n cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n\n return {real: divRealVals, imag: divImagVals};\n }\n\n return result;\n } else {\n const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n\n const rawOutput =\n fourierTransformByMatmul(data, inputSize, inverse) as Float32Array;\n\n return backend_util.splitRealAndImagArrays(rawOutput);\n }\n}\n\nfunction isExponentOf2(size: number): boolean {\n return (size & size - 1) === 0;\n}\n\n// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\nfunction fftRadix2(\n realVals: Float32Array, imagVals: Float32Array, size: number,\n inverse: boolean,\n cpuBackend: MathBackendCPU): {real: Float32Array, imag: Float32Array} {\n if (size === 1) {\n return {real: realVals, imag: imagVals};\n }\n\n const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n\n const half = size / 2;\n\n const evenComplex = backend_util.complexWithEvenIndex(data);\n\n const evenRealVals = evenComplex.real;\n const evenImagVals = evenComplex.imag;\n\n const evenShape = [evenRealVals.length];\n\n const evenRealInfo =\n cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);\n const evenImagInfo =\n cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);\n\n const evenTensorInfo = complex(\n {inputs: {real: evenRealInfo, imag: evenImagInfo}, backend: cpuBackend});\n\n const oddComplex = backend_util.complexWithOddIndex(data);\n\n const oddRealVals = oddComplex.real;\n const oddImagVals = oddComplex.imag;\n\n const oddShape = [oddRealVals.length];\n\n const oddRealInfo =\n cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);\n const oddImagInfo =\n cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);\n\n const oddTensorInfo = complex(\n {inputs: {real: oddRealInfo, imag: oddImagInfo}, backend: cpuBackend});\n\n // Recursive call for half part of original input.\n const $evenComplex =\n fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n\n const $evenRealVals = $evenComplex.real;\n const $evenImagVals = $evenComplex.imag;\n\n const $evenShape = [$evenRealVals.length];\n\n const $evenRealInfo =\n cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);\n const $evenImagInfo =\n cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);\n\n const $evenTensorInfo = complex({\n inputs: {real: $evenRealInfo, imag: $evenImagInfo},\n backend: cpuBackend\n });\n\n const $oddComplex =\n fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n\n const $oddRealVals = $oddComplex.real;\n const $oddImagVals = $oddComplex.imag;\n\n const $oddShape = [$oddRealVals.length];\n\n const $oddRealInfo =\n cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);\n const $oddImagInfo =\n cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);\n\n const $oddTensorInfo = complex(\n {inputs: {real: $oddRealInfo, imag: $oddImagInfo}, backend: cpuBackend});\n\n const e = backend_util.exponents(size, inverse);\n const eShape = [e.real.length];\n\n const eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);\n const eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);\n\n const complexInfo = complex(\n {inputs: {real: eRealInfo, imag: eImagInfo}, backend: cpuBackend});\n\n const exponentInfo =\n multiply(\n {inputs: {a: complexInfo, b: $oddTensorInfo}, backend: cpuBackend}) as\n TensorInfo;\n\n const addPart = add({\n inputs: {a: $evenTensorInfo, b: exponentInfo},\n backend: cpuBackend\n }) as TensorInfo;\n const subPart = sub({\n inputs: {a: $evenTensorInfo, b: exponentInfo},\n backend: cpuBackend\n }) as TensorInfo;\n\n const addPartReal = real({inputs: {input: addPart}, backend: cpuBackend});\n const subPartReal = real({inputs: {input: subPart}, backend: cpuBackend});\n\n const addPartImag = imag({inputs: {input: addPart}, backend: cpuBackend});\n const subPartImag = imag({inputs: {input: subPart}, backend: cpuBackend});\n\n const $real = concat({\n inputs: [addPartReal as Tensor, subPartReal as Tensor],\n backend: cpuBackend,\n attrs: {axis: 0}\n });\n const $imag = concat({\n inputs: [addPartImag as Tensor, subPartImag as Tensor],\n backend: cpuBackend,\n attrs: {axis: 0}\n });\n\n const $realVals = cpuBackend.data.get($real.dataId).values as Float32Array;\n const $imagVals = cpuBackend.data.get($imag.dataId).values as Float32Array;\n\n cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n cpuBackend.disposeIntermediateTensorInfo(addPart);\n cpuBackend.disposeIntermediateTensorInfo(subPart);\n cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n cpuBackend.disposeIntermediateTensorInfo($real);\n cpuBackend.disposeIntermediateTensorInfo($imag);\n\n return {real: $realVals, imag: $imagVals};\n}\n\n// Calculate fourier transform by multplying sinusoid matrix.\nfunction fourierTransformByMatmul(\n data: TypedArray, size: number, inverse: boolean): TypedArray {\n const ret = new Float32Array(size * 2);\n // TODO: Use matmul instead once it supports complex64 type.\n for (let r = 0; r < size; r++) {\n let real = 0.0;\n let imag = 0.0;\n for (let c = 0; c < size; c++) {\n const e = backend_util.exponent(r * c, size, inverse);\n const term = backend_util.getComplexWithIndex(data as Float32Array, c);\n real += term.real * e.real - term.imag * e.imag;\n imag += term.real * e.imag + term.imag * e.real;\n }\n if (inverse) {\n real /= size;\n imag /= size;\n }\n backend_util.assignToTypedArray(ret, real, imag, r);\n }\n return ret;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {FFT, FFTInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {fftBatch} from '../utils/fft_utils';\nimport {reshape} from './Reshape';\n\nexport function fft(args: {inputs: FFTInputs, backend: MathBackendCPU}):\n TensorInfo {\n const {inputs, backend} = args;\n const {input} = inputs;\n\n const inputSize = util.sizeFromShape(input.shape);\n\n // Collapse all outer dimensions to a single batch dimension.\n const innerDimensionSize = input.shape[input.shape.length - 1];\n const batch = inputSize / innerDimensionSize;\n\n const input2D = reshape({\n inputs: {x: input},\n backend,\n attrs: {shape: [batch, innerDimensionSize]}\n });\n\n const result = fftBatch(input2D, false, backend);\n\n const resultReshaped =\n reshape({inputs: {x: result}, backend, attrs: {shape: input.shape}});\n\n backend.disposeIntermediateTensorInfo(input2D);\n backend.disposeIntermediateTensorInfo(result);\n\n return resultReshaped;\n}\n\nexport const fftConfig: KernelConfig = {\n kernelName: FFT,\n backendName: 'cpu',\n kernelFunc: fft as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, DataValues, Fill, FillAttrs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport function fill(args: {backend: MathBackendCPU, attrs: FillAttrs}):\n TensorInfo {\n const {backend, attrs} = args;\n const {shape, value, dtype} = attrs;\n\n const $dtype = dtype || util.inferDtype(value);\n const values = util.getArrayFromDType($dtype, util.sizeFromShape(shape));\n fillValues(values, value, $dtype);\n\n return backend.makeTensorInfo(shape, $dtype, values);\n}\n\nexport const fillConfig: KernelConfig = {\n kernelName: Fill,\n backendName: 'cpu',\n kernelFunc: fill as {} as KernelFunc\n};\n\nfunction fillValues(\n values: DataValues, value: string|number, dtype: DataType): void {\n if (dtype === 'string') {\n (values as string[]).fill(value as string);\n } else {\n (values as TypedArray).fill(value as number);\n }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, NumericDataType, TypedArray} from '@tensorflow/tfjs-core';\nimport {FlipLeftRight, FlipLeftRightInputs, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport const flipLeftRightConfig: KernelConfig = {\n kernelName: FlipLeftRight,\n backendName: 'cpu',\n kernelFunc: ({inputs, attrs, backend}) => {\n const {image} = inputs as FlipLeftRightInputs;\n const cpuBackend = backend as MathBackendCPU;\n\n const output = util.getTypedArrayFromDType(\n image.dtype as NumericDataType, util.sizeFromShape(image.shape));\n const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n\n const imageVals = cpuBackend.data.get(image.dataId).values as TypedArray;\n\n for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n\n for (let row = 0; row < imageHeight; row++) {\n const rowOffset = row * (imageWidth * numChannels);\n\n for (let col = 0; col < imageWidth; col++) {\n const colOffset = col * numChannels;\n\n for (let channel = 0; channel < numChannels; channel++) {\n const coords = [batch, row, col, channel];\n\n const x = coords[2];\n\n const coordX = Math.round(imageWidth - x);\n const outIdx = batchOffset + rowOffset + colOffset + channel;\n\n let outputValue = imageVals[outIdx];\n // If the coordinate position falls within the image boundaries...\n if (coordX >= 0 && coordX < imageWidth) {\n // set the output to the image value at the coordinate position.\n const rotatedColOffset = coordX * numChannels;\n const imageIdx =\n batchOffset + rowOffset + rotatedColOffset + channel;\n outputValue = imageVals[imageIdx];\n }\n output[outIdx] = outputValue;\n }\n }\n }\n }\n\n const dataId = cpuBackend.write(output, image.shape, image.dtype);\n return {dataId, shape: image.shape, dtype: image.dtype};\n }\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {FloorDiv, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const floorDivImpl =\n createSimpleBinaryKernelImpl((a: number, b: number) => Math.floor(a / b));\nexport const floorDiv =\n binaryKernelFunc(FloorDiv, floorDivImpl, null /* complexImpl */, 'int32');\n\nexport const floorDivConfig: KernelConfig = {\n kernelName: FloorDiv,\n backendName: 'cpu',\n kernelFunc: floorDiv\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {applyActivation} from '../utils/fused_utils';\nimport {add} from './Add';\nimport {conv2D} from './Conv2D';\n\nexport function fusedConv2D(args: {\n inputs: FusedConv2DInputs,\n backend: MathBackendCPU,\n attrs: FusedConv2DAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, filter, bias, preluActivationWeights} = inputs;\n const {\n strides,\n pad,\n dataFormat,\n dilations,\n dimRoundingMode,\n activation,\n leakyreluAlpha\n } = attrs;\n\n let result = conv2D({\n inputs: {x, filter},\n backend,\n attrs: {strides, pad, dataFormat, dilations, dimRoundingMode}\n });\n\n if (bias) {\n const resultOld = result;\n result = add({inputs: {a: result, b: bias}, backend}) as TensorInfo;\n backend.disposeIntermediateTensorInfo(resultOld);\n }\n\n if (activation) {\n const resultOld = result;\n result = applyActivation(\n backend, result, activation, preluActivationWeights, leakyreluAlpha);\n backend.disposeIntermediateTensorInfo(resultOld);\n }\n\n return result;\n}\n\nexport const fusedConv2DConfig: KernelConfig = {\n kernelName: FusedConv2D,\n backendName: 'cpu',\n kernelFunc: fusedConv2D as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {applyActivation} from '../utils/fused_utils';\nimport {add} from './Add';\nimport {depthwiseConv2dNative} from './DepthwiseConv2dNative';\n\nexport function fusedDepthwiseConv2D(args: {\n inputs: FusedDepthwiseConv2DInputs,\n backend: MathBackendCPU,\n attrs: FusedDepthwiseConv2DAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, filter, bias, preluActivationWeights} = inputs;\n const {\n strides,\n pad,\n dataFormat,\n dilations,\n dimRoundingMode,\n activation,\n leakyreluAlpha\n } = attrs;\n\n let result = depthwiseConv2dNative({\n inputs: {x, filter},\n backend,\n attrs: {strides, pad, dataFormat, dilations, dimRoundingMode}\n });\n\n if (bias) {\n const oldResult = result;\n result = add({inputs: {a: result, b: bias}, backend}) as TensorInfo;\n backend.disposeIntermediateTensorInfo(oldResult);\n }\n if (activation) {\n const oldResult = result;\n result = applyActivation(\n backend, result, activation, preluActivationWeights, leakyreluAlpha);\n backend.disposeIntermediateTensorInfo(oldResult);\n }\n\n return result;\n}\n\nexport const fusedDepthwiseConv2DConfig: KernelConfig = {\n kernelName: FusedDepthwiseConv2D,\n backendName: 'cpu',\n kernelFunc: fusedDepthwiseConv2D as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, buffer, GatherNd, GatherNdInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport function gatherNd(\n args: {inputs: GatherNdInputs, backend: MathBackendCPU}): TensorInfo {\n const {inputs, backend} = args;\n const {params, indices} = inputs;\n\n const paramsSize = util.sizeFromShape(params.shape);\n\n const indicesShape = indices.shape;\n const sliceRank = indicesShape[indicesShape.length - 1];\n\n const [resultShape, numSlices, sliceSize, strides] =\n backend_util.prepareAndValidate(params, indices);\n if (numSlices === 0) {\n return backend.makeTensorInfo(resultShape, params.dtype, []);\n }\n\n const outBuf = buffer([numSlices, sliceSize], params.dtype);\n const indicesData = backend.data.get(indices.dataId).values as TypedArray;\n const paramsData = backend.data.get(params.dataId).values as TypedArray;\n\n for (let i = 0; i < numSlices; i++) {\n const index = [];\n let flattenIndex = 0;\n for (let j = 0; j < sliceRank; j++) {\n const dim = indicesData[i * sliceRank + j];\n flattenIndex += dim * strides[j];\n index.push(dim);\n }\n if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {\n throw new Error(\n `Invalid indices: ${index} does not index into ${params.shape}`);\n }\n\n for (let k = 0; k < sliceSize; k++) {\n outBuf.values[i * sliceSize + k] =\n paramsData[flattenIndex * sliceSize + k];\n }\n }\n\n return backend.makeTensorInfo(resultShape, outBuf.dtype, outBuf.values);\n}\n\nexport const gatherNdConfig: KernelConfig = {\n kernelName: GatherNd,\n backendName: 'cpu',\n kernelFunc: gatherNd as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, GatherV2, GatherV2Attrs, GatherV2Inputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {gatherV2Impl} from './GatherV2_impl';\nimport {reshape} from './Reshape';\n\nexport function gatherV2(args: {\n inputs: GatherV2Inputs,\n backend: MathBackendCPU,\n attrs: GatherV2Attrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, indices} = inputs;\n const {axis, batchDims} = attrs;\n\n assertNotComplex([x, indices], 'gatherV2');\n\n let $batchDims = batchDims;\n\n if (batchDims == null) {\n $batchDims = 0;\n }\n\n const indicesSize = util.sizeFromShape(indices.shape);\n\n const parsedAxis = util.parseAxisParam(axis, x.shape)[0];\n const shapeInfo = backend_util.segment_util.collectGatherOpShapeInfo(\n x, indices, parsedAxis, $batchDims);\n\n const flattenX = reshape({\n inputs: {x},\n backend,\n attrs: {\n shape: [\n shapeInfo.batchSize, shapeInfo.outerSize, shapeInfo.dimSize,\n shapeInfo.sliceSize\n ]\n }\n });\n\n const flattenIndex = reshape({\n inputs: {x: indices},\n backend,\n attrs: {shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize]}\n });\n\n const flattenOutputShape = [\n shapeInfo.batchSize, shapeInfo.outerSize, indicesSize / shapeInfo.batchSize,\n shapeInfo.sliceSize\n ];\n\n const indicesBuf = backend.bufferSync(flattenIndex);\n const xBuf = backend.bufferSync(flattenX);\n const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape);\n\n backend.disposeIntermediateTensorInfo(flattenX);\n backend.disposeIntermediateTensorInfo(flattenIndex);\n\n return backend.makeTensorInfo(\n shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n}\n\nexport const gatherV2Config: KernelConfig = {\n kernelName: GatherV2,\n backendName: 'cpu',\n kernelFunc: gatherV2 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GreaterEqual, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const greaterEqualImpl =\n createSimpleBinaryKernelImpl((a: number, b: number) => (a >= b) ? 1 : 0);\nexport const greaterEqual = binaryKernelFunc(\n GreaterEqual, greaterEqualImpl, null /* complexImpl */, 'bool');\n\nexport const greaterEqualConfig: KernelConfig = {\n kernelName: GreaterEqual,\n backendName: 'cpu',\n kernelFunc: greaterEqual\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IFFT, IFFTInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {fftBatch} from '../utils/fft_utils';\nimport {reshape} from './Reshape';\n\nexport function ifft(args: {inputs: IFFTInputs, backend: MathBackendCPU}):\n TensorInfo {\n const {inputs, backend} = args;\n const {input} = inputs;\n\n const inputSize = util.sizeFromShape(input.shape);\n\n // Collapse all outer dimensions to a single batch dimension.\n const innerDimensionSize = input.shape[input.shape.length - 1];\n const batch = inputSize / innerDimensionSize;\n\n const input2D = reshape({\n inputs: {x: input},\n backend,\n attrs: {shape: [batch, innerDimensionSize]}\n });\n\n const result = fftBatch(input2D, true, backend);\n\n const resultReshaped =\n reshape({inputs: {x: result}, backend, attrs: {shape: input.shape}});\n\n backend.disposeIntermediateTensorInfo(input2D);\n backend.disposeIntermediateTensorInfo(result);\n\n return resultReshaped;\n}\n\nexport const ifftConfig: KernelConfig = {\n kernelName: IFFT,\n backendName: 'cpu',\n kernelFunc: ifft as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IsFinite, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const isFinite =\n unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, 'bool');\n\nexport const isFiniteConfig: KernelConfig = {\n kernelName: IsFinite,\n backendName: 'cpu',\n kernelFunc: isFinite,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IsInf, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const isInf =\n unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, 'bool');\n\nexport const isInfConfig: KernelConfig = {\n kernelName: IsInf,\n backendName: 'cpu',\n kernelFunc: isInf,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IsNan, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const isNaN =\n unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, 'bool');\n\nexport const isNaNConfig: KernelConfig = {\n kernelName: IsNan,\n backendName: 'cpu',\n kernelFunc: isNaN,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, LessEqual} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const lessEqualImpl =\n createSimpleBinaryKernelImpl((a: number, b: number) => (a <= b) ? 1 : 0);\nexport const lessEqual =\n binaryKernelFunc(LessEqual, lessEqualImpl, null /* complexImpl */, 'bool');\n\nexport const lessEqualConfig: KernelConfig = {\n kernelName: LessEqual,\n backendName: 'cpu',\n kernelFunc: lessEqual\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, LinSpace, LinSpaceAttrs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {linSpaceImpl} from './LinSpace_impl';\n\nexport function linSpace(args: {backend: MathBackendCPU, attrs: LinSpaceAttrs}):\n TensorInfo {\n const {backend, attrs} = args;\n const {start, stop, num} = attrs;\n\n const outVals = linSpaceImpl(start, stop, num);\n\n return backend.makeTensorInfo([outVals.length], 'float32', outVals);\n}\n\nexport const linSpaceConfig: KernelConfig = {\n kernelName: LinSpace,\n backendName: 'cpu',\n kernelFunc: linSpace as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Log1p} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const log1p = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\n\nexport const log1pConfig: KernelConfig = {\n kernelName: Log1p,\n backendName: 'cpu',\n kernelFunc: log1p,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, LogicalAnd} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const logicalAndImpl =\n createSimpleBinaryKernelImpl((a: number, b: number) => a && b);\nexport const logicalAnd = binaryKernelFunc(\n LogicalAnd, logicalAndImpl, null /* complexImpl */, 'bool');\n\nexport const logicalAndConfig: KernelConfig = {\n kernelName: LogicalAnd,\n backendName: 'cpu',\n kernelFunc: logicalAnd\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, LogicalNot} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const logicalNot =\n unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, 'bool');\n\nexport const logicalNotConfig: KernelConfig = {\n kernelName: LogicalNot,\n backendName: 'cpu',\n kernelFunc: logicalNot,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, LogicalOr} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const logicalOrImpl =\n createSimpleBinaryKernelImpl((a: number, b: number) => a || b);\nexport const logicalOr =\n binaryKernelFunc(LogicalOr, logicalOrImpl, null /* complexImpl */, 'bool');\n\nexport const logicalOrConfig: KernelConfig = {\n kernelName: LogicalOr,\n backendName: 'cpu',\n kernelFunc: logicalOr\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, LRN, LRNAttrs, LRNInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function lRN(\n args: {inputs: LRNInputs, backend: MathBackendCPU, attrs: LRNAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {depthRadius, bias, alpha, beta} = attrs;\n\n assertNotComplex(x, 'LRN');\n\n const channels = x.shape[3];\n const maxD = channels - 1;\n const xValues = backend.data.get(x.dataId).values as TypedArray;\n const size = util.sizeFromShape(x.shape);\n const result = new Float32Array(size);\n\n function sumAcrossChannels(offset: number) {\n const currentChannel = offset % channels;\n let beginSumOffset =\n offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n const endSumOffset =\n offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);\n\n let sum = 0.0;\n for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n const z = xValues[beginSumOffset];\n sum += z * z;\n }\n return sum;\n }\n\n for (let offset = 0; offset < size; offset++) {\n const sum = sumAcrossChannels(offset);\n const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n result[offset] = val;\n }\n\n return backend.makeTensorInfo(x.shape, x.dtype, result);\n}\n\nexport const lRNConfig: KernelConfig = {\n kernelName: LRN,\n backendName: 'cpu',\n kernelFunc: lRN as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, LRNGrad, LRNGradAttrs, LRNGradInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function lRNGrad(\n args:\n {inputs: LRNGradInputs, backend: MathBackendCPU, attrs: LRNGradAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, y, dy} = inputs;\n const {depthRadius, bias, alpha, beta} = attrs;\n\n assertNotComplex(dy, 'LRNGrad');\n\n const dySize = util.sizeFromShape(dy.shape);\n\n const channels = dy.shape[3];\n const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n const xValues = backend.data.get(x.dataId).values as TypedArray;\n const yValues = backend.data.get(y.dataId).values as TypedArray;\n const result = new Float32Array(dySize);\n const size = dySize;\n\n for (let offset = 0; offset < size; offset++) {\n const currentChannel = offset % channels;\n const depthBegin =\n (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n const depthEnd = (offset - currentChannel) +\n Math.min(channels, currentChannel + depthRadius + 1);\n\n let norm = 0;\n for (let k = depthBegin; k < depthEnd; k++) {\n norm += Math.pow(xValues[k], 2);\n }\n norm = alpha * norm + bias;\n\n for (let k = depthBegin; k < depthEnd; k++) {\n let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm;\n if (offset === k) {\n dyi += Math.pow(norm, -beta);\n }\n dyi *= dyValues[offset];\n result[k] += dyi;\n }\n }\n\n return backend.makeTensorInfo(dy.shape, x.dtype, result);\n}\n\nexport const lRNGradConfig: KernelConfig = {\n kernelName: LRNGrad,\n backendName: 'cpu',\n kernelFunc: lRNGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelFunc, Max, MaxAttrs, MaxInputs, TensorInfo} from '@tensorflow/tfjs-core';\nimport {backend_util, KernelConfig} from '@tensorflow/tfjs-core';\nimport {TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nimport {maxImpl} from './Max_impl';\nimport {transposeImpl} from './Transpose_impl';\n\nexport function max(\n args: {inputs: MaxInputs, backend: MathBackendCPU, attrs: MaxAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {reductionIndices, keepDims} = attrs;\n const cpuBackend = backend;\n let xShape = x.shape;\n const xRank = xShape.length;\n\n const origAxes = util.parseAxisParam(reductionIndices, xShape);\n let axes = origAxes;\n const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n let xVals = cpuBackend.data.get(x.dataId).values as TypedArray;\n if (permutedAxes != null) {\n const newShape: number[] = new Array(xRank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = xShape[permutedAxes[i]];\n }\n\n xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n axes = backend_util.getInnerMostAxes(axes.length, xRank);\n\n xShape = newShape;\n }\n\n assertNotComplex(x, 'max');\n backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n const [maxOutShape, reduceShape] =\n backend_util.computeOutAndReduceShapes(xShape, axes);\n\n const reduceSize = util.sizeFromShape(reduceShape);\n\n const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n\n let outShape = maxOutShape;\n if (keepDims) {\n // reshape\n const newShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n outShape = newShape;\n }\n\n return {dataId, shape: outShape, dtype: x.dtype};\n}\n\nexport const maxConfig: KernelConfig = {\n kernelName: Max,\n backendName: 'cpu',\n kernelFunc: max as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {backend_util, KernelConfig, KernelFunc, MaxPool, MaxPoolAttrs, MaxPoolInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {pool} from '../utils/pool_utils';\nimport {identity} from './Identity';\n\nexport function maxPool(\n args:\n {inputs: MaxPoolInputs, backend: MathBackendCPU, attrs: MaxPoolAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n assertNotComplex(x, 'maxPool');\n const {filterSize, strides, pad, dimRoundingMode} = attrs;\n const dilations = 1;\n\n util.assert(\n backend_util.eitherStridesOrDilationsAreOne(strides, dilations),\n () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n\n const convInfo = backend_util.computePool2DInfo(\n x.shape as [number, number, number, number], filterSize, strides,\n dilations, pad, dimRoundingMode);\n let res: TensorInfo;\n\n if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n res = identity({inputs: {x}, backend});\n } else {\n const xValues = backend.data.get(x.dataId).values as TypedArray;\n const strides = util.computeStrides(x.shape);\n const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');\n res = backend.makeTensorInfo(\n convInfo.outShape, x.dtype, buffer.values as TypedArray);\n }\n return res;\n}\n\nexport const maxPoolConfig: KernelConfig = {\n kernelName: MaxPool,\n backendName: 'cpu',\n kernelFunc: maxPool as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, MaxPool3D, MaxPool3DAttrs, MaxPool3DInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {pool3d} from '../utils/pool_utils';\n\nexport function maxPool3D(args: {\n inputs: MaxPool3DInputs,\n backend: MathBackendCPU,\n attrs: MaxPool3DAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {filterSize, strides, pad, dimRoundingMode, dataFormat} = attrs;\n\n assertNotComplex(x, 'maxPool3d');\n\n const convInfo = backend_util.computePool3DInfo(\n x.shape as [number, number, number, number, number], filterSize, strides,\n 1 /* dilations */, pad, dimRoundingMode, dataFormat);\n\n const xValues = backend.data.get(x.dataId).values as TypedArray;\n const outBuf = pool3d(\n xValues, x.shape, x.dtype, util.computeStrides(x.shape), convInfo, 'max');\n\n return backend.makeTensorInfo(outBuf.shape, 'float32', outBuf.values);\n}\n\nexport const maxPool3DConfig: KernelConfig = {\n kernelName: MaxPool3D,\n backendName: 'cpu',\n kernelFunc: maxPool3D as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, buffer, KernelConfig, KernelFunc, MaxPool3DGrad, MaxPool3DGradAttrs, MaxPool3DGradInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {maxPool3dPositions} from '../utils/pool_utils';\n\nexport function maxPool3DGrad(args: {\n inputs: MaxPool3DGradInputs,\n backend: MathBackendCPU,\n attrs: MaxPool3DGradAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {dy, input} = inputs;\n const {filterSize, strides, pad, dimRoundingMode} = attrs;\n\n assertNotComplex([dy, input], 'maxPool3DGrad');\n\n const convInfo = backend_util.computePool3DInfo(\n input.shape as [number, number, number, number, number], filterSize,\n strides, 1 /* dilations */, pad, dimRoundingMode);\n\n const inputBuf = backend.bufferSync(input);\n const maxPosBuf = maxPool3dPositions(inputBuf, convInfo);\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const dx = buffer(input.shape, 'float32');\n\n const dyBuf = backend.bufferSync(dy);\n\n for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n // Shader code begins\n const dyDepthCorner = dxDepth - padFront;\n const dyRowCorner = dxRow - padTop;\n const dyColCorner = dxCol - padLeft;\n let dotProd = 0;\n for (let wDepth = 0; wDepth < effectiveFilterDepth;\n wDepth += dilationDepth) {\n const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n Math.floor(dyDepth) !== dyDepth) {\n continue;\n }\n for (let wRow = 0; wRow < effectiveFilterHeight;\n wRow += dilationHeight) {\n const dyRow = (dyRowCorner + wRow) / strideHeight;\n if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n Math.floor(dyRow) !== dyRow) {\n continue;\n }\n for (let wCol = 0; wCol < effectiveFilterWidth;\n wCol += dilationWidth) {\n const dyCol = (dyColCorner + wCol) / strideWidth;\n if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n Math.floor(dyCol) !== dyCol) {\n continue;\n }\n\n const maxPos = effectiveFilterDepth * effectiveFilterHeight *\n effectiveFilterWidth -\n 1 -\n (maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel) as\n number);\n const curPos =\n wDepth * effectiveFilterHeight * effectiveFilterWidth +\n wRow * effectiveFilterWidth + wCol;\n\n const mask = maxPos === curPos ? 1 : 0;\n if (mask === 0) {\n continue;\n }\n\n const pixel =\n dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n dotProd += pixel * mask;\n }\n }\n }\n dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n }\n }\n }\n }\n }\n\n return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\n\nexport const maxPool3DGradConfig: KernelConfig = {\n kernelName: MaxPool3DGrad,\n backendName: 'cpu',\n kernelFunc: maxPool3DGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {backend_util, buffer, KernelConfig, KernelFunc, MaxPoolGrad, MaxPoolGradAttrs, MaxPoolGradInputs, Rank, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {maxPoolPositions} from '../utils/pool_utils';\n\nexport function maxPoolGrad(args: {\n inputs: MaxPoolGradInputs,\n backend: MathBackendCPU,\n attrs: MaxPoolGradAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {dy, input, output} = inputs;\n const x = input;\n assertNotComplex([input, output], 'maxPoolGrad');\n const {filterSize, strides, pad, dimRoundingMode} = attrs;\n\n const convInfo = backend_util.computePool2DInfo(\n x.shape as [number, number, number, number], filterSize, strides,\n 1 /* dilations */, pad, dimRoundingMode);\n const xValues = backend.data.get(x.dataId).values as TypedArray;\n const maxPosBuf = buffer(\n convInfo.outShape, x.dtype,\n maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const dx =\n buffer<Rank.R4>(x.shape as [number, number, number, number], 'float32');\n\n const dyData = backend.data.get(dy.dataId).values as Float32Array;\n const dyBuf = buffer<Rank.R4>(\n dy.shape as [number, number, number, number], 'float32', dyData);\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n // Shader code begins.\n const dyRCorner = dxR - padTop;\n const dyCCorner = dxC - padLeft;\n let dotProd = 0;\n for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n const dyR = (dyRCorner + wR) / strideHeight;\n if (dyR < 0 || dyR >= convInfo.outHeight ||\n Math.floor(dyR) !== dyR) {\n continue;\n }\n for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n const dyC = (dyCCorner + wC) / strideWidth;\n if (dyC < 0 || dyC >= convInfo.outWidth ||\n Math.floor(dyC) !== dyC) {\n continue;\n }\n const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -\n (maxPosBuf.get(b, dyR, dyC, d) as number);\n const curPos = wR * effectiveFilterWidth + wC;\n\n const mask = maxPos === curPos ? 1 : 0;\n if (mask === 0) {\n continue;\n }\n\n const pixel = dyBuf.get(b, dyR, dyC, d);\n dotProd += pixel * mask;\n }\n }\n dx.set(dotProd, b, dxR, dxC, d);\n }\n }\n }\n }\n return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\n\nexport const maxPoolGradConfig: KernelConfig = {\n kernelName: MaxPoolGrad,\n backendName: 'cpu',\n kernelFunc: maxPoolGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {MaxPoolWithArgmax, MaxPoolWithArgmaxAttrs, MaxPoolWithArgmaxInputs} from '@tensorflow/tfjs-core';\nimport {backend_util, KernelConfig, TypedArray} from '@tensorflow/tfjs-core';\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nimport {maxPoolWithArgmaxImpl} from './MaxPoolWithArgmax_impl';\n\nexport const maxPoolWithArgmaxConfig: KernelConfig = {\n kernelName: MaxPoolWithArgmax,\n backendName: 'cpu',\n kernelFunc: ({inputs, attrs, backend}) => {\n const {x} = inputs as MaxPoolWithArgmaxInputs;\n const {filterSize, strides, pad, includeBatchInIndex} =\n attrs as {} as MaxPoolWithArgmaxAttrs;\n const cpuBackend = backend as MathBackendCPU;\n assertNotComplex(x, 'MaxPoolWithArgmax');\n\n const values = cpuBackend.data.get(x.dataId).values as TypedArray;\n const convInfo = backend_util.computePool2DInfo(\n x.shape as [number, number, number, number], filterSize, strides,\n [1, 1], pad);\n const [pooled, indexes] = maxPoolWithArgmaxImpl(\n values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n\n const pooledDataId =\n cpuBackend.write(pooled as Float32Array, convInfo.outShape, x.dtype);\n const indexesDataId =\n cpuBackend.write(indexes as Int32Array, convInfo.outShape, x.dtype);\n return [\n {dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype},\n {dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32'}\n ];\n }\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {backend_util, DataType, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {maxPoolPositions, pool} from '../utils/pool_utils';\nexport function maxPoolWithArgmaxImpl(\n xValues: TypedArray, xShape: number[], dtype: DataType,\n includeBatchInIndex: boolean, convInfo: backend_util.Conv2DInfo) {\n const strides = util.computeStrides(xShape);\n const maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');\n const maxPositions = maxPoolPositions(\n xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n\n return [maxPools.values, maxPositions.values];\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, Sum, SumAttrs, SumInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {zeros} from '../utils/zeros_impl';\nimport {cast} from './Cast';\nimport {identity} from './Identity';\nimport {reshape} from './Reshape';\nimport {transpose} from './Transpose';\n\nexport function sum(\n args: {inputs: SumInputs, backend: MathBackendCPU, attrs: SumAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {axis, keepDims} = attrs;\n\n assertNotComplex(x, 'sum');\n\n let $x;\n if (x.dtype === 'bool') {\n $x = cast({inputs: {x}, backend, attrs: {dtype: 'int32'}});\n } else {\n $x = identity({inputs: {x}, backend});\n }\n\n const xRank = $x.shape.length;\n const axes = util.parseAxisParam(axis, $x.shape);\n const permutation = backend_util.getAxesPermutation(axes, xRank);\n\n let reductionAxes = axes;\n let permutedX = $x;\n if (permutation != null) {\n permutedX =\n transpose({inputs: {x: $x}, backend, attrs: {perm: permutation}});\n reductionAxes = backend_util.getInnerMostAxes(reductionAxes.length, xRank);\n }\n\n backend_util.assertAxesAreInnerMostDims(\n 'sum', reductionAxes, permutedX.shape.length);\n\n const [outShape, reduceShape] =\n backend_util.computeOutAndReduceShapes(permutedX.shape, reductionAxes);\n const resultDtype = backend_util.upcastType(permutedX.dtype, 'int32');\n let result = zeros(backend, outShape, resultDtype);\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = backend.data.get(result.dataId).values as TypedArray;\n\n const aVals = backend.data.get(permutedX.dataId).values as TypedArray;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let sum = 0;\n for (let j = 0; j < reduceSize; ++j) {\n sum += aVals[offset + j];\n }\n vals[i] = sum;\n }\n\n if (keepDims) {\n const newShape = backend_util.expandShapeToKeepDim(result.shape, axes);\n const oldResult = result;\n result = reshape({inputs: {x: result}, backend, attrs: {shape: newShape}});\n backend.disposeIntermediateTensorInfo(oldResult);\n }\n\n backend.disposeIntermediateTensorInfo($x);\n\n if (permutation != null) {\n backend.disposeIntermediateTensorInfo(permutedX);\n }\n\n return result;\n}\n\nexport const sumConfig: KernelConfig = {\n kernelName: Sum,\n backendName: 'cpu',\n kernelFunc: sum as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, Mean, MeanAttrs, MeanInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {cast} from './Cast';\nimport {div} from './RealDiv';\nimport {sum} from './Sum';\n\nexport function mean(\n args: {inputs: MeanInputs, backend: MathBackendCPU, attrs: MeanAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {axis, keepDims} = attrs;\n\n const axes = util.parseAxisParam(axis, x.shape);\n const shapes = backend_util.computeOutAndReduceShapes(x.shape, axes);\n const reduceShape = shapes[1];\n const reduceSize = util.sizeFromShape(reduceShape);\n const toDispose = [];\n const reduceSizeScalar =\n backend.makeTensorInfo([], 'float32', new Float32Array([reduceSize]));\n toDispose.push(reduceSizeScalar);\n\n const $x = cast({inputs: {x}, backend, attrs: {dtype: 'float32'}});\n toDispose.push($x);\n\n const res =\n div({inputs: {a: $x, b: reduceSizeScalar}, backend}) as TensorInfo;\n toDispose.push(res);\n\n const result = sum({inputs: {x: res}, backend, attrs: {axis, keepDims}});\n\n toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));\n\n return result;\n}\n\nexport const meanConfig: KernelConfig = {\n kernelName: Mean,\n backendName: 'cpu',\n kernelFunc: mean as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, Min, MinAttrs, MinInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {reshape} from './Reshape';\nimport {transpose} from './Transpose';\n\nexport function min(\n args: {inputs: MinInputs, backend: MathBackendCPU, attrs: MinAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {axis, keepDims} = attrs;\n\n assertNotComplex(x, 'min');\n\n const origAxes = util.parseAxisParam(axis, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n if (permutedAxes != null) {\n $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});\n axes = backend_util.getInnerMostAxes(axes.length, x.shape.length);\n }\n\n backend_util.assertAxesAreInnerMostDims('min', axes, $x.shape.length);\n const [outShape, reduceShape] =\n backend_util.computeOutAndReduceShapes($x.shape, axes);\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = util.makeZerosTypedArray(util.sizeFromShape(outShape), $x.dtype);\n\n const aVals = backend.data.get($x.dataId).values as TypedArray;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let min = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (value < min) {\n min = value;\n }\n }\n vals[i] = min;\n }\n\n if (permutedAxes != null) {\n backend.disposeIntermediateTensorInfo($x);\n }\n\n const result = backend.makeTensorInfo(outShape, $x.dtype, vals);\n\n if (keepDims) {\n const expandedShape = backend_util.expandShapeToKeepDim(outShape, origAxes);\n const reshapedResult =\n reshape({inputs: {x: result}, backend, attrs: {shape: expandedShape}});\n\n backend.disposeIntermediateTensorInfo(result);\n\n return reshapedResult;\n }\n\n return result;\n}\n\nexport const minConfig: KernelConfig = {\n kernelName: Min,\n backendName: 'cpu',\n kernelFunc: min as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, MirrorPad, MirrorPadAttrs, MirrorPadInputs, NumericDataType, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function mirrorPad(args: {\n inputs: MirrorPadInputs,\n backend: MathBackendCPU,\n attrs: MirrorPadAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {paddings, mode} = attrs;\n\n assertNotComplex(x, 'mirrorPad');\n\n const outShape = paddings.map(\n (p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n\n const start = paddings.map(p => p[0]);\n const end = paddings.map((p, i) => p[0] + x.shape[i]);\n const offset = mode === 'reflect' ? 0 : 1;\n\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const xRank = x.shape.length;\n const xStrides = util.computeStrides(x.shape);\n\n const resultSize = util.sizeFromShape(outShape);\n const resultRank = outShape.length;\n const resultStrides = util.computeStrides(outShape);\n const resVals =\n util.getTypedArrayFromDType(x.dtype as NumericDataType, resultSize);\n\n for (let i = 0; i < resultSize; i++) {\n let coords = util.indexToLoc(i, resultRank, resultStrides);\n for (let i = 0; i < resultRank; i++) {\n if (coords[i] < start[i]) {\n coords[i] = start[i] * 2 - coords[i] - offset;\n } else if (coords[i] >= end[i]) {\n coords[i] = (end[i] - 1) * 2 - coords[i] + offset;\n }\n }\n coords = coords.map((c, i) => c - start[i]);\n\n const inIndex = util.locToIndex(coords, xRank, xStrides);\n\n resVals[i] = xVals[inIndex];\n }\n\n const outId = backend.write(resVals, outShape, x.dtype);\n\n return {dataId: outId, shape: outShape, dtype: x.dtype};\n}\n\nexport const mirrorPadConfig: KernelConfig = {\n kernelName: MirrorPad,\n backendName: 'cpu',\n kernelFunc: mirrorPad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Mod} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const modImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => {\n const rem = aValue % bValue;\n if ((aValue < 0 && bValue < 0) || (aValue >= 0 && bValue >= 0)) {\n return rem;\n } else {\n return (rem + bValue) % bValue;\n }\n}));\n\nexport const mod = binaryKernelFunc(Mod, modImpl);\n\nexport const modConfig: KernelConfig = {\n kernelName: Mod,\n backendName: 'cpu',\n kernelFunc: mod\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, Softmax, SoftmaxAttrs, SoftmaxInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nimport {exp} from './Exp';\nimport {max} from './Max';\nimport {div} from './RealDiv';\nimport {reshape} from './Reshape';\nimport {sub} from './Sub';\nimport {sum} from './Sum';\n\nexport function softmax(\n args:\n {inputs: SoftmaxInputs, backend: MathBackendCPU, attrs: SoftmaxAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {logits} = inputs;\n const {dim} = attrs;\n\n const logitsRank = logits.shape.length;\n\n let $dim = dim;\n if ($dim === -1) {\n $dim = logitsRank - 1;\n }\n if ($dim !== logitsRank - 1) {\n throw Error(\n 'Softmax along a non-last dimension is not yet supported. ' +\n `Logits was rank ${logitsRank} and dim was ${$dim}`);\n }\n\n const axes = util.parseAxisParam([$dim], logits.shape);\n const maxLogit = max({\n inputs: {x: logits},\n backend,\n attrs: {reductionIndices: axes, keepDims: false}\n });\n const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n\n const maxLogitReshaped =\n reshape({inputs: {x: maxLogit}, backend, attrs: {shape: expandedShape}});\n const a =\n sub({inputs: {a: logits, b: maxLogitReshaped}, backend}) as TensorInfo;\n const b = exp({inputs: {x: a}, backend}) as TensorInfo;\n const sumExp =\n sum({inputs: {x: b}, backend, attrs: {axis: axes, keepDims: false}});\n const sumReshaped =\n reshape({inputs: {x: sumExp}, backend, attrs: {shape: expandedShape}});\n\n const result = div({inputs: {a: b, b: sumReshaped}, backend}) as TensorInfo;\n\n backend.disposeIntermediateTensorInfo(maxLogit);\n backend.disposeIntermediateTensorInfo(maxLogitReshaped);\n backend.disposeIntermediateTensorInfo(a);\n backend.disposeIntermediateTensorInfo(b);\n backend.disposeIntermediateTensorInfo(sumExp);\n backend.disposeIntermediateTensorInfo(sumReshaped);\n\n return result;\n}\n\nexport const softmaxConfig: KernelConfig = {\n kernelName: Softmax,\n backendName: 'cpu',\n kernelFunc: softmax as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Multinomial, MultinomialAttrs, MultinomialInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nimport {softmax} from './Softmax';\n\nexport function multinomial(args: {\n inputs: MultinomialInputs,\n backend: MathBackendCPU,\n attrs: MultinomialAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {logits} = inputs;\n const {numSamples, seed, normalized} = attrs;\n\n assertNotComplex(logits, 'multinomial');\n\n const probabilities = normalized ?\n logits :\n softmax({inputs: {logits}, backend, attrs: {dim: -1}});\n\n const batchSize = probabilities.shape[0];\n const numEvents = probabilities.shape[1];\n const probVals = backend.data.get(probabilities.dataId).values as TypedArray;\n const resShape = [batchSize, numSamples];\n const resVals =\n util.makeZerosTypedArray(util.sizeFromShape(resShape), 'int32');\n\n for (let b = 0; b < batchSize; ++b) {\n const offset = b * numEvents;\n // The cdf won't include the last event. It will be implicit if no other\n // event happened.\n const cdf = new Float32Array(numEvents - 1);\n cdf[0] = probVals[offset];\n for (let event = 1; event < cdf.length; ++event) {\n cdf[event] = cdf[event - 1] + probVals[offset + event];\n }\n\n const random = seedrandom.alea(seed.toString());\n const outOffset = b * numSamples;\n for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n const r = random();\n\n // Assume last event happened by default.\n resVals[outOffset + sampleId] = cdf.length;\n\n for (let event = 0; event < cdf.length; event++) {\n if (r < cdf[event]) {\n resVals[outOffset + sampleId] = event;\n break;\n }\n }\n }\n }\n\n if (!normalized) {\n backend.disposeIntermediateTensorInfo(probabilities);\n }\n\n return backend.makeTensorInfo(resShape, 'int32', resVals);\n}\n\nexport const multinomialConfig: KernelConfig = {\n kernelName: Multinomial,\n backendName: 'cpu',\n kernelFunc: multinomial as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV3, NonMaxSuppressionV3Attrs, NonMaxSuppressionV3Inputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nconst nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function nonMaxSuppressionV3(args: {\n inputs: NonMaxSuppressionV3Inputs,\n backend: MathBackendCPU,\n attrs: NonMaxSuppressionV3Attrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {boxes, scores} = inputs;\n const {maxOutputSize, iouThreshold, scoreThreshold} = attrs;\n\n assertNotComplex(boxes, 'NonMaxSuppression');\n\n const boxesVals = backend.data.get(boxes.dataId).values as TypedArray;\n const scoresVals = backend.data.get(scores.dataId).values as TypedArray;\n\n const {selectedIndices} = nonMaxSuppressionV3Impl(\n boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n\n return backend.makeTensorInfo(\n [selectedIndices.length], 'int32', new Int32Array(selectedIndices));\n}\n\nexport const nonMaxSuppressionV3Config: KernelConfig = {\n kernelName: NonMaxSuppressionV3,\n backendName: 'cpu',\n kernelFunc: nonMaxSuppressionV3 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV4, NonMaxSuppressionV4Attrs, NonMaxSuppressionV4Inputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function nonMaxSuppressionV4(args: {\n inputs: NonMaxSuppressionV4Inputs,\n backend: MathBackendCPU,\n attrs: NonMaxSuppressionV4Attrs\n}): [TensorInfo, TensorInfo] {\n const {inputs, backend, attrs} = args;\n const {boxes, scores} = inputs;\n const {maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize} =\n attrs;\n\n assertNotComplex(boxes, 'NonMaxSuppressionPadded');\n\n const boxesVals = backend.data.get(boxes.dataId).values as TypedArray;\n const scoresVals = backend.data.get(scores.dataId).values as TypedArray;\n\n const {selectedIndices, validOutputs} = nonMaxSuppressionV4Impl(\n boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold,\n padToMaxOutputSize);\n\n return [\n backend.makeTensorInfo(\n [selectedIndices.length], 'int32', new Int32Array(selectedIndices)),\n backend.makeTensorInfo([], 'int32', new Int32Array([validOutputs]))\n ];\n}\nexport const nonMaxSuppressionV4Config: KernelConfig = {\n kernelName: NonMaxSuppressionV4,\n backendName: 'cpu',\n kernelFunc: nonMaxSuppressionV4 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV5, NonMaxSuppressionV5Attrs, NonMaxSuppressionV5Inputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function nonMaxSuppressionV5(args: {\n inputs: NonMaxSuppressionV5Inputs,\n backend: MathBackendCPU,\n attrs: NonMaxSuppressionV5Attrs\n}): [TensorInfo, TensorInfo] {\n const {inputs, backend, attrs} = args;\n const {boxes, scores} = inputs;\n const {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma} = attrs;\n\n assertNotComplex(boxes, 'NonMaxSuppressionWithScore');\n\n const boxesVals = backend.data.get(boxes.dataId).values as TypedArray;\n const scoresVals = backend.data.get(scores.dataId).values as TypedArray;\n\n const maxOutputSizeVal = maxOutputSize;\n const iouThresholdVal = iouThreshold;\n const scoreThresholdVal = scoreThreshold;\n const softNmsSigmaVal = softNmsSigma;\n\n const {selectedIndices, selectedScores} = nonMaxSuppressionV5Impl(\n boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal,\n scoreThresholdVal, softNmsSigmaVal);\n\n return [\n backend.makeTensorInfo(\n [selectedIndices.length], 'int32', new Int32Array(selectedIndices)),\n backend.makeTensorInfo(\n [selectedScores.length], 'float32', new Float32Array(selectedScores))\n ];\n}\n\nexport const nonMaxSuppressionV5Config: KernelConfig = {\n kernelName: NonMaxSuppressionV5,\n backendName: 'cpu',\n kernelFunc: nonMaxSuppressionV5 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, OneHot, OneHotAttrs, OneHotInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function oneHot(\n args: {inputs: OneHotInputs, backend: MathBackendCPU, attrs: OneHotAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {indices} = inputs;\n const {depth, onValue, offValue} = attrs;\n\n assertNotComplex(indices, 'oneHot');\n\n const indicesSize = util.sizeFromShape(indices.shape);\n\n const res = new Float32Array(indicesSize * depth);\n res.fill(offValue);\n const indicesVal = backend.data.get(indices.dataId).values as TypedArray;\n\n for (let event = 0; event < indicesSize; ++event) {\n if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n res[event * depth + indicesVal[event]] = onValue;\n }\n }\n\n return backend.makeTensorInfo([...indices.shape, depth], 'int32', res);\n}\n\nexport const oneHotConfig: KernelConfig = {\n kernelName: OneHot,\n backendName: 'cpu',\n kernelFunc: oneHot as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, TensorInfo, ZerosLike, ZerosLikeInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nimport {complex} from './Complex';\nimport {fill} from './Fill';\nimport {imag} from './Imag';\nimport {real} from './Real';\n\nexport function zerosLike(\n args: {inputs: ZerosLikeInputs, backend: MathBackendCPU}): TensorInfo {\n const {inputs, backend} = args;\n const {x} = inputs;\n\n if (x.dtype === 'string') {\n throw new Error('zerosLike is not supported for string tensors');\n } else if (x.dtype === 'complex64') {\n const realPart = real({inputs: {input: x}, backend});\n const r = zerosLike({inputs: {x: realPart}, backend});\n const imagPart = imag({inputs: {input: x}, backend});\n const i = zerosLike({inputs: {x: imagPart}, backend});\n\n const result = complex({inputs: {real: r, imag: i}, backend});\n\n backend.disposeIntermediateTensorInfo(realPart);\n backend.disposeIntermediateTensorInfo(r);\n backend.disposeIntermediateTensorInfo(imagPart);\n backend.disposeIntermediateTensorInfo(i);\n\n return result;\n } else {\n return fill({backend, attrs: {shape: x.shape, value: 0, dtype: x.dtype}});\n }\n}\n\nexport const zerosLikeConfig: KernelConfig = {\n kernelName: ZerosLike,\n backendName: 'cpu',\n kernelFunc: zerosLike as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, OnesLike, OnesLikeInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {complex} from './Complex';\nimport {fill} from './Fill';\nimport {imag} from './Imag';\nimport {real} from './Real';\nimport {zerosLike} from './ZerosLike';\n\nexport function onesLike(\n args: {inputs: OnesLikeInputs, backend: MathBackendCPU}): TensorInfo {\n const {inputs, backend} = args;\n const {x} = inputs;\n\n if (x.dtype === 'string') {\n throw new Error('onesLike is not supported for string tensors');\n } else if (x.dtype === 'complex64') {\n const realPart = real({inputs: {input: x}, backend});\n const r = onesLike({inputs: {x: realPart}, backend});\n const imagPart = imag({inputs: {input: x}, backend});\n const i = zerosLike({inputs: {x: imagPart}, backend});\n\n const result = complex({inputs: {real: r, imag: i}, backend});\n\n backend.disposeIntermediateTensorInfo(realPart);\n backend.disposeIntermediateTensorInfo(r);\n backend.disposeIntermediateTensorInfo(imagPart);\n backend.disposeIntermediateTensorInfo(i);\n\n return result;\n } else {\n return fill({backend, attrs: {shape: x.shape, value: 1, dtype: x.dtype}});\n }\n}\n\nexport const onesLikeConfig: KernelConfig = {\n kernelName: OnesLike,\n backendName: 'cpu',\n kernelFunc: onesLike as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Pack, PackAttrs, PackInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {concat} from './Concat';\nimport {expandDims} from './ExpandDims';\n\nexport function pack(\n args: {inputs: PackInputs, backend: MathBackendCPU, attrs: PackAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {axis} = attrs;\n\n if (inputs.length === 1) {\n return expandDims(\n {inputs: {input: inputs[0]}, backend, attrs: {dim: axis}});\n }\n\n const shape = inputs[0].shape;\n const dtype = inputs[0].dtype;\n\n inputs.forEach(t => {\n util.assertShapesMatch(\n shape, t.shape,\n 'All tensors passed to stack must have matching shapes');\n util.assert(\n dtype === t.dtype,\n () => 'All tensors passed to stack must have matching dtypes');\n });\n\n const intermediateTensorInfos: TensorInfo[] = [];\n const expandedTensors = inputs.map(t => {\n const expandedT =\n expandDims({inputs: {input: t}, backend, attrs: {dim: axis}});\n intermediateTensorInfos.push(expandedT);\n return expandedT;\n });\n\n const result = concat({inputs: expandedTensors, backend, attrs: {axis}});\n\n intermediateTensorInfos.forEach(\n t => backend.disposeIntermediateTensorInfo(t));\n\n return result;\n}\n\nexport const packConfig: KernelConfig = {\n kernelName: Pack,\n backendName: 'cpu',\n kernelFunc: pack as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, NumericDataType, PadV2, PadV2Attrs, PadV2Inputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function padV2(\n args: {inputs: PadV2Inputs, backend: MathBackendCPU, attrs: PadV2Attrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {paddings, constantValue} = attrs;\n\n assertNotComplex(x, 'pad');\n\n const outShape = paddings.map(\n (p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n\n const start = paddings.map(p => p[0]);\n\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const xSize = util.sizeFromShape(x.shape);\n const xRank = x.shape.length;\n const xStrides = util.computeStrides(x.shape);\n\n const resultSize = util.sizeFromShape(outShape);\n const resultRank = outShape.length;\n const resultStrides = util.computeStrides(outShape);\n const resVals =\n util.getTypedArrayFromDType(x.dtype as NumericDataType, resultSize);\n\n if (constantValue !== 0) {\n resVals.fill(constantValue);\n }\n\n for (let i = 0; i < xSize; i++) {\n const coords = util.indexToLoc(i, xRank, xStrides);\n const outCoords = coords.map((c, i) => c + start[i]);\n const outIndex = util.locToIndex(outCoords, resultRank, resultStrides);\n\n resVals[outIndex] = xVals[i];\n }\n\n const outId = backend.write(resVals, outShape, x.dtype);\n\n return {dataId: outId, shape: outShape, dtype: x.dtype};\n}\n\nexport const padV2Config: KernelConfig = {\n kernelName: PadV2,\n backendName: 'cpu',\n kernelFunc: padV2 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Pow} from '@tensorflow/tfjs-core';\n\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {binaryKernelFunc} from '../utils/binary_utils';\n\nexport const powImpl =\n createSimpleBinaryKernelImpl((a: number, b: number) => Math.pow(a, b));\nexport const pow = binaryKernelFunc(Pow, powImpl);\n\nexport const powConfig: KernelConfig = {\n kernelName: Pow,\n backendName: 'cpu',\n kernelFunc: pow\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Range, RangeAttrs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {rangeImpl} from './Range_impl';\n\nexport function range(args: {backend: MathBackendCPU, attrs: RangeAttrs}):\n TensorInfo {\n const {backend, attrs} = args;\n const {start, stop, dtype, step} = attrs;\n\n const values = rangeImpl(start, stop, step, dtype);\n return backend.makeTensorInfo([values.length], dtype, values);\n}\n\nexport const rangeConfig: KernelConfig = {\n kernelName: Range,\n backendName: 'cpu',\n kernelFunc: range as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Reciprocal} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const reciprocal = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\n\nexport const reciprocalConfig: KernelConfig = {\n kernelName: Reciprocal,\n backendName: 'cpu',\n kernelFunc: reciprocal,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, ResizeBilinear, ResizeBilinearAttrs, ResizeBilinearInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function resizeBilinear(args: {\n inputs: ResizeBilinearInputs,\n backend: MathBackendCPU,\n attrs: ResizeBilinearAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {images} = inputs;\n const {alignCorners, halfPixelCenters, size} = attrs;\n\n assertNotComplex(images, 'resizeBilinear');\n\n const imagesStrides = util.computeStrides(images.shape);\n const [newHeight, newWidth] = size;\n\n const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n const xValues = backend.data.get(images.dataId).values as TypedArray;\n const result = new Float32Array(\n util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n\n const effectiveInputSize: [number, number] = [\n (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n ];\n\n const effectiveOutputSize: [number, number] = [\n (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n ];\n let outputIdx = 0;\n const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n for (let b = 0; b < batch; b++) {\n for (let r = 0; r < newHeight; r++) {\n let sourceFracRow: number;\n if (halfPixelCenters) {\n sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5;\n } else {\n sourceFracRow = effectiveRowSizeRatio * r;\n }\n\n const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));\n const rowFrac = sourceFracRow - sourceRowFloor;\n const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n const topRowOffset =\n b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];\n const botRowOffset =\n b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];\n for (let c = 0; c < newWidth; c++) {\n let sourceFracCol: number;\n if (halfPixelCenters) {\n sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;\n } else {\n sourceFracCol = effectiveColSizeRatio * c;\n }\n const sourceColFloor = Math.max(0, Math.floor(sourceFracCol));\n const colFrac = sourceFracCol - sourceColFloor;\n const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];\n const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];\n const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];\n const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];\n for (let d = 0; d < numChannels; d++) {\n // Begin shader.\n\n // Compute the fractional index of the source.\n const topLeft = xValues[topLeftOffest + d];\n const bottomLeft = xValues[botLeftOffset + d];\n const topRight = xValues[topRightOffset + d];\n const bottomRight = xValues[botRightOffest + d];\n\n const top = topLeft + (topRight - topLeft) * colFrac;\n const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n const newValue = top + (bottom - top) * rowFrac;\n\n result[outputIdx++] = newValue;\n }\n }\n }\n }\n\n return backend.makeTensorInfo(\n [batch, newHeight, newWidth, numChannels], 'float32', result);\n}\n\nexport const resizeBilinearConfig: KernelConfig = {\n kernelName: ResizeBilinear,\n backendName: 'cpu',\n kernelFunc: resizeBilinear as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, ResizeBilinearGrad, ResizeBilinearGradAttrs, ResizeBilinearGradInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function resizeBilinearGrad(args: {\n inputs: ResizeBilinearGradInputs,\n backend: MathBackendCPU,\n attrs: ResizeBilinearGradAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {images, dy} = inputs;\n const {alignCorners} = attrs;\n\n assertNotComplex([dy, images], 'resizeBilinearGrad');\n\n const imagesStrides = util.computeStrides(images.shape);\n\n const [batch, xHeight, xWidth, depth] = images.shape;\n const [, yHeight, yWidth] = dy.shape;\n\n const output = new Float32Array(batch * xHeight * xWidth * depth);\n\n // In the backwards pass, we want to find the pixels that were generated\n // for each pixel in the input image the forward pass and add the\n // corresponding coefficient from dy to the gradient (with some\n // interpolation).\n\n const effectiveXSize: [number, number] = [\n (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n ];\n\n const effectiveYSize: [number, number] = [\n (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n ];\n\n const heightScale = effectiveXSize[0] / effectiveYSize[0];\n const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n // Reference implementation\n // tslint:disable-next-line:max-line-length\n // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n let offset = 0;\n for (let b = 0; b < batch; b++) {\n const bOffset = b * imagesStrides[0];\n for (let r = 0; r < yHeight; r++) {\n const dxR = r * heightScale;\n const topDxRIndex = Math.floor(dxR);\n const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n\n const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];\n const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];\n\n const dxRLerp = dxR - topDxRIndex;\n const inverseDxRLerp = 1.0 - dxRLerp;\n for (let c = 0; c < yWidth; c++) {\n const dxC = c * widthScale;\n const leftDxCIndex = Math.floor(dxC);\n const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n const dxCLerp = dxC - leftDxCIndex;\n const inverseDxCLerp = 1.0 - dxCLerp;\n\n const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];\n const topRightRCOffset =\n topDxROffset + rightDxCIndex * imagesStrides[2];\n const bottomLeftRCOffset =\n bottomDxROffset + leftDxCIndex * imagesStrides[2];\n const bottomRightRCOffset =\n bottomDxROffset + rightDxCIndex * imagesStrides[2];\n\n const inverseDxRLerpTimesInverseDxCLerp =\n inverseDxRLerp * inverseDxCLerp;\n const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n for (let d = 0; d < depth; d++) {\n const dyVal = dyValues[offset++];\n output[topLeftRCOffset + d] +=\n dyVal * inverseDxRLerpTimesInverseDxCLerp;\n output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;\n output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n }\n }\n }\n }\n\n return backend.makeTensorInfo(\n [batch, xWidth, xHeight, depth], 'float32', output);\n}\n\nexport const resizeBilinearGradConfig: KernelConfig = {\n kernelName: ResizeBilinearGrad,\n backendName: 'cpu',\n kernelFunc: resizeBilinearGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, ResizeNearestNeighbor, ResizeNearestNeighborAttrs, ResizeNearestNeighborInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function resizeNearestNeighbor(args: {\n inputs: ResizeNearestNeighborInputs,\n backend: MathBackendCPU,\n attrs: ResizeNearestNeighborAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {images} = inputs;\n const {alignCorners, halfPixelCenters, size} = attrs;\n\n assertNotComplex(images, 'resizeNearestNeighbor');\n\n const imagesStrides = util.computeStrides(images.shape);\n const [newHeight, newWidth] = size;\n\n const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n const xValues = backend.data.get(images.dataId).values as TypedArray;\n const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n\n const effectiveInputSize: [number, number] = [\n (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n ];\n\n const effectiveOutputSize: [number, number] = [\n (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n ];\n\n const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n\n let outputOffset = 0;\n for (let b = 0; b < batch; b++) {\n const batchOffset = b * imagesStrides[0];\n for (let r = 0; r < newHeight; r++) {\n const sourceFracRow = halfPixelCenters ?\n effectiveRowSizeRatio * (r + 0.5) :\n effectiveRowSizeRatio * r;\n let sourceNearestRow = Math.min(\n oldHeight - 1,\n alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));\n if (halfPixelCenters) {\n sourceNearestRow = Math.max(0, sourceNearestRow);\n }\n const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];\n for (let c = 0; c < newWidth; c++) {\n const sourceFracCol = halfPixelCenters ?\n effectiveColSizeRatio * (c + 0.5) :\n effectiveColSizeRatio * c;\n let sourceNearestCol = Math.min(\n oldWidth - 1,\n alignCorners ? Math.round(sourceFracCol) :\n Math.floor(sourceFracCol));\n if (halfPixelCenters) {\n sourceNearestCol = Math.max(0, sourceNearestCol);\n }\n const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];\n for (let d = 0; d < numChannels; d++) {\n // Begin shader.\n // Compute the fractional index of the source.\n const newVal = xValues[colOffset + d];\n output[outputOffset++] = newVal;\n }\n }\n }\n }\n\n return backend.makeTensorInfo(\n [batch, newHeight, newWidth, numChannels], images.dtype, output);\n}\n\nexport const resizeNearestNeighborConfig: KernelConfig = {\n kernelName: ResizeNearestNeighbor,\n backendName: 'cpu',\n kernelFunc: resizeNearestNeighbor as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, ResizeNearestNeighborGrad, ResizeNearestNeighborGradAttrs, ResizeNearestNeighborGradInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function resizeNearestNeighborGrad(args: {\n inputs: ResizeNearestNeighborGradInputs,\n backend: MathBackendCPU,\n attrs: ResizeNearestNeighborGradAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {images, dy} = inputs;\n const {alignCorners} = attrs;\n\n assertNotComplex([dy, images], 'resizeNearestNeighborGrad');\n\n const imagesStrides = util.computeStrides(images.shape);\n const dyStrides = util.computeStrides(dy.shape);\n const [batch, xHeight, xWidth, depth] = images.shape;\n const [, yHeight, yWidth] = dy.shape;\n\n const output = new Float32Array(batch * xHeight * xWidth * depth);\n const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n\n // In the backwards pass, we want to find the pixels that were generated\n // for each pixel in the input image the forward pass\n\n const effectiveXSize: [number, number] = [\n (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n ];\n\n const effectiveYSize: [number, number] = [\n (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n ];\n\n const heightScale = effectiveXSize[0] / effectiveYSize[0];\n const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n const invHeightScale = 1 / heightScale;\n const invWidthScale = 1 / widthScale;\n\n // This defines the size of the window of values around a particular\n // index in dy that we want to search for contributions to dx.\n const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n // Loop over the output space.\n for (let b = 0; b < batch; b++) {\n const batchOffset = b * imagesStrides[0];\n for (let r = 0; r < xHeight; r++) {\n const rowOffset = batchOffset + r * imagesStrides[1];\n\n // Compute bounds for where in dy we will look\n const startRLerp = Math.floor(r * invHeightScale);\n const startDyR = Math.floor(startRLerp - (winHeight / 2));\n for (let c = 0; c < xWidth; c++) {\n const colOffset = rowOffset + c * imagesStrides[2];\n\n // Compute bounds for where in dy we will look\n const startCLerp = Math.floor(c * invWidthScale);\n const startDyC = Math.floor(startCLerp - (winWidth / 2));\n\n for (let d = 0; d < depth; d++) {\n let accum = 0;\n // loop over dy\n\n for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n const dyR = dyRIndex + startDyR;\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= yHeight) {\n continue;\n }\n\n const dyROffset = batchOffset + dyR * dyStrides[1];\n const sourceFracRow = dyR * heightScale;\n const sourceNearestRow = Math.min(\n xHeight - 1,\n alignCorners ? Math.round(sourceFracRow) :\n Math.floor(sourceFracRow));\n if (r !== sourceNearestRow) {\n continue;\n }\n for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n const dyC = dyCIndex + startDyC;\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= yWidth) {\n continue;\n }\n\n const dyCOffset = dyROffset + dyC * dyStrides[2];\n const sourceFracCol = dyC * widthScale;\n const sourceNearestCol = Math.min(\n xWidth - 1,\n alignCorners ? Math.round(sourceFracCol) :\n Math.floor(sourceFracCol));\n\n if (c === sourceNearestCol) {\n accum += dyValues[dyCOffset + d];\n }\n }\n }\n output[colOffset + d] = accum;\n }\n }\n }\n }\n\n return backend.makeTensorInfo(images.shape, images.dtype, output);\n}\n\nexport const resizeNearestNeighborGradConfig: KernelConfig = {\n kernelName: ResizeNearestNeighborGrad,\n backendName: 'cpu',\n kernelFunc: resizeNearestNeighborGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Reverse, ReverseAttrs, ReverseInputs, TensorBuffer, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {identity} from './Identity';\n\nexport function reverse(\n args:\n {inputs: ReverseInputs, backend: MathBackendCPU, attrs: ReverseAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {dims} = attrs;\n\n assertNotComplex(x, 'reverse');\n\n const xRank = x.shape.length;\n\n const $dims = util.parseAxisParam(dims, x.shape);\n if (xRank === 0) {\n return identity({inputs: {x}, backend});\n }\n\n const outBuf = new TensorBuffer(x.shape, x.dtype);\n const xBuf = backend.bufferSync(x);\n\n for (let i = 0; i < outBuf.size; i++) {\n const outLoc = outBuf.indexToLoc(i);\n const inLoc = outLoc.slice();\n $dims.forEach(d => inLoc[d] = x.shape[d] - 1 - inLoc[d]);\n outBuf.set(xBuf.get(...inLoc), ...outLoc);\n }\n\n return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\n\nexport const reverseConfig: KernelConfig = {\n kernelName: Reverse,\n backendName: 'cpu',\n kernelFunc: reverse as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, NumericDataType, TypedArray} from '@tensorflow/tfjs-core';\nimport {backend_util, RotateWithOffset, RotateWithOffsetAttrs, RotateWithOffsetInputs, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport const rotateWithOffsetConfig: KernelConfig = {\n kernelName: RotateWithOffset,\n backendName: 'cpu',\n kernelFunc: ({inputs, attrs, backend}) => {\n const {image} = inputs as RotateWithOffsetInputs;\n const {radians, fillValue, center} = attrs as {} as RotateWithOffsetAttrs;\n const cpuBackend = backend as MathBackendCPU;\n\n const output = util.getTypedArrayFromDType(\n image.dtype as NumericDataType, util.sizeFromShape(image.shape));\n const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n\n const [centerX, centerY] =\n backend_util.getImageCenter(center, imageHeight, imageWidth);\n const fullOpacityValue = 255;\n\n const sinFactor = Math.sin(radians);\n const cosFactor = Math.cos(radians);\n const imageVals = cpuBackend.data.get(image.dataId).values as TypedArray;\n\n for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n\n for (let row = 0; row < imageHeight; row++) {\n const rowOffset = row * (imageWidth * numChannels);\n\n for (let col = 0; col < imageWidth; col++) {\n const colOffset = col * numChannels;\n\n for (let channel = 0; channel < numChannels; channel++) {\n const coords = [batch, row, col, channel];\n\n const x = coords[2];\n const y = coords[1];\n\n // coordX/coordY are the result of rotating and translating x/y.\n let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n coordX = Math.round(coordX + centerX);\n coordY = Math.round(coordY + centerY);\n\n let outputValue = fillValue;\n if (typeof fillValue !== 'number') {\n if (channel === 3) {\n outputValue = fullOpacityValue;\n } else {\n outputValue = fillValue[channel];\n }\n }\n\n // If the coordinate position falls within the image boundaries...\n if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&\n coordY < imageHeight) {\n // set the output to the image value at the coordinate position.\n const rotatedRowOffset = coordY * (imageWidth * numChannels);\n const rotatedColOffset = coordX * numChannels;\n const imageIdx =\n batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n outputValue = imageVals[imageIdx];\n }\n\n const outIdx = batchOffset + rowOffset + colOffset + channel;\n output[outIdx] = outputValue as number;\n }\n }\n }\n }\n\n const dataId = cpuBackend.write(output, image.shape, image.dtype);\n return {dataId, shape: image.shape, dtype: image.dtype};\n }\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Round} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const round = unaryKernelFunc(Round, (xi) => {\n // The algorithm is based on banker's rounding.\n const base = Math.floor(xi);\n if (xi - base < 0.5) {\n return Math.floor(xi);\n } else if (xi - base > 0.5) {\n return Math.ceil(xi);\n } else {\n if (base % 2.0 === 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n});\n\nexport const roundConfig: KernelConfig = {\n kernelName: Round,\n backendName: 'cpu',\n kernelFunc: round,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {buffer, Rank, ShapeMap, TensorBuffer, TypedArray} from '@tensorflow/tfjs-core';\n\nexport function scatterImpl<R extends Rank>(\n indices: TensorBuffer<R>, updates: TensorBuffer<R>, shape: number[],\n outputSize: number, sliceSize: number, numUpdates: number,\n sliceRank: number, strides: number[], defaultValue: number,\n sumDupeIndices: boolean): TensorBuffer<R> {\n const flattenShape = [outputSize / sliceSize, sliceSize];\n\n const indicesData = indices.values as TypedArray;\n const updatesData = updates.values as TypedArray;\n\n if (outputSize === 0) {\n return buffer(shape as ShapeMap[R], updates.dtype);\n }\n\n const outBuf = buffer(flattenShape, updates.dtype);\n (outBuf.values as TypedArray).fill(defaultValue);\n\n for (let i = 0; i < numUpdates; i++) {\n const index = [];\n let flattenIndex = 0;\n for (let j = 0; j < sliceRank; j++) {\n const dim = indicesData[i * sliceRank + j];\n index.push(dim);\n flattenIndex += dim * strides[j];\n }\n\n if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n }\n\n for (let k = 0; k < sliceSize; k++) {\n if (sumDupeIndices) {\n outBuf.values[flattenIndex * sliceSize + k] +=\n updatesData[i * sliceSize + k];\n } else {\n outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n updatesData[0] :\n updatesData[i * sliceSize + k];\n }\n }\n }\n\n return outBuf as TensorBuffer<R>;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, ScatterNd, ScatterNdAttrs, ScatterNdInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {scatterImpl} from './Scatter_impl';\n\nexport function scatterNd(args: {\n inputs: ScatterNdInputs,\n backend: MathBackendCPU,\n attrs: ScatterNdAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {indices, updates} = inputs;\n const {shape} = attrs;\n\n const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n backend_util.calculateShapes(updates, indices, shape);\n const sumDupeIndices = true;\n\n const indicesBuf = backend.bufferSync(indices);\n const updatesBuf = backend.bufferSync(updates);\n\n const outBuf = scatterImpl(\n indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates,\n sliceRank, strides, 0 /* defaultValue */, sumDupeIndices);\n\n return backend.makeTensorInfo(shape, outBuf.dtype, outBuf.values);\n}\n\nexport const scatterNdConfig: KernelConfig = {\n kernelName: ScatterNd,\n backendName: 'cpu',\n kernelFunc: scatterNd as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Select, SelectInputs, TensorInfo, TypedArray, upcastType, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function select(args: {inputs: SelectInputs, backend: MathBackendCPU}):\n TensorInfo {\n const {inputs, backend} = args;\n const {condition, t, e} = inputs;\n\n assertNotComplex([condition, t, e], 'select');\n const conditionRank = condition.shape.length;\n\n const values = backend.data.get(condition.dataId).values as TypedArray;\n const tValues = backend.data.get(t.dataId).values as TypedArray;\n const eValues = backend.data.get(e.dataId).values as TypedArray;\n const resultDtype = upcastType(t.dtype, e.dtype);\n const newValues =\n util.makeZerosTypedArray(util.sizeFromShape(t.shape), resultDtype);\n\n let index = 0;\n const offset =\n conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ?\n 1 :\n util.sizeFromShape(t.shape.slice(1));\n\n for (let i = 0; i < values.length; i++) {\n for (let j = 0; j < offset; j++) {\n if (values[i] === 1) {\n newValues[index++] = tValues[i];\n } else {\n newValues[index++] = eValues[i];\n }\n }\n }\n\n return backend.makeTensorInfo(t.shape, resultDtype, newValues);\n}\n\nexport const selectConfig: KernelConfig = {\n kernelName: Select,\n backendName: 'cpu',\n kernelFunc: select as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, Selu} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nconst scaleAlpha = backend_util.SELU_SCALEALPHA;\nconst scale = backend_util.SELU_SCALE;\n\nexport const selu = unaryKernelFunc(Selu, (xi) => {\n if (xi >= 0) {\n return scale * xi;\n } else {\n return scaleAlpha * (Math.exp(xi) - 1);\n }\n});\n\nexport const seluConfig: KernelConfig = {\n kernelName: Selu,\n backendName: 'cpu',\n kernelFunc: selu,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Sigmoid} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const sigmoid =\n unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\n\nexport const sigmoidConfig: KernelConfig = {\n kernelName: Sigmoid,\n backendName: 'cpu',\n kernelFunc: sigmoid,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Sign} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const sign = unaryKernelFunc(Sign, (xi) => {\n if (xi < 0) {\n return -1;\n } else if (xi > 0) {\n return 1;\n } else {\n return 0;\n }\n});\n\nexport const signConfig: KernelConfig = {\n kernelName: Sign,\n backendName: 'cpu',\n kernelFunc: sign,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Sin} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const sin = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\n\nexport const sinConfig: KernelConfig = {\n kernelName: Sin,\n backendName: 'cpu',\n kernelFunc: sin,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Sinh} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const sinh = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\n\nexport const sinhConfig: KernelConfig = {\n kernelName: Sinh,\n backendName: 'cpu',\n kernelFunc: sinh,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Softplus} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\n// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n\n// epsilon is the difference between 1.0 and the next representable float.\n// For a single precision 32 bit float this should be 2^-23, see:\n// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\nconst epsilon = 1.1920928955078125e-7;\nconst threshold = Math.log(epsilon) + 2.0;\n\nexport const softplus = unaryKernelFunc(Softplus, (xi) => {\n // Value above which exp(x) may overflow, but softplus(x) == x\n // is within machine epsilon.\n const tooLarge = xi > -threshold;\n\n // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n // is within machine epsilon.\n const tooSmall = xi < threshold;\n\n const expX = Math.exp(xi);\n let result;\n\n if (tooSmall) {\n result = expX;\n } else if (tooLarge) {\n result = xi;\n } else {\n result = Math.log(1.0 + expX);\n }\n return result;\n});\n\nexport const softplusConfig: KernelConfig = {\n kernelName: Softplus,\n backendName: 'cpu',\n kernelFunc: softplus,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, ReshapeAttrs, ReshapeInputs, SpaceToBatchND, SpaceToBatchNDAttrs, SpaceToBatchNDInputs, TensorInfo, TransposeAttrs, TransposeInputs, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nimport {padV2Config} from './PadV2';\nimport {reshape} from './Reshape';\nimport {transpose} from './Transpose';\n\nexport function spaceToBatchND(args: {\n inputs: SpaceToBatchNDInputs,\n backend: MathBackendCPU,\n attrs: SpaceToBatchNDAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {blockShape, paddings} = attrs;\n\n assertNotComplex([x], 'spaceToBatchND');\n\n const prod = util.sizeFromShape(blockShape);\n\n const completePaddings: Array<[number, number]> = [[0, 0]];\n completePaddings.push(...(paddings as Array<[number, number]>));\n\n for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n completePaddings.push([0, 0]);\n }\n\n const paddedX = padV2Config.kernelFunc({\n inputs: {x},\n backend,\n attrs: {paddings: completePaddings, constantValue: 0}\n }) as TensorInfo;\n\n const reshapedPaddedShape =\n backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n\n const permutedReshapedPaddedPermutation = backend_util.getPermuted(\n reshapedPaddedShape.length, blockShape.length, false);\n\n const flattenShape =\n backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n\n const reshapeInputs: ReshapeInputs = {x: paddedX};\n const reshapeAttrs: ReshapeAttrs = {shape: reshapedPaddedShape};\n const paddedXReshaped =\n reshape({inputs: reshapeInputs, backend, attrs: reshapeAttrs});\n\n const transposeInputs: TransposeInputs = {x: paddedXReshaped};\n const transposeAttrs:\n TransposeAttrs = {perm: permutedReshapedPaddedPermutation};\n const paddedXT =\n transpose({inputs: transposeInputs, backend, attrs: transposeAttrs});\n\n const resultReshapeInputs: ReshapeInputs = {x: paddedXT};\n const resultReshapeAttrs: ReshapeAttrs = {shape: flattenShape};\n const result = reshape(\n {inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs});\n\n backend.disposeIntermediateTensorInfo(paddedX);\n backend.disposeIntermediateTensorInfo(paddedXReshaped);\n backend.disposeIntermediateTensorInfo(paddedXT);\n\n return result;\n}\n\nexport const spaceToBatchNDConfig: KernelConfig = {\n kernelName: SpaceToBatchND,\n backendName: 'cpu',\n kernelFunc: spaceToBatchND as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, SparseToDense, SparseToDenseAttrs, SparseToDenseInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {scatterImpl} from './Scatter_impl';\n\nexport function sparseToDense(args: {\n inputs: SparseToDenseInputs,\n backend: MathBackendCPU,\n attrs: SparseToDenseAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {sparseIndices, sparseValues, defaultValue} = inputs;\n const {outputShape} = attrs;\n\n const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n const sumDupeIndices = false;\n\n const indicesBuf = backend.bufferSync(sparseIndices);\n const updatesBuf = backend.bufferSync(sparseValues);\n const $defaultValue =\n backend.data.get(defaultValue.dataId).values[0] as number;\n\n const outBuf = scatterImpl(\n indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates,\n sliceRank, strides, $defaultValue, sumDupeIndices);\n\n return backend.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n}\n\nexport const sparseToDenseConfig: KernelConfig = {\n kernelName: SparseToDense,\n backendName: 'cpu',\n kernelFunc: sparseToDense as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, SplitVAttrs, SplitVInputs} from '@tensorflow/tfjs-core';\nimport {KernelConfig, KernelFunc, SplitV, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {slice} from './Slice';\n\nexport function splitV(\n args: {inputs: SplitVInputs, backend: MathBackendCPU, attrs: SplitVAttrs}):\n TensorInfo[] {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {numOrSizeSplits, axis} = attrs;\n\n const $axis = util.parseAxisParam(axis, x.shape)[0];\n const splitSizes = backend_util.prepareSplitSize(x, numOrSizeSplits, $axis);\n\n const begin = new Array(x.shape.length).fill(0);\n const size = x.shape.slice();\n return splitSizes.map(s => {\n const sliceSize = [...size];\n sliceSize[$axis] = s;\n const sliceT =\n slice({inputs: {x}, backend, attrs: {begin, size: sliceSize}});\n begin[$axis] += s;\n return sliceT;\n });\n}\n\nexport const splitVConfig: KernelConfig = {\n kernelName: SplitV,\n backendName: 'cpu',\n kernelFunc: splitV as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Sqrt} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const sqrt = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\n\nexport const sqrtConfig: KernelConfig = {\n kernelName: Sqrt,\n backendName: 'cpu',\n kernelFunc: sqrt,\n};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Square, SquareInputs} from '@tensorflow/tfjs-core';\nimport {KernelConfig} from '@tensorflow/tfjs-core';\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport const squareConfig: KernelConfig = {\n kernelName: Square,\n backendName: 'cpu',\n kernelFunc: ({inputs, backend}) => {\n const {x} = inputs as SquareInputs;\n const cpuBackend = backend as MathBackendCPU;\n assertNotComplex(x, 'square');\n\n const values = cpuBackend.data.get(x.dataId).values as Float32Array;\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n const value = values[i];\n newValues[i] = value * value;\n }\n const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n return {dataId, shape: x.shape, dtype: x.dtype};\n }\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Step, StepAttrs} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const step = unaryKernelFunc(Step, (xi, attrs) => {\n const stepAttrs = attrs as {} as StepAttrs;\n if (isNaN(xi)) {\n return NaN;\n } else {\n return xi > 0 ? 1 : stepAttrs.alpha;\n }\n});\n\nexport const stepConfig: KernelConfig = {\n kernelName: Step,\n backendName: 'cpu',\n kernelFunc: step,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, slice_util, StridedSlice, StridedSliceAttrs, StridedSliceInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {reshape} from './Reshape';\nimport {slice} from './Slice';\nimport {stridedSliceImpl} from './StridedSlice_impl';\n\nexport function stridedSlice(args: {\n inputs: StridedSliceInputs,\n backend: MathBackendCPU,\n attrs: StridedSliceAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {\n begin,\n end,\n strides,\n beginMask,\n endMask,\n ellipsisMask,\n newAxisMask,\n shrinkAxisMask\n } = attrs;\n\n assertNotComplex(x, 'stridedSlice');\n\n const {nonStrided, $begin, $strides, size, newShape, outShape} =\n slice_util.sliceInfo(\n x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n newAxisMask, shrinkAxisMask);\n\n const $x = reshape({inputs: {x}, backend, attrs: {shape: newShape}});\n\n let result;\n if (nonStrided) {\n const sliced =\n slice({inputs: {x: $x}, backend, attrs: {begin: $begin, size}});\n result = reshape({inputs: {x: sliced}, backend, attrs: {shape: outShape}});\n\n backend.disposeIntermediateTensorInfo(sliced);\n } else if (outShape.some(axis => axis === 0)) {\n result = backend.makeTensorInfo(outShape, x.dtype, []);\n } else {\n const xBuf = backend.bufferSync($x);\n const outBuf = stridedSliceImpl(outShape, xBuf, $strides, $begin);\n\n result = backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n }\n\n const resultReshaped =\n reshape({inputs: {x: result}, backend, attrs: {shape: outShape}});\n\n backend.disposeIntermediateTensorInfo($x);\n backend.disposeIntermediateTensorInfo(result);\n\n return resultReshaped;\n}\n\nexport const stridedSliceConfig: KernelConfig = {\n kernelName: StridedSlice,\n backendName: 'cpu',\n kernelFunc: stridedSlice as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Tan} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const tan = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\n\nexport const tanConfig: KernelConfig = {\n kernelName: Tan,\n backendName: 'cpu',\n kernelFunc: tan,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Tanh} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../utils/unary_utils';\n\nexport const tanh = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\n\nexport const tanhConfig: KernelConfig = {\n kernelName: Tanh,\n backendName: 'cpu',\n kernelFunc: tanh,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, TensorInfo, UnsortedSegmentSum, UnsortedSegmentSumAttrs, UnsortedSegmentSumInputs, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {cast} from './Cast';\nimport {equal} from './Equal';\nimport {expandDims} from './ExpandDims';\nimport {multiply} from './Multiply';\nimport {pack} from './Pack';\nimport {sum} from './Sum';\n\nexport function unsortedSegmentSum(args: {\n inputs: UnsortedSegmentSumInputs,\n backend: MathBackendCPU,\n attrs: UnsortedSegmentSumAttrs\n}): TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x, segmentIds} = inputs;\n const {numSegments} = attrs;\n\n assertNotComplex(x, 'unsortedSegmentSum');\n\n const xRank = x.shape.length;\n const segmentIdsRank = segmentIds.shape.length;\n const res = [];\n const intermediates: TensorInfo[] = [];\n\n // Reshape the segment id's so that they can be broadcast with\n // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n const numIters = xRank - segmentIdsRank;\n let $segmentIds = segmentIds;\n\n for (let i = 0; i < numIters; ++i) {\n const expanded = expandDims(\n {inputs: {input: $segmentIds}, backend, attrs: {dim: i + 1}});\n $segmentIds = expanded;\n intermediates.push(expanded);\n }\n\n for (let i = 0; i < numSegments; ++i) {\n const scalarValue = util.createScalarValue(i as {} as 'int32', 'int32');\n const segmentId = backend.makeTensorInfo([], 'int32', scalarValue);\n const mask =\n equal({inputs: {a: segmentId, b: $segmentIds}, backend}) as TensorInfo;\n const maskCasted =\n cast({inputs: {x: mask}, backend, attrs: {dtype: 'float32'}});\n const mul =\n multiply({inputs: {a: maskCasted, b: x}, backend}) as TensorInfo;\n const sumTensorInfo =\n sum({inputs: {x: mul}, backend, attrs: {axis: 0, keepDims: false}});\n res.push(sumTensorInfo);\n intermediates.push(segmentId);\n intermediates.push(mask);\n intermediates.push(maskCasted);\n intermediates.push(mul);\n intermediates.push(sumTensorInfo);\n }\n\n const result = pack({inputs: res, backend, attrs: {axis: 0}});\n\n intermediates.forEach(t => backend.disposeIntermediateTensorInfo(t));\n\n return result;\n}\n\nexport const unsortedSegmentSumConfig: KernelConfig = {\n kernelName: UnsortedSegmentSum,\n backendName: 'cpu',\n kernelFunc: unsortedSegmentSum as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// We explicitly import the modular kernels so they get registered in the\n// global registry when we compile the library. A modular build would replace\n// the contents of this file and import only the kernels that are needed.\nimport {KernelConfig, registerKernel} from '@tensorflow/tfjs-core';\n\nimport {_fusedMatMulConfig} from './kernels/_FusedMatMul';\nimport {absConfig} from './kernels/Abs';\nimport {acosConfig} from './kernels/Acos';\nimport {acoshConfig} from './kernels/Acosh';\nimport {addConfig} from './kernels/Add';\nimport {addNConfig} from './kernels/AddN';\nimport {allConfig} from './kernels/All';\nimport {anyConfig} from './kernels/Any';\nimport {argMaxConfig} from './kernels/ArgMax';\nimport {argMinConfig} from './kernels/ArgMin';\nimport {asinConfig} from './kernels/Asin';\nimport {asinhConfig} from './kernels/Asinh';\nimport {atanConfig} from './kernels/Atan';\nimport {atan2Config} from './kernels/Atan2';\nimport {atanhConfig} from './kernels/Atanh';\nimport {avgPoolConfig} from './kernels/AvgPool';\nimport {avgPool3DConfig} from './kernels/AvgPool3D';\nimport {avgPool3DGradConfig} from './kernels/AvgPool3DGrad';\nimport {avgPoolGradConfig} from './kernels/AvgPoolGrad';\nimport {batchMatMulConfig} from './kernels/BatchMatMul';\nimport {batchNormConfig} from './kernels/BatchNorm';\nimport {batchToSpaceNDConfig} from './kernels/BatchToSpaceND';\nimport {bincountConfig} from './kernels/Bincount';\nimport {castConfig} from './kernels/Cast';\nimport {ceilConfig} from './kernels/Ceil';\nimport {clipConfig} from './kernels/Clip';\nimport {complexConfig} from './kernels/Complex';\nimport {complexAbsConfig} from './kernels/ComplexAbs';\nimport {concatConfig} from './kernels/Concat';\nimport {conv2DConfig} from './kernels/Conv2D';\nimport {conv2DBackpropFilterConfig} from './kernels/Conv2DBackpropFilter';\nimport {conv2DBackpropInputConfig} from './kernels/Conv2DBackpropInput';\nimport {conv3DConfig} from './kernels/Conv3D';\nimport {conv3DBackpropFilterV2Config} from './kernels/Conv3DBackpropFilterV2';\nimport {conv3DBackpropInputV2Config} from './kernels/Conv3DBackpropInputV2';\nimport {cosConfig} from './kernels/Cos';\nimport {coshConfig} from './kernels/Cosh';\nimport {cropAndResizeConfig} from './kernels/CropAndResize';\nimport {cumsumConfig} from './kernels/Cumsum';\nimport {denseBincountConfig} from './kernels/DenseBincount';\nimport {depthToSpaceConfig} from './kernels/DepthToSpace';\nimport {depthwiseConv2dNativeConfig} from './kernels/DepthwiseConv2dNative';\nimport {depthwiseConv2dNativeBackpropFilterConfig} from './kernels/DepthwiseConv2dNativeBackpropFilter';\nimport {depthwiseConv2dNativeBackpropInputConfig} from './kernels/DepthwiseConv2dNativeBackpropInput';\nimport {diagConfig} from './kernels/Diag';\nimport {dilation2dConfig} from './kernels/Dilation2D';\nimport {dilation2dBackpropFilterConfig} from './kernels/Dilation2DBackpropFilter';\nimport {dilation2dBackpropInputConfig} from './kernels/Dilation2DBackpropInput';\nimport {eluConfig} from './kernels/Elu';\nimport {eluGradConfig} from './kernels/EluGrad';\nimport {equalConfig} from './kernels/Equal';\nimport {erfConfig} from './kernels/Erf';\nimport {expConfig} from './kernels/Exp';\nimport {expandDimsConfig} from './kernels/ExpandDims';\nimport {expm1Config} from './kernels/Expm1';\nimport {fftConfig} from './kernels/FFT';\nimport {fillConfig} from './kernels/Fill';\nimport {flipLeftRightConfig} from './kernels/FlipLeftRight';\nimport {floorConfig} from './kernels/Floor';\nimport {floorDivConfig} from './kernels/FloorDiv';\nimport {fusedConv2DConfig} from './kernels/FusedConv2D';\nimport {fusedDepthwiseConv2DConfig} from './kernels/FusedDepthwiseConv2D';\nimport {gatherNdConfig} from './kernels/GatherNd';\nimport {gatherV2Config} from './kernels/GatherV2';\nimport {greaterConfig} from './kernels/Greater';\nimport {greaterEqualConfig} from './kernels/GreaterEqual';\nimport {identityConfig} from './kernels/Identity';\nimport {ifftConfig} from './kernels/IFFT';\nimport {imagConfig} from './kernels/Imag';\nimport {isFiniteConfig} from './kernels/IsFinite';\nimport {isInfConfig} from './kernels/IsInf';\nimport {isNaNConfig} from './kernels/IsNaN';\nimport {leakyReluConfig} from './kernels/LeakyRelu';\nimport {lessConfig} from './kernels/Less';\nimport {lessEqualConfig} from './kernels/LessEqual';\nimport {linSpaceConfig} from './kernels/LinSpace';\nimport {logConfig} from './kernels/Log';\nimport {log1pConfig} from './kernels/Log1p';\nimport {logicalAndConfig} from './kernels/LogicalAnd';\nimport {logicalNotConfig} from './kernels/LogicalNot';\nimport {logicalOrConfig} from './kernels/LogicalOr';\nimport {lRNConfig} from './kernels/LRN';\nimport {lRNGradConfig} from './kernels/LRNGrad';\nimport {maxConfig} from './kernels/Max';\nimport {maximumConfig} from './kernels/Maximum';\nimport {maxPoolConfig} from './kernels/MaxPool';\nimport {maxPool3DConfig} from './kernels/MaxPool3D';\nimport {maxPool3DGradConfig} from './kernels/MaxPool3DGrad';\nimport {maxPoolGradConfig} from './kernels/MaxPoolGrad';\nimport {maxPoolWithArgmaxConfig} from './kernels/MaxPoolWithArgmax';\nimport {meanConfig} from './kernels/Mean';\nimport {minConfig} from './kernels/Min';\nimport {minimumConfig} from './kernels/Minimum';\nimport {mirrorPadConfig} from './kernels/MirrorPad';\nimport {modConfig} from './kernels/Mod';\nimport {multinomialConfig} from './kernels/Multinomial';\nimport {multiplyConfig} from './kernels/Multiply';\nimport {negConfig} from './kernels/Neg';\nimport {nonMaxSuppressionV3Config} from './kernels/NonMaxSuppressionV3';\nimport {nonMaxSuppressionV4Config} from './kernels/NonMaxSuppressionV4';\nimport {nonMaxSuppressionV5Config} from './kernels/NonMaxSuppressionV5';\nimport {notEqualConfig} from './kernels/NotEqual';\nimport {oneHotConfig} from './kernels/OneHot';\nimport {onesLikeConfig} from './kernels/OnesLike';\nimport {packConfig} from './kernels/Pack';\nimport {padV2Config} from './kernels/PadV2';\nimport {powConfig} from './kernels/Pow';\nimport {preluConfig} from './kernels/Prelu';\nimport {prodConfig} from './kernels/Prod';\nimport {rangeConfig} from './kernels/Range';\nimport {realConfig} from './kernels/Real';\nimport {realDivConfig} from './kernels/RealDiv';\nimport {reciprocalConfig} from './kernels/Reciprocal';\nimport {reluConfig} from './kernels/Relu';\nimport {relu6Config} from './kernels/Relu6';\nimport {reshapeConfig} from './kernels/Reshape';\nimport {resizeBilinearConfig} from './kernels/ResizeBilinear';\nimport {resizeBilinearGradConfig} from './kernels/ResizeBilinearGrad';\nimport {resizeNearestNeighborConfig} from './kernels/ResizeNearestNeighbor';\nimport {resizeNearestNeighborGradConfig} from './kernels/ResizeNearestNeighborGrad';\nimport {reverseConfig} from './kernels/Reverse';\nimport {rotateWithOffsetConfig} from './kernels/RotateWithOffset';\nimport {roundConfig} from './kernels/Round';\nimport {rsqrtConfig} from './kernels/Rsqrt';\nimport {scatterNdConfig} from './kernels/ScatterNd';\nimport {selectConfig} from './kernels/Select';\nimport {seluConfig} from './kernels/Selu';\nimport {sigmoidConfig} from './kernels/Sigmoid';\nimport {signConfig} from './kernels/Sign';\nimport {sinConfig} from './kernels/Sin';\nimport {sinhConfig} from './kernels/Sinh';\nimport {sliceConfig} from './kernels/Slice';\nimport {softmaxConfig} from './kernels/Softmax';\nimport {softplusConfig} from './kernels/Softplus';\nimport {spaceToBatchNDConfig} from './kernels/SpaceToBatchND';\nimport {sparseToDenseConfig} from './kernels/SparseToDense';\nimport {splitVConfig} from './kernels/SplitV';\nimport {sqrtConfig} from './kernels/Sqrt';\nimport {squareConfig} from './kernels/Square';\nimport {squaredDifferenceConfig} from './kernels/SquaredDifference';\nimport {stepConfig} from './kernels/Step';\nimport {stridedSliceConfig} from './kernels/StridedSlice';\nimport {subConfig} from './kernels/Sub';\nimport {sumConfig} from './kernels/Sum';\nimport {tanConfig} from './kernels/Tan';\nimport {tanhConfig} from './kernels/Tanh';\nimport {tileConfig} from './kernels/Tile';\nimport {topKConfig} from './kernels/TopK';\nimport {transposeConfig} from './kernels/Transpose';\nimport {uniqueConfig} from './kernels/Unique';\nimport {unpackConfig} from './kernels/Unpack';\nimport {unsortedSegmentSumConfig} from './kernels/UnsortedSegmentSum';\nimport {zerosLikeConfig} from './kernels/ZerosLike';\n\n// List all kernel configs here\nconst kernelConfigs: KernelConfig[] = [\n _fusedMatMulConfig,\n absConfig,\n acosConfig,\n acoshConfig,\n addConfig,\n addNConfig,\n allConfig,\n anyConfig,\n argMaxConfig,\n argMinConfig,\n asinConfig,\n asinhConfig,\n atanConfig,\n atan2Config,\n atanhConfig,\n avgPoolConfig,\n avgPool3DConfig,\n avgPool3DGradConfig,\n avgPoolGradConfig,\n batchMatMulConfig,\n batchNormConfig,\n batchToSpaceNDConfig,\n bincountConfig,\n castConfig,\n ceilConfig,\n clipConfig,\n complexConfig,\n complexAbsConfig,\n concatConfig,\n conv2DBackpropFilterConfig,\n conv2DBackpropInputConfig,\n conv2DConfig,\n conv3DBackpropFilterV2Config,\n conv3DBackpropInputV2Config,\n conv3DConfig,\n cosConfig,\n coshConfig,\n cropAndResizeConfig,\n cumsumConfig,\n denseBincountConfig,\n depthToSpaceConfig,\n depthwiseConv2dNativeConfig,\n depthwiseConv2dNativeBackpropFilterConfig,\n depthwiseConv2dNativeBackpropInputConfig,\n diagConfig,\n dilation2dConfig,\n dilation2dBackpropInputConfig,\n dilation2dBackpropFilterConfig,\n realDivConfig,\n eluConfig,\n eluGradConfig,\n equalConfig,\n erfConfig,\n expConfig,\n expandDimsConfig,\n expm1Config,\n fftConfig,\n fillConfig,\n flipLeftRightConfig,\n floorConfig,\n floorDivConfig,\n fusedConv2DConfig,\n fusedDepthwiseConv2DConfig,\n gatherNdConfig,\n gatherV2Config,\n greaterConfig,\n greaterEqualConfig,\n identityConfig,\n ifftConfig,\n imagConfig,\n isFiniteConfig,\n isInfConfig,\n isNaNConfig,\n leakyReluConfig,\n lessConfig,\n lessEqualConfig,\n linSpaceConfig,\n logConfig,\n log1pConfig,\n logicalAndConfig,\n logicalNotConfig,\n logicalOrConfig,\n lRNConfig,\n lRNGradConfig,\n maximumConfig,\n maxPoolConfig,\n maxPool3DConfig,\n maxPool3DGradConfig,\n maxPoolGradConfig,\n maxPoolWithArgmaxConfig,\n maxConfig,\n meanConfig,\n minConfig,\n minimumConfig,\n mirrorPadConfig,\n modConfig,\n multinomialConfig,\n multiplyConfig,\n negConfig,\n nonMaxSuppressionV3Config,\n nonMaxSuppressionV4Config,\n nonMaxSuppressionV5Config,\n notEqualConfig,\n oneHotConfig,\n onesLikeConfig,\n packConfig,\n padV2Config,\n powConfig,\n preluConfig,\n prodConfig,\n rangeConfig,\n realConfig,\n reciprocalConfig,\n reluConfig,\n relu6Config,\n reshapeConfig,\n resizeBilinearConfig,\n resizeBilinearGradConfig,\n resizeNearestNeighborConfig,\n resizeNearestNeighborGradConfig,\n reverseConfig,\n rotateWithOffsetConfig,\n roundConfig,\n rsqrtConfig,\n scatterNdConfig,\n selectConfig,\n seluConfig,\n sigmoidConfig,\n signConfig,\n sinConfig,\n sinhConfig,\n sliceConfig,\n softmaxConfig,\n softplusConfig,\n spaceToBatchNDConfig,\n sparseToDenseConfig,\n splitVConfig,\n sqrtConfig,\n squareConfig,\n squaredDifferenceConfig,\n stepConfig,\n stridedSliceConfig,\n subConfig,\n sumConfig,\n tanConfig,\n tanhConfig,\n tileConfig,\n topKConfig,\n transposeConfig,\n uniqueConfig,\n unpackConfig,\n unsortedSegmentSumConfig,\n zerosLikeConfig\n];\n\nfor (const kernelConfig of kernelConfigs) {\n registerKernel(kernelConfig);\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, TensorInfo, Tile, TileAttrs, TileInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {tileImpl} from './Tile_impl';\n\nexport function tile(\n args: {inputs: TileInputs, backend: MathBackendCPU, attrs: TileAttrs}):\n TensorInfo {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {reps} = attrs;\n\n assertNotComplex(x, 'tile');\n const outBuf = tileImpl(backend.bufferSync(x), reps);\n\n return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\n\nexport const tileConfig: KernelConfig = {\n kernelName: Tile,\n backendName: 'cpu',\n kernelFunc: tile as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, NumericDataType, TensorInfo, TopK, TopKAttrs, TopKInputs, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {topKImpl} from './TopK_impl';\n\nexport function topK(\n args: {inputs: TopKInputs, backend: MathBackendCPU, attrs: TopKAttrs}):\n [TensorInfo, TensorInfo] {\n const {inputs, backend, attrs} = args;\n const {x} = inputs;\n const {k, sorted} = attrs;\n\n assertNotComplex(x, 'topk');\n\n const xVals = backend.data.get(x.dataId).values as TypedArray;\n const [allTopKVals, allTopKIndices] =\n topKImpl(xVals, x.shape, x.dtype as NumericDataType, k, sorted);\n\n return [\n backend.makeTensorInfo(\n allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n backend.makeTensorInfo(\n allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n ];\n}\n\nexport const topKConfig: KernelConfig = {\n kernelName: TopK,\n backendName: 'cpu',\n kernelFunc: topK as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, TensorInfo, Unique, UniqueAttrs, UniqueInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nimport {uniqueImpl} from './Unique_impl';\n\nexport function unique(\n args: {inputs: UniqueInputs, attrs: UniqueAttrs, backend: MathBackendCPU}):\n TensorInfo[] {\n const {inputs, attrs, backend} = args;\n const {axis} = attrs;\n const {x} = inputs;\n assertNotComplex(x, 'unique');\n\n const values = backend.data.get(x.dataId).values;\n const {outputValues, outputShape, indices} =\n uniqueImpl(values, axis, x.shape, x.dtype);\n return [\n backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n backend.makeTensorInfo([indices.length], 'int32', indices),\n ];\n}\n\nexport const uniqueConfig: KernelConfig = {\n kernelName: Unique,\n backendName: 'cpu',\n kernelFunc: unique as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, TensorInfo, Unpack, UnpackAttrs, UnpackInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {reshape} from './Reshape';\nimport {slice} from './Slice';\n\nexport function unpack(\n args: {inputs: UnpackInputs, backend: MathBackendCPU, attrs: UnpackAttrs}):\n TensorInfo[] {\n const {inputs, backend, attrs} = args;\n const {value} = inputs;\n let {axis} = attrs;\n\n if (axis < 0) {\n axis += value.shape.length;\n }\n\n const valueRank = value.shape.length;\n\n const num = value.shape[axis];\n const outShape: number[] = new Array(valueRank - 1);\n let outIndex = 0;\n for (let i = 0; i < valueRank; i++) {\n if (i !== axis) {\n outShape[outIndex++] = value.shape[i];\n }\n }\n\n const begin = new Array(valueRank).fill(0);\n const size = value.shape.slice();\n size[axis] = 1;\n const res = new Array(num);\n for (let i = 0; i < res.length; i++) {\n begin[axis] = i;\n const tempRes = slice({inputs: {x: value}, backend, attrs: {begin, size}});\n res[i] = reshape({inputs: {x: tempRes}, backend, attrs: {shape: outShape}});\n backend.disposeIntermediateTensorInfo(tempRes);\n }\n\n return res;\n}\n\nexport const unpackConfig: KernelConfig = {\n kernelName: Unpack,\n backendName: 'cpu',\n kernelFunc: unpack as {} as 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