TensorFlow Source
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{"version":3,"file":"Tanh.js","sourceRoot":"","sources":["../../src/kernels/Tanh.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAe,IAAI,EAAC,MAAM,uBAAuB,CAAC;AAEzD,OAAO,EAAC,eAAe,EAAC,MAAM,sBAAsB,CAAC;AAErD,MAAM,CAAC,MAAM,IAAI,GAAG,eAAe,CAAC,IAAI,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,IAAI,CAAC,IAAI,CAAC,EAAE,CAAC,CAAC,CAAC;AAEjE,MAAM,CAAC,MAAM,UAAU,GAAiB;IACtC,UAAU,EAAE,IAAI;IAChB,WAAW,EAAE,KAAK;IAClB,UAAU,EAAE,IAAI;CACjB,CAAC"}
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{"version":3,"file":"Tanh.js","sourceRoot":"","sources":["../../src/kernels/Tanh.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAe,IAAI,EAAC,MAAM,uBAAuB,CAAC;AAEzD,OAAO,EAAC,eAAe,EAAC,MAAM,sBAAsB,CAAC;AAErD,MAAM,CAAC,MAAM,IAAI,GAAG,eAAe,CAAC,IAAI,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,IAAI,CAAC,IAAI,CAAC,EAAE,CAAC,CAAC,CAAC;AAEjE,MAAM,CAAC,MAAM,UAAU,GAAiB;IACtC,UAAU,EAAE,IAAI;IAChB,WAAW,EAAE,KAAK;IAClB,UAAU,EAAE,IAAI;CACjB,CAAC","sourcesContent":["/**\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"]}
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{"version":3,"file":"Tile.js","sourceRoot":"","sources":["../../src/kernels/Tile.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAuC,IAAI,EAAwB,MAAM,uBAAuB,CAAC;AAGxG,OAAO,EAAC,gBAAgB,EAAC,MAAM,aAAa,CAAC;AAC7C,OAAO,EAAC,QAAQ,EAAC,MAAM,aAAa,CAAC;AAErC,MAAM,UAAU,IAAI,CAChB,IAAqE;IAEvE,MAAM,EAAC,MAAM,EAAE,OAAO,EAAE,KAAK,EAAC,GAAG,IAAI,CAAC;IACtC,MAAM,EAAC,CAAC,EAAC,GAAG,MAAM,CAAC;IACnB,MAAM,EAAC,IAAI,EAAC,GAAG,KAAK,CAAC;IAErB,gBAAgB,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC;IAC5B,MAAM,MAAM,GAAG,QAAQ,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC;IAErD,OAAO,OAAO,CAAC,cAAc,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,CAAC;AAC3E,CAAC;AAED,MAAM,CAAC,MAAM,UAAU,GAAiB;IACtC,UAAU,EAAE,IAAI;IAChB,WAAW,EAAE,KAAK;IAClB,UAAU,EAAE,IAAwB;CACrC,CAAC","sourcesContent":["/**\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"]}
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{"version":3,"file":"Tile_impl.js","sourceRoot":"","sources":["../../src/kernels/Tile_impl.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,MAAM,EAA+B,MAAM,uBAAuB,CAAC;AAE3E;;;GAGG;AAEH,MAAM,UAAU,QAAQ,CACpB,IAA+B,EAC/B,IAAc;IAChB,MAAM,QAAQ,GAAa,IAAI,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAChD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,QAAQ,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACxC,QAAQ,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC;KACvC;IACD,MAAM,MAAM,GAAG,MAAM,CAAC,QAAQ,EAAE,IAAI,CAAC,KAAK,CAAC,CAAC;IAC5C,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,CAAC,MAAM,EAAE,EAAE,CAAC,EAAE;QAC7C,MAAM,MAAM,GAAG,MAAM,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC;QAEpC,MAAM,WAAW,GAAa,IAAI,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QACnD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,WAAW,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;YAC3C,WAAW,CAAC,CAAC,CAAC,GAAG,MAAM,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;SAC5C;QAED,MAAM,aAAa,GAAG,IAAI,CAAC,UAAU,CAAC,WAAW,CAAC,CAAC;QAEnD,MAAM,CAAC,MAAM,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,MAAM,CAAC,aAAa,CAAC,CAAC;KAC/C;IACD,OAAO,MAAmC,CAAC;AAC7C,CAAC","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 {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"]}
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{"version":3,"file":"TopK_impl.js","sourceRoot":"","sources":["../../src/kernels/TopK_impl.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,yEAAyE;AAEzE,OAAO,EAAC,MAAM,EAAqE,IAAI,EAAC,MAAM,uBAAuB,CAAC;AAEtH,MAAM,UAAU,QAAQ,CACpB,CAAa,EAAE,MAAgB,EAAE,MAAuB,EAAE,CAAS,EACnE,MAAe;IAEjB,4EAA4E;IAC5E,MAAM,OAAO,GAAG,MAAM,CAAC,MAAM,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAC1C,MAAM,CAAC,KAAK,EAAE,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,MAAM,GAAG,OAAO,EAAE,OAAO,CAAC,CAAC;IACpD,MAAM,WAAW,GAAG,IAAI,CAAC,sBAAsB,CAAC,MAAM,EAAE,KAAK,GAAG,CAAC,CAAC,CAAC;IACnE,MAAM,cAAc,GAAG,IAAI,CAAC,sBAAsB,CAAC,OAAO,EAAE,KAAK,GAAG,CAAC,CAAC,CAAC;IAEvE,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,KAAK,EAAE,CAAC,EAAE,EAAE;QAC9B,MAAM,MAAM,GAAG,CAAC,GAAG,IAAI,CAAC;QACxB,MAAM,IAAI,GAAG,CAAC,CAAC,QAAQ,CAAC,MAAM,EAAE,MAAM,GAAG,IAAI,CAAC,CAAC;QAC/C,MAAM,SAAS,GAA0C,EAAE,CAAC;QAC5D,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;YACpC,SAAS,CAAC,IAAI,CAAC,EAAC,KAAK,EAAE,IAAI,CAAC,CAAC,CAAC,EAAE,KAAK,EAAE,CAAC,EAAC,CAAC,CAAC;SAC5C;QACD,SAAS,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC;QAE5C,MAAM,SAAS,GAAG,CAAC,GAAG,CAAC,CAAC;QACxB,MAAM,QAAQ,GAAG,WAAW,CAAC,QAAQ,CAAC,SAAS,EAAE,SAAS,GAAG,CAAC,CAAC,CAAC;QAChE,MAAM,WAAW,GAAG,cAAc,CAAC,QAAQ,CAAC,SAAS,EAAE,SAAS,GAAG,CAAC,CAAC,CAAC;QACtE,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,EAAE;YAC1B,QAAQ,CAAC,CAAC,CAAC,GAAG,SAAS,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC;YACjC,WAAW,CAAC,CAAC,CAAC,GAAG,SAAS,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC;SACrC;KACF;IACD,iEAAiE;IACjE,kBAAkB;IAClB,MAAM,WAAW,GAAG,MAAM,CAAC,KAAK,EAAE,CAAC;IACnC,WAAW,CAAC,WAAW,CAAC,MAAM,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;IAExC,OAAO;QACL,MAAM,CAAC,WAA0B,EAAE,MAAM,EAAE,WAAW,CAAC;QACvD,MAAM,CAAC,WAA0B,EAAE,OAAO,EAAE,cAAc,CAAC;KAC5D,CAAC;AACJ,CAAC","sourcesContent":["/**\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"]}
|
||||
Generated
Vendored
+24
@@ -0,0 +1,24 @@
|
||||
/**
|
||||
* @license
|
||||
* Copyright 2021 Google LLC. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
* =============================================================================
|
||||
*/
|
||||
import { KernelConfig, TensorInfo, TransformAttrs, TransformInputs } from '@tensorflow/tfjs-core';
|
||||
import { MathBackendCPU } from '../backend_cpu';
|
||||
export declare function transform(args: {
|
||||
inputs: TransformInputs;
|
||||
attrs: TransformAttrs;
|
||||
backend: MathBackendCPU;
|
||||
}): TensorInfo;
|
||||
export declare const transformConfig: KernelConfig;
|
||||
Generated
Vendored
+191
@@ -0,0 +1,191 @@
|
||||
/**
|
||||
* @license
|
||||
* Copyright 2021 Google LLC. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
* =============================================================================
|
||||
*/
|
||||
import { Transform, util } from '@tensorflow/tfjs-core';
|
||||
export function transform(args) {
|
||||
const { inputs, attrs, backend } = args;
|
||||
const { image, transforms } = inputs;
|
||||
const { interpolation, fillMode, fillValue, outputShape } = attrs;
|
||||
const [batch, imageHeight, imageWidth, numChannels] = image.shape;
|
||||
const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
|
||||
const outShape = [batch, outHeight, outWidth, numChannels];
|
||||
const strides = util.computeStrides(image.shape);
|
||||
const batchStride = strides[0];
|
||||
const rowStride = strides[1];
|
||||
const colStride = strides[2];
|
||||
const outVals = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(outShape));
|
||||
outVals.fill(fillValue);
|
||||
const imageVals = backend.data.get(image.dataId).values;
|
||||
const transformVals = backend.data.get(transforms.dataId).values;
|
||||
// Ref TF implementation:
|
||||
// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/image_ops.h
|
||||
for (let b = 0; b < batch; ++b) {
|
||||
const transform = transforms.shape[0] === 1 ?
|
||||
transformVals :
|
||||
transformVals.subarray(b * 8, b * 8 + 8);
|
||||
for (let outY = 0; outY < outHeight; ++outY) {
|
||||
for (let outX = 0; outX < outWidth; ++outX) {
|
||||
for (let channel = 0; channel < numChannels; ++channel) {
|
||||
let val;
|
||||
const projection = transform[6] * outX + transform[7] * outY + 1;
|
||||
if (projection === 0) {
|
||||
// Return the fill value for infinite coordinates,
|
||||
// which are outside the input image
|
||||
continue;
|
||||
}
|
||||
const inX = (transform[0] * outX + transform[1] * outY + transform[2]) /
|
||||
projection;
|
||||
const inY = (transform[3] * outX + transform[4] * outY + transform[5]) /
|
||||
projection;
|
||||
const x = mapCoord(inX, imageWidth, fillMode);
|
||||
const y = mapCoord(inY, imageHeight, fillMode);
|
||||
switch (interpolation) {
|
||||
case 'nearest':
|
||||
val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
|
||||
break;
|
||||
case 'bilinear':
|
||||
val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
|
||||
break;
|
||||
default:
|
||||
throw new Error(`Error in Transform: Expect 'nearest' or ` +
|
||||
`'bilinear', but got ${interpolation}`);
|
||||
}
|
||||
const ind = b * batchStride + outY * rowStride + outX * colStride + channel;
|
||||
outVals[ind] = val;
|
||||
}
|
||||
}
|
||||
}
|
||||
return backend.makeTensorInfo(outShape, image.dtype, outVals);
|
||||
}
|
||||
const dataId = backend.write(outVals, outShape, image.dtype);
|
||||
return { dataId, shape: image.shape, dtype: image.dtype };
|
||||
}
|
||||
export const transformConfig = {
|
||||
kernelName: Transform,
|
||||
backendName: 'cpu',
|
||||
kernelFunc: transform
|
||||
};
|
||||
function mapCoord(outCoord, len, mode) {
|
||||
switch (mode) {
|
||||
case 'reflect':
|
||||
return mapCoordReflect(outCoord, len);
|
||||
case 'wrap':
|
||||
return mapCoordWrap(outCoord, len);
|
||||
case 'nearest':
|
||||
return mapCoordNearest(outCoord, len);
|
||||
case 'constant':
|
||||
default:
|
||||
return mapCoordConstant(outCoord, len);
|
||||
}
|
||||
}
|
||||
function mapCoordReflect(outCoord, len) {
|
||||
// Reflect [abcd] to [dcba|abcd|dcba].
|
||||
let inCoord = outCoord;
|
||||
if (inCoord < 0) {
|
||||
if (len <= 1) {
|
||||
inCoord = 0;
|
||||
}
|
||||
else {
|
||||
const sz2 = 2 * len;
|
||||
if (inCoord < sz2) {
|
||||
inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;
|
||||
}
|
||||
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;
|
||||
}
|
||||
}
|
||||
else if (inCoord > len - 1) {
|
||||
if (len <= 1) {
|
||||
inCoord = 0;
|
||||
}
|
||||
else {
|
||||
const sz2 = 2 * len;
|
||||
inCoord -= sz2 * Math.trunc(inCoord / sz2);
|
||||
if (inCoord >= len) {
|
||||
inCoord = sz2 - inCoord - 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
// clamp is necessary because when outCoord = 3.5 and len = 4,
|
||||
// inCoord = 3.5 and will be rounded to 4 in nearest interpolation.
|
||||
return util.clamp(0, inCoord, len - 1);
|
||||
}
|
||||
function mapCoordWrap(outCoord, len) {
|
||||
// Wrap [abcd] to [abcd|abcd|abcd].
|
||||
let inCoord = outCoord;
|
||||
if (inCoord < 0) {
|
||||
if (len <= 1) {
|
||||
inCoord = 0;
|
||||
}
|
||||
else {
|
||||
const sz = len - 1;
|
||||
inCoord += len * (Math.trunc(-inCoord / sz) + 1);
|
||||
}
|
||||
}
|
||||
else if (inCoord > len - 1) {
|
||||
if (len <= 1) {
|
||||
inCoord = 0;
|
||||
}
|
||||
else {
|
||||
const sz = len - 1;
|
||||
inCoord -= len * Math.trunc(inCoord / sz);
|
||||
}
|
||||
}
|
||||
// clamp is necessary because when outCoord = -0.5 and len = 4,
|
||||
// inCoord = 3.5 and will be rounded to 4 in nearest interpolation.
|
||||
return util.clamp(0, inCoord, len - 1);
|
||||
}
|
||||
function mapCoordConstant(outCoord, len) {
|
||||
return outCoord;
|
||||
}
|
||||
function mapCoordNearest(outCoord, len) {
|
||||
return util.clamp(0, outCoord, len - 1);
|
||||
}
|
||||
function readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
|
||||
const ind = batch * batchStride + y * rowStride + x * colStride + channel;
|
||||
if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {
|
||||
return imageVals[ind];
|
||||
}
|
||||
else {
|
||||
return fillValue;
|
||||
}
|
||||
}
|
||||
function nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
|
||||
const $y = Math.round(y);
|
||||
const $x = Math.round(x);
|
||||
return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue);
|
||||
}
|
||||
function bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
|
||||
const yFloor = Math.floor(y);
|
||||
const xFloor = Math.floor(x);
|
||||
const yCeil = yFloor + 1;
|
||||
const xCeil = xFloor + 1;
|
||||
// f(x, yFloor) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yFloor)
|
||||
// + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yFloor)
|
||||
const valueYFloor = (xCeil - x) *
|
||||
readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) +
|
||||
(x - xFloor) *
|
||||
readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue);
|
||||
// f(x, yCeil) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yCeil)
|
||||
// + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yCeil)
|
||||
const valueYCeil = (xCeil - x) *
|
||||
readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) +
|
||||
(x - xFloor) *
|
||||
readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue);
|
||||
// f(x, y) = (yCeil - y) / (yCeil - yFloor) * f(x, yFloor)
|
||||
// + (y - yFloor) / (yCeil - yFloor) * f(x, yCeil)
|
||||
return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;
|
||||
}
|
||||
//# sourceMappingURL=Transform.js.map
|
||||
Generated
Vendored
+1
@@ -0,0 +1 @@
|
||||
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* limitations under the License.
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||||
* =============================================================================
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{"version":3,"file":"binary_types.js","sourceRoot":"","sources":["../../src/utils/binary_types.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG","sourcesContent":["/**\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, TypedArray} from '@tensorflow/tfjs-core';\n\nexport type SimpleBinaryOperation = (a: number, b: number) => number;\nexport type SimpleBinaryKernelImpl =\n (aShape: number[], bShape: number[], aVals: TypedArray, bVals: TypedArray,\n dtype: DataType) => [TypedArray, number[]];\nexport type ComplexBinaryOperation =\n (aReal: number, aImag: number, bReal: number, bImag: number) => {\n real: number, imag: number\n };\nexport type ComplexBinaryKernelImpl =\n (aShape: number[], bShape: number[], aRealVals: Float32Array,\n aImagVals: Float32Array, bRealVals: Float32Array,\n bImagVals: Float32Array) => [TypedArray, TypedArray, number[]];\n"]}
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-1
@@ -1 +1 @@
|
||||
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|
||||
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ine truncated
|
||||
Generated
Vendored
+4
@@ -20,6 +20,7 @@ import { leakyRelu } from '../kernels/LeakyRelu';
|
||||
import { prelu } from '../kernels/Prelu';
|
||||
import { relu } from '../kernels/Relu';
|
||||
import { relu6 } from '../kernels/Relu6';
|
||||
import { sigmoid } from '../kernels/Sigmoid';
|
||||
export function applyActivation(backend, x, activation, preluActivationWeights, leakyreluAlpha) {
|
||||
if (activation === 'linear') {
|
||||
return identity({ inputs: { x }, backend });
|
||||
@@ -39,6 +40,9 @@ export function applyActivation(backend, x, activation, preluActivationWeights,
|
||||
else if (activation === 'leakyrelu') {
|
||||
return leakyRelu({ inputs: { x }, backend, attrs: { alpha: leakyreluAlpha } });
|
||||
}
|
||||
else if (activation === 'sigmoid') {
|
||||
return sigmoid({ inputs: { x }, backend });
|
||||
}
|
||||
throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);
|
||||
}
|
||||
//# sourceMappingURL=fused_utils.js.map
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
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|
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|
||||
{"version":3,"file":"unary_utils.js","sourceRoot":"","sources":["../../src/utils/unary_utils.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAgD,IAAI,EAAC,MAAM,uBAAuB,CAAC;AAG1F,OAAO,EAAC,gBAAgB,EAAC,MAAM,aAAa,CAAC;AAI7C;;;;;;;GAOG;AACH,MAAM,UAAU,eAAe,CAC3B,IAAY,EAAE,EAAwB,EAAE,KAAgB;IAC1D,OAAO,CAAC,EAAC,MAAM,EAAE,KAAK,EAAE,OAAO,EAAC,EAAE,EAAE;QAClC,MAAM,EAAC,CAAC,EAAC,GAAG,MAAqB,CAAC;QAClC,gBAAgB,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC;QAC1B,IAAI,CAAC,CAAC,KAAK,KAAK,QAAQ,IAAI,KAAK,KAAK,QAAQ,EAAE;YAC9C,MAAM,IAAI,KAAK,CAAC,sDAAsD,CAAC,CAAC;SACzE;QAED,MAAM,UAAU,GAAG,OAAyB,CAAC;QAC7C,MAAM,MAAM,GAAG,UAAU,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,MAAoB,CAAC;QAClE,MAAM,KAAK,GAAG,IAAI,CAAC,aAAa,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC;QAC1C,MAAM,MAAM,GAAG,KAAK,IAAI,CAAC,CAAC,KAAK,CAAC;QAChC,MAAM,SAAS,GAAG,IAAI,CAAC,iBAAiB,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC;QACxD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,KAAK,EAAE,EAAE,CAAC,EAAE;YAC9B,SAAS,CAAC,CAAC,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC;SACrC;QACD,OAAO,UAAU,CAAC,cAAc,CAAC,CAAC,CAAC,KAAK,EAAE,MAAM,EAAE,SAAS,CAAC,CAAC;IAC/D,CAAC,CAAC;AACJ,CAAC;AAED;;;;;;;;GAQG;AACH,MAAM,UAAU,uBAAuB,CACnC,IAAY,EAAE,SAA0B,EAAE,KAAgB;IAC5D,OAAO,CAAC,EAAC,MAAM,EAAE,KAAK,EAAE,OAAO,EAAC,EAAE,EAAE;QAClC,MAAM,EAAC,CAAC,EAAC,GAAG,MAAqB,CAAC;QAClC,gBAAgB,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC;QAC1B,IAAI,CAAC,CAAC,KAAK,KAAK,QAAQ,IAAI,KAAK,KAAK,QAAQ,EAAE;YAC9C,MAAM,IAAI,KAAK,CAAC,sDAAsD,CAAC,CAAC;SACzE;QAED,MAAM,UAAU,GAAG,OAAyB,CAAC;QAC7C,MAAM,MAAM,GAAG,UAAU,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,MAAoB,CAAC;QAClE,MAAM,MAAM,GAAG,KAAK,IAAI,CAAC,CAAC,KAAK,CAAC;QAChC,MAAM,SAAS,GAAG,SAAS,CAAC,MAAM,EAAE,MAAM,EAAE,KAAK,CAAC,CAAC;QACnD,OAAO,UAAU,CAAC,cAAc,CAAC,CAAC,CAAC,KAAK,EAAE,MAAM,EAAE,SAAS,CAAC,CAAC;IAC/D,CAAC,CAAC;AACJ,CAAC","sourcesContent":["/**\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 Line truncated
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
{"version":3,"file":"zeros_impl.js","sourceRoot":"","sources":["../../src/utils/zeros_impl.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAuB,IAAI,EAAC,MAAM,uBAAuB,CAAC;AAEjE,OAAO,EAAC,OAAO,EAAC,MAAM,oBAAoB,CAAC;AAE3C;;;;;GAKG;AACH,MAAM,UAAU,KAAK,CACjB,OAAuB,EAAE,KAAe,EACxC,QAAkB,SAAS;IAC7B,IAAI,KAAK,KAAK,WAAW,EAAE;QACzB,MAAM,IAAI,GAAG,KAAK,CAAC,OAAO,EAAE,KAAK,EAAE,SAAS,CAAC,CAAC;QAC9C,MAAM,IAAI,GAAG,KAAK,CAAC,OAAO,EAAE,KAAK,EAAE,SAAS,CAAC,CAAC;QAE9C,OAAO,OAAO,CAAC,EAAC,MAAM,EAAE,EAAC,IAAI,EAAE,IAAI,EAAC,EAAE,OAAO,EAAC,CAAC,CAAC;KACjD;IAED,MAAM,MAAM,GAAG,IAAI,CAAC,mBAAmB,CAAC,IAAI,CAAC,aAAa,CAAC,KAAK,CAAC,EAAE,KAAK,CAAC,CAAC;IAE1E,OAAO,OAAO,CAAC,cAAc,CAAC,KAAK,EAAE,KAAK,EAAE,MAAM,CAAC,CAAC;AACtD,CAAC"}
|
||||
{"version":3,"file":"zeros_impl.js","sourceRoot":"","sources":["../../src/utils/zeros_impl.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAuB,IAAI,EAAC,MAAM,uBAAuB,CAAC;AAEjE,OAAO,EAAC,OAAO,EAAC,MAAM,oBAAoB,CAAC;AAE3C;;;;;GAKG;AACH,MAAM,UAAU,KAAK,CACjB,OAAuB,EAAE,KAAe,EACxC,QAAkB,SAAS;IAC7B,IAAI,KAAK,KAAK,WAAW,EAAE;QACzB,MAAM,IAAI,GAAG,KAAK,CAAC,OAAO,EAAE,KAAK,EAAE,SAAS,CAAC,CAAC;QAC9C,MAAM,IAAI,GAAG,KAAK,CAAC,OAAO,EAAE,KAAK,EAAE,SAAS,CAAC,CAAC;QAE9C,OAAO,OAAO,CAAC,EAAC,MAAM,EAAE,EAAC,IAAI,EAAE,IAAI,EAAC,EAAE,OAAO,EAAC,CAAC,CAAC;KACjD;IAED,MAAM,MAAM,GAAG,IAAI,CAAC,mBAAmB,CAAC,IAAI,CAAC,aAAa,CAAC,KAAK,CAAC,EAAE,KAAK,CAAC,CAAC;IAE1E,OAAO,OAAO,CAAC,cAAc,CAAC,KAAK,EAAE,KAAK,EAAE,MAAM,CAAC,CAAC;AACtD,CAAC","sourcesContent":["/**\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"]}
|
||||
Generated
Vendored
+34
-18
@@ -1,6 +1,6 @@
|
||||
/**
|
||||
* @license
|
||||
* Copyright 2017 Google LLC. All Rights Reserved.
|
||||
* Copyright 2021 Google LLC. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
@@ -39,6 +39,10 @@ export class MathBackendCPU extends KernelBackend {
|
||||
|
||||
data: DataStorage<TensorData<DataType>>;
|
||||
private firstUse = true;
|
||||
private static nextDataId = 0;
|
||||
private nextDataId(): number {
|
||||
return MathBackendCPU.nextDataId++;
|
||||
}
|
||||
|
||||
constructor() {
|
||||
super();
|
||||
@@ -63,7 +67,7 @@ export class MathBackendCPU extends KernelBackend {
|
||||
'\n============================');
|
||||
}
|
||||
}
|
||||
const dataId = {};
|
||||
const dataId = {id: this.nextDataId()};
|
||||
|
||||
this.data.set(dataId, {values, dtype, refCount: 1});
|
||||
|
||||
@@ -93,6 +97,15 @@ export class MathBackendCPU extends KernelBackend {
|
||||
return {dataId: outId, shape, dtype};
|
||||
}
|
||||
|
||||
/** Return refCount of a `TensorData`. */
|
||||
refCount(dataId: DataId): number {
|
||||
if (this.data.has(dataId)) {
|
||||
const tensorData = this.data.get(dataId);
|
||||
return tensorData.refCount;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
/** Increase refCount of a `TensorData`. */
|
||||
incRef(dataId: DataId): void {
|
||||
const tensorData = this.data.get(dataId);
|
||||
@@ -109,8 +122,8 @@ export class MathBackendCPU extends KernelBackend {
|
||||
|
||||
move(
|
||||
dataId: DataId, values: backend_util.BackendValues, shape: number[],
|
||||
dtype: DataType): void {
|
||||
this.data.set(dataId, {values, dtype, refCount: 1});
|
||||
dtype: DataType, refCount: number): void {
|
||||
this.data.set(dataId, {values, dtype, refCount});
|
||||
}
|
||||
|
||||
numDataIds(): number {
|
||||
@@ -155,31 +168,34 @@ export class MathBackendCPU extends KernelBackend {
|
||||
return engine().makeTensorFromDataId(dataId, shape, dtype, this) as T;
|
||||
}
|
||||
|
||||
disposeData(dataId: DataId): void {
|
||||
/**
|
||||
* Dispose the memory if the dataId has 0 refCount. Return true if the memory
|
||||
* is released or memory is not managed in this backend, false if memory is
|
||||
* not cleared.
|
||||
* @param dataId
|
||||
* @oaram force Optional, remove the data regardless of refCount
|
||||
*/
|
||||
disposeData(dataId: DataId, force = false): boolean {
|
||||
if (this.data.has(dataId)) {
|
||||
this.data.get(dataId).refCount--;
|
||||
if (!force && this.data.get(dataId).refCount > 0) {
|
||||
return false;
|
||||
}
|
||||
|
||||
const {complexTensorInfos} = this.data.get(dataId);
|
||||
|
||||
if (complexTensorInfos != null) {
|
||||
this.disposeData(complexTensorInfos.real.dataId);
|
||||
this.disposeData(complexTensorInfos.imag.dataId);
|
||||
this.disposeData(complexTensorInfos.real.dataId, true);
|
||||
this.disposeData(complexTensorInfos.imag.dataId, true);
|
||||
}
|
||||
|
||||
this.data.delete(dataId);
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
disposeIntermediateTensorInfo(tensorInfo: TensorInfo): void {
|
||||
const dataId = tensorInfo.dataId;
|
||||
|
||||
if (this.data.has(dataId)) {
|
||||
const tensorData = this.data.get(dataId);
|
||||
|
||||
tensorData.refCount--;
|
||||
|
||||
if (tensorData.refCount < 1) {
|
||||
this.disposeData(dataId);
|
||||
}
|
||||
}
|
||||
this.disposeData(tensorInfo.dataId);
|
||||
}
|
||||
|
||||
async time(f: () => void): Promise<BackendTimingInfo> {
|
||||
|
||||
Generated
Vendored
+2
-2
@@ -64,7 +64,7 @@ export function depthwiseConv2dNative(args: {
|
||||
const yOffset1 = b * y.strides[0];
|
||||
for (let yR = 0; yR < convInfo.outHeight; ++yR) {
|
||||
const yOffset2 = yOffset1 + yR * y.strides[1];
|
||||
const xRCorner = yR * convInfo.strideHeight - padLeft;
|
||||
const xRCorner = yR * convInfo.strideHeight - padTop;
|
||||
for (let wR = 0; wR < filterHeight; ++wR) {
|
||||
const xR = xRCorner + wR * dilationHeight;
|
||||
if (xR < 0 || xR >= convInfo.inHeight) {
|
||||
@@ -74,7 +74,7 @@ export function depthwiseConv2dNative(args: {
|
||||
const xOffset2 = xOffset1 + xR * xStrides[1];
|
||||
for (let yC = 0; yC < convInfo.outWidth; ++yC) {
|
||||
const yOffset3 = yOffset2 + yC * y.strides[2];
|
||||
const xCCorner = yC * convInfo.strideWidth - padTop;
|
||||
const xCCorner = yC * convInfo.strideWidth - padLeft;
|
||||
for (let wC = 0; wC < filterWidth; ++wC) {
|
||||
const xC = xCCorner + wC * dilationWidth;
|
||||
if (xC < 0 || xC >= convInfo.inWidth) {
|
||||
|
||||
Generated
Vendored
+102
@@ -0,0 +1,102 @@
|
||||
/**
|
||||
* @license
|
||||
* Copyright 2021 Google LLC. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
* =============================================================================
|
||||
*/
|
||||
|
||||
import {backend_util, Einsum, EinsumAttrs, EinsumInputs, KernelConfig, KernelFunc, Tensor, TensorInfo, util} from '@tensorflow/tfjs-core';
|
||||
|
||||
import {MathBackendCPU} from '../backend_cpu';
|
||||
|
||||
import {multiply} from './Multiply';
|
||||
import {reshape} from './Reshape';
|
||||
import {sum} from './Sum';
|
||||
import {transpose} from './Transpose';
|
||||
|
||||
export function einsum(
|
||||
args: {inputs: EinsumInputs, backend: MathBackendCPU, attrs: EinsumAttrs}):
|
||||
TensorInfo {
|
||||
const {inputs, backend, attrs} = args;
|
||||
const {equation} = attrs;
|
||||
const tensors = inputs as Tensor[];
|
||||
|
||||
const {allDims, summedDims, idDims} =
|
||||
backend_util.decodeEinsumEquation(equation, tensors.length);
|
||||
backend_util.checkEinsumDimSizes(allDims.length, idDims, tensors);
|
||||
const {path, steps} = backend_util.getEinsumComputePath(summedDims, idDims);
|
||||
|
||||
const nSteps = steps.length;
|
||||
let out: TensorInfo|null = null;
|
||||
let numDimsRemaining = allDims.length;
|
||||
const tensorsToDispose: TensorInfo[] = [];
|
||||
for (let i = 0; i < nSteps; ++i) {
|
||||
for (const idTerm of steps[i]) {
|
||||
const {permutationIndices: perm, expandDims: dimsToExpand} =
|
||||
backend_util.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
|
||||
let x: TensorInfo;
|
||||
if (backend_util.isIdentityPermutation(perm)) {
|
||||
x = tensors[idTerm];
|
||||
} else {
|
||||
x = transpose({inputs: {x: tensors[idTerm]}, backend, attrs: {perm}});
|
||||
tensorsToDispose.push(x);
|
||||
}
|
||||
const targetShape: number[] = x.shape.slice();
|
||||
for (let k = 0; k < dimsToExpand.length; ++k) {
|
||||
targetShape.splice(dimsToExpand[k], 0, 1);
|
||||
}
|
||||
|
||||
if (!util.arraysEqual(x.shape, targetShape)) {
|
||||
x = reshape({inputs: {x}, backend, attrs: {shape: targetShape}});
|
||||
tensorsToDispose.push(x);
|
||||
}
|
||||
if (out === null) {
|
||||
out = x;
|
||||
} else {
|
||||
// tslint:disable-next-line: no-unnecessary-type-assertion
|
||||
out = multiply({inputs: {a: x, b: out}, backend}) as TensorInfo;
|
||||
tensorsToDispose.push(out);
|
||||
}
|
||||
}
|
||||
if (i < nSteps - 1) {
|
||||
if (path[i] >= 0) {
|
||||
out = sum({
|
||||
inputs: {x: out},
|
||||
backend,
|
||||
attrs: {
|
||||
axis: path[i] - (allDims.length - numDimsRemaining),
|
||||
keepDims: false
|
||||
}
|
||||
});
|
||||
tensorsToDispose.push(out);
|
||||
}
|
||||
numDimsRemaining--;
|
||||
}
|
||||
}
|
||||
|
||||
// Clean up intermediate tensors.
|
||||
for (const tensorInfo of tensorsToDispose) {
|
||||
if (tensorInfo === out) {
|
||||
continue;
|
||||
}
|
||||
backend.disposeIntermediateTensorInfo(tensorInfo);
|
||||
}
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
export const einsumConfig: KernelConfig = {
|
||||
kernelName: Einsum,
|
||||
backendName: 'cpu',
|
||||
kernelFunc: einsum as {} as KernelFunc
|
||||
};
|
||||
Generated
Vendored
+76
@@ -0,0 +1,76 @@
|
||||
/**
|
||||
* @license
|
||||
* Copyright 2021 Google LLC. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
* =============================================================================
|
||||
*/
|
||||
|
||||
import {KernelConfig, KernelFunc, SparseFillEmptyRows, SparseFillEmptyRowsInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
|
||||
|
||||
import {MathBackendCPU} from '../backend_cpu';
|
||||
|
||||
import {sparseFillEmptyRowsImpl} from './SparseFillEmptyRows_impl';
|
||||
|
||||
export function sparseFillEmptyRows(args: {
|
||||
inputs: SparseFillEmptyRowsInputs,
|
||||
backend: MathBackendCPU
|
||||
}): [TensorInfo, TensorInfo, TensorInfo, TensorInfo] {
|
||||
const {inputs, backend} = args;
|
||||
const {indices, values, denseShape, defaultValue} = inputs;
|
||||
if (denseShape.shape.length !== 1) {
|
||||
throw new Error(`Dense shape must be a vector, saw:
|
||||
${denseShape.shape}`);
|
||||
}
|
||||
if (indices.shape.length !== 2) {
|
||||
throw new Error(`Indices must be a matrix, saw:
|
||||
${indices.shape}`);
|
||||
}
|
||||
if (values.shape.length !== 1) {
|
||||
throw new Error(`Values must be a vector, saw:
|
||||
${values.shape}`);
|
||||
}
|
||||
if (defaultValue.shape.length !== 0) {
|
||||
throw new Error(`Default value must be a scalar, saw:
|
||||
${defaultValue.shape}`);
|
||||
}
|
||||
|
||||
const $indices = backend.data.get(indices.dataId).values as TypedArray;
|
||||
const $values = backend.data.get(values.dataId).values as TypedArray;
|
||||
const $denseShape = backend.data.get(denseShape.dataId).values as TypedArray;
|
||||
const $defaultValue =
|
||||
backend.data.get(defaultValue.dataId).values[0] as number;
|
||||
|
||||
const [outputIndices, outputIndicesShape, outputValues,
|
||||
emptyRowIndicator, reverseIndexMap] =
|
||||
sparseFillEmptyRowsImpl(
|
||||
$indices, indices.shape, indices.dtype, $values, values.dtype,
|
||||
$denseShape, $defaultValue);
|
||||
return [
|
||||
backend.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
|
||||
backend.makeTensorInfo(
|
||||
[outputIndicesShape[0]], values.dtype, outputValues),
|
||||
backend.makeTensorInfo(
|
||||
[emptyRowIndicator.length], 'bool',
|
||||
new Uint8Array(
|
||||
emptyRowIndicator.map((value: boolean) => Number(value)))),
|
||||
backend.makeTensorInfo(
|
||||
[reverseIndexMap.length], indices.dtype,
|
||||
new Int32Array(reverseIndexMap)),
|
||||
];
|
||||
}
|
||||
|
||||
export const sparseFillEmptyRowsConfig: KernelConfig = {
|
||||
kernelName: SparseFillEmptyRows,
|
||||
backendName: 'cpu',
|
||||
kernelFunc: sparseFillEmptyRows as {} as KernelFunc,
|
||||
};
|
||||
Generated
Vendored
+137
@@ -0,0 +1,137 @@
|
||||
/**
|
||||
* @license
|
||||
* Copyright 2021 Google LLC. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
* =============================================================================
|
||||
*/
|
||||
|
||||
import {DataType, TypedArray, util} from '@tensorflow/tfjs-core';
|
||||
|
||||
export function sparseFillEmptyRowsImpl(
|
||||
indices: TypedArray, indicesShape: number[], indicesDType: DataType,
|
||||
values: TypedArray, valuesDType: DataType, denseShape: TypedArray,
|
||||
defaultValue: number):
|
||||
[TypedArray, number[], TypedArray, boolean[], number[]] {
|
||||
const indicesCount = indicesShape[0];
|
||||
const denseRows = denseShape[0];
|
||||
|
||||
const emptyRowIndicator: boolean[] = new Array(denseRows);
|
||||
const reverseIndexMap: number[] = new Array(indicesCount);
|
||||
|
||||
const rank = indicesShape[1];
|
||||
|
||||
if (denseRows === 0) {
|
||||
if (indicesCount !== 0) {
|
||||
throw new Error(`Received SparseTensor with denseShape[0] = 0 but
|
||||
indices.shape[0] = ${indicesCount}`);
|
||||
}
|
||||
const outputIndices = util.getArrayFromDType(indicesDType, 0) as TypedArray;
|
||||
const outputValues = util.getArrayFromDType(valuesDType, 0) as TypedArray;
|
||||
return [
|
||||
outputIndices, [0, rank], outputValues, emptyRowIndicator, reverseIndexMap
|
||||
];
|
||||
}
|
||||
|
||||
let rowsAreOrdered = true;
|
||||
let lastIndicesRow = 0;
|
||||
const csrOffset: number[] = new Array(denseRows).fill(0);
|
||||
|
||||
for (let i = 0; i < indicesCount; ++i) {
|
||||
// indices is a 2d tensor with shape of [N, rank]
|
||||
const row = indices[i * rank];
|
||||
if (row < 0) {
|
||||
throw new Error(`indices(${i}, 0) is invalid: ${row} < 0`);
|
||||
}
|
||||
if (row >= denseRows) {
|
||||
throw new Error(`indices(${i}, 0) is invalid: ${row} >= ${denseRows}`);
|
||||
}
|
||||
++csrOffset[row];
|
||||
rowsAreOrdered = rowsAreOrdered && (row >= lastIndicesRow);
|
||||
lastIndicesRow = row;
|
||||
}
|
||||
|
||||
let allRowsFull = true;
|
||||
for (let row = 0; row < denseRows; ++row) {
|
||||
// csrOffset here describes the number of elements in this dense row
|
||||
const rowEmpty = (csrOffset[row] === 0);
|
||||
emptyRowIndicator[row] = rowEmpty;
|
||||
allRowsFull = allRowsFull && !rowEmpty;
|
||||
// In filled version, each row has at least one element.
|
||||
csrOffset[row] = Math.max(csrOffset[row], 1);
|
||||
// Update csrOffset to represent the number of elements up to and
|
||||
// including denseRows + 1:
|
||||
// csrOffset[0] == #{elements of row 0}
|
||||
// csrOffset[1] == #{elements of row 1} + #{elements of row 0}
|
||||
// ..
|
||||
// csrOffset[i] == starting index for elements in row i + 1.
|
||||
if (row > 0) {
|
||||
csrOffset[row] += csrOffset[row - 1];
|
||||
}
|
||||
}
|
||||
|
||||
if (allRowsFull && rowsAreOrdered) {
|
||||
const outputIndices: TypedArray = indices;
|
||||
const outputValues: TypedArray = values;
|
||||
for (let i = 0; i < indicesCount; ++i) {
|
||||
reverseIndexMap[i] = i;
|
||||
}
|
||||
return [
|
||||
outputIndices, [indicesCount, rank], outputValues, emptyRowIndicator,
|
||||
reverseIndexMap
|
||||
];
|
||||
} else {
|
||||
const fullIndicesCount = csrOffset[denseRows - 1];
|
||||
const outputIndices =
|
||||
util.getArrayFromDType(indicesDType, fullIndicesCount * rank) as
|
||||
TypedArray;
|
||||
const outputValues =
|
||||
util.getArrayFromDType(valuesDType, fullIndicesCount) as TypedArray;
|
||||
const filledCount: number[] = new Array(denseRows).fill(0);
|
||||
|
||||
// Fill in values for rows that are not missing
|
||||
for (let i = 0; i < indicesCount; ++i) {
|
||||
// indices is a 2d tensor with shape of [N, rank]
|
||||
const row = indices[i * rank];
|
||||
const offset = filledCount[row];
|
||||
const outputI = ((row === 0) ? 0 : csrOffset[row - 1]) + offset;
|
||||
filledCount[row]++; // Increment the filled count for this row.
|
||||
for (let j = 0; j < rank; ++j) {
|
||||
// indices and outputIndices are 2d tensors with shape of [N, rank]
|
||||
outputIndices[outputI * rank + j] = indices[i * rank + j];
|
||||
}
|
||||
outputValues[outputI] = values[i];
|
||||
// We'll need this reverse index map to backprop correctly.
|
||||
reverseIndexMap[i] = outputI;
|
||||
}
|
||||
|
||||
// Fill in values for rows that are missing
|
||||
for (let row = 0; row < denseRows; ++row) {
|
||||
const rowCount = filledCount[row];
|
||||
if (rowCount === 0) { // We haven't filled this row
|
||||
const startingIndex = (row === 0) ? 0 : csrOffset[row - 1];
|
||||
// Remaining index values were set to zero already.
|
||||
// Just need to set the row index in the right location.
|
||||
// outputIndices is a 2d tensor with shape of [N, rank]
|
||||
outputIndices[startingIndex * rank + 0] = row;
|
||||
for (let col = 1; col < rank; ++col) {
|
||||
outputIndices[startingIndex * rank + col] = 0;
|
||||
}
|
||||
outputValues[startingIndex] = defaultValue;
|
||||
}
|
||||
}
|
||||
return [
|
||||
outputIndices, [indicesCount, rank], outputValues, emptyRowIndicator,
|
||||
reverseIndexMap
|
||||
];
|
||||
}
|
||||
}
|
||||
Generated
Vendored
+64
@@ -0,0 +1,64 @@
|
||||
/**
|
||||
* @license
|
||||
* Copyright 2021 Google LLC. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
* =============================================================================
|
||||
*/
|
||||
|
||||
import {KernelConfig, SparseReshape, SparseReshapeInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
|
||||
|
||||
import {MathBackendCPU} from '../backend_cpu';
|
||||
|
||||
import {sparseReshapeImpl} from './SparseReshape_impl';
|
||||
|
||||
export function sparseReshape(
|
||||
args: {inputs: SparseReshapeInputs, backend: MathBackendCPU}):
|
||||
[TensorInfo, TensorInfo] {
|
||||
const {inputs, backend} = args;
|
||||
const {inputIndices, inputShape, newShape} = inputs;
|
||||
if (inputIndices.shape.length !== 2) {
|
||||
throw new Error(`Input indices should be a matrix but received shape
|
||||
${inputIndices.shape}`);
|
||||
}
|
||||
if (inputShape.shape.length !== 1) {
|
||||
throw new Error(`Input shape should be a vector but received shape
|
||||
${inputShape.shape}`);
|
||||
}
|
||||
|
||||
if (newShape.shape.length !== 1) {
|
||||
throw new Error(
|
||||
`Target shape should be a vector but received shape ${newShape.shape}`);
|
||||
}
|
||||
|
||||
const $inputShape =
|
||||
Array.from(backend.data.get(inputShape.dataId).values as TypedArray);
|
||||
const $inputIndices =
|
||||
backend.data.get(inputIndices.dataId).values as TypedArray;
|
||||
const targetShape =
|
||||
Array.from(backend.data.get(newShape.dataId).values as TypedArray);
|
||||
|
||||
const [newIndices, indicesShape, outputShape] = sparseReshapeImpl(
|
||||
$inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape,
|
||||
targetShape);
|
||||
return [
|
||||
backend.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
|
||||
backend.makeTensorInfo(
|
||||
[outputShape.length], newShape.dtype, new Int32Array(outputShape)),
|
||||
];
|
||||
}
|
||||
|
||||
export const sparseReshapeConfig: KernelConfig = {
|
||||
kernelName: SparseReshape,
|
||||
backendName: 'cpu',
|
||||
kernelFunc: sparseReshape,
|
||||
};
|
||||
Generated
Vendored
+105
@@ -0,0 +1,105 @@
|
||||
/**
|
||||
* @license
|
||||
* Copyright 2021 Google LLC. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
* =============================================================================
|
||||
*/
|
||||
|
||||
import {DataType, TypedArray, util} from '@tensorflow/tfjs-core';
|
||||
|
||||
export function sparseReshapeImpl(
|
||||
inputIndices: TypedArray, inputIndicesShape: number[], inputDType: DataType,
|
||||
inputShape: number[],
|
||||
targetShape: number[]): [TypedArray, number[], number[]] {
|
||||
const denseSize = util.sizeFromShape(inputShape);
|
||||
const nnz = inputIndicesShape[0];
|
||||
const outputRank = targetShape.length;
|
||||
|
||||
// Compute the output shape. Determine product of specified dimensions, and
|
||||
// find the index of the unspecified one.
|
||||
const outputShape: number[] = [];
|
||||
let product = 1;
|
||||
let unknownIndex = -1;
|
||||
for (let d = 0; d < outputRank; ++d) {
|
||||
const size = targetShape[d];
|
||||
if (size === -1) {
|
||||
if (unknownIndex !== -1) {
|
||||
throw new Error(`only one output dimension may be -1, not both ${
|
||||
unknownIndex} and ${d}`);
|
||||
}
|
||||
unknownIndex = d;
|
||||
outputShape.push(1);
|
||||
} else {
|
||||
if (size < 0) {
|
||||
throw new Error(`size ${d} must be non-negative, not ${size}`);
|
||||
}
|
||||
product *= size;
|
||||
outputShape.push(size);
|
||||
}
|
||||
}
|
||||
if (unknownIndex !== -1) {
|
||||
if (product <= 0) {
|
||||
throw new Error(
|
||||
'reshape cannot infer the missing ' +
|
||||
'input size for an empty tensor unless all ' +
|
||||
'specified input sizes are non-zero');
|
||||
}
|
||||
const missing = Math.trunc(denseSize / product);
|
||||
if (product * missing !== denseSize) {
|
||||
throw new Error(`Input to reshape is a SparseTensor with ${denseSize}
|
||||
dense values, but the requested shape requires a multiple of ${
|
||||
product}. inputShape=${inputShape} outputShape= ${outputShape}`);
|
||||
}
|
||||
|
||||
outputShape[unknownIndex] = missing;
|
||||
}
|
||||
const outputSize = util.sizeFromShape(outputShape);
|
||||
if (outputSize !== denseSize) {
|
||||
throw new Error(`Input to reshape is a tensor with ${
|
||||
denseSize} dense values, but the requested shape has ${
|
||||
outputSize}. inputShape=${inputShape} outputShape=${outputShape}`);
|
||||
}
|
||||
|
||||
const inputRank = inputShape.length;
|
||||
const inputStrides: number[] = [];
|
||||
if (inputRank > 0) {
|
||||
inputStrides[inputRank - 1] = 1;
|
||||
for (let d = inputRank - 2; d >= 0; --d) {
|
||||
inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];
|
||||
}
|
||||
}
|
||||
|
||||
const outputStrides: number[] = [];
|
||||
if (outputRank > 0) {
|
||||
outputStrides[outputRank - 1] = 1;
|
||||
for (let d = outputRank - 2; d >= 0; --d) {
|
||||
outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];
|
||||
}
|
||||
}
|
||||
|
||||
const newIndices =
|
||||
util.getArrayFromDType(inputDType, nnz * outputRank) as TypedArray;
|
||||
for (let i = 0; i < nnz; ++i) {
|
||||
let id = 0;
|
||||
for (let j = 0; j < inputRank; ++j) {
|
||||
// inputIndices is a 2d tensor with shape of [nnz, inputRank]
|
||||
id += inputIndices[i * inputRank + j] * inputStrides[j];
|
||||
}
|
||||
for (let j = 0; j < outputRank; ++j) {
|
||||
// newIndices is a 2d tensor with shape of [nnz, outputRank]
|
||||
newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);
|
||||
id %= outputStrides[j];
|
||||
}
|
||||
}
|
||||
return [newIndices, [nnz, outputRank], outputShape];
|
||||
}
|
||||
Generated
Vendored
+251
@@ -0,0 +1,251 @@
|
||||
/**
|
||||
* @license
|
||||
* Copyright 2021 Google LLC. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
* =============================================================================
|
||||
*/
|
||||
|
||||
import {KernelConfig, KernelFunc, NumericDataType, TensorInfo, Transform, TransformAttrs, TransformInputs, TypedArray, util} from '@tensorflow/tfjs-core';
|
||||
|
||||
import {MathBackendCPU} from '../backend_cpu';
|
||||
|
||||
export function transform(args: {
|
||||
inputs: TransformInputs,
|
||||
attrs: TransformAttrs,
|
||||
backend: MathBackendCPU
|
||||
}): TensorInfo {
|
||||
const {inputs, attrs, backend} = args;
|
||||
const {image, transforms} = inputs;
|
||||
const {interpolation, fillMode, fillValue, outputShape} = attrs;
|
||||
|
||||
const [batch, imageHeight, imageWidth, numChannels] = image.shape;
|
||||
const [outHeight, outWidth] =
|
||||
outputShape != null ? outputShape : [imageHeight, imageWidth];
|
||||
const outShape = [batch, outHeight, outWidth, numChannels];
|
||||
|
||||
const strides = util.computeStrides(image.shape);
|
||||
const batchStride = strides[0];
|
||||
const rowStride = strides[1];
|
||||
const colStride = strides[2];
|
||||
|
||||
const outVals = util.getTypedArrayFromDType(
|
||||
image.dtype as NumericDataType, util.sizeFromShape(outShape));
|
||||
|
||||
outVals.fill(fillValue);
|
||||
|
||||
const imageVals = backend.data.get(image.dataId).values as TypedArray;
|
||||
const transformVals =
|
||||
backend.data.get(transforms.dataId).values as TypedArray;
|
||||
|
||||
// Ref TF implementation:
|
||||
// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/image_ops.h
|
||||
for (let b = 0; b < batch; ++b) {
|
||||
const transform = transforms.shape[0] === 1 ?
|
||||
transformVals :
|
||||
transformVals.subarray(b * 8, b * 8 + 8);
|
||||
|
||||
for (let outY = 0; outY < outHeight; ++outY) {
|
||||
for (let outX = 0; outX < outWidth; ++outX) {
|
||||
for (let channel = 0; channel < numChannels; ++channel) {
|
||||
let val;
|
||||
|
||||
const projection = transform[6] * outX + transform[7] * outY + 1;
|
||||
|
||||
if (projection === 0) {
|
||||
// Return the fill value for infinite coordinates,
|
||||
// which are outside the input image
|
||||
continue;
|
||||
}
|
||||
|
||||
const inX =
|
||||
(transform[0] * outX + transform[1] * outY + transform[2]) /
|
||||
projection;
|
||||
const inY =
|
||||
(transform[3] * outX + transform[4] * outY + transform[5]) /
|
||||
projection;
|
||||
|
||||
const x = mapCoord(inX, imageWidth, fillMode);
|
||||
const y = mapCoord(inY, imageHeight, fillMode);
|
||||
|
||||
switch (interpolation) {
|
||||
case 'nearest':
|
||||
val = nearestInterpolation(
|
||||
imageVals, imageHeight, imageWidth, batchStride, rowStride,
|
||||
colStride, b, y, x, channel, fillValue);
|
||||
break;
|
||||
case 'bilinear':
|
||||
val = bilinearInterpolation(
|
||||
imageVals, imageHeight, imageWidth, batchStride, rowStride,
|
||||
colStride, b, y, x, channel, fillValue);
|
||||
break;
|
||||
default:
|
||||
throw new Error(
|
||||
`Error in Transform: Expect 'nearest' or ` +
|
||||
`'bilinear', but got ${interpolation}`);
|
||||
}
|
||||
|
||||
const ind =
|
||||
b * batchStride + outY * rowStride + outX * colStride + channel;
|
||||
|
||||
outVals[ind] = val;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return backend.makeTensorInfo(outShape, image.dtype, outVals);
|
||||
}
|
||||
|
||||
const dataId = backend.write(outVals, outShape, image.dtype);
|
||||
return {dataId, shape: image.shape, dtype: image.dtype};
|
||||
}
|
||||
|
||||
export const transformConfig: KernelConfig = {
|
||||
kernelName: Transform,
|
||||
backendName: 'cpu',
|
||||
kernelFunc: transform as {} as KernelFunc
|
||||
};
|
||||
|
||||
function mapCoord(
|
||||
outCoord: number, len: number,
|
||||
mode: 'constant'|'reflect'|'wrap'|'nearest') {
|
||||
switch (mode) {
|
||||
case 'reflect':
|
||||
return mapCoordReflect(outCoord, len);
|
||||
case 'wrap':
|
||||
return mapCoordWrap(outCoord, len);
|
||||
case 'nearest':
|
||||
return mapCoordNearest(outCoord, len);
|
||||
case 'constant':
|
||||
default:
|
||||
return mapCoordConstant(outCoord, len);
|
||||
}
|
||||
}
|
||||
|
||||
function mapCoordReflect(outCoord: number, len: number): number {
|
||||
// Reflect [abcd] to [dcba|abcd|dcba].
|
||||
let inCoord = outCoord;
|
||||
if (inCoord < 0) {
|
||||
if (len <= 1) {
|
||||
inCoord = 0;
|
||||
} else {
|
||||
const sz2 = 2 * len;
|
||||
if (inCoord < sz2) {
|
||||
inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;
|
||||
}
|
||||
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;
|
||||
}
|
||||
} else if (inCoord > len - 1) {
|
||||
if (len <= 1) {
|
||||
inCoord = 0;
|
||||
} else {
|
||||
const sz2 = 2 * len;
|
||||
inCoord -= sz2 * Math.trunc(inCoord / sz2);
|
||||
if (inCoord >= len) {
|
||||
inCoord = sz2 - inCoord - 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
// clamp is necessary because when outCoord = 3.5 and len = 4,
|
||||
// inCoord = 3.5 and will be rounded to 4 in nearest interpolation.
|
||||
return util.clamp(0, inCoord, len - 1);
|
||||
}
|
||||
|
||||
function mapCoordWrap(outCoord: number, len: number): number {
|
||||
// Wrap [abcd] to [abcd|abcd|abcd].
|
||||
let inCoord = outCoord;
|
||||
if (inCoord < 0) {
|
||||
if (len <= 1) {
|
||||
inCoord = 0;
|
||||
} else {
|
||||
const sz = len - 1;
|
||||
inCoord += len * (Math.trunc(-inCoord / sz) + 1);
|
||||
}
|
||||
} else if (inCoord > len - 1) {
|
||||
if (len <= 1) {
|
||||
inCoord = 0;
|
||||
} else {
|
||||
const sz = len - 1;
|
||||
inCoord -= len * Math.trunc(inCoord / sz);
|
||||
}
|
||||
}
|
||||
// clamp is necessary because when outCoord = -0.5 and len = 4,
|
||||
// inCoord = 3.5 and will be rounded to 4 in nearest interpolation.
|
||||
return util.clamp(0, inCoord, len - 1);
|
||||
}
|
||||
|
||||
function mapCoordConstant(outCoord: number, len: number): number {
|
||||
return outCoord;
|
||||
}
|
||||
|
||||
function mapCoordNearest(outCoord: number, len: number): number {
|
||||
return util.clamp(0, outCoord, len - 1);
|
||||
}
|
||||
|
||||
function readWithFillValue(
|
||||
imageVals: TypedArray, imageHeight: number, imageWidth: number,
|
||||
batchStride: number, rowStride: number, colStride: number, batch: number,
|
||||
y: number, x: number, channel: number, fillValue: number): number {
|
||||
const ind = batch * batchStride + y * rowStride + x * colStride + channel;
|
||||
if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {
|
||||
return imageVals[ind];
|
||||
} else {
|
||||
return fillValue;
|
||||
}
|
||||
}
|
||||
|
||||
function nearestInterpolation(
|
||||
imageVals: TypedArray, imageHeight: number, imageWidth: number,
|
||||
batchStride: number, rowStride: number, colStride: number, batch: number,
|
||||
y: number, x: number, channel: number, fillValue: number): number {
|
||||
const $y = Math.round(y);
|
||||
const $x = Math.round(x);
|
||||
|
||||
return readWithFillValue(
|
||||
imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride,
|
||||
batch, $y, $x, channel, fillValue);
|
||||
}
|
||||
|
||||
function bilinearInterpolation(
|
||||
imageVals: TypedArray, imageHeight: number, imageWidth: number,
|
||||
batchStride: number, rowStride: number, colStride: number, batch: number,
|
||||
y: number, x: number, channel: number, fillValue: number) {
|
||||
const yFloor = Math.floor(y);
|
||||
const xFloor = Math.floor(x);
|
||||
const yCeil = yFloor + 1;
|
||||
const xCeil = xFloor + 1;
|
||||
// f(x, yFloor) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yFloor)
|
||||
// + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yFloor)
|
||||
const valueYFloor =
|
||||
(xCeil - x) *
|
||||
readWithFillValue(
|
||||
imageVals, imageHeight, imageWidth, batchStride, rowStride,
|
||||
colStride, batch, yFloor, xFloor, channel, fillValue) +
|
||||
(x - xFloor) *
|
||||
readWithFillValue(
|
||||
imageVals, imageHeight, imageWidth, batchStride, rowStride,
|
||||
colStride, batch, yFloor, xCeil, channel, fillValue);
|
||||
// f(x, yCeil) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yCeil)
|
||||
// + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yCeil)
|
||||
const valueYCeil =
|
||||
(xCeil - x) *
|
||||
readWithFillValue(
|
||||
imageVals, imageHeight, imageWidth, batchStride, rowStride,
|
||||
colStride, batch, yCeil, xFloor, channel, fillValue) +
|
||||
(x - xFloor) *
|
||||
readWithFillValue(
|
||||
imageVals, imageHeight, imageWidth, batchStride, rowStride,
|
||||
colStride, batch, yCeil, xCeil, channel, fillValue);
|
||||
// f(x, y) = (yCeil - y) / (yCeil - yFloor) * f(x, yFloor)
|
||||
// + (y - yFloor) / (yCeil - yFloor) * f(x, yCeil)
|
||||
return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;
|
||||
}
|
||||
Generated
Vendored
+8
@@ -67,6 +67,7 @@ import {diagConfig} from './kernels/Diag';
|
||||
import {dilation2dConfig} from './kernels/Dilation2D';
|
||||
import {dilation2dBackpropFilterConfig} from './kernels/Dilation2DBackpropFilter';
|
||||
import {dilation2dBackpropInputConfig} from './kernels/Dilation2DBackpropInput';
|
||||
import {einsumConfig} from './kernels/Einsum';
|
||||
import {eluConfig} from './kernels/Elu';
|
||||
import {eluGradConfig} from './kernels/EluGrad';
|
||||
import {equalConfig} from './kernels/Equal';
|
||||
@@ -154,6 +155,8 @@ import {sliceConfig} from './kernels/Slice';
|
||||
import {softmaxConfig} from './kernels/Softmax';
|
||||
import {softplusConfig} from './kernels/Softplus';
|
||||
import {spaceToBatchNDConfig} from './kernels/SpaceToBatchND';
|
||||
import {sparseFillEmptyRowsConfig} from './kernels/SparseFillEmptyRows';
|
||||
import {sparseReshapeConfig} from './kernels/SparseReshape';
|
||||
import {sparseToDenseConfig} from './kernels/SparseToDense';
|
||||
import {splitVConfig} from './kernels/SplitV';
|
||||
import {sqrtConfig} from './kernels/Sqrt';
|
||||
@@ -167,6 +170,7 @@ import {tanConfig} from './kernels/Tan';
|
||||
import {tanhConfig} from './kernels/Tanh';
|
||||
import {tileConfig} from './kernels/Tile';
|
||||
import {topKConfig} from './kernels/TopK';
|
||||
import {transformConfig} from './kernels/Transform';
|
||||
import {transposeConfig} from './kernels/Transpose';
|
||||
import {uniqueConfig} from './kernels/Unique';
|
||||
import {unpackConfig} from './kernels/Unpack';
|
||||
@@ -224,6 +228,7 @@ const kernelConfigs: KernelConfig[] = [
|
||||
dilation2dBackpropInputConfig,
|
||||
dilation2dBackpropFilterConfig,
|
||||
realDivConfig,
|
||||
einsumConfig,
|
||||
eluConfig,
|
||||
eluGradConfig,
|
||||
equalConfig,
|
||||
@@ -310,6 +315,8 @@ const kernelConfigs: KernelConfig[] = [
|
||||
softmaxConfig,
|
||||
softplusConfig,
|
||||
spaceToBatchNDConfig,
|
||||
sparseFillEmptyRowsConfig,
|
||||
sparseReshapeConfig,
|
||||
sparseToDenseConfig,
|
||||
splitVConfig,
|
||||
sqrtConfig,
|
||||
@@ -324,6 +331,7 @@ const kernelConfigs: KernelConfig[] = [
|
||||
tileConfig,
|
||||
topKConfig,
|
||||
transposeConfig,
|
||||
transformConfig,
|
||||
uniqueConfig,
|
||||
unpackConfig,
|
||||
unsortedSegmentSumConfig,
|
||||
|
||||
Generated
Vendored
+2
@@ -39,6 +39,8 @@ export {prodImpl} from './kernels/Prod';
|
||||
export {rangeImpl} from './kernels/Range_impl';
|
||||
export {rsqrtImpl} from './kernels/Rsqrt';
|
||||
export {sliceImpl} from './kernels/Slice';
|
||||
export {sparseFillEmptyRowsImpl} from './kernels/SparseFillEmptyRows_impl';
|
||||
export {sparseReshapeImpl} from './kernels/SparseReshape_impl';
|
||||
export {squaredDifferenceImpl} from './kernels/SquaredDifference';
|
||||
export {stridedSliceImpl} from './kernels/StridedSlice_impl';
|
||||
export {subImpl} from './kernels/Sub';
|
||||
|
||||
Generated
Vendored
+3
@@ -24,6 +24,7 @@ import {leakyRelu} from '../kernels/LeakyRelu';
|
||||
import {prelu} from '../kernels/Prelu';
|
||||
import {relu} from '../kernels/Relu';
|
||||
import {relu6} from '../kernels/Relu6';
|
||||
import {sigmoid} from '../kernels/Sigmoid';
|
||||
|
||||
export function applyActivation(
|
||||
backend: MathBackendCPU, x: TensorInfo, activation: backend_util.Activation,
|
||||
@@ -40,6 +41,8 @@ export function applyActivation(
|
||||
return prelu({inputs: {x, alpha: preluActivationWeights}, backend});
|
||||
} else if (activation === 'leakyrelu') {
|
||||
return leakyRelu({inputs: {x}, backend, attrs: {alpha: leakyreluAlpha}});
|
||||
} else if (activation === 'sigmoid') {
|
||||
return sigmoid({inputs: {x}, backend}) as TensorInfo;
|
||||
}
|
||||
throw new Error(
|
||||
`Activation ${activation} has not been implemented for the CPU backend.`);
|
||||
|
||||
Generated
Vendored
+1
-1
@@ -1,5 +1,5 @@
|
||||
/** @license See the LICENSE file. */
|
||||
|
||||
// This code is auto-generated, do not modify this file!
|
||||
const version = '3.0.0';
|
||||
const version = '3.6.0';
|
||||
export {version};
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
{"version":3,"file":"addn_gpu.js","sourceRoot":"","sources":["../src/addn_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAIH,MAAM,OAAO,WAAW;IAKtB,YAAY,WAAqB,EAAE,MAAkB;QAHrD,gBAAW,GAAa,EAAE,CAAC;QAIzB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,aAAa,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,CAAC;QAEnD,MAAM,QAAQ,GAAa,EAAE,CAAC;QAC9B,+CAA+C;QAC/C,IAAI,CAAC,aAAa,CAAC,OAAO,CAAC,QAAQ,CAAC,EAAE;YACpC,QAAQ,CAAC,IAAI,CAAC,UAAU,QAAQ,SAAS,QAAQ,gBAAgB,CAAC,CAAC;QACrE,CAAC,CAAC,CAAC;QAEH,qCAAqC;QACrC,MAAM,SAAS,GAAG,IAAI,CAAC,aAAa;aACb,GAAG,CAAC,QAAQ,CAAC,EAAE;YACd,OAAO,IAAI,QAAQ,EAAE,CAAC;QACxB,CAAC,CAAC;aACD,IAAI,CAAC,KAAK,CAAC,CAAC;QAEnC,IAAI,CAAC,QAAQ,GAAG;;UAEV,QAAQ,CAAC,IAAI,CAAC,YAAY,CAAC;;yBAEZ,SAAS;;;KAG7B,CAAC;IACJ,CAAC;CACF"}
|
||||
{"version":3,"file":"addn_gpu.js","sourceRoot":"","sources":["../src/addn_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAIH,MAAM,OAAO,WAAW;IAKtB,YAAY,WAAqB,EAAE,MAAkB;QAHrD,gBAAW,GAAa,EAAE,CAAC;QAIzB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,aAAa,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,CAAC;QAEnD,MAAM,QAAQ,GAAa,EAAE,CAAC;QAC9B,+CAA+C;QAC/C,IAAI,CAAC,aAAa,CAAC,OAAO,CAAC,QAAQ,CAAC,EAAE;YACpC,QAAQ,CAAC,IAAI,CAAC,UAAU,QAAQ,SAAS,QAAQ,gBAAgB,CAAC,CAAC;QACrE,CAAC,CAAC,CAAC;QAEH,qCAAqC;QACrC,MAAM,SAAS,GAAG,IAAI,CAAC,aAAa;aACb,GAAG,CAAC,QAAQ,CAAC,EAAE;YACd,OAAO,IAAI,QAAQ,EAAE,CAAC;QACxB,CAAC,CAAC;aACD,IAAI,CAAC,KAAK,CAAC,CAAC;QAEnC,IAAI,CAAC,QAAQ,GAAG;;UAEV,QAAQ,CAAC,IAAI,CAAC,YAAY,CAAC;;yBAEZ,SAAS;;;KAG7B,CAAC;IACJ,CAAC;CACF","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 {GPGPUProgram} from './gpgpu_math';\n\nexport class AddNProgram implements GPGPUProgram {\n variableNames: string[];\n outputShape: number[] = [];\n userCode: string;\n\n constructor(outputShape: number[], shapes: number[][]) {\n this.outputShape = outputShape;\n this.variableNames = shapes.map((_, i) => `T${i}`);\n\n const snippets: string[] = [];\n // Get target elements from every input tensor.\n this.variableNames.forEach(variable => {\n snippets.push(`float v${variable} = get${variable}AtOutCoords();`);\n });\n\n // Calculate the sum of all elements.\n const operation = this.variableNames\n .map(variable => {\n return `v${variable}`;\n })\n .join(' + ');\n\n this.userCode = `\n void main() {\n ${snippets.join('\\n ')}\n\n float result = ${operation};\n setOutput(result);\n }\n `;\n }\n}\n"]}
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
{"version":3,"file":"addn_packed_gpu.js","sourceRoot":"","sources":["../src/addn_packed_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAIH,MAAM,OAAO,iBAAiB;IAO5B,YAAY,WAAqB,EAAE,MAAkB;QALrD,gBAAW,GAAa,EAAE,CAAC;QAE3B,iBAAY,GAAG,IAAI,CAAC;QACpB,iBAAY,GAAG,IAAI,CAAC;QAGlB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,aAAa,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,CAAC;QAEnD,MAAM,QAAQ,GAAa,EAAE,CAAC;QAC9B,+CAA+C;QAC/C,IAAI,CAAC,aAAa,CAAC,OAAO,CAAC,QAAQ,CAAC,EAAE;YACpC,QAAQ,CAAC,IAAI,CAAC,SAAS,QAAQ,SAAS,QAAQ,gBAAgB,CAAC,CAAC;QACpE,CAAC,CAAC,CAAC;QAEH,qCAAqC;QACrC,MAAM,SAAS,GAAG,IAAI,CAAC,aAAa;aACb,GAAG,CAAC,QAAQ,CAAC,EAAE;YACd,OAAO,IAAI,QAAQ,EAAE,CAAC;QACxB,CAAC,CAAC;aACD,IAAI,CAAC,KAAK,CAAC,CAAC;QAEnC,IAAI,CAAC,QAAQ,GAAG;;UAEV,QAAQ,CAAC,IAAI,CAAC,YAAY,CAAC;;wBAEb,SAAS;;;KAG5B,CAAC;IACJ,CAAC;CACF"}
|
||||
{"version":3,"file":"addn_packed_gpu.js","sourceRoot":"","sources":["../src/addn_packed_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAIH,MAAM,OAAO,iBAAiB;IAO5B,YAAY,WAAqB,EAAE,MAAkB;QALrD,gBAAW,GAAa,EAAE,CAAC;QAE3B,iBAAY,GAAG,IAAI,CAAC;QACpB,iBAAY,GAAG,IAAI,CAAC;QAGlB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,aAAa,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,CAAC;QAEnD,MAAM,QAAQ,GAAa,EAAE,CAAC;QAC9B,+CAA+C;QAC/C,IAAI,CAAC,aAAa,CAAC,OAAO,CAAC,QAAQ,CAAC,EAAE;YACpC,QAAQ,CAAC,IAAI,CAAC,SAAS,QAAQ,SAAS,QAAQ,gBAAgB,CAAC,CAAC;QACpE,CAAC,CAAC,CAAC;QAEH,qCAAqC;QACrC,MAAM,SAAS,GAAG,IAAI,CAAC,aAAa;aACb,GAAG,CAAC,QAAQ,CAAC,EAAE;YACd,OAAO,IAAI,QAAQ,EAAE,CAAC;QACxB,CAAC,CAAC;aACD,IAAI,CAAC,KAAK,CAAC,CAAC;QAEnC,IAAI,CAAC,QAAQ,GAAG;;UAEV,QAAQ,CAAC,IAAI,CAAC,YAAY,CAAC;;wBAEb,SAAS;;;KAG5B,CAAC;IACJ,CAAC;CACF","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 {GPGPUProgram} from './gpgpu_math';\n\nexport class AddNPackedProgram implements GPGPUProgram {\n variableNames: string[];\n outputShape: number[] = [];\n userCode: string;\n packedInputs = true;\n packedOutput = true;\n\n constructor(outputShape: number[], shapes: number[][]) {\n this.outputShape = outputShape;\n this.variableNames = shapes.map((_, i) => `T${i}`);\n\n const snippets: string[] = [];\n // Get target elements from every input tensor.\n this.variableNames.forEach(variable => {\n snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);\n });\n\n // Calculate the sum of all elements.\n const operation = this.variableNames\n .map(variable => {\n return `v${variable}`;\n })\n .join(' + ');\n\n this.userCode = `\n void main() {\n ${snippets.join('\\n ')}\n\n vec4 result = ${operation};\n setOutput(result);\n }\n `;\n }\n}\n"]}
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
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|
||||
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|
||||
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|
||||
Generated
Vendored
+19
-11
@@ -48,6 +48,8 @@ export declare function getBinaryCache(webGLVersion: number): {
|
||||
export declare class MathBackendWebGL extends KernelBackend {
|
||||
texData: DataStorage<TextureData>;
|
||||
gpgpu: GPGPUContext;
|
||||
private static nextDataId;
|
||||
private nextDataId;
|
||||
private pendingRead;
|
||||
private pendingDisposal;
|
||||
dataRefCount: WeakMap<object, number>;
|
||||
@@ -57,40 +59,47 @@ export declare class MathBackendWebGL extends KernelBackend {
|
||||
private activeTimers;
|
||||
private uploadWaitMs;
|
||||
private downloadWaitMs;
|
||||
private cpuBackend;
|
||||
private lastGlFlushTime;
|
||||
private floatPrecisionValue;
|
||||
private textureManager;
|
||||
private binaryCache;
|
||||
private gpgpuCreatedLocally;
|
||||
private numMBBeforeWarning;
|
||||
private warnedAboutMemory;
|
||||
private warnedAboutCPUBackend;
|
||||
constructor(gpgpu?: GPGPUContext);
|
||||
numDataIds(): number;
|
||||
write(values: BackendValues, shape: number[], dtype: DataType): DataId;
|
||||
/** Return refCount of a `TensorData`. */
|
||||
refCount(dataId: DataId): number;
|
||||
/** Increase refCount of a `TextureData`. */
|
||||
incRef(dataId: DataId): void;
|
||||
/** Decrease refCount of a `TextureData`. */
|
||||
decRef(dataId: DataId): void;
|
||||
/**
|
||||
* Decrease refCount of a `TextureData` if it is a component of complex
|
||||
* tensor.
|
||||
*/
|
||||
decComplexRef(dataId: DataId): void;
|
||||
move(dataId: DataId, values: BackendValues, shape: number[], dtype: DataType): void;
|
||||
move(dataId: DataId, values: BackendValues, shape: number[], dtype: DataType, refCount: number): void;
|
||||
disposeIntermediateTensorInfo(tensorInfo: TensorInfo): void;
|
||||
readSync(dataId: DataId): BackendValues;
|
||||
read(dataId: DataId): Promise<BackendValues>;
|
||||
bufferSync<R extends Rank>(t: TensorInfo): TensorBuffer<R>;
|
||||
private checkNumericalProblems;
|
||||
private getValuesFromTexture;
|
||||
timerAvailable(): boolean;
|
||||
time(f: () => void): Promise<WebGLTimingInfo>;
|
||||
memory(): WebGLMemoryInfo;
|
||||
private startTimer;
|
||||
private endTimer;
|
||||
private getQueryTime;
|
||||
private pendingDeletes;
|
||||
disposeData(dataId: DataId): void;
|
||||
/**
|
||||
* Decrease the RefCount on the dataId and dispose the memory if the dataId
|
||||
* has 0 refCount. If there are pending read on the data, the disposal would
|
||||
* added to the pending delete queue. Return true if the dataId is removed
|
||||
* from backend or the backend does not contain the dataId, false if the
|
||||
* dataId is not removed. Memory may or may not be released even when dataId
|
||||
* is removed, which also depends on dataRefCount, see `releaseGPU`.
|
||||
* @param dataId
|
||||
* @oaram force Optional, remove the data regardless of refCount
|
||||
*/
|
||||
disposeData(dataId: DataId, force?: boolean): boolean;
|
||||
private releaseGPUData;
|
||||
getTexture(dataId: DataId): WebGLTexture;
|
||||
/**
|
||||
@@ -98,7 +107,6 @@ export declare class MathBackendWebGL extends KernelBackend {
|
||||
* tests.
|
||||
*/
|
||||
getDataInfo(dataId: DataId): TextureData;
|
||||
private getCPUBackend;
|
||||
shouldExecuteOnCPU(inputs: TensorInfo[], sizeThreshold?: number): boolean;
|
||||
getGPGPUContext(): GPGPUContext;
|
||||
where(condition: Tensor): Tensor2D;
|
||||
@@ -111,7 +119,7 @@ export declare class MathBackendWebGL extends KernelBackend {
|
||||
private packedReshape;
|
||||
private decode;
|
||||
runWebGLProgram(program: GPGPUProgram, inputs: TensorInfo[], outputDtype: DataType, customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void, preventEagerUnpackingOfOutput?: boolean): TensorInfo;
|
||||
compileAndRun<K extends TensorInfo>(program: GPGPUProgram, inputs: TensorInfo[], outputDtype?: DataType, customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void, preventEagerUnpackingOfOutput?: boolean): K;
|
||||
compileAndRun(program: GPGPUProgram, inputs: TensorInfo[], outputDtype?: DataType, customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void, preventEagerUnpackingOfOutput?: boolean): TensorInfo;
|
||||
private getAndSaveBinary;
|
||||
getTextureManager(): TextureManager;
|
||||
private disposed;
|
||||
|
||||
Generated
Vendored
+73
-84
@@ -79,8 +79,9 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
this.uploadWaitMs = 0;
|
||||
// Accumulated time spent (including blocking in downloading data from webgl.
|
||||
this.downloadWaitMs = 0;
|
||||
// record the last manual GL Flush time.
|
||||
this.lastGlFlushTime = 0;
|
||||
this.warnedAboutMemory = false;
|
||||
this.warnedAboutCPUBackend = false;
|
||||
this.pendingDeletes = 0;
|
||||
this.disposed = false;
|
||||
if (!env().getBool('HAS_WEBGL')) {
|
||||
@@ -103,10 +104,11 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
this.numMBBeforeWarning = numMBBeforeWarning();
|
||||
this.texData = new DataStorage(this, engine());
|
||||
}
|
||||
nextDataId() {
|
||||
return MathBackendWebGL.nextDataId++;
|
||||
}
|
||||
numDataIds() {
|
||||
return this.texData.numDataIds() +
|
||||
(this.cpuBackend ? this.cpuBackend.numDataIds() : 0) -
|
||||
this.pendingDeletes;
|
||||
return this.texData.numDataIds() - this.pendingDeletes;
|
||||
}
|
||||
write(values, shape, dtype) {
|
||||
if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||
|
||||
@@ -117,17 +119,18 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
throw new Error(`Cannot write to a complex64 dtype. ` +
|
||||
`Please use tf.complex(real, imag).`);
|
||||
}
|
||||
const dataId = {};
|
||||
this.texData.set(dataId, {
|
||||
shape,
|
||||
dtype,
|
||||
values,
|
||||
usage: TextureUsage.UPLOAD,
|
||||
refCount: 1,
|
||||
complexParentRefCount: 0
|
||||
});
|
||||
const dataId = { id: this.nextDataId() };
|
||||
this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });
|
||||
return dataId;
|
||||
}
|
||||
/** Return refCount of a `TensorData`. */
|
||||
refCount(dataId) {
|
||||
if (this.texData.has(dataId)) {
|
||||
const tensorData = this.texData.get(dataId);
|
||||
return tensorData.refCount;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
/** Increase refCount of a `TextureData`. */
|
||||
incRef(dataId) {
|
||||
const texData = this.texData.get(dataId);
|
||||
@@ -140,19 +143,7 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
texData.refCount--;
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Decrease refCount of a `TextureData` if it is a component of complex
|
||||
* tensor.
|
||||
*/
|
||||
decComplexRef(dataId) {
|
||||
if (this.texData.has(dataId)) {
|
||||
const texData = this.texData.get(dataId);
|
||||
if (texData.complexParentRefCount > 0) {
|
||||
texData.refCount--;
|
||||
}
|
||||
}
|
||||
}
|
||||
move(dataId, values, shape, dtype) {
|
||||
move(dataId, values, shape, dtype, refCount) {
|
||||
if (env().getBool('DEBUG')) {
|
||||
this.checkNumericalProblems(values);
|
||||
}
|
||||
@@ -160,24 +151,10 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
throw new Error(`Cannot write to a complex64 dtype. ` +
|
||||
`Please use tf.complex(real, imag).`);
|
||||
}
|
||||
this.texData.set(dataId, {
|
||||
shape,
|
||||
dtype,
|
||||
values,
|
||||
usage: TextureUsage.UPLOAD,
|
||||
refCount: 1,
|
||||
complexParentRefCount: 0
|
||||
});
|
||||
this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount });
|
||||
}
|
||||
disposeIntermediateTensorInfo(tensorInfo) {
|
||||
const dataId = tensorInfo.dataId;
|
||||
if (this.texData.has(dataId)) {
|
||||
const textureData = this.texData.get(dataId);
|
||||
textureData.refCount--;
|
||||
if (textureData.refCount < 1) {
|
||||
this.disposeData(dataId);
|
||||
}
|
||||
}
|
||||
this.disposeData(tensorInfo.dataId);
|
||||
}
|
||||
readSync(dataId) {
|
||||
const texData = this.texData.get(dataId);
|
||||
@@ -295,7 +272,9 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
subscribers.forEach(resolve => resolve(dTypeVals));
|
||||
if (this.pendingDisposal.has(dataId)) {
|
||||
this.pendingDisposal.delete(dataId);
|
||||
this.disposeData(dataId);
|
||||
if (this.disposeData(dataId)) {
|
||||
engine().removeDataId(dataId, this);
|
||||
}
|
||||
this.pendingDeletes--;
|
||||
}
|
||||
return dTypeVals;
|
||||
@@ -355,6 +334,9 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
this.disposeIntermediateTensorInfo(output);
|
||||
return vals;
|
||||
}
|
||||
timerAvailable() {
|
||||
return env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0;
|
||||
}
|
||||
async time(f) {
|
||||
const oldActiveTimers = this.activeTimers;
|
||||
const newActiveTimers = [];
|
||||
@@ -428,36 +410,49 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
const timerQuery = query;
|
||||
return timerQuery.endMs - timerQuery.startMs;
|
||||
}
|
||||
disposeData(dataId) {
|
||||
/**
|
||||
* Decrease the RefCount on the dataId and dispose the memory if the dataId
|
||||
* has 0 refCount. If there are pending read on the data, the disposal would
|
||||
* added to the pending delete queue. Return true if the dataId is removed
|
||||
* from backend or the backend does not contain the dataId, false if the
|
||||
* dataId is not removed. Memory may or may not be released even when dataId
|
||||
* is removed, which also depends on dataRefCount, see `releaseGPU`.
|
||||
* @param dataId
|
||||
* @oaram force Optional, remove the data regardless of refCount
|
||||
*/
|
||||
disposeData(dataId, force = false) {
|
||||
if (this.pendingDisposal.has(dataId)) {
|
||||
return;
|
||||
return false;
|
||||
}
|
||||
// No-op if already disposed.
|
||||
if (!this.texData.has(dataId)) {
|
||||
return true;
|
||||
}
|
||||
// if force flag is set, change refCount to 0, this would ensure disposal
|
||||
// when added to the pendingDisposal queue. Memory may or may not be
|
||||
// released, which also depends on dataRefCount, see `releaseGPU`.
|
||||
if (force) {
|
||||
this.texData.get(dataId).refCount = 0;
|
||||
}
|
||||
else {
|
||||
this.texData.get(dataId).refCount--;
|
||||
}
|
||||
if (!force && this.texData.get(dataId).refCount > 0) {
|
||||
return false;
|
||||
}
|
||||
if (this.pendingRead.has(dataId)) {
|
||||
this.pendingDisposal.add(dataId);
|
||||
this.pendingDeletes++;
|
||||
return;
|
||||
}
|
||||
// No-op if already disposed.
|
||||
if (!this.texData.has(dataId)) {
|
||||
return;
|
||||
}
|
||||
// Trying to dispose a textureData that has a 'kept' refCount, e.g. trying
|
||||
// to dispose a tensor whose data bucket is shared with a complex tensor. In
|
||||
// this case we are removing a reference to the textureData, but we
|
||||
// shouldn't actually dispose the texture.
|
||||
if (this.texData.get(dataId).complexParentRefCount > 0) {
|
||||
this.texData.get(dataId).refCount--;
|
||||
return;
|
||||
return false;
|
||||
}
|
||||
this.releaseGPUData(dataId);
|
||||
const { complexTensorInfos } = this.texData.get(dataId);
|
||||
if (complexTensorInfos != null) {
|
||||
this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--;
|
||||
this.disposeIntermediateTensorInfo(complexTensorInfos.real);
|
||||
this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--;
|
||||
this.disposeIntermediateTensorInfo(complexTensorInfos.imag);
|
||||
this.disposeData(complexTensorInfos.real.dataId, force);
|
||||
this.disposeData(complexTensorInfos.imag.dataId, force);
|
||||
}
|
||||
this.texData.delete(dataId);
|
||||
return true;
|
||||
}
|
||||
releaseGPUData(dataId) {
|
||||
const { texture, dtype, texShape, usage, isPacked, slice } = this.texData.get(dataId);
|
||||
@@ -490,15 +485,6 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
getDataInfo(dataId) {
|
||||
return this.texData.get(dataId);
|
||||
}
|
||||
getCPUBackend() {
|
||||
if (!env().getBool('WEBGL_CPU_FORWARD')) {
|
||||
return null;
|
||||
}
|
||||
if (this.cpuBackend == null) {
|
||||
this.cpuBackend = engine().findBackend('cpu');
|
||||
}
|
||||
return this.cpuBackend;
|
||||
}
|
||||
/*
|
||||
Tests whether all the inputs to an op are small and on the CPU. This heuristic
|
||||
determines when it would be faster to execute a kernel on the CPU. WebGL
|
||||
@@ -507,16 +493,7 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
sustainable strategy for optimizing backend execution of ops.
|
||||
*/
|
||||
shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {
|
||||
const cpuBackend = this.getCPUBackend();
|
||||
if (!env().getBool('IS_TEST') && !this.warnedAboutCPUBackend &&
|
||||
cpuBackend == null) {
|
||||
console.warn('Your application contains ops that are small enough to be ' +
|
||||
'executed on the CPU backend, however the CPU backend cannot ' +
|
||||
'be found. Consider importing the CPU backend ' +
|
||||
'(@tensorflow/tfjs-backend-cpu) for better performance.');
|
||||
this.warnedAboutCPUBackend = true;
|
||||
}
|
||||
return cpuBackend != null &&
|
||||
return env().getBool('WEBGL_CPU_FORWARD') &&
|
||||
inputs.every(input => this.texData.get(input.dataId).texture == null &&
|
||||
util.sizeFromShape(input.shape) < sizeThreshold);
|
||||
}
|
||||
@@ -531,7 +508,8 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
}
|
||||
packedUnaryOp(x, op, dtype) {
|
||||
const program = new UnaryOpPackedProgram(x.shape, op);
|
||||
return this.compileAndRun(program, [x], dtype);
|
||||
const outInfo = this.compileAndRun(program, [x], dtype);
|
||||
return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);
|
||||
}
|
||||
// TODO(msoulanille) remove this once the backend has been modularized
|
||||
// a copy is needed here to break a circular dependency.
|
||||
@@ -546,7 +524,8 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
return this.packedUnaryOp(x, unary_op.ABS, x.dtype);
|
||||
}
|
||||
const program = new UnaryOpProgram(x.shape, unary_op.ABS);
|
||||
return this.compileAndRun(program, [x]);
|
||||
const outInfo = this.compileAndRun(program, [x]);
|
||||
return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);
|
||||
}
|
||||
makeTensorInfo(shape, dtype, values) {
|
||||
let dataId;
|
||||
@@ -704,6 +683,15 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
query = this.endTimer(query);
|
||||
this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });
|
||||
}
|
||||
const glFlushThreshold = env().get('WEBGL_FLUSH_THRESHOLD');
|
||||
// Manually GL flush requested
|
||||
if (glFlushThreshold > 0) {
|
||||
const time = util.now();
|
||||
if ((time - this.lastGlFlushTime) > glFlushThreshold) {
|
||||
this.gpgpu.gl.flush();
|
||||
this.lastGlFlushTime = time;
|
||||
}
|
||||
}
|
||||
if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&
|
||||
preventEagerUnpackingOfOutput === false) {
|
||||
const unpacked = this.unpackTensor(output);
|
||||
@@ -715,7 +703,7 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
compileAndRun(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {
|
||||
outputDtype = outputDtype || inputs[0].dtype;
|
||||
const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);
|
||||
return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);
|
||||
return outInfo;
|
||||
}
|
||||
getAndSaveBinary(key, getBinary) {
|
||||
if (!(key in this.binaryCache)) {
|
||||
@@ -864,6 +852,7 @@ export class MathBackendWebGL extends KernelBackend {
|
||||
return shape[0] * shape[1] * util.bytesPerElement(dtype);
|
||||
}
|
||||
}
|
||||
MathBackendWebGL.nextDataId = 0;
|
||||
function float32ToTypedArray(a, dtype) {
|
||||
if (dtype === 'float32' || dtype === 'complex64') {
|
||||
return a;
|
||||
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
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ine truncated
|
||||
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{"version":3,"file":"backend_webgl_test_registry.js","sourceRoot":"","sources":["../src/backend_webgl_test_registry.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,iDAAiD;AACjD,OAAO,EAAc,eAAe,EAAC,MAAM,yCAAyC,CAAC;AAErF,MAAM,CAAC,MAAM,UAAU,GAAgB;IACrC,SAAS,EAAE,OAAO,CAAC,EAAE,CAAC,OAAO,CAAC,WAAW,KAAK,OAAO;CACtD,CAAC;AACF,MAAM,CAAC,MAAM,WAAW,GAAgB;IACtC,KAAK,EAAE,EAAC,YAAY,EAAE,IAAI,EAAC;CAC5B,CAAC;AAEF,eAAe,CAAC;IACd,IAAI,EAAE,QAAQ;IACd,WAAW,EAAE,OAAO;IACpB,KAAK,EAAE;QACL,eAAe,EAAE,CAAC;QAClB,mBAAmB,EAAE,KAAK;QAC1B,2BAA2B,EAAE,CAAC;KAC/B;IACD,UAAU,EAAE,IAAI;CACjB,CAAC,CAAC;AAEH,eAAe,CAAC;IACd,IAAI,EAAE,QAAQ;IACd,WAAW,EAAE,OAAO;IACpB,KAAK,EAAE;QACL,eAAe,EAAE,CAAC;QAClB,mBAAmB,EAAE,KAAK;QAC1B,2BAA2B,EAAE,CAAC;KAC/B;IACD,UAAU,EAAE,IAAI;CACjB,CAAC,CAAC","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\n// tslint:disable-next-line: no-imports-from-dist\nimport {Constraints, registerTestEnv} from '@tensorflow/tfjs-core/dist/jasmine_util';\n\nexport const WEBGL_ENVS: Constraints = {\n predicate: testEnv => testEnv.backendName === 'webgl'\n};\nexport const PACKED_ENVS: Constraints = {\n flags: {'WEBGL_PACK': true}\n};\n\nregisterTestEnv({\n name: 'webgl1',\n backendName: 'webgl',\n flags: {\n 'WEBGL_VERSION': 1,\n 'WEBGL_CPU_FORWARD': false,\n 'WEBGL_SIZE_UPLOAD_UNIFORM': 0\n },\n isDataSync: true\n});\n\nregisterTestEnv({\n name: 'webgl2',\n backendName: 'webgl',\n flags: {\n 'WEBGL_VERSION': 2,\n 'WEBGL_CPU_FORWARD': false,\n 'WEBGL_SIZE_UPLOAD_UNIFORM': 0\n },\n isDataSync: true\n});\n"]}
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|
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{"version":3,"file":"batchnorm_packed_gpu.js","sourceRoot":"","sources":["../src/batchnorm_packed_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,YAAY,EAAC,MAAM,uBAAuB,CAAC;AAGnD,MAAM,OAAO,sBAAsB;IAOjC,YACI,MAAgB,EAAE,SAAmB,EAAE,aAAuB,EAC9D,WAA0B,EAAE,UAAyB,EACrD,eAAuB;QAN3B,iBAAY,GAAG,IAAI,CAAC;QACpB,iBAAY,GAAG,IAAI,CAAC;QAMlB,IAAI,CAAC,aAAa,GAAG,CAAC,GAAG,EAAE,MAAM,EAAE,UAAU,CAAC,CAAC;QAC/C,YAAY,CAAC,0BAA0B,CAAC,MAAM,EAAE,SAAS,CAAC,CAAC;QAC3D,YAAY,CAAC,0BAA0B,CAAC,MAAM,EAAE,aAAa,CAAC,CAAC;QAE/D,IAAI,aAAa,GAAG,WAAW,CAAC;QAChC,IAAI,WAAW,IAAI,IAAI,EAAE;YACvB,YAAY,CAAC,0BAA0B,CAAC,MAAM,EAAE,WAAW,CAAC,CAAC;YAC7D,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC;YAClC,aAAa,GAAG,wBAAwB,CAAC;SAC1C;QAED,IAAI,YAAY,GAAG,WAAW,CAAC;QAC/B,IAAI,UAAU,IAAI,IAAI,EAAE;YACtB,YAAY,CAAC,0BAA0B,CAAC,MAAM,EAAE,UAAU,CAAC,CAAC;YAC5D,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;YACjC,YAAY,GAAG,uBAAuB,CAAC;SACxC;QAED,IAAI,CAAC,WAAW,GAAG,MAAM,CAAC;QAC1B,IAAI,CAAC,QAAQ,GAAG;;wBAEI,aAAa;uBACd,YAAY;;;;;;yDAMsB,eAAe;;;;KAInE,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2018 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} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class BatchNormPackedProgram implements GPGPUProgram {\n variableNames: string[];\n outputShape: number[];\n userCode: string;\n packedInputs = true;\n packedOutput = true;\n\n constructor(\n xShape: number[], meanShape: number[], varianceShape: number[],\n offsetShape: number[]|null, scaleShape: number[]|null,\n varianceEpsilon: number) {\n this.variableNames = ['x', 'mean', 'variance'];\n backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n\n let offsetSnippet = 'vec4(0.0)';\n if (offsetShape != null) {\n backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n this.variableNames.push('offset');\n offsetSnippet = 'getOffsetAtOutCoords()';\n }\n\n let scaleSnippet = 'vec4(1.0)';\n if (scaleShape != null) {\n backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n this.variableNames.push('scale');\n scaleSnippet = 'getScaleAtOutCoords()';\n }\n\n this.outputShape = xShape;\n this.userCode = `\n void main() {\n vec4 offset = ${offsetSnippet};\n vec4 scale = ${scaleSnippet};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n setOutput((x - mean) * inv + offset);\n }\n `;\n }\n}\n"]}
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{"version":3,"file":"binaryop_complex_gpu.js","sourceRoot":"","sources":["../src/binaryop_complex_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,YAAY,EAAC,MAAM,uBAAuB,CAAC;AAGnD,uBAAuB;AACvB,sDAAsD;AACtD,iBAAiB;AACjB,mBAAmB;AACnB,MAAM,CAAC,MAAM,gBAAgB,GAAG;IAC9B,IAAI,EAAE,uCAAuC;IAC7C,IAAI,EAAE,uCAAuC;CAC9C,CAAC;AAEF,MAAM,OAAO,sBAAsB;IAKjC,YAAY,EAAU,EAAE,MAAgB,EAAE,MAAgB;QAJ1D,kBAAa,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAC;QAKnD,IAAI,CAAC,WAAW,GAAG,YAAY,CAAC,0BAA0B,CAAC,MAAM,EAAE,MAAM,CAAC,CAAC;QAE3E,IAAI,CAAC,QAAQ,GAAG;;;UAGV,EAAE;;;;;;;;;;KAUP,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2018 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} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n REAL: 'return areal * breal - aimag * bimag;',\n IMAG: 'return areal * bimag + aimag * breal;'\n};\n\nexport class BinaryOpComplexProgram implements GPGPUProgram {\n variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n userCode: string;\n outputShape: number[];\n\n constructor(op: string, aShape: number[], bShape: number[]) {\n this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n\n this.userCode = `\n float binaryOpComplex(\n float areal, float aimag, float breal, float bimag) {\n ${op}\n }\n\n void main() {\n float areal = getARealAtOutCoords();\n float aimag = getAImagAtOutCoords();\n float breal = getBRealAtOutCoords();\n float bimag = getBImagAtOutCoords();\n setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n }\n `;\n }\n}\n"]}
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{"version":3,"file":"binaryop_gpu.js","sourceRoot":"","sources":["../src/binaryop_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,YAAY,EAAC,MAAM,uBAAuB,CAAC;AAInD,MAAM,CAAC,MAAM,iBAAiB,GAAG;;;CAGhC,CAAC;AAEF,MAAM,CAAC,MAAM,kBAAkB,GAAG,2BAA2B,CAAC;AAC9D,MAAM,OAAO,eAAe;IAK1B,YAAY,EAAU,EAAE,MAAgB,EAAE,MAAgB;QAJ1D,kBAAa,GAAG,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC;QAKzB,IAAI,CAAC,WAAW,GAAG,YAAY,CAAC,0BAA0B,CAAC,MAAM,EAAE,MAAM,CAAC,CAAC;QAC3E,IAAI,CAAC,QAAQ,GAAG;;UAEV,EAAE;;;;;;;;KAQP,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2017 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} from '@tensorflow/tfjs-core';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport const CHECK_NAN_SNIPPET = `\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;\n\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport class BinaryOpProgram implements GPGPUProgram {\n variableNames = ['A', 'B'];\n outputShape: number[];\n userCode: string;\n\n constructor(op: string, aShape: number[], bShape: number[]) {\n this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n this.userCode = `\n float binaryOperation(float a, float b) {\n ${op}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `;\n }\n}\n"]}
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|
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|
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||||
{"version":3,"file":"clip_gpu.js","sourceRoot":"","sources":["../src/clip_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAKH,MAAM,OAAO,WAAW;IAStB,YAAY,MAAgB;QAR5B,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QASpB,IAAI,CAAC,WAAW,GAAG,MAAM,CAAC;QAC1B,IAAI,CAAC,QAAQ,GAAG;;;;;;;;;;;;;KAaf,CAAC;IACJ,CAAC;IAED,kBAAkB,CAAC,GAAW,EAAE,GAAW;QACzC,OAAO,CAAC,KAAmB,EAAE,YAA0B,EAAE,EAAE;YACzD,IAAI,IAAI,CAAC,MAAM,IAAI,IAAI,EAAE;gBACvB,IAAI,CAAC,MAAM,GAAG,KAAK,CAAC,yBAAyB,CAAC,YAAY,EAAE,QAAQ,CAAC,CAAC;gBACtE,IAAI,CAAC,MAAM,GAAG,KAAK,CAAC,yBAAyB,CAAC,YAAY,EAAE,QAAQ,CAAC,CAAC;aACvE;YACD,KAAK,CAAC,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,MAAM,EAAE,GAAG,CAAC,CAAC;YACrC,KAAK,CAAC,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,MAAM,EAAE,GAAG,CAAC,CAAC;QACvC,CAAC,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2017 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 {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipProgram implements GPGPUProgram {\n variableNames = ['A'];\n userCode: string;\n outputShape: number[];\n\n // Caching uniform locations for speed.\n minLoc: WebGLUniformLocation;\n maxLoc: WebGLUniformLocation;\n\n constructor(aShape: number[]) {\n this.outputShape = aShape;\n this.userCode = `\n uniform float minVal;\n uniform float maxVal;\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `;\n }\n\n getCustomSetupFunc(min: number, max: number) {\n return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n if (this.minLoc == null) {\n this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n }\n gpgpu.gl.uniform1f(this.minLoc, min);\n gpgpu.gl.uniform1f(this.maxLoc, max);\n };\n }\n}\n"]}
|
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{"version":3,"file":"clip_packed_gpu.js","sourceRoot":"","sources":["../src/clip_packed_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAKH,MAAM,OAAO,iBAAiB;IAW5B,YAAY,MAAgB;QAV5B,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QACtB,iBAAY,GAAG,IAAI,CAAC;QACpB,iBAAY,GAAG,IAAI,CAAC;QASlB,IAAI,CAAC,WAAW,GAAG,MAAM,CAAC;QAC1B,IAAI,CAAC,QAAQ,GAAG;;;;;;;;;;;;;;KAcf,CAAC;IACJ,CAAC;IAED,kBAAkB,CAAC,GAAW,EAAE,GAAW;QACzC,OAAO,CAAC,KAAmB,EAAE,YAA0B,EAAE,EAAE;YACzD,IAAI,IAAI,CAAC,MAAM,IAAI,IAAI,EAAE;gBACvB,IAAI,CAAC,MAAM,GAAG,KAAK,CAAC,yBAAyB,CAAC,YAAY,EAAE,QAAQ,CAAC,CAAC;gBACtE,IAAI,CAAC,MAAM,GAAG,KAAK,CAAC,yBAAyB,CAAC,YAAY,EAAE,QAAQ,CAAC,CAAC;aACvE;YACD,KAAK,CAAC,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,MAAM,EAAE,GAAG,CAAC,CAAC;YACrC,KAAK,CAAC,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,MAAM,EAAE,GAAG,CAAC,CAAC;QACvC,CAAC,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2018 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 {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipPackedProgram implements GPGPUProgram {\n variableNames = ['A'];\n packedInputs = true;\n packedOutput = true;\n userCode: string;\n outputShape: number[];\n\n // Caching uniform locations for speed.\n minLoc: WebGLUniformLocation;\n maxLoc: WebGLUniformLocation;\n\n constructor(aShape: number[]) {\n this.outputShape = aShape;\n this.userCode = `\n uniform float minVal;\n uniform float maxVal;\n\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `;\n }\n\n getCustomSetupFunc(min: number, max: number) {\n return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n if (this.minLoc == null) {\n this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n }\n gpgpu.gl.uniform1f(this.minLoc, min);\n gpgpu.gl.uniform1f(this.maxLoc, max);\n };\n }\n}\n"]}
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{"version":3,"file":"complex_abs_gpu.js","sourceRoot":"","sources":["../src/complex_abs_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAIH,MAAM,OAAO,iBAAiB;IAK5B,YAAY,KAAe;QAJ3B,kBAAa,GAAG,CAAC,MAAM,EAAE,MAAM,CAAC,CAAC;QAK/B,IAAI,CAAC,WAAW,GAAG,KAAK,CAAC;QACzB,IAAI,CAAC,QAAQ,GAAG;;;;;;;;;;;;;KAaf,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2018 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 {GPGPUProgram} from './gpgpu_math';\n\nexport class ComplexAbsProgram implements GPGPUProgram {\n variableNames = ['real', 'imag'];\n userCode: string;\n outputShape: number[];\n\n constructor(shape: number[]) {\n this.outputShape = shape;\n this.userCode = `\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `;\n }\n}\n"]}
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-1
@@ -1 +1 @@
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|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
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|
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{"version":3,"file":"conv_gpu_depthwise.js","sourceRoot":"","sources":["../src/conv_gpu_depthwise.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAKH,MAAM,OAAO,sBAAsB;IAKjC,YACI,QAAiC,EAAE,OAAO,GAAG,KAAK,EAClD,aAAqB,IAAI,EAAE,kBAAkB,GAAG,KAAK,EACrD,iBAAiB,GAAG,KAAK;QAP7B,kBAAa,GAAG,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC;QAQzB,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,QAAQ,CAAC;QAErC,MAAM,QAAQ,GAAG,QAAQ,CAAC,QAAQ,CAAC;QACnC,MAAM,QAAQ,GAAG,QAAQ,CAAC,OAAO,CAAC;QAClC,MAAM,MAAM,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACpC,MAAM,OAAO,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QACtC,MAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,MAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,MAAM,cAAc,GAAG,QAAQ,CAAC,cAAc,CAAC;QAC/C,MAAM,aAAa,GAAG,QAAQ,CAAC,aAAa,CAAC;QAC7C,MAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,MAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,MAAM,UAAU,GAAG,QAAQ,CAAC,WAAW,GAAG,QAAQ,CAAC,UAAU,CAAC;QAE9D,IAAI,iBAAiB,GAAG,EAAE,EAAE,sBAAsB,GAAG,EAAE,CAAC;QACxD,IAAI,UAAU,EAAE;YACd,IAAI,kBAAkB,EAAE;gBACtB,iBAAiB,GAAG;;YAEhB,UAAU;UACZ,CAAC;aACJ;iBAAM,IAAI,iBAAiB,EAAE;gBAC5B,iBAAiB,GAAG;;YAEhB,UAAU;UACZ,CAAC;aACJ;iBAAM;gBACL,iBAAiB,GAAG;;cAEd,UAAU;;SAEf,CAAC;aACH;YAED,sBAAsB,GAAG,8BAA8B,CAAC;SACzD;QAED,MAAM,cAAc,GAAG,OAAO,CAAC,CAAC,CAAC,iCAAiC,CAAC,CAAC,CAAC,EAAE,CAAC;QACxE,IAAI,OAAO,EAAE;YACX,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC;SACjC;QAED,IAAI,kBAAkB,EAAE;YACtB,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,wBAAwB,CAAC,CAAC;SACnD;QACD,IAAI,iBAAiB,EAAE;YACrB,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,gBAAgB,CAAC,CAAC;SAC3C;QAED,IAAI,CAAC,QAAQ,GAAG;QACZ,iBAAiB;;oCAEW,YAAY,KAAK,WAAW;iCAC/B,MAAM,KAAK,OAAO;;;;;;;wBAO3B,UAAU;4BACN,UAAU;;;;;;;;;gCASN,YAAY;qCACP,cAAc;;gCAEnB,QAAQ;;;;kCAIN,WAAW;uCACN,aAAa;;kCAElB,QAAQ;;;;;;;;;;;UAWhC,cAAc;UACd,sBAAsB;;;KAG3B,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2017 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} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConv2DProgram implements GPGPUProgram {\n variableNames = ['x', 'W'];\n outputShape: number[];\n userCode: string;\n\n constructor(\n convInfo: backend_util.Conv2DInfo, addBias = false,\n activation: string = null, hasPreluActivation = false,\n hasLeakyReluAlpha = false) {\n this.outputShape = convInfo.outShape;\n\n const xNumRows = convInfo.inHeight;\n const xNumCols = convInfo.inWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n let activationSnippet = '', applyActivationSnippet = '';\n if (activation) {\n if (hasPreluActivation) {\n activationSnippet = `float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${activation}\n }`;\n } else if (hasLeakyReluAlpha) {\n activationSnippet = `float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${activation}\n }`;\n } else {\n activationSnippet = `\n float activation(float x) {\n ${activation}\n }\n `;\n }\n\n applyActivationSnippet = `result = activation(result);`;\n }\n\n const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n if (addBias) {\n this.variableNames.push('bias');\n }\n\n if (hasPreluActivation) {\n this.variableNames.push('preluActivationWeights');\n }\n if (hasLeakyReluAlpha) {\n this.variableNames.push('leakyreluAlpha');\n }\n\n this.userCode = `\n ${activationSnippet}\n\n const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${channelMul};\n int q = d2 - d1 * ${channelMul};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Line truncated
|
||||
Generated
Vendored
+158
-109
@@ -21,6 +21,7 @@ export class DepthwiseConvPacked2DProgram {
|
||||
this.packedInputs = true;
|
||||
this.packedOutput = true;
|
||||
this.outputShape = convInfo.outShape;
|
||||
const channelMul = convInfo.outChannels / convInfo.inChannels;
|
||||
const xNumRows = convInfo.inHeight;
|
||||
const xNumCols = convInfo.inWidth;
|
||||
const padTop = convInfo.padInfo.top;
|
||||
@@ -32,14 +33,14 @@ export class DepthwiseConvPacked2DProgram {
|
||||
const filterHeight = convInfo.filterHeight;
|
||||
const filterWidth = convInfo.filterWidth;
|
||||
const texelsAcross = filterWidth;
|
||||
let mainLoop = `int xR; int xC; int xCOffset;`;
|
||||
for (let r = 0; r < filterHeight; r++) {
|
||||
for (let c = 0; c < filterWidth; c++) {
|
||||
mainLoop += `
|
||||
vec4 xTexelR${r}C${c * 2} = vec4(0.);
|
||||
vec4 wR${r}C${c} = vec4(0.);
|
||||
vec4 xR${r}C${c} = vec4(0.);`;
|
||||
}
|
||||
let mainLoop = `
|
||||
int xR; int xC; int xCOffset;
|
||||
vec4 wTexel; vec4 previous; vec4 final;`;
|
||||
for (let c = 0; c < filterWidth; c++) {
|
||||
mainLoop += `
|
||||
vec4 xTexelC${c * 2};
|
||||
int xTexelC${c * 2}Ready;
|
||||
vec4 xC${c};`;
|
||||
}
|
||||
/**
|
||||
* This vectorized implementation works by gathering the values needed for
|
||||
@@ -50,14 +51,24 @@ export class DepthwiseConvPacked2DProgram {
|
||||
* values from a texture2D call at once.
|
||||
*/
|
||||
for (let r = 0; r < filterHeight; r++) {
|
||||
for (let texelC = 0; texelC < texelsAcross; texelC++) {
|
||||
const c = texelC * 2;
|
||||
for (let c = 0; c < filterWidth; c++) {
|
||||
mainLoop += `
|
||||
xR = xRCorner + ${r * dilationHeight};
|
||||
xC = xCCorner + ${c * dilationWidth};
|
||||
`;
|
||||
xTexelC${c * 2} = vec4(0.0);
|
||||
xTexelC${c * 2}Ready = 0;
|
||||
xC${c} = vec4(0.0);`;
|
||||
}
|
||||
mainLoop += `
|
||||
xR = xRCorner + ${r * dilationHeight};
|
||||
if (xR >=0 && xR < ${xNumRows}) {
|
||||
`;
|
||||
for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {
|
||||
const colIndex = texelC * 2;
|
||||
const c = colIndex * dilationWidth;
|
||||
mainLoop += `
|
||||
xC = xCCorner + ${c};
|
||||
`;
|
||||
if (strideWidth === 1) {
|
||||
if (c < filterWidth) {
|
||||
if (colIndex < filterWidth) {
|
||||
// If padding is odd, the outer texels have to be composed.
|
||||
if (padLeft % 2 === 1) {
|
||||
// TODO: Ensure vec4 previous does not result in redundant sample,
|
||||
@@ -69,45 +80,57 @@ export class DepthwiseConvPacked2DProgram {
|
||||
// - We subtract 2 to access the previous texel.
|
||||
mainLoop += `
|
||||
xCOffset = xC + 1;
|
||||
if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
|
||||
xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
|
||||
if (xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${c}Ready == 0) {
|
||||
xTexelC${c} = getX(batch, xR, xCOffset, d1);
|
||||
|
||||
// Need to manually clear unused channels in case
|
||||
// we're reading from recycled texture.
|
||||
if(xCOffset + 1 >= ${xNumCols}) {
|
||||
xTexelR${r}C${c}.zw = vec2(0.);
|
||||
if (xCOffset + 1 >= ${xNumCols}) {
|
||||
xTexelC${c}.zw = vec2(0.0);
|
||||
}
|
||||
} else {
|
||||
xTexelR${r}C${c} = vec4(0.);
|
||||
}
|
||||
|
||||
xCOffset = xC + 1 - 2;
|
||||
if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
|
||||
vec4 previous = getX(batch, xR, xCOffset, d1);
|
||||
|
||||
// Need to manually clear unused channels in case
|
||||
// we're reading from recycled texture.
|
||||
if(xCOffset + 1 >= ${xNumCols}) {
|
||||
previous.zw = vec2(0.);
|
||||
}
|
||||
|
||||
xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);
|
||||
} else {
|
||||
xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);
|
||||
xTexelC${c}Ready = 1;
|
||||
}
|
||||
`;
|
||||
// This texel has been read in previous iteration if the dilation
|
||||
// is 1.
|
||||
if (dilationWidth === 1 && c > 0) {
|
||||
mainLoop += `
|
||||
xC${colIndex} = vec4(xTexelC${c - 2}.zw, xTexelC${c}.xy);
|
||||
`;
|
||||
}
|
||||
else {
|
||||
mainLoop += `
|
||||
xCOffset = xC + 1 - 2;
|
||||
|
||||
if (xCOffset >= 0 && xCOffset < ${xNumCols}) {
|
||||
previous = getX(batch, xR, xCOffset, d1);
|
||||
|
||||
// Need to manually clear unused channels in case
|
||||
// we're reading from recycled texture.
|
||||
if (xCOffset + 1 >= ${xNumCols}) {
|
||||
previous.zw = vec2(0.0);
|
||||
}
|
||||
|
||||
xC${colIndex} = vec4(previous.zw, xTexelC${c}.xy);
|
||||
} else {
|
||||
xC${colIndex} = vec4(0.0, 0.0, xTexelC${c}.xy);
|
||||
}
|
||||
`;
|
||||
}
|
||||
}
|
||||
else {
|
||||
// Padding is even, so xRC corresponds to a single texel.
|
||||
mainLoop += `
|
||||
if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {
|
||||
xTexelR${r}C${c} = getX(batch, xR, xC, d1);
|
||||
} else {
|
||||
xTexelR${r}C${c} = vec4(0.);
|
||||
if (xC >= 0 && xC < ${xNumCols} && xTexelC${c}Ready == 0) {
|
||||
xTexelC${c} = getX(batch, xR, xC, d1);
|
||||
if (xC + 1 >= ${xNumCols}) {
|
||||
xTexelC${c}.zw = vec2(0.0);
|
||||
}
|
||||
xTexelC${c}Ready = 1;
|
||||
}
|
||||
|
||||
xR${r}C${c} = xTexelR${r}C${c};
|
||||
`;
|
||||
xC${colIndex} = xTexelC${c};
|
||||
`;
|
||||
}
|
||||
if (c + 1 < filterWidth) {
|
||||
// If dilation is even, the second entry should match the first
|
||||
@@ -123,130 +146,155 @@ export class DepthwiseConvPacked2DProgram {
|
||||
mainLoop += `
|
||||
xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};
|
||||
|
||||
if(xR >= 0 && xR < ${xNumRows} &&
|
||||
xCOffset >= 0 && xCOffset < ${xNumCols}) {
|
||||
xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);
|
||||
if (xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${c + 2}Ready == 0) {
|
||||
xTexelC${c + 2} = getX(batch, xR, xCOffset, d1);
|
||||
|
||||
// Need to manually clear unused channels in case
|
||||
// we're reading from recycled texture.
|
||||
if (xCOffset + 1 >= ${xNumCols}) {
|
||||
xTexelC${c + 2}.zw = vec2(0.0);
|
||||
}
|
||||
xTexelC${c + 2}Ready = 1;
|
||||
}
|
||||
`;
|
||||
`;
|
||||
// If dilation > 1 then the xRC's will not be able to share any
|
||||
// values, so each xRC will require two unique calls to getX.
|
||||
if (dilationWidth > 1) {
|
||||
mainLoop += `
|
||||
xCOffset -= 2;
|
||||
if(xR >= 0 && xR < ${xNumRows} &&
|
||||
xCOffset >= 0 && xCOffset < ${xNumCols}) {
|
||||
xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
|
||||
} else {
|
||||
xTexelR${r}C${c} = vec4(0.);
|
||||
if (xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${c}Ready == 0) {
|
||||
xTexelC${c} = getX(batch, xR, xCOffset, d1);
|
||||
xTexelC${c}Ready = 1;
|
||||
}
|
||||
`;
|
||||
`;
|
||||
}
|
||||
mainLoop += `
|
||||
xR${r}C${c + 1} = vec4(
|
||||
xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);
|
||||
`;
|
||||
xC${colIndex + 1} = vec4(xTexelC${c}.zw, xTexelC${c + 2}.xy);
|
||||
`;
|
||||
}
|
||||
else {
|
||||
mainLoop += `
|
||||
xCOffset = xC + ${nextTexelOffset};
|
||||
// If dilation is 1 and padding is odd, we have already read the
|
||||
// texel when constructing the previous x value. Here we can
|
||||
// simply skip the texture read.
|
||||
if (nextTexelOffset === 1) {
|
||||
mainLoop += `
|
||||
xC${colIndex + 1} = xTexelC${c};
|
||||
`;
|
||||
}
|
||||
else {
|
||||
mainLoop += `
|
||||
xCOffset = xC + ${nextTexelOffset};
|
||||
|
||||
if(xR >= 0 && xR < ${xNumRows} &&
|
||||
xCOffset >= 0 && xCOffset < ${xNumCols}) {
|
||||
xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);
|
||||
}
|
||||
if (xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${c + 2}Ready == 0) {
|
||||
xTexelC${c + 2} = getX(batch, xR, xCOffset, d1);
|
||||
if (xCOffset + 1 >= ${xNumCols}) {
|
||||
xTexelC${c + 2}.zw = vec2(0.0);
|
||||
}
|
||||
xTexelC${c + 2}Ready = 1;
|
||||
}
|
||||
|
||||
xR${r}C${c + 1} = xTexelR${r}C${c + 2};
|
||||
`;
|
||||
xC${colIndex + 1} = xTexelC${c + 2};
|
||||
`;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
else { // stride > 1
|
||||
else { // stride === 2
|
||||
if (c < filterWidth) {
|
||||
mainLoop += `
|
||||
if(xR >= 0 && xR < ${xNumRows}) {
|
||||
`;
|
||||
// Depending on whether padLeft is even or odd, we want either the
|
||||
// xy or zw channels from X texels for xR${r}C${c}. If padLeft is
|
||||
// even, xR${r}C${c + 1} is simply the zw channels of texels we've
|
||||
// already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will
|
||||
// need to come from the xy channels of a new texel, hence the `vec4
|
||||
// xy or zw channels from X texels for xC${colIndex}. If padLeft is
|
||||
// even, xC${colIndex +1} is simply the zw channels of texels we've
|
||||
// already sampled. But if padLeft is odd, xC{$c + 1}.zw will
|
||||
// need to come from the xy channels of a new texel, hence the `
|
||||
// vec4
|
||||
// final` initialized below.
|
||||
if (padLeft % 2 === 1) {
|
||||
mainLoop += `
|
||||
xCOffset = xC + 1 - ${strideWidth};
|
||||
if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
|
||||
xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
|
||||
} else {
|
||||
xTexelR${r}C${c} = vec4(0.);
|
||||
if(xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${c}Ready == 0) {
|
||||
xTexelC${c} = getX(batch, xR, xCOffset, d1);
|
||||
// Need to manually clear unused channels in case
|
||||
// we're reading from recycled texture.
|
||||
if (xCOffset + 1 >= ${xNumCols}) {
|
||||
xTexelC${c}.zw = vec2(0.0);
|
||||
}
|
||||
xTexelC${c}Ready = 1;
|
||||
}
|
||||
|
||||
if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {
|
||||
xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);
|
||||
} else {
|
||||
xTexelR${r}C${c + 2} = vec4(0.);
|
||||
if(xC + 1 >= 0 && xC + 1 < ${xNumCols} && xTexelC${c + 2}Ready == 0) {
|
||||
xTexelC${c + 2} = getX(batch, xR, xC + 1, d1);
|
||||
// Need to manually clear unused channels in case
|
||||
// we're reading from recycled texture.
|
||||
if (xC + 2 >= ${xNumCols}) {
|
||||
xTexelC${c + 2}.zw = vec2(0.0);
|
||||
}
|
||||
xTexelC${c + 2}Ready = 1;
|
||||
}
|
||||
|
||||
xR${r}C${c} = vec4(
|
||||
xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);
|
||||
xC${colIndex} = vec4(xTexelC${c}.zw, xTexelC${c + 2}.zw);
|
||||
`;
|
||||
if (c + 1 < filterWidth) {
|
||||
mainLoop += `
|
||||
vec4 final = vec4(0.);
|
||||
final = vec4(0.0);
|
||||
xCOffset = xC + 1 + ${strideWidth};
|
||||
if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
|
||||
final = getX(batch, xR, xCOffset, d1);
|
||||
}
|
||||
xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);
|
||||
xC${colIndex + 1} = vec4(xTexelC${c + 2}.xy, final.xy);
|
||||
`;
|
||||
}
|
||||
}
|
||||
else {
|
||||
mainLoop += `
|
||||
if(xC >= 0 && xC < ${xNumCols}) {
|
||||
xTexelR${r}C${c} = getX(batch, xR, xC, d1);
|
||||
} else {
|
||||
xTexelR${r}C${c} = vec4(0.);
|
||||
if(xC >= 0 && xC < ${xNumCols} && xTexelC${c}Ready == 0) {
|
||||
xTexelC${c} = getX(batch, xR, xC, d1);
|
||||
if (xC + 1 >= ${xNumCols}) {
|
||||
xTexelC${c}.zw = vec2(0.0);
|
||||
}
|
||||
xTexelC${c}Ready = 1;
|
||||
}
|
||||
|
||||
xCOffset = xC + ${strideWidth};
|
||||
if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
|
||||
xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);
|
||||
} else {
|
||||
xTexelR${r}C${c + 2} = vec4(0.);
|
||||
if(xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${c + 2}Ready == 0) {
|
||||
xTexelC${c + 2} = getX(batch, xR, xCOffset, d1);
|
||||
if (xCOffset + 1 >= ${xNumCols}) {
|
||||
xTexelC${c + 2}.zw = vec2(0.);
|
||||
}
|
||||
xTexelC${c + 2}Ready = 1;
|
||||
}
|
||||
|
||||
xR${r}C${c} = vec4(
|
||||
xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);
|
||||
xC${colIndex} = vec4(
|
||||
xTexelC${c}.xy, xTexelC${c + 2}.xy);
|
||||
`;
|
||||
if (c + 1 < filterWidth) {
|
||||
mainLoop += `
|
||||
xR${r}C${c + 1} = vec4(
|
||||
xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);
|
||||
xC${colIndex + 1} = vec4(xTexelC${c}.zw, xTexelC${c + 2}.zw);
|
||||
`;
|
||||
}
|
||||
}
|
||||
mainLoop += `}`;
|
||||
}
|
||||
}
|
||||
if (c < filterWidth) {
|
||||
// localize the dotProd accumulation within the loop, the theory is for
|
||||
// GPU with limited cache, accumulate sum across large amount of
|
||||
// veriables will cause lots of cache misses. (i.e. 5x5 filter will have
|
||||
// 50 variables)
|
||||
if (colIndex < filterWidth) {
|
||||
mainLoop += `
|
||||
vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);
|
||||
wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);
|
||||
wTexel = getW(${r}, ${c}, d1, q);
|
||||
dotProd += xC${colIndex} * vec4(wTexel.xz, wTexel.xz);
|
||||
`;
|
||||
if (c + 1 < filterWidth) {
|
||||
mainLoop += `
|
||||
vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);
|
||||
wR${r}C${c + 1} =
|
||||
vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;
|
||||
wTexel = getW(${r}, ${c + 1}, d1, q);
|
||||
dotProd += xC${colIndex + 1} * vec4(wTexel.xz, wTexel.xz);
|
||||
`;
|
||||
}
|
||||
}
|
||||
}
|
||||
mainLoop += `
|
||||
}
|
||||
for (let r = 0; r < filterHeight; r++) {
|
||||
for (let c = 0; c < filterWidth; c++) {
|
||||
mainLoop += `dotProd += xR${r}C${c} * wR${r}C${c};`;
|
||||
}
|
||||
`;
|
||||
}
|
||||
let activationSnippet = '', applyActivationSnippet = '';
|
||||
if (activation) {
|
||||
@@ -291,16 +339,17 @@ export class DepthwiseConvPacked2DProgram {
|
||||
int batch = coords.x;
|
||||
ivec2 xRCCorner = coords.yz * strides - pads;
|
||||
int d2 = coords.w;
|
||||
int d1 = d2;
|
||||
int q = 0;
|
||||
int d1 = d2 / ${channelMul};
|
||||
int q = d2 - d1 * ${channelMul};
|
||||
int xRCorner = xRCCorner.x;
|
||||
int xCCorner = xRCCorner.y;
|
||||
|
||||
vec4 dotProd = vec4(0.);
|
||||
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
|
||||
vec4 dotProd = vec4(0.000000000000001);
|
||||
|
||||
${mainLoop}
|
||||
|
||||
vec4 result = dotProd;
|
||||
vec4 result = dotProd - vec4(0.000000000000001);
|
||||
${addBiasSnippet}
|
||||
${applyActivationSnippet}
|
||||
setOutput(result);
|
||||
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
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|
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|
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{"version":3,"file":"decode_matrix_gpu.js","sourceRoot":"","sources":["../src/decode_matrix_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,kBAAkB,EAAC,MAAM,gBAAgB,CAAC;AAElD,OAAO,KAAK,WAAW,MAAM,wBAAwB,CAAC;AACtD,OAAO,EAAC,gBAAgB,EAAE,aAAa,EAAC,MAAM,YAAY,CAAC;AAE3D,MAAM,OAAO,mBAAmB;IAQ9B,YAAY,WAAqC;QAPjD,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAGtB,iBAAY,GAAG,KAAK,CAAC;QACrB,iBAAY,GAAG,IAAI,CAAC;QACpB,qBAAgB,GAAG,aAAa,CAAC,KAAK,CAAC;QAGrC,MAAM,QAAQ,GAAG,gBAAgB,CAAC,WAAW,CAAC,CAAC;QAC/C,MAAM,IAAI,GAAG,kBAAkB,EAAE,CAAC;QAClC,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAE/B,IAAI,CAAC,QAAQ,GAAG;;UAGZ,WAAW,CAAC,kCAAkC,CAC1C,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,EAAE,WAAW,CAAC;;;;;;iBAMxB,QAAQ,CAAC,CAAC,CAAC,KAAK,QAAQ,CAAC,CAAC,CAAC;wCACJ,QAAQ,CAAC,CAAC,CAAC;;;;;;;;;;UAUzC,IAAI,CAAC,MAAM;;KAEhB,CAAC;IACJ,CAAC;CACF","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 {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\nimport {getDenseTexShape, PackingScheme} from './tex_util';\n\nexport class DecodeMatrixProgram implements GPGPUProgram {\n variableNames = ['A'];\n userCode: string;\n outputShape: [number, number, number];\n packedInputs = false;\n packedOutput = true;\n outPackingScheme = PackingScheme.DENSE;\n\n constructor(outputShape: [number, number, number]) {\n const texShape = getDenseTexShape(outputShape);\n const glsl = getGlslDifferences();\n this.outputShape = outputShape;\n\n this.userCode = `\n ivec3 outCoordsFromFlatIndex(int index) {\n ${\n shader_util.getLogicalCoordinatesFromFlatIndex(\n ['r', 'c', 'd'], outputShape)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getA(rc.x, rc.y, rc.z);\n }\n\n ${glsl.output} = result;\n }\n `;\n }\n}\n"]}
|
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||||
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{"version":3,"file":"decode_matrix_packed_gpu.js","sourceRoot":"","sources":["../src/decode_matrix_packed_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,kBAAkB,EAAC,MAAM,gBAAgB,CAAC;AAElD,OAAO,KAAK,WAAW,MAAM,wBAAwB,CAAC;AACtD,OAAO,EAAC,gBAAgB,EAAE,aAAa,EAAC,MAAM,YAAY,CAAC;AAE3D,MAAM,OAAO,yBAAyB;IAQpC,YAAY,WAAqC;QAPjD,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAEtB,iBAAY,GAAG,IAAI,CAAC;QACpB,iBAAY,GAAG,IAAI,CAAC;QAEpB,qBAAgB,GAAG,aAAa,CAAC,KAAK,CAAC;QAGrC,MAAM,QAAQ,GAAG,gBAAgB,CAAC,WAAW,CAAC,CAAC;QAC/C,MAAM,IAAI,GAAG,kBAAkB,EAAE,CAAC;QAClC,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAE/B,IAAI,CAAC,QAAQ,GAAG;;UAGZ,WAAW,CAAC,kCAAkC,CAC1C,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,EAAE,WAAW,CAAC;;;;;;iBAMxB,QAAQ,CAAC,CAAC,CAAC,KAAK,QAAQ,CAAC,CAAC,CAAC;wCACJ,QAAQ,CAAC,CAAC,CAAC;;;;;;;;;;UAUzC,IAAI,CAAC,MAAM;;KAEhB,CAAC;IACJ,CAAC;CACF","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 {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\nimport {getDenseTexShape, PackingScheme} from './tex_util';\n\nexport class DecodeMatrixPackedProgram implements GPGPUProgram {\n variableNames = ['A'];\n userCode: string;\n packedInputs = true;\n packedOutput = true;\n outputShape: [number, number, number];\n outPackingScheme = PackingScheme.DENSE;\n\n constructor(outputShape: [number, number, number]) {\n const texShape = getDenseTexShape(outputShape);\n const glsl = getGlslDifferences();\n this.outputShape = outputShape;\n\n this.userCode = `\n ivec3 outCoordsFromFlatIndex(int index) {\n ${\n shader_util.getLogicalCoordinatesFromFlatIndex(\n ['r', 'c', 'd'], outputShape)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${glsl.output} = result;\n }\n `;\n }\n}\n"]}
|
||||
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|
||||
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|
||||
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|
||||
{"version":3,"file":"diag_gpu.js","sourceRoot":"","sources":["../src/diag_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAIH,MAAM,OAAO,WAAW;IAKtB,YAAY,IAAY;QAJxB,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAKpB,IAAI,CAAC,WAAW,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;QAChC,IAAI,CAAC,QAAQ,GAAG;;;;;;KAMf,CAAC;IACJ,CAAC;CACF"}
|
||||
{"version":3,"file":"diag_gpu.js","sourceRoot":"","sources":["../src/diag_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAIH,MAAM,OAAO,WAAW;IAKtB,YAAY,IAAY;QAJxB,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAKpB,IAAI,CAAC,WAAW,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;QAChC,IAAI,CAAC,QAAQ,GAAG;;;;;;KAMf,CAAC;IACJ,CAAC;CACF","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 {GPGPUProgram} from './gpgpu_math';\n\nexport class DiagProgram implements GPGPUProgram {\n variableNames = ['X'];\n outputShape: number[];\n userCode: string;\n\n constructor(size: number) {\n this.outputShape = [size, size];\n this.userCode = `\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `;\n }\n}\n"]}
|
||||
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|
||||
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|
||||
{"version":3,"file":"dilation_gpu.js","sourceRoot":"","sources":["../src/dilation_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAKH,MAAM,OAAO,iBAAiB;IAK5B,YAAY,QAAiC;QAJ7C,kBAAa,GAAG,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC;QAKzB,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,QAAQ,CAAC;QAErC,MAAM,EACJ,QAAQ,EACR,OAAO,EACP,OAAO,EACP,YAAY,EACZ,WAAW,EACX,YAAY,EACZ,WAAW,EACX,cAAc,EACd,aAAa,EACd,GAAG,QAAQ,CAAC;QAEb,MAAM,EAAC,GAAG,EAAE,MAAM,EAAE,IAAI,EAAE,OAAO,EAAC,GAAG,OAAO,CAAC;QAE7C,IAAI,CAAC,QAAQ,GAAG;oCACgB,YAAY,KAAK,WAAW;iCAC/B,MAAM,KAAK,OAAO;;;;;;;;;;;;;8BAarB,YAAY;iCACT,cAAc;;kCAEb,QAAQ;kCACR,WAAW;qCACR,aAAa;;sCAEZ,OAAO;;;;;;;;;;;;;;;;KAgBxC,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2017 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} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Dilation2DProgram implements GPGPUProgram {\n variableNames = ['x', 'W'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: backend_util.Conv2DInfo) {\n this.outputShape = convInfo.outShape;\n\n const {\n inHeight,\n inWidth,\n padInfo,\n strideHeight,\n strideWidth,\n filterHeight,\n filterWidth,\n dilationHeight,\n dilationWidth\n } = convInfo;\n\n const {top: padTop, left: padLeft} = padInfo;\n\n this.userCode = `\n const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${filterHeight}; h++) {\n int hIn = hBeg + h * ${dilationHeight};\n\n if (hIn >= 0 && hIn < ${inHeight}) {\n for (int w = 0; w < ${filterWidth}; w++) {\n int wIn = wBeg + w * ${dilationWidth};\n\n if (wIn >= 0 && wIn < ${inWidth}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `;\n }\n}\n"]}
|
||||
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|
||||
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|
||||
{"version":3,"file":"encode_float_gpu.js","sourceRoot":"","sources":["../src/encode_float_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,kBAAkB,EAAC,MAAM,gBAAgB,CAAC;AAElD,OAAO,EAAC,oBAAoB,EAAC,MAAM,wBAAwB,CAAC;AAC5D,OAAO,EAAC,YAAY,EAAC,MAAM,YAAY,CAAC;AAExC,MAAM,OAAO,kBAAkB;IAM7B,YAAY,WAAqB;QALjC,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAGtB,gBAAW,GAAG,YAAY,CAAC,QAAQ,CAAC;QAGlC,MAAM,IAAI,GAAG,kBAAkB,EAAE,CAAC;QAClC,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,QAAQ,GAAG;QACZ,oBAAoB;;;;UAIlB,IAAI,CAAC,MAAM;;KAEhB,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2018 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 {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {ENCODE_FLOAT_SNIPPET} from './shader_compiler_util';\nimport {TextureUsage} from './tex_util';\n\nexport class EncodeFloatProgram implements GPGPUProgram {\n variableNames = ['A'];\n userCode: string;\n outputShape: number[];\n outTexUsage = TextureUsage.DOWNLOAD;\n\n constructor(outputShape: number[]) {\n const glsl = getGlslDifferences();\n this.outputShape = outputShape;\n this.userCode = `\n ${ENCODE_FLOAT_SNIPPET}\n\n void main() {\n float x = getAAtOutCoords();\n ${glsl.output} = encode_float(x);\n }\n `;\n }\n}\n"]}
|
||||
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|
||||
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|
||||
{"version":3,"file":"encode_float_packed_gpu.js","sourceRoot":"","sources":["../src/encode_float_packed_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,kBAAkB,EAAC,MAAM,gBAAgB,CAAC;AAElD,OAAO,EAAC,oBAAoB,EAAC,MAAM,wBAAwB,CAAC;AAC5D,OAAO,EAAC,YAAY,EAAC,MAAM,YAAY,CAAC;AAExC,MAAM,OAAO,wBAAwB;IAQnC,YAAY,WAAqC;QAPjD,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAGtB,iBAAY,GAAG,IAAI,CAAC;QACpB,iBAAY,GAAG,KAAK,CAAC;QACrB,gBAAW,GAAG,YAAY,CAAC,QAAQ,CAAC;QAGlC,MAAM,IAAI,GAAG,kBAAkB,EAAE,CAAC;QAClC,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,QAAQ,GAAG;QACZ,oBAAoB;;;;;UAKlB,IAAI,CAAC,MAAM;;KAEhB,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2018 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 {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {ENCODE_FLOAT_SNIPPET} from './shader_compiler_util';\nimport {TextureUsage} from './tex_util';\n\nexport class EncodeFloatPackedProgram implements GPGPUProgram {\n variableNames = ['A'];\n userCode: string;\n outputShape: number[];\n packedInputs = true;\n packedOutput = false;\n outTexUsage = TextureUsage.DOWNLOAD;\n\n constructor(outputShape: [number, number, number]) {\n const glsl = getGlslDifferences();\n this.outputShape = outputShape;\n this.userCode = `\n ${ENCODE_FLOAT_SNIPPET}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${glsl.output} = encode_float(x);\n }\n `;\n }\n}\n"]}
|
||||
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|
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{"version":3,"file":"encode_matrix_gpu.js","sourceRoot":"","sources":["../src/encode_matrix_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,kBAAkB,EAAC,MAAM,gBAAgB,CAAC;AAElD,OAAO,KAAK,WAAW,MAAM,wBAAwB,CAAC;AAEtD,MAAM,OAAO,mBAAmB;IAK9B,YACI,WAAqC,EAAE,QAA0B,EACjE,mBAAmB,GAAG,KAAK;QAN/B,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAOpB,MAAM,IAAI,GAAG,kBAAkB,EAAE,CAAC;QAClC,MAAM,CAAC,MAAM,EAAE,KAAK,CAAC,GAAG,QAAQ,CAAC;QACjC,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAE/B,IAAI,MAAM,GAAG,QAAQ,CAAC;QACtB,IAAI,mBAAmB,EAAE;YACvB,MAAM,GAAG,4BAA4B,CAAC;SACvC;QAED,IAAI,CAAC,QAAQ,GAAG;QACZ,WAAW,CAAC,kBAAkB,CAAC,WAAW,CAAC;;;;;;;;;;8BAUrB,KAAK;kCACD,KAAK;iDACU,KAAK,OAAO,MAAM;wBAC3C,IAAI,CAAC,SAAS;;;;;;;;;;;;;;UAc5B,IAAI,CAAC,MAAM,WAAW,MAAM;;KAEjC,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2018 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 {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\n\nexport class EncodeMatrixProgram implements GPGPUProgram {\n variableNames = ['A'];\n userCode: string;\n outputShape: number[];\n\n constructor(\n outputShape: [number, number, number], texShape: [number, number],\n inputIsUnsignedByte = false) {\n const glsl = getGlslDifferences();\n const [height, width] = texShape;\n this.outputShape = outputShape;\n\n let output = `result`;\n if (inputIsUnsignedByte) {\n output = `floor(result * 255. + 0.5)`;\n }\n\n this.userCode = `\n ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n void main() {\n ivec3 coords = getOutputCoords();\n\n int flatIndex = getFlatIndex(coords);\n int offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n\n int r = flatIndex / ${width};\n int c = imod(flatIndex, ${width});\n vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n vec4 values = ${glsl.texture2D}(A, uv);\n\n float result;\n\n if(offset == 0) {\n result = values[0];\n } else if(offset == 1) {\n result = values[1];\n } else if(offset == 2) {\n result = values[2];\n } else {\n result = values[3];\n }\n\n ${glsl.output} = vec4(${output}, 0., 0., 0.);\n }\n `;\n }\n}\n"]}
|
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
{"version":3,"file":"fill_gpu.js","sourceRoot":"","sources":["../src/fill_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAKH,MAAM,OAAO,WAAW;IAOtB,YAAY,KAAe,EAAE,KAAa;QAL1C,gBAAW,GAAa,EAAE,CAAC;QAMzB,IAAI,CAAC,aAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAC3B,IAAI,CAAC,WAAW,GAAG,KAAK,CAAC;QAEzB,IAAI,CAAC,QAAQ,GAAG;;;;;;KAMf,CAAC;IACJ,CAAC;IAED,kBAAkB,CAAC,KAAa;QAC9B,OAAO,CAAC,KAAmB,EAAE,YAA0B,EAAE,EAAE;YACzD,IAAI,IAAI,CAAC,QAAQ,IAAI,IAAI,EAAE;gBACzB,IAAI,CAAC,QAAQ,GAAG,KAAK,CAAC,yBAAyB,CAAC,YAAY,EAAE,OAAO,CAAC,CAAC;aACxE;YACD,KAAK,CAAC,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,QAAQ,EAAE,KAAK,CAAC,CAAC;QAC3C,CAAC,CAAC;IACJ,CAAC;CACF","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 {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class FillProgram implements GPGPUProgram {\n variableNames: string[];\n outputShape: number[] = [];\n userCode: string;\n\n valueLoc: WebGLUniformLocation;\n\n constructor(shape: number[], value: number) {\n this.variableNames = ['x'];\n this.outputShape = shape;\n\n this.userCode = `\n uniform float value;\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `;\n }\n\n getCustomSetupFunc(value: number) {\n return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n if (this.valueLoc == null) {\n this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n }\n gpgpu.gl.uniform1f(this.valueLoc, value);\n };\n }\n}\n"]}
|
||||
Generated
Vendored
+18
-2
@@ -48,8 +48,7 @@ ENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));
|
||||
/** Whether we will pack the clip op. */
|
||||
ENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));
|
||||
/** Whether we will pack the depthwise conv op. */
|
||||
// TODO: https://github.com/tensorflow/tfjs/issues/1679
|
||||
ENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => false);
|
||||
ENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => ENV.getBool('WEBGL_PACK'));
|
||||
/** Whether we will pack binary ops. */
|
||||
ENV.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));
|
||||
/** Whether we will pack unary ops. */
|
||||
@@ -135,4 +134,21 @@ ENV.registerFlag('WEBGL_DELETE_TEXTURE_THRESHOLD', () => {
|
||||
`delete) or at least 0, but got ${threshold}.`);
|
||||
}
|
||||
});
|
||||
/**
|
||||
* Trigger a manual GL command flush if the threshold of time has passed since
|
||||
* previous Kernel execution. This can be useful for Andorid device where GL
|
||||
* command flush are delayed un til the end of javascript task. This value is
|
||||
* measured in millisecond. Typically you want to set this value to close to 1.
|
||||
*
|
||||
* Default value 1 for mobile chrome, and -1 for rest cases. -1 indicates that
|
||||
* we will not enforce manual flush and depend on system default flush schedule.
|
||||
*/
|
||||
ENV.registerFlag('WEBGL_FLUSH_THRESHOLD', () => {
|
||||
return device_util.isMobile() && ENV.getBool('IS_CHROME') ? 1 : -1;
|
||||
}, threshold => {
|
||||
if (threshold < 0 && threshold !== -1) {
|
||||
throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never ` +
|
||||
`manual flush) or at least 0, but got ${threshold}.`);
|
||||
}
|
||||
});
|
||||
//# sourceMappingURL=flags_webgl.js.map
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
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|
||||
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|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
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{"version":3,"file":"flip_left_right_gpu.js","sourceRoot":"","sources":["../src/flip_left_right_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAIH,MAAM,OAAO,oBAAoB;IAK/B,YAAY,UAA4C;QAJxD,kBAAa,GAAG,CAAC,OAAO,CAAC,CAAC;QAC1B,gBAAW,GAAa,EAAE,CAAC;QAIzB,MAAM,UAAU,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC;QACjC,IAAI,CAAC,WAAW,GAAG,UAAU,CAAC;QAE9B,IAAI,CAAC,QAAQ,GAAG;;;;;yBAKK,UAAU;;uCAEI,UAAU;;;;;;;KAO5C,CAAC;IACJ,CAAC;CACF"}
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{"version":3,"file":"flip_left_right_gpu.js","sourceRoot":"","sources":["../src/flip_left_right_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAIH,MAAM,OAAO,oBAAoB;IAK/B,YAAY,UAA4C;QAJxD,kBAAa,GAAG,CAAC,OAAO,CAAC,CAAC;QAC1B,gBAAW,GAAa,EAAE,CAAC;QAIzB,MAAM,UAAU,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC;QACjC,IAAI,CAAC,WAAW,GAAG,UAAU,CAAC;QAE9B,IAAI,CAAC,QAAQ,GAAG;;;;;yBAKK,UAAU;;uCAEI,UAAU;;;;;;;KAO5C,CAAC;IACJ,CAAC;CACF","sourcesContent":["/**\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 {GPGPUProgram} from './gpgpu_math';\n\nexport class FlipLeftRightProgram implements GPGPUProgram {\n variableNames = ['Image'];\n outputShape: number[] = [];\n userCode: string;\n\n constructor(imageShape: [number, number, number, number]) {\n const imageWidth = imageShape[2];\n this.outputShape = imageShape;\n\n this.userCode = `\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${imageWidth} - x;\n float outputValue;\n if(coordX >= 0 && coordX < ${imageWidth}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `;\n }\n}\n"]}
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|
||||
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|
||||
{"version":3,"file":"gather_gpu.js","sourceRoot":"","sources":["../src/gather_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAGH,OAAO,EAAC,iBAAiB,EAAC,MAAM,mBAAmB,CAAC;AAEpD,MAAM,OAAO,aAAa;IAMxB,YAAY,MAAgB,EAAE,WAAqB;QALnD,kBAAa,GAAG,CAAC,GAAG,EAAE,SAAS,CAAC,CAAC;QAM/B,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,IAAI,GAAG,WAAW,CAAC,MAAM,CAAC;QAC/B,MAAM,KAAK,GAAG,iBAAiB,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAC3C,MAAM,YAAY,GAAG,eAAe,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC;QAEhD,IAAI,CAAC,QAAQ,GAAG;;UAEV,KAAK;yBACU,YAAY;;KAEhC,CAAC;IACJ,CAAC;CACF;AAED,iEAAiE;AACjE,SAAS,eAAe,CAAC,MAAgB,EAAE,IAAY;IACrD,MAAM,aAAa,GAAG,CAAC,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,SAAS,CAAC,CAAC;IAEnE,MAAM,YAAY,GAAG,EAAE,CAAC;IACxB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACtC,IAAI,CAAC,KAAK,CAAC,EAAE;YACX,YAAY,CAAC,IAAI,CAAC,mCAAmC,CAAC,CAAC;SACxD;aAAM;YACL,YAAY,CAAC,IAAI,CAAC,GAAG,aAAa,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC;SAC1C;KACF;IACD,OAAO,YAAY,CAAC,IAAI,EAAE,CAAC;AAC7B,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2017 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 {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherProgram implements GPGPUProgram {\n variableNames = ['A', 'indices'];\n outputShape: number[];\n userCode: string;\n rank: number;\n\n constructor(aShape: number[], outputShape: number[]) {\n this.outputShape = outputShape;\n this.rank = outputShape.length;\n const dtype = getCoordsDataType(this.rank);\n const sourceCoords = getSourceCoords(aShape, 2);\n\n this.userCode = `\n void main() {\n ${dtype} resRC = getOutputCoords();\n setOutput(getA(${sourceCoords}));\n }\n `;\n }\n}\n\n// The input and output are always flattened into rank 4 tensors.\nfunction getSourceCoords(aShape: number[], axis: number): string {\n const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n\n const sourceCoords = [];\n for (let i = 0; i < aShape.length; i++) {\n if (i === 2) {\n sourceCoords.push('int(getIndices(resRC.x, resRC.z))');\n } else {\n sourceCoords.push(`${currentCoords[i]}`);\n }\n }\n return sourceCoords.join();\n}\n"]}
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
{"version":3,"file":"gather_nd_gpu.js","sourceRoot":"","sources":["../src/gather_nd_gpu.ts"],"names":[],"mappings":"AAiBA,OAAO,EAAC,iBAAiB,EAAC,MAAM,mBAAmB,CAAC;AAEpD,MAAM,OAAO,eAAe;IAI1B,YACY,QAAgB,EAAU,OAAiB,EAAE,KAAe;QAA5D,aAAQ,GAAR,QAAQ,CAAQ;QAAU,YAAO,GAAP,OAAO,CAAU;QAJvD,kBAAa,GAAG,CAAC,GAAG,EAAE,SAAS,CAAC,CAAC;QAK/B,IAAI,CAAC,WAAW,GAAG,KAAK,CAAC;QACzB,MAAM,WAAW,GAAG,iBAAiB,CAAC,OAAO,CAAC,MAAM,CAAC,CAAC;QACtD,MAAM,KAAK,GAAG,iBAAiB,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC;QAC9C,MAAM,YAAY,GAAG,IAAI,CAAC,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,SAAS,CAAC;QAClE,IAAI,CAAC,QAAQ,GAAG;UACV,WAAW,cAAc,WAAW,IAAI,IAAI,CAAC,OAAO;;YAElD,KAAK;;gCAEe,IAAI,CAAC,QAAQ;;sCAEP,YAAY;;;;OAI3C,CAAC;IACN,CAAC;CACF"}
|
||||
{"version":3,"file":"gather_nd_gpu.js","sourceRoot":"","sources":["../src/gather_nd_gpu.ts"],"names":[],"mappings":"AAiBA,OAAO,EAAC,iBAAiB,EAAC,MAAM,mBAAmB,CAAC;AAEpD,MAAM,OAAO,eAAe;IAI1B,YACY,QAAgB,EAAU,OAAiB,EAAE,KAAe;QAA5D,aAAQ,GAAR,QAAQ,CAAQ;QAAU,YAAO,GAAP,OAAO,CAAU;QAJvD,kBAAa,GAAG,CAAC,GAAG,EAAE,SAAS,CAAC,CAAC;QAK/B,IAAI,CAAC,WAAW,GAAG,KAAK,CAAC;QACzB,MAAM,WAAW,GAAG,iBAAiB,CAAC,OAAO,CAAC,MAAM,CAAC,CAAC;QACtD,MAAM,KAAK,GAAG,iBAAiB,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC;QAC9C,MAAM,YAAY,GAAG,IAAI,CAAC,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,SAAS,CAAC;QAClE,IAAI,CAAC,QAAQ,GAAG;UACV,WAAW,cAAc,WAAW,IAAI,IAAI,CAAC,OAAO;;YAElD,KAAK;;gCAEe,IAAI,CAAC,QAAQ;;sCAEP,YAAY;;;;OAI3C,CAAC;IACN,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2018 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 {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherNDProgram implements GPGPUProgram {\n variableNames = ['x', 'indices'];\n outputShape: number[];\n userCode: string;\n constructor(\n private sliceDim: number, private strides: number[], shape: number[]) {\n this.outputShape = shape;\n const stridesType = getCoordsDataType(strides.length);\n const dtype = getCoordsDataType(shape.length);\n const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n this.userCode = `\n ${stridesType} strides = ${stridesType}(${this.strides});\n void main() {\n ${dtype} coords = getOutputCoords();\n int flattenIndex = 0;\n for (int j = 0; j < ${this.sliceDim}; j++) {\n int index = round(getIndices(coords[0], j));\n flattenIndex += index * ${strideString};\n }\n setOutput(getX(flattenIndex, coords[1]));\n }\n `;\n }\n}\n"]}
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
{"version":3,"file":"glsl_version.js","sourceRoot":"","sources":["../src/glsl_version.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAC,GAAG,EAAC,MAAM,uBAAuB,CAAC;AAe1C,MAAM,UAAU,kBAAkB;IAChC,IAAI,OAAe,CAAC;IACpB,IAAI,SAAiB,CAAC;IACtB,IAAI,SAAiB,CAAC;IACtB,IAAI,SAAiB,CAAC;IACtB,IAAI,SAAiB,CAAC;IACtB,IAAI,MAAc,CAAC;IACnB,IAAI,YAAoB,CAAC;IACzB,IAAI,gBAAwB,CAAC;IAC7B,IAAI,gBAAwB,CAAC;IAC7B,IAAI,WAAmB,CAAC;IAExB,IAAI,GAAG,EAAE,CAAC,SAAS,CAAC,eAAe,CAAC,KAAK,CAAC,EAAE;QAC1C,OAAO,GAAG,iBAAiB,CAAC;QAC5B,SAAS,GAAG,IAAI,CAAC;QACjB,SAAS,GAAG,KAAK,CAAC;QAClB,SAAS,GAAG,IAAI,CAAC;QACjB,SAAS,GAAG,SAAS,CAAC;QACtB,MAAM,GAAG,aAAa,CAAC;QACvB,YAAY,GAAG,uBAAuB,CAAC;QAEvC,iEAAiE;QACjE,0EAA0E;QAC1E,wEAAwE;QACxE,wBAAwB;QACxB,gBAAgB,GAAG;;;;;;;;;;;KAWlB,CAAC;QACF,qEAAqE;QACrE,wCAAwC;QACxC,gBAAgB,GAAG,EAAE,CAAC;QACtB,WAAW,GAAG;;;;;;;;;KASb,CAAC;KACH;SAAM;QACL,OAAO,GAAG,EAAE,CAAC;QACb,SAAS,GAAG,WAAW,CAAC;QACxB,SAAS,GAAG,SAAS,CAAC;QACtB,SAAS,GAAG,SAAS,CAAC;QACtB,SAAS,GAAG,WAAW,CAAC;QACxB,MAAM,GAAG,cAAc,CAAC;QACxB,YAAY,GAAG,EAAE,CAAC;QAClB,sDAAsD;QACtD,gBAAgB,GAAG;;;;;;;;KAQlB,CAAC;QACF,gBAAgB,GAAG;;;;;;;;;KASlB,CAAC;QACF,WAAW,GAAG;;;;;;;;KAQb,CAAC;KACH;IAED,OAAO;QACL,OAAO;QACP,SAAS;QACT,SAAS;QACT,SAAS;QACT,SAAS;QACT,MAAM;QACN,YAAY;QACZ,gBAAgB;QAChB,gBAAgB;QAChB,WAAW;KACZ,CAAC;AACJ,CAAC"}
|
||||
{"version":3,"file":"glsl_version.js","sourceRoot":"","sources":["../src/glsl_version.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAC,GAAG,EAAC,MAAM,uBAAuB,CAAC;AAe1C,MAAM,UAAU,kBAAkB;IAChC,IAAI,OAAe,CAAC;IACpB,IAAI,SAAiB,CAAC;IACtB,IAAI,SAAiB,CAAC;IACtB,IAAI,SAAiB,CAAC;IACtB,IAAI,SAAiB,CAAC;IACtB,IAAI,MAAc,CAAC;IACnB,IAAI,YAAoB,CAAC;IACzB,IAAI,gBAAwB,CAAC;IAC7B,IAAI,gBAAwB,CAAC;IAC7B,IAAI,WAAmB,CAAC;IAExB,IAAI,GAAG,EAAE,CAAC,SAAS,CAAC,eAAe,CAAC,KAAK,CAAC,EAAE;QAC1C,OAAO,GAAG,iBAAiB,CAAC;QAC5B,SAAS,GAAG,IAAI,CAAC;QACjB,SAAS,GAAG,KAAK,CAAC;QAClB,SAAS,GAAG,IAAI,CAAC;QACjB,SAAS,GAAG,SAAS,CAAC;QACtB,MAAM,GAAG,aAAa,CAAC;QACvB,YAAY,GAAG,uBAAuB,CAAC;QAEvC,iEAAiE;QACjE,0EAA0E;QAC1E,wEAAwE;QACxE,wBAAwB;QACxB,gBAAgB,GAAG;;;;;;;;;;;KAWlB,CAAC;QACF,qEAAqE;QACrE,wCAAwC;QACxC,gBAAgB,GAAG,EAAE,CAAC;QACtB,WAAW,GAAG;;;;;;;;;KASb,CAAC;KACH;SAAM;QACL,OAAO,GAAG,EAAE,CAAC;QACb,SAAS,GAAG,WAAW,CAAC;QACxB,SAAS,GAAG,SAAS,CAAC;QACtB,SAAS,GAAG,SAAS,CAAC;QACtB,SAAS,GAAG,WAAW,CAAC;QACxB,MAAM,GAAG,cAAc,CAAC;QACxB,YAAY,GAAG,EAAE,CAAC;QAClB,sDAAsD;QACtD,gBAAgB,GAAG;;;;;;;;KAQlB,CAAC;QACF,gBAAgB,GAAG;;;;;;;;;KASlB,CAAC;QACF,WAAW,GAAG;;;;;;;;KAQb,CAAC;KACH;IAED,OAAO;QACL,OAAO;QACP,SAAS;QACT,SAAS;QACT,SAAS;QACT,SAAS;QACT,MAAM;QACN,YAAY;QACZ,gBAAgB;QAChB,gBAAgB;QAChB,WAAW;KACZ,CAAC;AACJ,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2018 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 {env} from '@tensorflow/tfjs-core';\n\nexport type GLSL = {\n version: string,\n attribute: string,\n varyingVs: string,\n varyingFs: string,\n texture2D: string,\n output: string,\n defineOutput: string,\n defineSpecialNaN: string,\n defineSpecialInf: string,\n defineRound: string\n};\n\nexport function getGlslDifferences(): GLSL {\n let version: string;\n let attribute: string;\n let varyingVs: string;\n let varyingFs: string;\n let texture2D: string;\n let output: string;\n let defineOutput: string;\n let defineSpecialNaN: string;\n let defineSpecialInf: string;\n let defineRound: string;\n\n if (env().getNumber('WEBGL_VERSION') === 2) {\n version = '#version 300 es';\n attribute = 'in';\n varyingVs = 'out';\n varyingFs = 'in';\n texture2D = 'texture';\n output = 'outputColor';\n defineOutput = 'out vec4 outputColor;';\n\n // Use custom isnan definition to work across differences between\n // implementations on various platforms. While this should happen in ANGLE\n // we still see differences between android and windows (on chrome) when\n // using isnan directly.\n defineSpecialNaN = `\n bool isnan_custom(float val) {\n return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n }\n\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan_custom(val.x),\n isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n }\n\n #define isnan(value) isnan_custom(value)\n `;\n // In webgl 2 we do not need to specify a custom isinf so there is no\n // need for a special INFINITY constant.\n defineSpecialInf = ``;\n defineRound = `\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `;\n } else {\n version = '';\n attribute = 'attribute';\n varyingVs = 'varying';\n varyingFs = 'varying';\n texture2D = 'texture2D';\n output = 'gl_FragColor';\n defineOutput = '';\n // WebGL1 has no built in isnan so we define one here.\n defineSpecialNaN = `\n #define isnan(value) isnan_custom(value)\n bool isnan_custom(float val) {\n return (val > 0. || val < 1. || val == 0.) ? false : true;\n }\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n `;\n defineSpecialInf = `\n uniform float INFINITY;\n\n bool isinf(float val) {\n return abs(val) == INFINITY;\n }\n bvec4 isinf(vec4 val) {\n return equal(abs(val), vec4(INFINITY));\n }\n `;\n defineRound = `\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 round(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `;\n }\n\n return {\n versioLine truncated
|
||||
Generated
Vendored
+2
-1
@@ -36,6 +36,7 @@ export declare class GPGPUContext {
|
||||
program: WebGLProgram | null;
|
||||
private disposed;
|
||||
private disjoint;
|
||||
private vertexShader;
|
||||
textureConfig: TextureConfig;
|
||||
constructor(gl?: WebGLRenderingContext);
|
||||
private readonly debug;
|
||||
@@ -43,7 +44,7 @@ export declare class GPGPUContext {
|
||||
createFloat32MatrixTexture(rows: number, columns: number): WebGLTexture;
|
||||
createFloat16MatrixTexture(rows: number, columns: number): WebGLTexture;
|
||||
createUnsignedBytesMatrixTexture(rows: number, columns: number): WebGLTexture;
|
||||
uploadPixelDataToTexture(texture: WebGLTexture, pixels: PixelData | ImageData | HTMLImageElement | HTMLCanvasElement): void;
|
||||
uploadPixelDataToTexture(texture: WebGLTexture, pixels: PixelData | ImageData | HTMLImageElement | HTMLCanvasElement | ImageBitmap): void;
|
||||
uploadDenseMatrixToTexture(texture: WebGLTexture, width: number, height: number, data: TypedArray): void;
|
||||
createFloat16PackedMatrixTexture(rows: number, columns: number): WebGLTexture;
|
||||
createPackedMatrixTexture(rows: number, columns: number): WebGLTexture;
|
||||
|
||||
Generated
Vendored
+4
-2
@@ -197,9 +197,11 @@ export class GPGPUContext {
|
||||
this.throwIfDisposed();
|
||||
const gl = this.gl;
|
||||
const fragmentShader = webgl_util.createFragmentShader(gl, fragmentShaderSource);
|
||||
const vertexShader = gpgpu_util.createVertexShader(gl);
|
||||
if (this.vertexShader == null) {
|
||||
this.vertexShader = gpgpu_util.createVertexShader(gl);
|
||||
}
|
||||
const program = webgl_util.createProgram(gl);
|
||||
webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));
|
||||
webgl_util.callAndCheck(gl, () => gl.attachShader(program, this.vertexShader));
|
||||
webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));
|
||||
webgl_util.linkProgram(gl, program);
|
||||
if (this.debug) {
|
||||
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
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-1
@@ -1 +1 @@
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ine truncated
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
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truncated
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truncated
|
||||
Generated
Vendored
+1
-1
@@ -32,7 +32,7 @@ export declare function getInternalFormatForFloat16PackedMatrixTexture(textureCo
|
||||
export declare function createFloat16PackedMatrixTexture(gl: WebGLRenderingContext, rows: number, columns: number, textureConfig: TextureConfig): WebGLTexture;
|
||||
export declare function bindVertexProgramAttributeStreams(gl: WebGLRenderingContext, program: WebGLProgram, vertexBuffer: WebGLBuffer): boolean;
|
||||
export declare function uploadDenseMatrixToTexture(gl: WebGLRenderingContext, texture: WebGLTexture, width: number, height: number, data: TypedArray, textureConfig: TextureConfig): void;
|
||||
export declare function uploadPixelDataToTexture(gl: WebGLRenderingContext, texture: WebGLTexture, pixels: PixelData | ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement): void;
|
||||
export declare function uploadPixelDataToTexture(gl: WebGLRenderingContext, texture: WebGLTexture, pixels: PixelData | ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | ImageBitmap): void;
|
||||
export declare function createBufferFromOutputTexture(gl2: WebGL2RenderingContext, rows: number, columns: number, textureConfig: TextureConfig): WebGLBuffer;
|
||||
export declare function downloadFloat32MatrixFromBuffer(gl: WebGLRenderingContext, buffer: WebGLBuffer, size: number): Float32Array;
|
||||
export declare function downloadByteEncodedFloatMatrixFromOutputTexture(gl: WebGLRenderingContext, rows: number, columns: number, textureConfig: TextureConfig): Float32Array;
|
||||
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
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truncated
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Generated
Vendored
+1
-1
@@ -1 +1 @@
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Generated
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{"version":3,"file":"im2col_packed_gpu.js","sourceRoot":"","sources":["../src/im2col_packed_gpu.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAGH,OAAO,EAAC,kBAAkB,EAAC,MAAM,gBAAgB,CAAC;AAGlD,MAAM,OAAO,mBAAmB;IAO9B,YACI,WAAqB,EAAE,UAAoB,EAC3C,QAAiC;QARrC,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QACtB,iBAAY,GAAG,IAAI,CAAC;QACpB,iBAAY,GAAG,IAAI,CAAC;QAOlB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAE/B,MAAM,EACJ,WAAW,EACX,UAAU,EACV,WAAW,EACX,YAAY,EACZ,OAAO,EACP,QAAQ,EACR,aAAa,EACb,cAAc,EACd,UAAU,EACX,GAAG,QAAQ,CAAC;QACb,MAAM,EAAC,IAAI,EAAE,GAAG,EAAC,GAAG,OAAO,CAAC;QAC5B,MAAM,gBAAgB,GAAG,UAAU,GAAG,WAAW,CAAC;QAClD,MAAM,IAAI,GAAG,kBAAkB,EAAE,CAAC;QAClC,MAAM,cAAc,GAAG,UAAU,KAAK,cAAc,CAAC;QACrD,MAAM,MAAM,GAAG,cAAc,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,MAAM,GAAG,cAAc,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAEtC,IAAI,QAAQ,GAAG,EAAE,CAAC;QAElB,KAAK,IAAI,GAAG,GAAG,CAAC,EAAE,GAAG,IAAI,CAAC,EAAE,GAAG,EAAE,EAAE;YACjC,KAAK,IAAI,GAAG,GAAG,CAAC,EAAE,GAAG,IAAI,CAAC,EAAE,GAAG,EAAE,EAAE;gBACjC,QAAQ,IAAI;gCACY,GAAG;yBACV,GAAG;;4BAEA,WAAW,CAAC,CAAC,CAAC,aAAa,WAAW,CAAC,CAAC,CAAC;0CAC3B,QAAQ,QAAQ,YAAY,MAC1D,GAAG;6BACc,cAAc,aAAa,gBAAgB;;sBAElD,UAAU,CAAC,MAAM,CAAC;;qDAEa,QAAQ,QACjD,WAAW,OAAO,IAAI;+BACH,aAAa,2BAChC,gBAAgB,QAAQ,UAAU;;wBAEtB,UAAU,CAAC,MAAM,CAAC;;2CAEC,UAAU;;sBAE/B,cAAc;;2BAET,GAAG,GAAG,CAAC,GAAG,GAAG;;;;;2BAKb,GAAG,GAAG,CAAC,GAAG,GAAG;;;;;;;SAO/B,CAAC;aACH;SACF;QAED,IAAI,CAAC,QAAQ,GAAG;;;;;;;;;UASV,QAAQ;;UAER,IAAI,CAAC,MAAM;;KAEhB,CAAC;IACJ,CAAC;CACF","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 {backend_util} from '@tensorflow/tfjs-core';\nimport {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Im2ColPackedProgram implements GPGPUProgram {\n variableNames = ['A'];\n packedInputs = true;\n packedOutput = true;\n outputShape: number[];\n userCode: string;\n\n constructor(\n outputShape: number[], inputShape: number[],\n convInfo: backend_util.Conv2DInfo) {\n this.outputShape = outputShape;\n\n const {\n filterWidth,\n inChannels,\n strideWidth,\n strideHeight,\n padInfo,\n outWidth,\n dilationWidth,\n dilationHeight,\n dataFormat\n } = convInfo;\n const {left, top} = padInfo;\n const itemsPerBlockRow = inChannels * filterWidth;\n const glsl = getGlslDifferences();\n const isChannelsLast = dataFormat === 'channelsLast';\n const rowDim = isChannelsLast ? 0 : 1;\n const colDim = isChannelsLast ? 1 : 2;\n\n let unrolled = ``;\n\n for (let row = 0; row <= 1; row++) {\n for (let col = 0; col <= 1; col++) {\n unrolled += `\n blockIndex = rc.y + ${col};\n pos = rc.x + ${row};\n\n if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {\n offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${\n top};\n d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n if(d0 < ${inputShape[rowDim]} && d0 >= 0) {\n\n offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${\n strideWidth}. - ${left}.);\n d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${\n itemsPerBlockRow}.) / ${inChannels}.));\n\n if(d1 < ${inputShape[colDim]} && d1 >= 0) {\n\n ch = int(mod(float(pos), ${inChannels}.));\n\n if (${isChannelsLast}) {\n innerDims = vec2(d1, ch);\n result[${row * 2 + col}] = getChannel(\n getA(d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${row * 2 + col}] = getChannel(\n getA(ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;\n }\n }\n\n this.userCode = `\n void main() {\n ivec2 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${unrolled}\n\n ${glsl.output} = result;\n }\n `;\n }\n}\n"]}
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
{"version":3,"file":"index.js","sourceRoot":"","sources":["../src/index.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,mDAAmD;AACnD,cAAc,QAAQ,CAAC;AACvB,OAAO,wBAAwB,CAAC"}
|
||||
{"version":3,"file":"index.js","sourceRoot":"","sources":["../src/index.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,mDAAmD;AACnD,cAAc,QAAQ,CAAC;AACvB,OAAO,wBAAwB,CAAC","sourcesContent":["/**\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// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n"]}
|
||||
Generated
Vendored
+1
-1
@@ -1 +1 @@
|
||||
{"version":3,"file":"arg_min_max.js","sourceRoot":"","sources":["../../src/kernel_utils/arg_min_max.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,YAAY,EAAE,GAAG,EAAc,IAAI,EAAC,MAAM,uBAAuB,CAAC;AAE1E,OAAO,EAAC,gBAAgB,EAAC,MAAM,kBAAkB,CAAC;AAClD,OAAO,EAAC,sBAAsB,EAAC,MAAM,yBAAyB,CAAC;AAE/D,OAAO,EAAC,OAAO,EAAC,MAAM,oBAAoB,CAAC;AAE3C,SAAS,SAAS,CACd,OAAyB,EAAE,CAAa,EAAE,UAAuB,EACjE,eAA2B,IAAI;IACjC,IAAI,SAAS,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IAC3B,IAAI,MAAM,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IACxB,IAAI,YAAY,IAAI,IAAI,EAAE;QACxB,SAAS,GAAG,YAAY,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,GAAG,YAAY,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;KAChC;IACD,MAAM,UAAU,GAAG,YAAY,CAAC,wBAAwB,CAAC,MAAM,CAAC,CAAC;IACjE,MAAM,UAAU,GACZ,EAAC,UAAU,EAAE,MAAM,EAAE,SAAS,EAAE,OAAO,EAAE,IAAI,CAAC,IAAI,CAAC,MAAM,GAAG,UAAU,CAAC,EAAC,CAAC;IAC7E,MAAM,OAAO,GACT,IAAI,gBAAgB,CAAC,UAAU,EAAE,UAAU,EAAE,YAAY,IAAI,IAAI,CAAC,CAAC;IACvE,MAAM,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC;IACnB,IAAI,YAAY,IAAI,IAAI,EAAE;QACxB,MAAM,CAAC,IAAI,CAAC,YAAY,CAAC,CAAC;KAC3B;IACD,MAAM,MAAM,GAAG,OAAO,CAAC,eAAe,CAAC,OAAO,EAAE,MAAM,EAAE,OAAO,CAAC,CAAC;IACjE,wCAAwC;IACxC,IAAI,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,CAAC,EAAE;QACzB,OAAO,MAAM,CAAC;KACf;IACD,MAAM,MAAM,GAAG,SAAS,CAAC,OAAO,EAAE,CAAC,EAAE,UAAU,EAAE,MAAM,CAAC,CAAC;IACzD,OAAO,CAAC,6BAA6B,CAAC,MAAM,CAAC,CAAC;IAC9C,OAAO,MAAM,CAAC;AAChB,CAAC;AAED,SAAS,eAAe,CACpB,OAAyB,EAAE,CAAa,EAAE,UAAuB,EACjE,eAA2B,IAAI;IACjC,MAAM,OAAO,GAAG,YAAY,IAAI,IAAI,CAAC,CAAC,CAAC,YAAY,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC;IACpE,MAAM,MAAM,GAAG,OAAO,CAAC,OAAO,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAC3C,MAAM,UAAU,GAAG,YAAY,CAAC,wBAAwB,CAAC,MAAM,CAAC,CAAC;IACjE,MAAM,OAAO,GAAG,IAAI,sBAAsB,CACtC,OAAO,EAAE,UAAU,EAAE,UAAU,EAAE,YAAY,IAAI,IAAI,CAAC,CAAC;IAC3D,MAAM,MAAM,GAAG,YAAY,IAAI,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,YAAY,CAAC,CAAC;IAC9D,MAAM,MAAM,GAAG,OAAO,CAAC,eAAe,CAAC,OAAO,EAAE,MAAM,EAAE,OAAO,CAAC,CAAC;IACjE,IAAI,MAAM,CAAC,KAAK,CAAC,MAAM,KAAK,CAAC,CAAC,KAAK,CAAC,MAAM,EAAE;QAC1C,MAAM,MAAM,GAAG,eAAe,CAAC,OAAO,EAAE,CAAC,EAAE,UAAU,EAAE,MAAM,CAAC,CAAC;QAC/D,OAAO,CAAC,6BAA6B,CAAC,MAAM,CAAC,CAAC;QAC9C,OAAO,MAAM,CAAC;KACf;IACD,OAAO,MAAM,CAAC;AAChB,CAAC;AAED,MAAM,UAAU,eAAe,CAC3B,OAAyB,EAAE,CAAa,EAAE,IAAY,EACtD,UAAuB;IACzB,MAAM,IAAI,GAAG,CAAC,IAAI,CAAC,CAAC;IACpB,YAAY,CAAC,0BAA0B,CACnC,KAAK,GAAG,UAAU,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,WAAW,EAAE,GAAG,UAAU,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,IAAI,EACtE,CAAC,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC;IACpB,IAAI,CAAC,GAAG,EAAE,CAAC,OAAO,CAAC,mBAAmB,CAAC,IAAI,CAAC,CAAC,KAAK,CAAC,MAAM,IAAI,CAAC,EAAE;QAC9D,MAAM,uBAAuB,GAAG,EAAE,CAAC;QACnC,MAAM,CAAC,QAAQ,EAAE,WAAW,CAAC,GACzB,YAAY,CAAC,yBAAyB,CAAC,CAAC,CAAC,KAAK,EAAE,IAAI,CAAC,CAAC;QAC1D,MAAM,MAAM,GAAG,IAAI,CAAC,aAAa,CAAC,WAAW,CAAC,CAAC;QAC/C,MAAM,GAAG,GAAG,OAAO,CAAC,EAAC,MAAM,EAAE,EAAC,CAAC,EAAC,EAAE,OAAO,EAAE,KAAK,EAAE,EAAC,KAAK,EAAE,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,EAAC,EAAC,CAAC,CAAC;QAC1E,uBAAuB,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC;QAElC,MAAM,OAAO,GAAG,SAAS,CAAC,OAAO,EAAE,GAAG,EAAE,UAAU,CAAC,CAAC;QACpD,uBAAuB,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QACtC,MAAM,QAAQ,GACV,OAAO,CAAC,EAAC,MAAM,EAAE,EAAC,CAAC,EAAE,OAAO,EAAC,EAAE,OAAO,EAAE,KAAK,EAAE,EAAC,KAAK,EAAE,QAAQ,EAAC,EAAC,CAAC,CAAC;QAEvE,uBAAuB,CAAC,OAAO,CAC3B,CAAC,CAAC,EAAE,CAAC,OAAO,CAAC,6BAA6B,CAAC,CAAC,CAAC,CAAC,CAAC;QACnD,OAAO,QAAQ,CAAC;KACjB;IACD,OAAO,eAAe,CAAC,OAAO,EAAE,CAAC,EAAE,UAAU,CAAC,CAAC;AACjD,CAAC"}
|
||||
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|
||||
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