onnxruntime-web
Version:
A Javascript library for running ONNX models on browsers
377 lines (330 loc) • 12.3 kB
text/typescript
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
import { AttributeWithCacheKey, createAttributeWithCacheKey } from '../../../attribute-with-cache-key';
import { Graph } from '../../../graph';
import { OperatorImplementation, OperatorInitialization } from '../../../operators';
import { Tensor } from '../../../tensor';
import { getGlsl } from '../glsl-source';
import { WebGLInferenceHandler } from '../inference-handler';
import { ProgramInfo, TextureType } from '../types';
export interface UpsampleAttributes extends AttributeWithCacheKey {
readonly opset: number;
readonly isResize: boolean;
readonly mode: string;
readonly scales: number[];
readonly extrapolationValue: number;
readonly coordinateTransformMode: string;
readonly useExtrapolation: boolean;
readonly needRoiInput: boolean;
readonly nearestMode: string;
readonly cubicCoefficientA: number;
readonly excludeOutside: boolean;
readonly useNearest2xOptimization: boolean;
readonly roiInputIdx: number;
readonly scalesInputIdx: number;
readonly sizesInputIdx: number;
}
const upsampleProgramMetadata = {
name: 'Upsample',
inputNames: ['X'],
inputTypes: [TextureType.unpacked],
};
export const upsample: OperatorImplementation<UpsampleAttributes> = (
inferenceHandler: WebGLInferenceHandler,
inputs: Tensor[],
attributes: UpsampleAttributes,
): Tensor[] => {
validateInputs(inputs, attributes);
const output = inferenceHandler.run(
{
...upsampleProgramMetadata,
cacheHint: attributes.cacheKey,
get: () => createUpsampleProgramInfo(inferenceHandler, inputs, attributes),
},
inputs,
);
return [output];
};
export const parseUpsampleAttributesV7: OperatorInitialization<UpsampleAttributes> = (
node: Graph.Node,
): UpsampleAttributes => parseUpsampleAttributes(node, 7);
export const parseUpsampleAttributesV9: OperatorInitialization<UpsampleAttributes> = (
node: Graph.Node,
): UpsampleAttributes => parseUpsampleAttributes(node, 9);
export const parseUpsampleAttributes = (node: Graph.Node, opset: number): UpsampleAttributes => {
const isResize = opset >= 10;
// processing node attributes
const mode = node.attributes.getString('mode', 'nearest');
if (mode !== 'nearest' && mode !== 'linear' && (opset < 11 || mode !== 'cubic')) {
throw new Error(`unrecognized mode: ${mode}`);
}
let scales: number[] = [];
if (opset < 9) {
scales = node.attributes.getFloats('scales');
scalesValidation(scales, mode, isResize);
}
const extrapolationValue = node.attributes.getFloat('extrapolation_value', 0.0);
const coordinateTransformMode =
opset > 10 ? node.attributes.getString('coordinate_transformation_mode', 'half_pixel') : 'asymmetric';
if (
[
'asymmetric',
'pytorch_half_pixel',
'tf_half_pixel_for_nn',
'align_corners',
'tf_crop_and_resize',
'half_pixel',
].indexOf(coordinateTransformMode) === -1
) {
throw new Error(`coordinate_transform_mode '${coordinateTransformMode}' is not supported`);
}
const needRoiInput = coordinateTransformMode === 'tf_crop_and_resize';
const useExtrapolation = needRoiInput;
const nearestMode =
mode === 'nearest' && opset >= 11 ? node.attributes.getString('nearest_mode', 'round_prefer_floor') : '';
if (['round_prefer_floor', 'round_prefer_ceil', 'floor', 'ceil', ''].indexOf(nearestMode) === -1) {
throw new Error(`nearest_mode '${nearestMode}' is not supported`);
}
const cubicCoefficientA = node.attributes.getFloat('cubic_coeff_a', -0.75);
const excludeOutside = node.attributes.getInt('exclude_outside', 0) !== 0;
if (excludeOutside && mode !== 'cubic') {
throw new Error('exclude_outside can be set to 1 only when mode is CUBIC.');
}
const useNearest2xOptimization =
opset < 11 ? true : mode === 'nearest' && coordinateTransformMode === 'asymmetric' && nearestMode === 'floor';
let roiInputIdx = 0;
let scalesInputIdx = 0;
let sizesInputIdx = 0;
if (opset > 10) {
// handle when roiInput is not given
if (node.inputs.length > 2) {
roiInputIdx = 1;
scalesInputIdx = 2;
sizesInputIdx = 3;
} else {
scalesInputIdx = 1;
sizesInputIdx = 2;
}
} else if (opset === 9) {
scalesInputIdx = 1;
}
return createAttributeWithCacheKey({
opset,
isResize,
mode,
scales,
extrapolationValue,
coordinateTransformMode,
useExtrapolation,
needRoiInput,
nearestMode,
cubicCoefficientA,
excludeOutside,
useNearest2xOptimization,
roiInputIdx,
scalesInputIdx,
sizesInputIdx,
});
};
const createUpsampleProgramInfo = (
inferenceHandler: WebGLInferenceHandler,
inputs: Tensor[],
attributes: UpsampleAttributes,
): ProgramInfo => {
const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);
const [inputWidth, inputHeight] = inferenceHandler.calculateTextureWidthAndHeight(
inputs[0].dims,
TextureType.unpacked,
);
const outputShape = inputs[0].dims.map((dim, i) => Math.floor(dim * attributes.scales[i]));
const [outputWidth, outputHeight] = inferenceHandler.calculateTextureWidthAndHeight(
outputShape,
TextureType.unpacked,
);
const dim = outputShape.length;
const outputPitches = new Array<number>(dim);
const inputPitches = new Array<number>(dim);
let precalculatedPitches = `
int output_pitches[${dim}];
int input_pitches[${dim}];
`;
for (let d = dim - 1; d >= 0; d--) {
outputPitches[d] = d === dim - 1 ? 1 : outputPitches[d + 1] * outputShape[d + 1];
inputPitches[d] = d === dim - 1 ? 1 : inputPitches[d + 1] * inputs[0].dims[d + 1];
precalculatedPitches += `
output_pitches[${d}] = ${outputPitches[d]};
input_pitches[${d}] = ${inputPitches[d]};
`;
}
const getInputFloatFunction = `
float getInputFloat(int index) {
vec2 coords = offsetToCoords(index, ${inputWidth}, ${inputHeight});
float value = getColorAsFloat(${glsl.texture2D}(X, coords));
return value;
}
`;
const shaderSource =
attributes.mode === 'nearest'
? // nearest
`
${getInputFloatFunction}
float process(int indices[${dim}]) {
int input_index = 0;
int output_index = coordsToOffset(TexCoords, ${outputWidth}, ${outputHeight});
${precalculatedPitches}
int d, m;
for (int dim = 0; dim < ${dim}; ++dim) {
d = output_index / output_pitches[dim];
m = output_index - d * output_pitches[dim];
output_index = m;
if (scales[dim] != 1 && d > 0) {
int d2 = d / scales[dim];
m = d - d2 * scales[dim];
d = d2;
}
input_index += input_pitches[dim] * d;
}
return getInputFloat(input_index);
}`
: dim === 4
? // bilinear 4D
`
${getInputFloatFunction}
float process(int indices[4]) {
int input_index = 0;
int output_index = coordsToOffset(TexCoords, ${outputWidth}, ${outputHeight});
${precalculatedPitches}
int m;
int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;
index_of_dim0 = output_index / output_pitches[0];
m = output_index - index_of_dim0 * output_pitches[0];
index_of_dim1 = m / output_pitches[1];
m = m - index_of_dim1 * output_pitches[1];
index_of_dim2 = m / output_pitches[2];
m = m - index_of_dim2 * output_pitches[2];
index_of_dim3 = m;
int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;
index_of_input_dim2 = index_of_dim2 / scales[2];
y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];
index_of_input_dim3 = index_of_dim3 / scales[3];
x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];
input_index = index_of_dim0 * input_pitches[0] +
index_of_dim1 * input_pitches[1] +
index_of_input_dim2 * input_pitches[2] +
index_of_input_dim3;
float x00 = getInputFloat(input_index);
float x10, x01, x11;
bool end_of_dim2 = false;
if (index_of_input_dim2 == (${inputs[0].dims[2]} - 1)) {
// It's the end in dimension 2
x01 = x00;
end_of_dim2 = true;
} else {
x01 = getInputFloat(input_index + input_pitches[2]);
}
if (index_of_input_dim3 == (input_pitches[2] - 1)) {
// It's the end in dimension 3
x10 = x00;
x11 = x01;
}
else {
x10 = getInputFloat(input_index + 1);
x11 = end_of_dim2 ? x10 : getInputFloat(input_index + input_pitches[2] + 1);
}
float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[2]);
float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]);
return y0 + float(x_offset) * (y1 - y0) / float(scales[3]);
}`
: // bilinear 2D
`
${getInputFloatFunction}
float process(int indices[2]) {
int input_index = 0;
int output_index = coordsToOffset(TexCoords, ${outputWidth}, ${outputHeight});
${precalculatedPitches}
int m;
int index_of_dim0, index_of_dim1;
index_of_dim0 = output_index / output_pitches[0];
m = output_index - index_of_dim0 * output_pitches[0];
index_of_dim1 = m;
int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;
index_of_input_dim0 = index_of_dim0 / scales[0];
y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];
index_of_input_dim1 = index_of_dim1 / scales[1];
x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];
input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;
float x00 = getInputFloat(input_index);
float x10, x01, x11;
bool end_of_dim0 = false;
if (index_of_input_dim0 == (${inputs[0].dims[0]} - 1)) {
// It's the end in dimension 0
x01 = x00;
end_of_dim0 = true;
} else {
x01 = getInputFloat(input_index + input_pitches[0]);
}
if (index_of_input_dim1 == (input_pitches[0] - 1)) {
// It's the end in dimension 1
x10 = x00;
x11 = x01;
}
else {
x10 = getInputFloat(input_index + 1);
x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1);
}
float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]);
float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]);
return y0 + float(x_offset) * (y1 - y0) / float(scales[1]);
}`;
return {
...upsampleProgramMetadata,
output: { dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked },
shaderSource,
variables: [
{
name: 'scales',
type: 'int',
arrayLength: attributes.scales.length,
data: attributes.scales.map((x) => Math.ceil(x)),
},
],
};
};
export const validateInputs = (inputs: Tensor[], attribute: UpsampleAttributes): void => {
if (
!inputs ||
(attribute.opset < 9 && inputs.length !== 1) ||
(attribute.opset >= 9 && attribute.opset < 11 && inputs.length !== 2) ||
(attribute.opset >= 11 && inputs.length < 2)
) {
throw new Error('invalid inputs.');
}
if (attribute.scales.length > 0 && inputs[0].dims.length !== attribute.scales.length) {
throw new Error('Invalid input shape.');
}
if (inputs[0].type === 'string') {
throw new Error('Invalid input tensor types.');
}
};
export const scalesValidation = (scales: number[], mode: string, isResize: boolean): void => {
if (!isResize) {
for (const scale of scales) {
if (scale < 1) {
throw new Error('Scale value should be greater than or equal to 1.');
}
}
} else {
for (const scale of scales) {
if (scale <= 0) {
throw new Error('Scale value should be greater than 0.');
}
}
}
if (mode === 'linear' || mode === 'cubic') {
if (scales.length !== 2 && (scales.length !== 4 || scales[0] !== 1 || scales[1] !== 1)) {
throw new Error(`'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') \
or 4-D inputs with the corresponding outermost 2 scale values being 1 \
in the ${isResize ? 'Resize' : 'Upsample'} opeartor.`);
}
}
};