chore: update node_modules with new binary files and dependencies
- Add new binary files for nodemon, onnxruntime-web, and xenova/transformers - Update various JavaScript and TypeScript files in node_modules - Remove unused files and dependencies - Add new test fixtures and documentation files
This commit is contained in:
73
node_modules/onnxruntime-web/lib/onnxjs/backends/webgl/ops/conv-grouped.js
generated
vendored
Normal file
73
node_modules/onnxruntime-web/lib/onnxjs/backends/webgl/ops/conv-grouped.js
generated
vendored
Normal file
@@ -0,0 +1,73 @@
|
||||
"use strict";
|
||||
// Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
// Licensed under the MIT License.
|
||||
Object.defineProperty(exports, "__esModule", { value: true });
|
||||
exports.createUnpackedGroupedConvProgramInfoLoader = void 0;
|
||||
const instrument_1 = require("../../../instrument");
|
||||
const glsl_source_1 = require("../glsl-source");
|
||||
const types_1 = require("../types");
|
||||
const conv_1 = require("./conv");
|
||||
const fuse_utils_1 = require("./fuse-utils");
|
||||
const createUnpackedGroupedConvProgramMetadata = (hasBias, cacheHint) => ({
|
||||
name: 'GroupedConv',
|
||||
inputNames: hasBias ? ['X', 'W', 'Bias'] : ['X', 'W'],
|
||||
inputTypes: hasBias ? [types_1.TextureType.unpacked, types_1.TextureType.unpacked, types_1.TextureType.unpacked] :
|
||||
[types_1.TextureType.unpacked, types_1.TextureType.unpacked],
|
||||
cacheHint
|
||||
});
|
||||
const createUnpackedGroupedConvProgramInfo = (inferenceHandler, inputs, metadata, attributes) => {
|
||||
const hasBias = inputs.length > 2;
|
||||
const processBias = hasBias ? 'value += getBias(output_channel);' : '';
|
||||
const xShape = inputs[0].dims.slice();
|
||||
const wShape = inputs[1].dims.slice();
|
||||
const outputChannelsPerGroup = wShape[0] / attributes.group;
|
||||
instrument_1.Logger.verbose('GroupedConv', `autpPad:${attributes.autoPad}, dilations:${attributes.dilations}, group:${attributes.group}, kernelShape:${attributes.kernelShape}, pads:${attributes.pads}, strides:${attributes.strides}`);
|
||||
const outputShape = (0, conv_1.calculateOutputShape)(xShape, wShape, attributes.dilations, attributes.pads, attributes.strides);
|
||||
const glsl = (0, glsl_source_1.getGlsl)(inferenceHandler.session.backend.glContext.version);
|
||||
const { activationFunction, applyActivation } = (0, fuse_utils_1.getActivationSnippet)(attributes);
|
||||
const shaderSource = `
|
||||
const ivec2 strides = ivec2(${attributes.strides[0]}, ${attributes.strides[1]});
|
||||
const ivec2 pads = ivec2(${attributes.pads[0]}, ${attributes.pads[1]});
|
||||
${activationFunction}
|
||||
void main() {
|
||||
ivec4 coords = getOutputCoords();
|
||||
int batch = coords.x;
|
||||
int output_channel = coords.y;
|
||||
ivec2 xRCCorner = coords.zw * strides - pads;
|
||||
int group_id = output_channel / ${outputChannelsPerGroup};
|
||||
|
||||
float value = 0.0;
|
||||
for (int wInChannel = 0; wInChannel < ${wShape[1]}; wInChannel++) {
|
||||
int input_channel = group_id * ${wShape[1]} + wInChannel;
|
||||
for (int wHeight = 0; wHeight < ${wShape[2]}; wHeight++) {
|
||||
int xHeight = xRCCorner.x + wHeight * ${attributes.dilations[0]};
|
||||
|
||||
if (xHeight < 0 || xHeight >= ${xShape[2]}) {
|
||||
continue;
|
||||
}
|
||||
|
||||
for (int wWidth = 0; wWidth < ${wShape[3]}; wWidth++) {
|
||||
int xWidth = xRCCorner.y + wWidth * ${attributes.dilations[1]};
|
||||
if (xWidth < 0 || xWidth >= ${xShape[3]}) {
|
||||
continue;
|
||||
}
|
||||
|
||||
float xVal = getX(batch, input_channel, xWidth, xHeight);
|
||||
float wVal = getW(output_channel, wInChannel, wWidth, wHeight);
|
||||
value += xVal*wVal;
|
||||
}
|
||||
}
|
||||
}
|
||||
${processBias}
|
||||
${applyActivation}
|
||||
${glsl.output} = vec4(value, .0, .0, .0);
|
||||
}
|
||||
`;
|
||||
return Object.assign(Object.assign({}, metadata), { output: { dims: outputShape, type: inputs[0].type, textureType: types_1.TextureType.unpacked }, shaderSource, hasMain: true });
|
||||
};
|
||||
const createUnpackedGroupedConvProgramInfoLoader = (inferenceHandler, inputs, attributes) => {
|
||||
const metadata = createUnpackedGroupedConvProgramMetadata(inputs.length > 2, attributes.cacheKey);
|
||||
return Object.assign(Object.assign({}, metadata), { get: () => createUnpackedGroupedConvProgramInfo(inferenceHandler, inputs, metadata, attributes) });
|
||||
};
|
||||
exports.createUnpackedGroupedConvProgramInfoLoader = createUnpackedGroupedConvProgramInfoLoader;
|
||||
//# sourceMappingURL=conv-grouped.js.map
|
||||
Reference in New Issue
Block a user