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Add OpenVINO SR-Model #1315

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Add OpenVINO SR-Model #1315

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23 changes: 19 additions & 4 deletions examples/super_resolution/SuperResolutionExample.js
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Original file line number Diff line number Diff line change
Expand Up @@ -13,17 +13,32 @@ class SuperResolutionExample extends BaseCameraExample {
};

const drawOutput = (outputTensor,srcElement, height, preOptions) => {
const width = height;
let width;
if(height == 1080) {
width = 1920;
}
else {
width = height;
}
const mean = preOptions.mean;
const offset = preOptions.std;
const bytes = new Uint8ClampedArray(width * height * 4);
const a = 255;

for (let i = 0; i < height * width; ++i) {
let j = i * 4;
let r = outputTensor[i * 3] * mean[0] + offset[0];
let g = outputTensor[i * 3 + 1] * mean[1] + offset[1];
let b = outputTensor[i * 3 + 2] * mean[2] + offset[2];
let r, g, b;
if(height == 1080) {
r = outputTensor[i * 3] * 255;
g = outputTensor[i * 3 + 1] * 255;
b = outputTensor[i * 3 + 2] * 255;
}
else {
r = outputTensor[i * 3] * mean[0] + offset[0];
g = outputTensor[i * 3 + 1] * mean[1] + offset[1];
b = outputTensor[i * 3 + 2] * mean[2] + offset[2];
}

bytes[j + 0] = Math.round(r);
bytes[j + 1] = Math.round(g);
bytes[j + 2] = Math.round(b);
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3 changes: 3 additions & 0 deletions examples/super_resolution/index.html
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Expand Up @@ -335,6 +335,9 @@ <h5 class='modal-title' id='modaltitle'>Subgraphs Summary</h5>
<script src='../util/tflite/schema/schema_generated.js'></script>
<script src='../util/tflite/TfLiteModelUtils.js'></script>
<script src='../util/tflite/TFliteModelImporter.js'></script>
<script src='../util/openvino/openvino.js'></script>
<script src='../util/openvino/OpenVINOModelUtils.js'></script>
<script src='../util/openvino/OpenVINOModelImporter.js'></script>

<script src='SuperResolutionExample.js'></script>
<script src='main.js'></script>
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30 changes: 30 additions & 0 deletions examples/util/modelZoo.js
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Expand Up @@ -746,6 +746,36 @@ const modelZoo = {
intro: 'Photo-realistic single image Super-Resolution using a generative adversarial network.',
paperUrl: 'https://arxiv.org/abs/1609.04802'
},
{
modelName: 'single-image-super-resolution-1032',
format: 'OpenVINO',
modelId: 'image-super-resolution-1032model',
modelSize: '120KB',
inputSize: [270, 480, 3],
outputSize: [1080, 1920, 3],
scale: 4,
modelFile: '../super_resolution/model/single-image-super-resolution-1032.bin',
preOptions: {
channelScheme: 'BGR',
},
intro: 'An Attention-Based Approach for Single Image Super Resolution',
paperUrl: 'https://arxiv.org/abs/1807.06779'
},
{
modelName: 'single-image-super-resolution-1033',
format: 'OpenVINO',
modelId: 'image-super-resolution-1033model',
modelSize: '122KB',
inputSize: [360, 640, 3],
outputSize: [1080, 1920, 3],
scale: 3,
modelFile: '../super_resolution/model/single-image-super-resolution-1033.bin',
preOptions: {
channelScheme: 'BGR',
},
intro: 'An Attention-Based Approach for Single Image Super Resolution',
paperUrl: 'https://arxiv.org/abs/1807.06779'
},
{
modelName: 'SRGAN 128x4 (TFLite)',
format: 'TFLite',
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91 changes: 78 additions & 13 deletions examples/util/openvino/OpenVINOModelImporter.js
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Expand Up @@ -403,17 +403,18 @@ class OpenVINOModelImporter {
}

let output = node.outputs[0];
const nextNode = graph.nodes[i+1];
if (nextNode && ['Clamp', 'ReLU'].includes(nextNode.operator) &&
node.outputs[0].graphId() === nextNode.inputs[0].graphId()) {
// Fuse relu
inputs.push(this._addScalarInt32(this._getFuseCode(nextNode)));
i++;
console.log(` fuse relu: output of ${nextNode.name}->${node.name}`);
output = nextNode.outputs[0];
} else {
inputs.push(this._addScalarInt32(this._nn.FUSED_NONE));
}
// const nextNode = graph.nodes[i+1];
// if (nextNode && ['Clamp', 'ReLU'].includes(nextNode.operator) &&
// node.outputs[0].graphId() === nextNode.inputs[0].graphId()) {
// // Fuse relu
// inputs.push(this._addScalarInt32(this._getFuseCode(nextNode)));
// i++;
// console.log(` fuse relu: output of ${nextNode.name}->${node.name}`);
// output = nextNode.outputs[0];
// } else {
// inputs.push(this._addScalarInt32(this._nn.FUSED_NONE));
// }
inputs.push(this._addScalarInt32(this._nn.FUSED_NONE));

// Add outputs
const outDims = output.shape();
Expand Down Expand Up @@ -693,10 +694,27 @@ class OpenVINOModelImporter {
console.log(` output shape: [${outDims}]`);

this._addOperation(this._nn.TRANSPOSE, inputs, outputs);
} else {
} else {
if(order.length === 6) {
console.log(` input shape: [${inDims}]`);
// no specific rules for tensor6D format so didn't reorder here
inputs.push(inputId);
inputs.push(this._addTensorInt32(order, [6]));

const outDims = output.shape();
const outputType = {
type: this._getTypeCode(output.dataType()), dimensions: outDims
};
const outputId = this._addNamedOperand(outputName, outputType);
outputs.push(outputId);
console.log(` output shape: [${outDims}]`);

this._addOperation(this._nn.TRANSPOSE, inputs, outputs);
}
else {
throw new Error(`Permuting to ${order} is not supported`);
}
}
} }
} break;
case 'Const': {
// initializer is contained in the node
Expand Down Expand Up @@ -891,6 +909,53 @@ class OpenVINOModelImporter {
outputs.push(outputId);
console.log(` output shape: [${outDims}]`);
} break;
case 'ReLU': {
const input = node.inputs[0];
inputs.push(this._getTensorId(input));
console.log(` inputs shape: ` +
`[${node.inputs.map((input) => input.shape()).join('], [')}]`);

const output = node.outputs[0];
const outDims = output.shape();
const outputType = {
type: this._getTypeCode(output.dataType()), dimensions: outDims
};
const outputId = this._addNamedOperand(output.graphId(), outputType);
outputs.push(outputId);
console.log(` output shape: [${outDims}]`);
opCode = this._nn.RELU;
} break;
case 'Power': {
const input = node.inputs[0];
inputs.push(this._getTensorId(input));
console.log(` inputs shape: ` +
`[${node.inputs.map((input) => input.shape()).join('], [')}]`);

const power = node.getInt('power',1);
const scale = node.getFloat('scale',1.0);
const shift = node.getInt('shift',0);

if(power === 1 && shift === 0) {
const dims = [1, 1, 1, 1];

inputs.push(this._addTensorFloat32(new Float32Array([scale]), dims));
inputs.push(this._addScalarInt32(this._nn.FUSED_NONE));

const output = node.outputs[0];
const outDims = output.shape();
const outputType = {
type: this._getTypeCode(output.dataType()), dimensions: outDims
};
const outputId = this._addNamedOperand(output.graphId(), outputType);
outputs.push(outputId);
console.log(` output shape: [${outDims}]`);

this._addOperation(this._nn.MUL,inputs,outputs);
}
else {
// TODO find ops to replace power
}
} break;
default: {
throw new Error(`${node.operator} is not supported.`);
}
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36 changes: 33 additions & 3 deletions examples/util/openvino/OpenVINOModelUtils.js
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Expand Up @@ -200,6 +200,26 @@ class OpenVINOModel {
const ctor = this._getConstructorFromType(tensor.type.dataType);
const length = size / ctor.BYTES_PER_ELEMENT;
const data = new ctor(this._weights, offset, length);
if (dimHints === 6) {
if (OpenVINOUtils.product(dimHints) !== length) {
throw new Error(`Product of ${dimHints} doesn't match the length ${length}`);
}
// 6d tensor permute
// NC[3]HW => NHWC[3]
const nhwc3Data = new ctor(data.length);
const [N, H, W, C1, C2, C3] = dimHints;
const C = C1 * C2 * C3;
for (let n = 0; n < N; ++n) {
for (let c = 0; c < C; ++c) {
for (let h = 0; h < H; ++h) {
for (let w = 0; w < W; ++w) {
nhwc3Data[n*H*W*C + h*W*C + w*C + c] = data[n*C*H*W + c*H*W + h*W + w];
}
}
}
}
return nhwc3Data;
}
if (typeof dimHints === 'undefined' || dimHints.length !== 4) {
return data;
}
Expand Down Expand Up @@ -233,11 +253,21 @@ class OpenVINOModel {

getTensorShape(arg) {
const dims = this._getTensorType(arg).shape.dimensions;
if (dims.length !== 4) {
if (dims.length !== 4 && dims.length !== 6) {
return dims;
} else {
const [N, C, H, W] = dims;
return [N, H, W, C];
if(dims.length === 4) {
const [N, C, H, W] = dims;
return [N, H, W, C];
}
else { if(dims[5] === 2 || dims[5] === 3) { //used in permute ops
const [N, C1, C2, C3, H, W] = dims;
return [N, C2, C3, H, W, C1];
} else { //used in reshape ops
const [N, C1, C2, C3, H, W] = dims;
return [N, H, W, C1, C2, C3];
}
}
}
}

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5 changes: 5 additions & 0 deletions src/nn/tfjs/TfjsModel.js
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Expand Up @@ -710,6 +710,11 @@ export default class TfjsModel {
const output = operands[outputs[0]];
output.assign(tf.sigmoid(input1));
} break;
case OperationCode.RELU: {
const input1 = operands[inputs[0]];
const output = operands[outputs[0]];
output.assign(tf.relu(input1));
} break;
default: {
throw new Error(`Operation ${op} is not supported`);
}
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