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Remove deprecated MLOperandDescriptor.type #240

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May 23, 2024
Merged

Remove deprecated MLOperandDescriptor.type #240

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2 changes: 1 addition & 1 deletion common/utils.js
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Expand Up @@ -121,7 +121,7 @@ export async function buildConstantByNpy(builder, url, targetType = 'float32') {
throw new Error(`Conversion from ${npArray.dataType} ` +
`to ${targetType} is not supported.`);
}
return builder.constant({dataType: type, type, dimensions}, typedArray);
return builder.constant({dataType: type, dimensions}, typedArray);
}

// Convert video frame to a canvas element
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1 change: 0 additions & 1 deletion face_recognition/facenet_nchw.js
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Expand Up @@ -134,7 +134,6 @@ export class FaceNetNchw {
this.context_ = await navigator.ml.createContext(contextOptions);
this.builder_ = new MLGraphBuilder(this.context_);
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion face_recognition/facenet_nhwc.js
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Expand Up @@ -135,7 +135,6 @@ export class FaceNetNhwc {
this.context_ = await navigator.ml.createContext(contextOptions);
this.builder_ = new MLGraphBuilder(this.context_);
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion facial_landmark_detection/face_landmark_nchw.js
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Expand Up @@ -70,7 +70,6 @@ export class FaceLandmarkNchw {
this.context_ = await navigator.ml.createContext(contextOptions);
this.builder_ = new MLGraphBuilder(this.context_);
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion facial_landmark_detection/face_landmark_nhwc.js
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Expand Up @@ -71,7 +71,6 @@ export class FaceLandmarkNhwc {
this.context_ = await navigator.ml.createContext(contextOptions);
this.builder_ = new MLGraphBuilder(this.context_);
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion facial_landmark_detection/ssd_mobilenetv2_face_nchw.js
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Expand Up @@ -122,7 +122,6 @@ ${nameArray[1]}`;
this.deviceType_ = contextOptions.deviceType;
this.builder_ = new MLGraphBuilder(this.context_);
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion facial_landmark_detection/ssd_mobilenetv2_face_nhwc.js
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Expand Up @@ -134,7 +134,6 @@ ${nameArray[1]}`;
this.deviceType_ = contextOptions.deviceType;
this.builder_ = new MLGraphBuilder(this.context_);
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion image_classification/mobilenet_nhwc.js
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Expand Up @@ -96,7 +96,6 @@ export class MobileNetV2Nhwc {
const autoPad = 'same-upper';
const filterLayout = 'ohwi';
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion image_classification/resnet50v2_nchw.js
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Expand Up @@ -101,7 +101,6 @@ export class ResNet50V2Nchw {
this.context_ = await navigator.ml.createContext(contextOptions);
this.builder_ = new MLGraphBuilder(this.context_);
const data = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion image_classification/resnet50v2_nhwc.js
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Expand Up @@ -123,7 +123,6 @@ export class ResNet50V2Nhwc {
this.context_ = await navigator.ml.createContext(contextOptions);
this.builder_ = new MLGraphBuilder(this.context_);
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion image_classification/squeezenet_nchw.js
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Expand Up @@ -44,7 +44,6 @@ export class SqueezeNetNchw {
this.context_ = await navigator.ml.createContext(contextOptions);
this.builder_ = new MLGraphBuilder(this.context_);
const data = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion image_classification/squeezenet_nhwc.js
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Expand Up @@ -55,7 +55,6 @@ export class SqueezeNetNhwc {
const strides = [2, 2];
const layout = 'nhwc';
const placeholder = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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14 changes: 7 additions & 7 deletions lenet/lenet.js
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Expand Up @@ -59,7 +59,7 @@ export class LeNet {
const add1BiasData =
new Float32Array(arrayBuffer, byteOffset, sizeOfShape(add1BiasShape));
const add1Bias = this.builder_.constant(
{type: 'float32', dataType: 'float32', dimensions: add1BiasShape},
{dataType: 'float32', dimensions: add1BiasShape},
add1BiasData,
);
byteOffset += sizeOfShape(add1BiasShape) * Float32Array.BYTES_PER_ELEMENT;
Expand Down Expand Up @@ -87,14 +87,14 @@ export class LeNet {
conv2FilterData, conv2FilterShape, this.oihwToOhwiPermutation_);
}
const conv2Filter = this.builder_.constant(
{type: 'float32', dataType: 'float32', dimensions: conv2FilterShape},
{dataType: 'float32', dimensions: conv2FilterShape},
conv2FilterData);
byteOffset +=
sizeOfShape(conv2FilterShape) * Float32Array.BYTES_PER_ELEMENT;

const add2BiasShape = [50];
const add2Bias = this.builder_.constant(
{type: 'float32', dataType: 'float32', dimensions: add2BiasShape},
{dataType: 'float32', dimensions: add2BiasShape},
new Float32Array(arrayBuffer, byteOffset, sizeOfShape(add2BiasShape)));
byteOffset += sizeOfShape(add2BiasShape) * Float32Array.BYTES_PER_ELEMENT;
conv2Options.bias = add2Bias;
Expand All @@ -120,15 +120,15 @@ export class LeNet {

const matmul1Shape = [500, 800];
const matmul1Weights = this.builder_.constant(
{type: 'float32', dataType: 'float32', dimensions: matmul1Shape},
{dataType: 'float32', dimensions: matmul1Shape},
new Float32Array(arrayBuffer, byteOffset, sizeOfShape(matmul1Shape)));
byteOffset += sizeOfShape(matmul1Shape) * Float32Array.BYTES_PER_ELEMENT;
const matmul1WeightsTransposed = this.builder_.transpose(matmul1Weights);
const matmul1 = this.builder_.gemm(reshape1, matmul1WeightsTransposed);

const add3BiasShape = [1, 500];
const add3Bias = this.builder_.constant(
{type: 'float32', dataType: 'float32', dimensions: add3BiasShape},
{dataType: 'float32', dimensions: add3BiasShape},
new Float32Array(arrayBuffer, byteOffset, sizeOfShape(add3BiasShape)));
byteOffset += sizeOfShape(add3BiasShape) * Float32Array.BYTES_PER_ELEMENT;
const add3 = this.builder_.add(matmul1, add3Bias);
Expand All @@ -140,15 +140,15 @@ export class LeNet {

const matmul2Shape = [10, 500];
const matmul2Weights = this.builder_.constant(
{type: 'float32', dataType: 'float32', dimensions: matmul2Shape},
{dataType: 'float32', dimensions: matmul2Shape},
new Float32Array(arrayBuffer, byteOffset, sizeOfShape(matmul2Shape)));
byteOffset += sizeOfShape(matmul2Shape) * Float32Array.BYTES_PER_ELEMENT;
const matmul2WeightsTransposed = this.builder_.transpose(matmul2Weights);
const matmul2 = this.builder_.gemm(reshape2, matmul2WeightsTransposed);

const add4BiasShape = [1, 10];
const add4Bias = this.builder_.constant(
{type: 'float32', dataType: 'float32', dimensions: add4BiasShape},
{dataType: 'float32', dimensions: add4BiasShape},
new Float32Array(arrayBuffer, byteOffset, sizeOfShape(add4BiasShape)));
const add4 = this.builder_.add(matmul2, add4Bias);

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3 changes: 0 additions & 3 deletions nsnet2/nsnet2.js
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Expand Up @@ -37,14 +37,12 @@ export class NSNet2 {
const biasFcOut4 = await buildConstantByNpy(this.builder_, baseUrl + 'fc_out_4_bias.npy');
// Build up the network.
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: [batchSize, frames, this.frameSize],
});
const relu20 = this.builder_.relu(this.builder_.add(this.builder_.matmul(input, weight172), biasFcIn0));
const transpose31 = this.builder_.transpose(relu20, {permutation: [1, 0, 2]});
const initialState92 = this.builder_.input('initialState92', {
type: 'float32',
dataType: 'float32',
dimensions: [1, batchSize, this.hiddenSize],
});
Expand All @@ -55,7 +53,6 @@ export class NSNet2 {
squeeze95Shape.splice(1, 1);
const squeeze95 = this.builder_.reshape(gru93, squeeze95Shape);
const initialState155 = this.builder_.input('initialState155', {
type: 'float32',
dataType: 'float32',
dimensions: [1, batchSize, this.hiddenSize],
});
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1 change: 0 additions & 1 deletion object_detection/ssd_mobilenetv1_nhwc.js
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Expand Up @@ -94,7 +94,6 @@ ${nameArray[1]}_BatchNorm_batchnorm`;
this.deviceType_ = contextOptions.deviceType;
this.builder_ = new MLGraphBuilder(this.context_);
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion object_detection/tiny_yolov2_nhwc.js
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Expand Up @@ -56,7 +56,6 @@ export class TinyYoloV2Nhwc {
this.context_ = await navigator.ml.createContext(contextOptions);
this.builder_ = new MLGraphBuilder(this.context_);
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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4 changes: 0 additions & 4 deletions rnnoise/rnnoise.js
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Expand Up @@ -52,7 +52,6 @@ export class RNNoise {
this.baseUrl_ + 'denoise_output_bias_0.npy');
// Build up the network.
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: [this.batchSize_, this.frames_, this.featureSize],
});
Expand All @@ -68,7 +67,6 @@ export class RNNoise {
[0, 3 * this.vadGruHiddenSize],
[1, 3 * this.vadGruHiddenSize]);
const vadGruInitialH = this.builder_.input('vadGruInitialH', {
type: 'float32',
dataType: 'float32',
dimensions: [1, this.batchSize_, this.vadGruHiddenSize],
});
Expand Down Expand Up @@ -96,7 +94,6 @@ export class RNNoise {
[0, 3 * this.noiseGruHiddenSize],
[1, 3 * this.noiseGruHiddenSize]);
const noiseGruInitialH = this.builder_.input('noiseGruInitialH', {
type: 'float32',
dataType: 'float32',
dimensions: [1, this.batchSize_, this.noiseGruHiddenSize],
});
Expand Down Expand Up @@ -124,7 +121,6 @@ export class RNNoise {
[0, 3 * this.denoiseGruHiddenSize],
[1, 3 * this.denoiseGruHiddenSize]);
const denoiseGruInitialH = this.builder_.input('denoiseGruInitialH', {
type: 'float32',
dataType: 'float32',
dimensions: [1, this.batchSize_, this.denoiseGruHiddenSize],
});
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1 change: 0 additions & 1 deletion semantic_segmentation/deeplabv3_mnv2_nchw.js
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Expand Up @@ -98,7 +98,6 @@ export class DeepLabV3MNV2Nchw {
const strides = [2, 2];

const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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1 change: 0 additions & 1 deletion semantic_segmentation/deeplabv3_mnv2_nhwc.js
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Expand Up @@ -88,7 +88,6 @@ export class DeepLabV3MNV2Nhwc {
this.builder_ = new MLGraphBuilder(this.context_);
const strides = [2, 2];
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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9 changes: 4 additions & 5 deletions style_transfer/fast_style_transfer_net.js
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Expand Up @@ -96,24 +96,23 @@ export class FastStyleTransferNet {
const padding1 = [0, 0, 1, 1];
const padding4 = [0, 0, 4, 4];
this.constAdd_ = this.builder_.constant(
{type: 'float32', dataType: 'float32', dimensions: [1]},
{dataType: 'float32', dimensions: [1]},
new Float32Array([9.999999717180685e-10]),
);
this.constPow_ = this.builder_.constant(
{type: 'float32', dataType: 'float32', dimensions: [1]},
{dataType: 'float32', dimensions: [1]},
new Float32Array([0.5]),
);
const constMul0 = this.builder_.constant(
{type: 'float32', dataType: 'float32', dimensions: [1]},
{dataType: 'float32', dimensions: [1]},
new Float32Array([150]),
);
const constAdd0 = this.builder_.constant(
{type: 'float32', dataType: 'float32', dimensions: [1]},
{dataType: 'float32', dimensions: [1]},
new Float32Array([127.5]),
);
// Build up the network.
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
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