Remove MLTensorUsage

code/.eslintrc.js

@@ -3,7 +3,6 @@ module.exports = {
     'CodeMirror': 'readonly',
     'executeCodeSnippet': 'readonly',
     'sizeOfShape': 'readonly',
-    'MLTensorUsage': 'readonly',
   },
   ignorePatterns: ['libs/'],
 };

code/samples/matmul.js

@@ -12,9 +12,7 @@ const graph = await builder.build({c});
 // Step 3: Bind input and output buffers to the graph and execute.
 const bufferA = new Float32Array(3*4).fill(1.0);
 const bufferB = new Float32Array(4*3).fill(0.8);
-descA.usage = MLTensorUsage.WRITE;
 descA.writable = true;
-descB.usage = MLTensorUsage.WRITE;
 descB.writable = true;
 const tensorA = await context.createTensor(descA);
 const tensorB = await context.createTensor(descB);
@@ -24,7 +22,6 @@ const tensorC = await context.createTensor({
   dataType: 'float32',
   dimensions: [3, 3],
   shape: [3, 3],
-  usage: MLTensorUsage.READ,
   readable: true,
 });
 context.dispatch(graph, {a: tensorA, b: tensorB}, {c: tensorC});

code/samples/mul_add.js

@@ -12,15 +12,13 @@ const graph = await builder.build({'C': C});
 // 3. Bind inputs to the graph and execute for the result.
 const bufferA = new Float32Array(4).fill(1.0);
 const bufferB = new Float32Array(4).fill(0.8);
-desc.usage = MLTensorUsage.WRITE;
 desc.writable = true;
 const tensorA = await context.createTensor(desc);
 const tensorB = await context.createTensor(desc);
 context.writeTensor(tensorA, bufferA);
 context.writeTensor(tensorB, bufferB);
 const tensorC = await context.createTensor({
   ...desc,
-  usage: MLTensorUsage.READ,
   readable: true,
   writable: false,
 });

code/samples/simple_graph.js

@@ -51,7 +51,6 @@ const graph = await builder.build({'output': output});
 const inputBuffer1 = new Float32Array(TENSOR_SIZE).fill(1);
 const inputBuffer2 = new Float32Array(TENSOR_SIZE).fill(1);
 
-desc.usage = MLTensorUsage.WRITE;
 desc.writable = true;
 const inputTensor1 = await context.createTensor(desc);
 const inputTensor2 = await context.createTensor(desc);
@@ -60,7 +59,6 @@ context.writeTensor(inputTensor2, inputBuffer2);
 
 const outputTensor = await context.createTensor({
   ...desc,
-  usage: MLTensorUsage.READ,
   readable: true,
   writable: false,
 });

face_recognition/.eslintrc.js

@@ -1,7 +1,6 @@
 module.exports = {
   globals: {
     'MLGraphBuilder': 'readonly',
-    'MLTensorUsage': 'readonly',
     'tf': 'readonly',
   },
 };

face_recognition/facenet_nchw.js

@@ -149,14 +149,12 @@ export class FaceNetNchw {
       shape: this.inputOptions.inputShape,
     };
     const input = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
 

face_recognition/facenet_nhwc.js

@@ -150,14 +150,12 @@ export class FaceNetNhwc {
       shape: this.inputOptions.inputShape,
     };
     const input = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
 

facial_landmark_detection/.eslintrc.js

@@ -1,7 +1,6 @@
 module.exports = {
   globals: {
     'MLGraphBuilder': 'readonly',
-    'MLTensorUsage': 'readonly',
     'tf': 'readonly',
   },
 };

facial_landmark_detection/face_landmark_nchw.js

@@ -78,14 +78,12 @@ export class FaceLandmarkNchw {
       shape: this.inputOptions.inputShape,
     };
     const input = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
 

facial_landmark_detection/face_landmark_nhwc.js

@@ -79,14 +79,12 @@ export class FaceLandmarkNhwc {
       shape: this.inputOptions.inputShape,
     };
     const input = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
 

facial_landmark_detection/ssd_mobilenetv2_face_nchw.js

@@ -125,15 +125,13 @@ ${nameArray[1]}`;
       shape: this.inputOptions.inputShape,
     };
     const input = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     for (const [key, value] of Object.entries(this.outputsInfo)) {
       this.outputTensors_[key] = await this.context_.createTensor({
         dataType: 'float32',
         dimensions: value,
         shape: value,
-        usage: MLTensorUsage.READ,
         readable: true,
       });
     }

facial_landmark_detection/ssd_mobilenetv2_face_nhwc.js

@@ -136,15 +136,13 @@ ${nameArray[1]}`;
       shape: this.inputOptions.inputShape,
     };
     const input = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     for (const [key, value] of Object.entries(this.outputsInfo)) {
       this.outputTensors_[key] = await this.context_.createTensor({
         dataType: 'float32',
         dimensions: value,
         shape: value,
-        usage: MLTensorUsage.READ,
         readable: true,
       });
     }

image_classification/.eslintrc.js

@@ -1,6 +1,5 @@
 module.exports = {
   globals: {
     'MLGraphBuilder': 'readonly',
-    'MLTensorUsage': 'readonly',
   },
 };

image_classification/efficientnet_fp16_nchw.js

@@ -83,14 +83,12 @@ export class EfficientNetFP16Nchw {
       shape: this.inputOptions.inputShape,
     };
     let data = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
     data = this.builder_.cast(data, 'float16');

image_classification/mobilenet_nchw.js

@@ -97,14 +97,12 @@ export class MobileNetV2Nchw {
       shape: this.inputOptions.inputShape,
     };
     let data = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
     if (this.dataType_ === 'float16') {

image_classification/mobilenet_nhwc.js

@@ -98,14 +98,12 @@ export class MobileNetV2Nhwc {
       shape: this.inputOptions.inputShape,
     };
     const input = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
     const conv0 = this.buildConv_(

image_classification/resnet50v1_fp16_nchw.js

@@ -84,14 +84,12 @@ export class ResNet50V1FP16Nchw {
       shape: this.inputOptions.inputShape,
     };
     let data = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
     data = this.builder_.cast(data, 'float16');

image_classification/resnet50v2_nchw.js

@@ -106,14 +106,12 @@ export class ResNet50V2Nchw {
       shape: this.inputOptions.inputShape,
     };
     const data = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
     const bn1 = this.buildBatchNorm_(data, '0', '', false);

image_classification/resnet50v2_nhwc.js

@@ -132,14 +132,12 @@ export class ResNet50V2Nhwc {
       shape: this.inputOptions.inputShape,
     };
     const input = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
     const conv1 = await this.buildConv_(

image_classification/squeezenet_nchw.js

@@ -51,14 +51,12 @@ export class SqueezeNetNchw {
       shape: this.inputOptions.inputShape,
     };
     const data = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
     const conv0 = this.buildConv_(data, 'conv0', {strides: [2, 2]});

image_classification/squeezenet_nhwc.js

@@ -66,14 +66,12 @@ export class SqueezeNetNhwc {
       shape: this.inputOptions.inputShape,
     };
     const placeholder = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
     const conv1 = this.buildConv_(

lenet/.eslintrc.cjs

@@ -2,7 +2,6 @@ module.exports = {
   ignorePatterns: ['libs/'],
   globals: {
     'MLGraphBuilder': 'readonly',
-    'MLTensorUsage': 'readonly',
     'BigInt64Array': 'readonly',
     'mnist': 'readonly',
   },

lenet/lenet.js

@@ -34,14 +34,12 @@ export class LeNet {
       shape: inputShape,
     };
     let input = this.builder_.input('input', inputDesc);
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
     this.outputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
       dimensions: this.outputShape_,
       shape: this.outputShape_,
-      usage: MLTensorUsage.READ,
       readable: true,
     });
     if (this.layout_ === 'nhwc') {

nnotepad/.eslintrc.js

@@ -1,5 +1,4 @@
 module.exports = {
   env: {'es6': true, 'browser': true, 'jquery': false, 'node': true},
   parserOptions: {ecmaVersion: 2021, sourceType: 'module'},
-  globals: {'MLTensorUsage': 'readonly'},
 };

nnotepad/js/nnotepad.js

@@ -40,7 +40,7 @@ class WebNNUtil {
     const isShapeMethod = typeof operand.shape === 'function';
     const operandShape = isShapeMethod ? operand.shape() : operand.shape;
     const operandDataType = isShapeMethod ? operand.dataType() :
-        operand.dataType;
+          operand.dataType;
     const size = [...operandShape].reduce((a, b) => a * b, 1);
     const ctor = WebNNUtil.dataTypeToBufferType(operandDataType);
     return Reflect.construct(ctor, [size]);
@@ -52,7 +52,6 @@ class WebNNUtil {
       dataType: isShapeMethod ? operand.dataType() : operand.dataType,
       dimensions: isShapeMethod ? operand.shape() : operand.shape,
       shape: isShapeMethod ? operand.shape() : operand.shape,
-      usage: MLTensorUsage.READ,
       readable: true,
     };
     const tensor = await context.createTensor(desc);

nsnet2/.eslintrc.js

@@ -2,7 +2,6 @@ module.exports = {
   ignorePatterns: ['libs/'],
   globals: {
     'MLGraphBuilder': 'readonly',
-    'MLTensorUsage': 'readonly',
     'tf': 'readonly',
   },
 };

nsnet2/nsnet2.js

@@ -46,7 +46,6 @@ export class NSNet2 {
     const inputDesc = {dataType: 'float32', dimensions: inputShape, shape: inputShape};
     const input = this.builder_.input('input', inputDesc);
 
-    inputDesc.usage = MLTensorUsage.WRITE;
     inputDesc.writable = true;
     this.inputTensor_ = await this.context_.createTensor(inputDesc);
 
@@ -64,7 +63,6 @@ export class NSNet2 {
     const squeeze95 = this.builder_.reshape(gru93, squeeze95Shape);
     const initialState155 = this.builder_.input('initialState155', initialStateDesc);
 
-    initialStateDesc.usage = MLTensorUsage.WRITE;
     initialStateDesc.writable = true;
     this.initialState92Tensor_ = await this.context_.createTensor(initialStateDesc);
     this.initialState155Tensor_ = await this.context_.createTensor(initialStateDesc);
@@ -73,15 +71,13 @@ export class NSNet2 {
       dataType: 'float32',
       dimensions: inputShape,
       shape: inputShape, // Same as inputShape.
-      usage: MLTensorUsage.READ,
       readable: true,
     });
     const gruOutputShape = [1, batchSize, this.hiddenSize];
     const gruOutputDesc = {
       dataType: 'float32',
       dimensions: gruOutputShape,
       shape: gruOutputShape,
-      usage: MLTensorUsage.READ,
       readable: true,
     };
     this.gru94Tensor_ = await this.context_.createTensor(gruOutputDesc);

object_detection/.eslintrc.js

@@ -1,6 +1,5 @@
 module.exports = {
   globals: {
     'MLGraphBuilder': 'readonly',
-    'MLTensorUsage': 'readonly',
   },
 };