implement Resize operator

lib/api/tensor-impl-utils.ts

@@ -15,7 +15,11 @@ export function fromInternalTensor(internalTensor: InternalTensor): ApiTensor {
       return new ApiTensor(new Float32Array(internalTensor.floatData), 'float32', internalTensor.dims);
     case 'string':
       return new ApiTensor(internalTensor.stringData, 'string', internalTensor.dims);
-    case 'int8' || 'uint8' || 'int16' || 'uint16' || 'uint32':
+    case 'int8':
+    case 'uint8':
+    case 'int16':
+    case 'uint16':
+    case 'uint32':
       return new ApiTensor(new Int32Array(internalTensor.integerData), 'int32', internalTensor.dims);
     case 'int32':
       return new ApiTensor(internalTensor.integerData as Int32Array, 'int32', internalTensor.dims);
@@ -57,7 +61,7 @@ export function matchElementType(type: TensorInterface.Type, element: TensorInte
 }
 
 export function validateIndices(indices: ReadonlyArray<number>) {
-  if (indices.length < 0 || indices.length > 6) {
+  if (indices.length > 6) {
     throw new RangeError(`Only rank 0 to 6 is supported for tensor shape.`);
   }
   for (const n of indices) {

lib/backends/cpu/op-resolve-rules.ts

@@ -33,7 +33,7 @@ import {CpuTranspose} from './ops/transpose';
 import * as unaryOps from './ops/unary-op';
 import {CpuUnaryOp} from './ops/unary-op';
 import {CpuUnsqueeze} from './ops/unsqueeze';
-import {CpuUpsample, CpuUpsampleV9} from './ops/upsample';
+import {CpuUpsample} from './ops/upsample';
 
 export const CPU_OP_RESOLVE_RULES: ReadonlyArray<OpSet.ResolveRule> = [
   ['Abs', '', '6+', () => new CpuUnaryOp(NUMBER_TYPES, unaryOps.abs)],
@@ -71,6 +71,7 @@ export const CPU_OP_RESOLVE_RULES: ReadonlyArray<OpSet.ResolveRule> = [
   ['InstanceNormalization', '', '6+', () => new CpuInstanceNormalization()],
   ['IsNaN', '', '9+', () => new CpuUnaryOp(FLOAT_TYPES, unaryOps.isNan, undefined, 'bool')],
   ['LeakyRelu', '', '6+', () => new CpuUnaryOp(FLOAT_TYPES, unaryOps.leakyRelu, unaryOps.leakyReluInitializer)],
+  ['Less', '', '7+', () => new CpuBinaryOp(NUMBER_TYPES, (a, b) => a < b ? 1 : 0, undefined, 'bool')],
   ['Log', '', '6+', () => new CpuUnaryOp(FLOAT_TYPES, unaryOps.log)],
   ['LRN', '', '1+', () => new CpuLrn()],
   ['MatMul', '', '1+', () => new CpuMatMul()],
@@ -92,6 +93,8 @@ export const CPU_OP_RESOLVE_RULES: ReadonlyArray<OpSet.ResolveRule> = [
   ['ReduceSumSquare', '', '1+', () => new cpuReduce.CpuReduceSumSquare()],
   ['Relu', '', '6+', () => new CpuUnaryOp(FLOAT_TYPES, unaryOps.relu)],
   ['Reshape', '', '5+', () => new CpuReshape()],
+  ['Resize', '', '10', () => new CpuUpsample(10)],
+  ['Resize', '', '11+', () => new CpuUpsample(11)],
   ['Shape', '', '1+', () => new CpuShape()],
   ['Sigmoid', '', '6+', () => new CpuUnaryOp(FLOAT_TYPES, unaryOps.sigmoid)],
   ['Sign', '', '9+', () => new CpuUnaryOp(NUMBER_TYPES, unaryOps.sign)],
@@ -109,7 +112,7 @@ export const CPU_OP_RESOLVE_RULES: ReadonlyArray<OpSet.ResolveRule> = [
   ['Tile', '', '6+', () => new CpuTile()],
   ['Transpose', '', '1+', () => new CpuTranspose()],
   ['Unsqueeze', '', '1+', () => new CpuUnsqueeze()],
-  ['Upsample', '', '7-8', () => new CpuUpsample()],
-  ['Upsample', '', '9', () => new CpuUpsampleV9()],
+  ['Upsample', '', '7-8', () => new CpuUpsample(7)],
+  ['Upsample', '', '9', () => new CpuUpsample(9)],
   ['Xor', '', '7+', () => new CpuBinaryOp(['bool'], (e1, e2) => (e1 ^ e2))],
 ];

lib/backends/cpu/ops/concat.ts

@@ -28,7 +28,7 @@ export function concat(x: Tensor[], axis: number) {
   // ensure all of the non-concatenated axes match each other
   // along the way, calculate the shape of the output tensor
   let concatAxisSize = inputShape[axis];
-  const outputShape = new Array<number>(inputShape.length);
+  const outputShape = inputShape.slice(0);
 
   for (let i = 1; i < x.length; i++) {
     const dataN = x[i];

lib/backends/cpu/ops/reshape.ts

@@ -16,7 +16,7 @@ export class CpuReshape extends Reshape {
 export function reshape(x: Tensor, shape: Tensor): Tensor {
   const reshapedDims = ShapeUtil.calculateReshapedDims(x.dims, shape.integerData);
   const output = new Tensor(reshapedDims, x.type);
-  const Y = output.floatData;
-  Y.set(x.floatData);
+  const Y = output.numberData;
+  Y.set(x.numberData);
   return output;
 }