Function stridedSlice

tfjs-backend-cpu/src/kernels/StridedSlice.ts:26–87

(args: {
  inputs: StridedSliceInputs,
  backend: MathBackendCPU,
  attrs: StridedSliceAttrs
})

import {stridedSliceImpl} from './StridedSlice_impl';

export function stridedSlice(args: {
  inputs: StridedSliceInputs,
  backend: MathBackendCPU,
  attrs: StridedSliceAttrs
}): TensorInfo {
  const {inputs, backend, attrs} = args;
  const {x} = inputs;
  const {
    begin,
    end,
    strides,
    beginMask,
    endMask,
    ellipsisMask,
    newAxisMask,
    shrinkAxisMask
  } = attrs;

  assertNotComplex(x, 'stridedSlice');

  const {
    finalShapeSparse,
    finalShape,
    isIdentity,
    sliceDim0,
    isSimpleSlice,
    begin: $begin,
    end: $end,
    strides: $strides
  } =
      slice_util.sliceInfo(
          x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,
          newAxisMask, shrinkAxisMask);

  let result;

  // ref:
  // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/strided_slice_op.cc
  if (isIdentity) {
    // Optimization #1, slice is a no-op plus reshape
    result = reshape({inputs: {x}, backend, attrs: {shape: finalShape}});
  } else if (sliceDim0 || isSimpleSlice) {
    // Optimization #2, slice is memory contiguous (only occurs in dim 0)
    util.assert(
        x.shape.length >= 1,
        () => `Input must have rank at least 1, got: ${x.shape.length}`);

    const size = slice_util.computeOutShape($begin, $end, $strides);
    // To tolerate begin[0] > end[0] (a 0-output slice), we min(begin, end).
    const sliced = slice({inputs: {x}, backend, attrs: {begin: $begin, size}});
    result =
        reshape({inputs: {x: sliced}, backend, attrs: {shape: finalShape}});
    backend.disposeIntermediateTensorInfo(sliced);
  } else {
    const xBuf = backend.bufferSync<Rank, 'float32'>(x);
    const outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin);

    result = backend.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values);