@tensorflow/tfjs-core/src/tensor_format.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */

import {DataType, TypedArray} from './types';
import {computeStrides, isString, rightPad, sizeFromShape} from './util';

// Maximum number of values before we decide to show ellipsis.
const FORMAT_LIMIT_NUM_VALS = 20;
// Number of first and last values to show when displaying a, b,...,y, z.
const FORMAT_NUM_FIRST_LAST_VALS = 3;
// Number of significant digits to show.
const FORMAT_NUM_SIG_DIGITS = 7;

export function tensorToString(
    vals: TypedArray|string[], shape: number[], dtype: DataType,
    verbose: boolean) {
  const strides = computeStrides(shape);
  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
  const rank = shape.length;
  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
  const lines = ['Tensor'];
  if (verbose) {
    lines.push(`  dtype: ${dtype}`);
    lines.push(`  rank: ${rank}`);
    lines.push(`  shape: [${shape}]`);
    lines.push(`  values:`);
  }
  lines.push(valsLines.map(l => '    ' + l).join('\n'));
  return lines.join('\n');
}

function computeMaxSizePerColumn(
    vals: TypedArray|string[], shape: number[], dtype: DataType,
    strides: number[]): number[] {
  const n = sizeFromShape(shape);
  const numCols = strides[strides.length - 1];
  const padPerCol = new Array(numCols).fill(0);
  const rank = shape.length;
  const valuesOrTuples =
      dtype === 'complex64' ? createComplexTuples(vals) : vals;

  if (rank > 1) {
    for (let row = 0; row < n / numCols; row++) {
      const offset = row * numCols;
      for (let j = 0; j < numCols; j++) {
        padPerCol[j] = Math.max(
            padPerCol[j],
            valToString(valuesOrTuples[offset + j], 0, dtype).length);
      }
    }
  }
  return padPerCol;
}

function valToString(
    val: number|string|[number, number], pad: number, dtype: DataType) {
  let valStr: string;
  if (Array.isArray(val)) {
    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +
        `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;
  } else if (isString(val)) {
    valStr = `'${val}'`;
  } else if (dtype === 'bool') {
    valStr = boolNumToString(val);
  } else {
    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
  }

  return rightPad(valStr, pad);
}

function boolNumToString(v: number): string {
  return v === 0 ? 'false' : 'true';
}

function subTensorToString(
    vals: TypedArray|string[], shape: number[], dtype: DataType,
    strides: number[], padPerCol: number[], isLast = true): string[] {
  const storagePerElement = dtype === 'complex64' ? 2 : 1;

  const size = shape[0];
  const rank = shape.length;
  if (rank === 0) {
    if (dtype === 'complex64') {
      const complexTuple = createComplexTuples(vals);
      return [valToString(complexTuple[0], 0, dtype)];
    }
    if (dtype === 'bool') {
      return [boolNumToString(vals[0] as number)];
    }
    return [vals[0].toString()];
  }

  if (rank === 1) {
    if (size > FORMAT_LIMIT_NUM_VALS) {
      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;

      let firstVals = Array.from<number|string|[number, number]>(
          vals.slice(0, firstValsSize));
      let lastVals = Array.from<number|string|[number, number]>(vals.slice(
          size - FORMAT_NUM_FIRST_LAST_VALS * storagePerElement, size));
      if (dtype === 'complex64') {
        firstVals = createComplexTuples(firstVals);
        lastVals = createComplexTuples(lastVals);
      }
      return [
        '[' +
        firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))
            .join(', ') +
        ', ..., ' +
        lastVals
            .map(
                (x, i) => valToString(
                    x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))
            .join(', ') +
        ']'
      ];
    }
    const displayVals: Array<number|string|[number, number]> =
        dtype === 'complex64' ? createComplexTuples(vals) :
                                Array.from<number|string>(vals);

    return [
      '[' +
      displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))
          .join(', ') +
      ']'
    ];
  }

  // The array is rank 2 or more.
  const subshape = shape.slice(1);
  const substrides = strides.slice(1);
  const stride = strides[0] * storagePerElement;
  const lines: string[] = [];
  if (size > FORMAT_LIMIT_NUM_VALS) {
    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
      const start = i * stride;
      const end = start + stride;
      lines.push(...subTensorToString(
          vals.slice(start, end), subshape, dtype, substrides, padPerCol,
          false /* isLast */));
    }
    lines.push('...');
    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
      const start = i * stride;
      const end = start + stride;
      lines.push(...subTensorToString(
          vals.slice(start, end), subshape, dtype, substrides, padPerCol,
          i === size - 1 /* isLast */));
    }
  } else {
    for (let i = 0; i < size; i++) {
      const start = i * stride;
      const end = start + stride;
      lines.push(...subTensorToString(
          vals.slice(start, end), subshape, dtype, substrides, padPerCol,
          i === size - 1 /* isLast */));
    }
  }
  const sep = rank === 2 ? ',' : '';
  lines[0] = '[' + lines[0] + sep;
  for (let i = 1; i < lines.length - 1; i++) {
    lines[i] = ' ' + lines[i] + sep;
  }
  let newLineSep = ',\n';
  for (let i = 2; i < rank; i++) {
    newLineSep += '\n';
  }
  lines[lines.length - 1] =
      ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);
  return lines;
}

function createComplexTuples(vals: Array<{}>|
                             TypedArray): Array<[number, number]> {
  const complexTuples: Array<[number, number]> = [];
  for (let i = 0; i < vals.length; i += 2) {
    complexTuples.push([vals[i], vals[i + 1]] as [number, number]);
  }
  return complexTuples;
}