[tfjs-layers] Have tfjs-layers inherit the global lint setting (tensorflow#1893)

DEV

- Upgrade layers to typescript 3.5.3
- Have tfjs-layers inherit the global lint setting.
- Fix lint issues.

Author: dsmilkov

tfjs-layers/.vscode/tasks.json

@@ -0,0 +1,27 @@
+{
+    "version": "2.0.0",
+    "tasks": [
+      {
+        "command": "yarn",
+        "label": "lint",
+        "type": "shell",
+        "args": [
+          "lint"
+        ],
+        "problemMatcher": {
+          "base": "$tslint5",
+          "owner": "tslint-type-checked",
+          "fileLocation": "absolute"
+        }
+      },
+      {
+        "command": "yarn",
+        "label": "build",
+        "type": "shell",
+        "args": ["build", "--pretty", "false", "--noEmit"],
+        "problemMatcher": [
+          "$tsc"
+        ]
+      }
+    ]
+  }

tfjs-layers/package.json

@@ -32,7 +32,7 @@
     "ts-node": "7.0.0",
     "tslint": "~5.11.0",
     "tslint-no-circular-imports": "^0.5.0",
-    "typescript": "3.3.3333",
+    "typescript": "3.5.3",
     "yalc": "~1.0.0-pre.21"
   },
   "scripts": {

tfjs-layers/src/activations_test.ts

@@ -13,10 +13,9 @@
  */
 import {scalar, tensor1d, tensor2d, tensor3d} from '@tensorflow/tfjs-core';
 
-import {Elu, HardSigmoid, Linear, Relu, Relu6, Selu, Sigmoid, Softmax, LogSoftmax, Softplus, Softsign, Tanh} from './activations';
+import {Elu, HardSigmoid, Linear, LogSoftmax, Relu, Relu6, Selu, Sigmoid, Softmax, Softplus, Softsign, Tanh} from './activations';
 import {describeMathCPUAndGPU, expectNoLeakedTensors, expectTensorsClose} from './utils/test_utils';
 
-
 describeMathCPUAndGPU('linear activation', () => {
   const initVals = new Float32Array([-1, 2, 0, 4, -5, 6]);
   const expectedVals = new Float32Array([-1, 2, 0, 4, -5, 6]);
@@ -88,7 +87,6 @@ describeMathCPUAndGPU('selu activation', () => {
   });
 });
 
-
 describeMathCPUAndGPU('relu activation', () => {
   const initVals = new Float32Array([-1, 2, 0, 4, -5, 6]);
   const expectedVals = new Float32Array([0, 2, 0, 4, 0, 6]);
@@ -286,15 +284,15 @@ describeMathCPUAndGPU('logsoftmax activation', () => {
     expectTensorsClose(logsoftmax(initX), expectedVals);
   });
   it('2D', () => {
-    const initX = tensor2d([[0, 1, 3, 9,], [0, 1, 3, 9]]);
+    const initX = tensor2d([[0, 1, 3, 9], [0, 1, 3, 9]]);
     const expectedVals = tensor2d(
-       [[-9.003, -8.003, -6.003, -0.003], [-9.003, -8.003, -6.003, -0.003]]);
+        [[-9.003, -8.003, -6.003, -0.003], [-9.003, -8.003, -6.003, -0.003]]);
     expectTensorsClose(logsoftmax(initX), expectedVals);
   });
   it('3D', () => {
-    const initX = tensor3d([[[0, 1, 3, 9,], [0, 1, 3, 9]]]);
+    const initX = tensor3d([[[0, 1, 3, 9], [0, 1, 3, 9]]]);
     const expectedVals = tensor3d(
-       [[[-9.003, -8.003, -6.003, -0.003], [-9.003, -8.003, -6.003, -0.003]]]);
+        [[[-9.003, -8.003, -6.003, -0.003], [-9.003, -8.003, -6.003, -0.003]]]);
     expectTensorsClose(logsoftmax(initX), expectedVals);
   });
   it('Does not leak', () => {

tfjs-layers/src/backend/tfjs_backend.ts

@@ -317,8 +317,8 @@ export function concatAlongFirstAxis(a: Tensor, b: Tensor): Tensor {
       return tfc.concat4d([a as Tensor4D, b as Tensor4D], 0);
     default:
       throw new ValueError(
-          'concatAlongFirstAxis() received an unsupported tensor rank: ' +
-          a.rank);
+          `concatAlongFirstAxis() received an unsupported ` +
+          `tensor rank: ${a.rank}`);
   }
 }
 
@@ -343,7 +343,6 @@ export function tile(x: Tensor, n: number|number[]): Tensor {
 
 /* Creation of random tensors. */
 
-
 /**
  * Get a tensor with normal distribution of values.
  *
@@ -547,7 +546,7 @@ export function pow(x: Tensor, a: Tensor|number): Tensor {
       throw new NotImplementedError(
           `Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);
     }
-    return tfc.pow(x, a as Tensor);
+    return tfc.pow(x, a);
   });
 }
 
@@ -559,8 +558,8 @@ function reshapeBias(xRank: number, bias: Tensor, dataFormat: string) {
 
   if (bias.rank !== 1 && bias.rank !== xRank) {
     throw new ValueError(
-        'Unexpected bias dimensions: ' + bias.rank +
-        '; expected it to be 1 or ' + xRank);
+        `Unexpected bias dimensions: ${bias.rank}` +
+        `; expected it to be 1 or ${xRank}`);
   }
 
   if (xRank === 5) {

tfjs-layers/src/backend/tfjs_backend_test.ts

@@ -22,7 +22,6 @@ import {LayerVariable} from '../variables';
 
 import * as K from './tfjs_backend';
 
-
 describe('TensorMath', () => {
   it('Setting and getting backend', () => {
     // Default deeplearn.js backend is WebGL (GPU).
@@ -318,7 +317,6 @@ describeMathCPUAndGPU('sliceAlongLastAxis', () => {
   });
 });
 
-
 describeMathCPUAndGPU('sliceAlongAxis', () => {
   it('1D', () => {
     const array1DData = [10, 20, 30, 40];
@@ -359,7 +357,6 @@ describeMathCPUAndGPU('sliceAlongAxis', () => {
         tensor3d([[[1], [3]], [[5], [7]]], [2, 2, 1]));
   });
 
-
   it('4D', () => {
     const array4DData = [[[[10, 1]]], [[[20, 2]]], [[[30, 3]]], [[[40, 4]]]];
     const x = tensor4d(array4DData, [4, 1, 1, 2]);
@@ -674,7 +671,6 @@ describeMathCPUAndGPU('Gather', () => {
   });
 });
 
-
 describeMathCPUAndGPU('Square', () => {
   it('Element-wise square', () => {
     expectTensorsClose(

tfjs-layers/src/base_callbacks.ts

@@ -252,9 +252,9 @@ export class BaseLogger extends BaseCallback {
         } else {
           this.totals[key] = 0;
         }
-        this.totals[key] = tidy(
-            () => add((this.totals[key] as Scalar), mul(value, batchSize)) as
-                Scalar);
+        const total: Scalar =
+            tidy(() => add((this.totals[key]), mul(value, batchSize)));
+        this.totals[key] = total;
         if (oldTotalsToDispose != null) {
           oldTotalsToDispose.dispose();
         }
@@ -272,8 +272,8 @@ export class BaseLogger extends BaseCallback {
           logs[key] = this.totals[key] as number / this.seen;
         } else {
           tidy(() => {
-            logs[key] = mul(div(1, this.seen) as Scalar,
-                            this.totals[key] as Scalar) as Scalar;
+            const log: Scalar = mul(div(1, this.seen), this.totals[key]);
+            logs[key] = log;
             (this.totals[key] as Tensor).dispose();
             keep(logs[key] as Scalar);
           });
@@ -470,7 +470,7 @@ export function standardizeCallbacks(
     callbacks = {} as BaseCallback;
   }
   if (callbacks instanceof BaseCallback) {
-    return [callbacks as BaseCallback];
+    return [callbacks];
   }
   if (Array.isArray(callbacks) && callbacks[0] instanceof BaseCallback) {
     return callbacks as BaseCallback[];

tfjs-layers/src/base_callbacks_test.ts

@@ -21,7 +21,6 @@ import * as tfl from './index';
 import {disposeTensorsInLogs, Logs, resolveScalarsInLogs, UnresolvedLogs} from './logs';
 import {describeMathCPUAndGPU} from './utils/test_utils';
 
-
 describe('BaseLogger Callback', () => {
   it('Records and averages losses in an epoch', async () => {
     const baseLogger = new BaseLogger();
@@ -170,7 +169,6 @@ describeMathCPUAndGPU('disposeTensorsInLogs', () => {
   });
 });
 
-
 describe('History Callback', () => {
   it('onTrainBegin', async () => {
     const history = new History();

tfjs-layers/src/callbacks.ts

@@ -24,7 +24,7 @@ export abstract class Callback extends BaseCallback {
     if (!(model instanceof LayersModel)) {
       throw new Error('model must be a LayersModel, not some other Container');
     }
-    this.model = model as LayersModel;
+    this.model = model;
   }
 }
 

tfjs-layers/src/common.ts

@@ -93,7 +93,7 @@ export function getUniqueTensorName(scopedName: string): string {
   nameMap.set(scopedName, nameMap.get(scopedName) + 1);
 
   if (index > 0) {
-    const result = scopedName + '_' + index;
+    const result = `${scopedName}_${index}`;
     // Mark the composed name as used in case someone wants
     // to call getUniqueTensorName("name_1").
     nameMap.set(result, 1);

tfjs-layers/src/common_test.ts

@@ -15,7 +15,6 @@
 import {checkDataFormat, checkPaddingMode, checkPoolMode, getUniqueTensorName, isValidTensorName} from './common';
 import {VALID_DATA_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES} from './keras_format/common';
 
-
 describe('checkDataFormat', () => {
   it('Valid values', () => {
     const extendedValues = VALID_DATA_FORMAT_VALUES.concat([undefined, null]);
@@ -85,7 +84,6 @@ describe('checkPoolMode', () => {
   });
 });
 
-
 describe('isValidTensorName', () => {
   it('Valid tensor names', () => {
     expect(isValidTensorName('a')).toEqual(true);