Apply no-consecutive-blank-lines in tslint (#165)

Lewuathe · dsmilkov · commit 9b8949fcd3f2 · 2017-10-03T10:55:31.000-04:00
* Apply no-consecutive-blank-lines in tslint

* Merge branch 'master' into apply-no-consecutive-blank-lines
diff --git a/demos/adder/adder.ts b/demos/adder/adder.ts
@@ -45,7 +45,6 @@ class Adder {
   }
 }
 
-
 const adder = new Adder();
 adder.setupSession();
 
diff --git a/demos/benchmarks/math-benchmark-run-groups.ts b/demos/benchmarks/math-benchmark-run-groups.ts
@@ -15,7 +15,6 @@
  * =============================================================================
  */
 
-
 import {BenchmarkRun, BenchmarkRunGroup} from './benchmark';
 import {ConvBenchmarkParams, ConvGPUBenchmark} from './conv_benchmarks';
 // tslint:disable-next-line:max-line-length
diff --git a/demos/complementary-color-prediction/complementary-color-prediction.ts b/demos/complementary-color-prediction/complementary-color-prediction.ts
@@ -18,7 +18,6 @@
 // tslint:disable-next-line:max-line-length
 import {Array1D, CostReduction, FeedEntry, Graph, InCPUMemoryShuffledInputProviderBuilder, NDArrayMath, NDArrayMathGPU, Session, SGDOptimizer, Tensor} from '../deeplearn';
 
-
 class ComplementaryColorModel {
   // Runs training.
   session: Session;
diff --git a/demos/homepage/index.ts b/demos/homepage/index.ts
@@ -70,7 +70,6 @@ function startCPPN() {
   const DEFAULT_NUM_LAYERS = 2;
   const WEIGHTS_STDEV = 0.6;
 
-
   const cppn = new CPPN(inferenceCanvas);
 
   cppn.setActivationFunction('tanh');
diff --git a/demos/model-builder/model-builder.ts b/demos/model-builder/model-builder.ts
@@ -301,8 +301,6 @@ export class ModelBuilder extends ModelBuilderPolymer {
     return applicationState === ApplicationState.IDLE;
   }
 
-
-
   private getTestData(): NDArray[][] {
     const data = this.dataSet.getData();
     if (data == null) {
diff --git a/demos/one_plus_one/one_plus_one.ts b/demos/one_plus_one/one_plus_one.ts
@@ -46,7 +46,6 @@ class Adder {
   }
 }
 
-
 const adder = new Adder();
 adder.setupSession();
 const result = adder.computeSum(1, 1);
diff --git a/src/data/input_provider_test.ts b/src/data/input_provider_test.ts
@@ -21,7 +21,6 @@ import {Array1D, Scalar} from '../math/ndarray';
 
 import {InCPUMemoryShuffledInputProviderBuilder} from './input_provider';
 
-
 describe('InCPUMemoryShuffledInputProviderBuilder', () => {
   let math: NDArrayMathCPU;
 
diff --git a/src/environment_test.ts b/src/environment_test.ts
@@ -78,7 +78,6 @@ describe('disjoint query timer enabled', () => {
     expect(env.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_ENABLED')).toBe(true);
   });
 
-
 });
 describe('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', () => {
   it('disjoint query timer disabled', () => {
diff --git a/src/graph/graph.ts b/src/graph/graph.ts
@@ -136,7 +136,6 @@ export class Graph {
         new FusedLinearCombinationNode(this, x1, x2, c1, c2));
   }
 
-
   /**
    * Adds two tensors (elementwise). Broadcasts if one of the tensors is scalar.
    * @param x1 The first input tensor.
diff --git a/src/graph/ops/convolution_test.ts b/src/graph/ops/convolution_test.ts
@@ -285,7 +285,6 @@ describe('Convolution', () => {
             dy.get(1, 0) * weights.get(1, 1, 0, 0) +
             dy.get(1, 1) * weights.get(1, 0, 0, 0));
 
-
     // Test dW.
     const dw = gradients.get(wTensor);
 
diff --git a/src/graph/ops/max_pool_test.ts b/src/graph/ops/max_pool_test.ts
@@ -24,7 +24,6 @@ import {SummedTensorArrayMap, TensorArrayMap} from '../tensor_array_map';
 
 import {MaxPool} from './max_pool';
 
-
 describe('Max pool', () => {
   let math: NDArrayMathCPU;
   let xTensor: Tensor;
diff --git a/src/graph/optimizers/rmsprop_optimizer.ts b/src/graph/optimizers/rmsprop_optimizer.ts
@@ -46,7 +46,6 @@ export class RMSPropOptimizer extends Optimizer {
     }
   }
 
-
   afterBatch(
       math: NDArrayMath, batchSize: number, runtime: SessionRuntime,
       activationArrayMap: TensorArrayMap,
diff --git a/src/graph/session_test.ts b/src/graph/session_test.ts
@@ -29,7 +29,6 @@ import {SGDOptimizer} from './optimizers/sgd_optimizer';
 import {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';
 import {FeedDictionary, FeedEntry, Session} from './session';
 
-
 describe('FeedDictionary', () => {
   it('ctor leaves dict empty if no args are passed', () => {
     expect(Object.keys(new FeedDictionary().dict).length).toEqual(0);
@@ -348,7 +347,6 @@ describe('Session', () => {
     });
   });
 
-
   it('Safe mode math, math scope train does not throw', () => {
     const x = g.placeholder('x', [2]);
     const w = g.variable('w', NDArray.zeros([1, 2]));
diff --git a/src/graph/session_util.ts b/src/graph/session_util.ts
@@ -135,7 +135,6 @@ export function loadInputsFromFeedDictionaryToTensorArrayMap(
   });
 }
 
-
 /**
  * @hidden
  */
@@ -219,7 +218,6 @@ export function disposeAndInitializeOperationInputGradients(
   });
 }
 
-
 /**
  * Calls underlying operation disposeTransientArrays methods which clean up any
  * NDArrays which operations may have created during a run.
diff --git a/src/graph_runner_test.ts b/src/graph_runner_test.ts
@@ -160,7 +160,6 @@ describe('Model runner', () => {
       expect(metric.get()).toEqual(.5);
     };
 
-
     graphRunner = new GraphRunner(math, session, fakeUserEvents);
 
     expect(graphRunner.getTotalBatchesTrained()).toEqual(0);
diff --git a/src/math/math.ts b/src/math/math.ts
@@ -29,7 +29,6 @@ export interface LSTMCell {
   (data: Array2D, c: Array2D, h: Array2D): [Array2D, Array2D];
 }
 
-
 export abstract class NDArrayMath {
   private ndarrayScopes: NDArray[][] = [];
   private activeScope: NDArray[];
@@ -68,7 +67,6 @@ export abstract class NDArrayMath {
     return result;
   }
 
-
   /**
    * In debug mode, the output of every math call will be downloaded to the CPU
    * and checked for NaNs. This significantly impacts performance.
@@ -1022,7 +1020,6 @@ export abstract class NDArrayMath {
   }
   protected abstract tanhInternal<T extends NDArray>(ndarray: T): T;
 
-
   /**
    * Computes step of the input NDArray element-wise, y = 1 if x > 0 | 0 if x <=
    * 0
diff --git a/src/math/math_cpu.ts b/src/math/math_cpu.ts
@@ -782,7 +782,6 @@ export class NDArrayMathCPU extends NDArrayMath {
           const xCMin = Math.max(0, xCCorner);
           const xCMax = Math.min(xCols, filterWidth + xCCorner);
 
-
           let minMaxValue =
               (poolType === 'max' ? Number.NEGATIVE_INFINITY :
                                     Number.POSITIVE_INFINITY);
diff --git a/src/math/math_gpu.ts b/src/math/math_gpu.ts
@@ -356,7 +356,6 @@ export class NDArrayMathGPU extends NDArrayMath {
     return this.compileAndRun(program, [a]) as T;
   }
 
-
   protected stepInternal<T extends NDArray>(a: T): T {
     const program = new UnaryOpProgram(a.shape, unary_op.STEP);
     return this.compileAndRun(program, [a]) as T;
diff --git a/src/math/math_gpu_test.ts b/src/math/math_gpu_test.ts
@@ -22,7 +22,6 @@ import {NDArrayMathGPU} from './math_gpu';
 import {Array1D, Array2D, Array3D, Array4D, Scalar} from './ndarray';
 import * as webgl_util from './webgl/webgl_util';
 
-
 describe('NDArrayMathGPU scope', () => {
   let math: NDArrayMathGPU;
   beforeEach(() => {
@@ -1708,7 +1707,6 @@ describe('NDArrayMathGPU sqrt', () => {
   });
 });
 
-
 describe('softmax', () => {
   let math: NDArrayMathGPU;
 
diff --git a/src/math/webgl/avg_pool_gpu_test.ts b/src/math/webgl/avg_pool_gpu_test.ts
@@ -20,7 +20,6 @@ import {NDArrayMathCPU} from '../math_cpu';
 import {Array3D} from '../ndarray';
 import * as pool_gpu_test_util from './pool_gpu_test_util';
 
-
 describe('avg_pool_gpu', () => {
   function uploadAvgPoolDownload(
       a: Float32Array, xShape: [number, number, number], fieldSize: number,
diff --git a/src/math/webgl/binaryop_gpu_test.ts b/src/math/webgl/binaryop_gpu_test.ts
@@ -95,7 +95,6 @@ describe('binaryop_gpu Sub', () => {
   });
 });
 
-
 describe('binaryop_gpu Mul', () => {
   function cpuMultiply(a: Float32Array, b: Float32Array): Float32Array {
     const result = new Float32Array(a.length);
diff --git a/src/math/webgl/copy_gpu_test.ts b/src/math/webgl/copy_gpu_test.ts
@@ -160,7 +160,6 @@ describe('copy_gpu', () => {
       sourceVals[i] = i;
     }
 
-
     const gpgpu = new GPGPUContext();
     const texManager = new TextureManager(gpgpu);
     initializeGPU(gpgpu, texManager);
diff --git a/src/math/webgl/gpgpu_context.ts b/src/math/webgl/gpgpu_context.ts
@@ -364,7 +364,6 @@ export class GPGPUContext {
     });
   }
 
-
   private downloadMatrixDriver(
       texture: WebGLTexture,
       downloadAndDecode: () => Float32Array): Float32Array {
diff --git a/src/math/webgl/pool_gpu.ts b/src/math/webgl/pool_gpu.ts
@@ -36,7 +36,6 @@ export class Pool2DProgram implements GPGPUProgram {
     const strideHeight = convInfo.strideHeight;
     const strideWidth = convInfo.strideWidth;
 
-
     const xNumRows = convInfo.inShape[0];
     const xNumCols = convInfo.inShape[1];
     const padTop = convInfo.padInfo.top;
diff --git a/src/math/webgl/slice_gpu_test.ts b/src/math/webgl/slice_gpu_test.ts
@@ -67,7 +67,6 @@ describe('slice1d_gpu', () => {
     expect(result.get(0)).toEqual(expected);
   });
 
-
   function doSlice1D(a: Array1D, start: number, size: number): Array1D {
     const program = new SliceProgram([size]);
     const result = Array1D.zeros([size]);
@@ -128,7 +127,6 @@ describe('slice2d_gpu', () => {
     expect(result.get(0, 0)).toEqual(expected);
   });
 
-
   function doSlice2D(
       a: Array2D, start: [number, number], size: [number, number]): Array2D {
     const program = new SliceProgram(size);
@@ -190,7 +188,6 @@ describe('slice3d_gpu', () => {
     expect(result.get(0, 0, 0)).toEqual(expected);
   });
 
-
   function doSlice3D(
       a: Array3D, start: [number, number, number],
       size: [number, number, number]): Array3D {
@@ -257,7 +254,6 @@ describe('slice4d_gpu', () => {
     expect(result.get(0, 0, 0, 0)).toEqual(expected);
   });
 
-
   function doSlice4D(
       a: Array4D, start: [number, number, number, number],
       size: [number, number, number, number]): Array4D {
diff --git a/src/math/webgl/webgl_util.ts b/src/math/webgl/webgl_util.ts
@@ -150,7 +150,6 @@ function logShaderSourceAndInfoLog(
 
   const lineNumber = +lineNumberRegexResult[1];
 
-
   const shaderLines = shaderSource.split('\n');
   const pad = ('' + shaderLines.length).length + 2;
   const linesWithLineNumbers = shaderLines.map(
diff --git a/tslint.json b/tslint.json
@@ -29,6 +29,7 @@
     "no-angle-bracket-type-assertion": true,
     "no-any": true,
     "no-construct": true,
+    "no-consecutive-blank-lines": true,
     "no-debugger": true,
     "no-default-export": true,
     "no-inferrable-types": true,

Original file line number	Diff line number	Diff line change
`@@ -45,7 +45,6 @@ class Adder {`
`45`	`45`	`}`
`46`	`46`	`}`
`47`	`47`
`48`		`-`
`49`	`48`	`const adder = new Adder();`
`50`	`49`	`adder.setupSession();`
`51`	`50`
Original file line number	Diff line number	Diff line change
`@@ -301,8 +301,6 @@ export class ModelBuilder extends ModelBuilderPolymer {`
`301`	`301`	`return applicationState === ApplicationState.IDLE;`
`302`	`302`	`}`
`303`	`303`
`304`		`-`
`305`		`-`
`306`	`304`	`private getTestData(): NDArray[][] {`
`307`	`305`	`const data = this.dataSet.getData();`
`308`	`306`	`if (data == null) {`
Original file line number	Diff line number	Diff line change
`@@ -46,7 +46,6 @@ class Adder {`
`46`	`46`	`}`
`47`	`47`	`}`
`48`	`48`
`49`		`-`
`50`	`49`	`const adder = new Adder();`
`51`	`50`	`adder.setupSession();`
`52`	`51`	`const result = adder.computeSum(1, 1);`
Original file line number	Diff line number	Diff line change
`@@ -136,7 +136,6 @@ export class Graph {`
`136`	`136`	`new FusedLinearCombinationNode(this, x1, x2, c1, c2));`
`137`	`137`	`}`
`138`	`138`
`139`		`-`
`140`	`139`	`/**`
`141`	`140`	`* Adds two tensors (elementwise). Broadcasts if one of the tensors is scalar.`
`142`	`141`	`* @param x1 The first input tensor.`