fix padding_test.ts & 'for .. in' to 'for .. of' (tensorflow#2388)

karikera · dsmilkov · commit fa00e253a28f · 2019-11-15T11:00:17.000+01:00
DEV

Fix for .. in for Array.
diff --git a/tfjs-core/src/engine.ts b/tfjs-core/src/engine.ts
@@ -935,8 +935,8 @@ export class Engine implements TensorTracker, DataMover {
         // This means that we are not computing higher-order gradients
         // and can clean up the tape.
         this.state.activeTape.forEach(node => {
-          for (const key in node.saved) {
-            node.saved[key].dispose();
+          for (const tensor of node.saved) {
+            tensor.dispose();
           }
         });
         this.state.activeTape = null;
diff --git a/tfjs-layers/src/engine/training_dataset.ts b/tfjs-layers/src/engine/training_dataset.ts
@@ -238,7 +238,7 @@ function standardizeDataIteratorOutput(
           `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +
           `${JSON.stringify(model.outputNames)})`);
 
-  for (const xIndex in flattenedXs) {
+  for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {
     tfc.util.assert(
         flattenedXs[xIndex].shape[0] === batchSize,
         () => `Batch size mismatch: input ` +
@@ -247,7 +247,7 @@ function standardizeDataIteratorOutput(
             `expected  ${batchSize} based on input ${model.inputNames[0]}.`);
   }
 
-  for (const yIndex in flattenedYs) {
+  for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {
     tfc.util.assert(
         flattenedYs[yIndex].shape[0] === batchSize,
         () => `Batch size mismatch: output ` +
diff --git a/tfjs-layers/src/layers/padding_test.ts b/tfjs-layers/src/layers/padding_test.ts
@@ -78,10 +78,10 @@ describeMathCPU('ZeroPadding2D: Symbolic', () => {
   const dataFormats: DataFormat[] =
       [undefined, 'channelsFirst', 'channelsLast'];
 
-  for (const dataFormat in dataFormats) {
+  for (const dataFormat of dataFormats) {
     it('Default padding 1-1-1-1, dataFormat=' + dataFormat, () => {
       const x = new SymbolicTensor('float32', [1, 2, 3, 4], null, [], null);
-      const layer = tfl.layers.zeroPadding2d();
+      const layer = tfl.layers.zeroPadding2d({dataFormat});
       const y = layer.apply(x) as SymbolicTensor;
       expect(y.dtype).toEqual('float32');
       if (dataFormat === 'channelsFirst') {
@@ -93,19 +93,19 @@ describeMathCPU('ZeroPadding2D: Symbolic', () => {
 
     it('All symmetric padding 2, dataFormat=' + dataFormat, () => {
       const x = new SymbolicTensor('float32', [1, 2, 3, 4], null, [], null);
-      const layer = tfl.layers.zeroPadding2d({padding: 2});
+      const layer = tfl.layers.zeroPadding2d({dataFormat, padding: 2});
       const y = layer.apply(x) as SymbolicTensor;
       expect(y.dtype).toEqual('float32');
       if (dataFormat === 'channelsFirst') {
-        expect(y.shape).toEqual([1, 6, 7, 8]);
+        expect(y.shape).toEqual([1, 2, 7, 8]);
       } else {
         expect(y.shape).toEqual([1, 6, 7, 4]);
       }
     });
 
     it('Symmetric padding 2-3, dataFormat=' + dataFormat, () => {
       const x = new SymbolicTensor('float32', [1, 2, 3, 4], null, [], null);
-      const layer = tfl.layers.zeroPadding2d({padding: [2, 3]});
+      const layer = tfl.layers.zeroPadding2d({dataFormat, padding: [2, 3]});
       const y = layer.apply(x) as SymbolicTensor;
       expect(y.dtype).toEqual('float32');
       if (dataFormat === 'channelsFirst') {
@@ -117,11 +117,12 @@ describeMathCPU('ZeroPadding2D: Symbolic', () => {
 
     it('Asymmetric padding 2-3-4-5, dataFormat=' + dataFormat, () => {
       const x = new SymbolicTensor('float32', [1, 2, 3, 4], null, [], null);
-      const layer = tfl.layers.zeroPadding2d({padding: [[2, 3], [4, 5]]});
+      const layer =
+          tfl.layers.zeroPadding2d({dataFormat, padding: [[2, 3], [4, 5]]});
       const y = layer.apply(x) as SymbolicTensor;
       expect(y.dtype).toEqual('float32');
       if (dataFormat === 'channelsFirst') {
-        expect(y.shape).toEqual([1, 2, 7, 13]);
+        expect(y.shape).toEqual([1, 2, 8, 13]);
       } else {
         expect(y.shape).toEqual([1, 7, 12, 4]);
       }
