[webgl] Add default arguments to CropAndResize.

tfjs-backend-cpu/src/kernels/CropAndResize.ts

@@ -30,6 +30,9 @@ export function cropAndResize(args: {
 
   const $method = method || 'bilinear';
   const $extrapolationValue = extrapolationValue || 0;
+  util.assert(
+      $method === 'bilinear' || $method === 'nearest',
+      () => `method must be bilinear or nearest, but was ${$method}`);
 
   const [batch, imageHeight, imageWidth, numChannels] = image.shape;
   const numBoxes = boxes.shape[0];

tfjs-backend-webgl/src/kernels/CropAndResize.ts

@@ -15,7 +15,7 @@
  * =============================================================================
  */
 
-import {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
+import {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
 
 import {MathBackendWebGL} from '../backend_webgl';
 import {CropAndResizeProgram} from '../crop_and_resize_gpu';
@@ -29,9 +29,15 @@ export const cropAndResize = (args: {
   const {image, boxes, boxInd} = inputs;
   const {cropSize, method, extrapolationValue} = attrs;
 
+  const $method = method || 'bilinear';
+  const $extrapolationValue = extrapolationValue || 0;
+  util.assert(
+      $method === 'bilinear' || $method === 'nearest',
+      () => `method must be bilinear or nearest, but was ${$method}`);
+
   const program = new CropAndResizeProgram(
       image.shape as [number, number, number, number],
-      boxes.shape as [number, number], cropSize, method, extrapolationValue);
+      boxes.shape as [number, number], cropSize, $method, $extrapolationValue);
   return backend.runWebGLProgram(program, [image, boxes, boxInd], 'float32');
 };
 

tfjs-core/src/ops/image/crop_and_resize.ts

@@ -82,9 +82,6 @@ function cropAndResize_(
   util.assert(
       cropSize[0] >= 1 && cropSize[1] >= 1,
       () => `cropSize must be atleast [1,1], but was ${cropSize}`);
-  util.assert(
-      method === 'bilinear' || method === 'nearest',
-      () => `method must be bilinear or nearest, but was ${method}`);
 
   const inputs:
       CropAndResizeInputs = {image: $image, boxes: $boxes, boxInd: $boxInd};

tfjs-core/src/ops/image/crop_and_resize_test.ts

@@ -45,6 +45,19 @@ describeWithFlags('cropAndResize', ALL_ENVS, () => {
     expectArraysClose(await output.data(), await image.data());
   });
 
+  it('5x5-bilinear, no arguments passed in for method or extrapolation',
+     async () => {
+       const image: tf.Tensor4D = tf.ones([1, 5, 5, 3]);
+       const boxes: tf.Tensor2D = tf.tensor2d([0, 0, 1, 1], [1, 4]);
+       const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
+
+       const output = tf.image.cropAndResize(image, boxes, boxInd, [5, 5]);
+
+       expect(output.shape).toEqual([1, 5, 5, 3]);
+       expect(output.dtype).toBe('float32');
+       expectArraysClose(await output.data(), await image.data());
+     });
+
   it('5x5-bilinear, just a crop, no resize', async () => {
     const image: tf.Tensor4D = tf.ones([1, 6, 6, 3]);
     const boxes: tf.Tensor2D = tf.tensor2d([0.5, 0.5, 1, 1], [1, 4]);

tfjs-node/src/kernels/CropAndResize.ts

@@ -15,7 +15,7 @@
  * =============================================================================
  */
 
-import {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, tensor1d} from '@tensorflow/tfjs';
+import {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, tensor1d, util} from '@tensorflow/tfjs';
 
 import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
 
@@ -28,12 +28,18 @@ export const cropAndResizeConfig: KernelConfig = {
     const {cropSize, method, extrapolationValue} =
         args.attrs as {} as CropAndResizeAttrs;
 
+    const $method = method || 'bilinear';
+    const $extrapolationValue = extrapolationValue || 0;
+    util.assert(
+        $method === 'bilinear' || $method === 'nearest',
+        () => `method must be bilinear or nearest, but was ${$method}`);
+
     const opAttrs = [
       createTensorsTypeOpAttr('T', image.dtype),
-      {name: 'method', type: backend.binding.TF_ATTR_STRING, value: method}, {
+      {name: 'method', type: backend.binding.TF_ATTR_STRING, value: $method}, {
         name: 'extrapolation_value',
         type: backend.binding.TF_ATTR_FLOAT,
-        value: extrapolationValue
+        value: $extrapolationValue
       }
     ];
     const cropSizeTensor = tensor1d(cropSize, 'int32');