Implementing gradients for tf.image.resize_bicubic.

PiperOrigin-RevId: 168547412

tensorflow/cc/ops/op_gen_overrides.pbtxt

@@ -134,6 +134,7 @@ op { name: "WholeFileReaderV2" rename_to: "WholeFileReader" }
 # image_ops
 op { name: "AdjustContrastv2" rename_to: "AdjustContrast" }
 op { name: "ResizeBilinearGrad" hide: true }
+op { name: "ResizeBicubicGrad" hide: true }
 op { name: "ResizeNearestNeighborGrad" hide: true }
 
 # io_ops

tensorflow/core/kernels/resize_bicubic_op.cc

@@ -180,6 +180,21 @@ static void ComputeXWeightsAndIndices(const ImageResizerState& resizer_state,
   }
 }
 
+static void ComputeGradientXWeightsAndIndices(
+    const ImageResizerGradientState& resizer_state,
+    std::vector<WeightsAndIndices>* x_wais) {
+  CachedInterpolationCalculator calc;
+  for (int64 x = 0; x < resizer_state.resized_width; ++x) {
+    GetWeightsAndIndices(resizer_state.width_scale, x,
+                         resizer_state.original_width, &(*x_wais)[x]);
+    auto& x_wai = (*x_wais)[x];
+    x_wai.advance = calc.Advance(x_wai.index_0, x_wai.index_1, x_wai.index_2,
+                                 x_wai.index_3);
+  }
+  // Do not scale, as we will be using these directly as tensor indices on the
+  // gradient pass.
+}
+
 template <typename T>
 static EIGEN_ALWAYS_INLINE float ComputeYInterpolation(
     int which, int channel_num, const WeightsAndIndices& y_wai,
@@ -365,6 +380,73 @@ inline void interpolate_with_caching(
   }
 }
 
+template <typename T>
+inline void ResizeBicubicGrad(typename TTypes<float, 4>::ConstTensor input_grad,
+                              const ImageResizerGradientState& resizer_state,
+                              typename TTypes<T, 4>::Tensor output_grad) {
+  // This function computes gradients for the ResizeBicubic op by iterating over
+  // the input_grad Tensor and using WeightsAndIndices to appropriately update
+  // the output gradient.
+  const float height_scale = resizer_state.height_scale;
+  const int64 original_height = resizer_state.original_height;
+  const int channels = resizer_state.channels;
+  const int64 resized_width = resizer_state.resized_width;
+  const int64 resized_height = resizer_state.resized_height;
+
+  output_grad.setZero();
+
+  std::vector<WeightsAndIndices> x_wais(resizer_state.resized_width);
+  ComputeGradientXWeightsAndIndices(resizer_state, &x_wais);
+  for (int64 b = 0; b < resizer_state.batch_size; ++b) {
+    for (int64 y = 0; y < resized_height; ++y) {
+      WeightsAndIndices y_wai;
+      GetWeightsAndIndices(height_scale, y, original_height, &y_wai);
+      for (int64 x = 0; x < resized_width; ++x) {
+        const WeightsAndIndices& x_wai = x_wais[x];
+        for (int64 c = 0; c < channels; ++c) {
+          T curr_input_grad = input_grad(b, y, x, c);
+          // row 0 of 0, 1, 2, 3
+          output_grad(b, y_wai.index_0, x_wai.index_0, c) +=
+              T(curr_input_grad * y_wai.weight_0 * x_wai.weight_0);
+          output_grad(b, y_wai.index_0, x_wai.index_1, c) +=
+              T(curr_input_grad * y_wai.weight_0 * x_wai.weight_1);
+          output_grad(b, y_wai.index_0, x_wai.index_2, c) +=
+              T(curr_input_grad * y_wai.weight_0 * x_wai.weight_2);
+          output_grad(b, y_wai.index_0, x_wai.index_3, c) +=
+              T(curr_input_grad * y_wai.weight_0 * x_wai.weight_3);
+          // row 1 of 0, 1, 2, 3
+          output_grad(b, y_wai.index_1, x_wai.index_0, c) +=
+              T(curr_input_grad * y_wai.weight_1 * x_wai.weight_0);
+          output_grad(b, y_wai.index_1, x_wai.index_1, c) +=
+              T(curr_input_grad * y_wai.weight_1 * x_wai.weight_1);
+          output_grad(b, y_wai.index_1, x_wai.index_2, c) +=
+              T(curr_input_grad * y_wai.weight_1 * x_wai.weight_2);
+          output_grad(b, y_wai.index_1, x_wai.index_3, c) +=
+              T(curr_input_grad * y_wai.weight_1 * x_wai.weight_3);
+          // row 2 of 0, 1, 2, 3
+          output_grad(b, y_wai.index_2, x_wai.index_0, c) +=
+              T(curr_input_grad * y_wai.weight_2 * x_wai.weight_0);
+          output_grad(b, y_wai.index_2, x_wai.index_1, c) +=
+              T(curr_input_grad * y_wai.weight_2 * x_wai.weight_1);
+          output_grad(b, y_wai.index_2, x_wai.index_2, c) +=
+              T(curr_input_grad * y_wai.weight_2 * x_wai.weight_2);
+          output_grad(b, y_wai.index_2, x_wai.index_3, c) +=
+              T(curr_input_grad * y_wai.weight_2 * x_wai.weight_3);
+          // row 3 of 0, 1, 2, 3
+          output_grad(b, y_wai.index_3, x_wai.index_0, c) +=
+              T(curr_input_grad * y_wai.weight_3 * x_wai.weight_0);
+          output_grad(b, y_wai.index_3, x_wai.index_1, c) +=
+              T(curr_input_grad * y_wai.weight_3 * x_wai.weight_1);
+          output_grad(b, y_wai.index_3, x_wai.index_2, c) +=
+              T(curr_input_grad * y_wai.weight_3 * x_wai.weight_2);
+          output_grad(b, y_wai.index_3, x_wai.index_3, c) +=
+              T(curr_input_grad * y_wai.weight_3 * x_wai.weight_3);
+        }
+      }
+    }
+  }
+}
+
 }  // namespace
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
@@ -394,6 +476,36 @@ class ResizeBicubicOp : public OpKernel {
   bool align_corners_;
 };
 
+template <typename Device, typename T>
+class ResizeBicubicOpGrad : public OpKernel {
+ public:
+  explicit ResizeBicubicOpGrad(OpKernelConstruction* context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context, context->GetAttr("align_corners", &align_corners_));
+  }
+
+  void Compute(OpKernelContext* context) override {
+    // Validate input.
+    // First argument is gradient with respect to resized image.
+    const Tensor& input = context->input(0);
+    const Tensor& original_image = context->input(1);
+
+    ImageResizerGradientState st(align_corners_);
+    st.ValidateAndCreateOutput(context, input, original_image);
+
+    if (!context->status().ok()) return;
+
+    typename TTypes<float, 4>::ConstTensor input_grad =
+        input.tensor<float, 4>();
+    typename TTypes<T, 4>::Tensor output_grad = st.output->tensor<T, 4>();
+
+    ResizeBicubicGrad<T>(input_grad, st, output_grad);
+  }
+
+ private:
+  bool align_corners_;
+};
+
 #define REGISTER_KERNEL(T)                            \
   REGISTER_KERNEL_BUILDER(Name("ResizeBicubic")       \
                               .Device(DEVICE_CPU)     \
@@ -405,4 +517,14 @@ TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNEL);
 
 #undef REGISTER_KERNEL
 
+#define REGISTER_GRAD_KERNEL(T)                                            \
+  REGISTER_KERNEL_BUILDER(                                                 \
+      Name("ResizeBicubicGrad").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      ResizeBicubicOpGrad<CPUDevice, T>);
+
+TF_CALL_float(REGISTER_GRAD_KERNEL);
+TF_CALL_double(REGISTER_GRAD_KERNEL);
+
+#undef REGISTER_GRAD_KERNEL
+
 }  // namespace tensorflow

tensorflow/core/ops/image_ops.cc

@@ -197,6 +197,31 @@ resized_images: 4-D with shape
   `[batch, new_height, new_width, channels]`.
 )doc");
 
+// --------------------------------------------------------------------------
+REGISTER_OP("ResizeBicubicGrad")
+    .Input("grads: float")
+    .Input("original_image: T")
+    .Output("output: T")
+    .Attr("T: {float, double}")
+    .Attr("align_corners: bool = false")
+    .SetShapeFn([](InferenceContext* c) {
+      c->set_output(0, c->input(1));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Computes the gradient of bicubic interpolation.
+
+grads: 4-D with shape `[batch, height, width, channels]`.
+original_image: 4-D with shape `[batch, orig_height, orig_width, channels]`,
+  The image tensor that was resized.
+align_corners: If true, rescale grads by (orig_height - 1) / (height - 1), which
+  exactly aligns the 4 corners of grads and original_image. If false, rescale by
+  orig_height / height. Treat similarly the width dimension.
+output: 4-D with shape `[batch, orig_height, orig_width, channels]`.
+  Gradients with respect to the input image. Input image must have been
+  float or double.
+)doc");
+
 // --------------------------------------------------------------------------
 REGISTER_OP("ResizeBilinear")
     .Input("images: T")

tensorflow/core/ops/ops.pbtxt

@@ -20176,6 +20176,43 @@ op {
   summary: "Resize `images` to `size` using bicubic interpolation."
   description: "Input images can be of different types but output images are always float."
 }
+op {
+  name: "ResizeBicubicGrad"
+  input_arg {
+    name: "grads"
+    description: "4-D with shape `[batch, height, width, channels]`."
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "original_image"
+    description: "4-D with shape `[batch, orig_height, orig_width, channels]`,\nThe image tensor that was resized."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    description: "4-D with shape `[batch, orig_height, orig_width, channels]`.\nGradients with respect to the input image. Input image must have been\nfloat or double."
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+  attr {
+    name: "align_corners"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If true, rescale grads by (orig_height - 1) / (height - 1), which\nexactly aligns the 4 corners of grads and original_image. If false, rescale by\norig_height / height. Treat similarly the width dimension."
+  }
+  summary: "Computes the gradient of bicubic interpolation."
+}
 op {
   name: "ResizeBilinear"
   input_arg {

tensorflow/python/ops/hidden_ops.txt

@@ -167,6 +167,7 @@ NonMaxSuppression
 NonMaxSuppressionV2
 RandomCrop
 ResizeBilinearGrad
+ResizeBicubicGrad
 ResizeNearestNeighborGrad
 SampleDistortedBoundingBox
 SampleDistortedBoundingBoxV2

tensorflow/python/ops/image_grad.py

@@ -73,6 +73,27 @@ def _ResizeBilinearGrad(op, grad):
   return [grad0, None]
 
 
+@ops.RegisterGradient("ResizeBicubic")
+def _ResizeBicubicGrad(op, grad):
+  """The derivatives for bicubic resizing.
+
+  Args:
+    op: The ResizeBicubic op.
+    grad: The tensor representing the gradient w.r.t. the output.
+
+  Returns:
+    The gradients w.r.t. the input.
+  """
+  allowed_types = [dtypes.float32, dtypes.float64]
+  grad0 = None
+  if op.inputs[0].dtype in allowed_types:
+    # pylint: disable=protected-access
+    grad0 = gen_image_ops._resize_bicubic_grad(
+        grad, op.inputs[0], align_corners=op.get_attr("align_corners"))
+    # pylint: enable=protected-access
+  return [grad0, None]
+
+
 @ops.RegisterGradient("CropAndResize")
 def _CropAndResizeGrad(op, grad):
   """The derivatives for crop_and_resize.

tensorflow/python/ops/image_grad_test.py

@@ -173,6 +173,67 @@ class ResizeBilinearOpTest(test.TestCase):
       self.assertAllClose(grad[False], grad[True], rtol=1e-4, atol=1e-4)
 
 
+class ResizeBicubicOpTest(test.TestCase):
+
+  def testShapeIsCorrectAfterOp(self):
+    in_shape = [1, 2, 2, 1]
+    out_shape = [1, 4, 6, 1]
+
+    x = np.arange(0, 4).reshape(in_shape).astype(np.float32)
+
+    for align_corners in [True, False]:
+      with self.test_session() as sess:
+        input_tensor = constant_op.constant(x, shape=in_shape)
+        resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3],
+                                              align_corners=align_corners)
+        self.assertEqual(out_shape, list(resize_out.get_shape()))
+
+        resize_out = sess.run(resize_out)
+        self.assertEqual(out_shape, list(resize_out.shape))
+
+  def testGradFromResizeToLargerInBothDims(self):
+    in_shape = [1, 2, 3, 1]
+    out_shape = [1, 4, 6, 1]
+
+    x = np.arange(0, 6).reshape(in_shape).astype(np.float32)
+
+    for align_corners in [True, False]:
+      with self.test_session():
+        input_tensor = constant_op.constant(x, shape=in_shape)
+        resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3],
+                                              align_corners=align_corners)
+        err = gradient_checker.compute_gradient_error(
+            input_tensor, in_shape, resize_out, out_shape, x_init_value=x)
+      self.assertLess(err, 1e-3)
+
+  def testGradFromResizeToSmallerInBothDims(self):
+    in_shape = [1, 4, 6, 1]
+    out_shape = [1, 2, 3, 1]
+
+    x = np.arange(0, 24).reshape(in_shape).astype(np.float32)
+
+    for align_corners in [True, False]:
+      with self.test_session():
+        input_tensor = constant_op.constant(x, shape=in_shape)
+        resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3],
+                                              align_corners=align_corners)
+        err = gradient_checker.compute_gradient_error(
+            input_tensor, in_shape, resize_out, out_shape, x_init_value=x)
+      self.assertLess(err, 1e-3)
+
+  def testGradOnUnsupportedType(self):
+    in_shape = [1, 4, 6, 1]
+    out_shape = [1, 2, 3, 1]
+
+    x = np.arange(0, 24).reshape(in_shape).astype(np.uint8)
+
+    with self.test_session():
+      input_tensor = constant_op.constant(x, shape=in_shape)
+      resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3])
+      grad = gradients_impl.gradients(input_tensor, [resize_out])
+      self.assertEqual([None], grad)
+
+
 class CropAndResizeOpTest(test.TestCase):
 
   def testShapeIsCorrectAfterOp(self):