1 files changed, 109 insertions, 0 deletions
diff --git a/tensorflow/core/kernels/resize_bilinear_op.cc b/tensorflow/core/kernels/resize_bilinear_op.cc
new file mode 100644
index 0000000000..5119b93508
--- /dev/null
+++ b/tensorflow/core/kernels/resize_bilinear_op.cc
@@ -0,0 +1,109 @@
+// See docs in ../ops/image_ops.cc
+#define EIGEN_USE_THREADS
+
+#include <memory>
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/public/status.h"
+#include "tensorflow/core/public/tensor.h"
+#include "tensorflow/core/public/tensor_shape.h"
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+
+namespace tensorflow {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+
+template <typename Device, typename T>
+class ResizeBilinearOp : public OpKernel {
+ public:
+  explicit ResizeBilinearOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& input = context->input(0);
+    OP_REQUIRES(context, input.dims() == 4,
+                errors::InvalidArgument("input must be 4-dimensional",
+                                        input.shape().ShortDebugString()));
+    const Tensor& shape_t = context->input(1);
+    OP_REQUIRES(context, shape_t.dims() == 1,
+                errors::InvalidArgument("shape_t must be 1-dimensional",
+                                        shape_t.shape().ShortDebugString()));
+    OP_REQUIRES(context, shape_t.NumElements() == 2,
+                errors::InvalidArgument("shape_t must have two elements",
+                                        shape_t.shape().ShortDebugString()));
+
+    auto Svec = shape_t.vec<int32>();
+    // Initialize shape to the batch size of the input, then add
+    // the rest of the dimensions
+    Tensor* output = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                0, TensorShape({input.dim_size(0), Svec(0),
+                                                Svec(1), input.dim_size(3)}),
+                                &output));
+
+    const int64 batch_size = input.dim_size(0);
+    const int64 in_height = input.dim_size(1);
+    const int64 in_width = input.dim_size(2);
+    const int64 channels = input.dim_size(3);
+    const int64 out_height = output->dim_size(1);
+    const int64 out_width = output->dim_size(2);
+
+    typename TTypes<T, 4>::ConstTensor input_data = input.tensor<T, 4>();
+    typename TTypes<float, 4>::Tensor output_data = output->tensor<float, 4>();
+
+    const float height_scale = in_height / static_cast<float>(out_height);
+    const float width_scale = in_width / static_cast<float>(out_width);
+
+    for (int b = 0; b < batch_size; ++b) {
+      for (int y = 0; y < out_height; ++y) {
+        const float in_y = y * height_scale;
+        const int top_y_index = static_cast<int>(floorf(in_y));
+        const int bottom_y_index =
+            std::min(static_cast<int64>(ceilf(in_y)), (in_height - 1));
+        const float y_lerp = in_y - top_y_index;
+        const float inverse_y_lerp = (1.0f - y_lerp);
+        for (int x = 0; x < out_width; ++x) {
+          const float in_x = x * width_scale;
+          const int left_x_index = static_cast<int>(floorf(in_x));
+          const int right_x_index =
+              std::min(static_cast<int64>(ceilf(in_x)), (in_width - 1));
+          const float x_lerp = in_x - left_x_index;
+          const float inverse_x_lerp = (1.0f - x_lerp);
+          for (int c = 0; c < channels; ++c) {
+            const float top_left = input_data(b, top_y_index, left_x_index, c);
+            const float top_right =
+                input_data(b, top_y_index, right_x_index, c);
+            const float bottom_left =
+                input_data(b, bottom_y_index, left_x_index, c);
+            const float bottom_right =
+                input_data(b, bottom_y_index, right_x_index, c);
+            const float top =
+                (top_left * inverse_x_lerp) + (top_right * x_lerp);
+            const float bottom =
+                (bottom_left * inverse_x_lerp) + (bottom_right * x_lerp);
+            output_data(b, y, x, c) =
+                (top * inverse_y_lerp) + (bottom * y_lerp);
+          }
+        }
+      }
+    }
+  }
+};
+
+#define REGISTER_KERNEL(T)                            \
+  REGISTER_KERNEL_BUILDER(Name("ResizeBilinear")      \
+                              .Device(DEVICE_CPU)     \
+                              .TypeConstraint<T>("T") \
+                              .HostMemory("size"),    \
+                          ResizeBilinearOp<CPUDevice, T>);
+
+REGISTER_KERNEL(uint8);
+REGISTER_KERNEL(int8);
+REGISTER_KERNEL(int32);
+REGISTER_KERNEL(float);
+REGISTER_KERNEL(double);
+#undef REGISTER_KERNEL
+
+}  // namespace tensorflow