1 files changed, 129 insertions, 0 deletions
diff --git a/Codes/flownet2/src/ops/preprocessing/kernels/flow_augmentation.cc b/Codes/flownet2/src/ops/preprocessing/kernels/flow_augmentation.cc
new file mode 100644
index 0000000..b5cc11f
--- /dev/null
+++ b/Codes/flownet2/src/ops/preprocessing/kernels/flow_augmentation.cc
@@ -0,0 +1,129 @@
+#define EIGEN_USE_THREADS
+
+#include "flow_augmentation.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice        GPUDevice;
+
+inline int clamp(int f, int a, int b) {
+  return std::max(a, std::min(f, b));
+}
+
+template<>
+void FillFlowAugmentation(const CPUDevice& device,
+                          typename TTypes<float, 4>::Tensor output,
+                          typename TTypes<float, 4>::ConstTensor flows,
+                          typename TTypes<float, 2>::ConstTensor transforms_from_a,
+                          typename TTypes<float, 2>::ConstTensor transforms_from_b) {
+  const int batch_size      = output.dimension(0);
+  const int out_height      = output.dimension(1);
+  const int out_width       = output.dimension(2);
+  const int src_height      = flows.dimension(1);
+  const int src_width       = flows.dimension(2);
+  const int src_total_count = flows.dimension(0) * flows.dimension(1) *
+                              flows.dimension(2) * flows.dimension(3);
+  float *output_ptr     = output.data();
+  const float *flow_ptr = flows.data();
+
+  for (int n = 0; n < batch_size; n++) {
+    const float *transMatA = transforms_from_a.data() + n * 6;
+    const float *transMatB = transforms_from_b.data() + n * 6;
+
+    for (int y = 0; y < out_height; y++) {
+      int outputIdxOffset = (n * out_height + y) * out_width;
+
+      for (int x = 0; x < out_width; x++) {
+        // Apply transformation matrix applied to first image
+        const float xpos1 = x * transMatA[0] + y * transMatA[1] + transMatA[2];
+        const float ypos1 = x * transMatA[3] + y * transMatA[4] + transMatA[5];
+
+        const int srcXIdx =
+          ((n * src_height + (int)(ypos1 + 0.5)) * src_width + (int)(xpos1 + 0.5)) * 2 + 0;
+        const int srcYIdx = srcXIdx + 1;
+
+        const float xpos2 = xpos1 + flow_ptr[clamp(srcXIdx, 0, src_total_count - 1)];
+        const float ypos2 = ypos1 + flow_ptr[clamp(srcYIdx, 0, src_total_count - 1)];
+
+        // Apply inverse of the transformation matrix applied to second image
+        const float xpos3 = xpos2 * transMatB[0] + ypos2 * transMatB[1] + transMatB[2];
+        const float ypos3 = xpos2 * transMatB[3] + ypos2 * transMatB[4] + transMatB[5];
+
+        output_ptr[(outputIdxOffset + x) * 2 + 0] = xpos3 - (float)x;
+        output_ptr[(outputIdxOffset + x) * 2 + 1] = ypos3 - (float)y;
+      }
+    }
+  }
+}
+
+template<typename Device>
+class FlowAugmentation : public OpKernel {
+  public:
+    explicit FlowAugmentation(OpKernelConstruction *ctx) : OpKernel(ctx) {
+      // Get the crop [height, width] tensor and verify its dimensions
+      OP_REQUIRES_OK(ctx, ctx->GetAttr("crop", &crop_));
+      OP_REQUIRES(ctx, crop_.size() == 2,
+                  errors::InvalidArgument("crop must be 2 dimensions"));
+    }
+
+    void Compute(OpKernelContext *ctx) override {
+      // Get the input images and transforms and verify their dimensions
+      const Tensor& flows_t             = ctx->input(0);
+      const Tensor& transforms_from_a_t = ctx->input(1);
+      const Tensor& transforms_from_b_t = ctx->input(2);
+
+      OP_REQUIRES(ctx, flows_t.dims() == 4,
+                  errors::InvalidArgument("Input images must have rank 4"));
+      OP_REQUIRES(ctx,
+                  (TensorShapeUtils::IsMatrix(transforms_from_a_t.shape()) &&
+                   transforms_from_a_t.dim_size(0) ==
+                   flows_t.dim_size(0) &&
+                   transforms_from_a_t.dim_size(1) == 6),
+                  errors::InvalidArgument(
+                    "Input transforms_from_a should be num_images x 6"));
+      OP_REQUIRES(ctx,
+                  (TensorShapeUtils::IsMatrix(transforms_from_b_t.shape()) &&
+                   transforms_from_b_t.dim_size(0) ==
+                   flows_t.dim_size(0) &&
+                   transforms_from_b_t.dim_size(1) == 6),
+                  errors::InvalidArgument(
+                    "Input transforms_from_b should be num_images x 6"));
+
+      // Allocate the memory for the output
+      Tensor *output_t;
+      OP_REQUIRES_OK(ctx, ctx->allocate_output(
+                       0,
+                       TensorShape({ flows_t.dim_size(0), crop_[0], crop_[1],
+                                     flows_t.dim_size(3) }), &output_t));
+
+      // Perform flow augmentation
+      auto flows             = flows_t.tensor<float, 4>();
+      auto transforms_from_a = transforms_from_a_t.tensor<float, 2>();
+      auto transforms_from_b = transforms_from_b_t.tensor<float, 2>();
+      auto output            = output_t->tensor<float, 4>();
+
+      FillFlowAugmentation(ctx->eigen_device<Device>(),
+                           output,
+                           flows,
+                           transforms_from_a,
+                           transforms_from_b);
+    }
+
+  private:
+    std::vector<int32>crop_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("FlowAugmentation")
+                        .Device(DEVICE_CPU),
+                        FlowAugmentation<CPUDevice>)
+
+#if GOOGLE_CUDA
+REGISTER_KERNEL_BUILDER(Name("FlowAugmentation")
+                        .Device(DEVICE_GPU),
+                        FlowAugmentation<GPUDevice>)
+#endif // GOOGLE_CUDA
+} // end namespace tensorflow