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// See docs in ../ops/image_ops.cc.
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/random/simple_philox.h"
#include "tensorflow/core/public/tensor.h"
#include "tensorflow/core/util/guarded_philox_random.h"
namespace tensorflow {
template <typename T>
class RandomCropOp : public OpKernel {
public:
explicit RandomCropOp(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context, generator_.Init(context));
}
void Compute(OpKernelContext* context) override {
const Tensor& input = context->input(0);
OP_REQUIRES(context, input.dims() == 3,
errors::InvalidArgument("input must be 3-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 shape_vec = shape_t.vec<int64>();
const int32 target_height = shape_vec(0);
const int32 target_width = shape_vec(1);
const int32 height = input.dim_size(0);
const int32 width = input.dim_size(1);
const int32 channels = input.dim_size(2);
// Initialize shape to the batch size of the input, then add
// the rest of the dimensions
Tensor* output = nullptr;
const auto output_shape =
TensorShape({target_height, target_width, channels});
OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
// If the target size matches the actual size, then do nothing.
if ((target_height == height) && (target_width == width)) {
*output = context->input(0);
}
// TODO(shlens): Implement edge case to guarantee output size dimensions.
// Edge case. The target dimensions are larger then the image, so
// zero-pad the image. This guarantees that the image will *always*
// be [target_height, target_width] in size.
OP_REQUIRES(context, width >= target_width, errors::FailedPrecondition(
"width must be >= target_width: width = ", width,
", target_width = ", target_width));
OP_REQUIRES(context, height >= target_height, errors::FailedPrecondition(
"height must be >= target_height: height = ", height,
", target_height = ", target_height));
int32 offset_height = 0;
int32 offset_width = 0;
auto local_gen = generator_.ReserveSamples32(2);
random::SimplePhilox random(&local_gen);
if (width > target_width) {
offset_width = random.Rand32() % (width - target_width + 1);
}
if (height > target_height) {
offset_height = random.Rand32() % (height - target_height + 1);
}
// TODO(shlens): Do this more efficiently with memcpy once padding is
// available for smaller images.
typename TTypes<T, 3>::ConstTensor input_data = input.tensor<T, 3>();
typename TTypes<T, 3>::Tensor output_data = output->tensor<T, 3>();
for (int y = 0; y < target_height; ++y) {
for (int x = 0; x < target_width; ++x) {
for (int c = 0; c < channels; ++c) {
output_data(y, x, c) =
input_data(y + offset_height, x + offset_width, c);
}
}
}
}
private:
GuardedPhiloxRandom generator_;
};
#define REGISTER_KERNELS(type) \
REGISTER_KERNEL_BUILDER( \
Name("RandomCrop").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
RandomCropOp<type>)
TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS);
#undef REGISTER_KERNELS
} // namespace tensorflow
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