Export fill mode for image_projective_transform
PiperOrigin-RevId: 300652382 Change-Id: I762197f7dfbd545445db7b3b330d463f5f66d856
This commit is contained in:
Zhenyu Tan
TensorFlower Gardener
parent
3614f46e1f
commit
287f14529c
@ -38,6 +38,12 @@ END
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name: "interpolation"
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description: <<END
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Interpolation method, "NEAREST" or "BILINEAR".
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END
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}
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attr {
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name: "fill_mode"
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description: <<END
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Fill mode, "REFLECT", "WRAP", or "CONSTANT".
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END
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}
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summary: "Applies the given transform to each of the images."
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@ -46,27 +46,39 @@ typedef Eigen::ThreadPoolDevice CPUDevice;
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using functor::FillProjectiveTransform;
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using generator::Interpolation;
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using generator::INTERPOLATION_BILINEAR;
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using generator::INTERPOLATION_NEAREST;
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using generator::ProjectiveGenerator;
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using generator::Mode;
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template <typename Device, typename T>
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class ImageProjectiveTransform : public OpKernel {
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class ImageProjectiveTransformV2 : public OpKernel {
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private:
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Interpolation interpolation_;
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Mode fill_mode_;
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public:
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explicit ImageProjectiveTransform(OpKernelConstruction* ctx) : OpKernel(ctx) {
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explicit ImageProjectiveTransformV2(OpKernelConstruction* ctx)
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: OpKernel(ctx) {
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string interpolation_str;
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OP_REQUIRES_OK(ctx, ctx->GetAttr("interpolation", &interpolation_str));
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if (interpolation_str == "NEAREST") {
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interpolation_ = INTERPOLATION_NEAREST;
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interpolation_ = Interpolation::NEAREST;
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} else if (interpolation_str == "BILINEAR") {
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interpolation_ = INTERPOLATION_BILINEAR;
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interpolation_ = Interpolation::BILINEAR;
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} else {
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LOG(ERROR) << "Invalid interpolation " << interpolation_str
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<< ". Supported types: NEAREST, BILINEAR";
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}
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string mode_str;
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OP_REQUIRES_OK(ctx, ctx->GetAttr("fill_mode", &mode_str));
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if (mode_str == "REFLECT") {
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fill_mode_ = Mode::REFLECT;
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} else if (mode_str == "WRAP") {
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fill_mode_ = Mode::WRAP;
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} else if (mode_str == "CONSTANT") {
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fill_mode_ = Mode::CONSTANT;
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} else {
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LOG(ERROR) << "Invalid mode " << mode_str
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<< ". Supported types: REFLECT, WRAP, CONSTANT";
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}
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}
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void Compute(OpKernelContext* ctx) override {
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@ -78,8 +90,7 @@ class ImageProjectiveTransform : public OpKernel {
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(TensorShapeUtils::IsMatrix(transform_t.shape()) &&
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(transform_t.dim_size(0) == images_t.dim_size(0) ||
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transform_t.dim_size(0) == 1) &&
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transform_t.dim_size(1) ==
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ProjectiveGenerator<Device, T>::kNumParameters),
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transform_t.dim_size(1) == 8),
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errors::InvalidArgument(
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"Input transform should be num_images x 8 or 1 x 8"));
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@ -116,15 +127,15 @@ class ImageProjectiveTransform : public OpKernel {
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auto transform = transform_t.matrix<float>();
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(FillProjectiveTransform<Device, T>(interpolation_))(
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ctx->eigen_device<Device>(), &output, images, transform);
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ctx->eigen_device<Device>(), &output, images, transform, fill_mode_);
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}
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};
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#define REGISTER(TYPE) \
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REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransformV2") \
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.Device(DEVICE_CPU) \
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.TypeConstraint<TYPE>("dtype"), \
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ImageProjectiveTransform<CPUDevice, TYPE>)
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#define REGISTER(TYPE) \
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REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransformV2") \
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.Device(DEVICE_CPU) \
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.TypeConstraint<TYPE>("dtype"), \
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ImageProjectiveTransformV2<CPUDevice, TYPE>)
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TF_CALL_uint8(REGISTER);
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TF_CALL_int32(REGISTER);
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@ -138,33 +149,48 @@ TF_CALL_double(REGISTER);
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#if GOOGLE_CUDA
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typedef Eigen::GpuDevice GPUDevice;
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typedef generator::Mode Mode;
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namespace functor {
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// NOTE(ringwalt): We get an undefined symbol error if we don't explicitly
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// instantiate the operator() in GCC'd code.
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#define DECLARE_FUNCTOR(TYPE) \
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#define DECLARE_PROJECT_FUNCTOR(TYPE) \
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template <> \
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void FillProjectiveTransform<GPUDevice, TYPE>::operator()( \
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const GPUDevice& device, OutputType* output, const InputType& images, \
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const TransformsType& transform) const; \
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const TransformsType& transform, const Mode fill_mode) const; \
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extern template struct FillProjectiveTransform<GPUDevice, TYPE>
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TF_CALL_uint8(DECLARE_FUNCTOR);
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TF_CALL_int32(DECLARE_FUNCTOR);
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TF_CALL_int64(DECLARE_FUNCTOR);
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TF_CALL_half(DECLARE_FUNCTOR);
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TF_CALL_float(DECLARE_FUNCTOR);
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TF_CALL_double(DECLARE_FUNCTOR);
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TF_CALL_uint8(DECLARE_PROJECT_FUNCTOR);
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TF_CALL_int32(DECLARE_PROJECT_FUNCTOR);
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TF_CALL_int64(DECLARE_PROJECT_FUNCTOR);
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TF_CALL_half(DECLARE_PROJECT_FUNCTOR);
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TF_CALL_float(DECLARE_PROJECT_FUNCTOR);
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TF_CALL_double(DECLARE_PROJECT_FUNCTOR);
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} // end namespace functor
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#define REGISTER(TYPE) \
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REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransformV2") \
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.Device(DEVICE_GPU) \
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.TypeConstraint<TYPE>("dtype") \
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.HostMemory("output_shape"), \
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ImageProjectiveTransform<GPUDevice, TYPE>)
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namespace generator {
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#define DECLARE_MAP_FUNCTOR(Mode) \
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template <> \
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float MapCoordinate<GPUDevice, Mode>::operator()(const float out_coord, \
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const DenseIndex len); \
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extern template struct MapCoordinate<GPUDevice, Mode>
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DECLARE_MAP_FUNCTOR(Mode::REFLECT);
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DECLARE_MAP_FUNCTOR(Mode::WRAP);
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DECLARE_MAP_FUNCTOR(Mode::CONSTANT);
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} // end namespace generator
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#define REGISTER(TYPE) \
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REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransformV2") \
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.Device(DEVICE_GPU) \
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.TypeConstraint<TYPE>("dtype") \
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.HostMemory("output_shape"), \
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ImageProjectiveTransformV2<GPUDevice, TYPE>)
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TF_CALL_uint8(REGISTER);
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TF_CALL_int32(REGISTER);
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@ -29,12 +29,67 @@ namespace tensorflow {
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namespace generator {
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enum Interpolation { INTERPOLATION_NEAREST, INTERPOLATION_BILINEAR };
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enum Interpolation { NEAREST, BILINEAR };
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enum Mode { REFLECT, WRAP, CONSTANT };
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using Eigen::array;
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using Eigen::DenseIndex;
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template <typename Device, typename T>
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template <typename Device, Mode M>
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struct MapCoordinate {
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float operator()(const float out_coord, const DenseIndex len);
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};
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template <typename Device>
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struct MapCoordinate<Device, Mode::REFLECT> {
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float operator()(const float out_coord,
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const DenseIndex len) {
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float in_coord = out_coord;
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// Reflect [abcd] to [dcba|abcd|dcba], periodically from [0, 2 * len)
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// over [abcddcba]
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const DenseIndex boundary = 2 * len;
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// Shift coordinate to (-boundary, boundary)
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in_coord -= boundary * static_cast<DenseIndex>(in_coord / boundary);
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// Convert negative coordinates from [-boundary, 0) to [0, boundary)
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if (in_coord < 0) {
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in_coord += boundary;
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}
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// Coordinate in_coord between [len, boundary) should reverse reflect
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// to coordinate to (bounary - 1 - in_coord) between [0, len)
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if (in_coord > len - 1) {
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in_coord = boundary - 1 - in_coord;
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}
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return in_coord;
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}
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};
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template <typename Device>
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struct MapCoordinate<Device, Mode::WRAP> {
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float operator()(const float out_coord,
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const DenseIndex len) {
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float in_coord = out_coord;
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// Wrap [abcd] to [abcd|abcd|abcd], periodically from [0, len)
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// over [abcd]
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const DenseIndex boundary = len;
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// Shift coordinate to (-boundary, boundary)
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in_coord -= boundary * static_cast<DenseIndex>(in_coord / boundary);
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// Shift negative coordinate from [-boundary, 0) to [0, boundary)
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if (in_coord < 0) {
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in_coord += boundary;
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}
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return in_coord;
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}
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};
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template <typename Device>
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struct MapCoordinate<Device, Mode::CONSTANT> {
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float operator()(const float out_coord,
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const DenseIndex len) {
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return out_coord;
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}
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};
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template <typename Device, typename T, Mode M>
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class ProjectiveGenerator {
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private:
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typename TTypes<T, 4>::ConstTensor input_;
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@ -42,8 +97,6 @@ class ProjectiveGenerator {
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const Interpolation interpolation_;
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public:
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static const int kNumParameters = 8;
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
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ProjectiveGenerator(typename TTypes<T, 4>::ConstTensor input,
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typename TTypes<float>::ConstMatrix transforms,
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@ -52,6 +105,7 @@ class ProjectiveGenerator {
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T
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operator()(const array<DenseIndex, 4>& coords) const {
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const T fill_value = T(0);
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const int64 output_y = coords[1];
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const int64 output_x = coords[2];
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const float* transform =
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@ -62,7 +116,7 @@ class ProjectiveGenerator {
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if (projection == 0) {
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// Return the fill value (0) for infinite coordinates,
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// which are outside the input image
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return T(0);
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return fill_value;
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}
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const float input_x =
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(transform[0] * output_x + transform[1] * output_y + transform[2]) /
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@ -71,22 +125,24 @@ class ProjectiveGenerator {
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(transform[3] * output_x + transform[4] * output_y + transform[5]) /
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projection;
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const T fill_value = T(0);
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// Map out-of-boundary input coordinates to in-boundary based on fill_mode.
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auto map_functor = MapCoordinate<Device, M>();
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const float x = map_functor(input_x, input_.dimension(2));
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const float y = map_functor(input_y, input_.dimension(1));
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const DenseIndex batch = coords[0];
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const DenseIndex channels = coords[3];
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switch (interpolation_) {
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case INTERPOLATION_NEAREST:
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// Switch the order of x and y again for indexing into the image.
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return nearest_interpolation(coords[0], input_y, input_x, coords[3],
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fill_value);
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case INTERPOLATION_BILINEAR:
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return bilinear_interpolation(coords[0], input_y, input_x, coords[3],
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fill_value);
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case NEAREST:
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return nearest_interpolation(batch, y, x, channels, fill_value);
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case BILINEAR:
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return bilinear_interpolation(batch, y, x, channels, fill_value);
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}
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// Unreachable; ImageProjectiveTransform only uses INTERPOLATION_NEAREST
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// or INTERPOLATION_BILINEAR.
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return T(0);
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return fill_value;
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}
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private:
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T
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nearest_interpolation(const DenseIndex batch, const float y, const float x,
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const DenseIndex channel, const T fill_value) const {
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@ -138,12 +194,10 @@ class ProjectiveGenerator {
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} // end namespace generator
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// NOTE(ringwalt): We MUST wrap the generate() call in a functor and explicitly
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// instantiate the functor in image_ops_gpu.cu.cc. Otherwise, we will be missing
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// some Eigen device code.
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namespace functor {
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using generator::Interpolation;
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using generator::Mode;
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using generator::ProjectiveGenerator;
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template <typename Device, typename T>
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@ -151,17 +205,32 @@ struct FillProjectiveTransform {
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typedef typename TTypes<T, 4>::Tensor OutputType;
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typedef typename TTypes<T, 4>::ConstTensor InputType;
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typedef typename TTypes<float, 2>::ConstTensor TransformsType;
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const Interpolation interpolation_;
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const Interpolation interpolation;
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FillProjectiveTransform(Interpolation interpolation)
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: interpolation_(interpolation) {}
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explicit FillProjectiveTransform(Interpolation interpolation)
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: interpolation(interpolation) {}
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EIGEN_ALWAYS_INLINE
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void operator()(const Device& device, OutputType* output,
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const InputType& images,
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const TransformsType& transform) const {
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output->device(device) = output->generate(
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ProjectiveGenerator<Device, T>(images, transform, interpolation_));
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const InputType& images, const TransformsType& transform,
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const Mode fill_mode) const {
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switch (fill_mode) {
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case Mode::REFLECT:
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output->device(device) =
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output->generate(ProjectiveGenerator<Device, T, Mode::REFLECT>(
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images, transform, interpolation));
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break;
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case Mode::WRAP:
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output->device(device) =
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output->generate(ProjectiveGenerator<Device, T, Mode::WRAP>(
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images, transform, interpolation));
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break;
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case Mode::CONSTANT:
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output->device(device) =
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output->generate(ProjectiveGenerator<Device, T, Mode::CONSTANT>(
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images, transform, interpolation));
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break;
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}
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}
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};
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@ -1028,7 +1028,6 @@ REGISTER_OP("GenerateBoundingBoxProposals")
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return Status::OK();
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});
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// TODO(ringwalt): Add a "fill_mode" attr with "constant", "mirror", etc.
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// TODO(ringwalt): Add a "fill_constant" argument for constant mode (default 0).
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// V2 op supports output_shape. V1 op is in contrib.
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REGISTER_OP("ImageProjectiveTransformV2")
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@ -1037,6 +1036,7 @@ REGISTER_OP("ImageProjectiveTransformV2")
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.Input("output_shape: int32")
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.Attr("dtype: {uint8, int32, int64, float16, float32, float64}")
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.Attr("interpolation: string")
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.Attr("fill_mode: string = 'CONSTANT'")
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.Output("transformed_images: dtype")
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.SetShapeFn([](InferenceContext* c) {
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ShapeHandle input;
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@ -20,6 +20,7 @@ from __future__ import print_function
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import numpy as np
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from tensorflow.python.compat import compat
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from tensorflow.python.eager import context
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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@ -418,9 +419,15 @@ class RandomTranslation(Layer):
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When represented as a single float, this value is used for both the upper
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and lower bound.
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fill_mode: Points outside the boundaries of the input are filled according
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to the given mode (one of `{'nearest', 'bilinear'}`).
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fill_value: Value used for points outside the boundaries of the input if
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`mode='constant'`.
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to the given mode (one of `{'constant', 'reflect', 'wrap'}`).
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- *reflect*: `(d c b a | a b c d | d c b a)`
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The input is extended by reflecting about the edge of the last pixel.
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- *constant*: `(k k k k | a b c d | k k k k)`
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The input is extended by filling all values beyond the edge with the
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same constant value k = 0.
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- *wrap*: `(a b c d | a b c d | a b c d)`
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The input is extended by wrapping around to the opposite edge.
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interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
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seed: Integer. Used to create a random seed.
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name: A string, the name of the layer.
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@ -440,8 +447,8 @@ class RandomTranslation(Layer):
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def __init__(self,
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height_factor,
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width_factor,
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fill_mode='nearest',
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fill_value=0.,
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fill_mode='reflect',
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interpolation='bilinear',
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seed=None,
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name=None,
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**kwargs):
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@ -471,11 +478,16 @@ class RandomTranslation(Layer):
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raise ValueError('`width_factor` must have values between [-1, 1], '
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'got {}'.format(width_factor))
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if fill_mode not in {'nearest', 'bilinear'}:
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if fill_mode not in {'reflect', 'wrap', 'constant'}:
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raise NotImplementedError(
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'`fill_mode` {} is not supported yet.'.format(fill_mode))
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'Unknown `fill_mode` {}. Only `reflect`, `wrap` and '
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'`constant` are supported.'.format(fill_mode))
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if interpolation not in {'nearest', 'bilinear'}:
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raise NotImplementedError(
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'Unknown `interpolation` {}. Only `nearest` and '
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'`bilinear` are supported.'.format(interpolation))
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self.fill_mode = fill_mode
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self.fill_value = fill_value
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self.interpolation = interpolation
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self.seed = seed
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self._rng = make_generator(self.seed)
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self.input_spec = InputSpec(ndim=4)
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@ -508,7 +520,8 @@ class RandomTranslation(Layer):
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return transform(
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inputs,
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get_translation_matrix(translations),
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interpolation=self.fill_mode)
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interpolation=self.interpolation,
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fill_mode=self.fill_mode)
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output = tf_utils.smart_cond(training, random_translated_inputs,
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lambda: inputs)
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@ -523,7 +536,7 @@ class RandomTranslation(Layer):
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'height_factor': self.height_factor,
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'width_factor': self.width_factor,
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'fill_mode': self.fill_mode,
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'fill_value': self.fill_value,
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'interpolation': self.interpolation,
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'seed': self.seed,
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}
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base_config = super(RandomTranslation, self).get_config()
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@ -565,7 +578,8 @@ def get_translation_matrix(translations, name=None):
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def transform(images,
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transforms,
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interpolation='nearest',
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fill_mode='reflect',
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interpolation='bilinear',
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||||
output_shape=None,
|
||||
name=None):
|
||||
"""Applies the given transform(s) to the image(s).
|
||||
@ -582,11 +596,33 @@ def transform(images,
|
||||
`k = c0 x + c1 y + 1`. The transforms are *inverted* compared to the
|
||||
transform mapping input points to output points. Note that gradients are
|
||||
not backpropagated into transformation parameters.
|
||||
interpolation: Interpolation mode. Supported values: "NEAREST", "BILINEAR".
|
||||
fill_mode: Points outside the boundaries of the input are filled according
|
||||
to the given mode (one of `{'constant', 'reflect', 'wrap'}`).
|
||||
interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
|
||||
output_shape: Output dimesion after the transform, [height, width]. If None,
|
||||
output is the same size as input image.
|
||||
name: The name of the op.
|
||||
|
||||
## Fill mode.
|
||||
Behavior for each valid value is as follows:
|
||||
|
||||
reflect (d c b a | a b c d | d c b a)
|
||||
The input is extended by reflecting about the edge of the last pixel.
|
||||
|
||||
constant (k k k k | a b c d | k k k k)
|
||||
The input is extended by filling all values beyond the edge with the same
|
||||
constant value k = 0.
|
||||
|
||||
wrap (a b c d | a b c d | a b c d)
|
||||
The input is extended by wrapping around to the opposite edge.
|
||||
|
||||
Input shape:
|
||||
4D tensor with shape: `(samples, height, width, channels)`,
|
||||
data_format='channels_last'.
|
||||
Output shape:
|
||||
4D tensor with shape: `(samples, height, width, channels)`,
|
||||
data_format='channels_last'.
|
||||
|
||||
Returns:
|
||||
Image(s) with the same type and shape as `images`, with the given
|
||||
transform(s) applied. Transformed coordinates outside of the input image
|
||||
@ -612,6 +648,13 @@ def transform(images,
|
||||
'new_height, new_width, instead got '
|
||||
'{}'.format(output_shape))
|
||||
|
||||
if compat.forward_compatible(2020, 3, 25):
|
||||
return image_ops.image_projective_transform_v2(
|
||||
images,
|
||||
output_shape=output_shape,
|
||||
transforms=transforms,
|
||||
fill_mode=fill_mode.upper(),
|
||||
interpolation=interpolation.upper())
|
||||
return image_ops.image_projective_transform_v2(
|
||||
images,
|
||||
output_shape=output_shape,
|
||||
@ -680,11 +723,24 @@ class RandomRotation(Layer):
|
||||
2 representing lower and upper bound for rotating clockwise and
|
||||
counter-clockwise. When represented as a single float, lower = upper.
|
||||
fill_mode: Points outside the boundaries of the input are filled according
|
||||
to the given mode (one of `{'constant', 'nearest', 'bilinear', 'reflect',
|
||||
'wrap'}`).
|
||||
to the given mode (one of `{'constant', 'reflect', 'wrap'}`).
|
||||
- *reflect*: `(d c b a | a b c d | d c b a)`
|
||||
The input is extended by reflecting about the edge of the last pixel.
|
||||
- *constant*: `(k k k k | a b c d | k k k k)`
|
||||
The input is extended by filling all values beyond the edge with the
|
||||
same constant value k = 0.
|
||||
- *wrap*: `(a b c d | a b c d | a b c d)`
|
||||
interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
|
||||
seed: Integer. Used to create a random seed.
|
||||
name: A string, the name of the layer.
|
||||
|
||||
Input shape:
|
||||
4D tensor with shape: `(samples, height, width, channels)`,
|
||||
data_format='channels_last'.
|
||||
Output shape:
|
||||
4D tensor with shape: `(samples, height, width, channels)`,
|
||||
data_format='channels_last'.
|
||||
|
||||
Raise:
|
||||
ValueError: if lower bound is not between [0, 1], or upper bound is
|
||||
negative.
|
||||
@ -692,7 +748,8 @@ class RandomRotation(Layer):
|
||||
|
||||
def __init__(self,
|
||||
factor,
|
||||
fill_mode='nearest',
|
||||
fill_mode='reflect',
|
||||
interpolation='bilinear',
|
||||
seed=None,
|
||||
name=None,
|
||||
**kwargs):
|
||||
@ -705,10 +762,16 @@ class RandomRotation(Layer):
|
||||
if self.lower < 0. or self.upper < 0.:
|
||||
raise ValueError('Factor cannot have negative values, '
|
||||
'got {}'.format(factor))
|
||||
if fill_mode not in {'nearest', 'bilinear'}:
|
||||
if fill_mode not in {'reflect', 'wrap', 'constant'}:
|
||||
raise NotImplementedError(
|
||||
'`fill_mode` {} is not supported yet.'.format(fill_mode))
|
||||
'Unknown `fill_mode` {}. Only `reflect`, `wrap` and '
|
||||
'`constant` are supported.'.format(fill_mode))
|
||||
if interpolation not in {'nearest', 'bilinear'}:
|
||||
raise NotImplementedError(
|
||||
'Unknown `interpolation` {}. Only `nearest` and '
|
||||
'`bilinear` are supported.'.format(interpolation))
|
||||
self.fill_mode = fill_mode
|
||||
self.interpolation = interpolation
|
||||
self.seed = seed
|
||||
self._rng = make_generator(self.seed)
|
||||
self.input_spec = InputSpec(ndim=4)
|
||||
@ -732,7 +795,8 @@ class RandomRotation(Layer):
|
||||
return transform(
|
||||
inputs,
|
||||
get_rotation_matrix(angles, img_hd, img_wd),
|
||||
interpolation=self.fill_mode)
|
||||
fill_mode=self.fill_mode,
|
||||
interpolation=self.interpolation)
|
||||
|
||||
output = tf_utils.smart_cond(training, random_rotated_inputs,
|
||||
lambda: inputs)
|
||||
@ -746,6 +810,7 @@ class RandomRotation(Layer):
|
||||
config = {
|
||||
'factor': self.factor,
|
||||
'fill_mode': self.fill_mode,
|
||||
'interpolation': self.interpolation,
|
||||
'seed': self.seed,
|
||||
}
|
||||
base_config = super(RandomRotation, self).get_config()
|
||||
@ -768,9 +833,14 @@ class RandomZoom(Layer):
|
||||
upper and lower bound. For instance, `width_factor=(0.2, 0.3)` result in
|
||||
an output zoom varying in the range `[original * 20%, original * 30%]`.
|
||||
fill_mode: Points outside the boundaries of the input are filled according
|
||||
to the given mode (one of `{'nearest', 'bilinear'}`).
|
||||
fill_value: Value used for points outside the boundaries of the input if
|
||||
`mode='constant'`.
|
||||
to the given mode (one of `{'constant', 'reflect', 'wrap'}`).
|
||||
- *reflect*: `(d c b a | a b c d | d c b a)`
|
||||
The input is extended by reflecting about the edge of the last pixel.
|
||||
- *constant*: `(k k k k | a b c d | k k k k)`
|
||||
The input is extended by filling all values beyond the edge with the
|
||||
same constant value k = 0.
|
||||
- *wrap*: `(a b c d | a b c d | a b c d)`
|
||||
interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
|
||||
seed: Integer. Used to create a random seed.
|
||||
name: A string, the name of the layer.
|
||||
|
||||
@ -790,8 +860,8 @@ class RandomZoom(Layer):
|
||||
def __init__(self,
|
||||
height_factor,
|
||||
width_factor,
|
||||
fill_mode='nearest',
|
||||
fill_value=0.,
|
||||
fill_mode='reflect',
|
||||
interpolation='bilinear',
|
||||
seed=None,
|
||||
name=None,
|
||||
**kwargs):
|
||||
@ -821,11 +891,16 @@ class RandomZoom(Layer):
|
||||
raise ValueError('`width_factor` cannot have lower bound larger than '
|
||||
'upper bound, got {}.'.format(width_factor))
|
||||
|
||||
if fill_mode not in {'nearest', 'bilinear'}:
|
||||
if fill_mode not in {'reflect', 'wrap', 'constant'}:
|
||||
raise NotImplementedError(
|
||||
'`fill_mode` {} is not supported yet.'.format(fill_mode))
|
||||
'Unknown `fill_mode` {}. Only `reflect`, `wrap` and '
|
||||
'`constant` are supported.'.format(fill_mode))
|
||||
if interpolation not in {'nearest', 'bilinear'}:
|
||||
raise NotImplementedError(
|
||||
'Unknown `interpolation` {}. Only `nearest` and '
|
||||
'`bilinear` are supported.'.format(interpolation))
|
||||
self.fill_mode = fill_mode
|
||||
self.fill_value = fill_value
|
||||
self.interpolation = interpolation
|
||||
self.seed = seed
|
||||
self._rng = make_generator(self.seed)
|
||||
self.input_spec = InputSpec(ndim=4)
|
||||
@ -857,7 +932,8 @@ class RandomZoom(Layer):
|
||||
dtype=inputs.dtype)
|
||||
return transform(
|
||||
inputs, get_zoom_matrix(zooms, img_hd, img_wd),
|
||||
interpolation=self.fill_mode)
|
||||
fill_mode=self.fill_mode,
|
||||
interpolation=self.interpolation)
|
||||
|
||||
output = tf_utils.smart_cond(training, random_zoomed_inputs,
|
||||
lambda: inputs)
|
||||
@ -872,7 +948,7 @@ class RandomZoom(Layer):
|
||||
'height_factor': self.height_factor,
|
||||
'width_factor': self.width_factor,
|
||||
'fill_mode': self.fill_mode,
|
||||
'fill_value': self.fill_value,
|
||||
'interpolation': self.interpolation,
|
||||
'seed': self.seed,
|
||||
}
|
||||
base_config = super(RandomZoom, self).get_config()
|
||||
|
||||
@ -21,6 +21,7 @@ from __future__ import print_function
|
||||
from absl.testing import parameterized
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.python.compat import compat
|
||||
from tensorflow.python.framework import errors
|
||||
from tensorflow.python.framework import test_util as tf_test_util
|
||||
from tensorflow.python.keras import keras_parameterized
|
||||
@ -509,6 +510,250 @@ class RandomTranslationTest(keras_parameterized.TestCase):
|
||||
self.assertEqual(layer_1.name, layer.name)
|
||||
|
||||
|
||||
@keras_parameterized.run_all_keras_modes(always_skip_v1=True)
|
||||
class RandomTransformTest(keras_parameterized.TestCase):
|
||||
|
||||
def _run_random_transform_with_mock(self,
|
||||
transform_matrix,
|
||||
expected_output,
|
||||
mode,
|
||||
interpolation='bilinear'):
|
||||
inp = np.arange(15).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
with self.cached_session(use_gpu=True):
|
||||
output = image_preprocessing.transform(
|
||||
inp, transform_matrix, fill_mode=mode, interpolation=interpolation)
|
||||
self.assertAllClose(expected_output, output)
|
||||
|
||||
def test_random_translation_reflect(self):
|
||||
# reflected output is (dcba|abcd|dcba)
|
||||
|
||||
if compat.forward_compatible(2020, 3, 25):
|
||||
# Test down shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[0., 1., 2.],
|
||||
[0., 1., 2.],
|
||||
[3., 4., 5.],
|
||||
[6., 7., 8],
|
||||
[9., 10., 11]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 0., 0., 1., -1., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'reflect')
|
||||
|
||||
# Test up shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[3., 4., 5.],
|
||||
[6., 7., 8],
|
||||
[9., 10., 11.],
|
||||
[12., 13., 14.],
|
||||
[12., 13., 14.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 0., 0., 1., 1., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'reflect')
|
||||
|
||||
# Test left shift by 1.
|
||||
# reflected output is (dcba|abcd|dcba)
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[1., 2., 2.],
|
||||
[4., 5., 5.],
|
||||
[7., 8., 8.],
|
||||
[10., 11., 11.],
|
||||
[13., 14., 14.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 1., 0., 1., 0., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'reflect')
|
||||
|
||||
# Test right shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[0., 0., 1.],
|
||||
[3., 3., 4],
|
||||
[6., 6., 7.],
|
||||
[9., 9., 10.],
|
||||
[12., 12., 13.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., -1., 0., 1., 0., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'reflect')
|
||||
|
||||
def test_random_translation_wrap(self):
|
||||
# warpped output is (abcd|abcd|abcd)
|
||||
|
||||
if compat.forward_compatible(2020, 3, 25):
|
||||
# Test down shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[12., 13., 14.],
|
||||
[0., 1., 2.],
|
||||
[3., 4., 5.],
|
||||
[6., 7., 8],
|
||||
[9., 10., 11]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 0., 0., 1., -1., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'wrap')
|
||||
|
||||
# Test up shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[3., 4., 5.],
|
||||
[6., 7., 8],
|
||||
[9., 10., 11.],
|
||||
[12., 13., 14.],
|
||||
[0., 1., 2.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 0., 0., 1., 1., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'wrap')
|
||||
|
||||
# Test left shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[1., 2., 0.],
|
||||
[4., 5., 3.],
|
||||
[7., 8., 6.],
|
||||
[10., 11., 9.],
|
||||
[13., 14., 12.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 1., 0., 1., 0., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'wrap')
|
||||
|
||||
# Test right shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[2., 0., 1.],
|
||||
[5., 3., 4],
|
||||
[8., 6., 7.],
|
||||
[11., 9., 10.],
|
||||
[14., 12., 13.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., -1., 0., 1., 0., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'wrap')
|
||||
|
||||
def test_random_translation_constant(self):
|
||||
# constant output is (0000|abcd|0000)
|
||||
|
||||
if compat.forward_compatible(2020, 3, 25):
|
||||
# Test down shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[0., 0., 0.],
|
||||
[0., 1., 2.],
|
||||
[3., 4., 5.],
|
||||
[6., 7., 8],
|
||||
[9., 10., 11]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 0., 0., 1., -1., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'constant')
|
||||
|
||||
# Test up shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[3., 4., 5.],
|
||||
[6., 7., 8],
|
||||
[9., 10., 11.],
|
||||
[12., 13., 14.],
|
||||
[0., 0., 0.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 0., 0., 1., 1., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'constant')
|
||||
|
||||
# Test left shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[1., 2., 0.],
|
||||
[4., 5., 0.],
|
||||
[7., 8., 0.],
|
||||
[10., 11., 0.],
|
||||
[13., 14., 0.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 1., 0., 1., 0., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'constant')
|
||||
|
||||
# Test right shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[0., 0., 1.],
|
||||
[0., 3., 4],
|
||||
[0., 6., 7.],
|
||||
[0., 9., 10.],
|
||||
[0., 12., 13.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., -1., 0., 1., 0., 0., 0.]])
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'constant')
|
||||
|
||||
def test_random_translation_nearest_interpolation(self):
|
||||
# nearest output is (aaaa|abcd|dddd)
|
||||
|
||||
if compat.forward_compatible(2020, 3, 25):
|
||||
# Test down shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[0., 0., 0.],
|
||||
[0., 1., 2.],
|
||||
[3., 4., 5.],
|
||||
[6., 7., 8],
|
||||
[9., 10., 11]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 0., 0., 1., -1., 0., 0.]])
|
||||
self._run_random_transform_with_mock(
|
||||
transform_matrix, expected_output,
|
||||
mode='constant', interpolation='nearest')
|
||||
|
||||
# Test up shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[3., 4., 5.],
|
||||
[6., 7., 8],
|
||||
[9., 10., 11.],
|
||||
[12., 13., 14.],
|
||||
[0., 0., 0.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 0., 0., 1., 1., 0., 0.]])
|
||||
self._run_random_transform_with_mock(
|
||||
transform_matrix, expected_output,
|
||||
mode='constant', interpolation='nearest')
|
||||
|
||||
# Test left shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[1., 2., 0.],
|
||||
[4., 5., 0.],
|
||||
[7., 8., 0.],
|
||||
[10., 11., 0.],
|
||||
[13., 14., 0.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., 1., 0., 1., 0., 0., 0.]])
|
||||
self._run_random_transform_with_mock(
|
||||
transform_matrix, expected_output,
|
||||
mode='constant', interpolation='nearest')
|
||||
|
||||
# Test right shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[0., 0., 1.],
|
||||
[0., 3., 4],
|
||||
[0., 6., 7.],
|
||||
[0., 9., 10.],
|
||||
[0., 12., 13.]]).reshape((1, 5, 3, 1)).astype(np.float32)
|
||||
# pyformat: enable
|
||||
transform_matrix = np.asarray([[1., 0., -1., 0., 1., 0., 0., 0.]])
|
||||
self._run_random_transform_with_mock(
|
||||
transform_matrix, expected_output,
|
||||
mode='constant', interpolation='nearest')
|
||||
|
||||
|
||||
@keras_parameterized.run_all_keras_modes(always_skip_v1=True)
|
||||
class RandomRotationTest(keras_parameterized.TestCase):
|
||||
|
||||
|
||||
@ -245,6 +245,7 @@ def _image_projective_transform_grad(op, grad):
|
||||
images = op.inputs[0]
|
||||
transforms = op.inputs[1]
|
||||
interpolation = op.get_attr("interpolation")
|
||||
fill_mode = op.get_attr("fill_mode")
|
||||
|
||||
image_or_images = ops.convert_to_tensor(images, name="images")
|
||||
transform_or_transforms = ops.convert_to_tensor(
|
||||
@ -267,5 +268,6 @@ def _image_projective_transform_grad(op, grad):
|
||||
images=grad,
|
||||
transforms=transforms,
|
||||
output_shape=array_ops.shape(image_or_images)[1:3],
|
||||
interpolation=interpolation)
|
||||
interpolation=interpolation,
|
||||
fill_mode=fill_mode)
|
||||
return [output, None, None]
|
||||
|
||||
@ -113,7 +113,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'factor\', \'fill_mode\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'nearest\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -113,7 +113,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'fill_value\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'nearest\', \'0.0\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -113,7 +113,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'fill_value\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'nearest\', \'0.0\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -1778,7 +1778,7 @@ tf_module {
|
||||
}
|
||||
member_method {
|
||||
name: "ImageProjectiveTransformV2"
|
||||
argspec: "args=[\'images\', \'transforms\', \'output_shape\', \'interpolation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
argspec: "args=[\'images\', \'transforms\', \'output_shape\', \'interpolation\', \'fill_mode\', \'name\'], varargs=None, keywords=None, defaults=[\'CONSTANT\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ImageSummary"
|
||||
|
||||
@ -113,7 +113,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'factor\', \'fill_mode\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'nearest\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -113,7 +113,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'fill_value\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'nearest\', \'0.0\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -113,7 +113,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'fill_value\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'nearest\', \'0.0\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -1778,7 +1778,7 @@ tf_module {
|
||||
}
|
||||
member_method {
|
||||
name: "ImageProjectiveTransformV2"
|
||||
argspec: "args=[\'images\', \'transforms\', \'output_shape\', \'interpolation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
argspec: "args=[\'images\', \'transforms\', \'output_shape\', \'interpolation\', \'fill_mode\', \'name\'], varargs=None, keywords=None, defaults=[\'CONSTANT\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ImageSummary"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user