Support fill_value for ImageProjectiveTransform
Fix conflict Fix typo Update number
This commit is contained in:
Tzu-Wei Sung
parent
9f86089e45
commit
13a9df90f4
@ -0,0 +1,63 @@
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op {
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graph_op_name: "ImageProjectiveTransformV3"
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visibility: HIDDEN
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in_arg {
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name: "images"
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description: <<END
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4-D with shape `[batch, height, width, channels]`.
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END
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}
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in_arg {
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name: "transforms"
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description: <<END
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2-D Tensor, `[batch, 8]` or `[1, 8]` matrix, where each row corresponds to a 3 x 3
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projective transformation matrix, with the last entry assumed to be 1. If there
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is one row, the same transformation will be applied to all images.
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END
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}
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in_arg {
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name: "output_shape"
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description: <<END
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1-D Tensor [new_height, new_width].
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END
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}
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in_arg {
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name: "fill_value"
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description: <<END
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float, the value to be filled when fill_mode is constant".
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END
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}
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out_arg {
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name: "transformed_images"
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description: <<END
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4-D with shape
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`[batch, new_height, new_width, channels]`.
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END
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}
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attr {
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name: "dtype"
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description: <<END
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Input dtype.
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END
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}
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attr {
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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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description: <<END
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If one row of `transforms` is `[a0, a1, a2, b0, b1, b2, c0, c1]`, then it maps
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the *output* point `(x, y)` to a transformed *input* point
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`(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`, where
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`k = c0 x + c1 y + 1`. If the transformed point lays outside of the input
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image, the output pixel is set to fill_value.
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END
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}
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@ -48,6 +48,68 @@ using functor::FillProjectiveTransform;
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using generator::Interpolation;
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using generator::Mode;
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template <typename Device, typename T>
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void DoImageProjectiveTransformOp(OpKernelContext* ctx,
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const Interpolation& interpolation,
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const Mode& fill_mode) {
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const Tensor& images_t = ctx->input(0);
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const Tensor& transform_t = ctx->input(1);
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OP_REQUIRES(ctx, images_t.shape().dims() == 4,
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errors::InvalidArgument("Input images must have rank 4"));
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OP_REQUIRES(ctx,
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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) == 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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int32 out_height, out_width;
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// Kernel is shared by legacy "ImageProjectiveTransform" op with 2 args.
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if (ctx->num_inputs() >= 3) {
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const Tensor& shape_t = ctx->input(2);
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OP_REQUIRES(ctx, shape_t.dims() == 1,
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errors::InvalidArgument("output shape must be 1-dimensional",
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shape_t.shape().DebugString()));
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OP_REQUIRES(ctx, shape_t.NumElements() == 2,
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errors::InvalidArgument("output shape must have two elements",
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shape_t.shape().DebugString()));
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auto shape_vec = shape_t.vec<int32>();
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out_height = shape_vec(0);
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out_width = shape_vec(1);
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OP_REQUIRES(ctx, out_height > 0 && out_width > 0,
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errors::InvalidArgument("output dimensions must be positive"));
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} else {
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// Shape is N (batch size), H (height), W (width), C (channels).
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out_height = images_t.shape().dim_size(1);
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out_width = images_t.shape().dim_size(2);
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}
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T fill_value(0);
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// Kernel is shared by "ImageProjectiveTransformV2" with 3 args.
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if (ctx->num_inputs() >= 4) {
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const Tensor& fill_value_t = ctx->input(3);
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OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(fill_value_t.shape()),
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errors::InvalidArgument("fill_value must be a scalar",
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fill_value_t.shape().DebugString()));
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fill_value = static_cast<T>(*(fill_value_t.scalar<float>().data()));
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}
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Tensor* output_t;
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OP_REQUIRES_OK(
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ctx, ctx->allocate_output(0,
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TensorShape({images_t.dim_size(0), out_height,
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out_width, images_t.dim_size(3)}),
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&output_t));
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auto output = output_t->tensor<T, 4>();
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auto images = images_t.tensor<T, 4>();
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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, fill_mode,
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fill_value);
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}
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template <typename Device, typename T>
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class ImageProjectiveTransformV2 : public OpKernel {
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private:
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@ -84,52 +146,7 @@ class ImageProjectiveTransformV2 : public OpKernel {
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}
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void Compute(OpKernelContext* ctx) override {
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const Tensor& images_t = ctx->input(0);
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const Tensor& transform_t = ctx->input(1);
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OP_REQUIRES(ctx, images_t.shape().dims() == 4,
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errors::InvalidArgument("Input images must have rank 4"));
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OP_REQUIRES(ctx,
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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) == 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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int32 out_height, out_width;
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// Kernel is shared by legacy "ImageProjectiveTransform" op with 2 args.
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if (ctx->num_inputs() >= 3) {
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const Tensor& shape_t = ctx->input(2);
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OP_REQUIRES(ctx, shape_t.dims() == 1,
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errors::InvalidArgument("output shape must be 1-dimensional",
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shape_t.shape().DebugString()));
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OP_REQUIRES(ctx, shape_t.NumElements() == 2,
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errors::InvalidArgument("output shape must have two elements",
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shape_t.shape().DebugString()));
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auto shape_vec = shape_t.vec<int32>();
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out_height = shape_vec(0);
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out_width = shape_vec(1);
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OP_REQUIRES(
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ctx, out_height > 0 && out_width > 0,
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errors::InvalidArgument("output dimensions must be positive"));
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} else {
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// Shape is N (batch size), H (height), W (width), C (channels).
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out_height = images_t.shape().dim_size(1);
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out_width = images_t.shape().dim_size(2);
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}
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Tensor* output_t;
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OP_REQUIRES_OK(ctx, ctx->allocate_output(
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0,
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TensorShape({images_t.dim_size(0), out_height,
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out_width, images_t.dim_size(3)}),
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&output_t));
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auto output = output_t->tensor<T, 4>();
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auto images = images_t.tensor<T, 4>();
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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, fill_mode_);
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DoImageProjectiveTransformOp<Device, T>(ctx, interpolation_, fill_mode_);
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}
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};
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@ -148,6 +165,29 @@ TF_CALL_double(REGISTER);
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#undef REGISTER
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template <typename Device, typename T>
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class ImageProjectiveTransformV3
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: public ImageProjectiveTransformV2<Device, T> {
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public:
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explicit ImageProjectiveTransformV3(OpKernelConstruction* ctx)
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: ImageProjectiveTransformV2<Device, T>(ctx) {}
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};
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#define REGISTER(TYPE) \
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REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransformV3") \
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.Device(DEVICE_CPU) \
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.TypeConstraint<TYPE>("dtype"), \
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ImageProjectiveTransformV3<CPUDevice, TYPE>)
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TF_CALL_uint8(REGISTER);
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TF_CALL_int32(REGISTER);
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TF_CALL_int64(REGISTER);
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TF_CALL_half(REGISTER);
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TF_CALL_float(REGISTER);
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TF_CALL_double(REGISTER);
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#undef REGISTER
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#if GOOGLE_CUDA
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typedef Eigen::GpuDevice GPUDevice;
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@ -161,7 +201,8 @@ namespace functor {
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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 Mode fill_mode) const; \
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const TransformsType& transform, const Mode fill_mode, \
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const TYPE fill_value) const; \
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extern template struct FillProjectiveTransform<GPUDevice, TYPE>
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TF_CALL_uint8(DECLARE_PROJECT_FUNCTOR);
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@ -204,6 +245,23 @@ TF_CALL_double(REGISTER);
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#undef REGISTER
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#define REGISTER(TYPE) \
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REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransformV3") \
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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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.HostMemory("fill_value"), \
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ImageProjectiveTransformV3<GPUDevice, TYPE>)
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TF_CALL_uint8(REGISTER);
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TF_CALL_int32(REGISTER);
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TF_CALL_int64(REGISTER);
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TF_CALL_half(REGISTER);
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TF_CALL_float(REGISTER);
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TF_CALL_double(REGISTER);
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#undef REGISTER
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#endif // GOOGLE_CUDA
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} // end namespace tensorflow
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@ -107,17 +107,20 @@ class ProjectiveGenerator {
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typename TTypes<T, 4>::ConstTensor input_;
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typename TTypes<float>::ConstMatrix transforms_;
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const Interpolation interpolation_;
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const T fill_value_;
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public:
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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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const Interpolation interpolation)
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: input_(input), transforms_(transforms), interpolation_(interpolation) {}
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const Interpolation interpolation, const T fill_value)
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: input_(input),
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transforms_(transforms),
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interpolation_(interpolation),
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fill_value_(fill_value) {}
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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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@ -126,9 +129,9 @@ class ProjectiveGenerator {
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: &transforms_.data()[transforms_.dimension(1) * coords[0]];
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float projection = transform[6] * output_x + transform[7] * output_y + 1.f;
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if (projection == 0) {
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// Return the fill value (0) for infinite coordinates,
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// Return the fill value for infinite coordinates,
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// which are outside the input image
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return fill_value;
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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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@ -146,13 +149,13 @@ class ProjectiveGenerator {
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const DenseIndex channels = coords[3];
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switch (interpolation_) {
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case NEAREST:
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return nearest_interpolation(batch, y, x, channels, fill_value);
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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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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 fill_value;
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return fill_value_;
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}
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T
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@ -225,27 +228,27 @@ struct FillProjectiveTransform {
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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, const TransformsType& transform,
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const Mode fill_mode) const {
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const Mode fill_mode, const T fill_value) const {
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switch (fill_mode) {
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case Mode::FILL_REFLECT:
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output->device(device) =
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output->generate(ProjectiveGenerator<Device, T, Mode::FILL_REFLECT>(
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images, transform, interpolation));
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images, transform, interpolation, fill_value));
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break;
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case Mode::FILL_WRAP:
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output->device(device) =
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output->generate(ProjectiveGenerator<Device, T, Mode::FILL_WRAP>(
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images, transform, interpolation));
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images, transform, interpolation, fill_value));
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break;
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case Mode::FILL_CONSTANT:
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output->device(device) = output->generate(
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ProjectiveGenerator<Device, T, Mode::FILL_CONSTANT>(
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images, transform, interpolation));
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images, transform, interpolation, fill_value));
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break;
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case Mode::FILL_NEAREST:
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output->device(device) =
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output->generate(ProjectiveGenerator<Device, T, Mode::FILL_NEAREST>(
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images, transform, interpolation));
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images, transform, interpolation, fill_value));
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break;
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}
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}
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@ -1146,8 +1146,9 @@ REGISTER_OP("GenerateBoundingBoxProposals")
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return Status::OK();
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});
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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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// V3 op supports fill_value.
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// V2 op supports output_shape.
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// V1 op is in contrib.
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REGISTER_OP("ImageProjectiveTransformV2")
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.Input("images: dtype")
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.Input("transforms: float32")
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@ -1163,4 +1164,20 @@ REGISTER_OP("ImageProjectiveTransformV2")
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c->Dim(input, 3));
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});
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REGISTER_OP("ImageProjectiveTransformV3")
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.Input("images: dtype")
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.Input("transforms: float32")
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.Input("output_shape: int32")
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.Input("fill_value: float32")
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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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TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
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return SetOutputToSizedImage(c, c->Dim(input, 0), 2 /* size_input_idx */,
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c->Dim(input, 3));
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});
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} // namespace tensorflow
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@ -19102,6 +19102,54 @@ op {
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}
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}
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}
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op {
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name: "ImageProjectiveTransformV3"
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input_arg {
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name: "images"
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type_attr: "dtype"
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}
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input_arg {
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name: "transforms"
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type: DT_FLOAT
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}
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input_arg {
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name: "output_shape"
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type: DT_INT32
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}
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input_arg {
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name: "fill_value"
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type: DT_FLOAT
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}
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output_arg {
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name: "transformed_images"
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type_attr: "dtype"
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}
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attr {
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name: "dtype"
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type: "type"
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allowed_values {
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list {
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type: DT_UINT8
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type: DT_INT32
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type: DT_INT64
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type: DT_HALF
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type: DT_FLOAT
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type: DT_DOUBLE
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}
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}
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}
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attr {
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name: "interpolation"
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type: "string"
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}
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attr {
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name: "fill_mode"
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type: "string"
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default_value {
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s: "CONSTANT"
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}
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}
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}
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op {
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name: "ImageSummary"
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input_arg {
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@ -50,7 +50,7 @@ auto OpGradientInfoInit(const T &a) {
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absl::optional<tensorflow::gtl::FlatSet<int>> OpGradientUnusedInputIndices(
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const tensorflow::string &op_name) {
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static std::array<OpIndexInfo, 349> a = {{
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static std::array<OpIndexInfo, 350> a = {{
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{"Acosh"},
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{"AllToAll", 1, {0}},
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{"ApproximateEqual"},
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@ -152,6 +152,7 @@ absl::optional<tensorflow::gtl::FlatSet<int>> OpGradientUnusedInputIndices(
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{"IdentityReader"},
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{"Imag"},
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{"ImageProjectiveTransformV2", 1, {2}},
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{"ImageProjectiveTransformV3", 2, {2, 3}},
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{"ImageSummary"},
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{"InitializeTable"},
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{"InitializeTableFromTextFile"},
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@ -412,7 +413,7 @@ absl::optional<tensorflow::gtl::FlatSet<int>> OpGradientUnusedInputIndices(
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absl::optional<tensorflow::gtl::FlatSet<int>> OpGradientUnusedOutputIndices(
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const tensorflow::string &op_name) {
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static std::array<OpIndexInfo, 465> a = {{
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static std::array<OpIndexInfo, 466> a = {{
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{"Abs"},
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{"AccumulateNV2"},
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{"Acos"},
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@ -568,6 +569,7 @@ absl::optional<tensorflow::gtl::FlatSet<int>> OpGradientUnusedOutputIndices(
|
||||
{"Igammac"},
|
||||
{"Imag"},
|
||||
{"ImageProjectiveTransformV2"},
|
||||
{"ImageProjectiveTransformV3"},
|
||||
{"ImageSummary"},
|
||||
{"InitializeTable"},
|
||||
{"InitializeTableFromTextFile"},
|
||||
|
||||
@ -21,6 +21,7 @@ from __future__ import print_function
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.python.eager import context
|
||||
from tensorflow.python.compat import compat
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.framework import tensor_shape
|
||||
@ -466,6 +467,8 @@ class RandomTranslation(PreprocessingLayer):
|
||||
The input is extended by wrapping around to the opposite edge.
|
||||
- *nearest*: `(a a a a | a b c d | d d d d)`
|
||||
The input is extended by the nearest pixel.
|
||||
fill_value: a float represents the value to be filled outside the
|
||||
boundaries when `fill_mode` is "constant".
|
||||
interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
|
||||
seed: Integer. Used to create a random seed.
|
||||
name: A string, the name of the layer.
|
||||
@ -487,6 +490,7 @@ class RandomTranslation(PreprocessingLayer):
|
||||
height_factor,
|
||||
width_factor,
|
||||
fill_mode='reflect',
|
||||
fill_value=0.0,
|
||||
interpolation='bilinear',
|
||||
seed=None,
|
||||
name=None,
|
||||
@ -522,6 +526,7 @@ class RandomTranslation(PreprocessingLayer):
|
||||
check_fill_mode_and_interpolation(fill_mode, interpolation)
|
||||
|
||||
self.fill_mode = fill_mode
|
||||
self.fill_value = fill_value
|
||||
self.interpolation = interpolation
|
||||
self.seed = seed
|
||||
self._rng = make_generator(self.seed)
|
||||
@ -559,7 +564,8 @@ class RandomTranslation(PreprocessingLayer):
|
||||
inputs,
|
||||
get_translation_matrix(translations),
|
||||
interpolation=self.interpolation,
|
||||
fill_mode=self.fill_mode)
|
||||
fill_mode=self.fill_mode,
|
||||
fill_value=self.fill_value)
|
||||
|
||||
output = control_flow_util.smart_cond(training, random_translated_inputs,
|
||||
lambda: inputs)
|
||||
@ -574,6 +580,7 @@ class RandomTranslation(PreprocessingLayer):
|
||||
'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,
|
||||
}
|
||||
@ -617,6 +624,7 @@ def get_translation_matrix(translations, name=None):
|
||||
def transform(images,
|
||||
transforms,
|
||||
fill_mode='reflect',
|
||||
fill_value=0.0,
|
||||
interpolation='bilinear',
|
||||
output_shape=None,
|
||||
name=None):
|
||||
@ -636,6 +644,8 @@ def transform(images,
|
||||
not backpropagated into transformation parameters.
|
||||
fill_mode: Points outside the boundaries of the input are filled according
|
||||
to the given mode (one of `{'constant', 'reflect', 'wrap', 'nearest'}`).
|
||||
fill_value: a float represents the value to be filled outside the
|
||||
boundaries when `fill_mode` is "constant".
|
||||
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.
|
||||
@ -689,6 +699,18 @@ def transform(images,
|
||||
'new_height, new_width, instead got '
|
||||
'{}'.format(output_shape))
|
||||
|
||||
fill_value = ops.convert_to_tensor_v2(
|
||||
fill_value, dtypes.float32, name='fill_value')
|
||||
|
||||
if compat.forward_compatible(2020, 8, 5):
|
||||
return gen_image_ops.ImageProjectiveTransformV3(
|
||||
images=images,
|
||||
output_shape=output_shape,
|
||||
fill_value=fill_value,
|
||||
transforms=transforms,
|
||||
fill_mode=fill_mode.upper(),
|
||||
interpolation=interpolation.upper())
|
||||
|
||||
return gen_image_ops.ImageProjectiveTransformV2(
|
||||
images=images,
|
||||
output_shape=output_shape,
|
||||
@ -774,6 +796,8 @@ class RandomRotation(PreprocessingLayer):
|
||||
The input is extended by wrapping around to the opposite edge.
|
||||
- *nearest*: `(a a a a | a b c d | d d d d)`
|
||||
The input is extended by the nearest pixel.
|
||||
fill_value: a float represents the value to be filled outside the
|
||||
boundaries when `fill_mode` is "constant".
|
||||
interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
|
||||
seed: Integer. Used to create a random seed.
|
||||
name: A string, the name of the layer.
|
||||
@ -793,6 +817,7 @@ class RandomRotation(PreprocessingLayer):
|
||||
def __init__(self,
|
||||
factor,
|
||||
fill_mode='reflect',
|
||||
fill_value=0.0,
|
||||
interpolation='bilinear',
|
||||
seed=None,
|
||||
name=None,
|
||||
@ -809,6 +834,7 @@ class RandomRotation(PreprocessingLayer):
|
||||
'got {}'.format(factor))
|
||||
check_fill_mode_and_interpolation(fill_mode, interpolation)
|
||||
self.fill_mode = fill_mode
|
||||
self.fill_value = fill_value
|
||||
self.interpolation = interpolation
|
||||
self.seed = seed
|
||||
self._rng = make_generator(self.seed)
|
||||
@ -834,6 +860,7 @@ class RandomRotation(PreprocessingLayer):
|
||||
inputs,
|
||||
get_rotation_matrix(angles, img_hd, img_wd),
|
||||
fill_mode=self.fill_mode,
|
||||
fill_value=self.fill_value,
|
||||
interpolation=self.interpolation)
|
||||
|
||||
output = control_flow_util.smart_cond(training, random_rotated_inputs,
|
||||
@ -848,6 +875,7 @@ class RandomRotation(PreprocessingLayer):
|
||||
config = {
|
||||
'factor': self.factor,
|
||||
'fill_mode': self.fill_mode,
|
||||
'fill_value': self.fill_value,
|
||||
'interpolation': self.interpolation,
|
||||
'seed': self.seed,
|
||||
}
|
||||
@ -889,6 +917,8 @@ class RandomZoom(PreprocessingLayer):
|
||||
The input is extended by wrapping around to the opposite edge.
|
||||
- *nearest*: `(a a a a | a b c d | d d d d)`
|
||||
The input is extended by the nearest pixel.
|
||||
fill_value: a float represents the value to be filled outside the
|
||||
boundaries when `fill_mode` is "constant".
|
||||
interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
|
||||
seed: Integer. Used to create a random seed.
|
||||
name: A string, the name of the layer.
|
||||
@ -914,11 +944,11 @@ class RandomZoom(PreprocessingLayer):
|
||||
negative.
|
||||
"""
|
||||
|
||||
# TODO(b/156526279): Add `fill_value` argument.
|
||||
def __init__(self,
|
||||
height_factor,
|
||||
width_factor=None,
|
||||
fill_mode='reflect',
|
||||
fill_value=0.0,
|
||||
interpolation='bilinear',
|
||||
seed=None,
|
||||
name=None,
|
||||
@ -951,6 +981,7 @@ class RandomZoom(PreprocessingLayer):
|
||||
check_fill_mode_and_interpolation(fill_mode, interpolation)
|
||||
|
||||
self.fill_mode = fill_mode
|
||||
self.fill_value = fill_value
|
||||
self.interpolation = interpolation
|
||||
self.seed = seed
|
||||
self._rng = make_generator(self.seed)
|
||||
@ -985,6 +1016,7 @@ class RandomZoom(PreprocessingLayer):
|
||||
return transform(
|
||||
inputs, get_zoom_matrix(zooms, img_hd, img_wd),
|
||||
fill_mode=self.fill_mode,
|
||||
fill_value=self.fill_value,
|
||||
interpolation=self.interpolation)
|
||||
|
||||
output = control_flow_util.smart_cond(training, random_zoomed_inputs,
|
||||
@ -1000,6 +1032,7 @@ class RandomZoom(PreprocessingLayer):
|
||||
'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,
|
||||
}
|
||||
|
||||
@ -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.distribute.mirrored_strategy import MirroredStrategy
|
||||
from tensorflow.python.framework import errors
|
||||
from tensorflow.python.framework import test_util as tf_test_util
|
||||
@ -698,11 +699,13 @@ class RandomTransformTest(keras_parameterized.TestCase):
|
||||
transform_matrix,
|
||||
expected_output,
|
||||
mode,
|
||||
fill_value=0.0,
|
||||
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)
|
||||
inp, transform_matrix, fill_mode=mode,
|
||||
fill_value=fill_value, interpolation=interpolation)
|
||||
self.assertAllClose(expected_output, output)
|
||||
|
||||
def test_random_translation_reflect(self):
|
||||
@ -871,7 +874,7 @@ class RandomTransformTest(keras_parameterized.TestCase):
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'nearest')
|
||||
|
||||
def test_random_translation_constant(self):
|
||||
def test_random_translation_constant_0(self):
|
||||
# constant output is (0000|abcd|0000)
|
||||
|
||||
# Test down shift by 1.
|
||||
@ -926,6 +929,62 @@ class RandomTransformTest(keras_parameterized.TestCase):
|
||||
self._run_random_transform_with_mock(transform_matrix, expected_output,
|
||||
'constant')
|
||||
|
||||
def test_random_translation_constant_1(self):
|
||||
with compat.forward_compatibility_horizon(2020, 8, 6):
|
||||
# constant output is (1111|abcd|1111)
|
||||
|
||||
# Test down shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[1., 1., 1.],
|
||||
[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', fill_value=1.0)
|
||||
|
||||
# Test up shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[3., 4., 5.],
|
||||
[6., 7., 8],
|
||||
[9., 10., 11.],
|
||||
[12., 13., 14.],
|
||||
[1., 1., 1.]]).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', fill_value=1.0)
|
||||
|
||||
# Test left shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[1., 2., 1.],
|
||||
[4., 5., 1.],
|
||||
[7., 8., 1.],
|
||||
[10., 11., 1.],
|
||||
[13., 14., 1.]]).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', fill_value=1.0)
|
||||
|
||||
# Test right shift by 1.
|
||||
# pyformat: disable
|
||||
expected_output = np.asarray(
|
||||
[[1., 0., 1.],
|
||||
[1., 3., 4],
|
||||
[1., 6., 7.],
|
||||
[1., 9., 10.],
|
||||
[1., 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', fill_value=1.0)
|
||||
|
||||
def test_random_translation_nearest_interpolation(self):
|
||||
# nearest output is (aaaa|abcd|dddd)
|
||||
|
||||
|
||||
@ -271,3 +271,38 @@ def _image_projective_transform_grad(op, grad):
|
||||
interpolation=interpolation,
|
||||
fill_mode=fill_mode)
|
||||
return [output, None, None]
|
||||
|
||||
|
||||
@ops.RegisterGradient("ImageProjectiveTransformV3")
|
||||
def _image_projective_transform_v3_grad(op, grad):
|
||||
"""Computes the gradient for ImageProjectiveTransform."""
|
||||
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(
|
||||
transforms, name="transforms", dtype=dtypes.float32)
|
||||
|
||||
if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES:
|
||||
raise TypeError("Invalid dtype %s." % image_or_images.dtype)
|
||||
if len(transform_or_transforms.get_shape()) == 1:
|
||||
transforms = transform_or_transforms[None]
|
||||
elif len(transform_or_transforms.get_shape()) == 2:
|
||||
transforms = transform_or_transforms
|
||||
else:
|
||||
raise TypeError("Transforms should have rank 1 or 2.")
|
||||
|
||||
# Invert transformations
|
||||
transforms = flat_transforms_to_matrices(transforms=transforms)
|
||||
inverse = linalg_ops.matrix_inverse(transforms)
|
||||
transforms = matrices_to_flat_transforms(inverse)
|
||||
output = gen_image_ops.image_projective_transform_v3(
|
||||
images=grad,
|
||||
transforms=transforms,
|
||||
output_shape=array_ops.shape(image_or_images)[1:3],
|
||||
interpolation=interpolation,
|
||||
fill_mode=fill_mode,
|
||||
fill_value=0.0)
|
||||
return [output, None, None, None]
|
||||
|
||||
@ -118,7 +118,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'factor\', \'fill_mode\', \'fill_value\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'0.0\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "adapt"
|
||||
|
||||
@ -118,7 +118,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'fill_value\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'0.0\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "adapt"
|
||||
|
||||
@ -118,7 +118,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'fill_value\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'reflect\', \'0.0\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "adapt"
|
||||
|
||||
@ -1880,6 +1880,10 @@ tf_module {
|
||||
name: "ImageProjectiveTransformV2"
|
||||
argspec: "args=[\'images\', \'transforms\', \'output_shape\', \'interpolation\', \'fill_mode\', \'name\'], varargs=None, keywords=None, defaults=[\'CONSTANT\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ImageProjectiveTransformV3"
|
||||
argspec: "args=[\'images\', \'transforms\', \'output_shape\', \'fill_value\', \'interpolation\', \'fill_mode\', \'name\'], varargs=None, keywords=None, defaults=[\'CONSTANT\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ImageSummary"
|
||||
argspec: "args=[\'tag\', \'tensor\', \'max_images\', \'bad_color\', \'name\'], varargs=None, keywords=None, defaults=[\'3\', \'dtype: DT_UINT8\\ntensor_shape {\\n dim {\\n size: 4\\n }\\n}\\nint_val: 255\\nint_val: 0\\nint_val: 0\\nint_val: 255\\n\', \'None\'], "
|
||||
|
||||
@ -118,7 +118,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'factor\', \'fill_mode\', \'fill_value\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'0.0\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "adapt"
|
||||
|
||||
@ -118,7 +118,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'fill_value\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'reflect\', \'0.0\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "adapt"
|
||||
|
||||
@ -118,7 +118,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'reflect\', \'bilinear\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'fill_value\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'reflect\', \'0.0\', \'bilinear\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "adapt"
|
||||
|
||||
@ -1880,6 +1880,10 @@ tf_module {
|
||||
name: "ImageProjectiveTransformV2"
|
||||
argspec: "args=[\'images\', \'transforms\', \'output_shape\', \'interpolation\', \'fill_mode\', \'name\'], varargs=None, keywords=None, defaults=[\'CONSTANT\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ImageProjectiveTransformV3"
|
||||
argspec: "args=[\'images\', \'transforms\', \'output_shape\', \'fill_value\', \'interpolation\', \'fill_mode\', \'name\'], varargs=None, keywords=None, defaults=[\'CONSTANT\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ImageSummary"
|
||||
argspec: "args=[\'tag\', \'tensor\', \'max_images\', \'bad_color\', \'name\'], varargs=None, keywords=None, defaults=[\'3\', \'dtype: DT_UINT8\\ntensor_shape {\\n dim {\\n size: 4\\n }\\n}\\nint_val: 255\\nint_val: 0\\nint_val: 0\\nint_val: 255\\n\', \'None\'], "
|
||||
|
||||
Loading…
Reference in New Issue
Block a user