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// Operators that deal with SummaryProtos (encoded as DT_STRING tensors) as
// inputs or outputs in various ways.
// See docs in ../ops/summary_ops.cc.
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/summary.pb.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/png/png_io.h"
namespace tensorflow {
class SummaryImageOp : public OpKernel {
public:
explicit SummaryImageOp(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("max_images", &max_images_));
const TensorProto* proto;
OP_REQUIRES_OK(context, context->GetAttr("bad_color", &proto));
OP_REQUIRES_OK(context, context->device()->MakeTensorFromProto(
*proto, AllocatorAttributes(), &bad_color_));
OP_REQUIRES(context, bad_color_.dtype() == DT_UINT8,
errors::InvalidArgument("bad_color must be uint8, got ",
DataTypeString(bad_color_.dtype())));
OP_REQUIRES(
context, TensorShapeUtils::IsVector(bad_color_.shape()),
errors::InvalidArgument("bad_color must be a vector, got shape ",
bad_color_.shape().ShortDebugString()));
}
void Compute(OpKernelContext* c) override {
const Tensor& tags = c->input(0);
const Tensor& tensor = c->input(1);
OP_REQUIRES(c, TensorShapeUtils::IsLegacyScalar(tags.shape()),
errors::InvalidArgument("Tags must have be a scalar"));
OP_REQUIRES(c, tensor.dims() == 4 &&
(tensor.dim_size(3) == 1 || tensor.dim_size(3) == 3 ||
tensor.dim_size(3) == 4),
errors::InvalidArgument(
"Tensor must be 4-D with last dim 1, 3, or 4, not ",
tensor.shape().DebugString()));
const string& base_tag = tags.scalar<string>()();
const int batch_size = tensor.dim_size(0);
const int h = tensor.dim_size(1);
const int w = tensor.dim_size(2);
const int hw = h * w; // Compact these two dims for simplicity
const int depth = tensor.dim_size(3);
auto tensor_eigen = tensor.shaped<float, 3>({batch_size, hw, depth});
OP_REQUIRES(c, bad_color_.dim_size(0) >= depth,
errors::InvalidArgument(
"expected depth <= bad_color.size, got depth = ", depth,
", bad_color.size = ", bad_color_.dim_size(0)));
auto bad_color_full = bad_color_.vec<uint8>();
typename TTypes<uint8>::Vec bad_color(bad_color_full.data(), depth);
// RGB (or gray or RGBA) is last dimension
Eigen::Tensor<uint8, 2, Eigen::RowMajor> image(hw, depth);
Summary s;
const int N = std::min<int>(max_images_, batch_size);
for (int i = 0; i < N; ++i) {
Summary::Value* v = s.add_value();
// The tag depends on the number of requested images (not the number
// produced.)
//
// Note that later on avisu uses "/" to figure out a consistent naming
// convention for display, so we append "/image" to guarantee that the
// image(s) won't be displayed in the global scope with no name.
if (max_images_ > 1) {
v->set_tag(strings::StrCat(base_tag, "/image/", i));
} else {
v->set_tag(strings::StrCat(base_tag, "/image"));
}
if (image.size()) {
typename TTypes<float>::ConstMatrix values(
&tensor_eigen(i, 0, 0),
Eigen::DSizes<Eigen::DenseIndex, 2>(hw, depth));
// Rescale the image to uint8 range.
//
// We are trying to generate an RCG image from a float tensor. We do
// not have any info about the expected range of values in the tensor
// but the generated image needs to have all RGB values within [0, 255].
//
// We use two different algorithms to generate these values. If the
// tensor has only positive values we scale them all by 255/max(values).
// If the tensor has both negative and positive values we scale them by
// the max of their absolute values and center them around 127.
//
// This works for most cases, but has the incovenient of not respecting
// the relative dynamic range across different instances of the tensor.
// Compute min and max ignoring nonfinite pixels
float image_min = std::numeric_limits<float>::infinity();
float image_max = -image_min;
for (int i = 0; i < hw; i++) {
bool finite = true;
for (int j = 0; j < depth; j++) {
if (!std::isfinite(values(i, j))) {
finite = false;
break;
}
}
if (finite) {
for (int j = 0; j < depth; j++) {
float value = values(i, j);
image_min = std::min(image_min, value);
image_max = std::max(image_max, value);
}
}
}
// Pick an affine transform into uint8
const float kZeroThreshold = 1e-6;
float scale, offset;
if (image_min < 0) {
float max_val = std::max(std::abs(image_min), std::abs(image_max));
scale = max_val < kZeroThreshold ? 0.0f : 127.0f / max_val;
offset = 128.0f;
} else {
scale = image_max < kZeroThreshold ? 0.0f : 255.0f / image_max;
offset = 0.0f;
}
// Transform image, turning nonfinite values to bad_color
for (int i = 0; i < hw; i++) {
bool finite = true;
for (int j = 0; j < depth; j++) {
if (!std::isfinite(values(i, j))) {
finite = false;
break;
}
}
if (finite) {
image.chip<0>(i) =
(values.chip<0>(i) * scale + offset).cast<uint8>();
} else {
image.chip<0>(i) = bad_color;
}
}
}
Summary::Image* si = v->mutable_image();
si->set_height(h);
si->set_width(w);
si->set_colorspace(depth);
OP_REQUIRES(c, png::WriteImageToBuffer(
image.data(), w, h, w * depth, depth, 8, -1,
si->mutable_encoded_image_string(), nullptr),
errors::Internal("PNG encoding failed"));
}
Tensor* summary_tensor = nullptr;
OP_REQUIRES_OK(c, c->allocate_output(0, TensorShape({}), &summary_tensor));
CHECK(s.SerializeToString(&summary_tensor->scalar<string>()()));
}
private:
int64 max_images_;
Tensor bad_color_;
};
REGISTER_KERNEL_BUILDER(Name("ImageSummary").Device(DEVICE_CPU),
SummaryImageOp);
} // namespace tensorflow
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