view mupdf-source/thirdparty/tesseract/src/lstm/networkscratch.h @ 17:dd9cdb856310

Remove PKG-INFO from the because it is regenerated automatically for the sdist
author Franz Glasner <fzglas.hg@dom66.de>
date Thu, 18 Sep 2025 17:40:40 +0200
parents b50eed0cc0ef
children
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///////////////////////////////////////////////////////////////////////
// File:        networkscratch.h
// Description: Scratch space for Network layers that hides distinction
//              between float/int implementations.
// Author:      Ray Smith
//
// (C) Copyright 2014, Google Inc.
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
///////////////////////////////////////////////////////////////////////

#ifndef TESSERACT_LSTM_NETWORKSCRATCH_H_
#define TESSERACT_LSTM_NETWORKSCRATCH_H_

#include <mutex>
#include "matrix.h"
#include "networkio.h"

namespace tesseract {

// Generic scratch space for network layers. Provides NetworkIO that can store
// a complete set (over time) of intermediates, and vector<float>
// scratch space that auto-frees after use. The aim here is to provide a set
// of temporary buffers to network layers that can be reused between layers
// and don't have to be reallocated on each call.
class NetworkScratch {
public:
  NetworkScratch() : int_mode_(false) {}
  ~NetworkScratch() = default;

  // Sets the network representation. If the representation is integer, then
  // default (integer) NetworkIOs are separated from the always-float variety.
  // This saves memory by having separate int-specific and float-specific
  // stacks. If the network representation is float, then all NetworkIOs go
  // to the float stack.
  void set_int_mode(bool int_mode) {
    int_mode_ = int_mode;
  }

  // Class that acts like a NetworkIO (by having an implicit cast operator),
  // yet actually holds a pointer to NetworkIOs in the source NetworkScratch,
  // and knows how to unstack the borrowed pointers on destruction.
  class IO {
  public:
    // The NetworkIO should be sized after construction.
    IO(const NetworkIO &src, NetworkScratch *scratch)
        : int_mode_(scratch->int_mode_ && src.int_mode()), scratch_space_(scratch) {
      network_io_ =
          int_mode_ ? scratch_space_->int_stack_.Borrow() : scratch_space_->float_stack_.Borrow();
    }
    // Default constructor for arrays. Use one of the Resize functions
    // below to initialize and size.
    IO() : int_mode_(false), network_io_(nullptr), scratch_space_(nullptr) {}

    ~IO() {
      if (scratch_space_ == nullptr) {
        ASSERT_HOST(network_io_ == nullptr);
      } else if (int_mode_) {
        scratch_space_->int_stack_.Return(network_io_);
      } else {
        scratch_space_->float_stack_.Return(network_io_);
      }
    }
    // Resizes the array (and stride), avoiding realloc if possible, to the
    // size from various size specs:
    // Same time size, given number of features.
    void Resize(const NetworkIO &src, int num_features, NetworkScratch *scratch) {
      if (scratch_space_ == nullptr) {
        int_mode_ = scratch->int_mode_ && src.int_mode();
        scratch_space_ = scratch;
        network_io_ =
            int_mode_ ? scratch_space_->int_stack_.Borrow() : scratch_space_->float_stack_.Borrow();
      }
      network_io_->Resize(src, num_features);
    }
    // Resizes to a specific size as a temp buffer. No batches, no y-dim.
    void Resize2d(bool int_mode, int width, int num_features, NetworkScratch *scratch) {
      if (scratch_space_ == nullptr) {
        int_mode_ = scratch->int_mode_ && int_mode;
        scratch_space_ = scratch;
        network_io_ =
            int_mode_ ? scratch_space_->int_stack_.Borrow() : scratch_space_->float_stack_.Borrow();
      }
      network_io_->Resize2d(int_mode, width, num_features);
    }
    // Resize forcing a float representation with the width of src and the given
    // number of features.
    void ResizeFloat(const NetworkIO &src, int num_features, NetworkScratch *scratch) {
      if (scratch_space_ == nullptr) {
        int_mode_ = false;
        scratch_space_ = scratch;
        network_io_ = scratch_space_->float_stack_.Borrow();
      }
      network_io_->ResizeFloat(src, num_features);
    }

    // Returns a ref to a NetworkIO that enables *this to be treated as if
    // it were just a NetworkIO*.
    NetworkIO &operator*() {
      return *network_io_;
    }
    NetworkIO *operator->() {
      return network_io_;
    }
    operator NetworkIO *() {
      return network_io_;
    }

  private:
    // True if this is from the always-float stack, otherwise the default stack.
    bool int_mode_;
    // The NetworkIO that we have borrowed from the scratch_space_.
    NetworkIO *network_io_;
    // The source scratch_space_. Borrowed pointer, used to free the
    // NetworkIO. Don't delete!
    NetworkScratch *scratch_space_;
  }; // class IO.

  // Class that acts like a fixed array of float, yet actually uses space
  // from a vector<float> in the source NetworkScratch, and knows how
  // to unstack the borrowed vector on destruction.
  class FloatVec {
  public:
    // The array will have size elements in it, uninitialized.
    FloatVec(int size, NetworkScratch *scratch) : vec_(nullptr), scratch_space_(scratch) {
      Init(size, scratch);
    }
    // Default constructor is for arrays. Use Init to setup.
    FloatVec() : vec_(nullptr), data_(nullptr), scratch_space_(nullptr) {}
    ~FloatVec() {
      if (scratch_space_ != nullptr) {
        scratch_space_->vec_stack_.Return(vec_);
      }
    }

    void Init(int /*size*/, int reserve, NetworkScratch *scratch) {
      if (scratch_space_ != nullptr && vec_ != nullptr) {
        scratch_space_->vec_stack_.Return(vec_);
      }
      scratch_space_ = scratch;
      vec_ = scratch_space_->vec_stack_.Borrow();
      // TODO: optimize.
      vec_->resize(reserve);
      data_ = &(*vec_)[0];
    }

    void Init(int size, NetworkScratch *scratch) {
      Init(size, size, scratch);
    }

    // Use the cast operator instead of operator[] so the FloatVec can be used
    // as a TFloat* argument to a function call.
    operator TFloat *() const {
      return data_;
    }
    TFloat *get() {
      return data_;
    }

  private:
    // Vector borrowed from the scratch space. Use Return to free it.
    std::vector<TFloat> *vec_;
    // Short-cut pointer to the underlying array.
    TFloat *data_;
    // The source scratch_space_. Borrowed pointer, used to free the
    // vector. Don't delete!
    NetworkScratch *scratch_space_;
  }; // class FloatVec

  // Class that acts like a 2-D array of TFloat, yet actually uses space
  // from the source NetworkScratch, and knows how to unstack the borrowed
  // array on destruction.
  class GradientStore {
  public:
    // Default constructor is for arrays. Use Init to setup.
    GradientStore() : array_(nullptr), scratch_space_(nullptr) {}
    ~GradientStore() {
      if (scratch_space_ != nullptr) {
        scratch_space_->array_stack_.Return(array_);
      }
    }

    void Init(int size1, int size2, NetworkScratch *scratch) {
      if (scratch_space_ != nullptr && array_ != nullptr) {
        scratch_space_->array_stack_.Return(array_);
      }
      scratch_space_ = scratch;
      array_ = scratch_space_->array_stack_.Borrow();
      array_->Resize(size1, size2, 0.0);
    }

    // Accessors to get to the underlying TransposedArray.
    TransposedArray *get() const {
      return array_;
    }
    const TransposedArray &operator*() const {
      return *array_;
    }

  private:
    // Array borrowed from the scratch space. Use Return to free it.
    TransposedArray *array_;
    // The source scratch_space_. Borrowed pointer, used to free the
    // vector. Don't delete!
    NetworkScratch *scratch_space_;
  }; // class GradientStore

  // Class that does the work of holding a stack of objects, a stack pointer
  // and a vector of in-use flags, so objects can be returned out of order.
  // It is safe to attempt to Borrow/Return in multiple threads.
  template <typename T>
  class Stack {
  public:
    Stack() = default;

    ~Stack() {
      for (auto data : stack_) {
        delete data;
      }
    }

    // Lends out the next free item, creating one if none available, sets
    // the used flags and increments the stack top.
    T *Borrow() {
      std::lock_guard<std::mutex> lock(mutex_);
      if (stack_top_ == stack_.size()) {
        stack_.push_back(new T);
        flags_.push_back(false);
      }
      flags_[stack_top_] = true;
      return stack_[stack_top_++];
    }
    // Takes back the given item, and marks it free. Item does not have to be
    // the most recently lent out, but free slots don't get re-used until the
    // blocking item is returned. The assumption is that there will only be
    // small, temporary variations from true stack use. (Determined by the order
    // of destructors within a local scope.)
    void Return(T *item) {
      std::lock_guard<std::mutex> lock(mutex_);
      // Linear search will do.
      int index = stack_top_;
      while (--index >= 0 && stack_[index] != item) {
      }
      if (index >= 0) {
        flags_[index] = false;
      }
      while (stack_top_ > 0 && !flags_[stack_top_ - 1]) {
        --stack_top_;
      }
    }

  private:
    std::vector<T *> stack_;
    std::vector<bool> flags_;
    unsigned stack_top_ = 0;
    std::mutex mutex_;
  }; // class Stack.

private:
  // If true, the network weights are int8_t, if false, float.
  bool int_mode_;
  // Stacks of NetworkIO and vector<float>. Once allocated, they are not
  // deleted until the NetworkScratch is deleted.
  Stack<NetworkIO> int_stack_;
  Stack<NetworkIO> float_stack_;
  Stack<std::vector<TFloat>> vec_stack_;
  Stack<TransposedArray> array_stack_;
};

} // namespace tesseract.

#endif // TESSERACT_LSTM_NETWORKSCRATCH_H_