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Remove Tensorflow support

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Tensorflow was never used because of missing models.

Signed-off-by: Stefan Weil <sw@weilnetz.de>
This commit is contained in:
Stefan Weil 2024-11-07 10:50:11 +01:00
parent daaa902a5e
commit d7c0a05ffa
13 changed files with 3 additions and 2050 deletions
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28
Makefile.am
View File

@ -110,7 +110,6 @@ lib_LTLIBRARIES = libtesseract.la
libtesseract_la_LDFLAGS = $(LEPTONICA_LIBS) libtesseract_la_LDFLAGS = $(LEPTONICA_LIBS)
libtesseract_la_LDFLAGS += $(libarchive_LIBS) libtesseract_la_LDFLAGS += $(libarchive_LIBS)
libtesseract_la_LDFLAGS += $(libcurl_LIBS) libtesseract_la_LDFLAGS += $(libcurl_LIBS)
libtesseract_la_LDFLAGS += $(TENSORFLOW_LIBS)
if T_WIN if T_WIN
libtesseract_la_LDFLAGS += -no-undefined -lws2_32 libtesseract_la_LDFLAGS += -no-undefined -lws2_32
else else
@ -509,10 +508,6 @@ libtesseract_lstm_la_CPPFLAGS += -I$(top_srcdir)/src/cutil
libtesseract_lstm_la_CPPFLAGS += -I$(top_srcdir)/src/dict libtesseract_lstm_la_CPPFLAGS += -I$(top_srcdir)/src/dict
libtesseract_lstm_la_CPPFLAGS += -I$(top_srcdir)/src/lstm libtesseract_lstm_la_CPPFLAGS += -I$(top_srcdir)/src/lstm
libtesseract_lstm_la_CPPFLAGS += -I$(top_srcdir)/src/viewer libtesseract_lstm_la_CPPFLAGS += -I$(top_srcdir)/src/viewer
if TENSORFLOW
libtesseract_lstm_la_CPPFLAGS += -DINCLUDE_TENSORFLOW
libtesseract_lstm_la_CPPFLAGS += -I/usr/include/tensorflow
endif
if !NO_TESSDATA_PREFIX if !NO_TESSDATA_PREFIX
libtesseract_lstm_la_CPPFLAGS += -DTESSDATA_PREFIX='"@datadir@"' libtesseract_lstm_la_CPPFLAGS += -DTESSDATA_PREFIX='"@datadir@"'
endif endif
@ -535,7 +530,6 @@ noinst_HEADERS += src/lstm/reversed.h
noinst_HEADERS += src/lstm/series.h noinst_HEADERS += src/lstm/series.h
noinst_HEADERS += src/lstm/static_shape.h noinst_HEADERS += src/lstm/static_shape.h
noinst_HEADERS += src/lstm/stridemap.h noinst_HEADERS += src/lstm/stridemap.h
noinst_HEADERS += src/lstm/tfnetwork.h
noinst_HEADERS += src/lstm/weightmatrix.h noinst_HEADERS += src/lstm/weightmatrix.h
noinst_LTLIBRARIES += libtesseract_lstm.la noinst_LTLIBRARIES += libtesseract_lstm.la
@ -556,10 +550,6 @@ libtesseract_lstm_la_SOURCES += src/lstm/reconfig.cpp
libtesseract_lstm_la_SOURCES += src/lstm/reversed.cpp libtesseract_lstm_la_SOURCES += src/lstm/reversed.cpp
libtesseract_lstm_la_SOURCES += src/lstm/series.cpp libtesseract_lstm_la_SOURCES += src/lstm/series.cpp
libtesseract_lstm_la_SOURCES += src/lstm/stridemap.cpp libtesseract_lstm_la_SOURCES += src/lstm/stridemap.cpp
if TENSORFLOW
libtesseract_lstm_la_SOURCES += src/lstm/tfnetwork.cpp
libtesseract_lstm_la_SOURCES += src/lstm/tfnetwork.pb.cc
endif
libtesseract_lstm_la_SOURCES += src/lstm/weightmatrix.cpp libtesseract_lstm_la_SOURCES += src/lstm/weightmatrix.cpp
# Rules for src/textord. # Rules for src/textord.
@ -730,7 +720,6 @@ tesseract_LDFLAGS = $(OPENMP_CXXFLAGS)
tesseract_LDADD = libtesseract.la tesseract_LDADD = libtesseract.la
tesseract_LDADD += $(LEPTONICA_LIBS) tesseract_LDADD += $(LEPTONICA_LIBS)
tesseract_LDADD += $(TENSORFLOW_LIBS)
tesseract_LDADD += $(libarchive_LIBS) tesseract_LDADD += $(libarchive_LIBS)
tesseract_LDADD += $(libcurl_LIBS) tesseract_LDADD += $(libcurl_LIBS)
@ -900,7 +889,6 @@ EXTRA_PROGRAMS += $(trainingtools)
extralib = libtesseract.la extralib = libtesseract.la
extralib += $(libarchive_LIBS) extralib += $(libarchive_LIBS)
extralib += $(LEPTONICA_LIBS) extralib += $(LEPTONICA_LIBS)
extralib += $(TENSORFLOW_LIBS)
if T_WIN if T_WIN
extralib += -lws2_32 extralib += -lws2_32
endif endif
@ -1008,7 +996,6 @@ fuzzer-api: unittest/fuzzers/fuzzer-api.cpp
$< \ $< \
$(builddir)/.libs/libtesseract.a \ $(builddir)/.libs/libtesseract.a \
$(LEPTONICA_LIBS) \ $(LEPTONICA_LIBS) \
$(TENSORFLOW_LIBS) \
$(libarchive_LIBS) \ $(libarchive_LIBS) \
$(libcurl_LIBS) \ $(libcurl_LIBS) \
-o $@ -o $@
@ -1028,7 +1015,6 @@ fuzzer-api-512x256: unittest/fuzzers/fuzzer-api.cpp
$< \ $< \
$(builddir)/.libs/libtesseract.a \ $(builddir)/.libs/libtesseract.a \
$(LEPTONICA_LIBS) \ $(LEPTONICA_LIBS) \
$(TENSORFLOW_LIBS) \
$(libarchive_LIBS) \ $(libarchive_LIBS) \
$(libcurl_LIBS) \ $(libcurl_LIBS) \
-o $@ -o $@
@ -1141,10 +1127,6 @@ endif # ENABLE_TRAINING
unittest_CPPFLAGS += -I$(top_srcdir)/src/viewer unittest_CPPFLAGS += -I$(top_srcdir)/src/viewer
unittest_CPPFLAGS += -I$(top_srcdir)/src/wordrec unittest_CPPFLAGS += -I$(top_srcdir)/src/wordrec
unittest_CPPFLAGS += -I$(top_srcdir)/unittest unittest_CPPFLAGS += -I$(top_srcdir)/unittest
if TENSORFLOW
unittest_CPPFLAGS += -DINCLUDE_TENSORFLOW
unittest_CPPFLAGS += -I/usr/include/tensorflow
endif # TENSORFLOW
# Build googletest: # Build googletest:
check_LTLIBRARIES = libgtest.la libgtest_main.la libgmock.la libgmock_main.la check_LTLIBRARIES = libgtest.la libgtest_main.la libgmock.la libgmock_main.la
@ -1172,7 +1154,6 @@ GTEST_LIBS = libgtest.la libgtest_main.la -lpthread
GMOCK_LIBS = libgmock.la libgmock_main.la GMOCK_LIBS = libgmock.la libgmock_main.la
TESS_LIBS = $(GTEST_LIBS) TESS_LIBS = $(GTEST_LIBS)
TESS_LIBS += libtesseract.la $(libarchive_LIBS) TESS_LIBS += libtesseract.la $(libarchive_LIBS)
TESS_LIBS += $(TENSORFLOW_LIBS)
TRAINING_LIBS = libtesseract_training.la TRAINING_LIBS = libtesseract_training.la
TRAINING_LIBS += $(TESS_LIBS) TRAINING_LIBS += $(TESS_LIBS)
unittest_CPPFLAGS += -isystem $(top_srcdir)/unittest/third_party/googletest/googletest/include unittest_CPPFLAGS += -isystem $(top_srcdir)/unittest/third_party/googletest/googletest/include
@ -1419,10 +1400,6 @@ networkio_test_CPPFLAGS = $(unittest_CPPFLAGS)
networkio_test_LDADD = $(TESS_LIBS) networkio_test_LDADD = $(TESS_LIBS)
normstrngs_test_SOURCES = unittest/normstrngs_test.cc normstrngs_test_SOURCES = unittest/normstrngs_test.cc
if TENSORFLOW
normstrngs_test_SOURCES += unittest/third_party/utf/rune.c
normstrngs_test_SOURCES += unittest/util/utf8/unilib.cc
endif # TENSORFLOW
normstrngs_test_CPPFLAGS = $(unittest_CPPFLAGS) normstrngs_test_CPPFLAGS = $(unittest_CPPFLAGS)
normstrngs_test_LDADD = $(TRAINING_LIBS) $(ICU_I18N_LIBS) $(ICU_UC_LIBS) normstrngs_test_LDADD = $(TRAINING_LIBS) $(ICU_I18N_LIBS) $(ICU_UC_LIBS)
@ -1441,11 +1418,6 @@ pagesegmode_test_CPPFLAGS = $(unittest_CPPFLAGS)
pagesegmode_test_LDADD = $(TRAINING_LIBS) $(LEPTONICA_LIBS) pagesegmode_test_LDADD = $(TRAINING_LIBS) $(LEPTONICA_LIBS)
pango_font_info_test_SOURCES = unittest/pango_font_info_test.cc pango_font_info_test_SOURCES = unittest/pango_font_info_test.cc
if TENSORFLOW
pango_font_info_test_SOURCES += unittest/third_party/utf/rune.c
pango_font_info_test_SOURCES += unittest/util/utf8/unicodetext.cc
pango_font_info_test_SOURCES += unittest/util/utf8/unilib.cc
endif # TENSORFLOW
pango_font_info_test_CPPFLAGS = $(unittest_CPPFLAGS) pango_font_info_test_CPPFLAGS = $(unittest_CPPFLAGS)
pango_font_info_test_LDADD = $(TRAINING_LIBS) $(LEPTONICA_LIBS) pango_font_info_test_LDADD = $(TRAINING_LIBS) $(LEPTONICA_LIBS)
pango_font_info_test_LDADD += $(ICU_I18N_LIBS) pango_font_info_test_LDADD += $(ICU_I18N_LIBS)

19
configure.ac
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@ -270,25 +270,6 @@ AC_ARG_WITH([curl],
AS_HELP_STRING([--with-curl], AS_HELP_STRING([--with-curl],
[Build with libcurl which supports processing an image URL @<:@default=check@:>@]), [Build with libcurl which supports processing an image URL @<:@default=check@:>@]),
[], [with_curl=check]) [], [with_curl=check])
AC_ARG_WITH([tensorflow],
AS_HELP_STRING([--with-tensorflow],
[support TensorFlow @<:@default=check@:>@]),
[], [with_tensorflow=check])
# Check whether to build with support for TensorFlow.
AM_CONDITIONAL([TENSORFLOW], false)
TENSORFLOW_LIBS=
AS_IF([test "x$with_tensorflow" != xno],
[AC_CHECK_HEADERS([tensorflow/core/framework/graph.pb.h],
[AC_SUBST([TENSORFLOW_LIBS], ["-lprotobuf -ltensorflow_cc"])
AM_CONDITIONAL([TENSORFLOW], true)
],
[if test "x$with_tensorflow" != xcheck; then
AC_MSG_FAILURE(
[--with-tensorflow was given, but test for libtensorflow-dev failed])
fi
])
])
# https://lists.apple.com/archives/unix-porting/2009/Jan/msg00026.html # https://lists.apple.com/archives/unix-porting/2009/Jan/msg00026.html
m4_define([MY_CHECK_FRAMEWORK], m4_define([MY_CHECK_FRAMEWORK],

9
src/lstm/network.cpp
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@ -38,9 +38,6 @@
#include "scrollview.h" #include "scrollview.h"
#include "series.h" #include "series.h"
#include "statistc.h" #include "statistc.h"
#ifdef INCLUDE_TENSORFLOW
# include "tfnetwork.h"
#endif
#include "tprintf.h" #include "tprintf.h"
namespace tesseract { namespace tesseract {
@ -287,11 +284,7 @@ Network *Network::CreateFromFile(TFile *fp) {
network = new Series(name); network = new Series(name);
break; break;
case NT_TENSORFLOW: case NT_TENSORFLOW:
#ifdef INCLUDE_TENSORFLOW tprintf("Unsupported TensorFlow model\n");
network = new TFNetwork(name);
#else
tprintf("TensorFlow not compiled in! -DINCLUDE_TENSORFLOW\n");
#endif
break; break;
// All variants of FullyConnected. // All variants of FullyConnected.
case NT_SOFTMAX: case NT_SOFTMAX:

143
src/lstm/tfnetwork.cpp
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@ -1,143 +0,0 @@
///////////////////////////////////////////////////////////////////////
// File: tfnetwork.cpp
// Description: Encapsulation of an entire tensorflow graph as a
// Tesseract Network.
// Author: Ray Smith
//
// (C) Copyright 2016, 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.
///////////////////////////////////////////////////////////////////////
#ifdef INCLUDE_TENSORFLOW
# include "tfnetwork.h"
# include <allheaders.h>
# include "input.h"
# include "networkscratch.h"
using tensorflow::Status;
using tensorflow::Tensor;
using tensorflow::TensorShape;
namespace tesseract {
TFNetwork::TFNetwork(const std::string &name) : Network(NT_TENSORFLOW, name, 0, 0) {}
int TFNetwork::InitFromProtoStr(const std::string &proto_str) {
if (!model_proto_.ParseFromString(proto_str))
return 0;
return InitFromProto();
}
// Writes to the given file. Returns false in case of error.
// Should be overridden by subclasses, but called by their Serialize.
bool TFNetwork::Serialize(TFile *fp) const {
if (!Network::Serialize(fp))
return false;
std::string proto_str;
model_proto_.SerializeToString(&proto_str);
// TODO: optimize and avoid copy from proto_str to data.
std::vector<char> data(proto_str.size());
memcpy(&data[0], proto_str.data(), proto_str.size());
return fp->Serialize(data);
}
// Reads from the given file. Returns false in case of error.
// Should be overridden by subclasses, but NOT called by their DeSerialize.
bool TFNetwork::DeSerialize(TFile *fp) {
std::vector<char> data;
if (!fp->DeSerialize(data))
return false;
if (!model_proto_.ParseFromArray(&data[0], data.size())) {
return false;
}
return InitFromProto();
}
// Runs forward propagation of activations on the input line.
// See Network for a detailed discussion of the arguments.
void TFNetwork::Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose,
NetworkScratch *scratch, NetworkIO *output) {
std::vector<std::pair<std::string, Tensor>> tf_inputs;
int depth = input_shape_.depth();
ASSERT_HOST(depth == input.NumFeatures());
// TODO(rays) Allow batching. For now batch_size = 1.
const StrideMap &stride_map = input.stride_map();
// TF requires a tensor of shape float[batch, height, width, depth].
TensorShape shape{1, stride_map.Size(FD_HEIGHT), stride_map.Size(FD_WIDTH), depth};
Tensor input_tensor(tensorflow::DT_FLOAT, shape);
// The flat() member gives a 1d array, with a data() member to get the data.
auto eigen_tensor = input_tensor.flat<float>();
memcpy(eigen_tensor.data(), input.f(0), input.Width() * depth * sizeof(input.f(0)[0]));
// Add the tensor to the vector of inputs.
tf_inputs.emplace_back(model_proto_.image_input(), input_tensor);
// Provide tensors giving the width and/or height of the image if they are
// required. Some tf ops require a separate tensor with knowledge of the
// size of the input as they cannot obtain it from the input tensor. This is
// usually true in the case of ops that process a batch of variable-sized
// objects.
if (!model_proto_.image_widths().empty()) {
TensorShape size_shape{1};
Tensor width_tensor(tensorflow::DT_INT64, size_shape);
auto eigen_wtensor = width_tensor.flat<tensorflow::int64>();
*eigen_wtensor.data() = stride_map.Size(FD_WIDTH);
tf_inputs.emplace_back(model_proto_.image_widths(), width_tensor);
}
if (!model_proto_.image_heights().empty()) {
TensorShape size_shape{1};
Tensor height_tensor(tensorflow::DT_INT64, size_shape);
auto eigen_htensor = height_tensor.flat<tensorflow::int64>();
*eigen_htensor.data() = stride_map.Size(FD_HEIGHT);
tf_inputs.emplace_back(model_proto_.image_heights(), height_tensor);
}
std::vector<std::string> target_layers = {model_proto_.output_layer()};
std::vector<Tensor> outputs;
Status s = session_->Run(tf_inputs, target_layers, {}, &outputs);
if (!s.ok())
tprintf("session->Run failed:%s\n", s.error_message().c_str());
ASSERT_HOST(s.ok());
ASSERT_HOST(outputs.size() == 1);
const Tensor &output_tensor = outputs[0];
// Check the dimensions of the output.
ASSERT_HOST(output_tensor.shape().dims() == 3);
int output_batch = output_tensor.shape().dim_size(0);
int output_steps = output_tensor.shape().dim_size(1);
int output_depth = output_tensor.shape().dim_size(2);
ASSERT_HOST(output_batch == 1);
ASSERT_HOST(output_depth == output_shape_.depth());
output->Resize2d(false, output_steps, output_depth);
auto eigen_output = output_tensor.flat<float>();
memcpy(output->f(0), eigen_output.data(), output_steps * output_depth * sizeof(output->f(0)[0]));
}
int TFNetwork::InitFromProto() {
spec_ = model_proto_.spec();
input_shape_.SetShape(model_proto_.batch_size(), std::max(0, model_proto_.y_size()),
std::max(0, model_proto_.x_size()), model_proto_.depth());
output_shape_.SetShape(model_proto_.batch_size(), 1, 0, model_proto_.num_classes());
output_shape_.set_loss_type(model_proto_.using_ctc() ? LT_CTC : LT_SOFTMAX);
ni_ = input_shape_.height();
no_ = output_shape_.depth();
// Initialize the session_ with the graph. Since we can't get the graph
// back from the session_, we have to keep the proto as well
tensorflow::SessionOptions options;
session_.reset(NewSession(options));
Status s = session_->Create(model_proto_.graph());
if (s.ok())
return model_proto_.global_step();
tprintf("Session_->Create returned '%s'\n", s.error_message().c_str());
return 0;
}
} // namespace tesseract
#endif // ifdef INCLUDE_TENSORFLOW

106
src/lstm/tfnetwork.h
View File

@ -1,106 +0,0 @@
///////////////////////////////////////////////////////////////////////
// File: tfnetwork.h
// Description: Encapsulation of an entire tensorflow graph as a
// Tesseract Network.
// Author: Ray Smith
//
// (C) Copyright 2016, 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_TFNETWORK_H_
#define TESSERACT_LSTM_TFNETWORK_H_
#ifdef INCLUDE_TENSORFLOW
# include <memory>
# include <string>
# include "network.h"
# include "static_shape.h"
# include "tensorflow/core/framework/graph.pb.h"
# include "tensorflow/core/public/session.h"
# include "tfnetwork.pb.h"
namespace tesseract {
class TFNetwork : public Network {
public:
explicit TFNetwork(const std::string &name);
virtual ~TFNetwork() = default;
// Returns the required shape input to the network.
StaticShape InputShape() const override {
return input_shape_;
}
// Returns the shape output from the network given an input shape (which may
// be partially unknown ie zero).
StaticShape OutputShape(const StaticShape &input_shape) const override {
return output_shape_;
}
std::string spec() const override {
return spec_;
}
// Deserializes *this from a serialized TFNetwork proto. Returns 0 if failed,
// otherwise the global step of the serialized graph.
int InitFromProtoStr(const std::string &proto_str);
// The number of classes in this network should be equal to those in the
// recoder_ in LSTMRecognizer.
int num_classes() const {
return output_shape_.depth();
}
// Writes to the given file. Returns false in case of error.
// Should be overridden by subclasses, but called by their Serialize.
bool Serialize(TFile *fp) const override;
// Reads from the given file. Returns false in case of error.
// Should be overridden by subclasses, but NOT called by their DeSerialize.
bool DeSerialize(TFile *fp) override;
// Runs forward propagation of activations on the input line.
// See Network for a detailed discussion of the arguments.
void Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose,
NetworkScratch *scratch, NetworkIO *output) override;
private:
// Runs backward propagation of errors on the deltas line.
// See Network for a detailed discussion of the arguments.
bool Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch,
NetworkIO *back_deltas) override {
tprintf("Must override Network::Backward for type %d\n", type_);
return false;
}
void DebugWeights() override {
tprintf("Must override Network::DebugWeights for type %d\n", type_);
}
int InitFromProto();
// The original network definition for reference.
std::string spec_;
// Input tensor parameters.
StaticShape input_shape_;
// Output tensor parameters.
StaticShape output_shape_;
// The tensor flow graph is contained in here.
std::unique_ptr<tensorflow::Session> session_;
// The serialized graph is also contained in here.
TFNetworkModel model_proto_;
};
} // namespace tesseract.
#endif // ifdef INCLUDE_TENSORFLOW
#endif // TESSERACT_TENSORFLOW_TFNETWORK_H_

929
src/lstm/tfnetwork.pb.cc
View File

@ -1,929 +0,0 @@
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tfnetwork.proto
#include "tfnetwork.pb.h"
#include <algorithm>
#include <google/protobuf/descriptor.h>
#include <google/protobuf/generated_message_reflection.h>
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/reflection_ops.h>
#include <google/protobuf/stubs/common.h>
#include <google/protobuf/stubs/port.h>
#include <google/protobuf/wire_format.h>
#include <google/protobuf/wire_format_lite_inl.h>
// This is a temporary google only hack
#ifdef GOOGLE_PROTOBUF_ENFORCE_UNIQUENESS
# include "third_party/protobuf/version.h"
#endif
// @@protoc_insertion_point(includes)
namespace protobuf_tensorflow_2fcore_2fframework_2fgraph_2eproto {
extern PROTOBUF_INTERNAL_EXPORT_protobuf_tensorflow_2fcore_2fframework_2fgraph_2eproto ::google::
protobuf::internal::SCCInfo<3>
scc_info_GraphDef;
} // namespace protobuf_tensorflow_2fcore_2fframework_2fgraph_2eproto
namespace tesseract {
class TFNetworkModelDefaultTypeInternal {
public:
::google::protobuf::internal::ExplicitlyConstructed<TFNetworkModel> _instance;
} _TFNetworkModel_default_instance_;
} // namespace tesseract
namespace protobuf_tfnetwork_2eproto {
static void InitDefaultsTFNetworkModel() {
GOOGLE_PROTOBUF_VERIFY_VERSION;
{
void *ptr = &::tesseract::_TFNetworkModel_default_instance_;
new (ptr)::tesseract::TFNetworkModel();
::google::protobuf::internal::OnShutdownDestroyMessage(ptr);
}
::tesseract::TFNetworkModel::InitAsDefaultInstance();
}
::google::protobuf::internal::SCCInfo<1> scc_info_TFNetworkModel = {
{ATOMIC_VAR_INIT(::google::protobuf::internal::SCCInfoBase::kUninitialized), 1,
InitDefaultsTFNetworkModel},
{
&protobuf_tensorflow_2fcore_2fframework_2fgraph_2eproto::scc_info_GraphDef.base,
}};
void InitDefaults() {
::google::protobuf::internal::InitSCC(&scc_info_TFNetworkModel.base);
}
::google::protobuf::Metadata file_level_metadata[1];
const ::google::protobuf::uint32 TableStruct::offsets[] GOOGLE_PROTOBUF_ATTRIBUTE_SECTION_VARIABLE(
protodesc_cold) = {
~0u, // no _has_bits_
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel,
_internal_metadata_),
~0u, // no _extensions_
~0u, // no _oneof_case_
~0u, // no _weak_field_map_
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, graph_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, global_step_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, spec_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, depth_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, x_size_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, y_size_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, batch_size_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, num_classes_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, using_ctc_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, image_input_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, image_widths_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, image_heights_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(::tesseract::TFNetworkModel, output_layer_),
};
static const ::google::protobuf::internal::MigrationSchema
schemas[] GOOGLE_PROTOBUF_ATTRIBUTE_SECTION_VARIABLE(protodesc_cold) = {
{0, -1, sizeof(::tesseract::TFNetworkModel)},
};
static ::google::protobuf::Message const *const file_default_instances[] = {
reinterpret_cast<const ::google::protobuf::Message *>(
&::tesseract::_TFNetworkModel_default_instance_),
};
void protobuf_AssignDescriptors() {
AddDescriptors();
AssignDescriptors("tfnetwork.proto", schemas, file_default_instances, TableStruct::offsets,
file_level_metadata, NULL, NULL);
}
void protobuf_AssignDescriptorsOnce() {
static ::google::protobuf::internal::once_flag once;
::google::protobuf::internal::call_once(once, protobuf_AssignDescriptors);
}
void protobuf_RegisterTypes(const ::std::string &) GOOGLE_PROTOBUF_ATTRIBUTE_COLD;
void protobuf_RegisterTypes(const ::std::string &) {
protobuf_AssignDescriptorsOnce();
::google::protobuf::internal::RegisterAllTypes(file_level_metadata, 1);
}
void AddDescriptorsImpl() {
InitDefaults();
static const char descriptor[] GOOGLE_PROTOBUF_ATTRIBUTE_SECTION_VARIABLE(protodesc_cold) = {
"\n\017tfnetwork.proto\022\ttesseract\032%tensorflow"
"/core/framework/graph.proto\"\233\002\n\016TFNetwor"
"kModel\022#\n\005graph\030\001 \001(\0132\024.tensorflow.Graph"
"Def\022\023\n\013global_step\030\002 \001(\003\022\014\n\004spec\030\003 \001(\t\022\r"
"\n\005depth\030\004 \001(\005\022\016\n\006x_size\030\005 \001(\005\022\016\n\006y_size\030"
"\006 \001(\005\022\022\n\nbatch_size\030\010 \001(\005\022\023\n\013num_classes"
"\030\t \001(\005\022\021\n\tusing_ctc\030\n \001(\010\022\023\n\013image_input"
"\030\013 \001(\t\022\024\n\014image_widths\030\014 \001(\t\022\025\n\rimage_he"
"ights\030\r \001(\t\022\024\n\014output_layer\030\016 \001(\tb\006proto"
"3"};
::google::protobuf::DescriptorPool::InternalAddGeneratedFile(descriptor, 361);
::google::protobuf::MessageFactory::InternalRegisterGeneratedFile("tfnetwork.proto",
&protobuf_RegisterTypes);
::protobuf_tensorflow_2fcore_2fframework_2fgraph_2eproto::AddDescriptors();
}
void AddDescriptors() {
static ::google::protobuf::internal::once_flag once;
::google::protobuf::internal::call_once(once, AddDescriptorsImpl);
}
// Force AddDescriptors() to be called at dynamic initialization time.
struct StaticDescriptorInitializer {
StaticDescriptorInitializer() {
AddDescriptors();
}
} static_descriptor_initializer;
} // namespace protobuf_tfnetwork_2eproto
namespace tesseract {
// ===================================================================
void TFNetworkModel::InitAsDefaultInstance() {
::tesseract::_TFNetworkModel_default_instance_._instance.get_mutable()->graph_ =
const_cast<::tensorflow::GraphDef *>(::tensorflow::GraphDef::internal_default_instance());
}
void TFNetworkModel::clear_graph() {
if (GetArenaNoVirtual() == NULL && graph_ != NULL) {
delete graph_;
}
graph_ = NULL;
}
#if !defined(_MSC_VER) || _MSC_VER >= 1900
const int TFNetworkModel::kGraphFieldNumber;
const int TFNetworkModel::kGlobalStepFieldNumber;
const int TFNetworkModel::kSpecFieldNumber;
const int TFNetworkModel::kDepthFieldNumber;
const int TFNetworkModel::kXSizeFieldNumber;
const int TFNetworkModel::kYSizeFieldNumber;
const int TFNetworkModel::kBatchSizeFieldNumber;
const int TFNetworkModel::kNumClassesFieldNumber;
const int TFNetworkModel::kUsingCtcFieldNumber;
const int TFNetworkModel::kImageInputFieldNumber;
const int TFNetworkModel::kImageWidthsFieldNumber;
const int TFNetworkModel::kImageHeightsFieldNumber;
const int TFNetworkModel::kOutputLayerFieldNumber;
#endif // !defined(_MSC_VER) || _MSC_VER >= 1900
TFNetworkModel::TFNetworkModel() : ::google::protobuf::Message(), _internal_metadata_(NULL) {
::google::protobuf::internal::InitSCC(&protobuf_tfnetwork_2eproto::scc_info_TFNetworkModel.base);
SharedCtor();
// @@protoc_insertion_point(constructor:tesseract.TFNetworkModel)
}
TFNetworkModel::TFNetworkModel(const TFNetworkModel &from)
: ::google::protobuf::Message(), _internal_metadata_(NULL) {
_internal_metadata_.MergeFrom(from._internal_metadata_);
spec_.UnsafeSetDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
if (from.spec().size() > 0) {
spec_.AssignWithDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
from.spec_);
}
image_input_.UnsafeSetDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
if (from.image_input().size() > 0) {
image_input_.AssignWithDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
from.image_input_);
}
image_widths_.UnsafeSetDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
if (from.image_widths().size() > 0) {
image_widths_.AssignWithDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
from.image_widths_);
}
image_heights_.UnsafeSetDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
if (from.image_heights().size() > 0) {
image_heights_.AssignWithDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
from.image_heights_);
}
output_layer_.UnsafeSetDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
if (from.output_layer().size() > 0) {
output_layer_.AssignWithDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
from.output_layer_);
}
if (from.has_graph()) {
graph_ = new ::tensorflow::GraphDef(*from.graph_);
} else {
graph_ = NULL;
}
::memcpy(&global_step_, &from.global_step_,
static_cast<size_t>(reinterpret_cast<char *>(&using_ctc_) -
reinterpret_cast<char *>(&global_step_)) +
sizeof(using_ctc_));
// @@protoc_insertion_point(copy_constructor:tesseract.TFNetworkModel)
}
void TFNetworkModel::SharedCtor() {
spec_.UnsafeSetDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
image_input_.UnsafeSetDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
image_widths_.UnsafeSetDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
image_heights_.UnsafeSetDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
output_layer_.UnsafeSetDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
::memset(&graph_, 0,
static_cast<size_t>(reinterpret_cast<char *>(&using_ctc_) -
reinterpret_cast<char *>(&graph_)) +
sizeof(using_ctc_));
}
TFNetworkModel::~TFNetworkModel() {
// @@protoc_insertion_point(destructor:tesseract.TFNetworkModel)
SharedDtor();
}
void TFNetworkModel::SharedDtor() {
spec_.DestroyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
image_input_.DestroyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
image_widths_.DestroyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
image_heights_.DestroyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
output_layer_.DestroyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
if (this != internal_default_instance())
delete graph_;
}
void TFNetworkModel::SetCachedSize(int size) const {
_cached_size_.Set(size);
}
const ::google::protobuf::Descriptor *TFNetworkModel::descriptor() {
::protobuf_tfnetwork_2eproto::protobuf_AssignDescriptorsOnce();
return ::protobuf_tfnetwork_2eproto::file_level_metadata[kIndexInFileMessages].descriptor;
}
const TFNetworkModel &TFNetworkModel::default_instance() {
::google::protobuf::internal::InitSCC(&protobuf_tfnetwork_2eproto::scc_info_TFNetworkModel.base);
return *internal_default_instance();
}
void TFNetworkModel::Clear() {
// @@protoc_insertion_point(message_clear_start:tesseract.TFNetworkModel)
::google::protobuf::uint32 cached_has_bits = 0;
// Prevent compiler warnings about cached_has_bits being unused
(void)cached_has_bits;
spec_.ClearToEmptyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
image_input_.ClearToEmptyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
image_widths_.ClearToEmptyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
image_heights_.ClearToEmptyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
output_layer_.ClearToEmptyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
if (GetArenaNoVirtual() == NULL && graph_ != NULL) {
delete graph_;
}
graph_ = NULL;
::memset(&global_step_, 0,
static_cast<size_t>(reinterpret_cast<char *>(&using_ctc_) -
reinterpret_cast<char *>(&global_step_)) +
sizeof(using_ctc_));
_internal_metadata_.Clear();
}
bool TFNetworkModel::MergePartialFromCodedStream(::google::protobuf::io::CodedInputStream *input) {
#define DO_(EXPRESSION) \
if (!GOOGLE_PREDICT_TRUE(EXPRESSION)) \
goto failure
::google::protobuf::uint32 tag;
// @@protoc_insertion_point(parse_start:tesseract.TFNetworkModel)
for (;;) {
::std::pair<::google::protobuf::uint32, bool> p = input->ReadTagWithCutoffNoLastTag(127u);
tag = p.first;
if (!p.second)
goto handle_unusual;
switch (::google::protobuf::internal::WireFormatLite::GetTagFieldNumber(tag)) {
// .tensorflow.GraphDef graph = 1;
case 1: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(10u /* 10 & 0xFF */)) {
DO_(::google::protobuf::internal::WireFormatLite::ReadMessage(input, mutable_graph()));
} else {
goto handle_unusual;
}
break;
}
// int64 global_step = 2;
case 2: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(16u /* 16 & 0xFF */)) {
DO_((::google::protobuf::internal::WireFormatLite::ReadPrimitive<
::google::protobuf::int64, ::google::protobuf::internal::WireFormatLite::TYPE_INT64>(
input, &global_step_)));
} else {
goto handle_unusual;
}
break;
}
// string spec = 3;
case 3: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(26u /* 26 & 0xFF */)) {
DO_(::google::protobuf::internal::WireFormatLite::ReadString(input,
this->mutable_spec()));
DO_(::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->spec().data(), static_cast<int>(this->spec().length()),
::google::protobuf::internal::WireFormatLite::PARSE,
"tesseract.TFNetworkModel.spec"));
} else {
goto handle_unusual;
}
break;
}
// int32 depth = 4;
case 4: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(32u /* 32 & 0xFF */)) {
DO_((::google::protobuf::internal::WireFormatLite::ReadPrimitive<
::google::protobuf::int32, ::google::protobuf::internal::WireFormatLite::TYPE_INT32>(
input, &depth_)));
} else {
goto handle_unusual;
}
break;
}
// int32 x_size = 5;
case 5: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(40u /* 40 & 0xFF */)) {
DO_((::google::protobuf::internal::WireFormatLite::ReadPrimitive<
::google::protobuf::int32, ::google::protobuf::internal::WireFormatLite::TYPE_INT32>(
input, &x_size_)));
} else {
goto handle_unusual;
}
break;
}
// int32 y_size = 6;
case 6: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(48u /* 48 & 0xFF */)) {
DO_((::google::protobuf::internal::WireFormatLite::ReadPrimitive<
::google::protobuf::int32, ::google::protobuf::internal::WireFormatLite::TYPE_INT32>(
input, &y_size_)));
} else {
goto handle_unusual;
}
break;
}
// int32 batch_size = 8;
case 8: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(64u /* 64 & 0xFF */)) {
DO_((::google::protobuf::internal::WireFormatLite::ReadPrimitive<
::google::protobuf::int32, ::google::protobuf::internal::WireFormatLite::TYPE_INT32>(
input, &batch_size_)));
} else {
goto handle_unusual;
}
break;
}
// int32 num_classes = 9;
case 9: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(72u /* 72 & 0xFF */)) {
DO_((::google::protobuf::internal::WireFormatLite::ReadPrimitive<
::google::protobuf::int32, ::google::protobuf::internal::WireFormatLite::TYPE_INT32>(
input, &num_classes_)));
} else {
goto handle_unusual;
}
break;
}
// bool using_ctc = 10;
case 10: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(80u /* 80 & 0xFF */)) {
DO_((::google::protobuf::internal::WireFormatLite::ReadPrimitive<
bool, ::google::protobuf::internal::WireFormatLite::TYPE_BOOL>(input, &using_ctc_)));
} else {
goto handle_unusual;
}
break;
}
// string image_input = 11;
case 11: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(90u /* 90 & 0xFF */)) {
DO_(::google::protobuf::internal::WireFormatLite::ReadString(
input, this->mutable_image_input()));
DO_(::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->image_input().data(), static_cast<int>(this->image_input().length()),
::google::protobuf::internal::WireFormatLite::PARSE,
"tesseract.TFNetworkModel.image_input"));
} else {
goto handle_unusual;
}
break;
}
// string image_widths = 12;
case 12: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(98u /* 98 & 0xFF */)) {
DO_(::google::protobuf::internal::WireFormatLite::ReadString(
input, this->mutable_image_widths()));
DO_(::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->image_widths().data(), static_cast<int>(this->image_widths().length()),
::google::protobuf::internal::WireFormatLite::PARSE,
"tesseract.TFNetworkModel.image_widths"));
} else {
goto handle_unusual;
}
break;
}
// string image_heights = 13;
case 13: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(106u /* 106 & 0xFF */)) {
DO_(::google::protobuf::internal::WireFormatLite::ReadString(
input, this->mutable_image_heights()));
DO_(::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->image_heights().data(), static_cast<int>(this->image_heights().length()),
::google::protobuf::internal::WireFormatLite::PARSE,
"tesseract.TFNetworkModel.image_heights"));
} else {
goto handle_unusual;
}
break;
}
// string output_layer = 14;
case 14: {
if (static_cast<::google::protobuf::uint8>(tag) ==
static_cast<::google::protobuf::uint8>(114u /* 114 & 0xFF */)) {
DO_(::google::protobuf::internal::WireFormatLite::ReadString(
input, this->mutable_output_layer()));
DO_(::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->output_layer().data(), static_cast<int>(this->output_layer().length()),
::google::protobuf::internal::WireFormatLite::PARSE,
"tesseract.TFNetworkModel.output_layer"));
} else {
goto handle_unusual;
}
break;
}
default: {
handle_unusual:
if (tag == 0) {
goto success;
}
DO_(::google::protobuf::internal::WireFormat::SkipField(
input, tag, _internal_metadata_.mutable_unknown_fields()));
break;
}
}
}
success:
// @@protoc_insertion_point(parse_success:tesseract.TFNetworkModel)
return true;
failure:
// @@protoc_insertion_point(parse_failure:tesseract.TFNetworkModel)
return false;
#undef DO_
}
void TFNetworkModel::SerializeWithCachedSizes(
::google::protobuf::io::CodedOutputStream *output) const {
// @@protoc_insertion_point(serialize_start:tesseract.TFNetworkModel)
::google::protobuf::uint32 cached_has_bits = 0;
(void)cached_has_bits;
// .tensorflow.GraphDef graph = 1;
if (this->has_graph()) {
::google::protobuf::internal::WireFormatLite::WriteMessageMaybeToArray(
1, this->_internal_graph(), output);
}
// int64 global_step = 2;
if (this->global_step() != 0) {
::google::protobuf::internal::WireFormatLite::WriteInt64(2, this->global_step(), output);
}
// string spec = 3;
if (this->spec().size() > 0) {
::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->spec().data(), static_cast<int>(this->spec().length()),
::google::protobuf::internal::WireFormatLite::SERIALIZE, "tesseract.TFNetworkModel.spec");
::google::protobuf::internal::WireFormatLite::WriteStringMaybeAliased(3, this->spec(), output);
}
// int32 depth = 4;
if (this->depth() != 0) {
::google::protobuf::internal::WireFormatLite::WriteInt32(4, this->depth(), output);
}
// int32 x_size = 5;
if (this->x_size() != 0) {
::google::protobuf::internal::WireFormatLite::WriteInt32(5, this->x_size(), output);
}
// int32 y_size = 6;
if (this->y_size() != 0) {
::google::protobuf::internal::WireFormatLite::WriteInt32(6, this->y_size(), output);
}
// int32 batch_size = 8;
if (this->batch_size() != 0) {
::google::protobuf::internal::WireFormatLite::WriteInt32(8, this->batch_size(), output);
}
// int32 num_classes = 9;
if (this->num_classes() != 0) {
::google::protobuf::internal::WireFormatLite::WriteInt32(9, this->num_classes(), output);
}
// bool using_ctc = 10;
if (this->using_ctc() != 0) {
::google::protobuf::internal::WireFormatLite::WriteBool(10, this->using_ctc(), output);
}
// string image_input = 11;
if (this->image_input().size() > 0) {
::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->image_input().data(), static_cast<int>(this->image_input().length()),
::google::protobuf::internal::WireFormatLite::SERIALIZE,
"tesseract.TFNetworkModel.image_input");
::google::protobuf::internal::WireFormatLite::WriteStringMaybeAliased(11, this->image_input(),
output);
}
// string image_widths = 12;
if (this->image_widths().size() > 0) {
::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->image_widths().data(), static_cast<int>(this->image_widths().length()),
::google::protobuf::internal::WireFormatLite::SERIALIZE,
"tesseract.TFNetworkModel.image_widths");
::google::protobuf::internal::WireFormatLite::WriteStringMaybeAliased(12, this->image_widths(),
output);
}
// string image_heights = 13;
if (this->image_heights().size() > 0) {
::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->image_heights().data(), static_cast<int>(this->image_heights().length()),
::google::protobuf::internal::WireFormatLite::SERIALIZE,
"tesseract.TFNetworkModel.image_heights");
::google::protobuf::internal::WireFormatLite::WriteStringMaybeAliased(13, this->image_heights(),
output);
}
// string output_layer = 14;
if (this->output_layer().size() > 0) {
::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->output_layer().data(), static_cast<int>(this->output_layer().length()),
::google::protobuf::internal::WireFormatLite::SERIALIZE,
"tesseract.TFNetworkModel.output_layer");
::google::protobuf::internal::WireFormatLite::WriteStringMaybeAliased(14, this->output_layer(),
output);
}
if ((_internal_metadata_.have_unknown_fields() &&
::google::protobuf::internal::GetProto3PreserveUnknownsDefault())) {
::google::protobuf::internal::WireFormat::SerializeUnknownFields(
(::google::protobuf::internal::GetProto3PreserveUnknownsDefault()
? _internal_metadata_.unknown_fields()
: _internal_metadata_.default_instance()),
output);
}
// @@protoc_insertion_point(serialize_end:tesseract.TFNetworkModel)
}
::google::protobuf::uint8 *TFNetworkModel::InternalSerializeWithCachedSizesToArray(
bool deterministic, ::google::protobuf::uint8 *target) const {
(void)deterministic; // Unused
// @@protoc_insertion_point(serialize_to_array_start:tesseract.TFNetworkModel)
::google::protobuf::uint32 cached_has_bits = 0;
(void)cached_has_bits;
// .tensorflow.GraphDef graph = 1;
if (this->has_graph()) {
target = ::google::protobuf::internal::WireFormatLite::InternalWriteMessageToArray(
1, this->_internal_graph(), deterministic, target);
}
// int64 global_step = 2;
if (this->global_step() != 0) {
target = ::google::protobuf::internal::WireFormatLite::WriteInt64ToArray(2, this->global_step(),
target);
}
// string spec = 3;
if (this->spec().size() > 0) {
::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->spec().data(), static_cast<int>(this->spec().length()),
::google::protobuf::internal::WireFormatLite::SERIALIZE, "tesseract.TFNetworkModel.spec");
target =
::google::protobuf::internal::WireFormatLite::WriteStringToArray(3, this->spec(), target);
}
// int32 depth = 4;
if (this->depth() != 0) {
target =
::google::protobuf::internal::WireFormatLite::WriteInt32ToArray(4, this->depth(), target);
}
// int32 x_size = 5;
if (this->x_size() != 0) {
target =
::google::protobuf::internal::WireFormatLite::WriteInt32ToArray(5, this->x_size(), target);
}
// int32 y_size = 6;
if (this->y_size() != 0) {
target =
::google::protobuf::internal::WireFormatLite::WriteInt32ToArray(6, this->y_size(), target);
}
// int32 batch_size = 8;
if (this->batch_size() != 0) {
target = ::google::protobuf::internal::WireFormatLite::WriteInt32ToArray(8, this->batch_size(),
target);
}
// int32 num_classes = 9;
if (this->num_classes() != 0) {
target = ::google::protobuf::internal::WireFormatLite::WriteInt32ToArray(9, this->num_classes(),
target);
}
// bool using_ctc = 10;
if (this->using_ctc() != 0) {
target = ::google::protobuf::internal::WireFormatLite::WriteBoolToArray(10, this->using_ctc(),
target);
}
// string image_input = 11;
if (this->image_input().size() > 0) {
::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->image_input().data(), static_cast<int>(this->image_input().length()),
::google::protobuf::internal::WireFormatLite::SERIALIZE,
"tesseract.TFNetworkModel.image_input");
target = ::google::protobuf::internal::WireFormatLite::WriteStringToArray(
11, this->image_input(), target);
}
// string image_widths = 12;
if (this->image_widths().size() > 0) {
::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->image_widths().data(), static_cast<int>(this->image_widths().length()),
::google::protobuf::internal::WireFormatLite::SERIALIZE,
"tesseract.TFNetworkModel.image_widths");
target = ::google::protobuf::internal::WireFormatLite::WriteStringToArray(
12, this->image_widths(), target);
}
// string image_heights = 13;
if (this->image_heights().size() > 0) {
::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->image_heights().data(), static_cast<int>(this->image_heights().length()),
::google::protobuf::internal::WireFormatLite::SERIALIZE,
"tesseract.TFNetworkModel.image_heights");
target = ::google::protobuf::internal::WireFormatLite::WriteStringToArray(
13, this->image_heights(), target);
}
// string output_layer = 14;
if (this->output_layer().size() > 0) {
::google::protobuf::internal::WireFormatLite::VerifyUtf8String(
this->output_layer().data(), static_cast<int>(this->output_layer().length()),
::google::protobuf::internal::WireFormatLite::SERIALIZE,
"tesseract.TFNetworkModel.output_layer");
target = ::google::protobuf::internal::WireFormatLite::WriteStringToArray(
14, this->output_layer(), target);
}
if ((_internal_metadata_.have_unknown_fields() &&
::google::protobuf::internal::GetProto3PreserveUnknownsDefault())) {
target = ::google::protobuf::internal::WireFormat::SerializeUnknownFieldsToArray(
(::google::protobuf::internal::GetProto3PreserveUnknownsDefault()
? _internal_metadata_.unknown_fields()
: _internal_metadata_.default_instance()),
target);
}
// @@protoc_insertion_point(serialize_to_array_end:tesseract.TFNetworkModel)
return target;
}
size_t TFNetworkModel::ByteSizeLong() const {
// @@protoc_insertion_point(message_byte_size_start:tesseract.TFNetworkModel)
size_t total_size = 0;
if ((_internal_metadata_.have_unknown_fields() &&
::google::protobuf::internal::GetProto3PreserveUnknownsDefault())) {
total_size += ::google::protobuf::internal::WireFormat::ComputeUnknownFieldsSize(
(::google::protobuf::internal::GetProto3PreserveUnknownsDefault()
? _internal_metadata_.unknown_fields()
: _internal_metadata_.default_instance()));
}
// string spec = 3;
if (this->spec().size() > 0) {
total_size += 1 + ::google::protobuf::internal::WireFormatLite::StringSize(this->spec());
}
// string image_input = 11;
if (this->image_input().size() > 0) {
total_size += 1 + ::google::protobuf::internal::WireFormatLite::StringSize(this->image_input());
}
// string image_widths = 12;
if (this->image_widths().size() > 0) {
total_size +=
1 + ::google::protobuf::internal::WireFormatLite::StringSize(this->image_widths());
}
// string image_heights = 13;
if (this->image_heights().size() > 0) {
total_size +=
1 + ::google::protobuf::internal::WireFormatLite::StringSize(this->image_heights());
}
// string output_layer = 14;
if (this->output_layer().size() > 0) {
total_size +=
1 + ::google::protobuf::internal::WireFormatLite::StringSize(this->output_layer());
}
// .tensorflow.GraphDef graph = 1;
if (this->has_graph()) {
total_size += 1 + ::google::protobuf::internal::WireFormatLite::MessageSize(*graph_);
}
// int64 global_step = 2;
if (this->global_step() != 0) {
total_size += 1 + ::google::protobuf::internal::WireFormatLite::Int64Size(this->global_step());
}
// int32 depth = 4;
if (this->depth() != 0) {
total_size += 1 + ::google::protobuf::internal::WireFormatLite::Int32Size(this->depth());
}
// int32 x_size = 5;
if (this->x_size() != 0) {
total_size += 1 + ::google::protobuf::internal::WireFormatLite::Int32Size(this->x_size());
}
// int32 y_size = 6;
if (this->y_size() != 0) {
total_size += 1 + ::google::protobuf::internal::WireFormatLite::Int32Size(this->y_size());
}
// int32 batch_size = 8;
if (this->batch_size() != 0) {
total_size += 1 + ::google::protobuf::internal::WireFormatLite::Int32Size(this->batch_size());
}
// int32 num_classes = 9;
if (this->num_classes() != 0) {
total_size += 1 + ::google::protobuf::internal::WireFormatLite::Int32Size(this->num_classes());
}
// bool using_ctc = 10;
if (this->using_ctc() != 0) {
total_size += 1 + 1;
}
int cached_size = ::google::protobuf::internal::ToCachedSize(total_size);
SetCachedSize(cached_size);
return total_size;
}
void TFNetworkModel::MergeFrom(const ::google::protobuf::Message &from) {
// @@protoc_insertion_point(generalized_merge_from_start:tesseract.TFNetworkModel)
GOOGLE_DCHECK_NE(&from, this);
const TFNetworkModel *source =
::google::protobuf::internal::DynamicCastToGenerated<const TFNetworkModel>(&from);
if (source == NULL) {
// @@protoc_insertion_point(generalized_merge_from_cast_fail:tesseract.TFNetworkModel)
::google::protobuf::internal::ReflectionOps::Merge(from, this);
} else {
// @@protoc_insertion_point(generalized_merge_from_cast_success:tesseract.TFNetworkModel)
MergeFrom(*source);
}
}
void TFNetworkModel::MergeFrom(const TFNetworkModel &from) {
// @@protoc_insertion_point(class_specific_merge_from_start:tesseract.TFNetworkModel)
GOOGLE_DCHECK_NE(&from, this);
_internal_metadata_.MergeFrom(from._internal_metadata_);
::google::protobuf::uint32 cached_has_bits = 0;
(void)cached_has_bits;
if (from.spec().size() > 0) {
spec_.AssignWithDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
from.spec_);
}
if (from.image_input().size() > 0) {
image_input_.AssignWithDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
from.image_input_);
}
if (from.image_widths().size() > 0) {
image_widths_.AssignWithDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
from.image_widths_);
}
if (from.image_heights().size() > 0) {
image_heights_.AssignWithDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
from.image_heights_);
}
if (from.output_layer().size() > 0) {
output_layer_.AssignWithDefault(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
from.output_layer_);
}
if (from.has_graph()) {
mutable_graph()->::tensorflow::GraphDef::MergeFrom(from.graph());
}
if (from.global_step() != 0) {
set_global_step(from.global_step());
}
if (from.depth() != 0) {
set_depth(from.depth());
}
if (from.x_size() != 0) {
set_x_size(from.x_size());
}
if (from.y_size() != 0) {
set_y_size(from.y_size());
}
if (from.batch_size() != 0) {
set_batch_size(from.batch_size());
}
if (from.num_classes() != 0) {
set_num_classes(from.num_classes());
}
if (from.using_ctc() != 0) {
set_using_ctc(from.using_ctc());
}
}
void TFNetworkModel::CopyFrom(const ::google::protobuf::Message &from) {
// @@protoc_insertion_point(generalized_copy_from_start:tesseract.TFNetworkModel)
if (&from == this)
return;
Clear();
MergeFrom(from);
}
void TFNetworkModel::CopyFrom(const TFNetworkModel &from) {
// @@protoc_insertion_point(class_specific_copy_from_start:tesseract.TFNetworkModel)
if (&from == this)
return;
Clear();
MergeFrom(from);
}
bool TFNetworkModel::IsInitialized() const {
return true;
}
void TFNetworkModel::Swap(TFNetworkModel *other) {
if (other == this)
return;
InternalSwap(other);
}
void TFNetworkModel::InternalSwap(TFNetworkModel *other) {
using std::swap;
spec_.Swap(&other->spec_, &::google::protobuf::internal::GetEmptyStringAlreadyInited(),
GetArenaNoVirtual());
image_input_.Swap(&other->image_input_,
&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
GetArenaNoVirtual());
image_widths_.Swap(&other->image_widths_,
&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
GetArenaNoVirtual());
image_heights_.Swap(&other->image_heights_,
&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
GetArenaNoVirtual());
output_layer_.Swap(&other->output_layer_,
&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
GetArenaNoVirtual());
swap(graph_, other->graph_);
swap(global_step_, other->global_step_);
swap(depth_, other->depth_);
swap(x_size_, other->x_size_);
swap(y_size_, other->y_size_);
swap(batch_size_, other->batch_size_);
swap(num_classes_, other->num_classes_);
swap(using_ctc_, other->using_ctc_);
_internal_metadata_.Swap(&other->_internal_metadata_);
}
::google::protobuf::Metadata TFNetworkModel::GetMetadata() const {
protobuf_tfnetwork_2eproto::protobuf_AssignDescriptorsOnce();
return ::protobuf_tfnetwork_2eproto::file_level_metadata[kIndexInFileMessages];
}
// @@protoc_insertion_point(namespace_scope)
} // namespace tesseract
namespace google {
namespace protobuf {
template <>
GOOGLE_PROTOBUF_ATTRIBUTE_NOINLINE ::tesseract::TFNetworkModel *
Arena::CreateMaybeMessage<::tesseract::TFNetworkModel>(Arena *arena) {
return Arena::CreateInternal<::tesseract::TFNetworkModel>(arena);
}
} // namespace protobuf
} // namespace google
// @@protoc_insertion_point(global_scope)

703
src/lstm/tfnetwork.pb.h
View File

@ -1,703 +0,0 @@
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tfnetwork.proto
#ifndef PROTOBUF_INCLUDED_tfnetwork_2eproto
#define PROTOBUF_INCLUDED_tfnetwork_2eproto
#include <string>
#include <google/protobuf/stubs/common.h>
#if GOOGLE_PROTOBUF_VERSION < 3006001
# error This file was generated by a newer version of protoc which is
# error incompatible with your Protocol Buffer headers. Please update
# error your headers.
#endif
#if 3006001 < GOOGLE_PROTOBUF_MIN_PROTOC_VERSION
# error This file was generated by an older version of protoc which is
# error incompatible with your Protocol Buffer headers. Please
# error regenerate this file with a newer version of protoc.
#endif
#include <google/protobuf/arena.h>
#include <google/protobuf/arenastring.h>
#include <google/protobuf/extension_set.h> // IWYU pragma: export
#include <google/protobuf/generated_message_table_driven.h>
#include <google/protobuf/generated_message_util.h>
#include <google/protobuf/inlined_string_field.h>
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/message.h>
#include <google/protobuf/metadata.h>
#include <google/protobuf/repeated_field.h> // IWYU pragma: export
#include <google/protobuf/unknown_field_set.h>
#include "tensorflow/core/framework/graph.pb.h"
// @@protoc_insertion_point(includes)
#define PROTOBUF_INTERNAL_EXPORT_protobuf_tfnetwork_2eproto
namespace protobuf_tfnetwork_2eproto {
// Internal implementation detail -- do not use these members.
struct TableStruct {
static const ::google::protobuf::internal::ParseTableField entries[];
static const ::google::protobuf::internal::AuxillaryParseTableField aux[];
static const ::google::protobuf::internal::ParseTable schema[1];
static const ::google::protobuf::internal::FieldMetadata field_metadata[];
static const ::google::protobuf::internal::SerializationTable serialization_table[];
static const ::google::protobuf::uint32 offsets[];
};
void AddDescriptors();
} // namespace protobuf_tfnetwork_2eproto
namespace tesseract {
class TFNetworkModel;
class TFNetworkModelDefaultTypeInternal;
extern TFNetworkModelDefaultTypeInternal _TFNetworkModel_default_instance_;
} // namespace tesseract
namespace google {
namespace protobuf {
template <>
::tesseract::TFNetworkModel *Arena::CreateMaybeMessage<::tesseract::TFNetworkModel>(Arena *);
} // namespace protobuf
} // namespace google
namespace tesseract {
// ===================================================================
class TFNetworkModel
: public ::google::protobuf::
Message /* @@protoc_insertion_point(class_definition:tesseract.TFNetworkModel)
*/
{
public:
TFNetworkModel();
virtual ~TFNetworkModel();
TFNetworkModel(const TFNetworkModel &from);
inline TFNetworkModel &operator=(const TFNetworkModel &from) {
CopyFrom(from);
return *this;
}
#if LANG_CXX11
TFNetworkModel(TFNetworkModel &&from) noexcept : TFNetworkModel() {
*this = ::std::move(from);
}
inline TFNetworkModel &operator=(TFNetworkModel &&from) noexcept {
if (GetArenaNoVirtual() == from.GetArenaNoVirtual()) {
if (this != &from)
InternalSwap(&from);
} else {
CopyFrom(from);
}
return *this;
}
#endif
static const ::google::protobuf::Descriptor *descriptor();
static const TFNetworkModel &default_instance();
static void InitAsDefaultInstance(); // FOR INTERNAL USE ONLY
static inline const TFNetworkModel *internal_default_instance() {
return reinterpret_cast<const TFNetworkModel *>(&_TFNetworkModel_default_instance_);
}
static constexpr int kIndexInFileMessages = 0;
void Swap(TFNetworkModel *other);
friend void swap(TFNetworkModel &a, TFNetworkModel &b) {
a.Swap(&b);
}
// implements Message ----------------------------------------------
inline TFNetworkModel *New() const final {
return CreateMaybeMessage<TFNetworkModel>(NULL);
}
TFNetworkModel *New(::google::protobuf::Arena *arena) const final {
return CreateMaybeMessage<TFNetworkModel>(arena);
}
void CopyFrom(const ::google::protobuf::Message &from) final;
void MergeFrom(const ::google::protobuf::Message &from) final;
void CopyFrom(const TFNetworkModel &from);
void MergeFrom(const TFNetworkModel &from);
void Clear() final;
bool IsInitialized() const final;
size_t ByteSizeLong() const final;
bool MergePartialFromCodedStream(::google::protobuf::io::CodedInputStream *input) final;
void SerializeWithCachedSizes(::google::protobuf::io::CodedOutputStream *output) const final;
::google::protobuf::uint8 *InternalSerializeWithCachedSizesToArray(
bool deterministic, ::google::protobuf::uint8 *target) const final;
int GetCachedSize() const final {
return _cached_size_.Get();
}
private:
void SharedCtor();
void SharedDtor();
void SetCachedSize(int size) const final;
void InternalSwap(TFNetworkModel *other);
private:
inline ::google::protobuf::Arena *GetArenaNoVirtual() const {
return NULL;
}
inline void *MaybeArenaPtr() const {
return NULL;
}
public:
::google::protobuf::Metadata GetMetadata() const final;
// nested types ----------------------------------------------------
// accessors -------------------------------------------------------
// string spec = 3;
void clear_spec();
static const int kSpecFieldNumber = 3;
const ::std::string &spec() const;
void set_spec(const ::std::string &value);
#if LANG_CXX11
void set_spec(::std::string &&value);
#endif
void set_spec(const char *value);
void set_spec(const char *value, size_t size);
::std::string *mutable_spec();
::std::string *release_spec();
void set_allocated_spec(::std::string *spec);
// string image_input = 11;
void clear_image_input();
static const int kImageInputFieldNumber = 11;
const ::std::string &image_input() const;
void set_image_input(const ::std::string &value);
#if LANG_CXX11
void set_image_input(::std::string &&value);
#endif
void set_image_input(const char *value);
void set_image_input(const char *value, size_t size);
::std::string *mutable_image_input();
::std::string *release_image_input();
void set_allocated_image_input(::std::string *image_input);
// string image_widths = 12;
void clear_image_widths();
static const int kImageWidthsFieldNumber = 12;
const ::std::string &image_widths() const;
void set_image_widths(const ::std::string &value);
#if LANG_CXX11
void set_image_widths(::std::string &&value);
#endif
void set_image_widths(const char *value);
void set_image_widths(const char *value, size_t size);
::std::string *mutable_image_widths();
::std::string *release_image_widths();
void set_allocated_image_widths(::std::string *image_widths);
// string image_heights = 13;
void clear_image_heights();
static const int kImageHeightsFieldNumber = 13;
const ::std::string &image_heights() const;
void set_image_heights(const ::std::string &value);
#if LANG_CXX11
void set_image_heights(::std::string &&value);
#endif
void set_image_heights(const char *value);
void set_image_heights(const char *value, size_t size);
::std::string *mutable_image_heights();
::std::string *release_image_heights();
void set_allocated_image_heights(::std::string *image_heights);
// string output_layer = 14;
void clear_output_layer();
static const int kOutputLayerFieldNumber = 14;
const ::std::string &output_layer() const;
void set_output_layer(const ::std::string &value);
#if LANG_CXX11
void set_output_layer(::std::string &&value);
#endif
void set_output_layer(const char *value);
void set_output_layer(const char *value, size_t size);
::std::string *mutable_output_layer();
::std::string *release_output_layer();
void set_allocated_output_layer(::std::string *output_layer);
// .tensorflow.GraphDef graph = 1;
bool has_graph() const;
void clear_graph();
static const int kGraphFieldNumber = 1;
private:
const ::tensorflow::GraphDef &_internal_graph() const;
public:
const ::tensorflow::GraphDef &graph() const;
::tensorflow::GraphDef *release_graph();
::tensorflow::GraphDef *mutable_graph();
void set_allocated_graph(::tensorflow::GraphDef *graph);
// int64 global_step = 2;
void clear_global_step();
static const int kGlobalStepFieldNumber = 2;
::google::protobuf::int64 global_step() const;
void set_global_step(::google::protobuf::int64 value);
// int32 depth = 4;
void clear_depth();
static const int kDepthFieldNumber = 4;
::google::protobuf::int32 depth() const;
void set_depth(::google::protobuf::int32 value);
// int32 x_size = 5;
void clear_x_size();
static const int kXSizeFieldNumber = 5;
::google::protobuf::int32 x_size() const;
void set_x_size(::google::protobuf::int32 value);
// int32 y_size = 6;
void clear_y_size();
static const int kYSizeFieldNumber = 6;
::google::protobuf::int32 y_size() const;
void set_y_size(::google::protobuf::int32 value);
// int32 batch_size = 8;
void clear_batch_size();
static const int kBatchSizeFieldNumber = 8;
::google::protobuf::int32 batch_size() const;
void set_batch_size(::google::protobuf::int32 value);
// int32 num_classes = 9;
void clear_num_classes();
static const int kNumClassesFieldNumber = 9;
::google::protobuf::int32 num_classes() const;
void set_num_classes(::google::protobuf::int32 value);
// bool using_ctc = 10;
void clear_using_ctc();
static const int kUsingCtcFieldNumber = 10;
bool using_ctc() const;
void set_using_ctc(bool value);
// @@protoc_insertion_point(class_scope:tesseract.TFNetworkModel)
private:
::google::protobuf::internal::InternalMetadataWithArena _internal_metadata_;
::google::protobuf::internal::ArenaStringPtr spec_;
::google::protobuf::internal::ArenaStringPtr image_input_;
::google::protobuf::internal::ArenaStringPtr image_widths_;
::google::protobuf::internal::ArenaStringPtr image_heights_;
::google::protobuf::internal::ArenaStringPtr output_layer_;
::tensorflow::GraphDef *graph_;
::google::protobuf::int64 global_step_;
::google::protobuf::int32 depth_;
::google::protobuf::int32 x_size_;
::google::protobuf::int32 y_size_;
::google::protobuf::int32 batch_size_;
::google::protobuf::int32 num_classes_;
bool using_ctc_;
mutable ::google::protobuf::internal::CachedSize _cached_size_;
friend struct ::protobuf_tfnetwork_2eproto::TableStruct;
};
// ===================================================================
// ===================================================================
#ifdef __GNUC__
# pragma GCC diagnostic push
# pragma GCC diagnostic ignored "-Wstrict-aliasing"
#endif // __GNUC__
// TFNetworkModel
// .tensorflow.GraphDef graph = 1;
inline bool TFNetworkModel::has_graph() const {
return this != internal_default_instance() && graph_ != NULL;
}
inline const ::tensorflow::GraphDef &TFNetworkModel::_internal_graph() const {
return *graph_;
}
inline const ::tensorflow::GraphDef &TFNetworkModel::graph() const {
const ::tensorflow::GraphDef *p = graph_;
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.graph)
return p != NULL ? *p
: *reinterpret_cast<const ::tensorflow::GraphDef *>(
&::tensorflow::_GraphDef_default_instance_);
}
inline ::tensorflow::GraphDef *TFNetworkModel::release_graph() {
// @@protoc_insertion_point(field_release:tesseract.TFNetworkModel.graph)
::tensorflow::GraphDef *temp = graph_;
graph_ = NULL;
return temp;
}
inline ::tensorflow::GraphDef *TFNetworkModel::mutable_graph() {
if (graph_ == NULL) {
auto *p = CreateMaybeMessage<::tensorflow::GraphDef>(GetArenaNoVirtual());
graph_ = p;
}
// @@protoc_insertion_point(field_mutable:tesseract.TFNetworkModel.graph)
return graph_;
}
inline void TFNetworkModel::set_allocated_graph(::tensorflow::GraphDef *graph) {
::google::protobuf::Arena *message_arena = GetArenaNoVirtual();
if (message_arena == NULL) {
delete reinterpret_cast<::google::protobuf::MessageLite *>(graph_);
}
if (graph) {
::google::protobuf::Arena *submessage_arena =
reinterpret_cast<::google::protobuf::MessageLite *>(graph)->GetArena();
if (message_arena != submessage_arena) {
graph = ::google::protobuf::internal::GetOwnedMessage(message_arena, graph, submessage_arena);
}
} else {
}
graph_ = graph;
// @@protoc_insertion_point(field_set_allocated:tesseract.TFNetworkModel.graph)
}
// int64 global_step = 2;
inline void TFNetworkModel::clear_global_step() {
global_step_ = GOOGLE_LONGLONG(0);
}
inline ::google::protobuf::int64 TFNetworkModel::global_step() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.global_step)
return global_step_;
}
inline void TFNetworkModel::set_global_step(::google::protobuf::int64 value) {
global_step_ = value;
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.global_step)
}
// string spec = 3;
inline void TFNetworkModel::clear_spec() {
spec_.ClearToEmptyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline const ::std::string &TFNetworkModel::spec() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.spec)
return spec_.GetNoArena();
}
inline void TFNetworkModel::set_spec(const ::std::string &value) {
spec_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(), value);
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.spec)
}
#if LANG_CXX11
inline void TFNetworkModel::set_spec(::std::string &&value) {
spec_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::move(value));
// @@protoc_insertion_point(field_set_rvalue:tesseract.TFNetworkModel.spec)
}
#endif
inline void TFNetworkModel::set_spec(const char *value) {
GOOGLE_DCHECK(value != NULL);
spec_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::string(value));
// @@protoc_insertion_point(field_set_char:tesseract.TFNetworkModel.spec)
}
inline void TFNetworkModel::set_spec(const char *value, size_t size) {
spec_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::string(reinterpret_cast<const char *>(value), size));
// @@protoc_insertion_point(field_set_pointer:tesseract.TFNetworkModel.spec)
}
inline ::std::string *TFNetworkModel::mutable_spec() {
// @@protoc_insertion_point(field_mutable:tesseract.TFNetworkModel.spec)
return spec_.MutableNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline ::std::string *TFNetworkModel::release_spec() {
// @@protoc_insertion_point(field_release:tesseract.TFNetworkModel.spec)
return spec_.ReleaseNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline void TFNetworkModel::set_allocated_spec(::std::string *spec) {
if (spec != NULL) {
} else {
}
spec_.SetAllocatedNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(), spec);
// @@protoc_insertion_point(field_set_allocated:tesseract.TFNetworkModel.spec)
}
// int32 depth = 4;
inline void TFNetworkModel::clear_depth() {
depth_ = 0;
}
inline ::google::protobuf::int32 TFNetworkModel::depth() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.depth)
return depth_;
}
inline void TFNetworkModel::set_depth(::google::protobuf::int32 value) {
depth_ = value;
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.depth)
}
// int32 x_size = 5;
inline void TFNetworkModel::clear_x_size() {
x_size_ = 0;
}
inline ::google::protobuf::int32 TFNetworkModel::x_size() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.x_size)
return x_size_;
}
inline void TFNetworkModel::set_x_size(::google::protobuf::int32 value) {
x_size_ = value;
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.x_size)
}
// int32 y_size = 6;
inline void TFNetworkModel::clear_y_size() {
y_size_ = 0;
}
inline ::google::protobuf::int32 TFNetworkModel::y_size() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.y_size)
return y_size_;
}
inline void TFNetworkModel::set_y_size(::google::protobuf::int32 value) {
y_size_ = value;
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.y_size)
}
// int32 batch_size = 8;
inline void TFNetworkModel::clear_batch_size() {
batch_size_ = 0;
}
inline ::google::protobuf::int32 TFNetworkModel::batch_size() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.batch_size)
return batch_size_;
}
inline void TFNetworkModel::set_batch_size(::google::protobuf::int32 value) {
batch_size_ = value;
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.batch_size)
}
// int32 num_classes = 9;
inline void TFNetworkModel::clear_num_classes() {
num_classes_ = 0;
}
inline ::google::protobuf::int32 TFNetworkModel::num_classes() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.num_classes)
return num_classes_;
}
inline void TFNetworkModel::set_num_classes(::google::protobuf::int32 value) {
num_classes_ = value;
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.num_classes)
}
// bool using_ctc = 10;
inline void TFNetworkModel::clear_using_ctc() {
using_ctc_ = false;
}
inline bool TFNetworkModel::using_ctc() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.using_ctc)
return using_ctc_;
}
inline void TFNetworkModel::set_using_ctc(bool value) {
using_ctc_ = value;
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.using_ctc)
}
// string image_input = 11;
inline void TFNetworkModel::clear_image_input() {
image_input_.ClearToEmptyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline const ::std::string &TFNetworkModel::image_input() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.image_input)
return image_input_.GetNoArena();
}
inline void TFNetworkModel::set_image_input(const ::std::string &value) {
image_input_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(), value);
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.image_input)
}
#if LANG_CXX11
inline void TFNetworkModel::set_image_input(::std::string &&value) {
image_input_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::move(value));
// @@protoc_insertion_point(field_set_rvalue:tesseract.TFNetworkModel.image_input)
}
#endif
inline void TFNetworkModel::set_image_input(const char *value) {
GOOGLE_DCHECK(value != NULL);
image_input_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::string(value));
// @@protoc_insertion_point(field_set_char:tesseract.TFNetworkModel.image_input)
}
inline void TFNetworkModel::set_image_input(const char *value, size_t size) {
image_input_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::string(reinterpret_cast<const char *>(value), size));
// @@protoc_insertion_point(field_set_pointer:tesseract.TFNetworkModel.image_input)
}
inline ::std::string *TFNetworkModel::mutable_image_input() {
// @@protoc_insertion_point(field_mutable:tesseract.TFNetworkModel.image_input)
return image_input_.MutableNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline ::std::string *TFNetworkModel::release_image_input() {
// @@protoc_insertion_point(field_release:tesseract.TFNetworkModel.image_input)
return image_input_.ReleaseNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline void TFNetworkModel::set_allocated_image_input(::std::string *image_input) {
if (image_input != NULL) {
} else {
}
image_input_.SetAllocatedNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
image_input);
// @@protoc_insertion_point(field_set_allocated:tesseract.TFNetworkModel.image_input)
}
// string image_widths = 12;
inline void TFNetworkModel::clear_image_widths() {
image_widths_.ClearToEmptyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline const ::std::string &TFNetworkModel::image_widths() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.image_widths)
return image_widths_.GetNoArena();
}
inline void TFNetworkModel::set_image_widths(const ::std::string &value) {
image_widths_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(), value);
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.image_widths)
}
#if LANG_CXX11
inline void TFNetworkModel::set_image_widths(::std::string &&value) {
image_widths_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::move(value));
// @@protoc_insertion_point(field_set_rvalue:tesseract.TFNetworkModel.image_widths)
}
#endif
inline void TFNetworkModel::set_image_widths(const char *value) {
GOOGLE_DCHECK(value != NULL);
image_widths_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::string(value));
// @@protoc_insertion_point(field_set_char:tesseract.TFNetworkModel.image_widths)
}
inline void TFNetworkModel::set_image_widths(const char *value, size_t size) {
image_widths_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::string(reinterpret_cast<const char *>(value), size));
// @@protoc_insertion_point(field_set_pointer:tesseract.TFNetworkModel.image_widths)
}
inline ::std::string *TFNetworkModel::mutable_image_widths() {
// @@protoc_insertion_point(field_mutable:tesseract.TFNetworkModel.image_widths)
return image_widths_.MutableNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline ::std::string *TFNetworkModel::release_image_widths() {
// @@protoc_insertion_point(field_release:tesseract.TFNetworkModel.image_widths)
return image_widths_.ReleaseNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline void TFNetworkModel::set_allocated_image_widths(::std::string *image_widths) {
if (image_widths != NULL) {
} else {
}
image_widths_.SetAllocatedNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
image_widths);
// @@protoc_insertion_point(field_set_allocated:tesseract.TFNetworkModel.image_widths)
}
// string image_heights = 13;
inline void TFNetworkModel::clear_image_heights() {
image_heights_.ClearToEmptyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline const ::std::string &TFNetworkModel::image_heights() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.image_heights)
return image_heights_.GetNoArena();
}
inline void TFNetworkModel::set_image_heights(const ::std::string &value) {
image_heights_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(), value);
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.image_heights)
}
#if LANG_CXX11
inline void TFNetworkModel::set_image_heights(::std::string &&value) {
image_heights_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::move(value));
// @@protoc_insertion_point(field_set_rvalue:tesseract.TFNetworkModel.image_heights)
}
#endif
inline void TFNetworkModel::set_image_heights(const char *value) {
GOOGLE_DCHECK(value != NULL);
image_heights_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::string(value));
// @@protoc_insertion_point(field_set_char:tesseract.TFNetworkModel.image_heights)
}
inline void TFNetworkModel::set_image_heights(const char *value, size_t size) {
image_heights_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::string(reinterpret_cast<const char *>(value), size));
// @@protoc_insertion_point(field_set_pointer:tesseract.TFNetworkModel.image_heights)
}
inline ::std::string *TFNetworkModel::mutable_image_heights() {
// @@protoc_insertion_point(field_mutable:tesseract.TFNetworkModel.image_heights)
return image_heights_.MutableNoArena(
&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline ::std::string *TFNetworkModel::release_image_heights() {
// @@protoc_insertion_point(field_release:tesseract.TFNetworkModel.image_heights)
return image_heights_.ReleaseNoArena(
&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline void TFNetworkModel::set_allocated_image_heights(::std::string *image_heights) {
if (image_heights != NULL) {
} else {
}
image_heights_.SetAllocatedNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
image_heights);
// @@protoc_insertion_point(field_set_allocated:tesseract.TFNetworkModel.image_heights)
}
// string output_layer = 14;
inline void TFNetworkModel::clear_output_layer() {
output_layer_.ClearToEmptyNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline const ::std::string &TFNetworkModel::output_layer() const {
// @@protoc_insertion_point(field_get:tesseract.TFNetworkModel.output_layer)
return output_layer_.GetNoArena();
}
inline void TFNetworkModel::set_output_layer(const ::std::string &value) {
output_layer_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(), value);
// @@protoc_insertion_point(field_set:tesseract.TFNetworkModel.output_layer)
}
#if LANG_CXX11
inline void TFNetworkModel::set_output_layer(::std::string &&value) {
output_layer_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::move(value));
// @@protoc_insertion_point(field_set_rvalue:tesseract.TFNetworkModel.output_layer)
}
#endif
inline void TFNetworkModel::set_output_layer(const char *value) {
GOOGLE_DCHECK(value != NULL);
output_layer_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::string(value));
// @@protoc_insertion_point(field_set_char:tesseract.TFNetworkModel.output_layer)
}
inline void TFNetworkModel::set_output_layer(const char *value, size_t size) {
output_layer_.SetNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
::std::string(reinterpret_cast<const char *>(value), size));
// @@protoc_insertion_point(field_set_pointer:tesseract.TFNetworkModel.output_layer)
}
inline ::std::string *TFNetworkModel::mutable_output_layer() {
// @@protoc_insertion_point(field_mutable:tesseract.TFNetworkModel.output_layer)
return output_layer_.MutableNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline ::std::string *TFNetworkModel::release_output_layer() {
// @@protoc_insertion_point(field_release:tesseract.TFNetworkModel.output_layer)
return output_layer_.ReleaseNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited());
}
inline void TFNetworkModel::set_allocated_output_layer(::std::string *output_layer) {
if (output_layer != NULL) {
} else {
}
output_layer_.SetAllocatedNoArena(&::google::protobuf::internal::GetEmptyStringAlreadyInited(),
output_layer);
// @@protoc_insertion_point(field_set_allocated:tesseract.TFNetworkModel.output_layer)
}
#ifdef __GNUC__
# pragma GCC diagnostic pop
#endif // __GNUC__
// @@protoc_insertion_point(namespace_scope)
} // namespace tesseract
// @@protoc_insertion_point(global_scope)
#endif // PROTOBUF_INCLUDED_tfnetwork_2eproto

74
src/lstm/tfnetwork.proto
View File

@ -1,74 +0,0 @@
// Protocol description for Tesseract
// Compile this file with the Protocol Compiler protoc to generate
// the files tfnetwork.pb.cc and tfnetwork.pb.h.
// This requires the protocol descriptions for TensorFlow
// (included in the TensorFlow sources).
// With TensorFlow sources installed in /usr/src/tensorflow/tensorflow-1.10.1,
// this command was used on Debian to generate the files:
// protoc --cpp_out=$PWD --proto_path=/usr/src/tensorflow/tensorflow-1.10.1 \
// --proto_path=$PWD src/lstm/tfnetwork.proto
syntax = "proto3";
package tesseract;
import "tensorflow/core/framework/graph.proto";
// This proto is the interface between a python TF graph builder/trainer and
// the C++ world. The writer of this proto must provide fields as documented
// by the comments below.
// The graph must have a placeholder for NetworkIO, Widths and Heights. The
// following python code creates the appropriate placeholders:
//
// input_layer = tf.placeholder(tf.float32,
// shape=[batch_size, xsize, ysize, depth_dim],
// name='NetworkIO')
// widths = tf.placeholder(tf.int32, shape=[batch_size], name='Widths')
// heights = tf.placeholder(tf.int32, shape=[batch_size], name='Heights')
// # Flip x and y to the TF convention.
// input_layer = tf.transpose(input_layer, [0, 2, 1, 3])
//
// The widths and heights will be set to indicate the post-scaling size of the
// input image(s).
// For now batch_size is ignored and set to 1.
// The graph should return a 2-dimensional float32 tensor called 'softmax' of
// shape [sequence_length, num_classes], where sequence_length is allowed to
// be variable, given by the tensor itself.
// TODO(rays) determine whether it is worth providing for batch_size >1 and if
// so, how.
message TFNetworkModel {
// The TF graph definition. Required.
tensorflow.GraphDef graph = 1;
// The training index. Required to be > 0.
int64 global_step = 2;
// The original network definition for reference. Optional
string spec = 3;
// Input tensor parameters.
// Values per pixel. Required to be 1 or 3. Inputs assumed to be float32.
int32 depth = 4;
// Image size. Required. Zero implies flexible sizes, fixed if non-zero.
// If x_size > 0, images will be cropped/padded to the given size, after
// any scaling required by the y_size.
// If y_size > 0, images will be scaled isotropically to the given height.
int32 x_size = 5;
int32 y_size = 6;
// Number of images in a batch. Optional.
int32 batch_size = 8;
// Output tensor parameters.
// Number of output classes. Required to match the depth of the softmax.
int32 num_classes = 9;
// True if this network needs CTC-like decoding, dropping duplicated labels.
// The decoder always drops the null character.
bool using_ctc = 10;
// Name of input image tensor.
string image_input = 11;
// Name of image height and width tensors.
string image_widths = 12;
string image_heights = 13;
// Name of output (softmax) tensor.
string output_layer = 14;
}

20
src/training/unicharset/lstmtrainer.cpp
View File

@ -36,9 +36,6 @@
#include "networkbuilder.h" #include "networkbuilder.h"
#include "ratngs.h" #include "ratngs.h"
#include "recodebeam.h" #include "recodebeam.h"
#ifdef INCLUDE_TENSORFLOW
# include "tfnetwork.h"
#endif
#include "tprintf.h" #include "tprintf.h"
namespace tesseract { namespace tesseract {
@ -186,23 +183,6 @@ bool LSTMTrainer::InitNetwork(const char *network_spec, int append_index,
return true; return true;
} }
// Initializes a trainer from a serialized TFNetworkModel proto.
// Returns the global step of TensorFlow graph or 0 if failed.
#ifdef INCLUDE_TENSORFLOW
int LSTMTrainer::InitTensorFlowNetwork(const std::string &tf_proto) {
delete network_;
TFNetwork *tf_net = new TFNetwork("TensorFlow");
training_iteration_ = tf_net->InitFromProtoStr(tf_proto);
if (training_iteration_ == 0) {
tprintf("InitFromProtoStr failed!!\n");
return 0;
}
network_ = tf_net;
ASSERT_HOST(recoder_.code_range() == tf_net->num_classes());
return training_iteration_;
}
#endif
// Resets all the iteration counters for fine tuning or traininng a head, // Resets all the iteration counters for fine tuning or traininng a head,
// where we want the error reporting to reset. // where we want the error reporting to reset.
void LSTMTrainer::InitIterations() { void LSTMTrainer::InitIterations() {

4
src/training/unicharset/lstmtrainer.h
View File

@ -119,10 +119,6 @@ public:
bool InitNetwork(const char *network_spec, int append_index, int net_flags, bool InitNetwork(const char *network_spec, int append_index, int net_flags,
float weight_range, float learning_rate, float momentum, float weight_range, float learning_rate, float momentum,
float adam_beta); float adam_beta);
// Initializes a trainer from a serialized TFNetworkModel proto.
// Returns the global step of TensorFlow graph or 0 if failed.
// Building a compatible TF graph: See tfnetwork.proto.
int InitTensorFlowNetwork(const std::string &tf_proto);
// Resets all the iteration counters for fine tuning or training a head, // Resets all the iteration counters for fine tuning or training a head,
// where we want the error reporting to reset. // where we want the error reporting to reset.
void InitIterations(); void InitIterations();

2
tesseract.pc.cmake
View File

@ -8,6 +8,6 @@ Description: An OCR Engine that was developed at HP Labs (1985-1995) and Google
URL: https://github.com/tesseract-ocr/tesseract URL: https://github.com/tesseract-ocr/tesseract
Version: @tesseract_VERSION@ Version: @tesseract_VERSION@
Requires.private: lept Requires.private: lept
Libs: -L${libdir} -l@tesseract_OUTPUT_NAME@ @libarchive_LIBS@ @libcurl_LIBS@ @TENSORFLOW_LIBS@ Libs: -L${libdir} -l@tesseract_OUTPUT_NAME@ @libarchive_LIBS@ @libcurl_LIBS@
Libs.private: Libs.private:
Cflags: -I${includedir} Cflags: -I${includedir}

2
tesseract.pc.in
View File

@ -11,6 +11,6 @@ Description: An OCR Engine that was developed at HP Labs (1985-1995) and Google
URL: https://github.com/tesseract-ocr/tesseract URL: https://github.com/tesseract-ocr/tesseract
Version: @VERSION@ Version: @VERSION@
Requires.private: lept Requires.private: lept
Libs: -L${libdir} -ltesseract @libarchive_LIBS@ @libcurl_LIBS@ @TENSORFLOW_LIBS@ Libs: -L${libdir} -ltesseract @libarchive_LIBS@ @libcurl_LIBS@
Libs.private: -lpthread Libs.private: -lpthread
Cflags: -I${includedir} Cflags: -I${includedir}

14
unittest/third_party/utf/utfdef.h vendored
View File

@ -1,14 +0,0 @@
#define uchar _utfuchar
#define ushort _utfushort
#define uint _utfuint
#define ulong _utfulong
#define vlong _utfvlong
#define uvlong _utfuvlong
typedef unsigned char uchar;
typedef unsigned short ushort;
typedef unsigned int uint;
typedef unsigned long ulong;
#define nelem(x) (sizeof(x) / sizeof((x)[0]))
#define nil ((void *)0)