tesseract  5.0.0
series.cpp
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1 // File: series.cpp
3 // Description: Runs networks in series on the same input.
4 // Author: Ray Smith
5 //
6 // (C) Copyright 2013, Google Inc.
7 // Licensed under the Apache License, Version 2.0 (the "License");
8 // you may not use this file except in compliance with the License.
9 // You may obtain a copy of the License at
10 // http://www.apache.org/licenses/LICENSE-2.0
11 // Unless required by applicable law or agreed to in writing, software
12 // distributed under the License is distributed on an "AS IS" BASIS,
13 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 // See the License for the specific language governing permissions and
15 // limitations under the License.
17 
18 #include "series.h"
19 
20 #include "fullyconnected.h"
21 #include "networkscratch.h"
22 #include "scrollview.h"
23 #include "tprintf.h"
24 
25 namespace tesseract {
26 
27 // ni_ and no_ will be set by AddToStack.
28 Series::Series(const char *name) : Plumbing(name) {
29  type_ = NT_SERIES;
30 }
31 
32 // Returns the shape output from the network given an input shape (which may
33 // be partially unknown ie zero).
34 StaticShape Series::OutputShape(const StaticShape &input_shape) const {
35  StaticShape result(input_shape);
36  int stack_size = stack_.size();
37  for (int i = 0; i < stack_size; ++i) {
38  result = stack_[i]->OutputShape(result);
39  }
40  return result;
41 }
42 
43 // Sets up the network for training. Initializes weights using weights of
44 // scale `range` picked according to the random number generator `randomizer`.
45 // Note that series has its own implementation just for debug purposes.
46 int Series::InitWeights(float range, TRand *randomizer) {
47  num_weights_ = 0;
48  tprintf("Num outputs,weights in Series:\n");
49  for (auto &i : stack_) {
50  int weights = i->InitWeights(range, randomizer);
51  tprintf(" %s:%d, %d\n", i->spec().c_str(), i->NumOutputs(), weights);
52  num_weights_ += weights;
53  }
54  tprintf("Total weights = %d\n", num_weights_);
55  return num_weights_;
56 }
57 
58 // Recursively searches the network for softmaxes with old_no outputs,
59 // and remaps their outputs according to code_map. See network.h for details.
60 int Series::RemapOutputs(int old_no, const std::vector<int> &code_map) {
61  num_weights_ = 0;
62  tprintf("Num (Extended) outputs,weights in Series:\n");
63  for (auto &i : stack_) {
64  int weights = i->RemapOutputs(old_no, code_map);
65  tprintf(" %s:%d, %d\n", i->spec().c_str(), i->NumOutputs(), weights);
66  num_weights_ += weights;
67  }
68  tprintf("Total weights = %d\n", num_weights_);
69  no_ = stack_.back()->NumOutputs();
70  return num_weights_;
71 }
72 
73 // Sets needs_to_backprop_ to needs_backprop and returns true if
74 // needs_backprop || any weights in this network so the next layer forward
75 // can be told to produce backprop for this layer if needed.
76 bool Series::SetupNeedsBackprop(bool needs_backprop) {
77  needs_to_backprop_ = needs_backprop;
78  for (auto &i : stack_) {
79  needs_backprop = i->SetupNeedsBackprop(needs_backprop);
80  }
81  return needs_backprop;
82 }
83 
84 // Returns an integer reduction factor that the network applies to the
85 // time sequence. Assumes that any 2-d is already eliminated. Used for
86 // scaling bounding boxes of truth data.
87 // WARNING: if GlobalMinimax is used to vary the scale, this will return
88 // the last used scale factor. Call it before any forward, and it will return
89 // the minimum scale factor of the paths through the GlobalMinimax.
90 int Series::XScaleFactor() const {
91  int factor = 1;
92  for (auto i : stack_) {
93  factor *= i->XScaleFactor();
94  }
95  return factor;
96 }
97 
98 // Provides the (minimum) x scale factor to the network (of interest only to
99 // input units) so they can determine how to scale bounding boxes.
100 void Series::CacheXScaleFactor(int factor) {
101  stack_[0]->CacheXScaleFactor(factor);
102 }
103 
104 // Runs forward propagation of activations on the input line.
105 // See NetworkCpp for a detailed discussion of the arguments.
106 void Series::Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose,
107  NetworkScratch *scratch, NetworkIO *output) {
108  int stack_size = stack_.size();
109  ASSERT_HOST(stack_size > 1);
110  // Revolving intermediate buffers.
111  NetworkScratch::IO buffer1(input, scratch);
112  NetworkScratch::IO buffer2(input, scratch);
113  // Run each network in turn, giving the output of n as the input to n + 1,
114  // with the final network providing the real output.
115  stack_[0]->Forward(debug, input, input_transpose, scratch, buffer1);
116  for (int i = 1; i < stack_size; i += 2) {
117  stack_[i]->Forward(debug, *buffer1, nullptr, scratch, i + 1 < stack_size ? buffer2 : output);
118  if (i + 1 == stack_size) {
119  return;
120  }
121  stack_[i + 1]->Forward(debug, *buffer2, nullptr, scratch,
122  i + 2 < stack_size ? buffer1 : output);
123  }
124 }
125 
126 // Runs backward propagation of errors on the deltas line.
127 // See NetworkCpp for a detailed discussion of the arguments.
128 bool Series::Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch,
129  NetworkIO *back_deltas) {
130  if (!IsTraining()) {
131  return false;
132  }
133  int stack_size = stack_.size();
134  ASSERT_HOST(stack_size > 1);
135  // Revolving intermediate buffers.
136  NetworkScratch::IO buffer1(fwd_deltas, scratch);
137  NetworkScratch::IO buffer2(fwd_deltas, scratch);
138  // Run each network in reverse order, giving the back_deltas output of n as
139  // the fwd_deltas input to n-1, with the 0 network providing the real output.
140  if (!stack_.back()->IsTraining() ||
141  !stack_.back()->Backward(debug, fwd_deltas, scratch, buffer1)) {
142  return false;
143  }
144  for (int i = stack_size - 2; i >= 0; i -= 2) {
145  if (!stack_[i]->IsTraining() ||
146  !stack_[i]->Backward(debug, *buffer1, scratch, i > 0 ? buffer2 : back_deltas)) {
147  return false;
148  }
149  if (i == 0) {
150  return needs_to_backprop_;
151  }
152  if (!stack_[i - 1]->IsTraining() ||
153  !stack_[i - 1]->Backward(debug, *buffer2, scratch, i > 1 ? buffer1 : back_deltas)) {
154  return false;
155  }
156  }
157  return needs_to_backprop_;
158 }
159 
160 // Splits the series after the given index, returning the two parts and
161 // deletes itself. The first part, up to network with index last_start, goes
162 // into start, and the rest goes into end.
163 void Series::SplitAt(unsigned last_start, Series **start, Series **end) {
164  *start = nullptr;
165  *end = nullptr;
166  if (last_start >= stack_.size()) {
167  tprintf("Invalid split index %u must be in range [0,%zu]!\n", last_start, stack_.size() - 1);
168  return;
169  }
170  auto *master_series = new Series("MasterSeries");
171  auto *boosted_series = new Series("BoostedSeries");
172  for (unsigned s = 0; s <= last_start; ++s) {
173  if (s + 1 == stack_.size() && stack_[s]->type() == NT_SOFTMAX) {
174  // Change the softmax to a tanh.
175  auto *fc = static_cast<FullyConnected *>(stack_[s]);
176  fc->ChangeType(NT_TANH);
177  }
178  master_series->AddToStack(stack_[s]);
179  stack_[s] = nullptr;
180  }
181  for (unsigned s = last_start + 1; s < stack_.size(); ++s) {
182  boosted_series->AddToStack(stack_[s]);
183  stack_[s] = nullptr;
184  }
185  *start = master_series;
186  *end = boosted_series;
187  delete this;
188 }
189 
190 // Appends the elements of the src series to this, removing from src and
191 // deleting it.
193  ASSERT_HOST(src->type() == NT_SERIES);
194  auto *src_series = static_cast<Series *>(src);
195  for (auto &s : src_series->stack_) {
196  AddToStack(s);
197  s = nullptr;
198  }
199  delete src;
200 }
201 
202 } // namespace tesseract.
#define ASSERT_HOST(x)
Definition: errcode.h:59
void tprintf(const char *format,...)
Definition: tprintf.cpp:41
@ NT_SOFTMAX
Definition: network.h:66
@ NT_SERIES
Definition: network.h:52
@ NT_TANH
Definition: network.h:63
void ChangeType(NetworkType type)
NetworkType type_
Definition: network.h:300
bool needs_to_backprop_
Definition: network.h:302
bool IsTraining() const
Definition: network.h:113
int32_t num_weights_
Definition: network.h:306
NetworkType type() const
Definition: network.h:110
virtual void AddToStack(Network *network)
Definition: plumbing.cpp:84
std::vector< Network * > stack_
Definition: plumbing.h:150
bool SetupNeedsBackprop(bool needs_backprop) override
Definition: series.cpp:76
bool Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch, NetworkIO *back_deltas) override
Definition: series.cpp:128
TESS_API void AppendSeries(Network *src)
Definition: series.cpp:192
TESS_API Series(const char *name)
Definition: series.cpp:28
int XScaleFactor() const override
Definition: series.cpp:90
TESS_API void SplitAt(unsigned last_start, Series **start, Series **end)
Definition: series.cpp:163
StaticShape OutputShape(const StaticShape &input_shape) const override
Definition: series.cpp:34
void CacheXScaleFactor(int factor) override
Definition: series.cpp:100
int InitWeights(float range, TRand *randomizer) override
Definition: series.cpp:46
int RemapOutputs(int old_no, const std::vector< int > &code_map) override
Definition: series.cpp:60
void Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose, NetworkScratch *scratch, NetworkIO *output) override
Definition: series.cpp:106