// Runs forward propagation of activations on the input line.
// See NetworkCpp for a detailed discussion of the arguments.
void Parallel::Forward(bool debug, const NetworkIO& input,
const TransposedArray* input_transpose,
NetworkScratch* scratch, NetworkIO* output) {
bool parallel_debug = false;
// If this parallel is a replicator of convolvers, or holds a 1-d LSTM pair,
// or a 2-d LSTM quad, do debug locally, and don't pass the flag on.
if (debug && type_ != NT_PARALLEL) {
parallel_debug = true;
debug = false;
}
int stack_size = stack_.size();
if (type_ == NT_PAR_2D_LSTM) {
// Special case, run parallel in parallel.
GenericVector<NetworkScratch::IO> results;
results.init_to_size(stack_size, NetworkScratch::IO());
for (int i = 0; i < stack_size; ++i) {
results[i].Resize(input, stack_[i]->NumOutputs(), scratch);
}
#ifdef _OPENMP
#pragma omp parallel for num_threads(stack_size)
#endif
for (int i = 0; i < stack_size; ++i) {
stack_[i]->Forward(debug, input, nullptr, scratch, results[i]);
}
// Now pack all the results (serially) into the output.
int out_offset = 0;
output->Resize(*results[0], NumOutputs());
for (int i = 0; i < stack_size; ++i) {
out_offset = output->CopyPacking(*results[i], out_offset);
}
} else {
// Revolving intermediate result.
NetworkScratch::IO result(input, scratch);
// Source for divided replicated.
NetworkScratch::IO source_part;
TransposedArray* src_transpose = nullptr;
if (IsTraining() && type_ == NT_REPLICATED) {
// Make a transposed copy of the input.
input.Transpose(&transposed_input_);
src_transpose = &transposed_input_;
}
// Run each network, putting the outputs into result.
int out_offset = 0;
for (int i = 0; i < stack_size; ++i) {
stack_[i]->Forward(debug, input, src_transpose, scratch, result);
// All networks must have the same output width
if (i == 0) {
output->Resize(*result, NumOutputs());
} else {
ASSERT_HOST(result->Width() == output->Width());
}
out_offset = output->CopyPacking(*result, out_offset);
}
}
if (parallel_debug) {
DisplayForward(*output);
}
}
// Runs forward propagation of activations on the input line.
// See NetworkCpp for a detailed discussion of the arguments.
void LSTM::Forward(bool debug, const NetworkIO& input,
const TransposedArray* input_transpose,
NetworkScratch* scratch, NetworkIO* output) {
input_map_ = input.stride_map();
input_width_ = input.Width();
if (softmax_ != NULL)
output->ResizeFloat(input, no_);
else if (type_ == NT_LSTM_SUMMARY)
output->ResizeXTo1(input, no_);
else
output->Resize(input, no_);
ResizeForward(input);
// Temporary storage of forward computation for each gate.
NetworkScratch::FloatVec temp_lines[WT_COUNT];
for (int i = 0; i < WT_COUNT; ++i) temp_lines[i].Init(ns_, scratch);
// Single timestep buffers for the current/recurrent output and state.
NetworkScratch::FloatVec curr_state, curr_output;
curr_state.Init(ns_, scratch);
ZeroVector<double>(ns_, curr_state);
curr_output.Init(ns_, scratch);
ZeroVector<double>(ns_, curr_output);
// Rotating buffers of width buf_width allow storage of the state and output
// for the other dimension, used only when working in true 2D mode. The width
// is enough to hold an entire strip of the major direction.
int buf_width = Is2D() ? input_map_.Size(FD_WIDTH) : 1;
GenericVector<NetworkScratch::FloatVec> states, outputs;
if (Is2D()) {
states.init_to_size(buf_width, NetworkScratch::FloatVec());
outputs.init_to_size(buf_width, NetworkScratch::FloatVec());
for (int i = 0; i < buf_width; ++i) {
states[i].Init(ns_, scratch);
ZeroVector<double>(ns_, states[i]);
outputs[i].Init(ns_, scratch);
ZeroVector<double>(ns_, outputs[i]);
}
}
// Used only if a softmax LSTM.
NetworkScratch::FloatVec softmax_output;
NetworkScratch::IO int_output;
if (softmax_ != NULL) {
softmax_output.Init(no_, scratch);
ZeroVector<double>(no_, softmax_output);
int rounded_softmax_inputs = gate_weights_[CI].RoundInputs(ns_);
if (input.int_mode())
int_output.Resize2d(true, 1, rounded_softmax_inputs, scratch);
softmax_->SetupForward(input, NULL);
}
NetworkScratch::FloatVec curr_input;
curr_input.Init(na_, scratch);
StrideMap::Index src_index(input_map_);
// Used only by NT_LSTM_SUMMARY.
StrideMap::Index dest_index(output->stride_map());
do {
int t = src_index.t();
// True if there is a valid old state for the 2nd dimension.
bool valid_2d = Is2D();
if (valid_2d) {
StrideMap::Index dim_index(src_index);
if (!dim_index.AddOffset(-1, FD_HEIGHT)) valid_2d = false;
}
// Index of the 2-D revolving buffers (outputs, states).
int mod_t = Modulo(t, buf_width); // Current timestep.
// Setup the padded input in source.
source_.CopyTimeStepGeneral(t, 0, ni_, input, t, 0);
if (softmax_ != NULL) {
source_.WriteTimeStepPart(t, ni_, nf_, softmax_output);
}
source_.WriteTimeStepPart(t, ni_ + nf_, ns_, curr_output);
if (Is2D())
source_.WriteTimeStepPart(t, ni_ + nf_ + ns_, ns_, outputs[mod_t]);
if (!source_.int_mode()) source_.ReadTimeStep(t, curr_input);
// Matrix multiply the inputs with the source.
PARALLEL_IF_OPENMP(GFS)
// It looks inefficient to create the threads on each t iteration, but the
// alternative of putting the parallel outside the t loop, a single around
// the t-loop and then tasks in place of the sections is a *lot* slower.
// Cell inputs.
if (source_.int_mode())
gate_weights_[CI].MatrixDotVector(source_.i(t), temp_lines[CI]);
else
gate_weights_[CI].MatrixDotVector(curr_input, temp_lines[CI]);
FuncInplace<GFunc>(ns_, temp_lines[CI]);
SECTION_IF_OPENMP
// Input Gates.
if (source_.int_mode())
gate_weights_[GI].MatrixDotVector(source_.i(t), temp_lines[GI]);
else
gate_weights_[GI].MatrixDotVector(curr_input, temp_lines[GI]);
FuncInplace<FFunc>(ns_, temp_lines[GI]);
SECTION_IF_OPENMP
// 1-D forget gates.
if (source_.int_mode())
gate_weights_[GF1].MatrixDotVector(source_.i(t), temp_lines[GF1]);
else
gate_weights_[GF1].MatrixDotVector(curr_input, temp_lines[GF1]);
FuncInplace<FFunc>(ns_, temp_lines[GF1]);
// 2-D forget gates.
if (Is2D()) {
if (source_.int_mode())
gate_weights_[GFS].MatrixDotVector(source_.i(t), temp_lines[GFS]);
else
gate_weights_[GFS].MatrixDotVector(curr_input, temp_lines[GFS]);
FuncInplace<FFunc>(ns_, temp_lines[GFS]);
}
SECTION_IF_OPENMP
// Output gates.
if (source_.int_mode())
gate_weights_[GO].MatrixDotVector(source_.i(t), temp_lines[GO]);
else
gate_weights_[GO].MatrixDotVector(curr_input, temp_lines[GO]);
FuncInplace<FFunc>(ns_, temp_lines[GO]);
END_PARALLEL_IF_OPENMP
// Apply forget gate to state.
MultiplyVectorsInPlace(ns_, temp_lines[GF1], curr_state);
if (Is2D()) {
// Max-pool the forget gates (in 2-d) instead of blindly adding.
inT8* which_fg_col = which_fg_[t];
memset(which_fg_col, 1, ns_ * sizeof(which_fg_col[0]));
if (valid_2d) {
const double* stepped_state = states[mod_t];
for (int i = 0; i < ns_; ++i) {
if (temp_lines[GF1][i] < temp_lines[GFS][i]) {
curr_state[i] = temp_lines[GFS][i] * stepped_state[i];
which_fg_col[i] = 2;
}
}
}
}
MultiplyAccumulate(ns_, temp_lines[CI], temp_lines[GI], curr_state);
// Clip curr_state to a sane range.
ClipVector<double>(ns_, -kStateClip, kStateClip, curr_state);
if (IsTraining()) {
// Save the gate node values.
node_values_[CI].WriteTimeStep(t, temp_lines[CI]);
node_values_[GI].WriteTimeStep(t, temp_lines[GI]);
node_values_[GF1].WriteTimeStep(t, temp_lines[GF1]);
node_values_[GO].WriteTimeStep(t, temp_lines[GO]);
if (Is2D()) node_values_[GFS].WriteTimeStep(t, temp_lines[GFS]);
}
FuncMultiply<HFunc>(curr_state, temp_lines[GO], ns_, curr_output);
if (IsTraining()) state_.WriteTimeStep(t, curr_state);
if (softmax_ != NULL) {
if (input.int_mode()) {
int_output->WriteTimeStepPart(0, 0, ns_, curr_output);
softmax_->ForwardTimeStep(NULL, int_output->i(0), t, softmax_output);
} else {
softmax_->ForwardTimeStep(curr_output, NULL, t, softmax_output);
}
output->WriteTimeStep(t, softmax_output);
if (type_ == NT_LSTM_SOFTMAX_ENCODED) {
CodeInBinary(no_, nf_, softmax_output);
}
} else if (type_ == NT_LSTM_SUMMARY) {
// Output only at the end of a row.
if (src_index.IsLast(FD_WIDTH)) {
output->WriteTimeStep(dest_index.t(), curr_output);
dest_index.Increment();
}
} else {
output->WriteTimeStep(t, curr_output);
}
// Save states for use by the 2nd dimension only if needed.
if (Is2D()) {
CopyVector(ns_, curr_state, states[mod_t]);
CopyVector(ns_, curr_output, outputs[mod_t]);
}
// Always zero the states at the end of every row, but only for the major
// direction. The 2-D state remains intact.
if (src_index.IsLast(FD_WIDTH)) {
ZeroVector<double>(ns_, curr_state);
ZeroVector<double>(ns_, curr_output);
}
} while (src_index.Increment());
#if DEBUG_DETAIL > 0
tprintf("Source:%s\n", name_.string());
source_.Print(10);
tprintf("State:%s\n", name_.string());
state_.Print(10);
tprintf("Output:%s\n", name_.string());
output->Print(10);
#endif
if (debug) DisplayForward(*output);
}
// Recognizes the image_data, returning the labels,
// scores, and corresponding pairs of start, end x-coords in coords.
bool LSTMRecognizer::RecognizeLine(const ImageData& image_data, bool invert,
bool debug, bool re_invert,
float* scale_factor, NetworkIO* inputs,
NetworkIO* outputs) {
// Maximum width of image to train on.
const int kMaxImageWidth = 2560;
// This ensures consistent recognition results.
SetRandomSeed();
int min_width = network_->XScaleFactor();
Pix* pix = Input::PrepareLSTMInputs(image_data, network_, min_width,
&randomizer_, scale_factor);
if (pix == NULL) {
tprintf("Line cannot be recognized!!\n");
return false;
}
if (network_->IsTraining() && pixGetWidth(pix) > kMaxImageWidth) {
tprintf("Image too large to learn!! Size = %dx%d\n", pixGetWidth(pix),
pixGetHeight(pix));
pixDestroy(&pix);
return false;
}
// Reduction factor from image to coords.
*scale_factor = min_width / *scale_factor;
inputs->set_int_mode(IsIntMode());
SetRandomSeed();
Input::PreparePixInput(network_->InputShape(), pix, &randomizer_, inputs);
network_->Forward(debug, *inputs, NULL, &scratch_space_, outputs);
// Check for auto inversion.
float pos_min, pos_mean, pos_sd;
OutputStats(*outputs, &pos_min, &pos_mean, &pos_sd);
if (invert && pos_min < 0.5) {
// Run again inverted and see if it is any better.
NetworkIO inv_inputs, inv_outputs;
inv_inputs.set_int_mode(IsIntMode());
SetRandomSeed();
pixInvert(pix, pix);
Input::PreparePixInput(network_->InputShape(), pix, &randomizer_,
&inv_inputs);
network_->Forward(debug, inv_inputs, NULL, &scratch_space_, &inv_outputs);
float inv_min, inv_mean, inv_sd;
OutputStats(inv_outputs, &inv_min, &inv_mean, &inv_sd);
if (inv_min > pos_min && inv_mean > pos_mean && inv_sd < pos_sd) {
// Inverted did better. Use inverted data.
if (debug) {
tprintf("Inverting image: old min=%g, mean=%g, sd=%g, inv %g,%g,%g\n",
pos_min, pos_mean, pos_sd, inv_min, inv_mean, inv_sd);
}
*outputs = inv_outputs;
*inputs = inv_inputs;
} else if (re_invert) {
// Inverting was not an improvement, so undo and run again, so the
// outputs match the best forward result.
SetRandomSeed();
network_->Forward(debug, *inputs, NULL, &scratch_space_, outputs);
}
}
pixDestroy(&pix);
if (debug) {
GenericVector<int> labels, coords;
LabelsFromOutputs(*outputs, &labels, &coords);
DisplayForward(*inputs, labels, coords, "LSTMForward", &debug_win_);
DebugActivationPath(*outputs, labels, coords);
}
return true;
}
