#include <algorithm>

#include <map>

#include <set>

#include <string>

#include <utility>

#include <vector>

#ifdef USE_HDF5

#include "hdf5.h"

#endif // USE_HDF5

#include "caffe/common.hpp"

#include "caffe/layer.hpp"

#include "caffe/net.hpp"

#include "caffe/parallel.hpp"

#include "caffe/proto/caffe.pb.h"

#include "caffe/util/hdf5.hpp"

#include "caffe/util/insert_splits.hpp"

#include "caffe/util/math_functions.hpp"

#include "caffe/util/upgrade_proto.hpp"

namespace caffe {

template <typename Dtype>

Net<Dtype>::Net(const NetParameter& param) {

Init(param);

}

template <typename Dtype>

Net<Dtype>::Net(const string& param_file, Phase phase,

const int level, const vector<string>* stages) {

NetParameter param;

ReadNetParamsFromTextFileOrDie(param_file, &param);

// Set phase, stages and level

param.mutable_state()->set_phase(phase);

if (stages != NULL) {

for (int i = 0; i < stages->size(); i++) {

param.mutable_state()->add_stage((*stages)[i]);

}

}

param.mutable_state()->set_level(level);

Init(param);

}

template <typename Dtype>

void Net<Dtype>::Init(const NetParameter& in_param) {

// Set phase from the state.

phase_ = in_param.state().phase();

// Filter layers based on their include/exclude rules and

// the current NetState.

NetParameter filtered_param;

FilterNet(in_param, &filtered_param);

LOG_IF(INFO, Caffe::root_solver())

<< "Initializing net from parameters: " << std::endl

<< filtered_param.DebugString();

// Create a copy of filtered_param with splits added where necessary.

NetParameter param;

InsertSplits(filtered_param, &param);

// Basically, build all the layers and set up their connections.

name_ = param.name();

map<string, int> blob_name_to_idx;

set<string> available_blobs;

memory_used_ = 0;

// For each layer, set up its input and output

bottom_vecs_.resize(param.layer_size());

top_vecs_.resize(param.layer_size());

bottom_id_vecs_.resize(param.layer_size());

param_id_vecs_.resize(param.layer_size());

top_id_vecs_.resize(param.layer_size());

bottom_need_backward_.resize(param.layer_size());

for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {

// Inherit phase from net if unset.

if (!param.layer(layer_id).has_phase()) {

param.mutable_layer(layer_id)->set_phase(phase_);

}

// Setup layer.

const LayerParameter& layer_param = param.layer(layer_id);

if (layer_param.propagate_down_size() > 0) {

CHECK_EQ(layer_param.propagate_down_size(),

layer_param.bottom_size())

<< "propagate_down param must be specified "

<< "either 0 or bottom_size times ";

}

layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));

layer_names_.push_back(layer_param.name());

LOG_IF(INFO, Caffe::root_solver())

<< "Creating Layer " << layer_param.name();

bool need_backward = false;

// Figure out this layer's input and output

for (int bottom_id = 0; bottom_id < layer_param.bottom_size();

++bottom_id) {

const int blob_id = AppendBottom(param, layer_id, bottom_id,

&available_blobs, &blob_name_to_idx);

// If a blob needs backward, this layer should provide it.

need_backward |= blob_need_backward_[blob_id];

}

int num_top = layer_param.top_size();

for (int top_id = 0; top_id < num_top; ++top_id) {

AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx);

// Collect Input layer tops as Net inputs.

if (layer_param.type() == "Input") {

const int blob_id = blobs_.size() - 1;

net_input_blob_indices_.push_back(blob_id);

net_input_blobs_.push_back(blobs_[blob_id].get());

}

}

// If the layer specifies that AutoTopBlobs() -> true and the LayerParameter

// specified fewer than the required number (as specified by

// ExactNumTopBlobs() or MinTopBlobs()), allocate them here.

Layer<Dtype>* layer = layers_[layer_id].get();

if (layer->AutoTopBlobs()) {

const int needed_num_top =

std::max(layer->MinTopBlobs(), layer->ExactNumTopBlobs());

for (; num_top < needed_num_top; ++num_top) {

// Add "anonymous" top blobs -- do not modify available_blobs or

// blob_name_to_idx as we don't want these blobs to be usable as input

// to other layers.

AppendTop(param, layer_id, num_top, NULL, NULL);

}

}

// After this layer is connected, set it up.

layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);

LOG_IF(INFO, Caffe::root_solver())

<< "Setting up " << layer_names_[layer_id];

for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {

if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {

blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));

}

blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);

LOG_IF(INFO, Caffe::root_solver())

<< "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();

if (layer->loss(top_id)) {

LOG_IF(INFO, Caffe::root_solver())

<< " with loss weight " << layer->loss(top_id);

}

memory_used_ += top_vecs_[layer_id][top_id]->count();

}

LOG_IF(INFO, Caffe::root_solver())

<< "Memory required for data: " << memory_used_ * sizeof(Dtype);

const int param_size = layer_param.param_size();

const int num_param_blobs = layers_[layer_id]->blobs().size();

CHECK_LE(param_size, num_param_blobs)

<< "Too many params specified for layer " << layer_param.name();

ParamSpec default_param_spec;

for (int param_id = 0; param_id < num_param_blobs; ++param_id) {

const ParamSpec* param_spec = (param_id < param_size) ?

&layer_param.param(param_id) : &default_param_spec;

const bool param_need_backward = param_spec->lr_mult() != 0;

need_backward |= param_need_backward;

layers_[layer_id]->set_param_propagate_down(param_id,

param_need_backward);

}

for (int param_id = 0; param_id < num_param_blobs; ++param_id) {

AppendParam(param, layer_id, param_id);

}

// Finally, set the backward flag

layer_need_backward_.push_back(need_backward);

if (need_backward) {

for (int top_id = 0; top_id < top_id_vecs_[layer_id].size(); ++top_id) {

blob_need_backward_[top_id_vecs_[layer_id][top_id]] = true;

}

}

}

// Go through the net backwards to determine which blobs contribute to the

// loss. We can skip backward computation for blobs that don't contribute

// to the loss.

// Also checks if all bottom blobs don't need backward computation (possible

// because the skip_propagate_down param) and so we can skip backward

// computation for the entire layer

set<string> blobs_under_loss;

set<string> blobs_skip_backp;

for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) {

bool layer_contributes_loss = false;

bool layer_skip_propagate_down = true;

for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {

const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];

if (layers_[layer_id]->loss(top_id) ||

(blobs_under_loss.find(blob_name) != blobs_under_loss.end())) {

layer_contributes_loss = true;

}

if (blobs_skip_backp.find(blob_name) == blobs_skip_backp.end()) {

layer_skip_propagate_down = false;

}

if (layer_contributes_loss && !layer_skip_propagate_down)

break;

}

// If this layer can skip backward computation, also all his bottom blobs

// don't need backpropagation

if (layer_need_backward_[layer_id] && layer_skip_propagate_down) {

layer_need_backward_[layer_id] = false;

for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();

++bottom_id) {

bottom_need_backward_[layer_id][bottom_id] = false;

}

}

if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }

if (Caffe::root_solver()) {

if (layer_need_backward_[layer_id]) {

LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";

} else {

LOG(INFO) << layer_names_[layer_id]

<< " does not need backward computation.";

}

}

for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();

++bottom_id) {

if (layer_contributes_loss) {

const string& blob_name =

blob_names_[bottom_id_vecs_[layer_id][bottom_id]];

blobs_under_loss.insert(blob_name);

} else {

bottom_need_backward_[layer_id][bottom_id] = false;

}

if (!bottom_need_backward_[layer_id][bottom_id]) {

const string& blob_name =

blob_names_[bottom_id_vecs_[layer_id][bottom_id]];

blobs_skip_backp.insert(blob_name);

}

}

}

// Handle force_backward if needed.

if (param.force_backward()) {

for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) {

layer_need_backward_[layer_id] = true;

for (int bottom_id = 0;

bottom_id < bottom_need_backward_[layer_id].size(); ++bottom_id) {

bottom_need_backward_[layer_id][bottom_id] =

bottom_need_backward_[layer_id][bottom_id] ||

layers_[layer_id]->AllowForceBackward(bottom_id);

blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] =

blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] ||

bottom_need_backward_[layer_id][bottom_id];

}

for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();

++param_id) {

layers_[layer_id]->set_param_propagate_down(param_id, true);

}

}

}

// In the end, all remaining blobs are considered output blobs.

for (set<string>::iterator it = available_blobs.begin();

it != available_blobs.end(); ++it) {

LOG_IF(INFO, Caffe::root_solver())

<< "This network produces output " << *it;

net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());

net_output_blob_indices_.push_back(blob_name_to_idx[*it]);

}

for (size_t blob_id = 0; blob_id < blob_names_.size(); ++blob_id) {

blob_names_index_[blob_names_[blob_id]] = blob_id;

}

for (size_t layer_id = 0; layer_id < layer_names_.size(); ++layer_id) {

layer_names_index_[layer_names_[layer_id]] = layer_id;

}

ShareWeights();

debug_info_ = param.debug_info();

LOG_IF(INFO, Caffe::root_solver()) << "Network initialization done.";

}

template <typename Dtype>

void Net<Dtype>::FilterNet(const NetParameter& param,

NetParameter* param_filtered) {

NetState net_state(param.state());

param_filtered->CopyFrom(param);

param_filtered->clear_layer();

for (int i = 0; i < param.layer_size(); ++i) {

const LayerParameter& layer_param = param.layer(i);

const string& layer_name = layer_param.name();

CHECK(layer_param.include_size() == 0 || layer_param.exclude_size() == 0)

<< "Specify either include rules or exclude rules; not both.";

// If no include rules are specified, the layer is included by default and

// only excluded if it meets one of the exclude rules.

bool layer_included = (layer_param.include_size() == 0);

for (int j = 0; layer_included && j < layer_param.exclude_size(); ++j) {

if (StateMeetsRule(net_state, layer_param.exclude(j), layer_name)) {

layer_included = false;

}

}

for (int j = 0; !layer_included && j < layer_param.include_size(); ++j) {

if (StateMeetsRule(net_state, layer_param.include(j), layer_name)) {

layer_included = true;

}

}

if (layer_included) {

param_filtered->add_layer()->CopyFrom(layer_param);

}

}

}

template <typename Dtype>

bool Net<Dtype>::StateMeetsRule(const NetState& state,

const NetStateRule& rule, const string& layer_name) {

// Check whether the rule is broken due to phase.

if (rule.has_phase()) {

if (rule.phase() != state.phase()) {

LOG_IF(INFO, Caffe::root_solver())

<< "The NetState phase (" << state.phase()

<< ") differed from the phase (" << rule.phase()

<< ") specified by a rule in layer " << layer_name;

return false;

}

}

// Check whether the rule is broken due to min level.

if (rule.has_min_level()) {

if (state.level() < rule.min_level()) {

LOG_IF(INFO, Caffe::root_solver())

<< "The NetState level (" << state.level()

<< ") is above the min_level (" << rule.min_level()

<< ") specified by a rule in layer " << layer_name;

return false;

}

}

// Check whether the rule is broken due to max level.

if (rule.has_max_level()) {

if (state.level() > rule.max_level()) {

LOG_IF(INFO, Caffe::root_solver())

<< "The NetState level (" << state.level()

<< ") is above the max_level (" << rule.max_level()

<< ") specified by a rule in layer " << layer_name;

return false;

}

}

// Check whether the rule is broken due to stage. The NetState must

// contain ALL of the rule's stages to meet it.

for (int i = 0; i < rule.stage_size(); ++i) {

// Check that the NetState contains the rule's ith stage.

bool has_stage = false;

for (int j = 0; !has_stage && j < state.stage_size(); ++j) {

if (rule.stage(i) == state.stage(j)) { has_stage = true; }

}

if (!has_stage) {

LOG_IF(INFO, Caffe::root_solver())

<< "The NetState did not contain stage '" << rule.stage(i)

<< "' specified by a rule in layer " << layer_name;

return false;

}

}

// Check whether the rule is broken due to not_stage. The NetState must

// contain NONE of the rule's not_stages to meet it.

for (int i = 0; i < rule.not_stage_size(); ++i) {

// Check that the NetState contains the rule's ith not_stage.

bool has_stage = false;

for (int j = 0; !has_stage && j < state.stage_size(); ++j) {

if (rule.not_stage(i) == state.stage(j)) { has_stage = true; }

}

if (has_stage) {

LOG_IF(INFO, Caffe::root_solver())

<< "The NetState contained a not_stage '" << rule.not_stage(i)

<< "' specified by a rule in layer " << layer_name;

return false;

}

}

return true;

}

// Helper for Net::Init: add a new top blob to the net.

template <typename Dtype>

void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,

const int top_id, set<string>* available_blobs,

map<string, int>* blob_name_to_idx) {

shared_ptr<LayerParameter> layer_param(

new LayerParameter(param.layer(layer_id)));

const string& blob_name = (layer_param->top_size() > top_id) ?

layer_param->top(top_id) : "(automatic)";

// Check if we are doing in-place computation

if (blob_name_to_idx && layer_param->bottom_size() > top_id &&

blob_name == layer_param->bottom(top_id)) {

// In-place computation

LOG_IF(INFO, Caffe::root_solver())

<< layer_param->name() << " -> " << blob_name << " (in-place)";

top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get());

top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]);

} else if (blob_name_to_idx &&

blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {

// If we are not doing in-place computation but have duplicated blobs,

// raise an error.

LOG(FATAL) << "Top blob '" << blob_name

<< "' produced by multiple sources.";

} else {

// Normal output.

if (Caffe::root_solver()) {

LOG(INFO) << layer_param->name() << " -> " << blob_name;

}

shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());

const int blob_id = blobs_.size();

blobs_.push_back(blob_pointer);

blob_names_.push_back(blob_name);

blob_need_backward_.push_back(false);

if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; }

top_id_vecs_[layer_id].push_back(blob_id);

top_vecs_[layer_id].push_back(blob_pointer.get());

}

if (available_blobs) { available_blobs->insert(blob_name); }

}

// Helper for Net::Init: add a new bottom blob to the net.

template <typename Dtype>

int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,

const int bottom_id, set<string>* available_blobs,

map<string, int>* blob_name_to_idx) {

const LayerParameter& layer_param = param.layer(layer_id);

const string& blob_name = layer_param.bottom(bottom_id);

if (available_blobs->find(blob_name) == available_blobs->end()) {

LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '"

<< layer_param.name() << "', bottom index " << bottom_id << ")";

}

const int blob_id = (*blob_name_to_idx)[blob_name];

LOG_IF(INFO, Caffe::root_solver())

<< layer_names_[layer_id] << " <- " << blob_name;

bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());

bottom_id_vecs_[layer_id].push_back(blob_id);

available_blobs->erase(blob_name);

bool need_backward = blob_need_backward_[blob_id];

// Check if the backpropagation on bottom_id should be skipped

if (layer_param.propagate_down_size() > 0) {

need_backward = layer_param.propagate_down(bottom_id);

}

bottom_need_backward_[layer_id].push_back(need_backward);

return blob_id;

}

template <typename Dtype>

void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,

const int param_id) {

const LayerParameter& layer_param = layers_[layer_id]->layer_param();

const int param_size = layer_param.param_size();

string param_name =

(param_size > param_id) ? layer_param.param(param_id).name() : "";

if (param_name.size()) {

param_display_names_.push_back(param_name);

} else {

ostringstream param_display_name;

param_display_name << param_id;

param_display_names_.push_back(param_display_name.str());

}

const int net_param_id = params_.size();

params_.push_back(layers_[layer_id]->blobs()[param_id]);

param_id_vecs_[layer_id].push_back(net_param_id);

param_layer_indices_.push_back(make_pair(layer_id, param_id));

ParamSpec default_param_spec;

const ParamSpec* param_spec = (layer_param.param_size() > param_id) ?

&layer_param.param(param_id) : &default_param_spec;

if (!param_size || !param_name.size() || (param_name.size() &&

param_names_index_.find(param_name) == param_names_index_.end())) {

// This layer "owns" this parameter blob -- it is either anonymous

// (i.e., not given a param_name) or explicitly given a name that we

// haven't already seen.

param_owners_.push_back(-1);

if (param_name.size()) {

param_names_index_[param_name] = net_param_id;

}

const int learnable_param_id = learnable_params_.size();

learnable_params_.push_back(params_[net_param_id].get());

learnable_param_ids_.push_back(learnable_param_id);

has_params_lr_.push_back(param_spec->has_lr_mult());

has_params_decay_.push_back(param_spec->has_decay_mult());

params_lr_.push_back(param_spec->lr_mult());

params_weight_decay_.push_back(param_spec->decay_mult());

} else {

// Named param blob with name we've seen before: share params

const int owner_net_param_id = param_names_index_[param_name];

param_owners_.push_back(owner_net_param_id);

const pair<int, int>& owner_index =

param_layer_indices_[owner_net_param_id];

const int owner_layer_id = owner_index.first;

const int owner_param_id = owner_index.second;

LOG_IF(INFO, Caffe::root_solver()) << "Sharing parameters '" << param_name

<< "' owned by "

<< "layer '" << layer_names_[owner_layer_id] << "', param "

<< "index " << owner_param_id;

Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();

Blob<Dtype>* owner_blob =

layers_[owner_layer_id]->blobs()[owner_param_id].get();

const int param_size = layer_param.param_size();

if (param_size > param_id && (layer_param.param(param_id).share_mode() ==

ParamSpec_DimCheckMode_PERMISSIVE)) {

// Permissive dimension checking -- only check counts are the same.

CHECK_EQ(this_blob->count(), owner_blob->count())

<< "Cannot share param '" << param_name << "' owned by layer '"

<< layer_names_[owner_layer_id] << "' with layer '"

<< layer_names_[layer_id] << "'; count mismatch. Owner layer param "

<< "shape is " << owner_blob->shape_string() << "; sharing layer "

<< "shape is " << this_blob->shape_string();

} else {

// Strict dimension checking -- all dims must be the same.

CHECK(this_blob->shape() == owner_blob->shape())

<< "Cannot share param '" << param_name << "' owned by layer '"

<< layer_names_[owner_layer_id] << "' with layer '"

<< layer_names_[layer_id] << "'; shape mismatch. Owner layer param "

<< "shape is " << owner_blob->shape_string() << "; sharing layer "

<< "expects shape " << this_blob->shape_string();

}

const int learnable_param_id = learnable_param_ids_[owner_net_param_id];

learnable_param_ids_.push_back(learnable_param_id);

if (param_spec->has_lr_mult()) {

if (has_params_lr_[learnable_param_id]) {

CHECK_EQ(param_spec->lr_mult(), params_lr_[learnable_param_id])

<< "Shared param '" << param_name << "' has mismatched lr_mult.";

} else {

has_params_lr_[learnable_param_id] = true;

params_lr_[learnable_param_id] = param_spec->lr_mult();

}

}

if (param_spec->has_decay_mult()) {

if (has_params_decay_[learnable_param_id]) {

CHECK_EQ(param_spec->decay_mult(),

params_weight_decay_[learnable_param_id])

<< "Shared param '" << param_name << "' has mismatched decay_mult.";

} else {

has_params_decay_[learnable_param_id] = true;

params_weight_decay_[learnable_param_id] = param_spec->decay_mult();

}

}

}

}

template <typename Dtype>

Dtype Net<Dtype>::ForwardFromTo(int start, int end) {

CHECK_GE(start, 0);

CHECK_LT(end, layers_.size());

Dtype loss = 0;

for (int i = start; i <= end; ++i) {

for (int c = 0; c < before_forward_.size(); ++c) {

before_forward_[c]->run(i);

}

Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);

loss += layer_loss;

if (debug_info_) { ForwardDebugInfo(i); }

for (int c = 0; c < after_forward_.size(); ++c) {

after_forward_[c]->run(i);

}

}

return loss;

}

template <typename Dtype>

Dtype Net<Dtype>::ForwardFrom(int start) {

return ForwardFromTo(start, layers_.size() - 1);

}

template <typename Dtype>

Dtype Net<Dtype>::ForwardTo(int end) {

return ForwardFromTo(0, end);

}

template <typename Dtype>

const vector<Blob<Dtype>*>& Net<Dtype>::Forward(Dtype* loss) {

if (loss != NULL) {

*loss = ForwardFromTo(0, layers_.size() - 1);

} else {

ForwardFromTo(0, layers_.size() - 1);

}

return net_output_blobs_;

}

template <typename Dtype>

const vector<Blob<Dtype>*>& Net<Dtype>::Forward(

const vector<Blob<Dtype>*> & bottom, Dtype* loss) {

LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: Forward(bottom, loss) "

<< "will be removed in a future version. Use Forward(loss).";

// Copy bottom to net bottoms

for (int i = 0; i < bottom.size(); ++i) {

net_input_blobs_[i]->CopyFrom(*bottom[i]);

}

return Forward(loss);

}

template <typename Dtype>

void Net<Dtype>::BackwardFromTo(int start, int end) {

CHECK_GE(end, 0);

CHECK_LT(start, layers_.size());

for (int i = start; i >= end; --i) {

for (int c = 0; c < before_backward_.size(); ++c) {

before_backward_[c]->run(i);

}

if (layer_need_backward_[i]) {

layers_[i]->Backward(

top_vecs_[i], bottom_need_backward_[i], bottom_vecs_[i]);

if (debug_info_) { BackwardDebugInfo(i); }

}

for (int c = 0; c < after_backward_.size(); ++c) {

after_backward_[c]->run(i);

}

}

}

template <typename Dtype>

void Net<Dtype>::ForwardDebugInfo(const int layer_id) {

for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {

const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];

const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];

const Dtype data_abs_val_mean = blob.asum_data() / blob.count();

LOG_IF(INFO, Caffe::root_solver())

<< " [Forward] "

<< "Layer " << layer_names_[layer_id]

<< ", top blob " << blob_name

<< " data: " << data_abs_val_mean;

}

for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();

++param_id) {

const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];

const int net_param_id = param_id_vecs_[layer_id][param_id];

const string& blob_name = param_display_names_[net_param_id];

const Dtype data_abs_val_mean = blob.asum_data() / blob.count();

LOG_IF(INFO, Caffe::root_solver())

<< " [Forward] "

<< "Layer " << layer_names_[layer_id]

<< ", param blob " << blob_name

<< " data: " << data_abs_val_mean;

}

}

template <typename Dtype>

void Net<Dtype>::BackwardDebugInfo(const int layer_id) {

const vector<Blob<Dtype>*>& bottom_vec = bottom_vecs_[layer_id];

for (int bottom_id = 0; bottom_id < bottom_vec.size(); ++bottom_id) {

if (!bottom_need_backward_[layer_id][bottom_id]) { continue; }

const Blob<Dtype>& blob = *bottom_vec[bottom_id];

const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];

const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();

LOG_IF(INFO, Caffe::root_solver())

<< " [Backward] "

<< "Layer " << layer_names_[layer_id]

<< ", bottom blob " << blob_name

<< " diff: " << diff_abs_val_mean;

}

for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();

++param_id) {

if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }

const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];

const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();

LOG_IF(INFO, Caffe::root_solver())

<< " [Backward] "

<< "Layer " << layer_names_[layer_id]

<< ", param blob " << param_id

<< " diff: " << diff_abs_val_mean;

}

}

template <typename Dtype>

void Net<Dtype>::UpdateDebugInfo(const int param_id) {

const Blob<Dtype>& blob = *params_[param_id];

const int param_owner = param_owners_[param_id];

const string& layer_name = layer_names_[param_layer_indices_[param_id].first];

const string& param_display_name = param_display_names_[param_id];

const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();

if (param_owner < 0) {

const Dtype data_abs_val_mean = blob.asum_data() / blob.count();

LOG_IF(INFO, Caffe::root_solver())

<< " [Update] Layer " << layer_name

<< ", param " << param_display_name

<< " data: " << data_abs_val_mean

<< "; diff: " << diff_abs_val_mean;

} else {

const string& owner_layer_name =

layer_names_[param_layer_indices_[param_owner].first];

LOG_IF(INFO, Caffe::root_solver())

<< " [Update] Layer " << layer_name

<< ", param blob " << param_display_name

<< " (owned by layer " << owner_layer_name << ", " << "param "

<< param_display_names_[param_owners_[param_id]] << ")"

<< " diff: " << diff_abs_val_mean;

}

}

template <typename Dtype>

void Net<Dtype>::ShareTrainedLayersWith(const Net* other) {

int num_source_layers = other->layers().size();

for (int i = 0; i < num_source_layers; ++i) {

Layer<Dtype>* source_layer = other->layers()[i].get();

const string& source_layer_name = other->layer_names()[i];

int target_layer_id = 0;

while (target_layer_id != layer_names_.size() &&

layer_names_[target_layer_id] != source_layer_name) {

++target_layer_id;

}

if (target_layer_id == layer_names_.size()) {

LOG(INFO) << "Ignoring source layer " << source_layer_name;

continue;

}

DLOG(INFO) << "Copying source layer " << source_layer_name;

vector<shared_ptr<Blob<Dtype> > >& target_blobs =

layers_[target_layer_id]->blobs();

CHECK_EQ(target_blobs.size(), source_layer->blobs().size())

<< "Incompatible number of blobs for layer " << source_layer_name;

for (int j = 0; j < target_blobs.size(); ++j) {

Blob<Dtype>* source_blob = source_layer->blobs()[j].get();

CHECK(target_blobs[j]->shape() == source_blob->shape())

<< "Cannot share param " << j << " weights from layer '"

<< source_layer_name << "'; shape mismatch. Source param shape is "

<< source_blob->shape_string() << "; target param shape is "

<< target_blobs[j]->shape_string();

target_blobs[j]->ShareData(*source_blob);

}

}

}

template <typename Dtype>

void Net<Dtype>::BackwardFrom(int start) {

BackwardFromTo(start, 0);

}

template <typename Dtype>

void Net<Dtype>::BackwardTo(int end) {

BackwardFromTo(layers_.size() - 1, end);

}

template <typename Dtype>

void Net<Dtype>::Backward() {

BackwardFromTo(layers_.size() - 1, 0);

if (debug_info_) {

Dtype asum_data = 0, asum_diff = 0, sumsq_data = 0, sumsq_diff = 0;

for (int i = 0; i < learnable_params_.size(); ++i) {

asum_data += learnable_params_[i]->asum_data();

asum_diff += learnable_params_[i]->asum_diff();

sumsq_data += learnable_params_[i]->sumsq_data();

sumsq_diff += learnable_params_[i]->sumsq_diff();

}

const Dtype l2norm_data = std::sqrt(sumsq_data);

const Dtype l2norm_diff = std::sqrt(sumsq_diff);

LOG(ERROR) << " [Backward] All net params (data, diff): "

<< "L1 norm = (" << asum_data << ", " << asum_diff << "); "

<< "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";

}

}

template <typename Dtype>

void Net<Dtype>::Reshape() {

for (int i = 0; i < layers_.size(); ++i) {

layers_[i]->Reshape(bottom_vecs_[i], top_vecs_[i]);

}

}

template <typename Dtype>

void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {

int num_source_layers = param.layer_size();

for (int i = 0; i < num_source_layers; ++i) {

const LayerParameter& source_layer = param.layer(i);

const string& source_layer_name = source_layer.name();

int target_layer_id = 0;

while (target_layer_id != layer_names_.size() &&

layer_names_[target_layer_id] != source_layer_name) {

++target_layer_id;

}

if (target_layer_id == layer_names_.size()) {

LOG(INFO) << "Ignoring source layer " << source_layer_name;

continue;

}

DLOG(INFO) << "Copying source layer " << source_layer_name;

vector<shared_ptr<Blob<Dtype> > >& target_blobs =

layers_[target_layer_id]->blobs();

CHECK_EQ(target_blobs.size(), source_layer.blobs_size())

<< "Incompatible number of blobs for layer " << source_layer_name;

for (int j = 0; j < target_blobs.size(); ++j) {

if (!target_blobs[j]->ShapeEquals(source_layer.blobs(j))) {

Blob<Dtype> source_blob;

const bool kReshape = true;

source_blob.FromProto(source_layer.blobs(j), kReshape);

LOG(FATAL) << "Cannot copy param " << j << " weights from layer '"

<< source_layer_name << "'; shape mismatch. Source param shape is "

<< source_blob.shape_string() << "; target param shape is "

<< target_blobs[j]->shape_string() << ". "

<< "To learn this layer's parameters from scratch rather than "

<< "copying from a saved net, rename the layer.";

}

const bool kReshape = false;

target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);

}

}

}

template <typename Dtype>

void Net<Dtype>::CopyTrainedLayersFrom(const string& trained_filename) {

if (H5Fis_hdf5(trained_filename.c_str())) {

CopyTrainedLayersFromHDF5(trained_filename);

} else {

CopyTrainedLayersFromBinaryProto(trained_filename);

}

}

template <typename Dtype>

void Net<Dtype>::CopyTrainedLayersFromBinaryProto(

const string& trained_filename) {

NetParameter param;

ReadNetParamsFromBinaryFileOrDie(trained_filename, &param);

CopyTrainedLayersFrom(param);

}

template <typename Dtype>

void Net<Dtype>::CopyTrainedLayersFromHDF5(const string& trained_filename) {

#ifdef USE_HDF5

hid_t file_hid = H5Fopen(trained_filename.c_str(), H5F_ACC_RDONLY,

H5P_DEFAULT);

CHECK_GE(file_hid, 0) << "Couldn't open " << trained_filename;

hid_t data_hid = H5Gopen2(file_hid, "data", H5P_DEFAULT);

CHECK_GE(data_hid, 0) << "Error reading weights from " << trained_filename;

int num_layers = hdf5_get_num_links(data_hid);

for (int i = 0; i < num_layers; ++i) {

string source_layer_name = hdf5_get_name_by_idx(data_hid, i);

if (!layer_names_index_.count(source_layer_name)) {

LOG(INFO) << "Ignoring source layer " << source_layer_name;

continue;

}

int target_layer_id = layer_names_index_[source_layer_name];

DLOG(INFO) << "Copying source layer " << source_layer_name;

vector<shared_ptr<Blob<Dtype> > >& target_blobs =

layers_[target_layer_id]->blobs();

hid_t layer_hid = H5Gopen2(data_hid, source_layer_name.c_str(),

H5P_DEFAULT);

CHECK_GE(layer_hid, 0)

<< "Error reading weights from " << trained_filename;

// Check that source layer doesn't have more params than target layer

int num_source_params = hdf5_get_num_links(layer_hid);

CHECK_LE(num_source_params, target_blobs.size())

<< "Incompatible number of blobs for layer " << source_layer_name;

for (int j = 0; j < target_blobs.size(); ++j) {

ostringstream oss;

oss << j;

string dataset_name = oss.str();

int target_net_param_id = param_id_vecs_[target_layer_id][j];

if (!H5Lexists(layer_hid, dataset_name.c_str(), H5P_DEFAULT)) {

// Target param doesn't exist in source weights...

if (param_owners_[target_net_param_id] != -1) {

// ...but it's weight-shared in target, so that's fine.

continue;

} else {

LOG(FATAL) << "Incompatible number of blobs for layer "

<< source_layer_name;

}

}

hdf5_load_nd_dataset(layer_hid, dataset_name.c_str(), 0, kMaxBlobAxes,

target_blobs[j].get());

}

H5Gclose(layer_hid);

}

H5Gclose(data_hid);

H5Fclose(file_hid);

#else

LOG(FATAL) << "CopyTrainedLayersFromHDF5 requires hdf5;"

<< " compile with USE_HDF5.";

#endif // USE_HDF5

}

template <typename Dtype>

void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {

param->Clear();

param->set_name(name_);

// Add bottom and top

DLOG(INFO) << "Serializing " << layers_.size() << " layers";

for (int i = 0; i < layers_.size(); ++i) {

LayerParameter* layer_param = param->add_layer();

layers_[i]->ToProto(layer_param, write_diff);

}

}

template <typename Dtype>

void Net<Dtype>::ToHDF5(const string& filename, bool write_diff) const {

// This code is taken from https://github.com/sh1r0/caffe-android-lib

#ifdef USE_HDF5

hid_t file_hid = H5Fcreate(filename.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT,

H5P_DEFAULT);

CHECK_GE(file_hid, 0)

<< "Couldn't open " << filename << " to save weights.";

hid_t data_hid = H5Gcreate2(file_hid, "data", H5P_DEFAULT, H5P_DEFAULT,

H5P_DEFAULT);

CHECK_GE(data_hid, 0) << "Error saving weights to " << filename << ".";

hid_t diff_hid = -1;

if (write_diff) {

diff_hid = H5Gcreate2(file_hid, "diff", H5P_DEFAULT, H5P_DEFAULT,

H5P_DEFAULT);

CHECK_GE(diff_hid, 0) << "Error saving weights to " << filename << ".";

}

for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) {

const LayerParameter& layer_param = layers_[layer_id]->layer_param();

string layer_name = layer_param.name();

hid_t layer_data_hid = H5Gcreate2(data_hid, layer_name.c_str(),

H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);

CHECK_GE(layer_data_hid, 0)

<< "Error saving weights to " << filename << ".";

hid_t layer_diff_hid = -1;

if (write_diff) {

layer_diff_hid = H5Gcreate2(diff_hid, layer_name.c_str(),

H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);

CHECK_GE(layer_diff_hid, 0)

<< "Error saving weights to " << filename << ".";

}

int num_params = layers_[layer_id]->blobs().size();

for (int param_id = 0; param_id < num_params; ++param_id) {

ostringstream dataset_name;

dataset_name << param_id;

const int net_param_id = param_id_vecs_[layer_id][param_id];

if (param_owners_[net_param_id] == -1) {

// Only save params that own themselves

hdf5_save_nd_dataset<Dtype>(layer_data_hid, dataset_name.str(),

*params_[net_param_id]);

}

if (write_diff) {

// Write diffs regardless of weight-sharing

hdf5_save_nd_dataset<Dtype>(layer_diff_hid, dataset_name.str(),

*params_[net_param_id], true);

}

}

H5Gclose(layer_data_hid);

if (write_diff) {

H5Gclose(layer_diff_hid);

}

}

H5Gclose(data_hid);

if (write_diff) {

H5Gclose(diff_hid);

}

H5Fclose(file_hid);

// This code is taken from https://github.com/sh1r0/caffe-android-lib

#else

LOG(FATAL) << "ToHDF5 requires hdf5; compile with USE_HDF5.";

#endif // USE_HDF5

}

template <typename Dtype>

void Net<Dtype>::Update() {

for (int i = 0; i < learnable_params_.size(); ++i) {

learnable_params_[i]->Update();

}

}

template <typename Dtype>

void Net<Dtype>::ClearParamDiffs() {

for (int i = 0; i < learnable_params_.size(); ++i) {

Blob<Dtype>* blob = learnable_params_[i];

switch (Caffe::mode()) {

case Caffe::CPU:

caffe_set(blob->count(), static_cast<Dtype>(0),

blob->mutable_cpu_diff());

break;

case Caffe::GPU:

#ifndef CPU_ONLY

caffe_gpu_set(blob->count(), static_cast<Dtype>(0),

blob->mutable_gpu_diff());

#else

NO_GPU;

#endif

break;

}

}

}

template <typename Dtype>

void Net<Dtype>::ShareWeights() {

for (int i = 0; i < params_.size(); ++i) {

if (param_owners_[i] < 0) { continue; }

params_[i]->ShareData(*params_[param_owners_[i]]);

params_[i]->ShareDiff(*params_[param_owners_[i]]);

}

}

template <typename Dtype>

bool Net<Dtype>::has_blob(const string& blob_name) const {

return blob_names_index_.find(blob_name) != blob_names_index_.end();

}

template <typename Dtype>

const shared_ptr<Blob<Dtype> > Net<Dtype>::blob_by_name(

const string& blob_name) const {

shared_ptr<Blob<Dtype> > blob_ptr;

if (has_blob(blob_name)) {

blob_ptr = blobs_[blob_names_index_.find(blob_name)->second];

} else {

blob_ptr.reset((Blob<Dtype>*)(NULL));

LOG(WARNING) << "Unknown blob name " << blob_name;

}

return blob_ptr;

}

template <typename Dtype>

bool Net<Dtype>::has_layer(const string& layer_name) const {

return layer_names_index_.find(layer_name) != layer_names_index_.end();

}

template <typename Dtype>

const shared_ptr<Layer<Dtype> > Net<Dtype>::layer_by_name(

const string& layer_name) const {

shared_ptr<Layer<Dtype> > layer_ptr;

if (has_layer(layer_name)) {

layer_ptr = layers_[layer_names_index_.find(layer_name)->second];

} else {

layer_ptr.reset((Layer<Dtype>*)(NULL));

LOG(WARNING) << "Unknown layer name " << layer_name;

}

return layer_ptr;

}

INSTANTIATE_CLASS(Net);

} // namespace caffe