#include <algorithm>

#include <map>

#include <set>

#include <string>

#include <utility>

#include <vector>

#include "caffe/common.hpp"

#include "caffe/layer.hpp"

#include "caffe/net.hpp"

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

#include "caffe/util/insert_splits.hpp"

#include "caffe/util/io.hpp"

#include "caffe/util/math_functions.hpp"

#include "caffe/util/upgrade_proto.hpp"

#include "caffe/test/test_caffe_main.hpp"

namespace caffe {

template <typename Dtype>

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

Init(param);

}

template <typename Dtype>

Net<Dtype>::Net(const string& param_file) {

NetParameter param;

ReadNetParamsFromTextFileOrDie(param_file, &param);

Init(param);

}

template <typename Dtype>

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

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

// the current NetState.

NetParameter filtered_param;

FilterNet(in_param, &filtered_param);

LOG(INFO) << "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 its connections.

name_ = param.name();

map<string, int> blob_name_to_idx;

set<string> available_blobs;

CHECK_EQ(param.input_size() * 4, param.input_dim_size())

<< "Incorrect input blob dimension specifications.";

memory_used_ = 0;

// set the input blobs

for (int input_id = 0; input_id < param.input_size(); ++input_id) {

const int layer_id = -1; // inputs have fake layer ID -1

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

}

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

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

bottom_vecs_.resize(param.layers_size());

top_vecs_.resize(param.layers_size());

bottom_id_vecs_.resize(param.layers_size());

top_id_vecs_.resize(param.layers_size());

bottom_need_backward_.resize(param.layers_size());

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

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

layers_.push_back(shared_ptr<Layer<Dtype> >(GetLayer<Dtype>(layer_param)));

layer_names_.push_back(layer_param.name());

LOG(INFO) << "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);

}

// 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.

LOG(INFO) << "Setting up " << layer_names_[layer_id];

layers_[layer_id]->SetUp(bottom_vecs_[layer_id], &top_vecs_[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(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->num() << " "

<< top_vecs_[layer_id][top_id]->channels() << " "

<< top_vecs_[layer_id][top_id]->height() << " "

<< top_vecs_[layer_id][top_id]->width() << " ("

<< top_vecs_[layer_id][top_id]->count() << ")";

if (layer->loss(top_id)) {

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

}

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

}

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

const int blobs_lr_size = layer_param.blobs_lr_size();

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

CHECK(blobs_lr_size == num_param_blobs || blobs_lr_size == 0)

<< "Incorrect blobs lr size: should be either 0 "

<< "or the same as the number of the layer's parameter blobs.";

if (blobs_lr_size) {

// Check if this layer needs backward operation itself

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

const bool param_need_backward = layer_param.blobs_lr(param_id) > 0;

need_backward |= param_need_backward;

layers_[layer_id]->set_param_propagate_down(param_id,

param_need_backward);

}

} else if (layers_[layer_id]->blobs().size()) {

// catch: if a layer param does not specify blobs_lr, we should assume the

// learning rate to be 1. Thus we will need to perform backward.

need_backward = true;

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

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

}

}

const int param_size = layer_param.param_size();

CHECK(param_size == num_param_blobs || param_size == 0)

<< "Incorrect param size: should be either 0 or the same as "

"the number of the layer's parameter blobs: " << num_param_blobs;

const int blob_share_mode_size = layer_param.blob_share_mode_size();

CHECK(blob_share_mode_size == num_param_blobs || blob_share_mode_size == 0)

<< "Incorrect blob_share_mode size: should be either 0 or the same as "

"the number of the layer's parameter blobs: " << num_param_blobs;

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.

set<string> blobs_under_loss;

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

bool layer_contributes_loss = false;

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;

break;

}

}

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

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;

}

}

}

// 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(INFO) << "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;

}

GetLearningRateAndWeightDecay();

LOG(INFO) << "Network initialization done.";

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

// Don't display debug info by default.

debug_info_ = false;

}

template <typename Dtype>

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

NetParameter* param_filtered) {

NetState net_state(param.state());

// Let the phase of the net be the current global phase provided in the Caffe

// singleton, unless explicitly provided by the state.

if (!net_state.has_phase()) {

switch (Caffe::phase()) {

case Caffe::TRAIN:

net_state.set_phase(TRAIN);

break;

case Caffe::TEST:

net_state.set_phase(TEST);

break;

default:

LOG(FATAL) << "Unknown phase: " << Caffe::phase();

}

}

param_filtered->CopyFrom(param);

param_filtered->clear_layers();

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

const LayerParameter& layer_param = param.layers(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_layers()->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(INFO) << "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(INFO) << "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(INFO) << "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(INFO) << "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(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i)

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

return false;

}

}

return true;

}

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((layer_id >= 0) ?

(new LayerParameter(param.layers(layer_id))) : NULL);

const string& blob_name = layer_param ?

(layer_param->top_size() > top_id ?

layer_param->top(top_id) : "(automatic)") : param.input(top_id);

// Check if we are doing in-place computation

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

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

// In-place computation

LOG(INFO) << 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) << "Duplicate blobs produced by multiple sources.";

} else {

// Normal output.

if (layer_param) {

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

} else {

LOG(INFO) << "Input " << top_id << " -> " << 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; }

if (layer_id == -1) {

// Set the (explicitly specified) dimensions of the input blob.

blob_pointer->Reshape(param.input_dim(top_id * 4),

param.input_dim(top_id * 4 + 1),

param.input_dim(top_id * 4 + 2),

param.input_dim(top_id * 4 + 3));

net_input_blob_indices_.push_back(blob_id);

net_input_blobs_.push_back(blob_pointer.get());

} else {

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); }

}

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.layers(layer_id);

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

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

LOG(FATAL) << "Unknown blob input " << blob_name

<< " (at index " << bottom_id << ") to layer " << layer_id;

}

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

LOG(INFO) << 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);

const bool need_backward = blob_need_backward_[blob_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 ? layer_param.param(param_id) : "";

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

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_size) {

param_names_index_[param_name] = net_param_id;

}

} 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(INFO) << "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 blob_share_mode_size = layer_param.blob_share_mode_size();

if (blob_share_mode_size > param_id &&

(layer_param.blob_share_mode(param_id) ==

LayerParameter_DimCheckMode_PERMISSIVE)) {

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

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

<< "Shared parameter blobs must have the same count.";

} else {

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

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

<< "Shared parameter blobs must have the same num.";

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

<< "Shared parameter blobs must have the same channels.";

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

<< "Shared parameter blobs must have the same height.";

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

<< "Shared parameter blobs must have the same width.";

}

layers_[layer_id]->blobs()[param_id]->ShareData(

*layers_[owner_layer_id]->blobs()[owner_param_id]);

}

}

template <typename Dtype>

void Net<Dtype>::GetLearningRateAndWeightDecay() {

LOG(INFO) << "Collecting Learning Rate and Weight Decay.";

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

vector<shared_ptr<Blob<Dtype> > >& layer_blobs = layers_[i]->blobs();

// push the learning rate mutlipliers

if (layers_[i]->layer_param().blobs_lr_size()) {

CHECK_EQ(layers_[i]->layer_param().blobs_lr_size(), layer_blobs.size());

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

float local_lr = layers_[i]->layer_param().blobs_lr(j);

CHECK_GE(local_lr, 0.);

params_lr_.push_back(local_lr);

}

} else {

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

params_lr_.push_back(1.);

}

}

// push the weight decay multipliers

if (layers_[i]->layer_param().weight_decay_size()) {

CHECK_EQ(layers_[i]->layer_param().weight_decay_size(),

layer_blobs.size());

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

float local_decay = layers_[i]->layer_param().weight_decay(j);

CHECK_GE(local_decay, 0.);

params_weight_decay_.push_back(local_decay);

}

} else {

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

params_weight_decay_.push_back(1.);

}

}

}

}

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) {

// LOG(ERROR) << "Forwarding " << layer_names_[i];

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

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

loss += layer_loss;

if (debug_info_) { ForwardDebugInfo(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>::ForwardPrefilled(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) {

// Copy bottom to internal bottom

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

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

}

return ForwardPrefilled(loss);

}

template <typename Dtype>

string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss) {

BlobProtoVector blob_proto_vec;

if (net_input_blobs_.size()) {

blob_proto_vec.ParseFromString(input_blob_protos);

CHECK_EQ(blob_proto_vec.blobs_size(), net_input_blobs_.size())

<< "Incorrect input size.";

for (int i = 0; i < blob_proto_vec.blobs_size(); ++i) {

net_input_blobs_[i]->FromProto(blob_proto_vec.blobs(i));

}

}

ForwardPrefilled(loss);

blob_proto_vec.Clear();

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

net_output_blobs_[i]->ToProto(blob_proto_vec.add_blobs());

}

string output;

blob_proto_vec.SerializeToString(&output);

return output;

}

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) {

if (layer_need_backward_[i]) {

layers_[i]->Backward(

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

if (debug_info_) { BackwardDebugInfo(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(INFO) << " [Forward] "

<< "Layer " << layer_names_[layer_id] << ", top 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(INFO) << " [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(INFO) << " [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(INFO) << " [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(INFO) << " [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(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()) {

DLOG(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_EQ(target_blobs[j]->num(), source_blob->num());

CHECK_EQ(target_blobs[j]->channels(), source_blob->channels());

CHECK_EQ(target_blobs[j]->height(), source_blob->height());

CHECK_EQ(target_blobs[j]->width(), source_blob->width());

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);

}

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.layers_size();

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

const LayerParameter& source_layer = param.layers(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()) {

DLOG(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) {

CHECK_EQ(target_blobs[j]->num(), source_layer.blobs(j).num());

CHECK_EQ(target_blobs[j]->channels(), source_layer.blobs(j).channels());

CHECK_EQ(target_blobs[j]->height(), source_layer.blobs(j).height());

CHECK_EQ(target_blobs[j]->width(), source_layer.blobs(j).width());

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

}

}

}

template <typename Dtype>

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

NetParameter param;

ReadNetParamsFromBinaryFileOrDie(trained_filename, &param);

CopyTrainedLayersFrom(param);

}

template <typename Dtype>

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

param->Clear();

param->set_name(name_);

// Add bottom and top

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

param->add_input(blob_names_[net_input_blob_indices_[i]]);

}

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

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

LayerParameter* layer_param = param->add_layers();

for (int j = 0; j < bottom_id_vecs_[i].size(); ++j) {

layer_param->add_bottom(blob_names_[bottom_id_vecs_[i][j]]);

}

for (int j = 0; j < top_id_vecs_[i].size(); ++j) {

layer_param->add_top(blob_names_[top_id_vecs_[i][j]]);

}

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

}

}

template <typename Dtype>

void Net<Dtype>::Update() {

// First, accumulate the diffs of any shared parameters into their owner's

// diff. (Assumes that the learning rate, weight decay, etc. have already been

// accounted for in the current diff.)

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

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

if (debug_info_) { UpdateDebugInfo(i); }

const int count = params_[i]->count();

const Dtype* this_diff;

Dtype* owner_diff;

switch (Caffe::mode()) {

case Caffe::CPU:

this_diff = params_[i]->cpu_diff();

owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();

caffe_add(count, this_diff, owner_diff, owner_diff);

break;

#ifndef CPU_ONLY

case Caffe::GPU:

this_diff = params_[i]->gpu_diff();

owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();

caffe_gpu_add(count, this_diff, owner_diff, owner_diff);

break;

#else

NO_GPU;

#endif

default:

LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();

}

}

// Now, update the owned parameters.

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

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

if (debug_info_) { UpdateDebugInfo(i); }

params_[i]->Update();

}

}

template <typename Dtype>

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

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) {

shared_ptr<Blob<Dtype> > blob_ptr;

if (has_blob(blob_name)) {

blob_ptr = blobs_[blob_names_index_[blob_name]];

} 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) {

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) {

shared_ptr<Layer<Dtype> > layer_ptr;

if (has_layer(layer_name)) {

layer_ptr = layers_[layer_names_index_[layer_name]];

} else {

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

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

}

return layer_ptr;

}

INSTANTIATE_CLASS(Net);

} // namespace caffe