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Created April 12, 2018 08:55
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NVcaffe-0.17: allow duplicate top blobs during inference to save memory
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net.cpp
#include <algorithm>
#include <map>
#include <set>
#include <boost/thread.hpp>
#include <hdf5.h>
#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/net.hpp"
#include "caffe/parallel.hpp"
#include "caffe/util/hdf5.hpp"
#include "caffe/util/insert_splits.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/util/signal_handler.h"
#include "caffe/util/upgrade_proto.hpp"
#include "caffe/test/test_caffe_main.hpp"
namespace caffe {
constexpr int Net::END_OF_ITERATION;
constexpr int Net::END_OF_TRAIN;
Net::Net(const NetParameter& param,
size_t solver_rank,
Flag* solver_init_flag,
const Net* root_net,
bool inner_net,
int level,
const vector<string>* stages)
: root_net_(root_net),
solver_(nullptr),
solver_rank_(solver_rank),
solver_init_flag_(solver_init_flag),
inner_net_(inner_net) {
Init(param);
}
Net::Net(const string& param_file,
Phase phase,
size_t solver_rank,
Flag* solver_init_flag,
const Net* root_net,
bool inner_net,
int level,
const vector<string>* stages)
: root_net_(root_net),
solver_(nullptr),
solver_rank_(solver_rank),
solver_init_flag_(solver_init_flag),
inner_net_(inner_net) {
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->at(i));
}
}
param.mutable_state()->set_level(level);
Init(param);
}
Net::~Net() {
}
void Net::Init(const NetParameter& in_param) {
CHECK(inner_net_ || Caffe::root_solver() || root_net_)
<< "root_net_ needs to be set for all non-root solvers";
// 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);
net_param_ = filtered_param;
batch_per_solver_ = caffe::P2PSync::divide_batch_size(&filtered_param);
LOG_IF(INFO, Caffe::root_solver())
<< "Initializing net from parameters: " << std::endl
<< filtered_param.DebugString();
infer_count_ = 0UL;
// 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;
gpu_top_memory_data_use_ = gpu_top_memory_diff_use_ = 0UL;
gpu_btm_memory_data_use_ = gpu_btm_memory_diff_use_ = 0UL;
gpu_shr_memory_data_use_ = gpu_shr_memory_diff_use_ = 0UL;
gpu_prm_memory_data_use_ = gpu_prm_memory_diff_use_ = 0UL;
gpu_shp_memory_data_use_ = gpu_shp_memory_diff_use_ = 0UL;
// 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());
// If user skips default math type we use default data type:
Type default_fmath, default_bmath;
if (in_param.has_default_forward_math()) {
default_fmath = in_param.default_forward_math();
} else {
default_fmath = in_param.default_forward_type();
LOG(INFO) << "Using " << Type_Name(default_fmath) << " as default forward math type";
}
if (in_param.has_default_backward_math()) {
default_bmath = in_param.default_backward_math();
} else {
default_bmath = in_param.default_backward_type();
LOG(INFO) << "Using " << Type_Name(default_bmath) << " as default backward math type";
}
wgrad_sq_.store(0LL);
global_grad_scale_coeff_ = 1.F;
has_global_grad_scale_param_ = in_param.has_global_grad_scale();
global_grad_scale_param_ = in_param.global_grad_scale();
global_grad_scale_adaptive_ = in_param.global_grad_scale_adaptive();
for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
// For non-root solvers, whether this layer is shared from root_net_.
bool share_from_root = !inner_net_ && !Caffe::root_solver()
&& root_net_->layers_[layer_id]->ShareInParallel();
const LayerParameter& layer_param = param.layer(layer_id);
LayerParameter* mutable_layer_param = param.mutable_layer(layer_id);
DLOG_IF(INFO, Caffe::root_solver())
<< "Setting types for Layer " << layer_param.name();
// Inherit phase from net if unset.
if (!layer_param.has_phase()) {
mutable_layer_param->set_phase(phase_);
}
const bool is_data_layer = layer_param.has_transform_param();
// Data&Math types
const bool fm_by_user = layer_param.has_forward_math();
if (!fm_by_user) {
if (layer_param.has_forward_type()) {
mutable_layer_param->set_forward_math(layer_param.forward_type());
} else {
mutable_layer_param->set_forward_math(default_fmath);
}
}
const bool bm_by_user = layer_param.has_backward_math();
if (!bm_by_user) {
if (layer_param.has_backward_type()) {
mutable_layer_param->set_backward_math(layer_param.backward_type());
} else {
mutable_layer_param->set_backward_math(default_bmath);
}
}
if (!layer_param.has_forward_type()) {
mutable_layer_param->set_forward_type(in_param.default_forward_type());
}
if (!layer_param.has_backward_type()) {
if (is_data_layer) {
// In majority of cases we manage to avoid redundant conversion:
mutable_layer_param->set_backward_type(FLOAT);
} else {
mutable_layer_param->set_backward_type(in_param.default_backward_type());
}
}
// Convolution algorithms
if (param.has_default_conv_algos_override() && layer_param.has_convolution_param() &&
!layer_param.convolution_param().has_conv_algos_override()) {
mutable_layer_param->mutable_convolution_param()->
set_conv_algos_override(param.default_conv_algos_override());
}
// cuDNN math
if (param.has_default_cudnn_math_override() &&
!layer_param.has_cudnn_math_override()) {
mutable_layer_param->set_cudnn_math_override(param.default_cudnn_math_override());
}
// Setup layer.
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 ";
}
if (share_from_root) {
LOG(INFO) << "Sharing layer " << layer_param.name() << " from root net";
layers_.push_back(root_net_->layers_[layer_id]);
layers_[layer_id]->SetShared(true);
} else {
layers_.push_back(LayerRegistry::CreateLayer(layer_param, solver_rank_));
}
layer_names_.push_back(layer_param.name());
LOG_IF(INFO, Caffe::root_solver())
<< "Created Layer " << layer_param.name() << " (" << layer_id << ")";
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.
LayerBase* 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);
}
}
layer->fm_by_user(fm_by_user);
layer->bm_by_user(bm_by_user);
layers_[layer_id]->set_net_initialized_flag(solver_init_flag_);
Flag* layer_inititialized_flag = layers_[layer_id]->layer_inititialized_flag();
if (layer_inititialized_flag != nullptr) {
layer_inititialized_flags_.push_back(layer_inititialized_flag);
}
// After this layer is connected, set it up.
if (share_from_root) {
// Set up size of top blobs using root_net_
const vector<Blob*>& base_top = root_net_->top_vecs_[layer_id];
const vector<Blob*>& this_top = this->top_vecs_[layer_id];
for (int top_id = 0; top_id < base_top.size(); ++top_id) {
this_top[top_id]->ReshapeLike(*base_top[top_id]);
LOG(INFO) << "Created top blob " << top_id << " (shape: "
<< this_top[top_id]->shape_string() << ") for shared layer "
<< layer_param.name();
}
} else {
layers_[layer_id]->set_parent_net(this);
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, 0.F);
}
blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);
LOG_IF(INFO, Caffe::root_solver())
<< Phase_Name(phase_) << " Top shape for layer " << layer_id << " '"
<< layer_names_[layer_id] << "' " << top_vecs_[layer_id][top_id]->shape_string();
if (layer->loss(top_id) != 0.F) {
LOG_IF(INFO, Caffe::root_solver())
<< " with loss weight " << layer->loss(top_id);
}
gpu_top_memory_data_use_ += top_vecs_[layer_id][top_id]->gpu_memory_data_use();
gpu_top_memory_diff_use_ += top_vecs_[layer_id][top_id]->gpu_memory_diff_use();
}
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) != 0.F ||
(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 (int blob_id = 0; blob_id < blob_names_.size(); ++blob_id) {
blob_names_index_[blob_names_[blob_id]] = blob_id;
}
for (int layer_id = 0; layer_id < layer_names_.size(); ++layer_id) {
layer_names_index_[layer_names_[layer_id]] = layer_id;
}
ShareWeights();
// invert param_layer_indices_ to give map of
// (level_id, local param_id) -> global param_id
for (int i = 0; i < param_layer_indices_.size(); ++i) {
layer_index_params_[param_layer_indices_[i]] = i;
}
learnable_space_size_[0] = 0UL;
learnable_space_size_[1] = 0UL;
reduce_buckets_ = (size_t) in_param.reduce_buckets();
if (Caffe::device_count() > 0) {
LOG_IF(INFO, Caffe::root_solver())
<< "Top memory (" << Phase_Name(phase_) << ") required for data: "
<< gpu_top_memory_data_use_ << " diff: " << gpu_top_memory_diff_use_;
LOG_IF(INFO, Caffe::root_solver())
<< "Bottom memory (" << Phase_Name(phase_) << ") required for data: "
<< gpu_btm_memory_data_use_ << " diff: " << gpu_btm_memory_diff_use_;
LOG_IF(INFO, Caffe::root_solver())
<< "Shared (in-place) memory (" << Phase_Name(phase_) << ") by data: "
<< gpu_shr_memory_data_use_ << " diff: " << gpu_shr_memory_diff_use_;
LOG_IF(INFO, Caffe::root_solver())
<< "Parameters memory (" << Phase_Name(phase_) << ") required for data: "
<< gpu_prm_memory_data_use_ << " diff: " << gpu_prm_memory_diff_use_;
LOG_IF(INFO, Caffe::root_solver())
<< "Parameters shared memory (" << Phase_Name(phase_) << ") by data: "
<< gpu_shp_memory_data_use_ << " diff: " << gpu_shp_memory_diff_use_;
}
debug_info_ = param.debug_info();
trained_layers_shared_ = false;
LOG_IF(INFO, Caffe::root_solver()) << "Network initialization done.";
}
void Net::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);
}
}
}
bool Net::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.
void Net::AppendTop(const NetParameter& param, const int layer_id, const int top_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.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]);
gpu_shr_memory_data_use_ += top_vecs_[layer_id].back()->gpu_memory_data_use();
gpu_shr_memory_diff_use_ += top_vecs_[layer_id].back()->gpu_memory_diff_use();
} else if (blob_name_to_idx && phase_ == TEST &&
blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {
// Allow duplicated top-blobs during inference to save memory.
LOG_IF(INFO, Caffe::root_solver())
<< layer_param.name() << " -> " << blob_name << " (duplicated)";
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]);
gpu_shr_memory_data_use_ += top_vecs_[layer_id].back()->gpu_memory_data_use();
gpu_shr_memory_diff_use_ += top_vecs_[layer_id].back()->gpu_memory_diff_use();
} else if (blob_name_to_idx &&
blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {
// Raise an error for duplicated top-blobs during training.
LOG(FATAL) << "Top blob '" << blob_name
<< "' produced by multiple sources.";
} else {
// Normal output.
if (Caffe::root_solver()) {
LOG(INFO) << layer_param.name() << " -> " << blob_name;
}
Type ftype = layer_param.has_forward_type() ? layer_param.forward_type() :
param.default_forward_type();
Type btype = layer_param.has_backward_type() ? layer_param.backward_type() :
param.default_backward_type();
shared_ptr<Blob> blob_pointer = Blob::create(ftype, btype);
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.
int Net::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);
gpu_btm_memory_data_use_ += bottom_vecs_[layer_id].back()->gpu_memory_data_use();
gpu_btm_memory_diff_use_ += bottom_vecs_[layer_id].back()->gpu_memory_diff_use();
return blob_id;
}
void Net::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]);
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* this_blob = layers_[layer_id]->blobs()[param_id].get();
Blob* 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();
}
}
}
}
float Net::ForwardFromTo(int start, int end) {
CHECK_GE(start, 0);
CHECK_LT(end, layers_.size());
float loss = 0;
for (int i = start; i <= end; ++i) {
// LOG(INFO) << " ****** [Forward] (" << i << ") Layer '" << layer_names_[i];
// << "' FT " << Type_Name(layers_[i]->forward_type())
// << " BT " << Type_Name(layers_[i]->backward_type());
float layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);
loss += layer_loss;
if (debug_info_) { ForwardDebugInfo(i); }
}
++infer_count_;
return loss;
}
float Net::ForwardFrom(int start) {
return ForwardFromTo(start, layers_.size() - 1);
}
float Net::ForwardTo(int end) {
return ForwardFromTo(0, end);
}
const vector<Blob*>& Net::Forward(float* loss) {
if (loss != NULL) {
*loss = ForwardFromTo(0, layers_.size() - 1);
} else {
ForwardFromTo(0, layers_.size() - 1);
}
return net_output_blobs_;
}
const vector<Blob*>& Net::Forward(const vector<Blob*>& bottom, float* 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);
}
float Net::ForwardBackward(bool apply_update) {
float loss;
Forward(&loss);
Backward(apply_update);
return loss;
}
void Net::BackwardFromTo(int start, int end) {
BackwardFromToAu(start, end, true);
}
void Net::BackwardFromToAu(int start, int end, bool apply_update) {
CHECK_GE(end, 0);
CHECK_LT(start, layers_.size());
for (int i = start; i >= end; --i) {
if (!layer_need_backward_[i]) {
continue;
}
layers_[i]->Backward(top_vecs_[i], bottom_need_backward_[i], bottom_vecs_[i]);
if (debug_info_) {
BackwardDebugInfo(i);
}
if (!apply_update) {
continue;
}
for (int j = 0; j < layers_[i]->blobs().size(); ++j) {
if (layers_[i]->skip_apply_update(j)) {
continue;
}
const int param_id = layer_index_params_[make_pair(i, j)];
if (param_owners_[param_id] < 0) {
const int lparam_id = learnable_param_ids_[param_id];
int t = (int)learnable_params_[lparam_id]->diff_type();
for (int type_id = 0; type_id < learnable_types().size(); ++type_id) {
if (t == learnable_types_[type_id]) {
reduction_queue_[type_id].push(lparam_id);
break;
}
}
} // leave it to the owner otherwise
}
}
if (apply_update) {
for (int type_id = 0; type_id < learnable_types_.size(); ++type_id) {
reduction_queue_[type_id].push(END_OF_ITERATION);
}
}
}
void Net::Finalize() {
for (int type_id = 0; type_id < learnable_types_.size(); ++type_id) {
reduction_queue_[type_id].push(END_OF_TRAIN);
}
}
size_t Net::received_contiguous_count(int type_id, const std::set<int>& au_ids, int& id_from_ret) {
if (learnable_params_.empty() || au_ids.empty() || param_id_vecs_.empty()) {
return 0;
}
size_t cnt_ret = 0UL, cnt = 0UL;
const int bottom = *au_ids.begin();
const int top = *au_ids.rbegin();
id_from_ret = -1;
const std::map<size_t, std::set<int>>& ltop = ltop_[type_id];
for (auto lit = ltop.rbegin(); lit != ltop.rend(); ++lit) {
if (lit->second.empty() || *lit->second.begin() > top) {
continue;
}
bool layer_complete = true;
for (auto p = lit->second.begin(); p != lit->second.end(); ++p) {
int param_id = *p;
if (param_id < bottom || au_ids.find(param_id) == au_ids.end()) {
layer_complete = false;
break;
}
cnt += lp_aligned_count(param_id);
}
if (layer_complete) {
id_from_ret = *lit->second.begin();
cnt_ret = cnt;
} else {
break;
}
}
return cnt_ret;
}
void Net::ReduceAndUpdate(int type_id) {
DLOG(INFO) << "[" << Caffe::current_device()
<< "] Entering ReduceAndUpdate thread " << lwp_id()
<< ", type_id " << type_id;
size_t bucket_size = 0UL;
cublasHandle_t handle = Caffe::cublas_handle(type_id);
CHECK_GE(reduce_buckets_, 0);
if (Caffe::solver_count() > 1 && reduce_buckets_ > 0) {
bucket_size = align_up<6>(learnable_space_size_[type_id] / reduce_buckets_);
}
std::set<int> au_ids;
const bool clip_grads = solver_->param().clip_gradients() >= 0.F;
const bool clear_grads = !solver_->param().snapshot_diff() && !clip_grads;
const bool use_buckets = reduce_buckets_ > 0;
float rate = -1.F;
while (!solver_->stop_reducing_requested(type_id)) {
const int param_id = reduction_queue_[type_id].pop();
SolverAction::Enum request = solver_->GetRequestedAction();
if (SolverAction::STOP == request) {
solver_->request_early_exit();
break;
}
if (param_id == END_OF_TRAIN) {
break;
}
if (rate < 0.F) {
rate = solver_->GetLearningRate();
}
if (param_id != END_OF_ITERATION) {
if (Caffe::solver_count() > 1) {
if (!use_buckets && !clip_grads) {
Reduce(type_id, param_id);
if (solver_->stop_reducing_requested(type_id)) {
break;
}
add_wgrad_sq(solver_->ApplyUpdate(param_id, handle, rate, true, clear_grads));
continue;
}
} else {
if (!clip_grads) {
this->learnable_params()[param_id]->scale_diff(1.F / global_grad_scale(), handle);
add_wgrad_sq(solver_->ApplyUpdate(param_id, handle, rate, true, clear_grads));
}
continue;
}
} else if (clip_grads && Caffe::solver_count() == 1) {
solver_->ClipGradientsAndNormalize(handle, type_id, au_ids);
for (int i : au_ids) {
add_wgrad_sq(solver_->ApplyUpdate(i, handle, rate, false, clear_grads));
}
au_ids.clear();
}
if (!learnable_params_.empty() && Caffe::solver_count() > 1) {
int id_from = -1;
// Is bucket big enough? Done with iteration?
const size_t received_count = received_contiguous_count(type_id, au_ids, id_from);
if (id_from >= 0) {
const size_t received_size = received_count * lp_size(id_from);
if ((received_size >= bucket_size && !clip_grads) || param_id == END_OF_ITERATION) {
//#ifdef DEBUG
// {
// size_t c = 0UL;
// for (int i : au_ids) {
// if (i < id_from) {
// continue;
// }
// c += lp_aligned_count(i);
// }
// CHECK_EQ(c, received_count);
// }
//#endif
CHECK_EQ((int) learnable_params_[id_from]->diff_type(), learnable_types_[type_id]);
ReduceBucket(type_id, received_count, learnable_params_[id_from]->diff_type(),
learnable_params_ptrs_[type_id][id_from]);
if (solver_->stop_reducing_requested(type_id)) {
break;
}
if (clip_grads) {
solver_->ClipGradientsAndNormalize(handle, type_id, au_ids);
}
for (int i : au_ids) {
add_wgrad_sq(solver_->ApplyUpdate(i, handle, rate, !clip_grads, clear_grads));
}
au_ids.erase(au_ids.find(id_from), au_ids.end());
}
}
}
if (param_id == END_OF_ITERATION) {
CHECK(au_ids.empty());
rate = -1.F;
solver_->iteration_complete_signal(type_id);
} else {
au_ids.emplace(param_id);
}
}
DLOG(INFO) << "[" << Caffe::current_device()
<< "] Leaving ReduceAndUpdate thread " << lwp_id();
}
void Net::add_wgrad_sq(float wgrad_sq) {
if (wgrad_sq > 0.F) {
wgrad_sq_.fetch_add(std::llround(wgrad_sq * GRAD_FACTOR));
}
}
float Net::wgrad_sq() {
return wgrad_sq_.exchange(0LL) / GRAD_FACTOR;
}
void Net::update_grad_scale() {
global_grad_scale_coeff_ = 1.F;
if (global_grad_scale_enabled()) {
if (global_grad_scale_adaptive_) {
const float wgsq = wgrad_sq();
if (wgsq > 0.F) {
global_grad_scale_coeff_ = std::sqrt(wgsq) * global_grad_scale_param_;
return;
}
}
global_grad_scale_coeff_ = global_grad_scale_param_;
}
}
void Net::Reduce(int type_id, int param_id) {
Solver::Callback* cb = solver_->callback();
cb->reduce_barrier(type_id);
{
unique_ptr<unique_lock<shared_mutex>> lock;
if (solver_->is_root()) {
lock.reset(new unique_lock<shared_mutex>(GPUMemory::read_write_mutex()));
}
cb->reduce_barrier(type_id);
cb->allreduce(type_id, param_id);
cb->reduce_barrier(type_id);
}
this->learnable_params()[param_id]->
scale_diff(1.F / (Caffe::solver_count() * global_grad_scale()),
Caffe::cublas_handle(type_id));
// Also need to barrier to make sure lock isn't undone
// until all have completed, but the current nature of
// NCCL makes this unnecessary.
// solver_->callback()->reduce_barrier();
}
void Net::ReduceBucket(int type_id, size_t count, Type bucket_type, void* bucket) {
Solver::Callback* cb = solver_->callback();
cb->reduce_barrier(type_id);
{
unique_ptr<unique_lock<shared_mutex>> lock;
if (solver_->is_root()) {
lock.reset(new unique_lock<shared_mutex>(GPUMemory::read_write_mutex()));
}
cb->reduce_barrier(type_id);
cb->allreduce_bucket(type_id, count, bucket, bucket_type);
cb->reduce_barrier(type_id);
}
Tensor::gpu_scal(count, bucket_type, bucket, 1.F / (Caffe::solver_count() * global_grad_scale()),
Caffe::cublas_handle(type_id));
}
void Net::ForwardDebugInfo(const int layer_id) {
LOG_IF(INFO, Caffe::root_solver())
<< "[Forward] Layer " << layer_names_[layer_id];
for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
const Blob& blob = *top_vecs_[layer_id][top_id];
const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
const double data_abs_val_mean = blob.asum_data() / blob.count();
LOG_IF(INFO, Caffe::root_solver())
<< " -> top blob " << blob_name
<< ", count: " << blob.count()
<< " data: " << data_abs_val_mean;
}
for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
++param_id) {
const Blob& 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 double data_abs_val_mean = blob.asum_data() / blob.count();
LOG_IF(INFO, Caffe::root_solver())
<< " -> param blob " << blob_name
<< ", count: " << blob.count()
<< " data: " << data_abs_val_mean;
}
}
void Net::BackwardDebugInfo(const int layer_id) {
LOG_IF(INFO, Caffe::root_solver())
<< "[Backward] Layer " << layer_names_[layer_id];
const vector<Blob*>& 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& blob = *bottom_vec[bottom_id];
const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
const double diff_abs_val_mean = blob.asum_diff() / blob.count();
LOG_IF(INFO, Caffe::root_solver())
<< " -> bottom blob " << blob_name
<< ", count: " << blob.count()
<< ", 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& blob = *layers_[layer_id]->blobs()[param_id];
double diff_abs_val_mean = blob.asum_diff() / blob.count();
LOG_IF(INFO, Caffe::root_solver())
<< " -> param blob " << param_id
<< ", count: " << blob.count()
<< ", diff: " << diff_abs_val_mean;
}
}
void Net::UpdateDebugInfo(const int param_id) {
const Blob& 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 double diff_abs_val_mean = blob.asum_diff() / blob.count();
if (param_owner < 0) {
double 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;
}
}
void Net::ShareTrainedLayersWith(const Net* other) {
int num_source_layers = other->layers().size();
for (int i = 0; i < num_source_layers; ++i) {
LayerBase* 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> >& 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* 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);
}
}
trained_layers_shared_ = true;
}
void Net::BackwardFrom(int start) {
BackwardFromTo(start, 0);
}
void Net::BackwardTo(int end) {
BackwardFromTo(layers_.size() - 1, end);
}
void Net::Backward(bool apply_update) {
BackwardFromToAu(layers_.size() - 1, 0, apply_update);
if (debug_info_) {
float asum_data = 0.F, asum_diff = 0.F, sumsq_data = 0.F, sumsq_diff = 0.F;
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 double l2norm_data = std::sqrt(sumsq_data);
const double 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 << ")";
}
}
void Net::Reshape() {
for (int i = 0; i < layers_.size(); ++i) {
layers_[i]->Reshape(bottom_vecs_[i], top_vecs_[i]);
}
}
void Net::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();
const string& source_layer_type = source_layer.type();
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> >& 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;
LOG(INFO) << "Copying source layer " << source_layer_name << " Type:"
<< source_layer_type << " #blobs=" << source_layer.blobs_size();
// check if BN is in legacy DIGITS format?
if (source_layer_type == "BatchNorm" && source_layer.blobs_size() == 5) {
for (int j = 0; j < target_blobs.size(); ++j) {
const bool kReshape = true;
target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);
DLOG(INFO) << target_blobs[j]->count();
}
if (target_blobs[4]->count() == 1) {
// old format: 0 - scale , 1 - bias, 2 - mean , 3 - var, 4 - reserved
// new format: 0 - mean , 1 - var, 2 - reserved , 3- scale, 4 - bias
LOG(INFO) << "BN legacy DIGITS format detected ... ";
std::swap(target_blobs[0], target_blobs[2]);
std::swap(target_blobs[1], target_blobs[3]);
// ==> 0 - mean , 1 -var, 2 - scale , 3 - bias; 4 - reserved
std::swap(target_blobs[2], target_blobs[4]);
std::swap(target_blobs[3], target_blobs[4]);
LOG(INFO) << "BN Transforming to new format completed.";
}
} else {
for (int j = 0; j < target_blobs.size(); ++j) {
if (!target_blobs[j]->ShapeEquals(source_layer.blobs(j))) {
shared_ptr<Blob> source_blob = Blob::create(target_blobs[j]->data_type(),
target_blobs[j]->diff_type());
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);
}
}
}
}
void Net::CopyTrainedLayersFrom(const string trained_filename) {
if (trained_filename.size() >= 3 &&
trained_filename.compare(trained_filename.size() - 3, 3, ".h5") == 0) {
CopyTrainedLayersFromHDF5(trained_filename);
} else {
CopyTrainedLayersFromBinaryProto(trained_filename);
}
}
void Net::CopyTrainedLayersFromBinaryProto(
const string trained_filename) {
NetParameter param;
ReadNetParamsFromBinaryFileOrDie(trained_filename, &param);
CopyTrainedLayersFrom(param);
}
void Net::CopyTrainedLayersFromHDF5(const string trained_filename) {
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> >& 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);
}
void Net::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);
}
}
void Net::ToHDF5(const string& filename, bool write_diff) const {
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(layer_data_hid, dataset_name.str(),
*params_[net_param_id]);
}
if (write_diff) {
// Write diffs regardless of weight-sharing
hdf5_save_nd_dataset(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);
}
void Net::Update() {
for (int i = 0; i < learnable_params_.size(); ++i) {
learnable_params_[i]->Update();
}
}
void Net::ClearParamDiffs() {
if (Caffe::mode() == Caffe::GPU) {
caffe_gpu_memset(learnable_space_[0].size(), 0, learnable_space_[0].data());
caffe_gpu_memset(learnable_space_[1].size(), 0, learnable_space_[1].data());
} else {
for (int i = 0; i < learnable_params_.size(); ++i) {
learnable_params_[i]->set_diff(0.F);
}
}
}
void Net::ShareWeights() {
for (int i = 0; i < params_.size(); ++i) {
if (param_owners_[i] < 0) {
gpu_prm_memory_data_use_ += params_[i]->gpu_memory_data_use();
gpu_prm_memory_diff_use_ += params_[i]->gpu_memory_diff_use();
continue;
}
// DLOG(INFO) << "param " << i << " has owner " << param_owners_[i];
params_[i]->ShareData(*params_[param_owners_[i]]);
params_[i]->ShareDiff(*params_[param_owners_[i]]);
gpu_shp_memory_data_use_ += params_[i]->gpu_memory_data_use();
gpu_shp_memory_diff_use_ += params_[i]->gpu_memory_diff_use();
}
}
bool Net::has_blob(const string& blob_name) const {
return blob_names_index_.find(blob_name) != blob_names_index_.end();
}
const shared_ptr<Blob> Net::blob_by_name(
const string& blob_name) const {
shared_ptr<Blob> blob_ptr;
if (has_blob(blob_name)) {
blob_ptr = blobs_[blob_names_index_.find(blob_name)->second];
} else {
LOG(WARNING) << "Unknown blob name " << blob_name;
}
return blob_ptr;
}
bool Net::has_layer(const string& layer_name) const {
return layer_names_index_.find(layer_name) != layer_names_index_.end();
}
const shared_ptr<LayerBase> Net::layer_by_name(
const string& layer_name) const {
shared_ptr<LayerBase> layer_ptr;
if (has_layer(layer_name)) {
layer_ptr = layers_[layer_names_index_.find(layer_name)->second];
} else {
LOG(WARNING) << "Unknown layer name " << layer_name;
}
return layer_ptr;
}
void Net::set_solver(Solver* s) {
solver_ = s;
for (auto& layer : layers_) {
layer->set_parent_net(this);
}
}
void Net::InitializeLearnableDiffSpace(int type_id) {
CHECK_GE(type_id, 0);
CHECK_LT(type_id, 2);
const Type t = (Type) learnable_types_[type_id];
if (learnable_params_ptrs_[type_id].size() == learnable_params_.size()) {
LOG(INFO) << print_current_device() << " Already reserved "
<< learnable_space_size_[type_id] << " bytes of shared learnable space for type "
<< Type_Name(t);
return;
}
learnable_space_size_[type_id] = 0UL;
learnable_params_ptrs_[type_id].resize(learnable_params_.size(), nullptr);
for (int i = 0; i < layers_.size(); ++i) {
for (int j = 0; j < layers_[i]->blobs().size(); ++j) {
if (!layers_[i]->skip_apply_update(j)) {
const int lip = layer_index_params_[make_pair(i, j)];
if (param_owners_[lip] < 0) {
const int param_id = learnable_param_ids_[lip];
if (learnable_params_[param_id]->diff_type() == t) {
learnable_space_size_[type_id] += lp_aligned_count(param_id) * lp_size(param_id);
}
}
}
}
}
// Size have at least one byte, otherwise cudaMalloc fails if net has no
// learnable parameters. Times two.
if (learnable_space_size_[type_id] < 2) {
learnable_space_size_[type_id] = 2;
}
LOG(INFO) << print_current_device() << " Reserving "
<< learnable_space_size_[type_id] << " bytes of shared learnable space for type "
<< Type_Name(t);
learnable_space_[type_id].reserve(learnable_space_size_[type_id]);
unsigned char* ptr = reinterpret_cast<unsigned char*>(learnable_space_[type_id].data());
caffe_gpu_memset(learnable_space_size_[type_id], 0, ptr);
for (int i = 0; i < layers_.size(); ++i) {
for (int j = 0; j < layers_[i]->blobs().size(); ++j) {
if (!layers_[i]->skip_apply_update(j)) {
const int lip = layer_index_params_[make_pair(i, j)];
if (param_owners_[lip] < 0) {
const int param_id = learnable_param_ids_[lip];
if (learnable_params_[param_id]->diff_type() == t) {
learnable_params_[param_id]->set_gpu_diff(ptr);
learnable_params_ptrs_[type_id][param_id] = static_cast<void*>(ptr);
ptr += lp_aligned_count(param_id) * lp_size(param_id);
learnable_params_mapped_.push_back(learnable_params_[param_id]);
ltop_[type_id][i].insert(param_id);
void *p = learnable_params_[param_id]->
current_mutable_data_memory(Caffe::mode() == Caffe::GPU);
(void) p;
}
}
} else {
DLOG(INFO) << print_current_device()
<< "** Skipping non-learnable blob from " << layers_[i]->name()
<< " of type " << layers_[i]->type();
}
}
}
}
const vector<Type>& Net::learnable_types(bool reset) {
if (reset || learnable_types_.empty()) {
learnable_types_.clear();
int type0 = -1;
int type1 = -1;
for (shared_ptr<Blob> lp : learnable_params_) {
Type t = lp->diff_type();
if (type0 < 0) {
type0 = (int) t;
learnable_types_.push_back(t);
} else if (type1 < 0 && type0 != (int) t) {
type1 = (int) t;
learnable_types_.push_back(t);
}
}
if (learnable_types_.empty() && solver_ != nullptr) {
learnable_types_.push_back(solver_->data_type());
}
CHECK_LE(learnable_types_.size(), 2);
}
return learnable_types_;
}
} // namespace caffe
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