Read Caffe : class : Layer
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LayerはNetを構成する基石であり、BlobがLayerを構成する基石であるように、まずcaffe.protoで定義されているLayerParameterがどのようなものかを見てみましょう.message LayerParameter {
optional string name = 1; // the layer name
optional string type = 2; // the layer type
repeated string bottom = 3; // the name of each bottom blob
repeated string top = 4; // the name of each top blob
// The train / test phase for computation.
optional Phase phase = 10;
// The amount of weight to assign each top blob in the objective.
// Each layer assigns a default value, usually of either 0 or 1,
// to each top blob.
repeated float loss_weight = 5;
// Specifies training parameters (multipliers on global learning constants,
// and the name and other settings used for weight sharing).
repeated ParamSpec param = 6;
// The blobs containing the numeric parameters of the layer.
repeated BlobProto blobs = 7;
// Specifies whether to backpropagate to each bottom. If unspecified,
// Caffe will automatically infer whether each input needs backpropagation
// to compute parameter gradients. If set to true for some inputs,
// backpropagation to those inputs is forced; if set false for some inputs,
// backpropagation to those inputs is skipped.
//
// The size must be either 0 or equal to the number of bottoms.
repeated bool propagate_down = 11;
// Rules controlling whether and when a layer is included in the network,
// based on the current NetState. You may specify a non-zero number of rules
// to include OR exclude, but not both. If no include or exclude rules are
// specified, the layer is always included. If the current NetState meets
// ANY (i.e., one or more) of the specified rules, the layer is
// included/excluded.
repeated NetStateRule include = 8;
repeated NetStateRule exclude = 9;
// Parameters for data pre-processing.
optional TransformationParameter transform_param = 100;
// Parameters shared by loss layers.
optional LossParameter loss_param = 101;
// Layer type-specific parameters.
//
// Note: certain layers may have more than one computational engine
// for their implementation. These layers include an Engine type and
// engine parameter for selecting the implementation.
// The default for the engine is set by the ENGINE switch at compile-time.
optional AccuracyParameter accuracy_param = 102;
optional ArgMaxParameter argmax_param = 103;
optional BatchNormParameter batch_norm_param = 139;
......
}
このうち、この
phaseは、TESTとTRAINのモードの下層で異なる表現を制御するパラメータであり、ある状態で排除される可能性がある.loss_weightはtop blob全体lossの計算に占める重みを制御するためのものであり、一般的にloss層については1とし、他の層については0とすればよい.ParamSpec paramトレーニングパラメータをカスタマイズします.これは、グローバル化されたトレーニングパラメータに乗じた乗子です.このクラスの具体的な状況を見てみましょう.// Specifies training parameters (multipliers on global learning constants,
// and the name and other settings used for weight sharing).
message ParamSpec {
// The names of the parameter blobs -- useful for sharing parameters among
// layers, but never required otherwise. To share a parameter between two
// layers, give it a (non-empty) name.
optional string name = 1;
// Whether to require shared weights to have the same shape, or just the same
// count -- defaults to STRICT if unspecified.
optional DimCheckMode share_mode = 2;
enum DimCheckMode {
// STRICT (default) requires that num, channels, height, width each match.
STRICT = 0;
// PERMISSIVE requires only the count (num*channels*height*width) to match.
PERMISSIVE = 1;
}
// The multiplier on the global learning rate for this parameter.
optional float lr_mult = 3 [default = 1.0];
// The multiplier on the global weight decay for this parameter.
optional float decay_mult = 4 [default = 1.0];
}
実はこのクラスの中のものはこのいくつかのパラメータについて、
, そしてこのblobのパラメータを共有するには、 を定義する必要があります.では、以前のLayerParameterの定義に戻ると、repeated BlobProto blobsは私たちの層のパラメータblobであることがわかります.例えば、一般的なCNNネットワークでは、weight blobとbias blobの2つが含まれている可能性があります.他のネットワークは、ネットワークの構造によって決定されるより多く含まれている可能性があります.propagate_downこのパラメータは、私たちがdown blobに勾配を伝達するかどうかを制限するbool配列であり、それはすべて0であるか、またはその大きさはdown blobの数と一致しなければならない.また、このレイヤがNetに含まれているか、含まれていないかを制御する2つのパラメータがあります.これは現在のNetStateによって決まります.NetStateとNetStateRuleの2つのクラスには、どのようなものが含まれているのかを見てみましょう.message NetState {
optional Phase phase = 1 [default = TEST];
optional int32 level = 2 [default = 0];
repeated string stage = 3;
}
message NetStateRule {
// Set phase to require the NetState have a particular phase (TRAIN or TEST)
// to meet this rule.
optional Phase phase = 1;
// Set the minimum and/or maximum levels in which the layer should be used.
// Leave undefined to meet the rule regardless of level.
optional int32 min_level = 2;
optional int32 max_level = 3;
// Customizable sets of stages to include or exclude.
// The net must have ALL of the specified stages and NONE of the specified
// "not_stage"s to meet the rule.
// (Use multiple NetStateRules to specify conjunctions of stages.)
repeated string stage = 4;
repeated string not_stage = 5;
}
これは非常に詳細ではありませんが、後でゆっくり分析することができますが、これは基本的にいくつかのルールを設定していることを推測することができます.その時、このレベルがこの
Netの中にあるのか、それとも中にないのかを判断することができます.次の2つの主要なパラメータTransformationParameterとLossParameterは後で詳しく共有します.どうせいくつかの特別なパラメータがあります.それから各層特有のパラメータです.これはprotoファイルでoptionalキーワードで限定されています.ここまで、まずLayer.hppに何が含まれているか見てみましょう.#ifndef CAFFE_LAYER_H_
#define CAFFE_LAYER_H_
#include
#include
#include
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/layer_factory.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/math_functions.hpp"
/**
Forward declare boost::thread instead of including boost/thread.hpp
to avoid a boost/NVCC issues (#1009, #1010) on OSX.
*/
namespace boost { class mutex; }
namespace caffe {
/**
* @brief An interface for the units of computation which can be composed into a
* Net.
*
* Layer%s must implement a Forward function, in which they take their input
* (bottom) Blob%s (if any) and compute their output Blob%s (if any).
* They may also implement a Backward function, in which they compute the error
* gradients with respect to their input Blob%s, given the error gradients with
* their output Blob%s.
*/
template
class Layer {
public:
/**
* You should not implement your own constructor. Any set up code should go
* to SetUp(), where the dimensions of the bottom blobs are provided to the
* layer.
*/
explicit Layer(const LayerParameter& param)
: layer_param_(param), is_shared_(false) {
// Set phase and copy blobs (if there are any).
phase_ = param.phase();
if (layer_param_.blobs_size() > 0) {
blobs_.resize(layer_param_.blobs_size());
for (int i = 0; i < layer_param_.blobs_size(); ++i) {
blobs_[i].reset(new Blob());
blobs_[i]->FromProto(layer_param_.blobs(i));
}
}
}
この明示的な構造関数は、
layer_param_という保護メンバーにパラメータを直接コピーするとともに、phase_というメンバーが一般的であるため、単独で付与される.また、最も重要なのは、blobsのパラメータをすべて自分で定義したBlobオブジェクトにコピーすることです.これにより、自分の操作が便利になります.なぜなら、BlobProto機能が弱いため、コピーの過程でBlobオブジェクトバンドのFromProto方法を使用しているので、非常に簡単明瞭です. virtual ~Layer() {}
/**
* @brief Implements common layer setup functionality.
*
* @param bottom the preshaped input blobs
* @param top
* the allocated but unshaped output blobs, to be shaped by Reshape
*
* Checks that the number of bottom and top blobs is correct.
* Calls LayerSetUp to do special layer setup for individual layer types,
* followed by Reshape to set up sizes of top blobs and internal buffers.
* Sets up the loss weight multiplier blobs for any non-zero loss weights.
* This method may not be overridden.
*/
void SetUp(const vector*>& bottom,
const vector*>& top) {
InitMutex();
CheckBlobCounts(bottom, top);
LayerSetUp(bottom, top);
Reshape(bottom, top);
SetLossWeights(top);
}
この
SetUp関数は各層の配置関数であり、InitMutex関数はマルチタスク時にロックされる.CheckBlobCountsは各blobが要求を満たすかどうかを検査する検査関数であり、LayerSetUpは各層自身の専門的な配置関数であり、継承時に実現する必要がある虚関数である.Reshapeも、最后にこのSetLossWeightsという関数を言って、私は前に见たことがありません.そして、その計算lossの方法は意外にもweights点积dataを使って、それから和を求めて、どうしてこのようにするのか分かりません. /**
* @brief Does layer-specific setup: your layer should implement this function
* as well as Reshape.
*
* @param bottom
* the preshaped input blobs, whose data fields store the input data for
* this layer
* @param top
* the allocated but unshaped output blobs
*
* This method should do one-time layer specific setup. This includes reading
* and processing relevent parameters from the layer_param_.
* Setting up the shapes of top blobs and internal buffers should be done in
* Reshape, which will be called before the forward pass to
* adjust the top blob sizes.
*/
virtual void LayerSetUp(const vector*>& bottom,
const vector*>& top) {}
見たかどうか、この関数は主にこの特殊層のパラメータを読み出して処理するので、例えば
conv_layerあ、stride,kernel_sizeなどのパラメータを読み出して、これはすべて特殊層ならではのパラメータです. /**
* @brief Whether a layer should be shared by multiple nets during data
* parallelism. By default, all layers except for data layers should
* not be shared. data layers should be shared to ensure each worker
* solver access data sequentially during data parallelism.
*/
virtual inline bool ShareInParallel() const { return false; }
/** @brief Return whether this layer is actually shared by other nets.
* If ShareInParallel() is true and using more than one GPU and the
* net has TRAIN phase, then this function is expected return true.
*/
inline bool IsShared() const { return is_shared_; }
/** @brief Set whether this layer is actually shared by other nets
* If ShareInParallel() is true and using more than one GPU and the
* net has TRAIN phase, then is_shared should be set true.
*/
inline void SetShared(bool is_shared) {
CHECK(ShareInParallel() || !is_shared)
<< type() << "Layer does not support sharing.";
is_shared_ = is_shared;
}
これらの関数は処理層のデータ共有問題であり、現在はまだ使えない.
/**
* @brief Adjust the shapes of top blobs and internal buffers to accommodate
* the shapes of the bottom blobs.
*
* @param bottom the input blobs, with the requested input shapes
* @param top the top blobs, which should be reshaped as needed
*
* This method should reshape top blobs as needed according to the shapes
* of the bottom (input) blobs, as well as reshaping any internal buffers
* and making any other necessary adjustments so that the layer can
* accommodate the bottom blobs.
*/
virtual void Reshape(const vector*>& bottom,
const vector*>& top) = 0;
bottom blobの構成に従ってtop blobの構成を調整し、内部キャッシュの形状をいくつか設定します.の /**
* @brief Given the bottom blobs, compute the top blobs and the loss.
*
* @param bottom
* the input blobs, whose data fields store the input data for this layer
* @param top
* the preshaped output blobs, whose data fields will store this layers'
* outputs
* \return The total loss from the layer.
*
* The Forward wrapper calls the relevant device wrapper function
* (Forward_cpu or Forward_gpu) to compute the top blob values given the
* bottom blobs. If the layer has any non-zero loss_weights, the wrapper
* then computes and returns the loss.
*
* Your layer should implement Forward_cpu and (optionally) Forward_gpu.
*/
inline Dtype Forward(const vector*>& bottom,
const vector*>& top);
これが順方向伝播の関数です
/**
* @brief Given the top blob error gradients, compute the bottom blob error
* gradients.
*
* @param top
* the output blobs, whose diff fields store the gradient of the error
* with respect to themselves
* @param propagate_down
* a vector with equal length to bottom, with each index indicating
* whether to propagate the error gradients down to the bottom blob at
* the corresponding index
* @param bottom
* the input blobs, whose diff fields will store the gradient of the error
* with respect to themselves after Backward is run
*
* The Backward wrapper calls the relevant device wrapper function
* (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the
* top blob diffs.
*
* Your layer should implement Backward_cpu and (optionally) Backward_gpu.
*/
inline void Backward(const vector*>& top,
const vector& propagate_down,
const vector*>& bottom);
同様に、逆伝搬関数;注意
top blobの勾配はそのdiffデータ体に置かれている. /**
* @brief Returns the vector of learnable parameter blobs.
*/
vector > >& blobs() {
return blobs_;
}
/**
* @brief Returns the layer parameter.
*/
const LayerParameter& layer_param() const { return layer_param_; }
/**
* @brief Writes the layer parameter to a protocol buffer
*/
virtual void ToProto(LayerParameter* param, bool write_diff = false);
/**
* @brief Returns the scalar loss associated with a top blob at a given index.
*/
inline Dtype loss(const int top_index) const {
return (loss_.size() > top_index) ? loss_[top_index] : Dtype(0);
}
/**
* @brief Sets the loss associated with a top blob at a given index.
*/
inline void set_loss(const int top_index, const Dtype value) {
if (loss_.size() <= top_index) {
loss_.resize(top_index + 1, Dtype(0));
}
loss_[top_index] = value;
}
还有,需要问一下この地方はBUGがあるのではありませんかを感じて、、、実现して问题があるようにしましょう、どのようにすべて
resizeから0まで、それから値を賦課することができて、それでは最后に1つだけ値があるのではありませんか、その他の前のはすべて0??mark以下先 /**
* @brief Returns the layer type.
*/
virtual inline const char* type() const { return ""; }
上のいくつかの関数はすべてとても軽くて、理解してすぐよかったです
/**
* @brief Returns the exact number of bottom blobs required by the layer,
* or -1 if no exact number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some exact number of bottom blobs.
*/
virtual inline int ExactNumBottomBlobs() const { return -1; }
/**
* @brief Returns the minimum number of bottom blobs required by the layer,
* or -1 if no minimum number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some minimum number of bottom blobs.
*/
virtual inline int MinBottomBlobs() const { return -1; }
/**
* @brief Returns the maximum number of bottom blobs required by the layer,
* or -1 if no maximum number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some maximum number of bottom blobs.
*/
virtual inline int MaxBottomBlobs() const { return -1; }
/**
* @brief Returns the exact number of top blobs required by the layer,
* or -1 if no exact number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some exact number of top blobs.
*/
virtual inline int ExactNumTopBlobs() const { return -1; }
/**
* @brief Returns the minimum number of top blobs required by the layer,
* or -1 if no minimum number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some minimum number of top blobs.
*/
virtual inline int MinTopBlobs() const { return -1; }
/**
* @brief Returns the maximum number of top blobs required by the layer,
* or -1 if no maximum number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some maximum number of top blobs.
*/
virtual inline int MaxTopBlobs() const { return -1; }
/**
* @brief Returns true if the layer requires an equal number of bottom and
* top blobs.
*
* This method should be overridden to return true if your layer expects an
* equal number of bottom and top blobs.
*/
virtual inline bool EqualNumBottomTopBlobs() const { return false; }
この関数の山はすべていくつかの
top blobとbottom blobのいくつかの制限関数で、この層のパラメータが正しく設定されているかどうかを検査するために使用され、これらはすべて虚関数であり、特定の層でカスタマイズすることができる. /**
* @brief Return whether "anonymous" top blobs are created automatically
* by the layer.
*
* If this method returns true, Net::Init will create enough "anonymous" top
* blobs to fulfill the requirement specified by ExactNumTopBlobs() or
* MinTopBlobs().
*/
virtual inline bool AutoTopBlobs() const { return false; }
この戻り値が
trueである場合、Netを構築する際に、制限条件に基づいて、要求を満たすために名前のないblobを構築する可能性がある.これはプログラムの頑丈性を強化することができるが、理解性を低下させる. /**
* @brief Return whether to allow force_backward for a given bottom blob
* index.
*
* If AllowForceBackward(i) == false, we will ignore the force_backward
* setting and backpropagate to blob i only if it needs gradient information
* (as is done when force_backward == false).
*/
virtual inline bool AllowForceBackward(const int bottom_index) const {
return true;
}
/**
* @brief Specifies whether the layer should compute gradients w.r.t. a
* parameter at a particular index given by param_id.
*
* You can safely ignore false values and always compute gradients
* for all parameters, but possibly with wasteful computation.
*/
inline bool param_propagate_down(const int param_id) {
return (param_propagate_down_.size() > param_id) ?
param_propagate_down_[param_id] : false;
}
/**
* @brief Sets whether the layer should compute gradients w.r.t. a
* parameter at a particular index given by param_id.
*/
inline void set_param_propagate_down(const int param_id, const bool value) {
if (param_propagate_down_.size() <= param_id) {
param_propagate_down_.resize(param_id + 1, true);
}
param_propagate_down_[param_id] = value;
}
この3つの関数はいずれも逆伝搬に関係している.
protected:
/** The protobuf that stores the layer parameters */
LayerParameter layer_param_;
/** The phase: TRAIN or TEST */
Phase phase_;
/** The vector that stores the learnable parameters as a set of blobs. */
vector > > blobs_;
/** Vector indicating whether to compute the diff of each param blob. */
vector param_propagate_down_;
/** The vector that indicates whether each top blob has a non-zero weight in
* the objective function. */
vector loss_;
保護メンバーには基本的なパラメータメンバーが含まれています.
/** @brief Using the CPU device, compute the layer output. */
virtual void Forward_cpu(const vector*>& bottom,
const vector*>& top) = 0;
/**
* @brief Using the GPU device, compute the layer output.
* Fall back to Forward_cpu() if unavailable.
*/
virtual void Forward_gpu(const vector*>& bottom,
const vector*>& top) {
// LOG(WARNING) << "Using CPU code as backup.";
return Forward_cpu(bottom, top);
}
/**
* @brief Using the CPU device, compute the gradients for any parameters and
* for the bottom blobs if propagate_down is true.
*/
virtual void Backward_cpu(const vector*>& top,
const vector& propagate_down,
const vector*>& bottom) = 0;
/**
* @brief Using the GPU device, compute the gradients for any parameters and
* for the bottom blobs if propagate_down is true.
* Fall back to Backward_cpu() if unavailable.
*/
virtual void Backward_gpu(const vector*>& top,
const vector& propagate_down,
const vector*>& bottom) {
// LOG(WARNING) << "Using CPU code as backup.";
Backward_cpu(top, propagate_down, bottom);
}
この4つの関数は、
cpuバージョンが強制的に実装され、gpuバージョンが選択的に実装される継承で実装されます. /**
* Called by the parent Layer's SetUp to check that the number of bottom
* and top Blobs provided as input match the expected numbers specified by
* the {ExactNum,Min,Max}{Bottom,Top}Blobs() functions.
*/
virtual void CheckBlobCounts(const vector*>& bottom,
const vector*>& top) {
if (ExactNumBottomBlobs() >= 0) {
CHECK_EQ(ExactNumBottomBlobs(), bottom.size())
<< type() << " Layer takes " << ExactNumBottomBlobs()
<< " bottom blob(s) as input.";
}
if (MinBottomBlobs() >= 0) {
CHECK_LE(MinBottomBlobs(), bottom.size())
<< type() << " Layer takes at least " << MinBottomBlobs()
<< " bottom blob(s) as input.";
}
if (MaxBottomBlobs() >= 0) {
CHECK_GE(MaxBottomBlobs(), bottom.size())
<< type() << " Layer takes at most " << MaxBottomBlobs()
<< " bottom blob(s) as input.";
}
if (ExactNumTopBlobs() >= 0) {
CHECK_EQ(ExactNumTopBlobs(), top.size())
<< type() << " Layer produces " << ExactNumTopBlobs()
<< " top blob(s) as output.";
}
if (MinTopBlobs() >= 0) {
CHECK_LE(MinTopBlobs(), top.size())
<< type() << " Layer produces at least " << MinTopBlobs()
<< " top blob(s) as output.";
}
if (MaxTopBlobs() >= 0) {
CHECK_GE(MaxTopBlobs(), top.size())
<< type() << " Layer produces at most " << MaxTopBlobs()
<< " top blob(s) as output.";
}
if (EqualNumBottomTopBlobs()) {
CHECK_EQ(bottom.size(), top.size())
<< type() << " Layer produces one top blob as output for each "
<< "bottom blob input.";
}
}
チェック関数;
/**
* Called by SetUp to initialize the weights associated with any top blobs in
* the loss function. Store non-zero loss weights in the diff blob.
*/
inline void SetLossWeights(const vector*>& top) {
const int num_loss_weights = layer_param_.loss_weight_size();
if (num_loss_weights) {
CHECK_EQ(top.size(), num_loss_weights) << "loss_weight must be "
"unspecified or specified once per top blob.";
for (int top_id = 0; top_id < top.size(); ++top_id) {
const Dtype loss_weight = layer_param_.loss_weight(top_id);
if (loss_weight == Dtype(0)) { continue; }
this->set_loss(top_id, loss_weight);
const int count = top[top_id]->count();
Dtype* loss_multiplier = top[top_id]->mutable_cpu_diff();
caffe_set(count, loss_weight, loss_multiplier);
}
}
}
loss_weightこのパラメータはtop blobのdiffデータボディに格納される. private:
/** Whether this layer is actually shared by other nets*/
bool is_shared_;
/** The mutex for sequential forward if this layer is shared */
shared_ptr<:mutex> forward_mutex_;
/** Initialize forward_mutex_ */
void InitMutex();
/** Lock forward_mutex_ if this layer is shared */
void Lock();
/** Unlock forward_mutex_ if this layer is shared */
void Unlock();
DISABLE_COPY_AND_ASSIGN(Layer);
}; // class Layer
これらのプライベートメンバーは実はプロセスに関係するもので、
boostのlockのようなものを使っています.
// Forward and backward wrappers. You should implement the cpu and
// gpu specific implementations instead, and should not change these
// functions.
template
inline Dtype Layer::Forward(const vector*>& bottom,
const vector*>& top) {
// Lock during forward to ensure sequential forward
Lock();
Dtype loss = 0;
Reshape(bottom, top);
switch (Caffe::mode()) {
case Caffe::CPU:
Forward_cpu(bottom, top);
for (int top_id = 0; top_id < top.size(); ++top_id) {
if (!this->loss(top_id)) { continue; }
const int count = top[top_id]->count();
const Dtype* data = top[top_id]->cpu_data();
const Dtype* loss_weights = top[top_id]->cpu_diff();
loss += caffe_cpu_dot(count, data, loss_weights);
}
break;
case Caffe::GPU:
Forward_gpu(bottom, top);
#ifndef CPU_ONLY
for (int top_id = 0; top_id < top.size(); ++top_id) {
if (!this->loss(top_id)) { continue; }
const int count = top[top_id]->count();
const Dtype* data = top[top_id]->gpu_data();
const Dtype* loss_weights = top[top_id]->gpu_diff();
Dtype blob_loss = 0;
caffe_gpu_dot(count, data, loss_weights, &blob_loss);
loss += blob_loss;
}
#endif
break;
default:
LOG(FATAL) << "Unknown caffe mode.";
}
Unlock();
return loss;
}
この関数はベースクラスの関数であり、私たちがずっと使用しているものでもあります.これは実は
wrapperです.私たちは具体的な層の開発時に中のForward_cpuを設計する必要があります.Backward_cpuなどの関数です.中には最初からLockをロックし、最後にUnlockをロックします.またReshapeもしましたが、私の考えではSetUpですでにやったはずなのに、どうやってまたやってみるのかということです.また、Caffe::GPUとCPU_ONLYが同時に存在する場合は、lossを計算しないでください.template
inline void Layer::Backward(const vector*>& top,
const vector& propagate_down,
const vector*>& bottom) {
switch (Caffe::mode()) {
case Caffe::CPU:
Backward_cpu(top, propagate_down, bottom);
break;
case Caffe::GPU:
Backward_gpu(top, propagate_down, bottom);
break;
default:
LOG(FATAL) << "Unknown caffe mode.";
}
}
// Serialize LayerParameter to protocol buffer
template
void Layer::ToProto(LayerParameter* param, bool write_diff) {
param->Clear();
param->CopyFrom(layer_param_);
param->clear_blobs();
for (int i = 0; i < blobs_.size(); ++i) {
blobs_[i]->ToProto(param->add_blobs(), write_diff);
}
}
これは
LayerParameter *に書かれています.この関数は実はio関数です.ただ、私たちは一連の操作を行った後、blob_の中のものをparamの中のblobsに書き込む必要があります.そうすれば成功します.
} // namespace caffe
#endif // CAFFE_LAYER_H_
最後に、このベースクラスの
cppファイルを見てみましょう.実はベースクラスなので、そのcppファイルはかなり簡単です.プロセスに関するロック解除および具体化layerテンプレートである.#include
#include "caffe/layer.hpp"
namespace caffe {
template
void Layer::InitMutex() {
forward_mutex_.reset(new boost::mutex());
}
template
void Layer::Lock() {
if (IsShared()) {
forward_mutex_->lock();
}
}
template
void Layer::Unlock() {
if (IsShared()) {
forward_mutex_->unlock();
}
}
INSTANTIATE_CLASS(Layer);
} // namespace caffe