From: Jeff Donahue Date: Thu, 24 Jul 2014 02:22:11 +0000 (-0700) Subject: Add gradient checks for infogain loss layer, letting it take the X-Git-Tag: submit/tizen/20180823.020014~653^2~45^2 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=7de3382561bbbe235358f4259328093ecf048e2d;p=platform%2Fupstream%2Fcaffeonacl.git Add gradient checks for infogain loss layer, letting it take the infogain matrix as the third bottom blob. --- diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp index 204f6c3..4b02f4e 100644 --- a/src/caffe/layers/infogain_loss_layer.cpp +++ b/src/caffe/layers/infogain_loss_layer.cpp @@ -18,14 +18,24 @@ void InfogainLossLayer::FurtherSetUp( CHECK_EQ(bottom[1]->channels(), 1); CHECK_EQ(bottom[1]->height(), 1); CHECK_EQ(bottom[1]->width(), 1); - - BlobProto blob_proto; - ReadProtoFromBinaryFile( - this->layer_param_.infogain_loss_param().source(), &blob_proto); - infogain_.FromProto(blob_proto); - CHECK_EQ(infogain_.num(), 1); - CHECK_EQ(infogain_.channels(), 1); - CHECK_EQ(infogain_.height(), infogain_.width()); + Blob* infogain = NULL; + if (bottom.size() < 3) { + CHECK(this->layer_param_.infogain_loss_param().has_source()) + << "Infogain matrix source must be specified."; + BlobProto blob_proto; + ReadProtoFromBinaryFile( + this->layer_param_.infogain_loss_param().source(), &blob_proto); + infogain_.FromProto(blob_proto); + infogain = &infogain_; + } else { + infogain = bottom[2]; + } + const int num = bottom[0]->num(); + const int dim = bottom[0]->count() / num; + CHECK_EQ(infogain->num(), 1); + CHECK_EQ(infogain->channels(), 1); + CHECK_EQ(infogain->height(), dim); + CHECK_EQ(infogain->width(), dim); } @@ -34,10 +44,14 @@ Dtype InfogainLossLayer::Forward_cpu(const vector*>& bottom, vector*>* top) { const Dtype* bottom_data = bottom[0]->cpu_data(); const Dtype* bottom_label = bottom[1]->cpu_data(); - const Dtype* infogain_mat = infogain_.cpu_data(); + const Dtype* infogain_mat = NULL; + if (bottom.size() < 3) { + infogain_mat = infogain_.cpu_data(); + } else { + infogain_mat = bottom[2]->cpu_data(); + } int num = bottom[0]->num(); int dim = bottom[0]->count() / bottom[0]->num(); - CHECK_EQ(infogain_.height(), dim); Dtype loss = 0; for (int i = 0; i < num; ++i) { int label = static_cast(bottom_label[i]); @@ -46,10 +60,11 @@ Dtype InfogainLossLayer::Forward_cpu(const vector*>& bottom, loss -= infogain_mat[label * dim + j] * log(prob); } } + loss /= num; if (top->size() == 1) { - (*top)[0]->mutable_cpu_data()[0] = loss / num; + (*top)[0]->mutable_cpu_data()[0] = loss; } - return loss / num; + return loss; } template @@ -60,14 +75,22 @@ void InfogainLossLayer::Backward_cpu(const vector*>& top, LOG(FATAL) << this->type_name() << " Layer cannot backpropagate to label inputs."; } + if (propagate_down.size() > 2 && propagate_down[2]) { + LOG(FATAL) << this->type_name() + << " Layer cannot backpropagate to infogain inputs."; + } if (propagate_down[0]) { const Dtype* bottom_data = (*bottom)[0]->cpu_data(); const Dtype* bottom_label = (*bottom)[1]->cpu_data(); - const Dtype* infogain_mat = infogain_.cpu_data(); + const Dtype* infogain_mat = NULL; + if (bottom->size() < 3) { + infogain_mat = infogain_.cpu_data(); + } else { + infogain_mat = (*bottom)[2]->cpu_data(); + } Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff(); int num = (*bottom)[0]->num(); int dim = (*bottom)[0]->count() / (*bottom)[0]->num(); - CHECK_EQ(infogain_.height(), dim); for (int i = 0; i < num; ++i) { int label = static_cast(bottom_label[i]); for (int j = 0; j < dim; ++j) { diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp new file mode 100644 index 0000000..99bad26 --- /dev/null +++ b/src/caffe/test/test_infogain_loss_layer.cpp @@ -0,0 +1,67 @@ +// Copyright 2014 BVLC and contributors. + +#include +#include +#include +#include + +#include "gtest/gtest.h" +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/loss_layers.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +#include "caffe/test/test_caffe_main.hpp" + +namespace caffe { + +template +class InfogainLossLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + InfogainLossLayerTest() + : blob_bottom_data_(new Blob(10, 5, 1, 1)), + blob_bottom_label_(new Blob(10, 1, 1, 1)), + blob_bottom_infogain_(new Blob(1, 1, 5, 5)) { + Caffe::set_random_seed(1701); + FillerParameter filler_param; + PositiveUnitballFiller filler(filler_param); + filler.Fill(this->blob_bottom_data_); + blob_bottom_vec_.push_back(blob_bottom_data_); + for (int i = 0; i < blob_bottom_label_->count(); ++i) { + blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5; + } + blob_bottom_vec_.push_back(blob_bottom_label_); + filler_param.set_min(0.1); + filler_param.set_max(2.0); + UniformFiller infogain_filler(filler_param); + infogain_filler.Fill(this->blob_bottom_infogain_); + blob_bottom_vec_.push_back(blob_bottom_infogain_); + } + virtual ~InfogainLossLayerTest() { + delete blob_bottom_data_; + delete blob_bottom_label_; + delete blob_bottom_infogain_; + } + Blob* const blob_bottom_data_; + Blob* const blob_bottom_label_; + Blob* const blob_bottom_infogain_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; +}; + +TYPED_TEST_CASE(InfogainLossLayerTest, TestDtypesAndDevices); + + +TYPED_TEST(InfogainLossLayerTest, TestGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + InfogainLossLayer layer(layer_param); + GradientChecker checker(1e-4, 2e-2, 1701, 1, 0.01); + checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_), + &(this->blob_top_vec_), 0, -1, -1); +} + +} // namespace caffe