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test_split_layer.cpp
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test_split_layer.cpp
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#include <cstring>
#include <string>
#include <vector>
#include "google/protobuf/text_format.h"
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/insert_splits.hpp"
#include "caffe/vision_layers.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
namespace caffe {
template <typename TypeParam>
class SplitLayerTest : public MultiDeviceTest<TypeParam> {
typedef typename TypeParam::Dtype Dtype;
protected:
SplitLayerTest()
: blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
blob_top_a_(new Blob<Dtype>()),
blob_top_b_(new Blob<Dtype>()) {
// fill the values
FillerParameter filler_param;
GaussianFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_);
blob_bottom_vec_.push_back(blob_bottom_);
blob_top_vec_.push_back(blob_top_a_);
blob_top_vec_.push_back(blob_top_b_);
}
virtual ~SplitLayerTest() {
delete blob_bottom_;
delete blob_top_a_;
delete blob_top_b_;
}
Blob<Dtype>* const blob_bottom_;
Blob<Dtype>* const blob_top_a_;
Blob<Dtype>* const blob_top_b_;
vector<Blob<Dtype>*> blob_bottom_vec_;
vector<Blob<Dtype>*> blob_top_vec_;
};
TYPED_TEST_CASE(SplitLayerTest, TestDtypesAndDevices);
TYPED_TEST(SplitLayerTest, TestSetup) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
SplitLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
EXPECT_EQ(this->blob_top_a_->num(), 2);
EXPECT_EQ(this->blob_top_a_->channels(), 3);
EXPECT_EQ(this->blob_top_a_->height(), 6);
EXPECT_EQ(this->blob_top_a_->width(), 5);
EXPECT_EQ(this->blob_top_b_->num(), 2);
EXPECT_EQ(this->blob_top_b_->channels(), 3);
EXPECT_EQ(this->blob_top_b_->height(), 6);
EXPECT_EQ(this->blob_top_b_->width(), 5);
}
TYPED_TEST(SplitLayerTest, Test) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
SplitLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
for (int i = 0; i < this->blob_bottom_->count(); ++i) {
Dtype bottom_value = this->blob_bottom_->cpu_data()[i];
EXPECT_EQ(bottom_value, this->blob_top_a_->cpu_data()[i]);
EXPECT_EQ(bottom_value, this->blob_top_b_->cpu_data()[i]);
}
}
TYPED_TEST(SplitLayerTest, TestGradient) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
SplitLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-2);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
class SplitLayerInsertionTest : public ::testing::Test {
protected:
void RunInsertionTest(
const string& input_param_string, const string& output_param_string) {
// Test that InsertSplits called on the proto specified by
// input_param_string results in the proto specified by
// output_param_string.
NetParameter input_param;
CHECK(google::protobuf::TextFormat::ParseFromString(
input_param_string, &input_param));
NetParameter expected_output_param;
CHECK(google::protobuf::TextFormat::ParseFromString(
output_param_string, &expected_output_param));
NetParameter actual_output_param;
InsertSplits(input_param, &actual_output_param);
EXPECT_EQ(expected_output_param.DebugString(),
actual_output_param.DebugString());
// Also test idempotence.
NetParameter double_split_insert_param;
InsertSplits(actual_output_param, &double_split_insert_param);
EXPECT_EQ(actual_output_param.DebugString(),
double_split_insert_param.DebugString());
}
};
TEST_F(SplitLayerInsertionTest, TestNoInsertion1) {
const string& input_proto =
"name: 'TestNetwork' "
"layer { "
" name: 'data' "
" type: 'Data' "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'innerprod' "
" type: 'InnerProduct' "
" bottom: 'data' "
" top: 'innerprod' "
"} "
"layer { "
" name: 'loss' "
" type: 'SoftmaxWithLoss' "
" bottom: 'innerprod' "
" bottom: 'label' "
"} ";
this->RunInsertionTest(input_proto, input_proto);
}
TEST_F(SplitLayerInsertionTest, TestNoInsertion2) {
const string& input_proto =
"name: 'TestNetwork' "
"layer { "
" name: 'data' "
" type: 'Data' "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'data_split' "
" type: 'Split' "
" bottom: 'data' "
" top: 'data_split_0' "
" top: 'data_split_1' "
"} "
"layer { "
" name: 'innerprod1' "
" type: 'InnerProduct' "
" bottom: 'data_split_0' "
" top: 'innerprod1' "
"} "
"layer { "
" name: 'innerprod2' "
" type: 'InnerProduct' "
" bottom: 'data_split_1' "
" top: 'innerprod2' "
"} "
"layer { "
" name: 'loss' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod1' "
" bottom: 'innerprod2' "
"} ";
this->RunInsertionTest(input_proto, input_proto);
}
TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
const string& input_proto =
"name: 'CaffeNet' "
"layer { "
" name: 'data' "
" type: 'Data' "
" data_param { "
" source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
" batch_size: 256 "
" } "
" transform_param { "
" crop_size: 227 "
" mirror: true "
" mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
" } "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'conv1' "
" type: 'Convolution' "
" convolution_param { "
" num_output: 96 "
" kernel_size: 11 "
" stride: 4 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" } "
" param { "
" lr_mult: 1 "
" decay_mult: 1 "
" } "
" param { "
" lr_mult: 2 "
" decay_mult: 0 "
" } "
" bottom: 'data' "
" top: 'conv1' "
"} "
"layer { "
" name: 'relu1' "
" type: 'ReLU' "
" bottom: 'conv1' "
" top: 'conv1' "
"} "
"layer { "
" name: 'pool1' "
" type: 'Pooling' "
" pooling_param { "
" pool: MAX "
" kernel_size: 3 "
" stride: 2 "
" } "
" bottom: 'conv1' "
" top: 'pool1' "
"} "
"layer { "
" name: 'norm1' "
" type: 'LRN' "
" lrn_param { "
" local_size: 5 "
" alpha: 0.0001 "
" beta: 0.75 "
" } "
" bottom: 'pool1' "
" top: 'norm1' "
"} "
"layer { "
" name: 'conv2' "
" type: 'Convolution' "
" convolution_param { "
" num_output: 256 "
" group: 2 "
" kernel_size: 5 "
" pad: 2 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" } "
" param { "
" lr_mult: 1 "
" decay_mult: 1 "
" } "
" param { "
" lr_mult: 2 "
" decay_mult: 0 "
" } "
" bottom: 'norm1' "
" top: 'conv2' "
"} "
"layer { "
" name: 'relu2' "
" type: 'ReLU' "
" bottom: 'conv2' "
" top: 'conv2' "
"} "
"layer { "
" name: 'pool2' "
" type: 'Pooling' "
" pooling_param { "
" pool: MAX "
" kernel_size: 3 "
" stride: 2 "
" } "
" bottom: 'conv2' "
" top: 'pool2' "
"} "
"layer { "
" name: 'norm2' "
" type: 'LRN' "
" lrn_param { "
" local_size: 5 "
" alpha: 0.0001 "
" beta: 0.75 "
" } "
" bottom: 'pool2' "
" top: 'norm2' "
"} "
"layer { "
" name: 'conv3' "
" type: 'Convolution' "
" convolution_param { "
" num_output: 384 "
" kernel_size: 3 "
" pad: 1 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" } "
" param { "
" lr_mult: 1 "
" decay_mult: 1 "
" } "
" param { "
" lr_mult: 2 "
" decay_mult: 0 "
" } "
" bottom: 'norm2' "
" top: 'conv3' "
"} "
"layer { "
" name: 'relu3' "
" type: 'ReLU' "
" bottom: 'conv3' "
" top: 'conv3' "
"} "
"layer { "
" name: 'conv4' "
" type: 'Convolution' "
" convolution_param { "
" num_output: 384 "
" group: 2 "
" kernel_size: 3 "
" pad: 1 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" } "
" param { "
" lr_mult: 1 "
" decay_mult: 1 "
" } "
" param { "
" lr_mult: 2 "
" decay_mult: 0 "
" } "
" bottom: 'conv3' "
" top: 'conv4' "
"} "
"layer { "
" name: 'relu4' "
" type: 'ReLU' "
" bottom: 'conv4' "
" top: 'conv4' "
"} "
"layer { "
" name: 'conv5' "
" type: 'Convolution' "
" convolution_param { "
" num_output: 256 "
" group: 2 "
" kernel_size: 3 "
" pad: 1 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" } "
" param { "
" lr_mult: 1 "
" decay_mult: 1 "
" } "
" param { "
" lr_mult: 2 "
" decay_mult: 0 "
" } "
" bottom: 'conv4' "
" top: 'conv5' "
"} "
"layer { "
" name: 'relu5' "
" type: 'ReLU' "
" bottom: 'conv5' "
" top: 'conv5' "
"} "
"layer { "
" name: 'pool5' "
" type: 'Pooling' "
" pooling_param { "
" kernel_size: 3 "
" pool: MAX "
" stride: 2 "
" } "
" bottom: 'conv5' "
" top: 'pool5' "
"} "
"layer { "
" name: 'fc6' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 4096 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.005 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" } "
" param { "
" lr_mult: 1 "
" decay_mult: 1 "
" } "
" param { "
" lr_mult: 2 "
" decay_mult: 0 "
" } "
" bottom: 'pool5' "
" top: 'fc6' "
"} "
"layer { "
" name: 'relu6' "
" type: 'ReLU' "
" bottom: 'fc6' "
" top: 'fc6' "
"} "
"layer { "
" name: 'drop6' "
" type: 'Dropout' "
" dropout_param { "
" dropout_ratio: 0.5 "
" } "
" bottom: 'fc6' "
" top: 'fc6' "
"} "
"layer { "
" name: 'fc7' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 4096 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.005 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" } "
" param { "
" lr_mult: 1 "
" decay_mult: 1 "
" } "
" param { "
" lr_mult: 2 "
" decay_mult: 0 "
" } "
" bottom: 'fc6' "
" top: 'fc7' "
"} "
"layer { "
" name: 'relu7' "
" type: 'ReLU' "
" bottom: 'fc7' "
" top: 'fc7' "
"} "
"layer { "
" name: 'drop7' "
" type: 'Dropout' "
" dropout_param { "
" dropout_ratio: 0.5 "
" } "
" bottom: 'fc7' "
" top: 'fc7' "
"} "
"layer { "
" name: 'fc8' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 1000 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0 "
" } "
" } "
" param { "
" lr_mult: 1 "
" decay_mult: 1 "
" } "
" param { "
" lr_mult: 2 "
" decay_mult: 0 "
" } "
" bottom: 'fc7' "
" top: 'fc8' "
"} "
"layer { "
" name: 'loss' "
" type: 'SoftmaxWithLoss' "
" bottom: 'fc8' "
" bottom: 'label' "
"} ";
this->RunInsertionTest(input_proto, input_proto);
}
TEST_F(SplitLayerInsertionTest, TestNoInsertionWithInPlace) {
const string& input_proto =
"name: 'TestNetwork' "
"layer { "
" name: 'data' "
" type: 'Data' "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'innerprod' "
" type: 'InnerProduct' "
" bottom: 'data' "
" top: 'innerprod' "
"} "
"layer { "
" name: 'relu' "
" type: 'ReLU' "
" bottom: 'innerprod' "
" top: 'innerprod' "
"} "
"layer { "
" name: 'loss' "
" type: 'SoftmaxWithLoss' "
" bottom: 'innerprod' "
" bottom: 'label' "
"} ";
this->RunInsertionTest(input_proto, input_proto);
}
TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
const string& input_proto =
"name: 'UnsharedWeightsNetwork' "
"force_backward: true "
"layer { "
" name: 'data' "
" type: 'DummyData' "
" dummy_data_param { "
" num: 5 "
" channels: 2 "
" height: 3 "
" width: 4 "
" data_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" } "
" top: 'data' "
"} "
"layer { "
" name: 'innerproduct1' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
" bias_term: false "
" weight_filler { "
" type: 'gaussian' "
" std: 10 "
" } "
" } "
" param { name: 'unsharedweights1' } "
" bottom: 'data' "
" top: 'innerproduct1' "
" loss_weight: 2.5 "
"} "
"layer { "
" name: 'innerproduct2' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
" bias_term: false "
" weight_filler { "
" type: 'gaussian' "
" std: 10 "
" } "
" } "
" param { name: 'unsharedweights2' } "
" bottom: 'data' "
" top: 'innerproduct2' "
"} "
"layer { "
" name: 'loss' "
" type: 'EuclideanLoss' "
" bottom: 'innerproduct1' "
" bottom: 'innerproduct2' "
"} ";
const string& expected_output_proto =
"name: 'UnsharedWeightsNetwork' "
"force_backward: true "
"layer { "
" name: 'data' "
" type: 'DummyData' "
" dummy_data_param { "
" num: 5 "
" channels: 2 "
" height: 3 "
" width: 4 "
" data_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" } "
" top: 'data' "
"} "
"layer { "
" name: 'data_data_0_split' "
" type: 'Split' "
" bottom: 'data' "
" top: 'data_data_0_split_0' "
" top: 'data_data_0_split_1' "
"} "
"layer { "
" name: 'innerproduct1' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
" bias_term: false "
" weight_filler { "
" type: 'gaussian' "
" std: 10 "
" } "
" } "
" param { name: 'unsharedweights1' } "
" bottom: 'data_data_0_split_0' "
" top: 'innerproduct1' "
"} "
"layer { "
" name: 'innerproduct1_innerproduct1_0_split' "
" type: 'Split' "
" bottom: 'innerproduct1' "
" top: 'innerproduct1_innerproduct1_0_split_0' "
" top: 'innerproduct1_innerproduct1_0_split_1' "
" loss_weight: 2.5 "
" loss_weight: 0 "
"} "
"layer { "
" name: 'innerproduct2' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
" bias_term: false "
" weight_filler { "
" type: 'gaussian' "
" std: 10 "
" } "
" } "
" param { name: 'unsharedweights2' } "
" bottom: 'data_data_0_split_1' "
" top: 'innerproduct2' "
"} "
"layer { "
" name: 'loss' "
" type: 'EuclideanLoss' "
" bottom: 'innerproduct1_innerproduct1_0_split_1' "
" bottom: 'innerproduct2' "
"} ";
this->RunInsertionTest(input_proto, expected_output_proto);
}
TEST_F(SplitLayerInsertionTest, TestInsertion) {
const string& input_proto =
"name: 'TestNetwork' "
"layer { "
" name: 'data' "
" type: 'Data' "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'innerprod1' "
" type: 'InnerProduct' "
" bottom: 'data' "
" top: 'innerprod1' "
"} "
"layer { "
" name: 'innerprod2' "
" type: 'InnerProduct' "
" bottom: 'data' "
" top: 'innerprod2' "
"} "
"layer { "
" name: 'innerprod3' "
" type: 'InnerProduct' "
" bottom: 'data' "
" top: 'innerprod3' "
"} "
"layer { "
" name: 'loss1' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod1' "
" bottom: 'innerprod2' "
"} "
"layer { "
" name: 'loss2' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod2' "
" bottom: 'innerprod3' "
"} ";
const string& expected_output_proto =
"name: 'TestNetwork' "
"layer { "
" name: 'data' "
" type: 'Data' "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'data_data_0_split' "
" type: 'Split' "
" bottom: 'data' "
" top: 'data_data_0_split_0' "
" top: 'data_data_0_split_1' "
" top: 'data_data_0_split_2' "
"} "
"layer { "
" name: 'innerprod1' "
" type: 'InnerProduct' "
" bottom: 'data_data_0_split_0' "
" top: 'innerprod1' "
"} "
"layer { "
" name: 'innerprod2' "
" type: 'InnerProduct' "
" bottom: 'data_data_0_split_1' "
" top: 'innerprod2' "
"} "
"layer { "
" name: 'innerprod2_innerprod2_0_split' "
" type: 'Split' "
" bottom: 'innerprod2' "
" top: 'innerprod2_innerprod2_0_split_0' "
" top: 'innerprod2_innerprod2_0_split_1' "
"} "
"layer { "
" name: 'innerprod3' "
" type: 'InnerProduct' "
" bottom: 'data_data_0_split_2' "
" top: 'innerprod3' "
"} "
"layer { "
" name: 'loss1' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod1' "
" bottom: 'innerprod2_innerprod2_0_split_0' "
"} "
"layer { "
" name: 'loss2' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod2_innerprod2_0_split_1' "
" bottom: 'innerprod3' "
"} ";
this->RunInsertionTest(input_proto, expected_output_proto);
}
TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) {
const string& input_proto =
"name: 'TestNetwork' "
"layer { "
" name: 'data' "
" type: 'Data' "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'innerprod1' "
" type: 'InnerProduct' "
" bottom: 'data' "
" top: 'innerprod1' "
"} "
"layer { "
" name: 'innerprod2' "
" type: 'InnerProduct' "
" bottom: 'label' "
" top: 'innerprod2' "
"} "
"layer { "
" name: 'innerprod3' "
" type: 'InnerProduct' "
" bottom: 'data' "
" top: 'innerprod3' "
"} "
"layer { "
" name: 'innerprod4' "
" type: 'InnerProduct' "
" bottom: 'label' "
" top: 'innerprod4' "
"} "
"layer { "
" name: 'loss1' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod1' "
" bottom: 'innerprod3' "
"} "
"layer { "
" name: 'loss2' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod2' "
" bottom: 'innerprod4' "
"} ";
const string& expected_output_proto =
"name: 'TestNetwork' "
"layer { "
" name: 'data' "
" type: 'Data' "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'data_data_0_split' "
" type: 'Split' "
" bottom: 'data' "
" top: 'data_data_0_split_0' "
" top: 'data_data_0_split_1' "
"} "
"layer { "
" name: 'label_data_1_split' "
" type: 'Split' "
" bottom: 'label' "
" top: 'label_data_1_split_0' "
" top: 'label_data_1_split_1' "
"} "
"layer { "
" name: 'innerprod1' "
" type: 'InnerProduct' "
" bottom: 'data_data_0_split_0' "
" top: 'innerprod1' "
"} "
"layer { "
" name: 'innerprod2' "
" type: 'InnerProduct' "
" bottom: 'label_data_1_split_0' "
" top: 'innerprod2' "
"} "
"layer { "
" name: 'innerprod3' "
" type: 'InnerProduct' "
" bottom: 'data_data_0_split_1' "
" top: 'innerprod3' "
"} "
"layer { "
" name: 'innerprod4' "
" type: 'InnerProduct' "
" bottom: 'label_data_1_split_1' "
" top: 'innerprod4' "
"} "
"layer { "
" name: 'loss1' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod1' "
" bottom: 'innerprod3' "
"} "
"layer { "
" name: 'loss2' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod2' "
" bottom: 'innerprod4' "
"} ";
this->RunInsertionTest(input_proto, expected_output_proto);
}
TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
const string& input_proto =
"name: 'TestNetwork' "
"input: 'data' "
"input_dim: 10 "
"input_dim: 3 "
"input_dim: 227 "
"input_dim: 227 "
"layer { "
" name: 'innerprod1' "
" type: 'InnerProduct' "
" bottom: 'data' "
" top: 'innerprod1' "
"} "
"layer { "
" name: 'innerprod2' "
" type: 'InnerProduct' "
" bottom: 'data' "
" top: 'innerprod2' "
"} "
"layer { "
" name: 'loss' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod1' "
" bottom: 'innerprod2' "
"} ";
const string& expected_output_proto =
"name: 'TestNetwork' "
"input: 'data' "
"input_dim: 10 "
"input_dim: 3 "
"input_dim: 227 "
"input_dim: 227 "
"layer { "
" name: 'data_input_0_split' "
" type: 'Split' "
" bottom: 'data' "
" top: 'data_input_0_split_0' "
" top: 'data_input_0_split_1' "
"} "
"layer { "
" name: 'innerprod1' "
" type: 'InnerProduct' "
" bottom: 'data_input_0_split_0' "
" top: 'innerprod1' "
"} "
"layer { "
" name: 'innerprod2' "
" type: 'InnerProduct' "
" bottom: 'data_input_0_split_1' "
" top: 'innerprod2' "
"} "
"layer { "
" name: 'loss' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod1' "
" bottom: 'innerprod2' "
"} ";
this->RunInsertionTest(input_proto, expected_output_proto);
}
TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
const string& input_proto =
"name: 'TestNetwork' "
"layer { "
" name: 'data' "
" type: 'Data' "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'innerprod1' "
" type: 'InnerProduct' "
" bottom: 'data' "
" top: 'innerprod1' "
"} "
"layer { "
" name: 'relu1' "
" type: 'ReLU' "
" bottom: 'innerprod1' "
" top: 'innerprod1' "
"} "
"layer { "
" name: 'innerprod2' "
" type: 'InnerProduct' "
" bottom: 'innerprod1' "
" top: 'innerprod2' "
"} "
"layer { "
" name: 'loss1' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod1' "
" bottom: 'label' "
"} "
"layer { "
" name: 'loss2' "
" type: 'EuclideanLoss' "
" bottom: 'innerprod2' "
" bottom: 'data' "
"} ";
const string& expected_output_proto =
"name: 'TestNetwork' "
"layer { "
" name: 'data' "
" type: 'Data' "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'data_data_0_split' "
" type: 'Split' "