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test_net.cpp
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2368 lines (2280 loc) · 71.8 KB
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#include <string>
#include <utility>
#include <vector>
#include "google/protobuf/text_format.h"
#include "gtest/gtest.h"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
#include "caffe/net.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
namespace caffe {
template <typename TypeParam>
class NetTest : public MultiDeviceTest<TypeParam> {
typedef typename TypeParam::Dtype Dtype;
protected:
NetTest() : seed_(1701) {}
virtual void InitNetFromProtoString(const string& proto) {
NetParameter param;
CHECK(google::protobuf::TextFormat::ParseFromString(proto, ¶m));
net_.reset(new Net<Dtype>(param));
}
virtual void CopyNetBlobs(const bool copy_diff,
vector<shared_ptr<Blob<Dtype> > >* blobs_copy) {
CHECK(net_);
const vector<shared_ptr<Blob<Dtype> > >& net_blobs = net_->blobs();
blobs_copy->clear();
blobs_copy->resize(net_blobs.size());
const bool kReshape = true;
for (int i = 0; i < net_blobs.size(); ++i) {
(*blobs_copy)[i].reset(new Blob<Dtype>());
(*blobs_copy)[i]->CopyFrom(*net_blobs[i], copy_diff, kReshape);
}
}
virtual void CopyNetParams(const bool copy_diff,
vector<shared_ptr<Blob<Dtype> > >* params_copy) {
CHECK(net_);
const vector<shared_ptr<Blob<Dtype> > >& net_params = net_->params();
params_copy->clear();
params_copy->resize(net_params.size());
const bool kReshape = true;
for (int i = 0; i < net_params.size(); ++i) {
(*params_copy)[i].reset(new Blob<Dtype>());
(*params_copy)[i]->CopyFrom(*net_params[i], copy_diff, kReshape);
}
}
virtual void InitTinyNet(const bool force_backward = false,
const bool accuracy_layer = false) {
string proto =
"name: 'TinyTestNetwork' "
"layer { "
" name: 'data' "
" type: 'DummyData' "
" dummy_data_param { "
" shape { "
" dim: 5 "
" dim: 2 "
" dim: 3 "
" dim: 4 "
" } "
" data_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" shape { "
" dim: 5 "
" } "
" data_filler { "
" type: 'constant' "
" value: 0 "
" } "
" } "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'innerproduct' "
" 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: 'data' "
" top: 'innerproduct' "
"} "
"layer { "
" name: 'loss' "
" type: 'SoftmaxWithLoss' "
" bottom: 'innerproduct' "
" bottom: 'label' "
" top: 'top_loss' "
"} ";
if (accuracy_layer) {
proto +=
"layer { "
" name: 'loss' "
" type: 'Accuracy' "
" bottom: 'innerproduct' "
" bottom: 'label' "
" top: 'accuracy' "
"} ";
}
if (force_backward) {
proto += "force_backward: true ";
}
InitNetFromProtoString(proto);
}
virtual void InitTinyNetEuclidean(const bool force_backward = false) {
string proto =
"name: 'TinyTestEuclidLossNetwork' "
"layer { "
" name: 'data' "
" type: 'DummyData' "
" dummy_data_param { "
" num: 5 "
" channels: 2 "
" height: 3 "
" width: 4 "
" num: 5 "
" channels: 1 "
" height: 1 "
" width: 1 "
" data_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" } "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'innerproduct' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 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: 'data' "
" top: 'innerproduct' "
"} "
"layer { "
" name: 'loss' "
" type: 'EuclideanLoss' "
" bottom: 'innerproduct' "
" bottom: 'label' "
"} ";
if (force_backward) {
proto += "force_backward: true ";
}
InitNetFromProtoString(proto);
}
virtual void InitTrickyNet(Dtype* loss_weight = NULL) {
ostringstream loss_weight_stream;
if (loss_weight) {
loss_weight_stream << " loss_weight: " << *loss_weight << " ";
}
const string& proto =
"name: 'TrickyTestNetwork' "
"layer { "
" name: 'data' "
" type: 'DummyData' "
" dummy_data_param { "
" num: 5 "
" channels: 2 "
" height: 3 "
" width: 4 "
" num: 5 "
" channels: 1 "
" height: 1 "
" width: 1 "
" data_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" } "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'innerproduct' "
" 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: 'data' "
" top: 'transformed_data' "
"} "
"layer { "
" name: 'innerproduct' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 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: 'label' "
" top: 'transformed_label' "
"} "
"layer { "
" name: 'loss' "
" type: 'SoftmaxWithLoss' " +
loss_weight_stream.str() +
" bottom: 'transformed_data' "
" bottom: 'transformed_label' "
"} ";
InitNetFromProtoString(proto);
}
// loss_weight is the loss weight for the 'EuclideanLoss' layer output.
// midnet_loss_weight is the loss weight for the first 'InnerProduct' layer
// output. Should both default to 0.0 if unspecified (i.e., if NULL is
// passed to this function).
virtual void InitUnsharedWeightsNet(const Dtype* loss_weight = NULL,
const Dtype* midnet_loss_weight = NULL,
const bool force_backward = false, const bool bias_term = false,
const Dtype blobs_lr_w1 = 1, const Dtype blobs_lr_b1 = 2,
const Dtype blobs_lr_w2 = 1, const Dtype blobs_lr_b2 = 2) {
string bias_str = bias_term ? "true ":"false ";
ostringstream proto;
proto << "name: 'UnsharedWeightsNetwork' ";
if (force_backward) {
proto << "force_backward: true ";
}
proto <<
"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: " << bias_str <<
" weight_filler { "
" type: 'gaussian' "
" std: 10 "
" } "
" } "
" param { "
" name: 'unsharedweights1' "
" lr_mult: " << blobs_lr_w1 <<
" } ";
if (bias_term) {
proto << " param { lr_mult: " << blobs_lr_b1 << " } ";
}
proto <<
" bottom: 'data' "
" top: 'innerproduct1' ";
if (midnet_loss_weight) {
proto << " loss_weight: " << *midnet_loss_weight << " ";
}
proto <<
"} "
"layer { "
" name: 'innerproduct2' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
" bias_term: " << bias_str <<
" weight_filler { "
" type: 'gaussian' "
" std: 10 "
" } "
" } "
" param { "
" name: 'unsharedweights2' "
" lr_mult: " << blobs_lr_w2 <<
" } ";
if (bias_term) {
proto << " param { lr_mult: " << blobs_lr_b2 << " } ";
}
proto <<
" bottom: 'data' "
" top: 'innerproduct2' "
"} "
"layer { "
" name: 'loss' "
" type: 'EuclideanLoss' ";
if (loss_weight) {
proto << " loss_weight: " << *loss_weight << " ";
}
proto <<
" bottom: 'innerproduct1' "
" bottom: 'innerproduct2' "
"} ";
InitNetFromProtoString(proto.str());
}
virtual void InitSharedWeightsNet() {
const string& proto =
"name: 'SharedWeightsNetwork' "
"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: 'sharedweights' } "
" bottom: 'data' "
" top: 'innerproduct1' "
"} "
"layer { "
" name: 'innerproduct2' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
" bias_term: false "
" weight_filler { "
" type: 'gaussian' "
" std: 10 "
" } "
" } "
" param { name: 'sharedweights' } "
" bottom: 'data' "
" top: 'innerproduct2' "
"} "
"layer { "
" name: 'loss' "
" type: 'EuclideanLoss' "
" bottom: 'innerproduct1' "
" bottom: 'innerproduct2' "
"} ";
InitNetFromProtoString(proto);
}
virtual void InitDiffDataUnsharedWeightsNet() {
const string& proto =
"name: 'DiffDataUnsharedWeightsNetwork' "
"layer { "
" name: 'data' "
" type: 'DummyData' "
" dummy_data_param { "
" num: 10 "
" channels: 10 "
" height: 1 "
" width: 1 "
" num: 10 "
" channels: 10 "
" height: 1 "
" width: 1 "
" data_filler { "
" type: 'gaussian' "
" std: 10 "
" } "
" } "
" top: 'data1' "
" top: 'data2' "
"} "
"layer { "
" name: 'innerproduct1' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
" bias_term: false "
" weight_filler { "
" type: 'constant' "
" value: 0.5 "
" } "
" } "
" param { name: 'unsharedweights1' } "
" bottom: 'data1' "
" top: 'innerproduct1' "
"} "
"layer { "
" name: 'innerproduct2' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
" bias_term: false "
" weight_filler { "
" type: 'constant' "
" value: 0.5 "
" } "
" } "
" param { name: 'unsharedweights2' } "
" bottom: 'innerproduct1' "
" top: 'innerproduct2' "
"} "
"layer { "
" name: 'loss' "
" type: 'EuclideanLoss' "
" bottom: 'data2' "
" bottom: 'innerproduct2' "
"} ";
InitNetFromProtoString(proto);
}
virtual void InitDiffDataSharedWeightsNet() {
const string& proto =
"name: 'DiffDataSharedWeightsNetwork' "
"layer { "
" name: 'data' "
" type: 'DummyData' "
" dummy_data_param { "
" num: 10 "
" channels: 10 "
" height: 1 "
" width: 1 "
" num: 10 "
" channels: 10 "
" height: 1 "
" width: 1 "
" data_filler { "
" type: 'gaussian' "
" std: 10 "
" } "
" } "
" top: 'data1' "
" top: 'data2' "
"} "
"layer { "
" name: 'innerproduct1' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
" bias_term: false "
" weight_filler { "
" type: 'constant' "
" value: 0.5 "
" } "
" } "
" param { name: 'sharedweights' } "
" bottom: 'data1' "
" top: 'innerproduct1' "
"} "
"layer { "
" name: 'innerproduct2' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
" bias_term: false "
" weight_filler { "
" type: 'constant' "
" value: 0.5 "
" } "
" } "
" param { name: 'sharedweights' } "
" bottom: 'innerproduct1' "
" top: 'innerproduct2' "
"} "
"layer { "
" name: 'loss' "
" type: 'EuclideanLoss' "
" bottom: 'data2' "
" bottom: 'innerproduct2' "
"} ";
InitNetFromProtoString(proto);
}
virtual void InitReshapableNet() {
const string& proto =
"name: 'ReshapableNetwork' "
"input: 'data' "
"input_dim: 1 "
"input_dim: 3 "
"input_dim: 100 "
"input_dim: 100 "
"layer { "
" name: 'conv1' "
" type: 'Convolution' "
" bottom: 'data' "
" top: 'conv1' "
" convolution_param { "
" num_output: 5 "
" kernel_size: 3 "
" stride: 2 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0.2 "
" } "
" } "
"} "
"layer { "
" name: 'relu1' "
" type: 'ReLU' "
" bottom: 'conv1' "
" top: 'conv1' "
"} "
"layer { "
" name: 'pool1' "
" type: 'Pooling' "
" bottom: 'conv1' "
" top: 'pool1' "
" pooling_param { "
" pool: MAX "
" kernel_size: 2 "
" stride: 2 "
" } "
"} "
"layer { "
" name: 'norm1' "
" type: 'LRN' "
" bottom: 'pool1' "
" top: 'norm1' "
" lrn_param { "
" local_size: 3 "
" } "
"} "
"layer { "
" name: 'softmax' "
" type: 'Softmax' "
" bottom: 'norm1' "
" top: 'softmax' "
"} ";
InitNetFromProtoString(proto);
}
virtual void InitSkipPropNet(bool test_skip_true) {
string proto =
"name: 'SkipPropTestNetwork' "
"layer { "
" name: 'data' "
" type: 'DummyData' "
" dummy_data_param { "
" shape { "
" dim: 5 "
" dim: 2 "
" dim: 3 "
" dim: 4 "
" } "
" data_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" shape { "
" dim: 5 "
" } "
" data_filler { "
" type: 'constant' "
" value: 0 "
" } "
" } "
" top: 'data' "
" top: 'label' "
"} "
"layer { "
" name: 'silence' "
" bottom: 'label' "
" type: 'Silence' "
"} "
"layer { "
" name: 'innerproduct' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 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: 'data' "
" top: 'innerproduct' "
"} "
"layer { "
" name: 'ip_fake_labels' "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 1 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0 "
" } "
" } "
" bottom: 'data' "
" top: 'fake_labels' "
"} "
"layer { "
" name: 'argmax' "
" bottom: 'fake_labels' "
" top: 'label_argmax' "
" type: 'ArgMax' "
"} "
"layer { "
" name: 'loss' "
" bottom: 'innerproduct' "
" bottom: 'label_argmax' ";
if (test_skip_true)
proto += " propagate_down: true "
" propagate_down: false ";
else
proto += " propagate_down: true "
" propagate_down: true ";
proto +=
" top: 'cross_entropy_loss' "
" type: 'SigmoidCrossEntropyLoss' "
" loss_weight: 0.1 "
"} ";
InitNetFromProtoString(proto);
}
int seed_;
shared_ptr<Net<Dtype> > net_;
};
TYPED_TEST_CASE(NetTest, TestDtypesAndDevices);
TYPED_TEST(NetTest, TestHasBlob) {
this->InitTinyNet();
EXPECT_TRUE(this->net_->has_blob("data"));
EXPECT_TRUE(this->net_->has_blob("label"));
EXPECT_TRUE(this->net_->has_blob("innerproduct"));
EXPECT_FALSE(this->net_->has_blob("loss"));
EXPECT_TRUE(this->net_->has_blob("top_loss"));
}
TYPED_TEST(NetTest, TestGetBlob) {
this->InitTinyNet();
EXPECT_EQ(this->net_->blob_by_name("data"), this->net_->blobs()[0]);
EXPECT_EQ(this->net_->blob_by_name("label"), this->net_->blobs()[1]);
EXPECT_EQ(this->net_->blob_by_name("innerproduct"), this->net_->blobs()[2]);
EXPECT_FALSE(this->net_->blob_by_name("loss"));
EXPECT_EQ(this->net_->blob_by_name("top_loss"), this->net_->blobs()[3]);
}
TYPED_TEST(NetTest, TestHasLayer) {
this->InitTinyNet();
EXPECT_TRUE(this->net_->has_layer("data"));
EXPECT_TRUE(this->net_->has_layer("innerproduct"));
EXPECT_TRUE(this->net_->has_layer("loss"));
EXPECT_FALSE(this->net_->has_layer("label"));
}
TYPED_TEST(NetTest, TestGetLayerByName) {
this->InitTinyNet();
EXPECT_EQ(this->net_->layer_by_name("data"), this->net_->layers()[0]);
EXPECT_EQ(this->net_->layer_by_name("innerproduct"), this->net_->layers()[1]);
EXPECT_EQ(this->net_->layer_by_name("loss"), this->net_->layers()[2]);
EXPECT_FALSE(this->net_->layer_by_name("label"));
}
TYPED_TEST(NetTest, TestBottomNeedBackward) {
this->InitTinyNet();
const vector<vector<bool> >& bottom_need_backward =
this->net_->bottom_need_backward();
EXPECT_EQ(3, bottom_need_backward.size());
EXPECT_EQ(0, bottom_need_backward[0].size());
EXPECT_EQ(1, bottom_need_backward[1].size());
EXPECT_EQ(false, bottom_need_backward[1][0]);
EXPECT_EQ(2, bottom_need_backward[2].size());
EXPECT_EQ(true, bottom_need_backward[2][0]);
EXPECT_EQ(false, bottom_need_backward[2][1]);
}
TYPED_TEST(NetTest, TestBottomNeedBackwardForce) {
const bool force_backward = true;
this->InitTinyNet(force_backward);
const vector<vector<bool> >& bottom_need_backward =
this->net_->bottom_need_backward();
EXPECT_EQ(3, bottom_need_backward.size());
EXPECT_EQ(0, bottom_need_backward[0].size());
EXPECT_EQ(1, bottom_need_backward[1].size());
EXPECT_EQ(true, bottom_need_backward[1][0]);
EXPECT_EQ(2, bottom_need_backward[2].size());
EXPECT_EQ(true, bottom_need_backward[2][0]);
EXPECT_EQ(false, bottom_need_backward[2][1]);
}
TYPED_TEST(NetTest, TestBottomNeedBackwardEuclideanForce) {
const bool force_backward = true;
this->InitTinyNetEuclidean(force_backward);
const vector<vector<bool> >& bottom_need_backward =
this->net_->bottom_need_backward();
EXPECT_EQ(3, bottom_need_backward.size());
EXPECT_EQ(0, bottom_need_backward[0].size());
EXPECT_EQ(1, bottom_need_backward[1].size());
EXPECT_EQ(true, bottom_need_backward[1][0]);
EXPECT_EQ(2, bottom_need_backward[2].size());
EXPECT_EQ(true, bottom_need_backward[2][0]);
EXPECT_EQ(true, bottom_need_backward[2][1]);
}
TYPED_TEST(NetTest, TestBottomNeedBackwardTricky) {
this->InitTrickyNet();
const vector<vector<bool> >& bottom_need_backward =
this->net_->bottom_need_backward();
EXPECT_EQ(4, bottom_need_backward.size());
EXPECT_EQ(0, bottom_need_backward[0].size());
EXPECT_EQ(1, bottom_need_backward[1].size());
EXPECT_EQ(false, bottom_need_backward[1][0]);
EXPECT_EQ(1, bottom_need_backward[2].size());
EXPECT_EQ(false, bottom_need_backward[2][0]);
EXPECT_EQ(2, bottom_need_backward[3].size());
EXPECT_EQ(true, bottom_need_backward[3][0]);
// The label input to the SoftmaxLossLayer should say it "needs backward"
// since it has weights under it, even though we expect this to cause a crash
// at training/test time.
EXPECT_EQ(true, bottom_need_backward[3][1]);
}
TYPED_TEST(NetTest, TestLossWeight) {
typedef typename TypeParam::Dtype Dtype;
// First, compute the loss and gradients with no loss_weight specified.
// In this case, the loss weight for the 'EuclideanLoss' layer should default
// to 1.
vector<Blob<Dtype>*> bottom;
Caffe::set_random_seed(this->seed_);
const bool kForceBackward = true;
this->InitUnsharedWeightsNet(NULL, NULL, kForceBackward);
const Dtype loss = this->net_->ForwardBackward(bottom);
const bool kCopyDiff = true;
vector<shared_ptr<Blob<Dtype> > > blob_grads;
this->CopyNetBlobs(kCopyDiff, &blob_grads);
vector<shared_ptr<Blob<Dtype> > > param_grads;
this->CopyNetParams(kCopyDiff, ¶m_grads);
// Check that the loss is non-trivial, otherwise the test doesn't prove much.
const Dtype kMinLossAbsValue = 1e-2;
ASSERT_GE(fabs(loss), kMinLossAbsValue);
const Dtype kErrorMargin = 1e-4;
const int kNumLossWeights = 6;
Dtype kLossWeights[kNumLossWeights] = {2, 0, 1, -1, -2.5, 3.7};
for (int i = 0; i < kNumLossWeights; ++i) {
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&kLossWeights[i], NULL, kForceBackward);
const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
<< "loss weight = " << kLossWeights[i];
const vector<shared_ptr<Blob<Dtype> > >& weighted_blobs =
this->net_->blobs();
ASSERT_EQ(blob_grads.size(), weighted_blobs.size());
for (int j = 0; j < blob_grads.size(); ++j) {
ASSERT_EQ(blob_grads[j]->count(), weighted_blobs[j]->count());
for (int k = 0; k < blob_grads[j]->count(); ++k) {
EXPECT_NEAR(blob_grads[j]->cpu_diff()[k] * kLossWeights[i],
weighted_blobs[j]->cpu_diff()[k], error_margin);
}
}
const vector<shared_ptr<Blob<Dtype> > >& weighted_params =
this->net_->params();
ASSERT_EQ(param_grads.size(), weighted_params.size());
for (int j = 0; j < param_grads.size(); ++j) {
ASSERT_EQ(param_grads[j]->count(), weighted_params[j]->count());
for (int k = 0; k < param_grads[j]->count(); ++k) {
EXPECT_NEAR(param_grads[j]->cpu_diff()[k] * kLossWeights[i],
weighted_params[j]->cpu_diff()[k], error_margin);
}
}
}
}
TYPED_TEST(NetTest, TestLossWeightMidNet) {
typedef typename TypeParam::Dtype Dtype;
vector<Blob<Dtype>*> bottom;
Caffe::set_random_seed(this->seed_);
const bool kForceBackward = true;
Dtype loss_weight = 0;
Dtype midnet_loss_weight = 1;
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
const Dtype loss = this->net_->ForwardBackward(bottom);
const bool kCopyDiff = true;
const bool kReshape = true;
Blob<Dtype> data_grad;
data_grad.CopyFrom(*this->net_->blob_by_name("data"), kCopyDiff, kReshape);
// Check that the loss is non-trivial, otherwise the test doesn't prove much.
const Dtype kMinLossAbsValue = 1e-2;
ASSERT_GE(fabs(loss), kMinLossAbsValue);
const Dtype kErrorMargin = 1e-4;
const int kNumLossWeights = 6;
Dtype kLossWeights[kNumLossWeights] = {2, 0, 1, -1, -2.5, 3.7};
for (int i = 0; i < kNumLossWeights; ++i) {
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &kLossWeights[i],
kForceBackward);
const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
<< "loss weight = " << kLossWeights[i];
const shared_ptr<Blob<Dtype> >& weighted_blob =
this->net_->blob_by_name("data");
ASSERT_EQ(data_grad.count(), weighted_blob->count());
for (int j = 0; j < data_grad.count(); ++j) {
EXPECT_NEAR(data_grad.cpu_diff()[j] * kLossWeights[i],
weighted_blob->cpu_diff()[j], error_margin);
}
}
}
TYPED_TEST(NetTest, TestComboLossWeight) {
typedef typename TypeParam::Dtype Dtype;
vector<Blob<Dtype>*> bottom;
Dtype loss_weight;
Dtype midnet_loss_weight;
const bool kForceBackward = true;
const Dtype kErrorMargin = 1e-4;
// Get the loss and gradients with 'EuclideanLoss' weight 1,
// 'InnerProduct' weight 1.
loss_weight = 1;
midnet_loss_weight = 1;
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
const Dtype loss = this->net_->ForwardBackward(bottom);
const bool kCopyDiff = true;
vector<shared_ptr<Blob<Dtype> > > blob_grads;
this->CopyNetBlobs(kCopyDiff, &blob_grads);
vector<shared_ptr<Blob<Dtype> > > param_grads;
this->CopyNetParams(kCopyDiff, ¶m_grads);
loss_weight = 2;
midnet_loss_weight = 1;
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
const Dtype loss_main_2 = this->net_->ForwardBackward(bottom);
vector<shared_ptr<Blob<Dtype> > > blob_grads_loss_2;
this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
vector<shared_ptr<Blob<Dtype> > > param_grads_loss_2;
this->CopyNetParams(kCopyDiff, ¶m_grads_loss_2);
loss_weight = 3;
midnet_loss_weight = 1;
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
const Dtype loss_main_3 = this->net_->ForwardBackward(bottom);
const vector<shared_ptr<Blob<Dtype> > >& blob_grads_loss_3 =
this->net_->blobs();
ASSERT_EQ(blob_grads.size(), blob_grads_loss_3.size());
ASSERT_EQ(blob_grads_loss_2.size(), blob_grads_loss_3.size());
for (int j = 0; j < blob_grads.size(); ++j) {
const string& blob_name = this->net_->blob_names()[j];
bool grad_should_change = true;
if (blob_name == "innerproduct1_innerproduct1_0_split_0") {
grad_should_change = false;
}
ASSERT_EQ(blob_grads[j]->count(), blob_grads_loss_3[j]->count());
ASSERT_EQ(blob_grads_loss_2[j]->count(), blob_grads_loss_3[j]->count());
for (int k = 0; k < blob_grads[j]->count(); ++k) {
const Dtype grad_diff_2 = blob_grads_loss_2[j]->cpu_diff()[k] -
blob_grads[j]->cpu_diff()[k];
const Dtype grad_diff_3 = blob_grads_loss_3[j]->cpu_diff()[k] -
blob_grads[j]->cpu_diff()[k];
if (grad_should_change) {
// Test non-triviality.
const Dtype kMinGradDiffAbsValue = 1e-4;
EXPECT_GT(fabs(grad_diff_2), kMinGradDiffAbsValue) << blob_name;
EXPECT_NEAR(2 * grad_diff_2, grad_diff_3, kErrorMargin) << blob_name;
} else {
EXPECT_EQ(0, grad_diff_2) << blob_name;
EXPECT_EQ(0, grad_diff_3) << blob_name;
}
}
}
loss_weight = 1;
midnet_loss_weight = 2;
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
const Dtype loss_midnet_2 = this->net_->ForwardBackward(bottom);
this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
this->CopyNetParams(kCopyDiff, ¶m_grads_loss_2);
loss_weight = 1;
midnet_loss_weight = 3;
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
const Dtype loss_midnet_3 = this->net_->ForwardBackward(bottom);
const vector<shared_ptr<Blob<Dtype> > >& blob_grads_midnet_loss_3 =
this->net_->blobs();
ASSERT_EQ(blob_grads.size(), blob_grads_midnet_loss_3.size());
ASSERT_EQ(blob_grads_loss_2.size(), blob_grads_midnet_loss_3.size());
const vector<string>& blob_names = this->net_->blob_names();
for (int j = 0; j < blob_grads.size(); ++j) {
const string& blob_name = blob_names[j];
bool grad_should_change = false;
if (blob_name == "innerproduct1" ||
blob_name == "innerproduct1_innerproduct1_0_split_0" ||
blob_name == "data_data_0_split_0" || blob_name == "data") {
grad_should_change = true;
}
ASSERT_EQ(blob_grads[j]->count(), blob_grads_midnet_loss_3[j]->count());