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Copy pathtest_memory_data_layer.cpp
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167 lines (150 loc) · 5.61 KB
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#include <string>
#include <vector>
#include "caffe/data_layers.hpp"
#include "caffe/filler.hpp"
#include "caffe/test/test_caffe_main.hpp"
namespace caffe {
template <typename TypeParam>
class MemoryDataLayerTest : public MultiDeviceTest<TypeParam> {
typedef typename TypeParam::Dtype Dtype;
protected:
MemoryDataLayerTest()
: data_(new Blob<Dtype>()),
labels_(new Blob<Dtype>()),
data_blob_(new Blob<Dtype>()),
label_blob_(new Blob<Dtype>()) {}
virtual void SetUp() {
batch_size_ = 8;
batches_ = 12;
channels_ = 4;
height_ = 7;
width_ = 11;
blob_top_vec_.push_back(data_blob_);
blob_top_vec_.push_back(label_blob_);
// pick random input data
FillerParameter filler_param;
GaussianFiller<Dtype> filler(filler_param);
data_->Reshape(batches_ * batch_size_, channels_, height_, width_);
labels_->Reshape(batches_ * batch_size_, 1, 1, 1);
filler.Fill(this->data_);
filler.Fill(this->labels_);
}
virtual ~MemoryDataLayerTest() {
delete data_blob_;
delete label_blob_;
delete data_;
delete labels_;
}
int batch_size_;
int batches_;
int channels_;
int height_;
int width_;
// we don't really need blobs for the input data, but it makes it
// easier to call Filler
Blob<Dtype>* const data_;
Blob<Dtype>* const labels_;
// blobs for the top of MemoryDataLayer
Blob<Dtype>* const data_blob_;
Blob<Dtype>* const label_blob_;
vector<Blob<Dtype>*> blob_bottom_vec_;
vector<Blob<Dtype>*> blob_top_vec_;
};
TYPED_TEST_CASE(MemoryDataLayerTest, TestDtypesAndDevices);
TYPED_TEST(MemoryDataLayerTest, TestSetup) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
MemoryDataParameter* md_param = layer_param.mutable_memory_data_param();
md_param->set_batch_size(this->batch_size_);
md_param->set_channels(this->channels_);
md_param->set_height(this->height_);
md_param->set_width(this->width_);
shared_ptr<Layer<Dtype> > layer(
new MemoryDataLayer<Dtype>(layer_param));
layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
EXPECT_EQ(this->data_blob_->num(), this->batch_size_);
EXPECT_EQ(this->data_blob_->channels(), this->channels_);
EXPECT_EQ(this->data_blob_->height(), this->height_);
EXPECT_EQ(this->data_blob_->width(), this->width_);
EXPECT_EQ(this->label_blob_->num(), this->batch_size_);
EXPECT_EQ(this->label_blob_->channels(), 1);
EXPECT_EQ(this->label_blob_->height(), 1);
EXPECT_EQ(this->label_blob_->width(), 1);
}
// run through a few batches and check that the right data appears
TYPED_TEST(MemoryDataLayerTest, TestForward) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
MemoryDataParameter* md_param = layer_param.mutable_memory_data_param();
md_param->set_batch_size(this->batch_size_);
md_param->set_channels(this->channels_);
md_param->set_height(this->height_);
md_param->set_width(this->width_);
shared_ptr<MemoryDataLayer<Dtype> > layer(
new MemoryDataLayer<Dtype>(layer_param));
layer->DataLayerSetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer->Reset(this->data_->mutable_cpu_data(),
this->labels_->mutable_cpu_data(), this->data_->num());
for (int i = 0; i < this->batches_ * 6; ++i) {
int batch_num = i % this->batches_;
layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
for (int j = 0; j < this->data_blob_->count(); ++j) {
EXPECT_EQ(this->data_blob_->cpu_data()[j],
this->data_->cpu_data()[
this->data_->offset(1) * this->batch_size_ * batch_num + j]);
}
for (int j = 0; j < this->label_blob_->count(); ++j) {
EXPECT_EQ(this->label_blob_->cpu_data()[j],
this->labels_->cpu_data()[this->batch_size_ * batch_num + j]);
}
}
}
TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter param;
MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
memory_data_param->set_batch_size(this->batch_size_);
memory_data_param->set_channels(this->channels_);
memory_data_param->set_height(this->height_);
memory_data_param->set_width(this->width_);
MemoryDataLayer<Dtype> layer(param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
vector<Datum> datum_vector(this->batch_size_);
const size_t count = this->channels_ * this->height_ * this->width_;
size_t pixel_index = 0;
for (int i = 0; i < this->batch_size_; ++i) {
LOG(ERROR) << "i " << i;
datum_vector[i].set_channels(this->channels_);
datum_vector[i].set_height(this->height_);
datum_vector[i].set_width(this->width_);
datum_vector[i].set_label(i);
vector<char> pixels(count);
for (int j = 0; j < count; ++j) {
pixels[j] = pixel_index++ % 256;
}
datum_vector[i].set_data(&(pixels[0]), count);
}
layer.AddDatumVector(datum_vector);
int data_index;
// Go through the data 5 times
for (int iter = 0; iter < 5; ++iter) {
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
const Dtype* data = this->data_blob_->cpu_data();
size_t index = 0;
for (int i = 0; i < this->batch_size_; ++i) {
const string& data_string = datum_vector[i].data();
EXPECT_EQ(i, this->label_blob_->cpu_data()[i]);
for (int c = 0; c < this->channels_; ++c) {
for (int h = 0; h < this->height_; ++h) {
for (int w = 0; w < this->width_; ++w) {
data_index = (c * this->height_ + h) * this->width_ + w;
EXPECT_EQ(static_cast<Dtype>(
static_cast<uint8_t>(data_string[data_index])),
data[index++]);
}
}
}
}
}
}
} // namespace caffe