TYPED_TEST(DataTransformTest, TestCropSize) {
TransformationParameter transform_param;
const bool unique_pixels = false; // all pixels the same equal to label
const int label = 0;
const int channels = 3;
const int height = 4;
const int width = 5;
const int crop_size = 2;
transform_param.set_crop_size(crop_size);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
DataTransformer<TypeParam> transformer(transform_param, TEST);
transformer.InitRand();
Blob<TypeParam> blob(1, channels, crop_size, crop_size);
for (int iter = 0; iter < this->num_iter_; ++iter) {
transformer.Transform(datum, &blob);
EXPECT_EQ(blob.num(), 1);
EXPECT_EQ(blob.channels(), datum.channels());
EXPECT_EQ(blob.height(), crop_size);
EXPECT_EQ(blob.width(), crop_size);
for (int j = 0; j < blob.count(); ++j) {
EXPECT_EQ(blob.cpu_data()[j], label);
}
}
}
TYPED_TEST(DataTransformTest, TestMeanValues) {
TransformationParameter transform_param;
const bool unique_pixels = false; // pixels are equal to label
const int_tp label = 0;
const int_tp channels = 3;
const int_tp height = 4;
const int_tp width = 5;
transform_param.add_mean_value(0);
transform_param.add_mean_value(1);
transform_param.add_mean_value(2);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
DataTransformer<TypeParam>* transformer =
new DataTransformer<TypeParam>(transform_param, TEST,
Caffe::GetDefaultDevice());
transformer->InitRand();
transformer->Transform(datum, blob);
for (int_tp c = 0; c < channels; ++c) {
for (int_tp j = 0; j < height * width; ++j) {
EXPECT_EQ(blob->cpu_data()[blob->offset(0, c) + j], label - c);
}
}
}
TYPED_TEST(DataTransformTest, TestCropSize) {
TransformationParameter transform_param;
const bool unique_pixels = false; // all pixels the same equal to label
const int_tp label = 0;
const int_tp channels = 3;
const int_tp height = 4;
const int_tp width = 5;
const int_tp crop_size = 2;
transform_param.set_crop_size(crop_size);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
DataTransformer<TypeParam>* transformer =
new DataTransformer<TypeParam>(transform_param, TEST,
Caffe::GetDefaultDevice());
transformer->InitRand();
Blob<TypeParam>* blob =
new Blob<TypeParam>(1, channels, crop_size, crop_size);
for (int_tp iter = 0; iter < this->num_iter_; ++iter) {
transformer->Transform(datum, blob);
EXPECT_EQ(blob->num(), 1);
EXPECT_EQ(blob->channels(), datum.channels());
EXPECT_EQ(blob->height(), crop_size);
EXPECT_EQ(blob->width(), crop_size);
for (int_tp j = 0; j < blob->count(); ++j) {
EXPECT_EQ(blob->cpu_data()[j], label);
}
}
}
TYPED_TEST(DataTransformTest, TestMirrorTest) {
TransformationParameter transform_param;
const bool unique_pixels = true; // pixels are consecutive ints [0,size]
const int_tp label = 0;
const int_tp channels = 3;
const int_tp height = 4;
const int_tp width = 5;
const int_tp size = channels * height * width;
transform_param.set_mirror(true);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
int_tp num_matches = this->NumSequenceMatches(transform_param, datum, TEST);
EXPECT_LT(num_matches, size * this->num_iter_);
}
TYPED_TEST(DataTransformTest, TestCropTest) {
TransformationParameter transform_param;
const bool unique_pixels = true; // pixels are consecutive ints [0,size]
const int label = 0;
const int channels = 3;
const int height = 4;
const int width = 5;
const int crop_size = 2;
const int size = channels * crop_size * crop_size;
transform_param.set_crop_size(crop_size);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
int num_matches = this->NumSequenceMatches(transform_param, datum, TEST);
EXPECT_EQ(num_matches, size * this->num_iter_);
}
TYPED_TEST(DataTransformTest, TestMeanValue) {
TransformationParameter transform_param;
const bool unique_pixels = false; // pixels are equal to label
const int label = 0;
const int channels = 3;
const int height = 4;
const int width = 5;
const int mean_value = 2;
transform_param.add_mean_value(mean_value);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
Blob<TypeParam> blob(1, channels, height, width);
DataTransformer<TypeParam> transformer(transform_param, TEST);
transformer.InitRand();
transformer.Transform(datum, &blob);
for (int j = 0; j < blob.count(); ++j) {
EXPECT_EQ(blob.cpu_data()[j], label - mean_value);
}
}
TYPED_TEST(DataTransformTest, TestCropMirrorTest) {
TransformationParameter transform_param;
const bool unique_pixels = true; // pixels are consecutive ints [0,size]
const int label = 0;
const int channels = 3;
const int height = 4;
const int width = 5;
const int crop_size = 2;
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
transform_param.set_crop_size(crop_size);
int num_matches_crop = this->NumSequenceMatches(transform_param, datum, TEST);
transform_param.set_mirror(true);
int num_matches_crop_mirror =
this->NumSequenceMatches(transform_param, datum, TEST);
// When doing crop and mirror we expect less num_matches than just crop
EXPECT_LT(num_matches_crop_mirror, num_matches_crop);
}
TYPED_TEST(DataTransformTest, TestEmptyTransformUniquePixels) {
TransformationParameter transform_param;
const bool unique_pixels = true; // pixels are consecutive ints [0,size]
const int label = 0;
const int channels = 3;
const int height = 4;
const int width = 5;
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
Blob<TypeParam> blob(1, 3, 4, 5);
DataTransformer<TypeParam> transformer(transform_param, TEST);
transformer.InitRand();
transformer.Transform(datum, &blob);
EXPECT_EQ(blob.num(), 1);
EXPECT_EQ(blob.channels(), datum.channels());
EXPECT_EQ(blob.height(), datum.height());
EXPECT_EQ(blob.width(), datum.width());
for (int j = 0; j < blob.count(); ++j) {
EXPECT_EQ(blob.cpu_data()[j], j);
}
}
TYPED_TEST(DataTransformTest, TestMeanValue) {
TransformationParameter transform_param;
const bool unique_pixels = false; // pixels are equal to label
const int label = 0;
const int channels = 3;
const int height = 4;
const int width = 5;
const int mean_value = 2;
transform_param.add_mean_value(mean_value);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
DataTransformer<TypeParam>* transformer =
new DataTransformer<TypeParam>(transform_param, TEST,
Caffe::GetDefaultDeviceContext());
transformer->InitRand();
transformer->Transform(datum, blob);
for (int j = 0; j < blob->count(); ++j) {
EXPECT_EQ(blob->cpu_data()[j], label - mean_value);
}
}
TYPED_TEST(DataTransformTest, TestMeanFile) {
TransformationParameter transform_param;
const bool unique_pixels = true; // pixels are consecutive ints [0,size]
const int_tp label = 0;
const int_tp channels = 3;
const int_tp height = 4;
const int_tp width = 5;
const int_tp size = channels * height * width;
// Create a mean file
string* mean_file = new string();
MakeTempFilename(mean_file);
BlobProto blob_mean;
blob_mean.set_num(1);
blob_mean.set_channels(channels);
blob_mean.set_height(height);
blob_mean.set_width(width);
for (int_tp j = 0; j < size; ++j) {
blob_mean.add_data(j);
}
LOG(INFO) << "Using temporary mean_file " << *mean_file;
WriteProtoToBinaryFile(blob_mean, *mean_file);
transform_param.set_mean_file(*mean_file);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
DataTransformer<TypeParam>* transformer =
new DataTransformer<TypeParam>(transform_param, TEST,
Caffe::GetDefaultDevice());
transformer->InitRand();
transformer->Transform(datum, blob);
for (int_tp j = 0; j < blob->count(); ++j) {
EXPECT_EQ(blob->cpu_data()[j], 0);
}
}
TYPED_TEST(DataTransformTest, TestMeanValues) {
TransformationParameter transform_param;
const bool unique_pixels = false; // pixels are equal to label
const int label = 0;
const int channels = 3;
const int height = 4;
const int width = 5;
transform_param.add_mean_value(0);
transform_param.add_mean_value(1);
transform_param.add_mean_value(2);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
Blob<TypeParam> blob(1, channels, height, width);
DataTransformer<TypeParam> transformer(transform_param, TEST);
transformer.InitRand();
transformer.Transform(datum, &blob);
for (int c = 0; c < channels; ++c) {
for (int j = 0; j < height * width; ++j) {
EXPECT_EQ(blob.cpu_data()[blob.offset(0, c) + j], label - c);
}
}
}
TYPED_TEST(DataTransformTest, TestEmptyTransform) {
TransformationParameter transform_param;
const bool unique_pixels = false; // all pixels the same equal to label
const int label = 0;
const int channels = 3;
const int height = 4;
const int width = 5;
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
DataTransformer<TypeParam>* transformer =
new DataTransformer<TypeParam>(transform_param, TEST);
transformer->InitRand();
transformer->Transform(datum, blob);
EXPECT_EQ(blob->num(), 1);
EXPECT_EQ(blob->channels(), datum.channels());
EXPECT_EQ(blob->height(), datum.height());
EXPECT_EQ(blob->width(), datum.width());
for (int j = 0; j < blob->count(); ++j) {
EXPECT_EQ(blob->cpu_data()[j], label);
}
}
TYPED_TEST(DataTransformTest, TestEmptyTransformUniquePixels) {
TransformationParameter transform_param;
const bool unique_pixels = true; // pixels are consecutive ints [0,size]
const int_tp label = 0;
const int_tp channels = 3;
const int_tp height = 4;
const int_tp width = 5;
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
Blob<TypeParam>* blob = new Blob<TypeParam>(1, 3, 4, 5);
DataTransformer<TypeParam>* transformer =
new DataTransformer<TypeParam>(transform_param, TEST,
Caffe::GetDefaultDevice());
transformer->InitRand();
transformer->Transform(datum, blob);
EXPECT_EQ(blob->num(), 1);
EXPECT_EQ(blob->channels(), datum.channels());
EXPECT_EQ(blob->height(), datum.height());
EXPECT_EQ(blob->width(), datum.width());
for (int_tp j = 0; j < blob->count(); ++j) {
EXPECT_EQ(blob->cpu_data()[j], j);
}
}
TYPED_TEST(DataTransformTest, TestMeanFile) {
TransformationParameter transform_param;
const bool unique_pixels = true; // pixels are consecutive ints [0,size]
const int label = 0;
const int channels = 3;
const int height = 4;
const int width = 5;
const int size = channels * height * width;
// Create a mean file
string mean_file;
MakeTempFilename(&mean_file);
BlobProto blob_mean;
blob_mean.set_num(1);
blob_mean.set_channels(channels);
blob_mean.set_height(height);
blob_mean.set_width(width);
for (int j = 0; j < size; ++j) {
blob_mean.add_data(j);
}
LOG(INFO) << "Using temporary mean_file " << mean_file;
WriteProtoToBinaryFile(blob_mean, mean_file);
transform_param.set_mean_file(mean_file);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
Blob<TypeParam> blob(1, channels, height, width);
DataTransformer<TypeParam> transformer(transform_param, TEST);
transformer.InitRand();
transformer.Transform(datum, &blob);
for (int j = 0; j < blob.count(); ++j) {
EXPECT_EQ(blob.cpu_data()[j], 0);
}
}
