CaffeOnACL/src/caffe/data_transformer.cpp at master · 2php/CaffeOnACL · GitHub

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#ifdef USE_OPENCV

#include <opencv2/core/core.hpp>

#endif // USE_OPENCV

#include <string>

#include <vector>

#include "caffe/data_transformer.hpp"

#include "caffe/util/io.hpp"

#include "caffe/util/math_functions.hpp"

#include "caffe/util/rng.hpp"

namespace caffe {

template<typename Dtype>

DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param,

Phase phase)

: param_(param), phase_(phase) {

// check if we want to use mean_file

if (param_.has_mean_file()) {

CHECK_EQ(param_.mean_value_size(), 0) <<

"Cannot specify mean_file and mean_value at the same time";

const string& mean_file = param.mean_file();

if (Caffe::root_solver()) {

LOG(INFO) << "Loading mean file from: " << mean_file;

}

BlobProto blob_proto;

ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);

data_mean_.FromProto(blob_proto);

}

// check if we want to use mean_value

if (param_.mean_value_size() > 0) {

CHECK(param_.has_mean_file() == false) <<

"Cannot specify mean_file and mean_value at the same time";

for (int c = 0; c < param_.mean_value_size(); ++c) {

mean_values_.push_back(param_.mean_value(c));

}

template<typename Dtype>

void DataTransformer<Dtype>::Transform(const Datum& datum,

Dtype* transformed_data) {

const string& data = datum.data();

const int datum_channels = datum.channels();

const int datum_height = datum.height();

const int datum_width = datum.width();

const int crop_size = param_.crop_size();

const Dtype scale = param_.scale();

const bool do_mirror = param_.mirror() && Rand(2);

const bool has_mean_file = param_.has_mean_file();

const bool has_uint8 = data.size() > 0;

const bool has_mean_values = mean_values_.size() > 0;

CHECK_GT(datum_channels, 0);

CHECK_GE(datum_height, crop_size);

CHECK_GE(datum_width, crop_size);

Dtype* mean = NULL;

if (has_mean_file) {

CHECK_EQ(datum_channels, data_mean_.channels());

CHECK_EQ(datum_height, data_mean_.height());

CHECK_EQ(datum_width, data_mean_.width());

mean = data_mean_.mutable_cpu_data();

}

if (has_mean_values) {

CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) <<

"Specify either 1 mean_value or as many as channels: " << datum_channels;

if (datum_channels > 1 && mean_values_.size() == 1) {

// Replicate the mean_value for simplicity

for (int c = 1; c < datum_channels; ++c) {

mean_values_.push_back(mean_values_[0]);

}

int height = datum_height;

int width = datum_width;

int h_off = 0;

int w_off = 0;

if (crop_size) {

height = crop_size;

width = crop_size;

// We only do random crop when we do training.

if (phase_ == TRAIN) {

h_off = Rand(datum_height - crop_size + 1);

w_off = Rand(datum_width - crop_size + 1);

} else {

h_off = (datum_height - crop_size) / 2;

w_off = (datum_width - crop_size) / 2;

}

Dtype datum_element;

int top_index, data_index;

for (int c = 0; c < datum_channels; ++c) {

for (int h = 0; h < height; ++h) {

for (int w = 0; w < width; ++w) {

data_index = (c * datum_height + h_off + h) * datum_width + w_off + w;

if (do_mirror) {

top_index = (c * height + h) * width + (width - 1 - w);

} else {

top_index = (c * height + h) * width + w;

}

if (has_uint8) {

datum_element =

static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));

} else {

datum_element = datum.float_data(data_index);

}

if (has_mean_file) {

transformed_data[top_index] =

(datum_element - mean[data_index]) * scale;

} else {

if (has_mean_values) {

transformed_data[top_index] =

(datum_element - mean_values_[c]) * scale;

} else {

transformed_data[top_index] = datum_element * scale;

}

template<typename Dtype>

void DataTransformer<Dtype>::Transform(const Datum& datum,

Blob<Dtype>* transformed_blob) {

// If datum is encoded, decode and transform the cv::image.

if (datum.encoded()) {

#ifdef USE_OPENCV

CHECK(!(param_.force_color() && param_.force_gray()))

<< "cannot set both force_color and force_gray";

cv::Mat cv_img;

if (param_.force_color() || param_.force_gray()) {

// If force_color then decode in color otherwise decode in gray.

cv_img = DecodeDatumToCVMat(datum, param_.force_color());

} else {

cv_img = DecodeDatumToCVMatNative(datum);

}

// Transform the cv::image into blob.

return Transform(cv_img, transformed_blob);

#else

LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV.";

#endif // USE_OPENCV

} else {

if (param_.force_color() || param_.force_gray()) {

LOG(ERROR) << "force_color and force_gray only for encoded datum";

}

const int crop_size = param_.crop_size();

const int datum_channels = datum.channels();

const int datum_height = datum.height();

const int datum_width = datum.width();

// Check dimensions.

const int channels = transformed_blob->channels();

const int height = transformed_blob->height();

const int width = transformed_blob->width();

const int num = transformed_blob->num();

CHECK_EQ(channels, datum_channels);

CHECK_LE(height, datum_height);

CHECK_LE(width, datum_width);

CHECK_GE(num, 1);

if (crop_size) {

CHECK_EQ(crop_size, height);

CHECK_EQ(crop_size, width);

} else {

CHECK_EQ(datum_height, height);

CHECK_EQ(datum_width, width);

}

Dtype* transformed_data = transformed_blob->mutable_cpu_data();

Transform(datum, transformed_data);

}

template<typename Dtype>

void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,

Blob<Dtype>* transformed_blob) {

const int datum_num = datum_vector.size();

const int num = transformed_blob->num();

const int channels = transformed_blob->channels();

const int height = transformed_blob->height();

const int width = transformed_blob->width();

CHECK_GT(datum_num, 0) << "There is no datum to add";

CHECK_LE(datum_num, num) <<

"The size of datum_vector must be no greater than transformed_blob->num()";

Blob<Dtype> uni_blob(1, channels, height, width);

for (int item_id = 0; item_id < datum_num; ++item_id) {

int offset = transformed_blob->offset(item_id);

uni_blob.set_cpu_data(transformed_blob->mutable_cpu_data() + offset);

Transform(datum_vector[item_id], &uni_blob);

}

#ifdef USE_OPENCV

template<typename Dtype>

void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,

Blob<Dtype>* transformed_blob) {

const int mat_num = mat_vector.size();

const int num = transformed_blob->num();

const int channels = transformed_blob->channels();

const int height = transformed_blob->height();

const int width = transformed_blob->width();

CHECK_GT(mat_num, 0) << "There is no MAT to add";

CHECK_EQ(mat_num, num) <<

"The size of mat_vector must be equals to transformed_blob->num()";

Blob<Dtype> uni_blob(1, channels, height, width);

for (int item_id = 0; item_id < mat_num; ++item_id) {

int offset = transformed_blob->offset(item_id);

uni_blob.set_cpu_data(transformed_blob->mutable_cpu_data() + offset);

Transform(mat_vector[item_id], &uni_blob);

}

template<typename Dtype>

void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,

Blob<Dtype>* transformed_blob) {

const int crop_size = param_.crop_size();

const int img_channels = cv_img.channels();

const int img_height = cv_img.rows;

const int img_width = cv_img.cols;

// Check dimensions.

const int channels = transformed_blob->channels();

const int height = transformed_blob->height();

const int width = transformed_blob->width();

const int num = transformed_blob->num();

CHECK_EQ(channels, img_channels);

CHECK_LE(height, img_height);

CHECK_LE(width, img_width);

CHECK_GE(num, 1);

CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";

const Dtype scale = param_.scale();

const bool do_mirror = param_.mirror() && Rand(2);

const bool has_mean_file = param_.has_mean_file();

const bool has_mean_values = mean_values_.size() > 0;

CHECK_GT(img_channels, 0);

CHECK_GE(img_height, crop_size);

CHECK_GE(img_width, crop_size);

Dtype* mean = NULL;

if (has_mean_file) {

CHECK_EQ(img_channels, data_mean_.channels());

CHECK_EQ(img_height, data_mean_.height());

CHECK_EQ(img_width, data_mean_.width());

mean = data_mean_.mutable_cpu_data();

}

if (has_mean_values) {

CHECK(mean_values_.size() == 1 || mean_values_.size() == img_channels) <<

"Specify either 1 mean_value or as many as channels: " << img_channels;

if (img_channels > 1 && mean_values_.size() == 1) {

// Replicate the mean_value for simplicity

for (int c = 1; c < img_channels; ++c) {

mean_values_.push_back(mean_values_[0]);

}

int h_off = 0;

int w_off = 0;

cv::Mat cv_cropped_img = cv_img;

if (crop_size) {

CHECK_EQ(crop_size, height);

CHECK_EQ(crop_size, width);

// We only do random crop when we do training.

if (phase_ == TRAIN) {

h_off = Rand(img_height - crop_size + 1);

w_off = Rand(img_width - crop_size + 1);

} else {

h_off = (img_height - crop_size) / 2;

w_off = (img_width - crop_size) / 2;

}

cv::Rect roi(w_off, h_off, crop_size, crop_size);

cv_cropped_img = cv_img(roi);

} else {

CHECK_EQ(img_height, height);

CHECK_EQ(img_width, width);

}

CHECK(cv_cropped_img.data);

Dtype* transformed_data = transformed_blob->mutable_cpu_data();

int top_index;

for (int h = 0; h < height; ++h) {

const uchar* ptr = cv_cropped_img.ptr<uchar>(h);

int img_index = 0;

for (int w = 0; w < width; ++w) {

for (int c = 0; c < img_channels; ++c) {

if (do_mirror) {

top_index = (c * height + h) * width + (width - 1 - w);

} else {

top_index = (c * height + h) * width + w;

}

// int top_index = (c * height + h) * width + w;

Dtype pixel = static_cast<Dtype>(ptr[img_index++]);

if (has_mean_file) {

int mean_index = (c * img_height + h_off + h) * img_width + w_off + w;

transformed_data[top_index] =

(pixel - mean[mean_index]) * scale;

} else {

if (has_mean_values) {

transformed_data[top_index] =

(pixel - mean_values_[c]) * scale;

} else {

transformed_data[top_index] = pixel * scale;

}

#endif // USE_OPENCV

template<typename Dtype>

void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,

Blob<Dtype>* transformed_blob) {

const int crop_size = param_.crop_size();

const int input_num = input_blob->num();

const int input_channels = input_blob->channels();

const int input_height = input_blob->height();

const int input_width = input_blob->width();

if (transformed_blob->count() == 0) {

// Initialize transformed_blob with the right shape.

if (crop_size) {

transformed_blob->Reshape(input_num, input_channels,

crop_size, crop_size);

} else {

transformed_blob->Reshape(input_num, input_channels,

input_height, input_width);

}

const int num = transformed_blob->num();

const int channels = transformed_blob->channels();

const int height = transformed_blob->height();

const int width = transformed_blob->width();

const int size = transformed_blob->count();

CHECK_LE(input_num, num);

CHECK_EQ(input_channels, channels);

CHECK_GE(input_height, height);

CHECK_GE(input_width, width);

const Dtype scale = param_.scale();

const bool do_mirror = param_.mirror() && Rand(2);

const bool has_mean_file = param_.has_mean_file();

const bool has_mean_values = mean_values_.size() > 0;

int h_off = 0;

int w_off = 0;

if (crop_size) {

CHECK_EQ(crop_size, height);

CHECK_EQ(crop_size, width);

// We only do random crop when we do training.

if (phase_ == TRAIN) {

h_off = Rand(input_height - crop_size + 1);

w_off = Rand(input_width - crop_size + 1);

} else {

h_off = (input_height - crop_size) / 2;

w_off = (input_width - crop_size) / 2;

}

} else {

CHECK_EQ(input_height, height);

CHECK_EQ(input_width, width);

}

Dtype* input_data = input_blob->mutable_cpu_data();

if (has_mean_file) {

CHECK_EQ(input_channels, data_mean_.channels());

CHECK_EQ(input_height, data_mean_.height());

CHECK_EQ(input_width, data_mean_.width());

for (int n = 0; n < input_num; ++n) {

int offset = input_blob->offset(n);

caffe_sub(data_mean_.count(), input_data + offset,

data_mean_.cpu_data(), input_data + offset);

}

if (has_mean_values) {

CHECK(mean_values_.size() == 1 || mean_values_.size() == input_channels) <<

"Specify either 1 mean_value or as many as channels: " << input_channels;

if (mean_values_.size() == 1) {

caffe_add_scalar(input_blob->count(), -(mean_values_[0]), input_data);

} else {

for (int n = 0; n < input_num; ++n) {

for (int c = 0; c < input_channels; ++c) {

int offset = input_blob->offset(n, c);

caffe_add_scalar(input_height * input_width, -(mean_values_[c]),

input_data + offset);

}

Dtype* transformed_data = transformed_blob->mutable_cpu_data();

for (int n = 0; n < input_num; ++n) {

int top_index_n = n * channels;

int data_index_n = n * channels;

for (int c = 0; c < channels; ++c) {

int top_index_c = (top_index_n + c) * height;

int data_index_c = (data_index_n + c) * input_height + h_off;

for (int h = 0; h < height; ++h) {

int top_index_h = (top_index_c + h) * width;

int data_index_h = (data_index_c + h) * input_width + w_off;

if (do_mirror) {

int top_index_w = top_index_h + width - 1;

for (int w = 0; w < width; ++w) {

transformed_data[top_index_w-w] = input_data[data_index_h + w];

}

} else {

for (int w = 0; w < width; ++w) {

transformed_data[top_index_h + w] = input_data[data_index_h + w];

}

if (scale != Dtype(1)) {

DLOG(INFO) << "Scale: " << scale;

caffe_scal(size, scale, transformed_data);

}

template<typename Dtype>

vector<int> DataTransformer<Dtype>::InferBlobShape(const Datum& datum) {

if (datum.encoded()) {

#ifdef USE_OPENCV

CHECK(!(param_.force_color() && param_.force_gray()))

<< "cannot set both force_color and force_gray";

cv::Mat cv_img;

if (param_.force_color() || param_.force_gray()) {

// If force_color then decode in color otherwise decode in gray.

cv_img = DecodeDatumToCVMat(datum, param_.force_color());

} else {

cv_img = DecodeDatumToCVMatNative(datum);

}

// InferBlobShape using the cv::image.

return InferBlobShape(cv_img);

#else

LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV.";

#endif // USE_OPENCV

}

const int crop_size = param_.crop_size();

const int datum_channels = datum.channels();

const int datum_height = datum.height();

const int datum_width = datum.width();

// Check dimensions.

CHECK_GT(datum_channels, 0);

CHECK_GE(datum_height, crop_size);

CHECK_GE(datum_width, crop_size);

// Build BlobShape.

vector<int> shape(4);

shape[0] = 1;

shape[1] = datum_channels;

shape[2] = (crop_size)? crop_size: datum_height;

shape[3] = (crop_size)? crop_size: datum_width;

return shape;

}

template<typename Dtype>

vector<int> DataTransformer<Dtype>::InferBlobShape(

const vector<Datum> & datum_vector) {

const int num = datum_vector.size();

CHECK_GT(num, 0) << "There is no datum to in the vector";

// Use first datum in the vector to InferBlobShape.

vector<int> shape = InferBlobShape(datum_vector[0]);

// Adjust num to the size of the vector.

shape[0] = num;

return shape;

}

#ifdef USE_OPENCV

template<typename Dtype>

vector<int> DataTransformer<Dtype>::InferBlobShape(const cv::Mat& cv_img) {

const int crop_size = param_.crop_size();

const int img_channels = cv_img.channels();

const int img_height = cv_img.rows;

const int img_width = cv_img.cols;

// Check dimensions.

CHECK_GT(img_channels, 0);

CHECK_GE(img_height, crop_size);

CHECK_GE(img_width, crop_size);

// Build BlobShape.

vector<int> shape(4);

shape[0] = 1;

shape[1] = img_channels;

shape[2] = (crop_size)? crop_size: img_height;

shape[3] = (crop_size)? crop_size: img_width;

return shape;

}

template<typename Dtype>

vector<int> DataTransformer<Dtype>::InferBlobShape(

const vector<cv::Mat> & mat_vector) {

const int num = mat_vector.size();

CHECK_GT(num, 0) << "There is no cv_img to in the vector";

// Use first cv_img in the vector to InferBlobShape.

vector<int> shape = InferBlobShape(mat_vector[0]);

// Adjust num to the size of the vector.

shape[0] = num;

return shape;

}

#endif // USE_OPENCV

template <typename Dtype>

void DataTransformer<Dtype>::InitRand() {

const bool needs_rand = param_.mirror() ||

(phase_ == TRAIN && param_.crop_size());

if (needs_rand) {

const unsigned int rng_seed = caffe_rng_rand();

rng_.reset(new Caffe::RNG(rng_seed));

} else {

rng_.reset();

}

template <typename Dtype>

int DataTransformer<Dtype>::Rand(int n) {

CHECK(rng_);

CHECK_GT(n, 0);

caffe::rng_t* rng =

static_cast<caffe::rng_t*>(rng_->generator());

return ((*rng)() % n);

}

INSTANTIATE_CLASS(DataTransformer);

} // namespace caffe

CaffeOnACL/src/caffe/data_transformer.cpp at master · 2php/CaffeOnACL · GitHub

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