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image_input_op.h
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image_input_op.h
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#ifndef CAFFE2_IMAGE_IMAGE_INPUT_OP_H_
#define CAFFE2_IMAGE_IMAGE_INPUT_OP_H_
#include <opencv2/opencv.hpp>
#include <algorithm>
#include <iostream>
#include "c10/core/thread_pool.h"
#include "caffe2/core/common.h"
#include "caffe2/core/db.h"
#include "caffe2/image/transform_gpu.h"
#include "caffe2/operators/prefetch_op.h"
#include "caffe2/proto/caffe2_legacy.pb.h"
#include "caffe2/utils/cast.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
class CUDAContext;
template <class Context>
class ImageInputOp final : public PrefetchOperator<Context> {
// SINGLE_LABEL: single integer label for multi-class classification
// MULTI_LABEL_SPARSE: sparse active label indices for multi-label
// classification MULTI_LABEL_DENSE: dense label embedding vector for label
// embedding regression MULTI_LABEL_WEIGHTED_SPARSE: sparse active label
// indices with per-label weights for multi-label classification
// SINGLE_LABEL_WEIGHTED: single integer label for multi-class classification
// with weighted sampling EMBEDDING_LABEL: an array of floating numbers
// representing dense embedding.
// It is useful for model distillation
enum LABEL_TYPE {
SINGLE_LABEL = 0,
MULTI_LABEL_SPARSE = 1,
MULTI_LABEL_DENSE = 2,
MULTI_LABEL_WEIGHTED_SPARSE = 3,
SINGLE_LABEL_WEIGHTED = 4,
EMBEDDING_LABEL = 5,
};
// INCEPTION_STYLE: Random crop with size 8% - 100% image area and aspect
// ratio in [3/4, 4/3]. Reference: GoogleNet paper
enum SCALE_JITTER_TYPE {
NO_SCALE_JITTER = 0,
INCEPTION_STYLE = 1
// TODO(zyan3): ResNet-style random scale jitter
};
public:
using OperatorBase::OutputSize;
using PrefetchOperator<Context>::context_;
using PrefetchOperator<Context>::prefetch_thread_;
explicit ImageInputOp(const OperatorDef& operator_def, Workspace* ws);
~ImageInputOp() {
PrefetchOperator<Context>::Finalize();
}
bool Prefetch() override;
bool CopyPrefetched() override;
private:
using BoundingBox = struct {
bool valid;
int ymin;
int xmin;
int height;
int width;
};
// Structure to store per-image information
// This can be modified by the DecodeAnd* so needs
// to be privatized per launch.
using PerImageArg = struct { BoundingBox bounding_params; };
bool GetImageAndLabelAndInfoFromDBValue(
const string& value,
cv::Mat* img,
PerImageArg& info,
int item_id,
std::mt19937* randgen);
void DecodeAndTransform(
const std::string& value,
float* image_data,
int item_id,
const int channels,
std::size_t thread_index);
void DecodeAndTransposeOnly(
const std::string& value,
uint8_t* image_data,
int item_id,
const int channels,
std::size_t thread_index);
bool ApplyTransformOnGPU(
const std::vector<std::int64_t>& dims,
const c10::Device& type);
unique_ptr<db::DBReader> owned_reader_;
const db::DBReader* reader_;
Tensor prefetched_image_;
Tensor prefetched_label_;
vector<Tensor> prefetched_additional_outputs_;
Tensor prefetched_image_on_device_;
Tensor prefetched_label_on_device_;
vector<Tensor> prefetched_additional_outputs_on_device_;
// Default parameters for images
PerImageArg default_arg_;
int batch_size_;
LABEL_TYPE label_type_;
int num_labels_;
bool color_;
bool color_jitter_;
float img_saturation_;
float img_brightness_;
float img_contrast_;
bool color_lighting_;
float color_lighting_std_;
std::vector<std::vector<float>> color_lighting_eigvecs_;
std::vector<float> color_lighting_eigvals_;
SCALE_JITTER_TYPE scale_jitter_type_;
int scale_;
// Minsize is similar to scale except that it will only
// force the image to scale up if it is too small. In other words,
// it ensures that both dimensions of the image are at least minsize_
int minsize_;
bool warp_;
int crop_;
std::vector<float> mean_;
std::vector<float> std_;
Tensor mean_gpu_;
Tensor std_gpu_;
bool mirror_;
bool is_test_;
bool use_caffe_datum_;
bool gpu_transform_;
bool mean_std_copied_ = false;
// thread pool for parse + decode
int num_decode_threads_;
int additional_inputs_offset_;
int additional_inputs_count_;
std::vector<int> additional_output_sizes_;
std::shared_ptr<TaskThreadPool> thread_pool_;
// Output type for GPU transform path
TensorProto_DataType output_type_;
// random minsize
vector<int> random_scale_;
bool random_scaling_;
// Working variables
std::vector<std::mt19937> randgen_per_thread_;
// number of exceptions produced by opencv while reading image data
std::atomic<long> num_decode_errors_in_batch_{0};
// opencv exceptions tolerance
float max_decode_error_ratio_;
};
template <class Context>
ImageInputOp<Context>::ImageInputOp(
const OperatorDef& operator_def,
Workspace* ws)
: PrefetchOperator<Context>(operator_def, ws),
reader_(nullptr),
batch_size_(
OperatorBase::template GetSingleArgument<int>("batch_size", 0)),
label_type_(static_cast<LABEL_TYPE>(
OperatorBase::template GetSingleArgument<int>("label_type", 0))),
num_labels_(
OperatorBase::template GetSingleArgument<int>("num_labels", 0)),
color_(OperatorBase::template GetSingleArgument<int>("color", 1)),
color_jitter_(
OperatorBase::template GetSingleArgument<int>("color_jitter", 0)),
img_saturation_(OperatorBase::template GetSingleArgument<float>(
"img_saturation",
0.4)),
img_brightness_(OperatorBase::template GetSingleArgument<float>(
"img_brightness",
0.4)),
img_contrast_(
OperatorBase::template GetSingleArgument<float>("img_contrast", 0.4)),
color_lighting_(
OperatorBase::template GetSingleArgument<int>("color_lighting", 0)),
color_lighting_std_(OperatorBase::template GetSingleArgument<float>(
"color_lighting_std",
0.1)),
scale_jitter_type_(static_cast<SCALE_JITTER_TYPE>(
OperatorBase::template GetSingleArgument<int>(
"scale_jitter_type",
0))),
scale_(OperatorBase::template GetSingleArgument<int>("scale", -1)),
minsize_(OperatorBase::template GetSingleArgument<int>("minsize", -1)),
warp_(OperatorBase::template GetSingleArgument<int>("warp", 0)),
crop_(OperatorBase::template GetSingleArgument<int>("crop", -1)),
mirror_(OperatorBase::template GetSingleArgument<int>("mirror", 0)),
is_test_(OperatorBase::template GetSingleArgument<int>(
OpSchema::Arg_IsTest,
0)),
use_caffe_datum_(
OperatorBase::template GetSingleArgument<int>("use_caffe_datum", 0)),
gpu_transform_(OperatorBase::template GetSingleArgument<int>(
"use_gpu_transform",
0)),
num_decode_threads_(
OperatorBase::template GetSingleArgument<int>("decode_threads", 4)),
additional_output_sizes_(
OperatorBase::template GetRepeatedArgument<int>("output_sizes", {})),
thread_pool_(std::make_shared<TaskThreadPool>(num_decode_threads_)),
// output type only supported with CUDA and use_gpu_transform for now
output_type_(
cast::GetCastDataType(ArgumentHelper(operator_def), "output_type")),
random_scale_(OperatorBase::template GetRepeatedArgument<int>(
"random_scale",
{-1, -1})),
max_decode_error_ratio_(OperatorBase::template GetSingleArgument<float>(
"max_decode_error_ratio",
1.0)) {
if ((random_scale_[0] == -1) || (random_scale_[1] == -1)) {
random_scaling_ = false;
} else {
random_scaling_ = true;
minsize_ = random_scale_[0];
}
mean_ = OperatorBase::template GetRepeatedArgument<float>(
"mean_per_channel",
{OperatorBase::template GetSingleArgument<float>("mean", 0.)});
std_ = OperatorBase::template GetRepeatedArgument<float>(
"std_per_channel",
{OperatorBase::template GetSingleArgument<float>("std", 1.)});
if (additional_output_sizes_.size() == 0) {
additional_output_sizes_ = std::vector<int>(OutputSize() - 2, 1);
} else {
CAFFE_ENFORCE(
additional_output_sizes_.size() == OutputSize() - 2,
"If the output sizes are specified, they must be specified for all "
"additional outputs");
}
additional_inputs_count_ = OutputSize() - 2;
default_arg_.bounding_params = {
false,
OperatorBase::template GetSingleArgument<int>("bounding_ymin", -1),
OperatorBase::template GetSingleArgument<int>("bounding_xmin", -1),
OperatorBase::template GetSingleArgument<int>("bounding_height", -1),
OperatorBase::template GetSingleArgument<int>("bounding_width", -1),
};
if (operator_def.input_size() == 0) {
LOG(ERROR) << "You are using an old ImageInputOp format that creates "
"a local db reader. Consider moving to the new style "
"that takes in a DBReader blob instead.";
string db_name = OperatorBase::template GetSingleArgument<string>("db", "");
CAFFE_ENFORCE_GT(db_name.size(), 0, "Must specify a db name.");
owned_reader_.reset(new db::DBReader(
OperatorBase::template GetSingleArgument<string>("db_type", "leveldb"),
db_name));
reader_ = owned_reader_.get();
}
// hard-coded PCA eigenvectors and eigenvalues, based on RBG channel order
color_lighting_eigvecs_.push_back(
std::vector<float>{-144.7125f, 183.396f, 102.2295f});
color_lighting_eigvecs_.push_back(
std::vector<float>{-148.104f, -1.1475f, -207.57f});
color_lighting_eigvecs_.push_back(
std::vector<float>{-148.818f, -177.174f, 107.1765f});
color_lighting_eigvals_ = std::vector<float>{0.2175f, 0.0188f, 0.0045f};
CAFFE_ENFORCE_GT(batch_size_, 0, "Batch size should be nonnegative.");
if (use_caffe_datum_) {
CAFFE_ENFORCE(
label_type_ == SINGLE_LABEL || label_type_ == SINGLE_LABEL_WEIGHTED,
"Caffe datum only supports single integer label");
}
if (label_type_ != SINGLE_LABEL && label_type_ != SINGLE_LABEL_WEIGHTED) {
CAFFE_ENFORCE_GT(
num_labels_,
0,
"Number of labels must be set for using either sparse label indices or dense label embedding.");
}
if (label_type_ == MULTI_LABEL_WEIGHTED_SPARSE ||
label_type_ == SINGLE_LABEL_WEIGHTED) {
additional_inputs_offset_ = 3;
} else {
additional_inputs_offset_ = 2;
}
CAFFE_ENFORCE(
(scale_ > 0) != (minsize_ > 0),
"Must provide one and only one of scaling or minsize");
CAFFE_ENFORCE_GT(crop_, 0, "Must provide the cropping value.");
CAFFE_ENFORCE_GE(
scale_ > 0 ? scale_ : minsize_,
crop_,
"The scale/minsize value must be no smaller than the crop value.");
CAFFE_ENFORCE_EQ(
mean_.size(),
std_.size(),
"The mean and std. dev vectors must be of the same size.");
CAFFE_ENFORCE(
mean_.size() == 1 || mean_.size() == 3,
"The mean and std. dev vectors must be of size 1 or 3");
CAFFE_ENFORCE(
!use_caffe_datum_ || OutputSize() == 2,
"There can only be 2 outputs if the Caffe datum format is used");
CAFFE_ENFORCE(
random_scale_.size() == 2, "Must provide [scale_min, scale_max]");
CAFFE_ENFORCE_GE(
random_scale_[1],
random_scale_[0],
"random scale must provide a range [min, max]");
if (default_arg_.bounding_params.ymin < 0 ||
default_arg_.bounding_params.xmin < 0 ||
default_arg_.bounding_params.height < 0 ||
default_arg_.bounding_params.width < 0) {
default_arg_.bounding_params.valid = false;
} else {
default_arg_.bounding_params.valid = true;
}
if (mean_.size() == 1) {
// We are going to extend to 3 using the first value
mean_.resize(3, mean_[0]);
std_.resize(3, std_[0]);
}
LOG(INFO) << "Creating an image input op with the following setting: ";
LOG(INFO) << " Using " << num_decode_threads_ << " CPU threads;";
if (gpu_transform_) {
LOG(INFO) << " Performing transformation on GPU";
}
LOG(INFO) << " Outputting in batches of " << batch_size_ << " images;";
LOG(INFO) << " Treating input image as "
<< (color_ ? "color " : "grayscale ") << "image;";
if (default_arg_.bounding_params.valid) {
LOG(INFO) << " Applying a default bounding box of Y ["
<< default_arg_.bounding_params.ymin << "; "
<< default_arg_.bounding_params.ymin +
default_arg_.bounding_params.height
<< ") x X [" << default_arg_.bounding_params.xmin << "; "
<< default_arg_.bounding_params.xmin +
default_arg_.bounding_params.width
<< ")";
}
if (scale_ > 0 && !random_scaling_) {
LOG(INFO) << " Scaling image to " << scale_
<< (warp_ ? " with " : " without ") << "warping;";
} else {
if (random_scaling_) {
// randomly set min_size_ for each image
LOG(INFO) << " Randomly scaling shortest side between "
<< random_scale_[0] << " and " << random_scale_[1];
} else {
// Here, minsize_ > 0
LOG(INFO) << " Ensuring minimum image size of " << minsize_
<< (warp_ ? " with " : " without ") << "warping;";
}
}
LOG(INFO) << " " << (is_test_ ? "Central" : "Random")
<< " cropping image to " << crop_
<< (mirror_ ? " with " : " without ") << "random mirroring;";
LOG(INFO) << "Label Type: " << label_type_;
LOG(INFO) << "Num Labels: " << num_labels_;
auto mit = mean_.begin();
auto sit = std_.begin();
for (int i = 0; mit != mean_.end() && sit != std_.end(); ++mit, ++sit, ++i) {
LOG(INFO) << " Default [Channel " << i << "] Subtract mean " << *mit
<< " and divide by std " << *sit << ".";
// We actually will use the inverse of std, so inverse it here
*sit = 1.f / *sit;
}
LOG(INFO) << " Outputting images as "
<< OperatorBase::template GetSingleArgument<string>(
"output_type", "unknown")
<< ".";
std::mt19937 meta_randgen(time(nullptr));
for (int i = 0; i < num_decode_threads_; ++i) {
randgen_per_thread_.emplace_back(meta_randgen());
}
ReinitializeTensor(
&prefetched_image_,
{int64_t(batch_size_),
int64_t(crop_),
int64_t(crop_),
int64_t(color_ ? 3 : 1)},
at::dtype<uint8_t>().device(CPU));
std::vector<int64_t> sizes;
if (label_type_ != SINGLE_LABEL && label_type_ != SINGLE_LABEL_WEIGHTED) {
sizes = std::vector<int64_t>{int64_t(batch_size_), int64_t(num_labels_)};
} else {
sizes = std::vector<int64_t>{batch_size_};
}
// data type for prefetched_label_ is actually not known here..
ReinitializeTensor(&prefetched_label_, sizes, at::dtype<int>().device(CPU));
for (int i = 0; i < additional_output_sizes_.size(); ++i) {
prefetched_additional_outputs_on_device_.emplace_back();
prefetched_additional_outputs_.emplace_back();
}
}
// Inception-stype scale jittering
template <class Context>
bool RandomSizedCropping(cv::Mat* img, const int crop, std::mt19937* randgen) {
cv::Mat scaled_img;
bool inception_scale_jitter = false;
int im_height = img->rows, im_width = img->cols;
int area = im_height * im_width;
std::uniform_real_distribution<> area_dis(0.08, 1.0);
std::uniform_real_distribution<> aspect_ratio_dis(3.0 / 4.0, 4.0 / 3.0);
cv::Mat cropping;
for (int i = 0; i < 10; ++i) {
int target_area = int(ceil(area_dis(*randgen) * area));
float aspect_ratio = aspect_ratio_dis(*randgen);
int nh = floor(std::sqrt(((float)target_area / aspect_ratio)));
int nw = floor(std::sqrt(((float)target_area * aspect_ratio)));
if (nh >= 1 && nh <= im_height && nw >= 1 && nw <= im_width) {
int height_offset =
std::uniform_int_distribution<>(0, im_height - nh)(*randgen);
int width_offset =
std::uniform_int_distribution<>(0, im_width - nw)(*randgen);
cv::Rect ROI(width_offset, height_offset, nw, nh);
cropping = (*img)(ROI);
cv::resize(
cropping, scaled_img, cv::Size(crop, crop), 0, 0, cv::INTER_AREA);
*img = scaled_img;
inception_scale_jitter = true;
break;
}
}
return inception_scale_jitter;
}
template <class Context>
bool ImageInputOp<Context>::GetImageAndLabelAndInfoFromDBValue(
const string& value,
cv::Mat* img,
PerImageArg& info,
int item_id,
std::mt19937* randgen) {
//
// recommend using --caffe2_use_fatal_for_enforce=1 when using ImageInputOp
// as this function runs on a worker thread and the exceptions from
// CAFFE_ENFORCE are silently dropped by the thread worker functions
//
cv::Mat src;
// Use the default information for images
info = default_arg_;
if (use_caffe_datum_) {
// The input is a caffe datum format.
CaffeDatum datum;
CAFFE_ENFORCE(datum.ParseFromString(value));
prefetched_label_.mutable_data<int>()[item_id] = datum.label();
if (datum.encoded()) {
// encoded image in datum.
// count the number of exceptions from opencv imdecode
try {
src = cv::imdecode(
cv::Mat(
1,
datum.data().size(),
CV_8UC1,
const_cast<char*>(datum.data().data())),
color_ ? cv::IMREAD_COLOR : cv::IMREAD_GRAYSCALE);
if (src.rows == 0 || src.cols == 0) {
num_decode_errors_in_batch_++;
src = cv::Mat::zeros(cv::Size(224, 224), CV_8UC3);
}
} catch (cv::Exception& e) {
num_decode_errors_in_batch_++;
src = cv::Mat::zeros(cv::Size(224, 224), CV_8UC3);
}
} else {
// Raw image in datum.
CAFFE_ENFORCE(datum.channels() == 3 || datum.channels() == 1);
int src_c = datum.channels();
src.create(
datum.height(), datum.width(), (src_c == 3) ? CV_8UC3 : CV_8UC1);
if (src_c == 1) {
memcpy(src.ptr<uchar>(0), datum.data().data(), datum.data().size());
} else {
// Datum stores things in CHW order, let's do HWC for images to make
// things more consistent with conventional image storage.
for (int c = 0; c < 3; ++c) {
const char* datum_buffer =
datum.data().data() + datum.height() * datum.width() * c;
uchar* ptr = src.ptr<uchar>(0) + c;
for (int h = 0; h < datum.height(); ++h) {
for (int w = 0; w < datum.width(); ++w) {
*ptr = *(datum_buffer++);
ptr += 3;
}
}
}
}
}
} else {
// The input is a caffe2 format.
TensorProtos protos;
CAFFE_ENFORCE(protos.ParseFromString(value));
const TensorProto& image_proto = protos.protos(0);
const TensorProto& label_proto = protos.protos(1);
// add handle protos
vector<TensorProto> additional_output_protos;
int start = additional_inputs_offset_;
int end = start + additional_inputs_count_;
for (int i = start; i < end; ++i) {
additional_output_protos.push_back(protos.protos(i));
}
if (protos.protos_size() == end + 1) {
// We have bounding box information
const TensorProto& bounding_proto = protos.protos(end);
DCHECK_EQ(bounding_proto.data_type(), TensorProto::INT32);
DCHECK_EQ(bounding_proto.int32_data_size(), 4);
info.bounding_params.valid = true;
info.bounding_params.ymin = bounding_proto.int32_data(0);
info.bounding_params.xmin = bounding_proto.int32_data(1);
info.bounding_params.height = bounding_proto.int32_data(2);
info.bounding_params.width = bounding_proto.int32_data(3);
}
if (image_proto.data_type() == TensorProto::STRING) {
// encoded image string.
DCHECK_EQ(image_proto.string_data_size(), 1);
const string& encoded_image_str = image_proto.string_data(0);
int encoded_size = encoded_image_str.size();
// We use a cv::Mat to wrap the encoded str so we do not need a copy.
// count the number of exceptions from opencv imdecode
try {
src = cv::imdecode(
cv::Mat(
1,
&encoded_size,
CV_8UC1,
const_cast<char*>(encoded_image_str.data())),
color_ ? cv::IMREAD_COLOR : cv::IMREAD_GRAYSCALE);
if (src.rows == 0 || src.cols == 0) {
num_decode_errors_in_batch_++;
src = cv::Mat::zeros(cv::Size(224, 224), CV_8UC3);
}
} catch (cv::Exception& e) {
num_decode_errors_in_batch_++;
src = cv::Mat::zeros(cv::Size(224, 224), CV_8UC3);
}
} else if (image_proto.data_type() == TensorProto::BYTE) {
// raw image content.
int src_c = (image_proto.dims_size() == 3) ? image_proto.dims(2) : 1;
CAFFE_ENFORCE(src_c == 3 || src_c == 1);
src.create(
image_proto.dims(0),
image_proto.dims(1),
(src_c == 3) ? CV_8UC3 : CV_8UC1);
memcpy(
src.ptr<uchar>(0),
image_proto.byte_data().data(),
image_proto.byte_data().size());
} else {
LOG(FATAL) << "Unknown image data type.";
}
// TODO: if image decoding was unsuccessful, set label to 0
if (label_proto.data_type() == TensorProto::FLOAT) {
if (label_type_ == SINGLE_LABEL || label_type_ == SINGLE_LABEL_WEIGHTED) {
DCHECK_EQ(label_proto.float_data_size(), 1);
prefetched_label_.mutable_data<float>()[item_id] =
label_proto.float_data(0);
} else if (label_type_ == MULTI_LABEL_SPARSE) {
float* label_data =
prefetched_label_.mutable_data<float>() + item_id * num_labels_;
memset(label_data, 0, sizeof(float) * num_labels_);
for (int i = 0; i < label_proto.float_data_size(); ++i) {
label_data[(int)label_proto.float_data(i)] = 1.0;
}
} else if (label_type_ == MULTI_LABEL_WEIGHTED_SPARSE) {
const TensorProto& weight_proto = protos.protos(2);
float* label_data =
prefetched_label_.mutable_data<float>() + item_id * num_labels_;
memset(label_data, 0, sizeof(float) * num_labels_);
for (int i = 0; i < label_proto.float_data_size(); ++i) {
label_data[(int)label_proto.float_data(i)] =
weight_proto.float_data(i);
}
} else if (
label_type_ == MULTI_LABEL_DENSE || label_type_ == EMBEDDING_LABEL) {
CAFFE_ENFORCE(label_proto.float_data_size() == num_labels_);
float* label_data =
prefetched_label_.mutable_data<float>() + item_id * num_labels_;
for (int i = 0; i < label_proto.float_data_size(); ++i) {
label_data[i] = label_proto.float_data(i);
}
} else {
LOG(ERROR) << "Unknown label type:" << label_type_;
}
} else if (label_proto.data_type() == TensorProto::INT32) {
if (label_type_ == SINGLE_LABEL || label_type_ == SINGLE_LABEL_WEIGHTED) {
DCHECK_EQ(label_proto.int32_data_size(), 1);
prefetched_label_.mutable_data<int>()[item_id] =
label_proto.int32_data(0);
} else if (label_type_ == MULTI_LABEL_SPARSE) {
int* label_data =
prefetched_label_.mutable_data<int>() + item_id * num_labels_;
memset(label_data, 0, sizeof(int) * num_labels_);
for (int i = 0; i < label_proto.int32_data_size(); ++i) {
label_data[label_proto.int32_data(i)] = 1;
}
} else if (label_type_ == MULTI_LABEL_WEIGHTED_SPARSE) {
const TensorProto& weight_proto = protos.protos(2);
float* label_data =
prefetched_label_.mutable_data<float>() + item_id * num_labels_;
memset(label_data, 0, sizeof(float) * num_labels_);
for (int i = 0; i < label_proto.int32_data_size(); ++i) {
label_data[label_proto.int32_data(i)] = weight_proto.float_data(i);
}
} else if (
label_type_ == MULTI_LABEL_DENSE || label_type_ == EMBEDDING_LABEL) {
CAFFE_ENFORCE(label_proto.int32_data_size() == num_labels_);
int* label_data =
prefetched_label_.mutable_data<int>() + item_id * num_labels_;
for (int i = 0; i < label_proto.int32_data_size(); ++i) {
label_data[i] = label_proto.int32_data(i);
}
} else {
LOG(ERROR) << "Unknown label type:" << label_type_;
}
} else {
LOG(FATAL) << "Unsupported label data type.";
}
for (int i = 0; i < additional_output_protos.size(); ++i) {
auto additional_output_proto = additional_output_protos[i];
if (additional_output_proto.data_type() == TensorProto::FLOAT) {
float* additional_output =
prefetched_additional_outputs_[i].template mutable_data<float>() +
item_id * additional_output_proto.float_data_size();
for (int j = 0; j < additional_output_proto.float_data_size(); ++j) {
additional_output[j] = additional_output_proto.float_data(j);
}
} else if (additional_output_proto.data_type() == TensorProto::INT32) {
int* additional_output =
prefetched_additional_outputs_[i].template mutable_data<int>() +
item_id * additional_output_proto.int32_data_size();
for (int j = 0; j < additional_output_proto.int32_data_size(); ++j) {
additional_output[j] = additional_output_proto.int32_data(j);
}
} else if (additional_output_proto.data_type() == TensorProto::INT64) {
int64_t* additional_output =
prefetched_additional_outputs_[i].template mutable_data<int64_t>() +
item_id * additional_output_proto.int64_data_size();
for (int j = 0; j < additional_output_proto.int64_data_size(); ++j) {
additional_output[j] = additional_output_proto.int64_data(j);
}
} else if (additional_output_proto.data_type() == TensorProto::UINT8) {
uint8_t* additional_output =
prefetched_additional_outputs_[i].template mutable_data<uint8_t>() +
item_id * additional_output_proto.int32_data_size();
for (int j = 0; j < additional_output_proto.int32_data_size(); ++j) {
additional_output[j] =
static_cast<uint8_t>(additional_output_proto.int32_data(j));
}
} else {
LOG(FATAL) << "Unsupported output type.";
}
}
}
//
// convert source to the color format requested from Op
//
int out_c = color_ ? 3 : 1;
if (out_c == src.channels()) {
*img = src;
} else {
cv::cvtColor(
src, *img, (out_c == 1) ? cv::COLOR_BGR2GRAY : cv::COLOR_GRAY2BGR);
}
// Note(Yangqing): I believe that the mat should be created continuous.
CAFFE_ENFORCE(img->isContinuous());
// Sanity check now that we decoded everything
// Ensure that the bounding box is legit
if (info.bounding_params.valid &&
(src.rows < info.bounding_params.ymin + info.bounding_params.height ||
src.cols < info.bounding_params.xmin + info.bounding_params.width)) {
info.bounding_params.valid = false;
}
// Apply the bounding box if requested
if (info.bounding_params.valid) {
// If we reach here, we know the parameters are sane
cv::Rect bounding_box(
info.bounding_params.xmin,
info.bounding_params.ymin,
info.bounding_params.width,
info.bounding_params.height);
*img = (*img)(bounding_box);
/*
LOG(INFO) << "Did bounding with ymin:"
<< info.bounding_params.ymin << " xmin:" <<
info.bounding_params.xmin
<< " height:" << info.bounding_params.height
<< " width:" << info.bounding_params.width << "\n";
LOG(INFO) << "Bounded matrix: " << img;
*/
} else {
// LOG(INFO) << "No bounding\n";
}
cv::Mat scaled_img;
bool inception_scale_jitter = false;
if (scale_jitter_type_ == INCEPTION_STYLE) {
if (!is_test_) {
// Inception-stype scale jittering is only used for training
inception_scale_jitter =
RandomSizedCropping<Context>(img, crop_, randgen);
// if a random crop is still not found, do simple random cropping later
}
}
if ((scale_jitter_type_ == NO_SCALE_JITTER) ||
(scale_jitter_type_ == INCEPTION_STYLE && !inception_scale_jitter)) {
int scaled_width, scaled_height;
int scale_to_use = scale_ > 0 ? scale_ : minsize_;
// set the random minsize
if (random_scaling_) {
scale_to_use = std::uniform_int_distribution<>(
random_scale_[0], random_scale_[1])(*randgen);
}
if (warp_) {
scaled_width = scale_to_use;
scaled_height = scale_to_use;
} else if (img->rows > img->cols) {
scaled_width = scale_to_use;
scaled_height = static_cast<float>(img->rows) * scale_to_use / img->cols;
} else {
scaled_height = scale_to_use;
scaled_width = static_cast<float>(img->cols) * scale_to_use / img->rows;
}
if ((scale_ > 0 &&
(scaled_height != img->rows || scaled_width != img->cols)) ||
(scaled_height > img->rows || scaled_width > img->cols)) {
// We rescale in all cases if we are using scale_
// but only to make the image bigger if using minsize_
/*
LOG(INFO) << "Scaling to " << scaled_width << " x " << scaled_height
<< " From " << img->cols << " x " << img->rows;
*/
cv::resize(
*img,
scaled_img,
cv::Size(scaled_width, scaled_height),
0,
0,
cv::INTER_AREA);
*img = scaled_img;
}
}
// TODO(Yangqing): return false if any error happens.
return true;
}
// assume HWC order and color channels BGR
template <class Context>
void Saturation(
float* img,
const int img_size,
const float alpha_rand,
std::mt19937* randgen) {
float alpha = 1.0f +
std::uniform_real_distribution<float>(-alpha_rand, alpha_rand)(*randgen);
// BGR to Gray scale image: R -> 0.299, G -> 0.587, B -> 0.114
int p = 0;
for (int h = 0; h < img_size; ++h) {
for (int w = 0; w < img_size; ++w) {
float gray_color = img[3 * p] * 0.114f + img[3 * p + 1] * 0.587f +
img[3 * p + 2] * 0.299f;
for (int c = 0; c < 3; ++c) {
img[3 * p + c] = img[3 * p + c] * alpha + gray_color * (1.0f - alpha);
}
p++;
}
}
}
// assume HWC order and color channels BGR
template <class Context>
void Brightness(
float* img,
const int img_size,
const float alpha_rand,
std::mt19937* randgen) {
float alpha = 1.0f +
std::uniform_real_distribution<float>(-alpha_rand, alpha_rand)(*randgen);
int p = 0;
for (int h = 0; h < img_size; ++h) {
for (int w = 0; w < img_size; ++w) {
for (int c = 0; c < 3; ++c) {
img[p++] *= alpha;
}
}
}
}
// assume HWC order and color channels BGR
template <class Context>
void Contrast(
float* img,
const int img_size,
const float alpha_rand,
std::mt19937* randgen) {
float gray_mean = 0;
int p = 0;
for (int h = 0; h < img_size; ++h) {
for (int w = 0; w < img_size; ++w) {
// BGR to Gray scale image: R -> 0.299, G -> 0.587, B -> 0.114
gray_mean += img[3 * p] * 0.114f + img[3 * p + 1] * 0.587f +
img[3 * p + 2] * 0.299f;
p++;
}
}
gray_mean /= (img_size * img_size);
float alpha = 1.0f +
std::uniform_real_distribution<float>(-alpha_rand, alpha_rand)(*randgen);
p = 0;
for (int h = 0; h < img_size; ++h) {
for (int w = 0; w < img_size; ++w) {
for (int c = 0; c < 3; ++c) {
img[p] = img[p] * alpha + gray_mean * (1.0f - alpha);
p++;
}
}
}
}
// assume HWC order and color channels BGR
template <class Context>
void ColorJitter(
float* img,
const int img_size,
const float saturation,
const float brightness,
const float contrast,
std::mt19937* randgen) {
std::srand(unsigned(std::time(0)));
std::vector<int> jitter_order{0, 1, 2};
// obtain a time-based seed:
unsigned seed = std::chrono::system_clock::now().time_since_epoch().count();
std::shuffle(
jitter_order.begin(),
jitter_order.end(),
std::default_random_engine(seed));
for (int i = 0; i < 3; ++i) {
if (jitter_order[i] == 0) {
Saturation<Context>(img, img_size, saturation, randgen);
} else if (jitter_order[i] == 1) {
Brightness<Context>(img, img_size, brightness, randgen);
} else {
Contrast<Context>(img, img_size, contrast, randgen);
}
}
}
// assume HWC order and color channels BGR
template <class Context>
void ColorLighting(
float* img,
const int img_size,
const float alpha_std,
const std::vector<std::vector<float>>& eigvecs,
const std::vector<float>& eigvals,
std::mt19937* randgen) {
std::normal_distribution<float> d(0, alpha_std);
std::vector<float> alphas(3);
for (int i = 0; i < 3; ++i) {
alphas[i] = d(*randgen);
}
std::vector<float> delta_rgb(3, 0.0);
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
delta_rgb[i] += eigvecs[i][j] * eigvals[j] * alphas[j];
}
}
int p = 0;
for (int h = 0; h < img_size; ++h) {
for (int w = 0; w < img_size; ++w) {
for (int c = 0; c < 3; ++c) {
img[p++] += delta_rgb[2 - c];
}
}
}
}
// assume HWC order and color channels BGR
// mean subtraction and scaling.
template <class Context>
void ColorNormalization(
float* img,
const int img_size,
const int channels,
const std::vector<float>& mean,
const std::vector<float>& std) {
int p = 0;
for (int h = 0; h < img_size; ++h) {
for (int w = 0; w < img_size; ++w) {
for (int c = 0; c < channels; ++c) {
img[p] = (img[p] - mean[c]) * std[c];
p++;
}
}
}
}
// Factored out image transformation
template <class Context>
void TransformImage(
const cv::Mat& scaled_img,
const int channels,
float* image_data,
const bool color_jitter,
const float saturation,
const float brightness,
const float contrast,
const bool color_lighting,
const float color_lighting_std,
const std::vector<std::vector<float>>& color_lighting_eigvecs,
const std::vector<float>& color_lighting_eigvals,
const int crop,
const bool mirror,
const std::vector<float>& mean,
const std::vector<float>& std,
std::mt19937* randgen,
std::bernoulli_distribution* mirror_this_image,
bool is_test = false) {
CAFFE_ENFORCE_GE(
scaled_img.rows, crop, "Image height must be bigger than crop.");
CAFFE_ENFORCE_GE(
scaled_img.cols, crop, "Image width must be bigger than crop.");
// find the cropped region, and copy it to the destination matrix
int width_offset, height_offset;
if (is_test) {
width_offset = (scaled_img.cols - crop) / 2;
height_offset = (scaled_img.rows - crop) / 2;
} else {
width_offset =
std::uniform_int_distribution<>(0, scaled_img.cols - crop)(*randgen);
height_offset =
std::uniform_int_distribution<>(0, scaled_img.rows - crop)(*randgen);
}
float* image_data_ptr = image_data;
if (!is_test && mirror && (*mirror_this_image)(*randgen)) {
// Copy mirrored image.
for (int h = height_offset; h < height_offset + crop; ++h) {
for (int w = width_offset + crop - 1; w >= width_offset; --w) {
const uint8_t* cv_data = scaled_img.ptr(h) + w * channels;
for (int c = 0; c < channels; ++c) {
*(image_data_ptr++) = static_cast<float>(cv_data[c]);
}
}
}
} else {
// Copy normally.
for (int h = height_offset; h < height_offset + crop; ++h) {
for (int w = width_offset; w < width_offset + crop; ++w) {
const uint8_t* cv_data = scaled_img.ptr(h) + w * channels;
for (int c = 0; c < channels; ++c) {