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#include "roi_pool_op.h"
#include <cfloat>
namespace caffe2 {
using std::max;
using std::min;
template <>
C10_EXPORT bool RoIPoolOp<float, CPUContext>::RunOnDevice() {
const auto& X = Input(0); // Input data to pool
const auto& R = Input(1); // RoIs
auto* Y = Output(0); // RoI pooled data
auto* A = is_test_ ? nullptr : Output(1); // argmaxes
// Each ROI is of the form [batch_index x1 y1 x2 y2]
CAFFE_ENFORCE_EQ(R.dim32(1), 5);
// TODO: Handle the storage_order properly to get the NCWH.
int batch_size = X.dim32(0);
int channels = X.dim32(1);
int height = X.dim32(2);
int width = X.dim32(3);
int num_rois = R.dim32(0);
Y->Resize(num_rois, channels, pooled_height_, pooled_width_);
if (!is_test_) {
A->Resize(Y->sizes());
}
const float* Xdata = X.data<float>();
const float* rois = R.data<float>();
float* Ydata = Y->template mutable_data<float>();
int* argmax_data = is_test_ ? nullptr : A->template mutable_data<int>();
// For each ROI R = [batch_index x1 y1 x2 y2]: max pool over R
for (int n = 0; n < num_rois; ++n) {
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
int roi_batch_id = rois[0];
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
int roi_start_w = round(rois[1] * spatial_scale_);
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
int roi_start_h = round(rois[2] * spatial_scale_);
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
int roi_end_w = round(rois[3] * spatial_scale_);
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
int roi_end_h = round(rois[4] * spatial_scale_);
CAFFE_ENFORCE_GE(roi_batch_id, 0);
CAFFE_ENFORCE_LT(roi_batch_id, batch_size);
// Force malformed ROIs to be 1x1
int roi_height = max(roi_end_h - roi_start_h + 1, 1);
int roi_width = max(roi_end_w - roi_start_w + 1, 1);
const float bin_size_h =
static_cast<float>(roi_height) / static_cast<float>(pooled_height_);
const float bin_size_w =
static_cast<float>(roi_width) / static_cast<float>(pooled_width_);
const float* batch_data = Xdata + roi_batch_id * X.size_from_dim(1);
for (int c = 0; c < channels; ++c) {
for (int ph = 0; ph < pooled_height_; ++ph) {
for (int pw = 0; pw < pooled_width_; ++pw) {
// Compute pooling region for this output unit:
// start (included) = floor(ph * roi_height / pooled_height_)
// end (excluded) = ceil((ph + 1) * roi_height / pooled_height_)
int hstart =
static_cast<int>(floor(static_cast<float>(ph) * bin_size_h));
int wstart =
static_cast<int>(floor(static_cast<float>(pw) * bin_size_w));
int hend =
static_cast<int>(ceil(static_cast<float>(ph + 1) * bin_size_h));
int wend =
static_cast<int>(ceil(static_cast<float>(pw + 1) * bin_size_w));
// Add roi offsets and clip to input boundaries
hstart = min(max(hstart + roi_start_h, 0), height);
hend = min(max(hend + roi_start_h, 0), height);
wstart = min(max(wstart + roi_start_w, 0), width);
wend = min(max(wend + roi_start_w, 0), width);
const int pool_index = ph * pooled_width_ + pw;
// Define an empty pooling region to be zero
bool is_empty = (hend <= hstart) || (wend <= wstart);
Ydata[pool_index] = is_empty ? 0 : -FLT_MAX;
if (!is_test_) {
// If nothing is pooled, argmax = -1 causes nothing to be backprop'd
argmax_data[pool_index] = -1;
}
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
const int index = h * width + w;
if (batch_data[index] > Ydata[pool_index]) {
Ydata[pool_index] = batch_data[index];
if (!is_test_) {
argmax_data[pool_index] = index;
}
}
}
}
}
}
// Increment all data pointers by one channel
batch_data += X.size_from_dim(2);
Ydata += Y->size_from_dim(2);
if (!is_test_) {
argmax_data += A->size_from_dim(2);
}
}
// Increment ROI data pointer
rois += R.size_from_dim(1);
}
return true;
}
REGISTER_CPU_OPERATOR(RoIPool, RoIPoolOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(RoIPoolGradient, RoIPoolGradientOp<float, CPUContext>);
// Input: X, rois
// Output case #1: Y, argmaxes (train mode)
// Output case #2: Y (test mode)
OPERATOR_SCHEMA(RoIPool)
.NumInputs(2)
.NumOutputs({1, 2})
.TensorInferenceFunction([](const OperatorDef& def,
const vector<TensorShape>& in) {
ArgumentHelper helper(def);
const StorageOrder order = StringToStorageOrder(
helper.GetSingleArgument<string>("order", "NCHW"));
const TensorShape& X = in[0];
const int num_channels =
(order == StorageOrder::NCHW ? X.dims(1) : X.dims(3));
const TensorShape& R = in[1];
const int num_rois = R.dims(0);
const int pooled_height = helper.GetSingleArgument<int>("pooled_h", 1);
const int pooled_width = helper.GetSingleArgument<int>("pooled_w", 1);
TensorShape Y = CreateTensorShape(
vector<int>({num_rois, num_channels, pooled_height, pooled_width}),
X.data_type());
bool is_test = helper.GetSingleArgument<int>(OpSchema::Arg_IsTest, 0);
if (!is_test) {
TensorShape argmaxes = Y;
argmaxes.set_data_type(TensorProto_DataType_INT32);
return vector<TensorShape>({Y, argmaxes});
} else {
return vector<TensorShape>({Y});
}
})
.SetDoc(R"DOC(
Carries out ROI Pooling for Faster-RCNN.
Depending on the mode, there are multiple output cases:
Output case #1: Y, argmaxes (train mode)
Output case #2: Y (test mode)
)DOC")
.Arg(
"is_test",
"If set, run in test mode and skip computation of argmaxes (used for "
"gradient computation). Only one output tensor is produced. "
"(Default: false).")
.Arg("order", "A StorageOrder string (Default: \"NCHW\").")
.Arg("pooled_h", "The pooled output height (Default: 1).")
.Arg("pooled_w", "The pooled output width (Default: 1).")
.Arg(
"spatial_scale",
"Multiplicative spatial scale factor to translate ROI coords from "
"their input scale to the scale used when pooling (Default: 1.0).")
.Input(
0,
"X",
"The input 4-D tensor of data. Only NCHW order is currently supported.")
.Input(
1,
"rois",
"RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of "
"shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].")
.Output(
0,
"Y",
"RoI pooled output 4-D tensor of shape "
"(num_rois, channels, pooled_h, pooled_w).")
.Output(
1,
"argmaxes",
"Argmaxes corresponding to indices in X used for gradient computation. "
"Only output if arg \"is_test\" is false.");
// Input: X, rois, argmaxes, dY (aka "gradOutput")
// Output: dX (aka "gradInput")
OPERATOR_SCHEMA(RoIPoolGradient).NumInputs(4).NumOutputs(1);
class GetRoIPoolGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"RoIPoolGradient",
"",
vector<string>{I(0), I(1), O(1), GO(0)},
vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(RoIPool, GetRoIPoolGradient);
} // namespace caffe2
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