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Merge a15f377 into 3bcc796
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Johnson-Wang authored Apr 12, 2021
2 parents 3bcc796 + a15f377 commit bedc0df
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4 changes: 3 additions & 1 deletion mmcv/ops/__init__.py
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Expand Up @@ -24,6 +24,7 @@
rel_roi_point_to_rel_img_point)
from .psa_mask import PSAMask
from .roi_align import RoIAlign, roi_align
from .roi_align_rotated import RoIAlignRotated, roi_align_rotated
from .roi_pool import RoIPool, roi_pool
from .saconv import SAConv2d
from .sync_bn import SyncBatchNorm
Expand All @@ -44,5 +45,6 @@
'ConvTranspose2d', 'Linear', 'MaxPool2d', 'CrissCrossAttention', 'PSAMask',
'point_sample', 'rel_roi_point_to_rel_img_point', 'SimpleRoIAlign',
'SAConv2d', 'TINShift', 'tin_shift', 'box_iou_rotated', 'nms_rotated',
'upfirdn2d', 'FusedBiasLeakyReLU', 'fused_bias_leakyrelu'
'upfirdn2d', 'FusedBiasLeakyReLU', 'fused_bias_leakyrelu',
'RoIAlignRotated', 'roi_align_rotated'
]
2 changes: 2 additions & 0 deletions mmcv/ops/box_iou_rotated.py
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Expand Up @@ -16,8 +16,10 @@ def box_iou_rotated(bboxes1, bboxes2, mode='iou', aligned=False):
Arguments:
boxes1 (Tensor): rotated bboxes 1. \
It has shape (N, 5), indicating (x, y, w, h, theta) for each row.
Note that theta is in radian.
boxes2 (Tensor): rotated bboxes 2. \
It has shape (M, 5), indicating (x, y, w, h, theta) for each row.
Note that theta is in radian.
mode (str): "iou" (intersection over union) or iof (intersection over
foreground).
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7 changes: 7 additions & 0 deletions mmcv/ops/csrc/onnxruntime/cpu/onnxruntime_register.cpp
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Expand Up @@ -4,12 +4,14 @@
#include "nms.h"
#include "ort_mmcv_utils.h"
#include "roi_align.h"
#include "roi_align_rotated.h"
#include "soft_nms.h"

const char *c_MMCVOpDomain = "mmcv";
SoftNmsOp c_SoftNmsOp;
NmsOp c_NmsOp;
MMCVRoiAlignCustomOp c_MMCVRoiAlignCustomOp;
MMCVRoIAlignRotatedCustomOp c_MMCVRoIAlignRotatedCustomOp;
GridSampleOp c_GridSampleOp;

OrtStatus *ORT_API_CALL RegisterCustomOps(OrtSessionOptions *options,
Expand All @@ -34,6 +36,11 @@ OrtStatus *ORT_API_CALL RegisterCustomOps(OrtSessionOptions *options,
return status;
}

if (auto status =
ortApi->CustomOpDomain_Add(domain, &c_MMCVRoIAlignRotatedCustomOp)) {
return status;
}

if (auto status = ortApi->CustomOpDomain_Add(domain, &c_GridSampleOp)) {
return status;
}
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246 changes: 246 additions & 0 deletions mmcv/ops/csrc/onnxruntime/cpu/roi_align_rotated.cpp
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@@ -0,0 +1,246 @@
#include "roi_align_rotated.h"

#include "../ort_mmcv_utils.h"

// Implementation taken from
// https://github.com/facebookresearch/detectron2/blob/master/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp
struct PreCalc {
int pos1;
int pos2;
int pos3;
int pos4;
float w1;
float w2;
float w3;
float w4;
};

void pre_calc_for_bilinear_interpolate(
const int height, const int width, const int pooled_height,
const int pooled_width, const int iy_upper, const int ix_upper,
float roi_start_h, float roi_start_w, float bin_size_h, float bin_size_w,
int roi_bin_grid_h, int roi_bin_grid_w, float roi_center_h,
float roi_center_w, float cos_theta, float sin_theta,
std::vector<PreCalc> &pre_calc) {
int pre_calc_index = 0;
for (int ph = 0; ph < pooled_height; ph++) {
for (int pw = 0; pw < pooled_width; pw++) {
for (int iy = 0; iy < iy_upper; iy++) {
const float yy =
roi_start_h + ph * bin_size_h +
static_cast<float>(iy + .5f) * bin_size_h /
static_cast<float>(roi_bin_grid_h); // e.g., 0.5, 1.5
for (int ix = 0; ix < ix_upper; ix++) {
const float xx = roi_start_w + pw * bin_size_w +
static_cast<float>(ix + .5f) * bin_size_w /
static_cast<float>(roi_bin_grid_w);

// Rotate by theta around the center and translate
// In image space, (y, x) is the order for Right Handed System,
// and this is essentially multiplying the point by a rotation matrix
// to rotate it counterclockwise through angle theta.
float y = yy * cos_theta - xx * sin_theta + roi_center_h;
float x = yy * sin_theta + xx * cos_theta + roi_center_w;
// deal with: inverse elements are out of feature map boundary
if (y < -1.0 || y > height || x < -1.0 || x > width) {
// empty
PreCalc pc;
pc.pos1 = 0;
pc.pos2 = 0;
pc.pos3 = 0;
pc.pos4 = 0;
pc.w1 = 0;
pc.w2 = 0;
pc.w3 = 0;
pc.w4 = 0;
pre_calc[pre_calc_index] = pc;
pre_calc_index += 1;
continue;
}

if (y < 0) {
y = 0;
}
if (x < 0) {
x = 0;
}

int y_low = (int)y;
int x_low = (int)x;
int y_high;
int x_high;

if (y_low >= height - 1) {
y_high = y_low = height - 1;
y = (float)y_low;
} else {
y_high = y_low + 1;
}

if (x_low >= width - 1) {
x_high = x_low = width - 1;
x = (float)x_low;
} else {
x_high = x_low + 1;
}

float ly = y - y_low;
float lx = x - x_low;
float hy = 1. - ly, hx = 1. - lx;
float w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;

// save weights and indices
PreCalc pc;
pc.pos1 = y_low * width + x_low;
pc.pos2 = y_low * width + x_high;
pc.pos3 = y_high * width + x_low;
pc.pos4 = y_high * width + x_high;
pc.w1 = w1;
pc.w2 = w2;
pc.w3 = w3;
pc.w4 = w4;
pre_calc[pre_calc_index] = pc;

pre_calc_index += 1;
}
}
}
}
}

void ROIAlignRotatedForwardCPU(const int nthreads, const float *input,
const float *rois, float *output,
const float &spatial_scale, const int aligned,
const int clockwise, const int channels,
const int height, const int width,
const int pooled_height, const int pooled_width,
const int sampling_ratio) {
int n_rois = nthreads / channels / pooled_width / pooled_height;
// (n, c, ph, pw) is an element in the pooled output
// can be parallelized using omp
// #pragma omp parallel for num_threads(32)
for (int n = 0; n < n_rois; n++) {
int index_n = n * channels * pooled_width * pooled_height;

const float *current_roi = rois + n * 6;
int roi_batch_ind = current_roi[0];

// Do not use rounding; this implementation detail is critical
float offset = aligned ? (float)0.5 : (float)0.0;
float roi_center_w = current_roi[1] * spatial_scale - offset;
float roi_center_h = current_roi[2] * spatial_scale - offset;
float roi_width = current_roi[3] * spatial_scale;
float roi_height = current_roi[4] * spatial_scale;
// float theta = current_roi[5] * M_PI / 180.0;
float theta = current_roi[5]; // Radian angle by default
if (clockwise) {
theta = -theta;
}
float cos_theta = cos(theta);
float sin_theta = sin(theta);
if (!aligned) { // for backward-compatibility only
roi_width = std::max(roi_width, (float)1.);
roi_height = std::max(roi_height, (float)1.);
}

float bin_size_h =
static_cast<float>(roi_height) / static_cast<float>(pooled_height);
float bin_size_w =
static_cast<float>(roi_width) / static_cast<float>(pooled_width);

// We use roi_bin_grid to sample the grid and mimic integral
int roi_bin_grid_h = (sampling_ratio > 0)
? sampling_ratio
: ceil(roi_height / pooled_height); // e.g., = 2
int roi_bin_grid_w =
(sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);

// We do average (integral) pooling inside a bin
const float count =
std::max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4

// we want to precalculate indices and weights shared by all channels,
// this is the key point of optimization
std::vector<PreCalc> pre_calc(roi_bin_grid_h * roi_bin_grid_w *
pooled_width * pooled_height);

// roi_start_h and roi_start_w are computed wrt the center of RoI (x, y).
// Appropriate translation needs to be applied after.
float roi_start_h = -roi_height / 2.0;
float roi_start_w = -roi_width / 2.0;

pre_calc_for_bilinear_interpolate(
height, width, pooled_height, pooled_width, roi_bin_grid_h,
roi_bin_grid_w, roi_start_h, roi_start_w, bin_size_h, bin_size_w,
roi_bin_grid_h, roi_bin_grid_w, roi_center_h, roi_center_w, cos_theta,
sin_theta, pre_calc);

for (int c = 0; c < channels; c++) {
int index_n_c = index_n + c * pooled_width * pooled_height;
const float *offset_input =
input + (roi_batch_ind * channels + c) * height * width;
int pre_calc_index = 0;

for (int ph = 0; ph < pooled_height; ph++) {
for (int pw = 0; pw < pooled_width; pw++) {
int index = index_n_c + ph * pooled_width + pw;

float output_val = 0.;
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
PreCalc pc = pre_calc[pre_calc_index];
output_val += pc.w1 * offset_input[pc.pos1] +
pc.w2 * offset_input[pc.pos2] +
pc.w3 * offset_input[pc.pos3] +
pc.w4 * offset_input[pc.pos4];

pre_calc_index += 1;
}
}
output_val /= count;

output[index] = output_val;
} // for pw
} // for ph
} // for c
} // for n
}

void MMCVRoIAlignRotatedKernel::Compute(OrtKernelContext *context) {
// Setup inputs
const OrtValue *input_X = ort_.KernelContext_GetInput(context, 0);
const float *X_data =
reinterpret_cast<const float *>(ort_.GetTensorData<float>(input_X));
const OrtValue *input_rois = ort_.KernelContext_GetInput(context, 1);
const float *rois = reinterpret_cast<const float *>(
ort_.GetTensorData<const float *>(input_rois));

// Setup output
OrtTensorDimensions out_dimensions(ort_, input_X);
OrtTensorDimensions roi_dimensions(ort_, input_rois);

int batch_size = out_dimensions.data()[0];
int input_channels = out_dimensions.data()[1];
int input_height = out_dimensions.data()[2];
int input_width = out_dimensions.data()[3];

out_dimensions.data()[0] = roi_dimensions.data()[0];
out_dimensions.data()[2] = aligned_height_;
out_dimensions.data()[3] = aligned_width_;

OrtValue *output = ort_.KernelContext_GetOutput(
context, 0, out_dimensions.data(), out_dimensions.size());
float *out = ort_.GetTensorMutableData<float>(output);
OrtTensorTypeAndShapeInfo *output_info = ort_.GetTensorTypeAndShape(output);
ort_.ReleaseTensorTypeAndShapeInfo(output_info);

// TODO: forward here
int output_size = out_dimensions.data()[0];
for (auto i = 1; i < out_dimensions.size(); ++i) {
output_size *= out_dimensions.data()[i];
}
ROIAlignRotatedForwardCPU(output_size, X_data, rois, out, spatial_scale_,
aligned_, clockwise_, input_channels, input_height,
input_width, aligned_height_, aligned_width_,
sampling_ratio_);
}
61 changes: 61 additions & 0 deletions mmcv/ops/csrc/onnxruntime/roi_align_rotated.h
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@@ -0,0 +1,61 @@
#ifndef ONNXRUNTIME_ROI_ALIGN_ROTATED_H
#define ONNXRUNTIME_ROI_ALIGN_ROTATED_H

#include <assert.h>
#include <onnxruntime_cxx_api.h>

#include <cmath>
#include <mutex>
#include <string>
#include <vector>

struct MMCVRoIAlignRotatedKernel {
public:
MMCVRoIAlignRotatedKernel(Ort::CustomOpApi ort, const OrtKernelInfo* info)
: ort_(ort) {
aligned_height_ =
ort_.KernelInfoGetAttribute<int64_t>(info, "output_height");
aligned_width_ = ort_.KernelInfoGetAttribute<int64_t>(info, "output_width");
sampling_ratio_ =
ort_.KernelInfoGetAttribute<int64_t>(info, "sampling_ratio");
spatial_scale_ = ort_.KernelInfoGetAttribute<float>(info, "spatial_scale");
aligned_ = ort_.KernelInfoGetAttribute<int64_t>(info, "aligned");
clockwise_ = ort_.KernelInfoGetAttribute<int64_t>(info, "clockwise");
}

void Compute(OrtKernelContext* context);

private:
Ort::CustomOpApi ort_;
int aligned_height_;
int aligned_width_;
float spatial_scale_;
int sampling_ratio_;
int aligned_;
int clockwise_;
};

struct MMCVRoIAlignRotatedCustomOp
: Ort::CustomOpBase<MMCVRoIAlignRotatedCustomOp,
MMCVRoIAlignRotatedKernel> {
void* CreateKernel(Ort::CustomOpApi api, const OrtKernelInfo* info) {
return new MMCVRoIAlignRotatedKernel(api, info);
}
const char* GetName() const { return "MMCVRoIAlignRotated"; }

size_t GetInputTypeCount() const { return 2; }
ONNXTensorElementDataType GetInputType(size_t) const {
return ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT;
}

size_t GetOutputTypeCount() const { return 1; }
ONNXTensorElementDataType GetOutputType(size_t) const {
return ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT;
}

// force cpu
const char* GetExecutionProviderType() const {
return "CPUExecutionProvider";
}
};
#endif // ONNXRUNTIME_ROI_ALIGN_ROTATED_H
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