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fix roi_align ci for parrots (#708) 

* fix roi_align ci for parrots

* fix lint
pull/714/head
BigBigDream 2020-12-13 20:01:25 +08:00 committed by GitHub
parent b7136e3953
commit 02920db2cc
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 572 additions and 33 deletions
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111
mmcv/ops/csrc/parrots/roi_align.cpp
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@ -1,19 +1,90 @@
// Copyright (c) 2018, SenseTime.
#include "parrots_cpp_helper.hpp"
void ROIAlignForwardCUDAKernelLauncher(const DArrayLite input,
const DArrayLite rois, DArrayLite output,
DArrayLite argmax_y, DArrayLite argmax_x,
int aligned_height, int aligned_width,
float spatial_scale, int sampling_ratio,
int pool_mode, bool aligned,
cudaStream_t stream);
void ROIAlignForwardCPULauncher(DArrayLite input, DArrayLite rois,
DArrayLite output, DArrayLite argmax_y,
DArrayLite argmax_x, int aligned_height,
int aligned_width, float spatial_scale,
int sampling_ratio, int pool_mode,
bool aligned);
void ROIAlignBackwardCPULauncher(DArrayLite grad_output, DArrayLite rois,
DArrayLite argmax_y, DArrayLite argmax_x,
DArrayLite grad_input, int aligned_height,
int aligned_width, float spatial_scale,
int sampling_ratio, int pool_mode,
bool aligned);
void ROIAlignForwardCUDAKernelLauncher(DArrayLite input, DArrayLite rois,
DArrayLite output, DArrayLite argmax_y,
DArrayLite argmax_x, int aligned_height,
int aligned_width, float spatial_scale,
int sampling_ratio, int pool_mode,
bool aligned, cudaStream_t stream);
void ROIAlignBackwardCUDAKernelLauncher(
const DArrayLite grad_output, const DArrayLite rois,
const DArrayLite argmax_y, const DArrayLite argmax_x, DArrayLite grad_input,
int aligned_height, int aligned_width, float spatial_scale,
int sampling_ratio, int pool_mode, bool aligned, cudaStream_t stream);
DArrayLite grad_output, DArrayLite rois, DArrayLite argmax_y,
DArrayLite argmax_x, DArrayLite grad_input, int aligned_height,
int aligned_width, float spatial_scale, int sampling_ratio, int pool_mode,
bool aligned, cudaStream_t stream);
void roi_align_forward_cpu(HostContext& ctx, const SSElement& attr,
const OperatorBase::in_list_t& ins,
OperatorBase::out_list_t& outs) {
int aligned_height;
int aligned_width;
float spatial_scale;
int sampling_ratio;
int pool_mode;
bool aligned;
SSAttrs(attr)
.get<int>("aligned_height", aligned_height)
.get<int>("aligned_width", aligned_width)
.get<float>("spatial_scale", spatial_scale)
.get<int>("sampling_ratio", sampling_ratio)
.get<int>("pool_mode", pool_mode)
.get<bool>("aligned", aligned)
.done();
auto& input = ins[0];
auto& rois = ins[1];
auto& output = outs[0];
auto& argmax_y = outs[1];
auto& argmax_x = outs[2];
ROIAlignForwardCPULauncher(input, rois, output, argmax_y, argmax_x,
aligned_height, aligned_width, spatial_scale,
sampling_ratio, pool_mode, aligned);
}
void roi_align_backward_cpu(HostContext& ctx, const SSElement& attr,
const OperatorBase::in_list_t& ins,
OperatorBase::out_list_t& outs) {
int aligned_height;
int aligned_width;
float spatial_scale;
int sampling_ratio;
int pool_mode;
bool aligned;
SSAttrs(attr)
.get<int>("aligned_height", aligned_height)
.get<int>("aligned_width", aligned_width)
.get<float>("spatial_scale", spatial_scale)
.get<int>("sampling_ratio", sampling_ratio)
.get<int>("pool_mode", pool_mode)
.get<bool>("aligned", aligned)
.done();
auto& grad_output = ins[0];
auto& rois = ins[1];
auto& argmax_y = ins[2];
auto& argmax_x = ins[3];
auto& grad_input = outs[0];
ROIAlignBackwardCPULauncher(grad_output, rois, argmax_y, argmax_x, grad_input,
aligned_height, aligned_width, spatial_scale,
sampling_ratio, pool_mode, aligned);
}
void roi_align_forward_cuda(CudaContext& ctx, const SSElement& attr,
const OperatorBase::in_list_t& ins,
@ -33,8 +104,8 @@ void roi_align_forward_cuda(CudaContext& ctx, const SSElement& attr,
.get<bool>("aligned", aligned)
.done();
const auto& input = ins[0];
const auto& rois = ins[1];
auto& input = ins[0];
auto& rois = ins[1];
auto& output = outs[0];
auto& argmax_y = outs[1];
auto& argmax_x = outs[2];
@ -63,10 +134,10 @@ void roi_align_backward_cuda(CudaContext& ctx, const SSElement& attr,
.get<bool>("aligned", aligned)
.done();
const auto& grad_output = ins[0];
const auto& rois = ins[1];
const auto& argmax_y = ins[2];
const auto& argmax_x = ins[3];
auto& grad_output = ins[0];
auto& rois = ins[1];
auto& argmax_y = ins[2];
auto& argmax_x = ins[3];
auto& grad_input = outs[0];
cudaStream_t stream = getStreamNative<CudaDevice>(ctx.getStream());
@ -84,7 +155,10 @@ PARROTS_EXTENSION_REGISTER(roi_align_forward)
.attr("aligned")
.input(2)
.output(3)
.apply(roi_align_forward_cpu)
#ifdef PARROTS_USE_CUDA
.apply(roi_align_forward_cuda)
#endif
.done();
PARROTS_EXTENSION_REGISTER(roi_align_backward)
@ -96,5 +170,8 @@ PARROTS_EXTENSION_REGISTER(roi_align_backward)
.attr("aligned")
.input(4)
.output(1)
.apply(roi_align_backward_cpu)
#ifdef PARROTS_USE_CUDA
.apply(roi_align_backward_cuda)
#endif
.done();

430
mmcv/ops/csrc/parrots/roi_align_cpu.cpp 100644
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@ -0,0 +1,430 @@
// Modified from
// https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/ROIAlign
// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#include <iostream>
#include "parrots_cpp_helper.hpp"
// implementation taken from Caffe2
template <typename T>
struct PreCalc {
int pos1;
int pos2;
int pos3;
int pos4;
T w1;
T w2;
T w3;
T w4;
};
template <typename T>
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,
T roi_start_h, T roi_start_w, T bin_size_h, T bin_size_w,
int roi_bin_grid_h, int roi_bin_grid_w, std::vector<PreCalc<T>>& 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 T yy = roi_start_h + ph * bin_size_h +
static_cast<T>(iy + .5f) * bin_size_h /
static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
for (int ix = 0; ix < ix_upper; ix++) {
const T xx = roi_start_w + pw * bin_size_w +
static_cast<T>(ix + .5f) * bin_size_w /
static_cast<T>(roi_bin_grid_w);
T x = xx;
T y = yy;
// deal with: inverse elements are out of feature map boundary
if (y < -1.0 || y > height || x < -1.0 || x > width) {
// empty
PreCalc<T> 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 = (T)y_low;
} else {
y_high = y_low + 1;
}
if (x_low >= width - 1) {
x_high = x_low = width - 1;
x = (T)x_low;
} else {
x_high = x_low + 1;
}
T ly = y - y_low;
T lx = x - x_low;
T hy = 1. - ly, hx = 1. - lx;
T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
// save weights and indices
PreCalc<T> 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;
}
}
}
}
}
template <typename T>
void ROIAlignForward(const int nthreads, const T* input, const T* rois,
T* output, T* argmax_y, T* argmax_x,
const int pooled_height, const int pooled_width,
const T spatial_scale, const int sampling_ratio,
const int pool_mode, // 0 - max pool, 1 - avg pool
const bool aligned, const int channels, const int height,
const int width) {
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 T* offset_rois = rois + n * 5;
int roi_batch_ind = offset_rois[0];
// Do not use rounding; this implementation detail is critical
T offset = aligned ? (T)0.5 : (T)0.0;
T roi_start_w = offset_rois[1] * spatial_scale - offset;
T roi_start_h = offset_rois[2] * spatial_scale - offset;
T roi_end_w = offset_rois[3] * spatial_scale - offset;
T roi_end_h = offset_rois[4] * spatial_scale - offset;
T roi_width = roi_end_w - roi_start_w;
T roi_height = roi_end_h - roi_start_h;
if (aligned) {
PARROTS_CHECKARGS(roi_width >= 0 && roi_height >= 0)
<< "ROIs in ROIAlign cannot have non-negative size!";
} else { // for backward-compatibility only
roi_width = std::max(roi_width, (T)1.);
roi_height = std::max(roi_height, (T)1.);
}
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(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);
// When the grid is empty, output zeros == 0/1, instead of NaN.
const T 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<T>> pre_calc(roi_bin_grid_h * roi_bin_grid_w *
pooled_width * pooled_height);
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, pre_calc);
for (int c = 0; c < channels; c++) {
int index_n_c = index_n + c * pooled_width * pooled_height;
const T* 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;
T output_val = 0.;
T maxval = -10000;
T maxidx_y = -1.f, maxidx_x = -1.f;
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
const T y = roi_start_h + ph * bin_size_h +
static_cast<T>(iy + .5f) * bin_size_h /
static_cast<T>(roi_bin_grid_h);
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
const T x = roi_start_w + pw * bin_size_w +
static_cast<T>(ix + .5f) * bin_size_w /
static_cast<T>(roi_bin_grid_w);
PreCalc<T> pc = pre_calc[pre_calc_index];
T 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];
if (val > maxval) {
maxval = val;
maxidx_y = y;
maxidx_x = x;
}
output_val += val;
pre_calc_index += 1;
}
}
if (pool_mode == 0) {
// We do max pooling inside a bin
output[index] = maxval;
argmax_y[index] = maxidx_y;
argmax_x[index] = maxidx_x;
} else if (pool_mode == 1) {
// We do average (integral) pooling inside a bin
output[index] = output_val / count;
} // if
} // for pw
} // for ph
} // for c
} // for n
}
template <typename T>
void bilinear_interpolate_gradient(const int height, const int width, T y, T x,
T& w1, T& w2, T& w3, T& w4, int& x_low,
int& x_high, int& y_low, int& y_high,
const int index /* index for debug only*/) {
// deal with cases that inverse elements are out of feature map boundary
if (y < -1.0 || y > height || x < -1.0 || x > width) {
// empty
w1 = w2 = w3 = w4 = 0.;
x_low = x_high = y_low = y_high = -1;
return;
}
if (y <= 0) y = 0;
if (x <= 0) x = 0;
y_low = (int)y;
x_low = (int)x;
if (y_low >= height - 1) {
y_high = y_low = height - 1;
y = (T)y_low;
} else {
y_high = y_low + 1;
}
if (x_low >= width - 1) {
x_high = x_low = width - 1;
x = (T)x_low;
} else {
x_high = x_low + 1;
}
T ly = y - y_low;
T lx = x - x_low;
T hy = 1. - ly, hx = 1. - lx;
// reference in forward
// T v1 = input[y_low * width + x_low];
// T v2 = input[y_low * width + x_high];
// T v3 = input[y_high * width + x_low];
// T v4 = input[y_high * width + x_high];
// T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
return;
}
template <class T>
inline void add(T* address, const T& val) {
*address += val;
}
template <typename T>
void ROIAlignBackward(const int nthreads, const T* grad_output, const T* rois,
const T* argmax_y, const T* argmax_x, T* grad_input,
const int pooled_height, const int pooled_width,
const T spatial_scale, const int sampling_ratio,
const int pool_mode, // 0 - max pool, 1 - avg pool
const bool aligned, const int channels, const int height,
const int width, const int n_stride, const int c_stride,
const int h_stride, const int w_stride) {
for (int index = 0; index < nthreads; index++) {
// (n, c, ph, pw) is an element in the pooled output
int pw = index % pooled_width;
int ph = (index / pooled_width) % pooled_height;
int c = (index / pooled_width / pooled_height) % channels;
int n = index / pooled_width / pooled_height / channels;
const T* offset_rois = rois + n * 5;
int roi_batch_ind = offset_rois[0];
// Do not use rounding; this implementation detail is critical
T offset = aligned ? (T)0.5 : (T)0.0;
T roi_start_w = offset_rois[1] * spatial_scale - offset;
T roi_start_h = offset_rois[2] * spatial_scale - offset;
T roi_end_w = offset_rois[3] * spatial_scale - offset;
T roi_end_h = offset_rois[4] * spatial_scale - offset;
T roi_width = roi_end_w - roi_start_w;
T roi_height = roi_end_h - roi_start_h;
if (aligned) {
PARROTS_CHECKARGS(roi_width >= 0 && roi_height >= 0)
<< "ROIs in ROIAlign do not have non-negative size!";
} else { // for backward-compatibility only
roi_width = std::max(roi_width, (T)1.);
roi_height = std::max(roi_height, (T)1.);
}
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
T* offset_grad_input =
grad_input + ((roi_batch_ind * channels + c) * height * width);
int output_offset = n * n_stride + c * c_stride;
const T* offset_grad_output = grad_output + output_offset;
const T grad_output_this_bin =
offset_grad_output[ph * h_stride + pw * w_stride];
if (pool_mode == 0) {
// We do max pooling inside a bin
T y = argmax_y[index], x = argmax_x[index];
if (y != -1.f) {
T w1, w2, w3, w4;
int x_low, x_high, y_low, y_high;
bilinear_interpolate_gradient(height, width, y, x, w1, w2, w3, w4,
x_low, x_high, y_low, y_high, index);
T g1 = grad_output_this_bin * w1;
T g2 = grad_output_this_bin * w2;
T g3 = grad_output_this_bin * w3;
T g4 = grad_output_this_bin * w4;
if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
// atomic add is not needed for now since it is single threaded
add(offset_grad_input + y_low * width + x_low, static_cast<T>(g1));
add(offset_grad_input + y_low * width + x_high, static_cast<T>(g2));
add(offset_grad_input + y_high * width + x_low, static_cast<T>(g3));
add(offset_grad_input + y_high * width + x_high, static_cast<T>(g4));
} // if
} // mode
} else if (pool_mode == 1) {
// We do average (integral) pooling inside a bin
// 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);
const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
const T y = roi_start_h + ph * bin_size_h +
static_cast<T>(iy + .5f) * bin_size_h /
static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
const T x = roi_start_w + pw * bin_size_w +
static_cast<T>(ix + .5f) * bin_size_w /
static_cast<T>(roi_bin_grid_w);
T w1, w2, w3, w4;
int x_low, x_high, y_low, y_high;
bilinear_interpolate_gradient(height, width, y, x, w1, w2, w3, w4,
x_low, x_high, y_low, y_high, index);
T g1 = grad_output_this_bin * w1 / count;
T g2 = grad_output_this_bin * w2 / count;
T g3 = grad_output_this_bin * w3 / count;
T g4 = grad_output_this_bin * w4 / count;
if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
// atomic add is not needed for now since it is single threaded
add(offset_grad_input + y_low * width + x_low, static_cast<T>(g1));
add(offset_grad_input + y_low * width + x_high, static_cast<T>(g2));
add(offset_grad_input + y_high * width + x_low, static_cast<T>(g3));
add(offset_grad_input + y_high * width + x_high,
static_cast<T>(g4));
} // if
} // ix
} // iy
} // mode
} // for
} // ROIAlignBackward
void ROIAlignForwardCPULauncher(DArrayLite input, DArrayLite rois,
DArrayLite output, DArrayLite argmax_y,
DArrayLite argmax_x, int aligned_height,
int aligned_width, float spatial_scale,
int sampling_ratio, int pool_mode,
bool aligned) {
int output_size = output.size();
int channels = input.dim(1);
int height = input.dim(2);
int width = input.dim(3);
PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF(
input.elemType().prim(), ([&] {
ROIAlignForward<scalar_t>(
output_size, input.ptr<scalar_t>(), rois.ptr<scalar_t>(),
output.ptr<scalar_t>(), argmax_y.ptr<scalar_t>(),
argmax_x.ptr<scalar_t>(), aligned_height, aligned_width,
static_cast<scalar_t>(spatial_scale), sampling_ratio, pool_mode,
aligned, channels, height, width);
}));
}
void ROIAlignBackwardCPULauncher(DArrayLite grad_output, DArrayLite rois,
DArrayLite argmax_y, DArrayLite argmax_x,
DArrayLite grad_input, int aligned_height,
int aligned_width, float spatial_scale,
int sampling_ratio, int pool_mode,
bool aligned) {
int output_size = grad_output.size();
int channels = grad_input.dim(1);
int height = grad_input.dim(2);
int width = grad_input.dim(3);
// get stride values to ensure indexing into gradients is correct.
int n_stride = grad_output.stride(0);
int c_stride = grad_output.stride(1);
int h_stride = grad_output.stride(2);
int w_stride = grad_output.stride(3);
PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF(
grad_output.elemType().prim(), ([&] {
ROIAlignBackward<scalar_t>(
output_size, grad_output.ptr<scalar_t>(), rois.ptr<scalar_t>(),
argmax_y.ptr<scalar_t>(), argmax_x.ptr<scalar_t>(),
grad_input.ptr<scalar_t>(), aligned_height, aligned_width,
static_cast<scalar_t>(spatial_scale), sampling_ratio, pool_mode,
aligned, channels, height, width, n_stride, c_stride, h_stride,
w_stride);
}));
}

29
mmcv/ops/csrc/parrots/roi_align_cuda.cu
View File

@ -1,13 +1,12 @@
#include "parrots_cuda_helper.hpp"
#include "roi_align_cuda_kernel.cuh"
void ROIAlignForwardCUDAKernelLauncher(const DArrayLite input,
const DArrayLite rois, DArrayLite output,
DArrayLite argmax_y, DArrayLite argmax_x,
int aligned_height, int aligned_width,
float spatial_scale, int sampling_ratio,
int pool_mode, bool aligned,
cudaStream_t stream) {
void ROIAlignForwardCUDAKernelLauncher(DArrayLite input, DArrayLite rois,
DArrayLite output, DArrayLite argmax_y,
DArrayLite argmax_x, int aligned_height,
int aligned_width, float spatial_scale,
int sampling_ratio, int pool_mode,
bool aligned, cudaStream_t stream) {
int output_size = output.size();
int channels = input.dim(1);
int height = input.dim(2);
@ -20,18 +19,18 @@ void ROIAlignForwardCUDAKernelLauncher(const DArrayLite input,
output_size, input.ptr<scalar_t>(), rois.ptr<scalar_t>(),
output.ptr<scalar_t>(), argmax_y.ptr<scalar_t>(),
argmax_x.ptr<scalar_t>(), aligned_height, aligned_width,
spatial_scale, sampling_ratio, pool_mode, aligned, channels,
height, width);
static_cast<scalar_t>(spatial_scale), sampling_ratio, pool_mode,
aligned, channels, height, width);
}));
PARROTS_CUDA_CHECK(cudaGetLastError());
}
void ROIAlignBackwardCUDAKernelLauncher(
const DArrayLite grad_output, const DArrayLite rois,
const DArrayLite argmax_y, const DArrayLite argmax_x, DArrayLite grad_input,
int aligned_height, int aligned_width, float spatial_scale,
int sampling_ratio, int pool_mode, bool aligned, cudaStream_t stream) {
DArrayLite grad_output, DArrayLite rois, DArrayLite argmax_y,
DArrayLite argmax_x, DArrayLite grad_input, int aligned_height,
int aligned_width, float spatial_scale, int sampling_ratio, int pool_mode,
bool aligned, cudaStream_t stream) {
int output_size = grad_output.size();
int channels = grad_input.dim(1);
int height = grad_input.dim(2);
@ -44,8 +43,8 @@ void ROIAlignBackwardCUDAKernelLauncher(
output_size, grad_output.ptr<scalar_t>(), rois.ptr<scalar_t>(),
argmax_y.ptr<scalar_t>(), argmax_x.ptr<scalar_t>(),
grad_input.ptr<scalar_t>(), aligned_height, aligned_width,
spatial_scale, sampling_ratio, pool_mode, aligned, channels,
height, width);
static_cast<scalar_t>(spatial_scale), sampling_ratio, pool_mode,
aligned, channels, height, width);
}));
PARROTS_CUDA_CHECK(cudaGetLastError());

29
mmcv/ops/csrc/parrots_cpp_helper.hpp
View File

@ -8,4 +8,33 @@
using namespace parrots;
#define PARROTS_PRIVATE_CASE_TYPE(prim_type, type, ...) \
case prim_type: { \
using scalar_t = type; \
return __VA_ARGS__(); \
}
#define PARROTS_DISPATCH_FLOATING_TYPES(TYPE, ...) \
[&] { \
const auto& the_type = TYPE; \
switch (the_type) { \
PARROTS_PRIVATE_CASE_TYPE(Prim::Float64, double, __VA_ARGS__) \
PARROTS_PRIVATE_CASE_TYPE(Prim::Float32, float, __VA_ARGS__) \
default: \
PARROTS_NOTSUPPORTED; \
} \
}()
#define PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF(TYPE, ...) \
[&] { \
const auto& the_type = TYPE; \
switch (the_type) { \
PARROTS_PRIVATE_CASE_TYPE(Prim::Float64, double, __VA_ARGS__) \
PARROTS_PRIVATE_CASE_TYPE(Prim::Float32, float, __VA_ARGS__) \
PARROTS_PRIVATE_CASE_TYPE(Prim::Float16, float16, __VA_ARGS__) \
default: \
PARROTS_NOTSUPPORTED; \
} \
}()
#endif // PARROTS_CPP_HELPER

6
tests/test_ops/test_roi_align.py
View File

@ -56,7 +56,11 @@ def _test_roialign_gradcheck(device, dtype):
froipool = RoIAlign((pool_h, pool_w), spatial_scale, sampling_ratio)
gradcheck(froipool, (x, rois), eps=1e-5, atol=1e-5)
if torch.__version__ == 'parrots':
gradcheck(
froipool, (x, rois), no_grads=[rois], delta=1e-5, pt_atol=1e-5)
else:
gradcheck(froipool, (x, rois), eps=1e-5, atol=1e-5)
def _test_roialign_allclose(device, dtype):