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// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
// This file is modified from https://github.com/pytorch/pytorch/blob/master/modules/detectron/sigmoid_focal_loss_op.cu
// Cheng-Yang Fu
// cyfu@cs.unc.edu
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <THC/THC.h>
#include <THC/THCAtomics.cuh>
#include <THC/THCDeviceUtils.cuh>
#include <cfloat>
// TODO make it in a common file
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \
i += blockDim.x * gridDim.x)
template <typename T>
__global__ void SigmoidFocalLossForward(const int nthreads,
const T* logits,
const int* targets,
const int num_classes,
const float gamma,
const float alpha,
const int num,
T* losses) {
CUDA_1D_KERNEL_LOOP(i, nthreads) {
int n = i / num_classes;
int d = i % num_classes; // current class[0~79];
int t = targets[n]; // target class [1~80];
// Decide it is positive or negative case.
T c1 = (t == (d+1));
T c2 = (t>=0 & t != (d+1));
T zn = (1.0 - alpha);
T zp = (alpha);
// p = 1. / 1. + expf(-x); p = sigmoid(x)
T p = 1. / (1. + expf(-logits[i]));
// (1-p)**gamma * log(p) where
T term1 = powf((1. - p), gamma) * logf(max(p, FLT_MIN));
// p**gamma * log(1-p)
T term2 = powf(p, gamma) *
(-1. * logits[i] * (logits[i] >= 0) -
logf(1. + expf(logits[i] - 2. * logits[i] * (logits[i] >= 0))));
losses[i] = 0.0;
losses[i] += -c1 * term1 * zp;
losses[i] += -c2 * term2 * zn;
} // CUDA_1D_KERNEL_LOOP
} // SigmoidFocalLossForward
template <typename T>
__global__ void SigmoidFocalLossBackward(const int nthreads,
const T* logits,
const int* targets,
const T* d_losses,
const int num_classes,
const float gamma,
const float alpha,
const int num,
T* d_logits) {
CUDA_1D_KERNEL_LOOP(i, nthreads) {
int n = i / num_classes;
int d = i % num_classes; // current class[0~79];
int t = targets[n]; // target class [1~80], 0 is background;
// Decide it is positive or negative case.
T c1 = (t == (d+1));
T c2 = (t>=0 & t != (d+1));
T zn = (1.0 - alpha);
T zp = (alpha);
// p = 1. / 1. + expf(-x); p = sigmoid(x)
T p = 1. / (1. + expf(-logits[i]));
// (1-p)**g * (1 - p - g*p*log(p)
T term1 = powf((1. - p), gamma) *
(1. - p - (p * gamma * logf(max(p, FLT_MIN))));
// (p**g) * (g*(1-p)*log(1-p) - p)
T term2 = powf(p, gamma) *
((-1. * logits[i] * (logits[i] >= 0) -
logf(1. + expf(logits[i] - 2. * logits[i] * (logits[i] >= 0)))) *
(1. - p) * gamma - p);
d_logits[i] = 0.0;
d_logits[i] += -c1 * term1 * zp;
d_logits[i] += -c2 * term2 * zn;
d_logits[i] = d_logits[i] * d_losses[i];
} // CUDA_1D_KERNEL_LOOP
} // SigmoidFocalLossBackward
at::Tensor SigmoidFocalLoss_forward_cuda(
const at::Tensor& logits,
const at::Tensor& targets,
const int num_classes,
const float gamma,
const float alpha) {
AT_ASSERTM(logits.device().is_cuda(), "logits must be a CUDA tensor");
AT_ASSERTM(targets.device().is_cuda(), "targets must be a CUDA tensor");
AT_ASSERTM(logits.dim() == 2, "logits should be NxClass");
const int num_samples = logits.size(0);
auto losses = at::empty({num_samples, logits.size(1)}, logits.options());
auto losses_size = num_samples * logits.size(1);
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
dim3 grid(std::min(THCCeilDiv(losses_size, 512L), 4096L));
dim3 block(512);
if (losses.numel() == 0) {
THCudaCheck(cudaGetLastError());
return losses;
}
AT_DISPATCH_FLOATING_TYPES(logits.scalar_type(), "SigmoidFocalLoss_forward", [&] {
SigmoidFocalLossForward<scalar_t><<<grid, block, 0, stream>>>(
losses_size,
logits.contiguous().data_ptr<scalar_t>(),
targets.contiguous().data_ptr<int>(),
num_classes,
gamma,
alpha,
num_samples,
losses.data_ptr<scalar_t>());
});
THCudaCheck(cudaGetLastError());
return losses;
}
at::Tensor SigmoidFocalLoss_backward_cuda(
const at::Tensor& logits,
const at::Tensor& targets,
const at::Tensor& d_losses,
const int num_classes,
const float gamma,
const float alpha) {
AT_ASSERTM(logits.device().is_cuda(), "logits must be a CUDA tensor");
AT_ASSERTM(targets.device().is_cuda(), "targets must be a CUDA tensor");
AT_ASSERTM(d_losses.device().is_cuda(), "d_losses must be a CUDA tensor");
AT_ASSERTM(logits.dim() == 2, "logits should be NxClass");
const int num_samples = logits.size(0);
AT_ASSERTM(logits.size(1) == num_classes, "logits.size(1) should be num_classes");
auto d_logits = at::zeros({num_samples, num_classes}, logits.options());
auto d_logits_size = num_samples * logits.size(1);
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
dim3 grid(std::min(THCCeilDiv(d_logits_size, 512L), 4096L));
dim3 block(512);
if (d_logits.numel() == 0) {
THCudaCheck(cudaGetLastError());
return d_logits;
}
AT_DISPATCH_FLOATING_TYPES(logits.scalar_type(), "SigmoidFocalLoss_backward", [&] {
SigmoidFocalLossBackward<scalar_t><<<grid, block, 0, stream>>>(
d_logits_size,
logits.contiguous().data_ptr<scalar_t>(),
targets.contiguous().data_ptr<int>(),
d_losses.contiguous().data_ptr<scalar_t>(),
num_classes,
gamma,
alpha,
num_samples,
d_logits.data_ptr<scalar_t>());
});
THCudaCheck(cudaGetLastError());
return d_logits;
}
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