-
Notifications
You must be signed in to change notification settings - Fork 40
/
focal_loss_layer.cu
216 lines (190 loc) · 7.78 KB
/
focal_loss_layer.cu
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
#include <algorithm>
#include <cfloat>
#include <vector>
#include "caffe/layers/focal_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
template <typename Dtype>
__global__ void LogOpGPU(const int nthreads,
const Dtype* in, Dtype* out, const Dtype eps)
{
CUDA_KERNEL_LOOP(index, nthreads) {
out[index] = log(max(in[index], eps));
}
}
template <typename Dtype>
void FocalLossLayer<Dtype>::compute_intermediate_values_of_gpu() {
// compute the corresponding variables
const int count = prob_.count();
const Dtype* prob_data = prob_.gpu_data();
const Dtype* ones_data = ones_.gpu_data();
Dtype* log_prob_data = log_prob_.mutable_gpu_data();
Dtype* power_prob_data = power_prob_.mutable_gpu_data();
/// log(p_t)
const int nthreads = prob_.count();
const Dtype eps = Dtype(FLT_MIN); // where FLT_MIN = 1.17549e-38, here u can change it
// more stable
// NOLINT_NEXT_LINE(whitespace/operators)
LogOpGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
CAFFE_CUDA_NUM_THREADS>>>(nthreads, prob_data, log_prob_data, eps);
/// caffe_gpu_log(count, prob_data, log_prob_data);
/// (1 - p_t) ^ gamma
caffe_gpu_sub(count, ones_data, prob_data, power_prob_data);
caffe_gpu_powx(count, power_prob_.gpu_data(), gamma_, power_prob_data);
caffe_gpu_scal(count, alpha_, power_prob_data);
}
template <typename Dtype>
__global__ void FocalLossForwardGPU(const int nthreads,
const Dtype* log_prob_data,
const Dtype* power_prob_data,
const Dtype* label,
Dtype* loss,
const int num,
const int dim,
const int spatial_dim,
const bool has_ignore_label_,
const int ignore_label_,
Dtype* counts)
{
CUDA_KERNEL_LOOP(index, nthreads) {
const int n = index / spatial_dim;
const int s = index % spatial_dim;
const int label_value = static_cast<int>(label[n * spatial_dim + s]);
if (has_ignore_label_ && label_value == ignore_label_) {
loss[index] = 0;
counts[index] = 0;
} else {
int ind = n * dim + label_value * spatial_dim + s;
// loss[index] = -max(power_prob_data[ind] * log_prob_data[ind], Dtype(log(Dtype(FLT_MIN))));
loss[index] = -power_prob_data[ind] * log_prob_data[ind];
counts[index] = 1;
}
}
}
template <typename Dtype>
void FocalLossLayer<Dtype>::Forward_gpu(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top)
{
// The forward pass computes the softmax prob values.
softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
// compute all needed values
compute_intermediate_values_of_gpu();
// const Dtype* prob_data = prob_.gpu_data();
const Dtype* log_prob_data = log_prob_.gpu_data();
const Dtype* power_prob_data = power_prob_.gpu_data();
const Dtype* label = bottom[1]->gpu_data();
const int dim = prob_.count() / outer_num_;
const int nthreads = outer_num_ * inner_num_;
// Since this memory is not used for anything until it is overwritten
// on the backward pass, we use it here to avoid having to allocate new GPU
// memory to accumulate intermediate results in the kernel.
Dtype* loss_data = bottom[0]->mutable_gpu_diff();
// Similarly, this memory is never used elsewhere, and thus we can use it
// to avoid having to allocate additional GPU memory.
Dtype* counts = prob_.mutable_gpu_diff();
// NOLINT_NEXT_LINE(whitespace/operators)
FocalLossForwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
CAFFE_CUDA_NUM_THREADS>>>(nthreads, log_prob_data, power_prob_data,
label, loss_data,outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts);
Dtype loss;
caffe_gpu_asum(nthreads, loss_data, &loss);
Dtype valid_count = -1;
// Only launch another CUDA kernel if we actually need the count of valid
// outputs.
if (normalization_ == LossParameter_NormalizationMode_VALID &&
has_ignore_label_) {
caffe_gpu_asum(nthreads, counts, &valid_count);
}
top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_,
valid_count);
if (top.size() == 2) {
top[1]->ShareData(prob_);
}
}
template <typename Dtype>
__global__ void FocalLossBackwardGPU(const int nthreads,
const Dtype* top,
const Dtype* label,
const Dtype* prob_data,
const Dtype* log_prob_data,
const Dtype* power_prob_data,
Dtype* bottom_diff,
const int num,
const int dim,
const int spatial_dim,
const Dtype gamma,
const bool has_ignore_label_,
const int ignore_label_,
const Dtype eps,
Dtype* counts)
{
const int channels = dim / spatial_dim;
CUDA_KERNEL_LOOP(index, nthreads) {
const int n = index / spatial_dim;
const int s = index % spatial_dim;
const int label_value = static_cast<int>(label[n * spatial_dim + s]);
if (has_ignore_label_ && label_value == ignore_label_) {
for (int c = 0; c < channels; ++c) {
bottom_diff[n * dim + c * spatial_dim + s] = 0;
}
counts[index] = 0;
} else {
// the gradient from FL w.r.t p_t, here ignore the `sign`
int ind_i = n * dim + label_value * spatial_dim + s; // index of ground-truth label
Dtype grad = 0 - gamma * (power_prob_data[ind_i] / max(1 - prob_data[ind_i], eps))
* log_prob_data[ind_i] * prob_data[ind_i]
+ power_prob_data[ind_i];
// the gradient w.r.t input data x
for (int c = 0; c < channels; ++c) {
int ind_j = n * dim + c * spatial_dim + s;
if(c == label_value) {
// if i == j, (here i,j are refered for derivative of softmax)
bottom_diff[ind_j] = grad * (prob_data[ind_i] - 1);
} else {
// if i != j, (here i,j are refered for derivative of softmax)
bottom_diff[ind_j] = grad * prob_data[ind_j];
}
}
// count
counts[index] = 1;
}
}
}
template <typename Dtype>
void FocalLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
if (propagate_down[1]) {
LOG(FATAL) << this->type()
<< " Layer cannot backpropagate to label inputs.";
}
if (propagate_down[0]) {
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
const Dtype* prob_data = prob_.gpu_data();
const Dtype* top_data = top[0]->gpu_data();
const Dtype* label = bottom[1]->gpu_data();
const int dim = prob_.count() / outer_num_;
const int nthreads = outer_num_ * inner_num_;
const Dtype eps = 1e-10;
// intermidiate
const Dtype* log_prob_data = log_prob_.gpu_data();
const Dtype* power_prob_data = power_prob_.gpu_data();
// Since this memory is never used for anything else,
// we use to to avoid allocating new GPU memory.
Dtype* counts = prob_.mutable_gpu_diff();
// NOLINT_NEXT_LINE(whitespace/operators)
FocalLossBackwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
CAFFE_CUDA_NUM_THREADS>>>(nthreads, top_data, label, prob_data, log_prob_data, power_prob_data,
bottom_diff, outer_num_, dim, inner_num_, gamma_, has_ignore_label_, ignore_label_, eps, counts);
// Only launch another CUDA kernel if we actually need the count of valid outputs.
Dtype valid_count = -1;
if (normalization_ == LossParameter_NormalizationMode_VALID &&
has_ignore_label_) {
caffe_gpu_asum(nthreads, counts, &valid_count);
}
// Scale gradient
const Dtype loss_weight = top[0]->cpu_diff()[0] / get_normalizer(normalization_, valid_count);
caffe_gpu_scal(prob_.count(), loss_weight , bottom_diff);
}
}
INSTANTIATE_LAYER_GPU_FUNCS(FocalLossLayer);
} // namespace caffe