virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ /// Read the normalization mode parameter and compute the normalizer based
+ /// on the blob size. If normalization_mode is VALID, the count of valid
+ /// outputs will be read from valid_count, unless it is -1 in which case
+ /// all outputs are assumed to be valid.
+ virtual Dtype get_normalizer(
+ LossParameter_NormalizationMode normalization_mode, int valid_count);
+
/// The internal SigmoidLayer used to map predictions to probabilities.
shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
/// sigmoid_output stores the output of the SigmoidLayer.
bool has_ignore_label_;
/// The label indicating that an instance should be ignored.
int ignore_label_;
+ /// How to normalize the loss.
+ LossParameter_NormalizationMode normalization_;
+ Dtype normalizer_;
+ int outer_num_, inner_num_;
};
} // namespace caffe
+#include <algorithm>
#include <vector>
#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
if (has_ignore_label_) {
ignore_label_ = this->layer_param_.loss_param().ignore_label();
}
+ if (this->layer_param_.loss_param().has_normalization()) {
+ normalization_ = this->layer_param_.loss_param().normalization();
+ } else if (this->layer_param_.loss_param().has_normalize()) {
+ normalization_ = this->layer_param_.loss_param().normalize() ?
+ LossParameter_NormalizationMode_VALID :
+ LossParameter_NormalizationMode_BATCH_SIZE;
+ } else {
+ normalization_ = LossParameter_NormalizationMode_BATCH_SIZE;
+ }
}
template <typename Dtype>
void SigmoidCrossEntropyLossLayer<Dtype>::Reshape(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
LossLayer<Dtype>::Reshape(bottom, top);
+ outer_num_ = bottom[0]->shape(0); // batch size
+ inner_num_ = bottom[0]->count(1); // instance size: |output| == |target|
CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
"SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
sigmoid_layer_->Reshape(sigmoid_bottom_vec_, sigmoid_top_vec_);
}
+// TODO(shelhamer) loss normalization should be pulled up into LossLayer,
+// instead of duplicated here and in SoftMaxWithLossLayer
+template <typename Dtype>
+Dtype SigmoidCrossEntropyLossLayer<Dtype>::get_normalizer(
+ LossParameter_NormalizationMode normalization_mode, int valid_count) {
+ Dtype normalizer;
+ switch (normalization_mode) {
+ case LossParameter_NormalizationMode_FULL:
+ normalizer = Dtype(outer_num_ * inner_num_);
+ break;
+ case LossParameter_NormalizationMode_VALID:
+ if (valid_count == -1) {
+ normalizer = Dtype(outer_num_ * inner_num_);
+ } else {
+ normalizer = Dtype(valid_count);
+ }
+ break;
+ case LossParameter_NormalizationMode_BATCH_SIZE:
+ normalizer = Dtype(outer_num_);
+ break;
+ case LossParameter_NormalizationMode_NONE:
+ normalizer = Dtype(1);
+ break;
+ default:
+ LOG(FATAL) << "Unknown normalization mode: "
+ << LossParameter_NormalizationMode_Name(normalization_mode);
+ }
+ // Some users will have no labels for some examples in order to 'turn off' a
+ // particular loss in a multi-task setup. The max prevents NaNs in that case.
+ return std::max(Dtype(1.0), normalizer);
+}
+
template <typename Dtype>
void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
sigmoid_bottom_vec_[0] = bottom[0];
sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
// Compute the loss (negative log likelihood)
- const int count = bottom[0]->count();
- const int num = bottom[0]->num();
// Stable version of loss computation from input data
const Dtype* input_data = bottom[0]->cpu_data();
const Dtype* target = bottom[1]->cpu_data();
+ int valid_count = 0;
Dtype loss = 0;
- for (int i = 0; i < count; ++i) {
+ for (int i = 0; i < bottom[0]->count(); ++i) {
const int target_value = static_cast<int>(target[i]);
if (has_ignore_label_ && target_value == ignore_label_) {
continue;
}
loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
+ ++valid_count;
}
- top[0]->mutable_cpu_data()[0] = loss / num;
+ normalizer_ = get_normalizer(normalization_, valid_count);
+ top[0]->mutable_cpu_data()[0] = loss / normalizer_;
}
template <typename Dtype>
if (propagate_down[0]) {
// First, compute the diff
const int count = bottom[0]->count();
- const int num = bottom[0]->num();
const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
const Dtype* target = bottom[1]->cpu_data();
Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
caffe_sub(count, sigmoid_output_data, target, bottom_diff);
- // Scale down gradient
- const Dtype loss_weight = top[0]->cpu_diff()[0];
- caffe_scal(count, loss_weight / num, bottom_diff);
// Zero out gradient of ignored targets.
if (has_ignore_label_) {
for (int i = 0; i < count; ++i) {
}
}
}
+ // Scale down gradient
+ Dtype loss_weight = top[0]->cpu_diff()[0] / normalizer_;
+ caffe_scal(count, loss_weight, bottom_diff);
}
}
namespace caffe {
+
template <typename Dtype>
__global__ void SigmoidCrossEntropyLossForwardGPU(const int nthreads,
- const Dtype* input_data, const Dtype* target, Dtype* loss) {
+ const Dtype* input_data, const Dtype* target, Dtype* loss,
+ const bool has_ignore_label_, const int ignore_label_,
+ Dtype* counts) {
CUDA_KERNEL_LOOP(i, nthreads) {
- loss[i] = input_data[i] * (target[i] - (input_data[i] >= 0)) -
- log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
+ const int target_value = static_cast<int>(target[i]);
+ if (has_ignore_label_ && target_value == ignore_label_) {
+ loss[i] = 0;
+ counts[i] = 0;
+ } else {
+ loss[i] = input_data[i] * (target[i] - (input_data[i] >= 0)) -
+ log(1 + exp(input_data[i] - 2 * input_data[i] *
+ (input_data[i] >= 0)));
+ counts[i] = 1;
+ }
}
}
template <typename Dtype>
-__global__ void SigmoidCrossEntropyLossIgnoreGPU(const int count,
- const int ignore_label, const Dtype* target, Dtype* reference) {
- CUDA_KERNEL_LOOP(index, count) {
- const int target_value = static_cast<int>(target[index]);
+__global__ void SigmoidCrossEntropyLossIgnoreDiffGPU(const int count,
+ const int ignore_label, const Dtype* target, Dtype* diff) {
+ CUDA_KERNEL_LOOP(i, count) {
+ const int target_value = static_cast<int>(target[i]);
if (target_value == ignore_label) {
- reference[index] = 0;
+ diff[i] = 0;
}
}
}
+
template <typename Dtype>
void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
// Compute the loss (negative log likelihood)
const int count = bottom[0]->count();
- const int num = bottom[0]->num();
// Stable version of loss computation from input data
const Dtype* input_data = bottom[0]->gpu_data();
const Dtype* target = bottom[1]->gpu_data();
// 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();
+ Dtype* count_data = bottom[1]->mutable_gpu_diff();
+ Dtype valid_count;
// NOLINT_NEXT_LINE(whitespace/operators)
SigmoidCrossEntropyLossForwardGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
- CAFFE_CUDA_NUM_THREADS>>>(count, input_data, target, loss_data);
- // Zero out loss of ignored targets.
- if (has_ignore_label_) {
- // NOLINT_NEXT_LINE(whitespace/operators)
- SigmoidCrossEntropyLossIgnoreGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
- CAFFE_CUDA_NUM_THREADS>>>(count, ignore_label_, target, loss_data);
+ CAFFE_CUDA_NUM_THREADS>>>(count, input_data, target, loss_data,
+ has_ignore_label_, ignore_label_, count_data);
+ // Only launch another CUDA kernel if we actually need the valid count.
+ if (normalization_ == LossParameter_NormalizationMode_VALID &&
+ has_ignore_label_) {
+ caffe_gpu_asum(count, count_data, &valid_count);
+ } else {
+ valid_count = count;
}
Dtype loss;
caffe_gpu_asum(count, loss_data, &loss);
- top[0]->mutable_cpu_data()[0] = loss / num;
+ normalizer_ = get_normalizer(normalization_, valid_count);
+ top[0]->mutable_cpu_data()[0] = loss / normalizer_;
}
template <typename Dtype>
if (propagate_down[0]) {
// First, compute the diff
const int count = bottom[0]->count();
- const int num = bottom[0]->num();
const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
const Dtype* target = bottom[1]->gpu_data();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
caffe_copy(count, sigmoid_output_data, bottom_diff);
caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
- // Scale down gradient
- const Dtype loss_weight = top[0]->cpu_diff()[0];
- caffe_gpu_scal(count, loss_weight / num, bottom_diff);
// Zero out gradient of ignored targets.
if (has_ignore_label_) {
// NOLINT_NEXT_LINE(whitespace/operators)
- SigmoidCrossEntropyLossIgnoreGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
+ SigmoidCrossEntropyLossIgnoreDiffGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
CAFFE_CUDA_NUM_THREADS>>>(count, ignore_label_, target, bottom_diff);
}
+ // Scale down gradient
+ Dtype loss_weight = top[0]->cpu_diff()[0] / normalizer_;
+ caffe_gpu_scal(count, loss_weight, bottom_diff);
}
}
optional int32 ignore_label = 1;
// How to normalize the loss for loss layers that aggregate across batches,
// spatial dimensions, or other dimensions. Currently only implemented in
- // SoftmaxWithLoss layer.
+ // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
enum NormalizationMode {
// Divide by the number of examples in the batch times spatial dimensions.
// Outputs that receive the ignore label will NOT be ignored in computing
// Do not normalize the loss.
NONE = 3;
}
+ // For historical reasons, the default normalization for
+ // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
optional NormalizationMode normalization = 3 [default = VALID];
// Deprecated. Ignored if normalization is specified. If normalization
// is not specified, then setting this to false will be equivalent to
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