void BatchNormLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
BatchNormParameter param = this->layer_param_.batch_norm_param();
moving_average_fraction_ = param.moving_average_fraction();
use_global_stats_ = this->phase_ == TEST;
if (param.has_use_global_stats())
use_global_stats_ = param.use_global_stats();
if (bottom[0]->num_axes() == 1)
channels_ = 1;
else
channels_ = bottom[0]->shape(1);
eps_ = param.eps();
if (this->blobs_.size() > 0) {
LOG(INFO) << "Skipping parameter initialization";
} else {
this->blobs_.resize(3);
vector<int> sz;
sz.push_back(channels_);
this->blobs_[0].reset(new Blob<Dtype>(sz));
this->blobs_[1].reset(new Blob<Dtype>(sz));
sz[0]=1;
this->blobs_[2].reset(new Blob<Dtype>(sz));
for (int i = 0; i < 3; ++i) {
caffe_set(this->blobs_[i]->count(), Dtype(0),
this->blobs_[i]->mutable_cpu_data());
}
}
}
void BatchNormLayer<Dtype, MItype, MOtype>::LayerSetUp(
const vector<Blob<MItype>*>& bottom,
const vector<Blob<MOtype>*>& top) {
BatchNormParameter param = this->layer_param_.batch_norm_param();
moving_average_fraction_ = param.moving_average_fraction();
use_global_stats_ = this->phase_ == TEST;
if (param.has_use_global_stats())
use_global_stats_ = param.use_global_stats();
if (bottom[0]->num_axes() == 1)
channels_ = 1;
else
channels_ = bottom[0]->shape(1);
eps_ = param.eps();
if (this->blobs_.size() > 0) {
LOG(INFO)<< "Skipping parameter initialization";
} else {
this->blobs_.resize(3);
vector<int_tp> sz;
sz.push_back(channels_);
this->blobs_[0].reset(new Blob<Dtype>(sz, this->device_));
this->blobs_[1].reset(new Blob<Dtype>(sz, this->device_));
sz[0]=1;
this->blobs_[2].reset(new Blob<Dtype>(sz, this->device_));
for (int_tp i = 0; i < 3; ++i) {
caffe_set(this->blobs_[i]->count(), Dtype(0),
this->blobs_[i]->mutable_cpu_data());
}
}
// Mask statistics from optimization by setting local learning rates
// for mean, variance, and the bias correction to zero.
for (int_tp i = 0; i < this->blobs_.size(); ++i) {
if (this->layer_param_.param_size() == i) {
ParamSpec* fixed_param_spec = this->layer_param_.add_param();
fixed_param_spec->set_lr_mult(0.f);
} else {
CHECK_EQ(this->layer_param_.param(i).lr_mult(), 0.f)
<< "Cannot configure batch normalization statistics as layer "
<< "parameters.";
}
}
this->InitializeQuantizers(bottom, top);
}
