| 38 | |
| 39 | template <typename Dtype> |
| 40 | void BiasLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom, |
| 41 | const vector<Blob<Dtype>*>& top) { |
| 42 | const BiasParameter& param = this->layer_param_.bias_param(); |
| 43 | Blob<Dtype>* bias = (bottom.size() > 1) ? bottom[1] : this->blobs_[0].get(); |
| 44 | // Always set axis == 0 in special case where bias is a scalar |
| 45 | // (num_axes == 0). Mathematically equivalent for any choice of axis, so the |
| 46 | // actual setting can be safely ignored; and computation is most efficient |
| 47 | // with axis == 0 and (therefore) outer_dim_ == 1. |
| 48 | const int axis = (bias->num_axes() == 0) ? |
| 49 | 0 : bottom[0]->CanonicalAxisIndex(param.axis()); |
| 50 | CHECK_GE(bottom[0]->num_axes(), axis + bias->num_axes()) |
| 51 | << "bias blob's shape extends past bottom[0]'s shape when applied " |
| 52 | << "starting with bottom[0] axis = " << axis; |
| 53 | for (int i = 0; i < bias->num_axes(); ++i) { |
| 54 | CHECK_EQ(bottom[0]->shape(axis + i), bias->shape(i)) |
| 55 | << "dimension mismatch between bottom[0]->shape(" << axis + i |
| 56 | << ") and bias->shape(" << i << ")"; |
| 57 | } |
| 58 | outer_dim_ = bottom[0]->count(0, axis); |
| 59 | bias_dim_ = bias->count(); |
| 60 | inner_dim_ = bottom[0]->count(axis + bias->num_axes()); |
| 61 | dim_ = bias_dim_ * inner_dim_; |
| 62 | if (bottom[0] != top[0]) { |
| 63 | top[0]->ReshapeLike(*bottom[0]); |
| 64 | } |
| 65 | bias_multiplier_.Reshape(vector<int>(1, inner_dim_)); |
| 66 | if (bias_multiplier_.cpu_data()[inner_dim_ - 1] != Dtype(1)) { |
| 67 | caffe_set(inner_dim_, Dtype(1), bias_multiplier_.mutable_cpu_data()); |
| 68 | } |
| 69 | } |
| 70 | |
| 71 | template <typename Dtype> |
| 72 | void BiasLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom, |
nothing calls this directly
no test coverage detected