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Add LSTMLayer and LSTMUnitLayer, with tests
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| #include <string> | ||
| #include <vector> | ||
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| #include "caffe/blob.hpp" | ||
| #include "caffe/common.hpp" | ||
| #include "caffe/filler.hpp" | ||
| #include "caffe/layer.hpp" | ||
| #include "caffe/sequence_layers.hpp" | ||
| #include "caffe/util/math_functions.hpp" | ||
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| namespace caffe { | ||
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| template <typename Dtype> | ||
| void LSTMLayer<Dtype>::RecurrentInputBlobNames(vector<string>* names) const { | ||
| names->resize(2); | ||
| (*names)[0] = "h_0"; | ||
| (*names)[1] = "c_0"; | ||
| } | ||
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| template <typename Dtype> | ||
| void LSTMLayer<Dtype>::RecurrentOutputBlobNames(vector<string>* names) const { | ||
| names->resize(2); | ||
| (*names)[0] = "h_" + this->int_to_str(this->T_); | ||
| (*names)[1] = "c_T"; | ||
| } | ||
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| template <typename Dtype> | ||
| void LSTMLayer<Dtype>::OutputBlobNames(vector<string>* names) const { | ||
| names->resize(1); | ||
| (*names)[0] = "h"; | ||
| } | ||
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| template <typename Dtype> | ||
| void LSTMLayer<Dtype>::FillUnrolledNet(NetParameter* net_param) const { | ||
| const int num_output = this->layer_param_.recurrent_param().num_output(); | ||
| CHECK_GT(num_output, 0) << "num_output must be positive"; | ||
| const FillerParameter& weight_filler = | ||
| this->layer_param_.recurrent_param().weight_filler(); | ||
| const FillerParameter& bias_filler = | ||
| this->layer_param_.recurrent_param().bias_filler(); | ||
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| // Add generic LayerParameter's (without bottoms/tops) of layer types we'll | ||
| // use to save redundant code. | ||
| LayerParameter hidden_param; | ||
| hidden_param.set_type("InnerProduct"); | ||
| hidden_param.mutable_inner_product_param()->set_num_output(num_output * 4); | ||
| hidden_param.mutable_inner_product_param()->set_bias_term(false); | ||
| hidden_param.mutable_inner_product_param()->set_axis(2); | ||
| hidden_param.mutable_inner_product_param()-> | ||
| mutable_weight_filler()->CopyFrom(weight_filler); | ||
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| LayerParameter biased_hidden_param(hidden_param); | ||
| biased_hidden_param.mutable_inner_product_param()->set_bias_term(true); | ||
| biased_hidden_param.mutable_inner_product_param()-> | ||
| mutable_bias_filler()->CopyFrom(bias_filler); | ||
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| LayerParameter sum_param; | ||
| sum_param.set_type("Eltwise"); | ||
| sum_param.mutable_eltwise_param()->set_operation( | ||
| EltwiseParameter_EltwiseOp_SUM); | ||
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| LayerParameter slice_param; | ||
| slice_param.set_type("Slice"); | ||
| slice_param.mutable_slice_param()->set_axis(0); | ||
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| LayerParameter split_param; | ||
| split_param.set_type("Split"); | ||
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| BlobShape input_shape; | ||
| input_shape.add_dim(1); // c_0 and h_0 are a single timestep | ||
| input_shape.add_dim(this->N_); | ||
| input_shape.add_dim(num_output); | ||
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| net_param->add_input("c_0"); | ||
| net_param->add_input_shape()->CopyFrom(input_shape); | ||
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| net_param->add_input("h_0"); | ||
| net_param->add_input_shape()->CopyFrom(input_shape); | ||
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| LayerParameter* cont_slice_param = net_param->add_layer(); | ||
| cont_slice_param->CopyFrom(slice_param); | ||
| cont_slice_param->set_name("cont_slice"); | ||
| cont_slice_param->add_bottom("cont"); | ||
| cont_slice_param->mutable_slice_param()->set_axis(1); | ||
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| // Add layer to transform all timesteps of x to the hidden state dimension. | ||
| // W_xc_x = W_xc * x + b_c | ||
| { | ||
| LayerParameter* x_transform_param = net_param->add_layer(); | ||
| x_transform_param->CopyFrom(biased_hidden_param); | ||
| x_transform_param->set_name("x_transform"); | ||
| x_transform_param->add_param()->set_name("W_xc"); | ||
| x_transform_param->add_param()->set_name("b_c"); | ||
| x_transform_param->add_bottom("x"); | ||
| x_transform_param->add_top("W_xc_x"); | ||
| } | ||
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| if (this->static_input_) { | ||
| // Add layer to transform x_static to the gate dimension. | ||
| // W_xc_x_static = W_xc_static * x_static | ||
| LayerParameter* x_static_transform_param = net_param->add_layer(); | ||
| x_static_transform_param->CopyFrom(hidden_param); | ||
| x_static_transform_param->mutable_inner_product_param()->set_axis(1); | ||
| x_static_transform_param->set_name("W_xc_x_static"); | ||
| x_static_transform_param->add_param()->set_name("W_xc_static"); | ||
| x_static_transform_param->add_bottom("x_static"); | ||
| x_static_transform_param->add_top("W_xc_x_static"); | ||
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| LayerParameter* reshape_param = net_param->add_layer(); | ||
| reshape_param->set_type("Reshape"); | ||
| BlobShape* new_shape = | ||
| reshape_param->mutable_reshape_param()->mutable_shape(); | ||
| new_shape->add_dim(1); // One timestep. | ||
| new_shape->add_dim(this->N_); | ||
| new_shape->add_dim( | ||
| x_static_transform_param->inner_product_param().num_output()); | ||
| reshape_param->add_bottom("W_xc_x_static"); | ||
| reshape_param->add_top("W_xc_x_static"); | ||
| } | ||
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| LayerParameter* x_slice_param = net_param->add_layer(); | ||
| x_slice_param->CopyFrom(slice_param); | ||
| x_slice_param->add_bottom("W_xc_x"); | ||
| x_slice_param->set_name("W_xc_x_slice"); | ||
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| LayerParameter output_concat_layer; | ||
| output_concat_layer.set_name("h_concat"); | ||
| output_concat_layer.set_type("Concat"); | ||
| output_concat_layer.add_top("h"); | ||
| output_concat_layer.mutable_concat_param()->set_axis(0); | ||
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| for (int t = 1; t <= this->T_; ++t) { | ||
| string tm1s = this->int_to_str(t - 1); | ||
| string ts = this->int_to_str(t); | ||
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| cont_slice_param->add_top("cont_" + ts); | ||
| x_slice_param->add_top("W_xc_x_" + ts); | ||
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| // Add layers to flush the hidden state when beginning a new | ||
| // sequence, as indicated by cont_t. | ||
| // h_conted_{t-1} := cont_t * h_{t-1} | ||
| // | ||
| // Normally, cont_t is binary (i.e., 0 or 1), so: | ||
| // h_conted_{t-1} := h_{t-1} if cont_t == 1 | ||
| // 0 otherwise | ||
| { | ||
| LayerParameter* cont_h_param = net_param->add_layer(); | ||
| cont_h_param->CopyFrom(sum_param); | ||
| cont_h_param->mutable_eltwise_param()->set_coeff_blob(true); | ||
| cont_h_param->set_name("h_conted_" + tm1s); | ||
| cont_h_param->add_bottom("h_" + tm1s); | ||
| cont_h_param->add_bottom("cont_" + ts); | ||
| cont_h_param->add_top("h_conted_" + tm1s); | ||
| } | ||
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| // Add layer to compute | ||
| // W_hc_h_{t-1} := W_hc * h_conted_{t-1} | ||
| { | ||
| LayerParameter* w_param = net_param->add_layer(); | ||
| w_param->CopyFrom(hidden_param); | ||
| w_param->set_name("transform_" + ts); | ||
| w_param->add_param()->set_name("W_hc"); | ||
| w_param->add_bottom("h_conted_" + tm1s); | ||
| w_param->add_top("W_hc_h_" + tm1s); | ||
| w_param->mutable_inner_product_param()->set_axis(2); | ||
| } | ||
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| // Add the outputs of the linear transformations to compute the gate input. | ||
| // gate_input_t := W_hc * h_conted_{t-1} + W_xc * x_t + b_c | ||
| // = W_hc_h_{t-1} + W_xc_x_t + b_c | ||
| { | ||
| LayerParameter* input_sum_layer = net_param->add_layer(); | ||
| input_sum_layer->CopyFrom(sum_param); | ||
| input_sum_layer->set_name("gate_input_" + ts); | ||
| input_sum_layer->add_bottom("W_hc_h_" + tm1s); | ||
| input_sum_layer->add_bottom("W_xc_x_" + ts); | ||
| if (this->static_input_) { | ||
| input_sum_layer->add_bottom("W_xc_x_static"); | ||
| } | ||
| input_sum_layer->add_top("gate_input_" + ts); | ||
| } | ||
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| // Add LSTMUnit layer to compute the cell & hidden vectors c_t and h_t. | ||
| // Inputs: c_{t-1}, gate_input_t = (i_t, f_t, o_t, g_t), cont_t | ||
| // Outputs: c_t, h_t | ||
| // [ i_t' ] | ||
| // [ f_t' ] := gate_input_t | ||
| // [ o_t' ] | ||
| // [ g_t' ] | ||
| // i_t := \sigmoid[i_t'] | ||
| // f_t := \sigmoid[f_t'] | ||
| // o_t := \sigmoid[o_t'] | ||
| // g_t := \tanh[g_t'] | ||
| // c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t) | ||
| // h_t := o_t .* \tanh[c_t] | ||
| { | ||
| LayerParameter* lstm_unit_param = net_param->add_layer(); | ||
| lstm_unit_param->set_type("LSTMUnit"); | ||
| lstm_unit_param->add_bottom("c_" + tm1s); | ||
| lstm_unit_param->add_bottom("gate_input_" + ts); | ||
| lstm_unit_param->add_bottom("cont_" + ts); | ||
| lstm_unit_param->add_top("c_" + ts); | ||
| lstm_unit_param->add_top("h_" + ts); | ||
| lstm_unit_param->set_name("unit_" + ts); | ||
| } | ||
| output_concat_layer.add_bottom("h_" + ts); | ||
| } // for (int t = 1; t <= this->T_; ++t) | ||
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| { | ||
| LayerParameter* c_T_copy_param = net_param->add_layer(); | ||
| c_T_copy_param->CopyFrom(split_param); | ||
| c_T_copy_param->add_bottom("c_" + this->int_to_str(this->T_)); | ||
| c_T_copy_param->add_top("c_T"); | ||
| } | ||
| net_param->add_layer()->CopyFrom(output_concat_layer); | ||
| } | ||
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| INSTANTIATE_CLASS(LSTMLayer); | ||
| REGISTER_LAYER_CLASS(LSTM); | ||
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| } // namespace caffe |
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