minor compadability update, added MetaInfo to weights in full translation test SEDGE

Author: Daniel

tests/full_translation_test_SEDGE/network.hpp

@@ -5,12 +5,12 @@
 
 // const auto __attribute__(( section(".data") )) network=layers::Sequence(
 const auto network=layers::Sequence(
-    layers::Sedge<float,Complex<float>>(A0, B0_1_48, B0_bias, C0_1_96, C0_bias, Matrix<float, "E", 1>{1}, LeakyReLU<float>),
-    layers::Sedge<float,Complex<float>>(A1, B1_1_48, B1_bias, C1_1_96, C1_bias, Matrix<float, "E", 1>{1}, LeakyReLU<float>),
-    layers::Sedge<float,Complex<float>>(A2, B2_1_48, B2_bias, C2_1_96, C2_bias, Matrix<float, "E", 1>{1}, LeakyReLU<float>),
-    layers::Sedge<float,Complex<float>>(A3, B3_1_48, B3_bias, C3_1_96, C3_bias, Matrix<float, "E", 1>{1}, LeakyReLU<float>),
-    layers::Sedge<float,Complex<float>>(A4, B4_1_48, B4_bias, C4_1_96, C4_bias, Matrix<float, "E", 1>{1}, LeakyReLU<float>),
-    layers::Sedge<float,Complex<float>>(A5, B5_1_48, B5_bias, C5_1_96, C5_bias, Matrix<float, "E", 1>{1}, LeakyReLU<float>),
+    layers::Sedge<float,Complex<float>>(A0, B0_1_48, B0_bias, C0_1_96, C0_bias, SkipLayer0_1_112, LeakyReLU<float>),
+    layers::Sedge<float,Complex<float>>(A1, B1_1_48, B1_bias, C1_1_96, C1_bias, SkipLayer1_1_112, LeakyReLU<float>),
+    layers::Sedge<float,Complex<float>>(A2, B2_1_48, B2_bias, C2_1_96, C2_bias, SkipLayer2_1_112, LeakyReLU<float>),
+    layers::Sedge<float,Complex<float>>(A3, B3_1_48, B3_bias, C3_1_96, C3_bias, SkipLayer3_1_112, LeakyReLU<float>),
+    layers::Sedge<float,Complex<float>>(A4, B4_1_48, B4_bias, C4_1_96, C4_bias, SkipLayer4_1_112, LeakyReLU<float>),
+    layers::Sedge<float,Complex<float>>(A5, B5_1_48, B5_bias, C5_1_96, C5_bias, SkipLayer5_1_112, LeakyReLU<float>),
     layers::SumReduction<"S">(),
     layers::Linear<float>(W_1_112, b, PassThrough<float>)
 );

tests/full_translation_test_SEDGE/test_SEDGE.py

@@ -114,6 +114,10 @@
     file.write(f'#define NUMBER_OF_LAYERS {number_of_ssm_layers}\n')
 
     file.write('\n')
+    file.write('std::string MetaInfo = R"(\n')
+    file.write('This model was generated by the full translation test Script.\n')
+    file.write(f'It contains {number_of_ssm_layers} SSM layers.\n')
+    file.write(')";\n')
     step_scale = []
     for index, layer in enumerate(range(len(weights_dict))):
         if 'step_scale' in weights_dict[index].keys():
@@ -128,15 +132,15 @@
     file.write(f'constexpr unsigned int step_scale[] = {{{"".join([f"{x}," for x in step_scale_tmp])[:-1]}}};\n')
     file.write(f'constexpr unsigned int step_scale_index_offsets[] = {{{"".join([f"{x}," for x in index_ofsets])[:-1]}}};\n')
 
-    write_network_weights(weights_dict, step_scale, file)
+    write_network_weights(weights_dict, step_scale, file, os.path.join(os.path.dirname(__file__),include_path, "include"))
 
 with open("network.hpp", "w") as file:
     file.write('#pragma once\n')
     file.write(f'#include "include/NeuralNetwork.hpp"\n')
     file.write('#include "weights.inc"\n')
     file.write('#include "weights_unrolled.inc"\n')
     file.write('\n')
-    write_network(weights_dict, file, B_KP, C_KP, S_KP, W_KP)
+    write_network(weights_dict, file,os.path.join(os.path.dirname(__file__),include_path, "include"), B_KP, C_KP, S_KP, W_KP)
 
 total_number_of_weights = 0
 for index,_ in enumerate(weights_dict):

tests/full_translation_test_SEDGE/test_SEdge.cpp

@@ -177,11 +177,16 @@ auto getStepScaleTImes() {
     return array;
 }
 
+std::string getMetaInfo(){
+    return MetaInfo;
+}
+
 PYBIND11_MODULE(CppSEdge, m) {
     m.doc() = "Runs the Network";
 
     m.def("run", &run_SEdge, "Runs the network, takes input as a numpy array");
     m.def("printModelInfo", &printModelInfo, "Prints the model information");
     m.def("getInputSize", &getInputSize, "Returns the input size of the network");
     m.def("getStepScaleTimes", &getStepScaleTImes, "Returns the step scale times of the network");
+    m.def("getMetaInfo", &getMetaInfo, "Returns the meta information of the network");
 }

tests/full_translation_test_SEDGE/write_network.py

@@ -2,16 +2,12 @@
 import sys
 import numpy as np
 
-
-include_path = '../../'
-
-sys.path.append(os.path.join(os.path.dirname(__file__),include_path, "include"))
-from write_weights import write_weight
-
 complex_types = [np.complex64, np.complex128]
 
+def write_network_weights(weights_dict, step_scale, file, Cpp_NN_include_path):
+    sys.path.append(Cpp_NN_include_path)
+    from write_weights import write_weight
 
-def write_network_weights(weights_dict, step_scale, file):
     for index, layer in enumerate(weights_dict):
         print(weights_dict[index].keys())
         if 'A' in weights_dict[index].keys():
@@ -66,7 +62,10 @@ def write_network_weights(weights_dict, step_scale, file):
 
 
 
-def write_network(weights_dict, file, B_KP=(12*4), C_KP=(12*8), S_KP=(14*8), W_KP=(14*8)):
+def write_network(weights_dict, file, Cpp_NN_include_path, B_KP=(12*4), C_KP=(12*8), S_KP=(14*8), W_KP=(14*8)):
+    sys.path.append(Cpp_NN_include_path)
+    from write_weights import write_weight
+    
     file.write('// const auto __attribute__(( section(".data") )) network=layers::Sequence(\n')
     file.write('const auto network=layers::Sequence(\n')
     string = ""