LSTM params

tests/imuodom/imuodom.cpp

@@ -14,6 +14,9 @@ const char *c2_bin = "../tests/imuodom/layers/conv1d_9.bin";
 const char *c3_bin = "../tests/imuodom/layers/conv1d_10.bin";
 const char *c4_bin = "../tests/imuodom/layers/conv1d_11.bin";
 const char *c5_bin = "../tests/imuodom/layers/conv1d_12.bin";
+const char *l0_bin = "../tests/imuodom/layers/bidirectional_3.bin";
+const char *l1_bin = "../tests/imuodom/layers/bidirectional_4.bin";
+const char *d0_bin = "../tests/imuodom/layers/dense_3.bin";
 
 int main() {
 
@@ -48,8 +51,9 @@ int main() {
     tk::dnn::Layer *concat_l[3] = { &x0_2, &x1_2, &x2_2 };
     tk::dnn::Route concat (&net, concat_l, 3);
 
-    tk::dnn::LSTM lstm0(&net, 128, true, "ciao");
-    tk::dnn::LSTM lstm1(&net, 128, false, "ciao");
+    //tk::dnn::LSTM lstm0(&net, 128, true, l0_bin);
+    //tk::dnn::LSTM lstm1(&net, 128, false, l1_bin);
+    //tk::dnn::Dense d0 (&net, 3, d0_bin);
 
     net.print();
 
@@ -62,10 +66,12 @@ int main() {
     TIMER_STOP
 
     // Print real test
-    std::cout<<"\n==== CHECK RESULT ====\n";
-    dnnType *out;
-    dnnType *out_h;
-    readBinaryFile(output_bin, dim.tot(), &out_h, &out);
-    checkResult(dim.tot(), data, out);
+    //std::cout<<"\n==== CHECK RESULT ====\n";
+    //dnnType *out;
+    //dnnType *out_h;
+    //readBinaryFile(output_bin, dim.tot(), &out_h, &out);
+    //checkResult(dim.tot(), data, out);
+
+    printDeviceVector(100, data);
     return 0;
 }

tests/imuodom/infer.py

@@ -27,6 +27,7 @@ if __name__ == '__main__':
 
     weights = model.get_weights()
 
+    np.random.seed(2)
     x_angle = np.random.rand(1,100,4)
     x_gyro = np.random.rand(1,100,3)
     x_acc = np.random.rand(1,100,3)

tests/weights_exporter.py

@@ -24,6 +24,8 @@ def export_layer(name, weights, bias):
         weights = weights.transpose(3,2,0,1)
     elif(weights.ndim == 3):
         weights = weights.transpose(2,1,0)
+    elif(weights.ndim == 2):
+        weights = weights.transpose(1,0)
     else:
         print("Ndim", weights.ndim)
         raise("not implemented with dim" )
@@ -40,13 +42,19 @@ def export_layer(name, weights, bias):
     bin_write(f, bias)
     print ("WEIGHTS saved\n")
 
-def export_bidir(name, weights):
+def export_bidir(name, params):
     print ("########    EXPORT", name, "LAYER    ########")
  
-    for w in weights:
-        print(np.shape(w))
+    f = open(name + ".bin", mode='wb')
 
 
+    for w in params:
+        #w = w.transpose()
+        print(np.shape(w))
+        bin_write(f, w)
+   
+    print("WEIGHTS saved\n")
+
 #https://github.com/fchollet/keras/wiki/Converting-convolution-kernels-from-Theano-to-TensorFlow-and-vice-versa
 if __name__ == '__main__':
     print("DATA FORMAT: ", keras.backend.image_data_format())
@@ -64,6 +72,7 @@ if __name__ == '__main__':
     model = load_model(args.model)
     model.summary()
 
+
     weights = model.get_weights()
 
     ws = np.shape(weights)
@@ -90,6 +99,7 @@ if __name__ == '__main__':
         elif name.startswith("dense"):
             export_layer(args.output + "/" + name, wgs[0], wgs[1])
         elif name.startswith("bidirectional"):
+            wgs = l.forward_layer.get_weights()
             export_bidir(args.output + "/" + name, wgs)
         else:
             print ("skip:", name, "has no weights")