initial attempt at passing in tx EOO bits via function call (Python)

drowe67 · Nov 28, 2024 · 3f8a23b · 3f8a23b
1 parent 048ecc7
commit 3f8a23b
Show file tree

Hide file tree

Showing 4 changed files with 35 additions and 23 deletions.
diff --git a/CMakeLists.txt b/CMakeLists.txt
@@ -556,10 +556,8 @@ add_test(NAME c_decoder_aux_mpp
 
 add_test(NAME radae_eoo_data_py
         COMMAND sh -c "cd ${CMAKE_SOURCE_DIR}; \
-                       ./inference.sh model19_check3/checkpoints/checkpoint_epoch_100.pth wav/brian_g8sez.wav /dev/null \
-                       --EbNodB 10 --freq_offset 13 \
-                       --rate_Fs --pilots --pilot_eq --eq_ls --cp 0.004 --bottleneck 3 --time_offset -16 --write_rx rx.f32  \
-                       --prepend_noise 1 --append_noise 1 --end_of_over --auxdata --correct_freq_offset; \
+                       ${CMAKE_CURRENT_BINARY_DIR}/src/lpcnet_demo -features wav/brian_g8sez.wav features_in.f32; \
+                       cat features_in.f32 | python3 radae_txe.py --eoo_data_test > rx.f32; \
                        cat rx.f32 | python3 radae_rxe.py -v 2 --eoo_data_test > /dev/null")
                        set_tests_properties(radae_eoo_data_py PROPERTIES PASS_REGULAR_EXPRESSION "PASS")
 

diff --git a/radae/radae.py b/radae/radae.py
@@ -220,22 +220,6 @@ def __init__(self,
 
         # experimental EOO data symbols (quick and dirty supplimentary txt channel)
         self.Nseoo = (Ns-1)*Nc  # number of EOO data symbols
-        # use a customer RNG to avoid upsetting some other rather delicate ctests (TODO fix this sensitvity later)
-        g = torch.Generator().manual_seed(1)
-        eoo_bits = torch.sign(torch.rand(self.Nseoo*bps,generator=g)-0.5)
-        self.eoo_bits = eoo_bits
-        eoo_syms = eoo_bits[::2] + 1j*eoo_bits[1::2]
-        eoo_syms = torch.reshape(eoo_syms,(1,Ns-1,Nc))
-
-        eoo_tx = torch.matmul(eoo_syms,self.Winv)
-        if self.Ncp:
-            eoo_tx_cp = torch.zeros((1,Ns-1,self.M+Ncp),dtype=torch.complex64)
-            eoo_tx_cp[:,:,Ncp:] = eoo_tx
-            eoo_tx_cp[:,:,:Ncp] = eoo_tx_cp[:,:,-Ncp:]
-            eoo_tx = torch.reshape(eoo_tx_cp,(1,(Ns-1)*(self.M+Ncp)))*self.pilot_gain
-            if self.bottleneck == 3:
-                eoo_tx = torch.tanh(torch.abs(eoo_tx)) * torch.exp(1j*torch.angle(eoo_tx))
-            self.eoo[0,2*(M+Ncp):Nmf] = eoo_tx
 
         print(f"Rs: {Rs:5.2f} Rs': {Rs_dash:5.2f} Ts': {Ts_dash:5.3f} Nsmf: {Nsmf:3d} Ns: {Ns:3d} Nc: {Nc:3d} M: {self.M:d} Ncp: {self.Ncp:d}", file=sys.stderr)
 
@@ -454,6 +438,22 @@ def est_snr(self, r, time_offset=0):
         SNR_est = Ct/(torch.dot(torch.conj(p),p) - Ct)
         return SNR_est.real
 
+    def set_eoo_bits(self, eoo_bits):
+        Ns = self.Ns; Ncp = self.Ncp; M = self.M; Nc = self.Nc; Nmf = int((Ns+1)*(M+Ncp))
+
+        eoo_syms = eoo_bits[::2] + 1j*eoo_bits[1::2]
+        eoo_syms = torch.reshape(eoo_syms,(1,Ns-1,Nc))
+
+        eoo_tx = torch.matmul(eoo_syms,self.Winv)
+        if self.Ncp:
+            eoo_tx_cp = torch.zeros((1,Ns-1,self.M+Ncp),dtype=torch.complex64)
+            eoo_tx_cp[:,:,Ncp:] = eoo_tx
+            eoo_tx_cp[:,:,:Ncp] = eoo_tx_cp[:,:,-Ncp:]
+            eoo_tx = torch.reshape(eoo_tx_cp,(1,(Ns-1)*(self.M+Ncp)))*self.pilot_gain
+            if self.bottleneck == 3:
+                eoo_tx = torch.tanh(torch.abs(eoo_tx)) * torch.exp(1j*torch.angle(eoo_tx))
+            self.eoo[0,2*(M+Ncp):Nmf] = eoo_tx
+
     def forward(self, features, H, G=None):
 
         (num_batches, num_ten_ms_timesteps, num_features) = features.shape

diff --git a/radae_rxe.py b/radae_rxe.py
@@ -347,8 +347,10 @@ def do_radae_rx(self, buffer_complex, floats_out):
       if (ret & 1) and args.use_stdout:
          sys.stdout.buffer.write(floats_out)
       if (ret & 2) and args.eoo_data_test:
-         n_bits = rx.model.Nseoo*rx.model.bps
-         tx_bits = rx.model.eoo_bits.cpu().detach().numpy().flatten()
+         # same RNG as tx
+         g = torch.Generator().manual_seed(1)
+         tx_bits = torch.sign(torch.rand(rx.model.Nseoo*rx.model.bps,generator=g)-0.5).detach().numpy()
+         n_bits = len(tx_bits)
          n_errors = sum(floats_out[:n_bits]*tx_bits < 0)
          ber = n_errors/n_bits
          print(f"EOO data n_bits: {n_bits} n_errors: {n_errors} BER: {ber:5.2f}", file=sys.stderr)

diff --git a/radae_txe.py b/radae_txe.py
@@ -143,10 +143,17 @@ def do_eoo(self,tx_out):
    parser.add_argument('--noauxdata', dest="auxdata", action='store_false', help='disable injection of auxillary data symbols')
    parser.add_argument('--txbpf', action='store_true', help='enable Tx BPF')
    parser.add_argument('--bypass_enc', action='store_true', help='Bypass core encoder, read z from stdin')
+   parser.add_argument('--eoo_data_test', action='store_true', help='experimental EOO data test - tx test frame')
    parser.set_defaults(auxdata=True)
    args = parser.parse_args()
    tx = radae_tx(model_name=args.model_name, auxdata=args.auxdata, txbpf_en=args.txbpf, bypass_enc=args.bypass_enc)
-
+
+   if args.eoo_data_test:
+      # use a custom  RNG to avoid upsetting some other rather delicate ctests (TODO fix this sensitvity later)
+      g = torch.Generator().manual_seed(1)
+      tx_bits = torch.sign(torch.rand(tx.model.Nseoo*tx.model.bps,generator=g)-0.5)
+      tx.model.set_eoo_bits(tx_bits)
+
    tx_out = np.zeros(tx.Nmf,dtype=np.csingle)
    while True:
       buffer = sys.stdin.buffer.read(tx.n_floats_in*struct.calcsize("f"))
@@ -159,3 +166,8 @@ def do_eoo(self,tx_out):
    eoo_out = np.zeros(tx.Neoo,dtype=np.csingle)
    tx.do_eoo(eoo_out)
    sys.stdout.buffer.write(eoo_out)
+   if args.eoo_data_test:
+      # trailing silence so Rx has enough sample to process EOO frame
+      eoo_out = np.zeros(tx.Neoo,dtype=np.csingle)
+      sys.stdout.buffer.write(eoo_out)
+