shithub: opus

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Fix signed-unsigned biases

--- a/dnn/nnet.c

+++ b/dnn/nnet.c

@@ -324,8 +324,13 @@

       zrh[i] = gru->bias[i] + gru_b_condition[i];

 #endif

    sparse_sgemv_accum8x4(zrh, gru->input_weights, 3*N, M, gru->input_weights_idx, input);

+#ifdef USE_SU_BIAS

    for (i=0;i<3*N;i++)

+      recur[i] = gru->subias[3*N + i];

+#else

+   for (i=0;i<3*N;i++)

       recur[i] = gru->bias[3*N + i];

+#endif

    sgemv_accum8x4(recur, gru->recurrent_weights, 3*N, N, stride, state);

    for (i=0;i<2*N;i++)

       zrh[i] += recur[i];

--- a/dnn/training_tf2/dump_lpcnet.py

+++ b/dnn/training_tf2/dump_lpcnet.py

@@ -140,8 +140,8 @@

     qweight = printSparseVector(f, weights[0][:gru_a_size, :], name + '_weights', have_diag=False)

     f.write('#ifdef DOT_PROD\n')

-    qweight = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127)

-    printVector(f, qweight, name + '_recurrent_weights', dotp=True, dtype='qweight')

+    qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127)

+    printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight')

     f.write('#else /*DOT_PROD*/\n')

     printVector(f, weights[1], name + '_recurrent_weights')

     f.write('#endif /*DOT_PROD*/\n')

@@ -149,6 +149,7 @@

     printVector(f, weights[-1], name + '_bias')

     subias = weights[-1].copy()

     subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0)

+    subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0)

     printVector(f, subias, name + '_subias')

     if hasattr(self, 'activation'):

         activation = self.activation.__name__.upper()

-- 

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