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Code Issues Projects Releases Packages Wiki Activity Actions 4

Managing to actually use sparse matrices

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Now 2x real-time!
This commit is contained in:
Jean-Marc Valin 2018-11-28 20:20:17 -05:00
parent 4de3e53a73
commit d961d009a0
3 changed files with 14 additions and 4 deletions
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12
dnn/dump_lpcnet.py
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@ -67,11 +67,19 @@ def printSparseVector(f, A, name):
A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))
A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))
printVector(f, diag, name + '_diag')
idx = np.zeros((0,), dtype='int')
for i in range(3*N//16):
pos = idx.shape[0]
idx = np.append(idx, -1)
nb_nonzero = 0
for j in range(N):
W = np.concatenate([W, A[j, i*16:(i+1)*16]])
if np.sum(np.abs(A[j, i*16:(i+1)*16])) > 1e-10:
nb_nonzero = nb_nonzero + 1
idx = np.append(idx, j)
W = np.concatenate([W, A[j, i*16:(i+1)*16]])
idx[pos] = nb_nonzero
printVector(f, W, name)
idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)
#idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)
printVector(f, idx, name + '_idx', dtype='int')
return;