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wip 8x4 sparseness

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This commit is contained in:
Jean-Marc Valin 2020-12-19 19:25:59 -05:00
parent 8e405b44e0
commit cc28518699
3 changed files with 13 additions and 9 deletions
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10
dnn/training_tf2/dump_lpcnet.py
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@ -66,15 +66,17 @@ def printSparseVector(f, A, name):
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):
for i in range(3*N//8):
pos = idx.shape[0]
idx = np.append(idx, -1)
nb_nonzero = 0
for j in range(N):
if np.sum(np.abs(A[j, i*16:(i+1)*16])) > 1e-10:
for j in range(N//4):
block = A[j*4:(j+1)*4, i*8:(i+1)*8]
if np.sum(np.abs(block)) > 1e-10:
nb_nonzero = nb_nonzero + 1
idx = np.append(idx, j)
W = np.concatenate([W, A[j, i*16:(i+1)*16]])
vblock = block.transpose((1,0)).reshape((-1,))
W = np.concatenate([W, vblock])
idx[pos] = nb_nonzero
printVector(f, W, name)
#idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)

8
dnn/training_tf2/lpcnet.py
View file

@ -74,12 +74,14 @@ class Sparsify(Callback):
A = p[:, k*N:(k+1)*N]
A = A - np.diag(np.diag(A))
#A = np.transpose(A, (1, 0))
L=np.reshape(A, (N, N//16, 16))
L=np.reshape(A, (N//4, 4, N//8, 8))
S=np.sum(L*L, axis=-1)
S=np.sum(S, axis=1)
SS=np.sort(np.reshape(S, (-1,)))
thresh = SS[round(N*N//16*(1-density))]
thresh = SS[round(N*N//32*(1-density))]
mask = (S>=thresh).astype('float32');
mask = np.repeat(mask, 16, axis=1)
mask = np.repeat(mask, 4, axis=0)
mask = np.repeat(mask, 8, axis=1)
mask = np.minimum(1, mask + np.diag(np.ones((N,))))
#mask = np.transpose(mask, (1, 0))
p[:, k*N:(k+1)*N] = p[:, k*N:(k+1)*N]*mask

4
dnn/training_tf2/train_lpcnet.py
View file

@ -102,7 +102,7 @@ del pred
del in_exc
# dump models to disk as we go
checkpoint = ModelCheckpoint('lpcnet32c_384_10_G16_{epoch:02d}.h5')
checkpoint = ModelCheckpoint('lpcnet32v_384_10_G16_{epoch:02d}.h5')
#Set this to True to adapt an existing model (e.g. on new data)
adaptation = False
@ -120,5 +120,5 @@ else:
decay = 5e-5
model.compile(optimizer=Adam(lr, decay=decay, beta_2=0.99), loss='sparse_categorical_crossentropy')
model.save_weights('lpcnet32c_384_10_G16_00.h5');
model.save_weights('lpcnet32v_384_10_G16_00.h5');
model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify])