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import numpy as np
from wavegru_mod import WaveGRU
def extract_weight_mask(net):
data = {}
data["embed_weight"] = net.embed.weight
data["gru_h_zrh_weight"] = net.rnn.h_zrh_fc.weight
data["gru_h_zrh_mask"] = net.gru_pruner.h_zrh_fc_mask
data["gru_h_zrh_bias"] = net.rnn.h_zrh_fc.bias
data["o1_weight"] = net.o1.weight
data["o1_mask"] = net.o1_pruner.mask
data["o1_bias"] = net.o1.bias
data["o2_weight"] = net.o2.weight
data["o2_mask"] = net.o2_pruner.mask
data["o2_bias"] = net.o2.bias
return data
def load_wavegru_cpp(data, repeat_factor):
"""load wavegru weight to cpp object"""
embed = data["embed_weight"]
rnn_dim = data["gru_h_zrh_bias"].shape[0] // 3
net = WaveGRU(rnn_dim, repeat_factor)
net.load_embed(embed)
m = np.ascontiguousarray(data["gru_h_zrh_weight"].T)
mask = np.ascontiguousarray(data["gru_h_zrh_mask"].T)
b = data["gru_h_zrh_bias"]
o1 = np.ascontiguousarray(data["o1_weight"].T)
masko1 = np.ascontiguousarray(data["o1_mask"].T)
o1b = data["o1_bias"]
o2 = np.ascontiguousarray(data["o2_weight"].T)
masko2 = np.ascontiguousarray(data["o2_mask"].T)
o2b = data["o2_bias"]
net.load_weights(m, mask, b, o1, masko1, o1b, o2, masko2, o2b)
return net
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