def preprocess_data_1(_X, _X_test, _seq_len, _nb_ch):
# 把数据_seq_len毎に切开
# split into sequences
_X = utils.split_in_seqs(_X, _seq_len)
_X_test = utils.split_in_seqs(_X_test, _seq_len)
_X = utils.split_multi_channels(_X, _nb_ch)
_X_test = utils.split_multi_channels(_X_test, _nb_ch)
return _X, _X_test
def preprocess_data(_X, _Y, _X_test, _Y_test, _seq_len, _nb_ch):
# split into sequences
_X = utils.split_in_seqs(_X, _seq_len)
_Y = utils.split_in_seqs(_Y, _seq_len)
_X_test = utils.split_in_seqs(_X_test, _seq_len)
_Y_test = utils.split_in_seqs(_Y_test, _seq_len)
_X = utils.split_multi_channels(_X, _nb_ch)
_X_test = utils.split_multi_channels(_X_test, _nb_ch)
return _X, _Y, _X_test, _Y_test
def preprocess_data(_X, _Y, _X_test, _Y_test, _seq_len, _nb_ch):
# split into sequences
print "before split multi channel: ", _X.shape
_X = split_in_seqs(_X, 1)
#_Y = split_in_seqs(_Y, 1)
_X_test = split_in_seqs(_X_test, 1)
# _Y_test = split_in_seqs(_Y_test, 1)
print "split multi channel: ", _X.shape
_X = split_multi_channels(_X, _nb_ch)
_X_test = split_multi_channels(_X_test, _nb_ch)
# _X = _X.reshape(-1, 1, 40, 2).astype('float32')
# _X_test = _X_test.reshape(-1, 1, 40, 2).astype('float32')
# add extra label
_Y_extra = np.zeros((_Y.shape[0],7))
_Y_extra[:,:-1] = _Y
for i in range( _Y.shape[0] ):
if sum(_Y[i]) > 0:
_Y_extra[i][6] = 0
else:
_Y_extra[i][6] = 1
_Y_test_extra = np.zeros((_Y_test.shape[0],7))
_Y_test_extra[:,:-1] = _Y_test
for i in range( _Y_test.shape[0] ):
if sum(_Y_test[i]) > 0:
_Y_test_extra[i][6] = 0
else:
_Y_test_extra[i][6] = 1
# np.apply_along_axis( addLabel, axis=1, arr=b )
return _X, _Y_extra, _X_test, _Y_test_extra
for file in filename:
print('count = ', count)
mix = utils.get_input(dir_mix + file)
s1 = utils.get_input(dir_s1 + file)
s2 = utils.get_input(dir_s2 + file)
spec0, spec_mix, VAD = utils.get_features(mix)
norm_spec_mix = norm_fea(spec_mix)
_, spec_s1, _ = utils.get_features(s1)
_, spec_s2, _ = utils.get_features(s2)
mask_1 = spec_s1 > spec_s2
mask_2 = spec_s1 <= spec_s2
splitted_spec_mix = utils.split_in_seqs(norm_spec_mix.T,
norm_spec_mix.shape[1])
splitted_VAD = utils.split_in_seqs(VAD.T,
spec_mix.shape[1]).astype('float32')
#splitted_spec_mix = utils.split_in_seqs(norm_spec_mix.T,200)
splitted_spec_mix_tensor = torch.from_numpy(splitted_spec_mix)
model = DC_net(glob_constant.input_dim, glob_constant.hidden_dim,
splitted_spec_mix_tensor.size(0), glob_constant.Embedding,
4, glob_constant.dropout)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
with torch.no_grad():
hidden = model.init_hidden()
embedding = model(splitted_spec_mix_tensor, hidden)
embeddings = embedding.numpy()
