def test_dpcl_separator_forward_backward_complex(input_dim, rnn_type, layer,
unit, dropout, num_spk, emb_D,
nonlinear):
model = DPCLSeparator(
input_dim=input_dim,
rnn_type=rnn_type,
layer=layer,
unit=unit,
dropout=dropout,
num_spk=num_spk,
emb_D=emb_D,
nonlinear=nonlinear,
)
model.train()
real = torch.rand(2, 10, input_dim)
imag = torch.rand(2, 10, input_dim)
x = ComplexTensor(real, imag)
x_lens = torch.tensor([10, 8], dtype=torch.long)
masked, flens, others = model(x, ilens=x_lens)
assert "tf_embedding" in others
others["tf_embedding"].abs().mean().backward()
def test_dpcl_separator_invalid_type():
with pytest.raises(ValueError):
DPCLSeparator(
input_dim=10,
rnn_type="rnn",
layer=2,
unit=10,
dropout=0.1,
num_spk=2,
emb_D=40,
nonlinear="fff",
)
def test_dpcl_separator_output():
x = torch.rand(2, 10, 10)
x_lens = torch.tensor([10, 8], dtype=torch.long)
for num_spk in range(1, 4):
model = DPCLSeparator(
input_dim=10,
rnn_type="rnn",
layer=2,
unit=10,
dropout=0.1,
num_spk=num_spk,
emb_D=40,
nonlinear="relu",
)
model.eval()
specs, _, others = model(x, x_lens)
assert isinstance(specs, list)
assert isinstance(others, dict)
assert len(specs) == num_spk, len(specs)
for n in range(num_spk):
assert "tf_embedding" in others