def test_forward_lstm(input_size, hidden_size, num_layers, bidirectional):
# Test conversion from pytorch implementation
lstm = nn.LSTM(input_size,
hidden_size,
num_layers,
bidirectional=bidirectional)
lstm_man = DistillerLSTM.from_pytorch_impl(lstm)
lstm.eval()
lstm_man.eval()
h = lstm_man.init_hidden(BATCH_SIZE)
x = torch.rand(SEQUENCE_SIZE, BATCH_SIZE, input_size)
out_true = lstm(x, h)
out_pred = lstm_man(x, h)
assert_output(out_true, out_pred)
# Test conversion to pytorch implementation
lstm_man = DistillerLSTM(input_size,
hidden_size,
num_layers,
bidirectional=bidirectional)
lstm = lstm_man.to_pytorch_impl()
lstm.eval()
lstm_man.eval()
h = lstm_man.init_hidden(BATCH_SIZE)
x = torch.rand(SEQUENCE_SIZE, BATCH_SIZE, input_size)
out_true = lstm(x, h)
out_pred = lstm_man(x, h)
assert_output(out_true, out_pred)
def test_packed_sequence(input_size, hidden_size, num_layers, input_lengths,
bidirectional):
lstm = nn.LSTM(input_size,
hidden_size,
num_layers,
bidirectional=bidirectional)
lstm_man = DistillerLSTM.from_pytorch_impl(lstm)
lstm.eval()
lstm_man.eval()
h = lstm_man.init_hidden(BATCH_SIZE)
x = pack_sequence(
[torch.rand(length, input_size) for length in input_lengths])
out_true = lstm(x)
out_pred = lstm_man(x)
assert_output(out_true, out_pred)
def test_conversion():
lc_man = DistillerLSTMCell(3, 5)
lc_pth = lc_man.to_pytorch_impl()
lc_man1 = DistillerLSTMCell.from_pytorch_impl(lc_pth)
assert (lc_man.fc_gate_x.weight == lc_man1.fc_gate_x.weight).all()
assert (lc_man.fc_gate_h.weight == lc_man1.fc_gate_h.weight).all()
l_man = DistillerLSTM(3, 5, 2)
l_pth = l_man.to_pytorch_impl()
l_man1 = DistillerLSTM.from_pytorch_impl(l_pth)
for i in range(l_man.num_layers):
assert (l_man1.cells[i].fc_gate_x.weight ==
l_man.cells[i].fc_gate_x.weight).all()
assert (l_man1.cells[i].fc_gate_h.weight ==
l_man.cells[i].fc_gate_h.weight).all()
assert (l_man1.cells[i].fc_gate_x.bias == l_man.cells[i].fc_gate_x.bias
).all()
assert (l_man1.cells[i].fc_gate_h.bias == l_man.cells[i].fc_gate_h.bias
).all()