add lstm tests

testdata/dnn/onnx/generate_onnx_models.py

@@ -859,6 +859,39 @@ hidden_lstm = HiddenLSTM(features, hidden, num_layers=3, is_bidirectional=True)
 save_data_and_model("hidden_lstm_bi", input, hidden_lstm, version=11, export_params=True)
 
 
+batch = 5
+features = 4
+hidden = 3
+seq_len = 2
+num_layers=1
+bidirectional=True
+
+class LSTM(nn.Module):
+
+    def __init__(self):
+        super(LSTM, self).__init__()
+        self.lstm = nn.LSTM(features, hidden, num_layers, bidirectional=bidirectional)
+        self.h0 = torch.from_numpy(np.ones((num_layers + int(bidirectional), batch, hidden), dtype=np.float32))
+        self.c0 = torch.from_numpy(np.ones((num_layers + int(bidirectional), batch, hidden), dtype=np.float32))
+
+    def forward(self, x):
+        a, (b, c) = self.lstm(x, (self.h0, self.c0))
+        if bidirectional:
+            return torch.cat((a, b, c), dim=2)
+        else:
+            return torch.cat((a, b, c), dim=0)
+
+
+input_ = Variable(torch.randn(seq_len, batch, features))
+lstm = LSTM()
+save_data_and_model("lstm_cell_bidirectional", input_, lstm, export_params=True)
+
+bidirectional = False
+input_ = Variable(torch.randn(seq_len, batch, features))
+lstm = LSTM()
+save_data_and_model("lstm_cell_forward", input_, lstm, export_params=True)
+
+
 class MatMul(nn.Module):
     def __init__(self):
         super(MatMul, self).__init__()