cc9194708510631c93528e8337a4526b03dbdb7d,onnx_tf/handlers/backend/lstm.py,LSTM,_custom_getter,#Any#Any#Any#Any#Any#Any#,45

Before Change

        w_b = tf.transpose(tf.concat([w_b_i, w_b_c, w_b_f, w_b_o], 0))
        r_b = tf.transpose(tf.concat([r_b_i, r_b_c, r_b_f, r_b_o], 0))
        return tf.Variable(tf.add(w_b, r_b))
      return getter(name, *args, **kwargs)
    // Only use_peepholes is True,
    // will try to get w_f_diag, w_i_diag, w_o_diag
    // onnx P[iof]
    if names[-1] in ["w_f_diag", "w_i_diag", "w_o_diag"]:

After Change

      return weight_variable

    if names[-1] == "bias":
      bias_variable = tensor_dict[get_variable_name(node, cls.bias_var_name)]
      if len(node.inputs) >= 4:
        // onnx Wb[iofc], Rb[iofc]
        if is_bidirectional:
          b = tf.split(tensor_dict[node.inputs[3]], 2)[index]
        else:
          b = tensor_dict[node.inputs[3]]
        w_b, r_b = tf.split(tf.squeeze(b), 2)
        w_b_i, w_b_o, w_b_f, w_b_c = tf.split(w_b, 4)
        r_b_i, r_b_o, r_b_f, r_b_c = tf.split(r_b, 4)
        w_b = tf.transpose(tf.concat([w_b_i, w_b_c, w_b_f, w_b_o], 0))
        r_b = tf.transpose(tf.concat([r_b_i, r_b_c, r_b_f, r_b_o], 0))
        bias_variable.assign(tf.add(w_b, r_b))

      return bias_variable

    // Only use_peepholes is True,
    // will try to get w_f_diag, w_i_diag, w_o_diag
    // onnx P[iof]
    if names[-1] in ["w_f_diag", "w_i_diag", "w_o_diag"]:
      if is_bidirectional:
        p = tf.split(tensor_dict[node.inputs[7]], 2)[index]
      else:
        p = tensor_dict[node.inputs[7]]
      if names[-1] == "w_f_diag":
        w_f_variable = tensor_dict[get_variable_name(
            node, cls.peephole_weight_forget_var_name)]
        w_f_variable.assign(tf.split(p, 3, axis=1)[2])
        return w_f_variable
      if names[-1] == "w_i_diag":
        w_i_variable = tensor_dict[get_variable_name(
            node, cls.peephole_weight_input_var_name)]
        w_i_variable.assign(tf.split(p, 3, axis=1)[0])

Instances