def _sum_deterministic_policy(self, model_names, path):
     net = core.Net('DeterministicPolicy')
     C2.set_net(net)
     output = 'ActionProbabilities'
     workspace.FeedBlob(output, np.array([1.0]))
     model_outputs = []
     for model in model_names:
         model_output = '{}_Output'.format(model)
         workspace.FeedBlob(model_output, np.array([1.0], dtype=np.float32))
         model_outputs.append(model_output)
     max_action = C2.FlattenToVec(
         C2.ArgMax(C2.Transpose(C2.Sum(*model_outputs)))
     )
     one_blob = C2.NextBlob('one')
     workspace.FeedBlob(one_blob, np.array([1.0], dtype=np.float32))
     C2.net().SparseToDense(
         [
             max_action,
             one_blob,
             model_outputs[0],
         ],
         [output],
     )
     meta = PredictorExportMeta(
         net,
         [one_blob],
         model_outputs,
         [output],
     )
     save_to_db('minidb', path, meta)
def save_sum_deterministic_policy(model_names, path, db_type):
    net = core.Net("DeterministicPolicy")
    C2.set_net(net)
    output = "ActionProbabilities"
    workspace.FeedBlob(output, np.array([1.0]))
    model_outputs = []
    for model in model_names:
        model_output = "{}_Output".format(model)
        workspace.FeedBlob(model_output, np.array([[1.0]], dtype=np.float32))
        model_outputs.append(model_output)
    max_action = C2.FlattenToVec(C2.ArgMax(C2.Transpose(C2.Sum(*model_outputs))))
    one_blob = C2.NextBlob("one")
    workspace.FeedBlob(one_blob, np.array([1.0], dtype=np.float32))
    C2.net().SparseToDense([max_action, one_blob, model_outputs[0]], [output])
    meta = PredictorExportMeta(net, [one_blob], model_outputs, [output])
    save_to_db(db_type, path, meta)