Function TEST_CASE

tests/validation/CL/LSTMLayerQuantized.cpp:74–210

TEST_SUITE(IntegrationTestCase)
TEST_SUITE(MultSmallerEq1)
TEST_CASE(RunSmall, framework::DatasetMode::PRECOMMIT)
{
    const int batch_size  = 2;
    const int input_size  = 2;
    const int output_size = 4;

    QuantizationInfo qasymm(1.f / 128.f, 128);
    QuantizationInfo qweights(1.f / 128.f, 128);
    QuantizationInfo qsymm_3(8.f / 32768.f, 0);
    QuantizationInfo qsymm_4(16.f / 32768.f, 0);

    TensorShape input_shape{ input_size, batch_size };
    TensorShape input_weights_shape{ input_size, output_size };
    TensorShape recurrent_weights_shape{ output_size, output_size };
    TensorShape output_shape{ output_size, batch_size};
    TensorShape bias_shape{ output_size };

    auto input_to_input_weights      = create_tensor<CLTensor>(input_weights_shape, DataType::QASYMM8, 1, qweights);
    auto input_to_forget_weights     = create_tensor<CLTensor>(input_weights_shape, DataType::QASYMM8, 1, qweights);
    auto input_to_cell_weights       = create_tensor<CLTensor>(input_weights_shape, DataType::QASYMM8, 1, qweights);
    auto input_to_output_weights     = create_tensor<CLTensor>(input_weights_shape, DataType::QASYMM8, 1, qweights);
    auto recurrent_to_input_weights  = create_tensor<CLTensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights);
    auto recurrent_to_forget_weights = create_tensor<CLTensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights);
    auto recurrent_to_cell_weights   = create_tensor<CLTensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights);
    auto recurrent_to_output_weights = create_tensor<CLTensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights);
    auto input_gate_bias             = create_tensor<CLTensor>(bias_shape, DataType::S32);
    auto forget_gate_bias            = create_tensor<CLTensor>(bias_shape, DataType::S32);
    auto cell_gate_bias              = create_tensor<CLTensor>(bias_shape, DataType::S32);
    auto output_gate_bias            = create_tensor<CLTensor>(bias_shape, DataType::S32);

    // LSTM input
    auto input = create_tensor<CLTensor>(input_shape, DataType::QASYMM8, 1, qasymm);

    // LSTM output state
    auto output_state = create_tensor<CLTensor>(output_shape, DataType::QASYMM8, 1, qasymm);

    // LSTM cell state
    auto cell_state = create_tensor<CLTensor>(output_shape, DataType::QSYMM16, 1, qsymm_4);

    CLLSTMLayerQuantized lstmq;

    lstmq.configure(&input, &input_to_input_weights, &input_to_forget_weights, &input_to_cell_weights, &input_to_output_weights,
                    &recurrent_to_input_weights, &recurrent_to_forget_weights, &recurrent_to_cell_weights, &recurrent_to_output_weights,
                    &input_gate_bias, &forget_gate_bias, &cell_gate_bias, &output_gate_bias, &cell_state, &output_state, &cell_state, &output_state);

    input.allocator()->allocate();
    input_to_input_weights.allocator()->allocate();
    input_to_forget_weights.allocator()->allocate();
    input_to_cell_weights.allocator()->allocate();
    input_to_output_weights.allocator()->allocate();
    recurrent_to_input_weights.allocator()->allocate();
    recurrent_to_forget_weights.allocator()->allocate();
    recurrent_to_cell_weights.allocator()->allocate();
    recurrent_to_output_weights.allocator()->allocate();
    input_gate_bias.allocator()->allocate();
    forget_gate_bias.allocator()->allocate();
    cell_gate_bias.allocator()->allocate();
    output_gate_bias.allocator()->allocate();