Skip to content

N
ngraph
Commit 5136e1b5 authored by Adam Rogowiec's avatar Adam Rogowiec
Browse files
Options

Unit tests for GRUCell.

parent da4e9a0e
Hide whitespace changes
Inline Side-by-side
Showing with 311 additions and 0 deletions
test/backend_fused_op.in.cpp
View file @ 5136e1b5
......@@ -1421,3 +1421,231 @@ NGRAPH_TEST(${BACKEND_NAME}, rnn_cell_activation_function)
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, gru_cell_bias_clip)
{
const size_t batch_size = 2;
const size_t input_size = 3;
const size_t hidden_size = 3;
const size_t gates_count = 3;
float clip = 2.88f;
bool linear_before_reset = false;
const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
const auto W =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
const auto R =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto B = make_shared<op::Parameter>(element::f32, Shape{2 * gates_count * hidden_size});
const auto gru_cell = make_shared<op::GRUCell>(X,
W,
R,
H_t,
hidden_size,
B,
vector<string>{"sigmoid", "tanh"},
vector<float>{},
vector<float>{},
clip,
linear_before_reset);
auto function = make_shared<Function>(gru_cell, ParameterVector{X, W, R, H_t, B});
auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");
// X
test_case.add_input<float>(
{0.52421564f, 0.78845507f, 0.9372873f, 0.59783894f, 0.18278378f, 0.2084126f});
// W
test_case.add_input<float>(
{0.5815369f, 0.16559383f, 0.08464007f, 0.843122f, 0.73968244f, 0.11359601f, 0.8295078f,
0.9240567f, 0.10007995f, 0.20573162f, 0.09002485f, 0.2839569f, 0.3096991f, 0.5638341f,
0.5787327f, 0.84552664f, 0.16263747f, 0.7243242f, 0.8049057f, 0.43966424f, 0.46294412f,
0.9833361f, 0.31369713f, 0.1719934f, 0.4937093f, 0.6353004f, 0.77982515f});
// R
test_case.add_input<float>(
{0.16510165f, 0.52435565f, 0.2788478f, 0.99427545f, 0.1623331f, 0.01389796f, 0.99669236f,
0.53901845f, 0.8737506f, 0.9254788f, 0.21172932f, 0.11634306f, 0.40111724f, 0.37497616f,
0.2903471f, 0.6796794f, 0.65131867f, 0.78163475f, 0.12058706f, 0.45591718f, 0.791677f,
0.76497287f, 0.9895242f, 0.7845312f, 0.51267904f, 0.49030215f, 0.08498167f});
// Ht
test_case.add_input<float>(
{0.45738035f, 0.996877f, 0.82882977f, 0.47492632f, 0.88471466f, 0.57833236f});
// B
test_case.add_input<float>({0.8286678f,
0.9153158f,
0.9581612f,
0.6639213f,
0.84239805f,
0.5282445f,
0.14153397f,
0.22404431f,
0.6549655f,
0.9175602f,
0.14958014f,
0.49230585f,
0.63162816f,
0.4161903f,
0.22148274f,
0.50496656f,
0.34798595f,
0.6699164f});
test_case.add_expected_output<float>(
Shape{batch_size, hidden_size},
{0.48588726f, 0.99670005f, 0.83759373f, 0.5023099f, 0.89410484f, 0.60011315f});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, gru_cell_linear_before_reset)
{
const size_t batch_size = 2;
const size_t input_size = 3;
const size_t hidden_size = 3;
const size_t gates_count = 3;
float clip = 2.88f;
bool linear_before_reset = true;
const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
const auto W =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
const auto R =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto B = make_shared<op::Parameter>(element::f32, Shape{2 * gates_count * hidden_size});
const auto gru_cell = make_shared<op::GRUCell>(X,
W,
R,
H_t,
hidden_size,
B,
vector<string>{"sigmoid", "tanh"},
vector<float>{},
vector<float>{},
clip,
linear_before_reset);
auto function = make_shared<Function>(gru_cell, ParameterVector{X, W, R, H_t, B});
auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");
// X
test_case.add_input<float>(
{0.12249453f, 0.6127907f, 0.5001741f, 0.5124603f, 0.04329684f, 0.023834f});
// W
test_case.add_input<float>(
{0.72259396f, 0.11561195f, 0.9457856f, 0.19037509f, 0.6964006f, 0.33459795f, 0.5468904f,
0.85646594f, 0.5101311f, 0.9712257f, 0.3687071f, 0.60280246f, 0.56943774f, 0.7475505f,
0.2490578f, 0.86977345f, 0.85542053f, 0.29660386f, 0.49717373f, 0.7473479f, 0.53454477f,
0.15974349f, 0.5804805f, 0.14303213f, 0.07514781f, 0.5865731f, 0.76409274f});
// R
test_case.add_input<float>(
{0.91382647f, 0.41527033f, 0.28040004f, 0.23601337f, 0.04471736f, 0.03888785f, 0.06308217f,
0.44844428f, 0.29384327f, 0.49037653f, 0.50421673f, 0.7366393f, 0.63143945f, 0.00277612f,
0.37198433f, 0.06966069f, 0.4613444f, 0.10999731f, 0.78273284f, 0.21453214f, 0.10751773f,
0.18332677f, 0.1326976f, 0.9998985f, 0.19263928f, 0.10979804f, 0.52575564f});
// Ht
test_case.add_input<float>(
{0.8598948f, 0.41189128f, 0.72824323f, 0.53940123f, 0.31485787f, 0.04053852f});
// B
test_case.add_input<float>({0.09875853f,
0.37801138f,
0.7729636f,
0.78493553f,
0.5662702f,
0.12406381f,
0.66729516f,
0.7752771f,
0.78819966f,
0.5151927f,
0.708666f,
0.55303884f,
0.03424145f,
0.81109315f,
0.30524766f,
0.6606634f,
0.99040645f,
0.21112025f});
test_case.add_expected_output<float>(
Shape{batch_size, hidden_size},
{0.8709214f, 0.48411977f, 0.74495184f, 0.6074972f, 0.44572943f, 0.1467715f});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, gru_cell_activation_function)
{
const size_t batch_size = 2;
const size_t input_size = 3;
const size_t hidden_size = 3;
const size_t gates_count = 3;
float clip = 2.88f;
bool linear_before_reset = true;
const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
const auto W =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
const auto R =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto B = make_shared<op::Parameter>(element::f32, Shape{2 * gates_count * hidden_size});
const auto gru_cell = make_shared<op::GRUCell>(X,
W,
R,
H_t,
hidden_size,
B,
vector<string>{"hardsigmoid", "hardsigmoid"},
vector<float>{1.8345f, 1.8345f},
vector<float>{3.05f, 3.05f},
clip,
linear_before_reset);
auto function = make_shared<Function>(gru_cell, ParameterVector{X, W, R, H_t, B});
auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");
// X
test_case.add_input<float>(
{0.12249453f, 0.6127907f, 0.5001741f, 0.5124603f, 0.04329684f, 0.023834f});
// W
test_case.add_input<float>(
{0.72259396f, 0.11561195f, 0.9457856f, 0.19037509f, 0.6964006f, 0.33459795f, 0.5468904f,
0.85646594f, 0.5101311f, 0.9712257f, 0.3687071f, 0.60280246f, 0.56943774f, 0.7475505f,
0.2490578f, 0.86977345f, 0.85542053f, 0.29660386f, 0.49717373f, 0.7473479f, 0.53454477f,
0.15974349f, 0.5804805f, 0.14303213f, 0.07514781f, 0.5865731f, 0.76409274f});
// R
test_case.add_input<float>(
{0.91382647f, 0.41527033f, 0.28040004f, 0.23601337f, 0.04471736f, 0.03888785f, 0.06308217f,
0.44844428f, 0.29384327f, 0.49037653f, 0.50421673f, 0.7366393f, 0.63143945f, 0.00277612f,
0.37198433f, 0.06966069f, 0.4613444f, 0.10999731f, 0.78273284f, 0.21453214f, 0.10751773f,
0.18332677f, 0.1326976f, 0.9998985f, 0.19263928f, 0.10979804f, 0.52575564f});
// Ht
test_case.add_input<float>(
{0.8598948f, 0.41189128f, 0.72824323f, 0.53940123f, 0.31485787f, 0.04053852f});
// B
test_case.add_input<float>({0.09875853f,
0.37801138f,
0.7729636f,
0.78493553f,
0.5662702f,
0.12406381f,
0.66729516f,
0.7752771f,
0.78819966f,
0.5151927f,
0.708666f,
0.55303884f,
0.03424145f,
0.81109315f,
0.30524766f,
0.6606634f,
0.99040645f,
0.21112025f});
test_case.add_expected_output<float>(
Shape{batch_size, hidden_size},
{0.8598948f, 0.41189128f, 0.72824323f, 0.53940123f, 0.31485787f, 0.04053852f});
test_case.run();
}
test/type_prop.cpp
View file @ 5136e1b5
......@@ -14775,3 +14775,86 @@ TEST(type_prop, rnn_cell_invalid_input)
EXPECT_HAS_SUBSTRING(error.what(), std::string("Input tensor B must have shape"));
}
}
TEST(type_prop, gru_cell)
{
const size_t batch_size = 2;
const size_t input_size = 3;
const size_t hidden_size = 3;
const size_t gates_count = 3;
const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
const auto W = make_shared<op::Parameter>(element::f32,
Shape{gates_count * hidden_size, input_size});
const auto R = make_shared<op::Parameter>(element::f32,
Shape{gates_count * hidden_size, hidden_size});
const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto gru_cell = make_shared<op::GRUCell>(X, W, R, H_t, hidden_size);
EXPECT_EQ(gru_cell->output(0).get_element_type(), element::f32);
EXPECT_EQ(gru_cell->output(0).get_shape(), (Shape{batch_size, hidden_size}));
}
TEST(type_prop, gru_cell_invalid_input)
{
const size_t batch_size = 2;
const size_t input_size = 3;
const size_t hidden_size = 3;
const size_t gates_count = 3;
const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
auto R = make_shared<op::Parameter>(element::f32,
Shape{gates_count * hidden_size, hidden_size});
auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
// Invalid W tensor shape.
auto W = make_shared<op::Parameter>(element::f32, Shape{hidden_size, input_size});
try
{
const auto gru_cell = make_shared<op::GRUCell>(X, W, R, H_t, hidden_size);
FAIL() << "GRUCell node was created with invalid data.";
}
catch (const NodeValidationFailure& error)
{
EXPECT_HAS_SUBSTRING(error.what(), std::string("Input tensor W must have shape"));
}
// Invalid R tensor shape.
W = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
R = make_shared<op::Parameter>(element::f32, Shape{hidden_size, 1});
try
{
const auto gru_cell = make_shared<op::GRUCell>(X, W, R, H_t, hidden_size);
FAIL() << "GRUCell node was created with invalid data.";
}
catch (const NodeValidationFailure& error)
{
EXPECT_HAS_SUBSTRING(error.what(), std::string("Input tensor R must have shape"));
}
// Invalid H_t tensor shape.
R = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
H_t = make_shared<op::Parameter>(element::f32, Shape{4, hidden_size});
try
{
const auto gru_cell = make_shared<op::GRUCell>(X, W, R, H_t, hidden_size);
FAIL() << "GRUCell node was created with invalid data.";
}
catch (const NodeValidationFailure& error)
{
EXPECT_HAS_SUBSTRING(error.what(), std::string("Input tensor H_t must have shape"));
}
// Invalid B tensor shape.
H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
auto B = make_shared<op::Parameter>(element::f32, Shape{hidden_size});
try
{
const auto gru_cell = make_shared<op::GRUCell>(X, W, R, H_t, hidden_size, B);
FAIL() << "GRUCell node was created with invalid data.";
}
catch (const NodeValidationFailure& error)
{
EXPECT_HAS_SUBSTRING(error.what(), std::string("Input tensor B must have shape"));
}
}
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment