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Commit 090f16ec authored by mbencer's avatar mbencer
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Fixed problem with NormalizeL2

parent 4954b8d5
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Showing with 61 additions and 61 deletions
src/ngraph/op/fused/normalize_l2.cpp
View file @ 090f16ec
......@@ -48,16 +48,6 @@ void op::NormalizeL2::pre_validate_and_infer_types()
NODE_VALIDATION_CHECK(this, data_pshape.is_static(), "Input data must be static.");
NODE_VALIDATION_CHECK(this, axes_pshape.is_static(), "Input axes must be static.");
const Shape data_shape{data_pshape.to_shape()};
// Input data must be 2, 3 or 4D tensor.
NODE_VALIDATION_CHECK(this,
(data_shape.size() >= 2 && data_shape.size() <= 4),
"Input tensor rank must be 2, 3 or 4 dimensional (actual input "
"shape: ",
data_shape,
").");
NODE_VALIDATION_CHECK(this,
static_cast<size_t>(axes_pshape.rank()) == 1,
"Input axes must have rank equals 1 (axes shape: ",
......@@ -71,7 +61,7 @@ NodeVector op::NormalizeL2::decompose_op() const
const Shape input_shape{data.get_shape()};
// Reshape to 4D tensor.
if (input_shape.size() != 4)
if (input_shape.size() < 4)
{
Shape data_shape(4 - input_shape.size(), 1);
copy(begin(input_shape), end(input_shape), back_inserter(data_shape));
......@@ -92,12 +82,12 @@ NodeVector op::NormalizeL2::decompose_op() const
auto builder_bias_mode =
(m_eps_mode == EpsMode::MAX) ? builder::BiasMode::MAX : builder::BiasMode::ADD;
Output<Node> norm = builder::l2_norm(data, reduction_axes, m_eps, builder_bias_mode);
norm = make_broadcast_node(norm, data.get_shape(), 0);
norm = numpy_style_broadcast(norm, data.get_shape());
data = data / norm;
// get back original input tensor rank
if (input_shape.size() != 4)
if (input_shape.size() < 4)
{
data = builder::reshape(data, input_shape);
}
......
test/backend/fused_op.in.cpp
View file @ 090f16ec
......@@ -691,11 +691,11 @@ NGRAPH_TEST(${BACKEND_NAME}, normalize_across_empty_axes_input)
test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 1);
}
NGRAPH_TEST(${BACKEND_NAME}, normalize_across_hw_4d)
NGRAPH_TEST(${BACKEND_NAME}, normalize_across_h_4d)
{
Shape data_shape{1, 2, 3, 4};
auto data = make_shared<op::Parameter>(element::f32, data_shape);
const auto axes = make_shared<op::Constant>(element::u64, Shape{2}, vector<int64_t>{2, 3});
const auto axes = make_shared<op::Constant>(element::u64, Shape{1}, vector<int64_t>{1});
float eps{1e-6f};
auto eps_mode = op::EpsMode::ADD;
......@@ -710,10 +710,62 @@ NGRAPH_TEST(${BACKEND_NAME}, normalize_across_hw_4d)
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
data_shape, {0.03922323f, 0.07844646f, 0.11766968f, 0.15689291f, 0.19611613f, 0.23533936f,
0.2745626f, 0.31378582f, 0.35300905f, 0.39223227f, 0.43145549f, 0.47067872f,
0.1994109f, 0.2147502f, 0.2300895f, 0.2454288f, 0.26076809f, 0.2761074f,
0.29144669f, 0.306786f, 0.32212529f, 0.3374646f, 0.35280389f, 0.3681432f});
data_shape, {0.0766965f, 0.14142136f, 0.19611613f, 0.24253564f, 0.28216633f, 0.31622776f,
0.34570536f, 0.37139067f, 0.39391932f, 0.41380295f, 0.43145549f, 0.44721359f,
0.99705452f, 0.98994946f, 0.98058069f, 0.97014254f, 0.95936549f, 0.94868332f,
0.93834311f, 0.92847669f, 0.91914505f, 0.91036648f, 0.90213418f, 0.89442718f});
test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 1);
}
NGRAPH_TEST(${BACKEND_NAME}, normalize_across_1axis_5d)
{
Shape data_shape{1, 2, 2, 2, 3};
auto data = make_shared<op::Parameter>(element::f32, data_shape);
const auto axes = make_shared<op::Constant>(element::u64, Shape{1}, vector<int64_t>{1});
float eps{1e-6f};
auto eps_mode = op::EpsMode::ADD;
auto normalize = make_shared<op::NormalizeL2>(data, axes, eps, eps_mode);
auto function = make_shared<Function>(NodeVector{normalize}, ParameterVector{data});
auto test_case = test::NgraphTestCase(function, "${BACKEND_NAME}");
vector<float> input_data(shape_size(data_shape));
iota(begin(input_data), end(input_data), 1);
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
data_shape, {0.0766965f, 0.14142136f, 0.19611613f, 0.24253564f, 0.28216633f, 0.31622776f,
0.34570536f, 0.37139067f, 0.39391932f, 0.41380295f, 0.43145549f, 0.44721359f,
0.99705452f, 0.98994946f, 0.98058069f, 0.97014254f, 0.95936549f, 0.94868332f,
0.93834311f, 0.92847669f, 0.91914505f, 0.91036648f, 0.90213418f, 0.89442718f});
test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 1);
}
NGRAPH_TEST(${BACKEND_NAME}, normalize_across_123axes_5d)
{
Shape data_shape{1, 2, 2, 2, 3};
auto data = make_shared<op::Parameter>(element::f32, data_shape);
const auto axes = make_shared<op::Constant>(element::u64, Shape{3}, vector<int64_t>{1, 2, 3});
float eps{1e-6f};
auto eps_mode = op::EpsMode::ADD;
auto normalize = make_shared<op::NormalizeL2>(data, axes, eps, eps_mode);
auto function = make_shared<Function>(NodeVector{normalize}, ParameterVector{data});
auto test_case = test::NgraphTestCase(function, "${BACKEND_NAME}");
vector<float> input_data(shape_size(data_shape));
iota(begin(input_data), end(input_data), 1);
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
data_shape, {0.02638899f, 0.04956816f, 0.070014f, 0.10555596f, 0.1239204f, 0.140028f,
0.18472293f, 0.19827265f, 0.210042f, 0.26388991f, 0.27262488f, 0.280056f,
0.34305686f, 0.34697714f, 0.35007f, 0.42222384f, 0.42132938f, 0.420084f,
0.50139081f, 0.49568161f, 0.49009803f, 0.58055776f, 0.57003385f, 0.560112f});
test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 1);
}
......
test/type_prop/normalize.cpp
View file @ 090f16ec
......@@ -20,48 +20,6 @@
using namespace std;
using namespace ngraph;
TEST(type_prop, normalize_invalid_input_tensor_rank)
{
Shape data_shape{1, 2, 3, 4, 5};
auto data = make_shared<op::Parameter>(element::f32, data_shape);
auto axes = make_shared<op::Parameter>(element::u64, Shape{1, 2});
float eps{1e-6f};
auto eps_mode = op::EpsMode::ADD;
try
{
auto normalize = make_shared<op::NormalizeL2>(data, axes, eps, eps_mode);
// Should have thrown, so fail if it didn't
FAIL() << "Invalid input tensor rank.";
}
catch (const NodeValidationFailure& error)
{
EXPECT_HAS_SUBSTRING(error.what(),
std::string("Input tensor rank must be 2, 3 or 4 dimensional"));
}
catch (...)
{
FAIL() << "Deduced type check failed for unexpected reason";
}
data = make_shared<op::Parameter>(element::f32, Shape{2});
try
{
auto normalize = make_shared<op::NormalizeL2>(data, axes, eps, eps_mode);
// Should have thrown, so fail if it didn't
FAIL() << "Invalid input tensor rank.";
}
catch (const NodeValidationFailure& error)
{
EXPECT_HAS_SUBSTRING(error.what(),
std::string("Input tensor rank must be 2, 3 or 4 dimensional"));
}
catch (...)
{
FAIL() << "Deduced type check failed for unexpected reason";
}
}
TEST(type_prop, normalize_invalid_axes_rank)
{
......
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