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[ONNX] Use kernel shape in MaxPool to create default strides/dilations/paddings (#3522)

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Mateusz Tabaka 2020-12-10 12:59:47 +01:00 committed by GitHub
parent 312db9a713
commit 3db6b54815
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GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 59 additions and 29 deletions
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19
ngraph/frontend/onnx_import/include/onnx_import/utils/pooling_factory.hpp
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@ -41,13 +41,12 @@ namespace ngraph
/// - AveragePool
/// - MaxPool
///
/// This base class holds all common attributes like srides, dilations,
/// This class holds all common attributes like srides, dilations,
/// paddings, kernel shape and auto_pad type.
///
/// \see GlobalPoolingFactory
class PoolingFactory
{
public:
explicit PoolingFactory(const Node& node);
virtual ~PoolingFactory() = default;
///
@ -63,8 +62,6 @@ namespace ngraph
OutputVector make_max_pool() const;
protected:
explicit PoolingFactory(const Node& node);
Node m_onnx_node;
const OutputVector m_inputs;
Shape m_kernel_shape;
@ -75,18 +72,6 @@ namespace ngraph
ngraph::op::PadType m_auto_pad;
ngraph::op::RoundingType m_rounding_type;
};
///
/// \brief Factory class which generates sub-graphs for ONNX 'local' pooling
/// operators.
/// \note For a 'local' pooling operation, the kernel shape attribute is required
class LocalPoolingFactory : public PoolingFactory
{
public:
explicit LocalPoolingFactory(const Node& node);
virtual ~LocalPoolingFactory() = default;
};
} // namespace pooling
} // namespace onnx_import
} // namespace ngraph

2
ngraph/frontend/onnx_import/src/op/average_pool.cpp
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@ -28,7 +28,7 @@ namespace ngraph
{
OutputVector average_pool(const Node& node)
{
return pooling::LocalPoolingFactory(node).make_avg_pool();
return pooling::PoolingFactory(node).make_avg_pool();
}
} // namespace set_1

2
ngraph/frontend/onnx_import/src/op/max_pool.cpp
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@ -31,7 +31,7 @@ namespace ngraph
{
OutputVector max_pool(const Node& node)
{
auto max_pool = pooling::LocalPoolingFactory(node).make_max_pool();
auto max_pool = pooling::PoolingFactory(node).make_max_pool();
max_pool.emplace_back(std::make_shared<NullNode>()); // Indices (optional)
return max_pool;
}

14
ngraph/frontend/onnx_import/src/utils/pooling_factory.cpp
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@ -31,12 +31,13 @@ namespace ngraph
PoolingFactory::PoolingFactory(const Node& node)
: m_onnx_node{node}
, m_inputs{node.get_ng_inputs()}
, m_strides{convpool::get_strides(node)}
, m_dilations{convpool::get_dilations(node)}
, m_kernel_shape(node.get_attribute_value<std::vector<std::size_t>>("kernel_shape"))
, m_strides{convpool::get_strides(node, m_kernel_shape.size())}
, m_dilations{convpool::get_dilations(node, m_kernel_shape.size())}
, m_auto_pad{convpool::get_auto_pad(node)}
, m_rounding_type{convpool::get_rounding_type(node)}
{
const auto paddings = convpool::get_pads(node);
const auto paddings = convpool::get_pads(node, m_kernel_shape.size());
const CoordinateDiff& padding_above{paddings.second};
const CoordinateDiff& padding_below{paddings.first};
m_padding_below = Shape{std::begin(padding_below), std::end(padding_below)};
@ -67,13 +68,6 @@ namespace ngraph
m_rounding_type,
m_auto_pad)};
}
LocalPoolingFactory::LocalPoolingFactory(const Node& node)
: PoolingFactory(node)
{
// Kernel shape is required
m_kernel_shape = node.get_attribute_value<std::vector<std::size_t>>("kernel_shape");
}
} // namespace pooling
} // namespace onnx_import
} // namespace ngraph

35
ngraph/test/models/onnx/dynamic_shapes/max_pool_dyn_rank_without_default_attrs.prototxt Normal file
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@ -0,0 +1,35 @@
ir_version: 3
producer_name: "nGraph ONNX Importer"
graph {
node {
input: "x"
output: "y"
op_type: "MaxPool"
attribute {
name: "kernel_shape"
ints: 2
ints: 2
type: INTS
}
}
name: "compute_graph"
input {
name: "x"
type {
tensor_type {
elem_type: 1
}
}
}
output {
name: "y"
type {
tensor_type {
elem_type: 1
}
}
}
}
opset_import {
version: 7
}

15
ngraph/test/onnx/onnx_import_dyn_shapes.in.cpp
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@ -1285,3 +1285,18 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_dyn_shapes_reduce_max_dynamic_input_rank_negat
test_case.add_expected_output<float>(Shape{2, 1}, {4, 8});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_max_pool_dyn_rank_without_default_attrs)
{
auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/dynamic_shapes/max_pool_dyn_rank_without_default_attrs.prototxt"));
auto test_case = test::TestCase<TestEngine, TestCaseType::DYNAMIC>(function);
Shape input_shape{1, 1, 4, 4};
std::vector<float> input(shape_size(input_shape));
std::iota(input.begin(), input.end(), 0);
test_case.add_input<float>(input_shape, input);
test_case.add_expected_output<float>(Shape{1, 1, 3, 3}, {5, 6, 7, 9, 10, 11, 13, 14, 15});
test_case.run();
}

1
ngraph/test/runtime/ie/unit_test.manifest
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@ -205,6 +205,7 @@ onnx_dyn_shapes_flatten_neg_axis
onnx_model_range_positive_step
onnx_model_range_negative_step
onnx_dyn_shapes_slice_1_3d_input_21_axes_ends_max
onnx_model_max_pool_dyn_rank_without_default_attrs
# LSTMSequence Layer is not instance of RNNLayer class
# (Constant W, B, R inputs are required)