Enable importing TinyYOLOv2 (#1121)
* Fixed bugs in TinyYOLOv2 ops * Added tests * styles applied * styles applied * code review remarks introduced * code review remarks (unit tests added)
This commit is contained in:
Mateusz Bencer
GitHub
parent
95677afe29
commit
f4b76d4e5e
@ -62,6 +62,32 @@ void op::v1::Convolution::validate_and_infer_types()
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const PartialShape& filters_shape = get_input_partial_shape(1);
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element::Type filters_et = get_input_element_type(1);
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PartialShape result_shape = PartialShape::dynamic();
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if (data_batch_shape.rank().is_static())
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{
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result_shape =
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std::vector<Dimension>(data_batch_shape.rank().get_length(), Dimension::dynamic());
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if (data_batch_shape.rank().get_length() > 1)
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{
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result_shape[0] = data_batch_shape[0]; // batch size
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}
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}
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if (filters_shape.rank().is_static() && filters_shape.rank().get_length() > 1)
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{
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result_shape[1] = filters_shape[0]; // filter channel size
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}
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element::Type result_et;
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NODE_VALIDATION_CHECK(
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this,
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element::Type::merge(result_et, data_batch_et, filters_et),
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"Element types for data batch and filters do not match (data batch element type: ",
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data_batch_et,
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", filters element type: ",
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filters_et,
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").");
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if (m_strides.size() == 0)
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{
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m_strides = conv_default_strides(this, data_batch_shape, filters_shape);
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@ -84,34 +110,28 @@ void op::v1::Convolution::validate_and_infer_types()
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if (m_auto_pad == PadType::SAME_UPPER || m_auto_pad == PadType::SAME_LOWER)
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{
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if (data_batch_shape.is_static() && filters_shape.is_static())
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bool auto_padding_applied = false;
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if (filters_shape.is_static())
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{
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m_pads_begin.clear();
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m_pads_end.clear();
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auto filter_shape = filters_shape.to_shape();
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filter_shape.erase(filter_shape.begin(), filter_shape.begin() + 2); // Remove {O,I}
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infer_auto_padding(data_batch_shape.to_shape(),
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filter_shape,
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m_strides,
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m_dilations,
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m_auto_pad,
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m_pads_end,
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m_pads_begin);
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auto_padding_applied = try_apply_auto_padding(data_batch_shape,
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filter_shape,
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m_strides,
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m_dilations,
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m_auto_pad,
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m_pads_end,
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m_pads_begin);
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}
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if (!auto_padding_applied)
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{
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set_output_type(0, result_et, result_shape);
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return;
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}
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}
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element::Type result_et;
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PartialShape result_shape;
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NODE_VALIDATION_CHECK(
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this,
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element::Type::merge(result_et, data_batch_et, filters_et),
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"Element types for data batch and filters do not match (data batch element type: ",
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data_batch_et,
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", filters element type: ",
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filters_et,
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").");
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result_shape = infer_convolution_forward(this,
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data_batch_shape,
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Strides(m_strides.size(), 1), // dummy data dilations
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@ -130,26 +130,26 @@ void op::v0::MaxPool::update_auto_padding(const PartialShape& in_shape,
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}
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}
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void op::v1::MaxPool::update_auto_padding(const PartialShape& in_shape,
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bool op::v1::MaxPool::update_auto_padding(const PartialShape& in_shape,
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Shape& new_pads_end,
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Shape& new_pads_begin)
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{
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bool update_auto_padding_succeed = true;
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if (m_auto_pad == PadType::SAME_UPPER || m_auto_pad == PadType::SAME_LOWER)
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{
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if (in_shape.is_static())
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{
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CoordinateDiff pads_end, pads_begin;
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infer_auto_padding(in_shape.to_shape(),
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m_kernel,
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m_strides,
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Strides(m_kernel.size(), 1), // No dilation
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m_auto_pad,
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pads_end,
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pads_begin);
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new_pads_end = Shape(pads_end.begin(), pads_end.end());
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new_pads_begin = Shape(pads_begin.begin(), pads_begin.end());
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}
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CoordinateDiff pads_end, pads_begin;
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update_auto_padding_succeed =
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try_apply_auto_padding(in_shape,
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m_kernel,
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m_strides,
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Strides(m_kernel.size(), 1), // No dilation
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m_auto_pad,
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pads_end,
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pads_begin);
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new_pads_end = Shape(pads_end.begin(), pads_end.end());
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new_pads_begin = Shape(pads_begin.begin(), pads_begin.end());
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}
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return update_auto_padding_succeed;
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}
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op::v0::MaxPool::MaxPool(const Output<Node>& arg,
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@ -240,8 +240,22 @@ void op::v1::MaxPool::validate_and_infer_types()
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}
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const PartialShape& arg_shape = get_input_partial_shape(0);
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auto output_shape = PartialShape::dynamic();
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if (arg_shape.rank().is_static())
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{
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output_shape = std::vector<Dimension>(arg_shape.rank().get_length(), Dimension::dynamic());
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if (arg_shape.rank().get_length() > 1)
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{
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output_shape[0] = arg_shape[0]; // batch size
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}
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if (arg_shape.rank().get_length() > 2)
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{
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output_shape[1] = arg_shape[1]; // channel size
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}
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}
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update_auto_padding(arg_shape, m_pads_end, m_pads_begin);
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const bool update_auto_padding_succeed =
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update_auto_padding(arg_shape, m_pads_end, m_pads_begin);
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// infer_batched_forward_pooling wants CoordinateDiffs for these, while the pooling ops for
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// now still take Shape (no negative padding).
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@ -250,14 +264,16 @@ void op::v1::MaxPool::validate_and_infer_types()
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set_output_type(0,
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get_input_element_type(0),
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infer_batched_pooling_forward(this,
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arg_shape,
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pads_begin,
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pads_end,
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m_kernel,
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m_strides,
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true,
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m_rounding_type == op::RoundingType::CEIL));
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update_auto_padding_succeed
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? infer_batched_pooling_forward(this,
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arg_shape,
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pads_begin,
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pads_end,
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m_kernel,
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m_strides,
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true,
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m_rounding_type == op::RoundingType::CEIL)
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: output_shape);
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}
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shared_ptr<Node> op::v1::MaxPool::clone_with_new_inputs(const OutputVector& new_args) const
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@ -240,7 +240,7 @@ namespace ngraph
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op::RoundingType m_rounding_type{op::RoundingType::FLOOR};
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private:
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void update_auto_padding(const PartialShape& in_shape,
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bool update_auto_padding(const PartialShape& in_shape,
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Shape& new_pads_end,
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Shape& new_pads_begin);
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};
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@ -608,11 +608,45 @@ void ngraph::infer_auto_padding(const Shape& image_shape,
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const op::PadType pad_type,
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CoordinateDiff& padding_above,
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CoordinateDiff& padding_below)
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{
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const auto image_dims = std::vector<Dimension>(std::begin(image_shape), std::end(image_shape));
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// because image_shape is fully known result of try_apply_infer_auto_padding is ignored
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try_apply_auto_padding(image_dims,
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filter_shape,
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filter_strides,
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filter_dilations,
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pad_type,
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padding_above,
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padding_below);
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}
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bool ngraph::try_apply_auto_padding(const PartialShape& image_shape,
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const Shape& filter_shape,
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const Strides& filter_strides,
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const Strides& filter_dilations,
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const op::PadType pad_type,
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CoordinateDiff& padding_above,
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CoordinateDiff& padding_below)
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{
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NGRAPH_CHECK(pad_type == op::PadType::SAME_UPPER || pad_type == op::PadType::SAME_LOWER);
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if (image_shape.rank().is_dynamic())
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{
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return false;
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}
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const auto image_dims = static_cast<std::vector<Dimension>>(image_shape);
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const bool are_spatial_dims_static =
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std::all_of(std::begin(image_dims) + 2, std::end(image_dims), [](const Dimension& dim) {
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return dim.is_static();
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});
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if (!are_spatial_dims_static)
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{
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return false;
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}
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for (size_t i = 0; i < static_cast<size_t>(filter_shape.size()); i++)
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{
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int64_t image_size = static_cast<int64_t>(image_shape[i + 2]);
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int64_t image_size = static_cast<int64_t>(image_dims[i + 2]);
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int64_t filter_size = (static_cast<int64_t>(filter_shape[i]) - 1) * filter_dilations[i] + 1;
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int64_t filter_stride = static_cast<int64_t>(filter_strides[i]);
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auto output_size = (image_size + filter_stride - 1) / filter_stride;
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@ -624,6 +658,7 @@ void ngraph::infer_auto_padding(const Shape& image_shape,
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padding_below.push_back(pad_type == op::PadType::SAME_UPPER ? padding_lhs : padding_rhs);
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padding_above.push_back(pad_type == op::PadType::SAME_UPPER ? padding_rhs : padding_lhs);
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}
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return true;
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}
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PartialShape ngraph::infer_slice_shape(const Node* node,
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@ -89,6 +89,29 @@ namespace ngraph
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const PartialShape& gamma_shape,
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const PartialShape& beta_shape);
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/// \brief Apply auto padding to padding_above and padding_below inputs
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/// if all needed informations are known.
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///
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/// \param image_shape The shape of input image.
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/// \param filter_shape The shape of filter input.
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/// \param filter_strides The strides of applied padding.
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/// \param filter_dilations The dilations of applied padding.
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/// \param pad_type The type of padding. Auto padding is applied only
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/// for SAME_UPPER and SAME_LOWER mode.
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/// \param padding_above The beginning of padding shape.
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/// \param end The beginning of padding shape.
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///
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/// \return true if auto padding was applied successfully (all needed informations such as
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/// spatial dims are known), false otherwise.
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NGRAPH_API
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bool try_apply_auto_padding(const PartialShape& image_shape,
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const Shape& filter_shape,
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const Strides& filter_strides,
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const Strides& filter_dilations,
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const op::PadType pad_type,
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CoordinateDiff& padding_above,
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CoordinateDiff& padding_below);
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NGRAPH_API
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void infer_auto_padding(const Shape& image_shape,
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const Shape& filter_shape,
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@ -2557,6 +2557,91 @@ TEST(type_prop, conv_v1_partial_rank)
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ASSERT_TRUE(conv->get_output_partial_shape(0).is_dynamic());
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}
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TEST(type_prop, conv_v1_partial_auto_padding_same)
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{
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const PartialShape data_batch_shape{1, 1, 5, 5};
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const PartialShape filters_shape{1, 1, 3, 3};
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Strides strides{1, 1};
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CoordinateDiff pads_begin{0, 0};
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CoordinateDiff pads_end{0, 0};
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Strides dilations{1, 1};
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const auto auto_pad = op::PadType::SAME_LOWER;
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auto data_batch = make_shared<op::Parameter>(element::f32, data_batch_shape);
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auto filters = make_shared<op::Parameter>(element::f32, filters_shape);
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auto conv = make_shared<op::v1::Convolution>(
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data_batch, filters, strides, pads_begin, pads_end, dilations, auto_pad);
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ASSERT_TRUE(conv->get_output_partial_shape(0).same_scheme(PartialShape{1, 1, 5, 5}));
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ASSERT_EQ(conv->get_pads_begin(), (CoordinateDiff{1, 1}));
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ASSERT_EQ(conv->get_pads_end(), (CoordinateDiff{1, 1}));
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}
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TEST(type_prop, conv_v1_partial_auto_padding_same_nc_dims_dynamic_same_lower)
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{
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const PartialShape data_batch_shape{Dimension::dynamic(), Dimension::dynamic(), 5, 5};
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const PartialShape filters_shape{1, 1, 3, 3};
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Strides strides{1, 1};
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CoordinateDiff pads_begin{0, 0};
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CoordinateDiff pads_end{0, 0};
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Strides dilations{1, 1};
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const auto auto_pad = op::PadType::SAME_LOWER;
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auto data_batch = make_shared<op::Parameter>(element::f32, data_batch_shape);
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auto filters = make_shared<op::Parameter>(element::f32, filters_shape);
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auto conv = make_shared<op::v1::Convolution>(
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data_batch, filters, strides, pads_begin, pads_end, dilations, auto_pad);
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ASSERT_TRUE(conv->get_output_partial_shape(0).same_scheme({Dimension::dynamic(), 1, 5, 5}));
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ASSERT_EQ(conv->get_pads_begin(), (CoordinateDiff{1, 1}));
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ASSERT_EQ(conv->get_pads_end(), (CoordinateDiff{1, 1}));
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}
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TEST(type_prop, conv_v1_partial_auto_padding_same_nc_dims_dynamic_same_upper)
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{
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const PartialShape data_batch_shape{Dimension::dynamic(), Dimension::dynamic(), 5, 5};
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const PartialShape filters_shape{1, 1, 2, 2};
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Strides strides{1, 1};
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CoordinateDiff pads_begin{0, 0};
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CoordinateDiff pads_end{0, 0};
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Strides dilations{1, 1};
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const auto auto_pad = op::PadType::SAME_UPPER;
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auto data_batch = make_shared<op::Parameter>(element::f32, data_batch_shape);
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auto filters = make_shared<op::Parameter>(element::f32, filters_shape);
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auto conv = make_shared<op::v1::Convolution>(
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data_batch, filters, strides, pads_begin, pads_end, dilations, auto_pad);
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ASSERT_TRUE(conv->get_output_partial_shape(0).same_scheme({Dimension::dynamic(), 1, 5, 5}));
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ASSERT_EQ(conv->get_pads_begin(), (CoordinateDiff{0, 0}));
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ASSERT_EQ(conv->get_pads_end(), (CoordinateDiff{1, 1}));
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}
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TEST(type_prop, conv_v1_partial_auto_padding_same_spatial_dims_dynamic)
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{
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const PartialShape data_batch_shape{1, 1, Dimension::dynamic(), 5};
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const PartialShape filters_shape{1, 1, 3, 3};
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Strides strides{1, 1};
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CoordinateDiff pads_begin{0, 0};
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CoordinateDiff pads_end{0, 0};
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Strides dilations{1, 1};
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const auto auto_pad = op::PadType::SAME_LOWER;
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auto data_batch = make_shared<op::Parameter>(element::f32, data_batch_shape);
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auto filters = make_shared<op::Parameter>(element::f32, filters_shape);
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auto conv = make_shared<op::v1::Convolution>(
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data_batch, filters, strides, pads_begin, pads_end, dilations, auto_pad);
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ASSERT_TRUE(conv->get_output_partial_shape(0).same_scheme(
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{1, 1, Dimension::dynamic(), Dimension::dynamic()}));
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ASSERT_EQ(conv->get_pads_begin(), (CoordinateDiff{}));
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ASSERT_EQ(conv->get_pads_end(), (CoordinateDiff{}));
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}
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TEST(type_prop, deformable_conv_incorrect_group)
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{
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const PartialShape data_batch_shape{1, 3, 96, 96};
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@ -588,3 +588,82 @@ TEST(type_prop, max_pool_partial_rank_static_dynamic_padded_window_not_too_big)
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ASSERT_TRUE(mp->get_output_partial_shape(0).same_scheme(
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PartialShape{5, Dimension::dynamic(), 1, Dimension::dynamic(), 1, 3}));
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}
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TEST(type_prop, max_pool_auto_padding)
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{
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const PartialShape arg_shape{1, 3, 32, 32};
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const Strides strides{1, 1};
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const Shape pads_begin{0, 0};
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const Shape pads_end{0, 0};
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const Shape kernel_shape{2, 2};
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const auto rounding_mode = op::RoundingType::FLOOR;
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const auto auto_pad = op::PadType::SAME_LOWER;
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auto arg = make_shared<op::Parameter>(element::f32, arg_shape);
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auto mp = make_shared<op::v1::MaxPool>(
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arg, strides, pads_begin, pads_end, kernel_shape, rounding_mode, auto_pad);
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ASSERT_TRUE(mp->get_output_partial_shape(0).same_scheme({1, 3, 32, 32}));
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ASSERT_EQ(mp->get_pads_begin(), (Shape{1, 1}));
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ASSERT_EQ(mp->get_pads_end(), (Shape{0, 0}));
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}
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TEST(type_prop, max_pool_auto_padding_nc_dims_dynamic_same_lower)
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{
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const PartialShape arg_shape{Dimension::dynamic(), Dimension::dynamic(), 32, 32};
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const Strides strides{1, 1};
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const Shape pads_begin{0, 0};
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const Shape pads_end{0, 0};
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const Shape kernel_shape{2, 2};
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const auto rounding_mode = op::RoundingType::FLOOR;
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const auto auto_pad = op::PadType::SAME_LOWER;
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auto arg = make_shared<op::Parameter>(element::f32, arg_shape);
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auto mp = make_shared<op::v1::MaxPool>(
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arg, strides, pads_begin, pads_end, kernel_shape, rounding_mode, auto_pad);
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ASSERT_TRUE(mp->get_output_partial_shape(0).same_scheme(
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{Dimension::dynamic(), Dimension::dynamic(), 32, 32}));
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ASSERT_EQ(mp->get_pads_begin(), (Shape{1, 1}));
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ASSERT_EQ(mp->get_pads_end(), (Shape{0, 0}));
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}
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TEST(type_prop, max_pool_auto_padding_nc_dims_dynamic_same_upper)
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{
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const PartialShape arg_shape{Dimension::dynamic(), Dimension::dynamic(), 32, 32};
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const Strides strides{1, 1};
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const Shape pads_begin{0, 0};
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const Shape pads_end{0, 0};
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const Shape kernel_shape{2, 2};
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const auto rounding_mode = op::RoundingType::FLOOR;
|
||||
const auto auto_pad = op::PadType::SAME_UPPER;
|
||||
|
||||
auto arg = make_shared<op::Parameter>(element::f32, arg_shape);
|
||||
auto mp = make_shared<op::v1::MaxPool>(
|
||||
arg, strides, pads_begin, pads_end, kernel_shape, rounding_mode, auto_pad);
|
||||
|
||||
ASSERT_TRUE(mp->get_output_partial_shape(0).same_scheme(
|
||||
{Dimension::dynamic(), Dimension::dynamic(), 32, 32}));
|
||||
ASSERT_EQ(mp->get_pads_begin(), (Shape{0, 0}));
|
||||
ASSERT_EQ(mp->get_pads_end(), (Shape{1, 1}));
|
||||
}
|
||||
|
||||
TEST(type_prop, max_pool_auto_padding_spatial_dims_dynamic)
|
||||
{
|
||||
const PartialShape arg_shape{1, 3, 32, Dimension::dynamic()};
|
||||
const Strides strides{1, 1};
|
||||
const Shape pads_begin{0, 0};
|
||||
const Shape pads_end{0, 0};
|
||||
const Shape kernel_shape{2, 2};
|
||||
const auto rounding_mode = op::RoundingType::FLOOR;
|
||||
const auto auto_pad = op::PadType::SAME_LOWER;
|
||||
|
||||
auto arg = make_shared<op::Parameter>(element::f32, arg_shape);
|
||||
auto mp = make_shared<op::v1::MaxPool>(
|
||||
arg, strides, pads_begin, pads_end, kernel_shape, rounding_mode, auto_pad);
|
||||
|
||||
ASSERT_TRUE(mp->get_output_partial_shape(0).same_scheme(
|
||||
{1, 3, Dimension::dynamic(), Dimension::dynamic()}));
|
||||
ASSERT_EQ(mp->get_pads_begin(), (Shape{}));
|
||||
ASSERT_EQ(mp->get_pads_end(), (Shape{}));
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user