Reference ROIAlign-3 implementation (#2128)
This commit is contained in:
Bartosz Lesniewski
GitHub
parent
3f52e64c25
commit
9af49a7f95
@ -76,6 +76,9 @@ namespace ngraph
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int get_sampling_ratio() const { return m_sampling_ratio; }
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float get_spatial_scale() const { return m_spatial_scale; }
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PoolingMode get_mode() const { return m_mode; }
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bool evaluate(const HostTensorVector& outputs,
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const HostTensorVector& inputs) const override;
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private:
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PoolingMode mode_from_string(const std::string& mode) const;
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@ -86,9 +89,9 @@ namespace ngraph
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float m_spatial_scale;
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PoolingMode m_mode;
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};
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}
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} // namespace v3
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using v3::ROIAlign;
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}
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} // namespace op
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std::ostream& operator<<(std::ostream& s, const op::v3::ROIAlign::PoolingMode& mode);
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@ -106,4 +109,4 @@ namespace ngraph
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"AttributeAdapter<op::v3::ROIAlign::PoolingMode>", 3};
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const DiscreteTypeInfo& get_type_info() const override { return type_info; }
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};
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}
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} // namespace ngraph
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@ -0,0 +1,244 @@
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//*****************************************************************************
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// Copyright 2017-2020 Intel Corporation
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//*****************************************************************************
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#pragma once
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#include <algorithm>
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#include "ngraph/coordinate_transform.hpp"
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#include "ngraph/op/roi_align.hpp" // for ROIAlign:PoolingMode
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#include "ngraph/shape.hpp"
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namespace ngraph
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{
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namespace runtime
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{
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namespace reference
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{
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using ROIPoolingMode = op::v3::ROIAlign::PoolingMode;
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template <typename T>
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void roi_align(const T* feature_maps,
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const T* rois,
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const int64_t* batch_indices,
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T* out,
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const Shape& feature_maps_shape,
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const Shape& rois_shape,
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const Shape& batch_indices_shape,
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const Shape& out_shape,
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const int pooled_height,
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const int pooled_width,
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const int sampling_ratio,
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const float spatial_scale,
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const ROIPoolingMode& pooling_mode)
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{
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auto C = feature_maps_shape[1];
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auto feature_map_height = feature_maps_shape[2];
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auto feature_map_width = feature_maps_shape[3];
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auto num_rois = rois_shape[0];
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CoordinateTransform feature_maps_transform(feature_maps_shape);
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CoordinateTransform rois_transform(rois_shape);
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CoordinateTransform out_transform(out_shape);
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for (unsigned int roi_index = 0; roi_index < num_rois; roi_index++)
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{
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// Get ROI`s corners
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T x1 = rois[rois_transform.index({roi_index, 0})] * spatial_scale;
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T y1 = rois[rois_transform.index({roi_index, 1})] * spatial_scale;
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T x2 = rois[rois_transform.index({roi_index, 2})] * spatial_scale;
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T y2 = rois[rois_transform.index({roi_index, 3})] * spatial_scale;
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T roi_width = std::max(x2 - x1, static_cast<T>(1.0));
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T roi_height = std::max(y2 - y1, static_cast<T>(1.0));
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T bin_width = roi_width / pooled_width;
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T bin_height = roi_height / pooled_height;
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auto sampling_ratio_x =
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sampling_ratio == 0 ? static_cast<int>(ceil(bin_width)) : sampling_ratio;
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auto sampling_ratio_y =
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sampling_ratio == 0 ? static_cast<int>(ceil(bin_height)) : sampling_ratio;
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uint64_t num_samples_in_bin = sampling_ratio_x * sampling_ratio_y;
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T sample_distance_x = bin_width / static_cast<T>(sampling_ratio_x);
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T sample_distance_y = bin_height / static_cast<T>(sampling_ratio_y);
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std::vector<std::pair<unsigned int, unsigned int>> pooling_points;
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std::vector<T> pooling_weights;
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pooling_points.reserve(4 * num_samples_in_bin * pooled_height * pooled_width);
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pooling_weights.reserve(4 * num_samples_in_bin * pooled_height * pooled_width);
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// Save the sample coords and weights as they will be identical across all
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// channels
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for (unsigned int y_bin_ind = 0; y_bin_ind < pooled_height; y_bin_ind++)
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{
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for (unsigned int x_bin_ind = 0; x_bin_ind < pooled_width; x_bin_ind++)
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{
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for (unsigned int y_sample_ind = 0; y_sample_ind < sampling_ratio_y;
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y_sample_ind++)
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{
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T sample_y = y1 + static_cast<T>(y_bin_ind) * bin_height +
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sample_distance_y * (static_cast<T>(y_sample_ind) +
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static_cast<T>(0.5f));
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for (int64_t x_sample_ind = 0; x_sample_ind < sampling_ratio_x;
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x_sample_ind++)
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{
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T sample_x = x1 + static_cast<T>(x_bin_ind) * bin_width +
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sample_distance_x * (static_cast<T>(x_sample_ind) +
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static_cast<T>(0.5f));
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if (sample_x < -1.0 || sample_x > feature_map_width ||
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sample_y < -1.0 || sample_y > feature_map_height)
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{
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// For this sample we save 4x point (0,0) with weight 0
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pooling_points.insert(pooling_points.end(), 4, {0, 0});
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pooling_weights.insert(pooling_weights.end(), 4, {0});
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continue;
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}
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sample_x = std::max(sample_x, T{0});
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sample_y = std::max(sample_y, T{0});
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auto sample_y_low = static_cast<unsigned int>(sample_y);
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auto sample_x_low = static_cast<unsigned int>(sample_x);
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unsigned int sample_y_high;
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unsigned int sample_x_high;
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if (sample_y_low >= feature_map_height - 1)
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{
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sample_y_high = sample_y_low = feature_map_height - 1;
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sample_y = static_cast<T>(sample_y_low);
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}
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else
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{
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sample_y_high = sample_y_low + 1;
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}
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if (sample_x_low >= feature_map_height - 1)
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{
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sample_x_high = sample_x_low = feature_map_width - 1;
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sample_x = static_cast<T>(sample_x_low);
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}
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else
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{
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sample_x_high = sample_x_low + 1;
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}
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pooling_points.push_back({sample_y_low, sample_x_low});
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pooling_points.push_back({sample_y_low, sample_x_high});
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pooling_points.push_back({sample_y_high, sample_x_low});
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pooling_points.push_back({sample_y_high, sample_x_high});
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// weight calculation for bilinear interpolation
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auto ly = sample_y - static_cast<T>(sample_y_low);
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auto lx = sample_x - static_cast<T>(sample_x_low);
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auto hy = static_cast<T>(1.) - ly;
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auto hx = static_cast<T>(1.) - lx;
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pooling_weights.push_back(hy * hx);
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pooling_weights.push_back(hy * lx);
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pooling_weights.push_back(ly * hx);
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pooling_weights.push_back(ly * lx);
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}
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}
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}
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}
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std::vector<T> tmp_out;
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for (unsigned int channel_index = 0; channel_index < C; channel_index++)
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{
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tmp_out.reserve(pooled_height * pooled_width);
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unsigned int sample_index = 0;
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for (unsigned int y_bin_ind = 0; y_bin_ind < pooled_height; y_bin_ind++)
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{
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for (unsigned int x_bin_ind = 0; x_bin_ind < pooled_width; x_bin_ind++)
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{
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T pooled_value = 0;
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for (unsigned int bin_sample_ind = 0;
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bin_sample_ind < num_samples_in_bin;
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bin_sample_ind++)
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{
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// the four parts are values of the four closest surrounding
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// neighbours of considered sample, then basing on all sampled
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// values in bin we calculate pooled value
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auto sample_part_1 = feature_maps[feature_maps_transform.index(
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{static_cast<unsigned int>(batch_indices[roi_index]),
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channel_index,
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pooling_points[sample_index].first,
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pooling_points[sample_index].second})];
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auto sample_part_2 = feature_maps[feature_maps_transform.index(
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{static_cast<unsigned int>(batch_indices[roi_index]),
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channel_index,
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pooling_points[sample_index + 1].first,
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pooling_points[sample_index + 1].second})];
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auto sample_part_3 = feature_maps[feature_maps_transform.index(
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{static_cast<unsigned int>(batch_indices[roi_index]),
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channel_index,
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pooling_points[sample_index + 2].first,
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pooling_points[sample_index + 2].second})];
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auto sample_part_4 = feature_maps[feature_maps_transform.index(
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{static_cast<unsigned int>(batch_indices[roi_index]),
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channel_index,
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pooling_points[sample_index + 3].first,
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pooling_points[sample_index + 3].second})];
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switch (pooling_mode)
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{
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case ROIPoolingMode::MAX:
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{
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T sample_value = std::max(
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{pooling_weights[sample_index] * sample_part_1,
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pooling_weights[sample_index + 1] * sample_part_2,
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pooling_weights[sample_index + 2] * sample_part_3,
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pooling_weights[sample_index + 3] * sample_part_4});
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pooled_value = sample_value > pooled_value ? sample_value
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: pooled_value;
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break;
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}
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case ROIPoolingMode::AVG:
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default:
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{
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T sample_value =
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pooling_weights[sample_index] * sample_part_1 +
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pooling_weights[sample_index + 1] * sample_part_2 +
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pooling_weights[sample_index + 2] * sample_part_3 +
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pooling_weights[sample_index + 3] * sample_part_4;
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pooled_value += sample_value / (num_samples_in_bin);
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}
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}
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sample_index += 4;
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}
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tmp_out.push_back(pooled_value);
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}
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}
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// save the calculations for all bins across this channel
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auto output_channel_offset =
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out_transform.index({static_cast<unsigned int>(roi_index),
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static_cast<unsigned int>(channel_index),
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static_cast<unsigned int>(0),
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static_cast<unsigned int>(0)});
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std::copy(tmp_out.begin(), tmp_out.end(), out + output_channel_offset);
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tmp_out.clear();
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}
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}
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return;
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}
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} // namespace reference
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} // namespace runtime
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} // namespace ngraph
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@ -16,6 +16,10 @@
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#include "roi_align.hpp"
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#include "ngraph/runtime/host_tensor.hpp"
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#include "ngraph/runtime/reference/roi_align.hpp"
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#include "util.hpp" // for host_tensor_2_vector
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using namespace std;
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using namespace ngraph;
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@ -67,6 +71,14 @@ void op::v3::ROIAlign::validate_and_infer_types()
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" and: ",
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get_input_element_type(1));
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NODE_VALIDATION_CHECK(
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this,
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get_input_element_type(0) == get_input_element_type(1),
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"Type of feature maps (inputs) and rois is expected to be the same. Got: ",
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get_input_element_type(0),
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" and: ",
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get_input_element_type(1));
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NODE_VALIDATION_CHECK(this,
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get_input_element_type(2).is_integral_number(),
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"The data type for batch indices is expected to be an integer. Got: ",
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@ -190,4 +202,87 @@ namespace ngraph
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{
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return s << as_string(type);
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}
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} // namespace ngraph
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namespace
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{
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bool evaluate_roi_align(const HostTensorVector& args,
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const HostTensorPtr& out,
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const int pooled_height,
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const int pooled_width,
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const int sampling_ratio,
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const float spatial_scale,
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const op::v3::ROIAlign::PoolingMode& pooling_mode)
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{
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auto feature_maps = args[0];
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auto rois = args[1];
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auto batch_indices = args[2];
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std::vector<int64_t> batch_indices_vec_scaled_up =
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host_tensor_2_vector<int64_t>(batch_indices);
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switch (feature_maps->get_element_type())
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{
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case element::Type_t::bf16:
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{
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runtime::reference::roi_align<bfloat16>(feature_maps->get_data_ptr<bfloat16>(),
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rois->get_data_ptr<bfloat16>(),
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batch_indices_vec_scaled_up.data(),
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out->get_data_ptr<bfloat16>(),
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feature_maps->get_shape(),
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rois->get_shape(),
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batch_indices->get_shape(),
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out->get_shape(),
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pooled_height,
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pooled_width,
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sampling_ratio,
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spatial_scale,
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pooling_mode);
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break;
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}
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case element::Type_t::f16:
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{
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runtime::reference::roi_align<float16>(feature_maps->get_data_ptr<float16>(),
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rois->get_data_ptr<float16>(),
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batch_indices_vec_scaled_up.data(),
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out->get_data_ptr<float16>(),
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feature_maps->get_shape(),
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rois->get_shape(),
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batch_indices->get_shape(),
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out->get_shape(),
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pooled_height,
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pooled_width,
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sampling_ratio,
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spatial_scale,
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pooling_mode);
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break;
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}
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case element::Type_t::f32:
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{
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runtime::reference::roi_align<float>(feature_maps->get_data_ptr<float>(),
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rois->get_data_ptr<float>(),
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batch_indices_vec_scaled_up.data(),
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out->get_data_ptr<float>(),
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feature_maps->get_shape(),
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rois->get_shape(),
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batch_indices->get_shape(),
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out->get_shape(),
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pooled_height,
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pooled_width,
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sampling_ratio,
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spatial_scale,
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pooling_mode);
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break;
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}
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default: NGRAPH_UNREACHABLE("unsupported input type for roi_align");
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}
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return true;
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}
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} // namespace
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bool op::v3::ROIAlign::evaluate(const HostTensorVector& outputs,
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const HostTensorVector& inputs) const
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{
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return evaluate_roi_align(
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inputs, outputs[0], m_pooled_h, m_pooled_w, m_sampling_ratio, m_spatial_scale, m_mode);
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}
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@ -77,6 +77,7 @@ set(SRC
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op_eval/non_zero.cpp
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op_eval/reduce_l1.cpp
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op_eval/reduce_l2.cpp
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op_eval/roi_align.cpp
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op_eval/softplus.cpp
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op_eval/split.cpp
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op_eval/swish.cpp
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110
ngraph/test/models/onnx/roi_align_f32.prototxt
Normal file
110
ngraph/test/models/onnx/roi_align_f32.prototxt
Normal file
@ -0,0 +1,110 @@
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ir_version: 3
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producer_name: "onnx-importer-test"
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graph {
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node{
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input: "feature_maps"
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input: "rois"
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input: "batch_indices"
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output: "Y"
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op_type: "RoiAlign"
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attribute{
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name: "output_height"
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i: 3
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type: INT
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}
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attribute{
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name: "output_width"
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i: 4
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type: INT
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}
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attribute{
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name: "sampling_ratio"
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i: 2
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type: INT
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}
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attribute{
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name: "spatial_scale"
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f: 0.0625
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type: FLOAT
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}
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attribute{
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name: "mode"
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s: "avg"
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type: STRING
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}
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}
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name: "test-model"
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input{
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name: "feature_maps"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 1
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}
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dim {
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dim_value: 3
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}
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dim {
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dim_value: 5
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}
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dim {
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dim_value: 5
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}
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}
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}
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}
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}
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input{
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name: "rois"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 5
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}
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dim {
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dim_value: 4
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||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
input{
|
||||
name:"batch_indices"
|
||||
type{
|
||||
tensor_type{
|
||||
elem_type: 6
|
||||
shape {
|
||||
dim {
|
||||
dim_value: 5
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
output{
|
||||
name: "Y"
|
||||
type {
|
||||
tensor_type {
|
||||
elem_type: 1
|
||||
shape {
|
||||
dim {
|
||||
dim_value: 5
|
||||
}
|
||||
dim {
|
||||
dim_value: 3
|
||||
}
|
||||
dim {
|
||||
dim_value: 3
|
||||
}
|
||||
dim {
|
||||
dim_value: 4
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -748,7 +748,7 @@ namespace
|
||||
-1.21722605, 0.82919357, 0.55765697, 0.12657686, 0.63432172, 0.75425957, -2.43721014,
|
||||
-1.24478184, 2.65316853, 1.19509542, -0.95523998, 0.5149006, -0.01151649, 0.68327026,
|
||||
-0.4589638, -0.46554745, 0.21055324, 0.39266729, 2.05098086, 1.83207919};
|
||||
}
|
||||
} // namespace
|
||||
|
||||
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_softmax_axis_0)
|
||||
{
|
||||
@ -2468,6 +2468,48 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_empty_initializers_handling)
|
||||
test_case.run_with_tolerance_as_fp(2.0e-5f);
|
||||
}
|
||||
|
||||
NGRAPH_TEST(${BACKEND_NAME}, onnx_roi_align_f32)
|
||||
{
|
||||
const auto function = onnx_import::import_onnx_model(
|
||||
file_util::path_join(SERIALIZED_ZOO, "onnx/roi_align_f32.prototxt"));
|
||||
|
||||
auto test_case = test::TestCase<TestEngine>(function);
|
||||
test_case.add_input<float>({0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.,
|
||||
13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
|
||||
26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,
|
||||
39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51.,
|
||||
52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64.,
|
||||
65., 66., 67., 68., 69., 70., 71., 72., 73., 74.});
|
||||
|
||||
test_case.add_input<float>({7., 5., 7., 5., -15., -15., -15., -15., -10., 21.,
|
||||
-10., 21., 13., 8., 13., 8., -14., 19., -14., 19.});
|
||||
|
||||
test_case.add_input<int32_t>({0, 0, 0, 0, 0});
|
||||
test_case.add_expected_output<float>(
|
||||
Shape{5, 3, 3, 4},
|
||||
{2.95833f, 3.20833f, 3.45833f, 3.70833f, 4.625f, 4.875f, 5.125f, 5.375f, 6.29167f,
|
||||
6.54167f, 6.79167f, 7.04167f, 27.9583f, 28.2083f, 28.4583f, 28.7083f, 29.625f, 29.875f,
|
||||
30.125f, 30.375f, 31.2917f, 31.5417f, 31.7917f, 32.0417f, 52.9583f, 53.2083f, 53.4583f,
|
||||
53.7083f, 54.625f, 54.875f, 55.125f, 55.375f, 56.2917f, 56.5417f, 56.7917f, 57.0417f,
|
||||
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
|
||||
0.f, 0.f, 0.f, 25.f, 25.f, 25.f, 25.f, 25.f, 25.f,
|
||||
25.f, 25.f, 25.f, 25.f, 25.f, 25.f, 50.f, 50.f, 50.f,
|
||||
50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f,
|
||||
7.39583f, 7.39583f, 7.42708f, 7.64583f, 9.0625f, 9.0625f, 9.09375f, 9.3125f, 10.7292f,
|
||||
10.7292f, 10.7604f, 10.9792f, 32.3958f, 32.3958f, 32.4271f, 32.6458f, 34.0625f, 34.0625f,
|
||||
34.0938f, 34.3125f, 35.7292f, 35.7292f, 35.7604f, 35.9792f, 57.3958f, 57.3958f, 57.4271f,
|
||||
57.6458f, 59.0625f, 59.0625f, 59.0938f, 59.3125f, 60.7292f, 60.7292f, 60.7604f, 60.9792f,
|
||||
4.27083f, 4.52083f, 4.77083f, 5.02083f, 5.9375f, 6.1875f, 6.4375f, 6.6875f, 7.60417f,
|
||||
7.85417f, 8.10417f, 8.35417f, 29.2708f, 29.5208f, 29.7708f, 30.0208f, 30.9375f, 31.1875f,
|
||||
31.4375f, 31.6875f, 32.6042f, 32.8542f, 33.1042f, 33.3542f, 54.2708f, 54.5208f, 54.7708f,
|
||||
55.0208f, 55.9375f, 56.1875f, 56.4375f, 56.6875f, 57.6042f, 57.8542f, 58.1042f, 58.3542f,
|
||||
6.77083f, 6.77083f, 6.77083f, 6.80208f, 8.4375f, 8.4375f, 8.4375f, 8.46875f, 10.1042f,
|
||||
10.1042f, 10.1042f, 10.1354f, 31.7708f, 31.7708f, 31.7708f, 31.8021f, 33.4375f, 33.4375f,
|
||||
33.4375f, 33.4688f, 35.1042f, 35.1042f, 35.1042f, 35.1354f, 56.7708f, 56.7708f, 56.7708f,
|
||||
56.8021f, 58.4375f, 58.4375f, 58.4375f, 58.4688f, 60.1042f, 60.1042f, 60.1042f, 60.1354f});
|
||||
test_case.run_with_tolerance_as_fp(1.0e-4f);
|
||||
}
|
||||
|
||||
NGRAPH_TEST(${BACKEND_NAME}, quant_dequant_pattern)
|
||||
{
|
||||
const auto function = onnx_import::import_onnx_model(
|
||||
|
||||
166
ngraph/test/op_eval/roi_align.cpp
Normal file
166
ngraph/test/op_eval/roi_align.cpp
Normal file
@ -0,0 +1,166 @@
|
||||
//*****************************************************************************
|
||||
// Copyright 2017-2020 Intel Corporation
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
//*****************************************************************************
|
||||
|
||||
#include <numeric>
|
||||
#include <vector>
|
||||
|
||||
#include "gtest/gtest.h"
|
||||
|
||||
#include "ngraph/op/roi_align.hpp"
|
||||
#include "ngraph/runtime/host_tensor.hpp"
|
||||
#include "ngraph/validation_util.hpp"
|
||||
#include "runtime/backend.hpp"
|
||||
#include "util/all_close_f.hpp"
|
||||
#include "util/test_tools.hpp"
|
||||
#include "util/type_prop.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace ngraph;
|
||||
|
||||
TEST(op_eval, roi_align_avg_pool)
|
||||
{
|
||||
const int N = 1;
|
||||
const int C = 3;
|
||||
const int H = 5;
|
||||
const int W = 5;
|
||||
const int num_rois = 5;
|
||||
const int pooled_height = 3;
|
||||
const int pooled_width = 4;
|
||||
const auto data_shape = Shape{N, C, H, W};
|
||||
const auto rois_shape = Shape{num_rois, 4};
|
||||
|
||||
const auto data = make_shared<op::Parameter>(element::f32, data_shape);
|
||||
const auto rois = make_shared<op::Parameter>(element::f32, rois_shape);
|
||||
const auto batch_indices = make_shared<op::Parameter>(element::i32, Shape{num_rois});
|
||||
|
||||
auto roi_align = make_shared<op::v3::ROIAlign>(
|
||||
data, rois, batch_indices, pooled_height, pooled_width, 2, 1.0f / 16.0f, "avg");
|
||||
|
||||
auto f = make_shared<Function>(roi_align, ParameterVector{data, rois, batch_indices});
|
||||
|
||||
std::vector<float> data_vec{0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.,
|
||||
13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
|
||||
26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,
|
||||
39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51.,
|
||||
52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64.,
|
||||
65., 66., 67., 68., 69., 70., 71., 72., 73., 74.};
|
||||
|
||||
std::vector<float> rois_vec{7., 5., 7., 5., -15., -15., -15., -15., -10., 21.,
|
||||
-10., 21., 13., 8., 13., 8., -14., 19., -14., 19.};
|
||||
|
||||
std::vector<int64_t> batch_indices_vec{0, 0, 0, 0, 0};
|
||||
|
||||
auto result = make_shared<HostTensor>();
|
||||
|
||||
ASSERT_TRUE(f->evaluate({result},
|
||||
{make_host_tensor<element::Type_t::f32>(data_shape, data_vec),
|
||||
make_host_tensor<element::Type_t::f32>(rois_shape, rois_vec),
|
||||
make_host_tensor<element::Type_t::i64>(Shape{num_rois})}));
|
||||
|
||||
std::vector<float> expected_vec{
|
||||
2.95833f, 3.20833f, 3.45833f, 3.70833f, 4.625f, 4.875f, 5.125f, 5.375f, 6.29167f,
|
||||
6.54167f, 6.79167f, 7.04167f, 27.9583f, 28.2083f, 28.4583f, 28.7083f, 29.625f, 29.875f,
|
||||
30.125f, 30.375f, 31.2917f, 31.5417f, 31.7917f, 32.0417f, 52.9583f, 53.2083f, 53.4583f,
|
||||
53.7083f, 54.625f, 54.875f, 55.125f, 55.375f, 56.2917f, 56.5417f, 56.7917f, 57.0417f,
|
||||
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
|
||||
0.f, 0.f, 0.f, 25.f, 25.f, 25.f, 25.f, 25.f, 25.f,
|
||||
25.f, 25.f, 25.f, 25.f, 25.f, 25.f, 50.f, 50.f, 50.f,
|
||||
50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f,
|
||||
7.39583f, 7.39583f, 7.42708f, 7.64583f, 9.0625f, 9.0625f, 9.09375f, 9.3125f, 10.7292f,
|
||||
10.7292f, 10.7604f, 10.9792f, 32.3958f, 32.3958f, 32.4271f, 32.6458f, 34.0625f, 34.0625f,
|
||||
34.0938f, 34.3125f, 35.7292f, 35.7292f, 35.7604f, 35.9792f, 57.3958f, 57.3958f, 57.4271f,
|
||||
57.6458f, 59.0625f, 59.0625f, 59.0938f, 59.3125f, 60.7292f, 60.7292f, 60.7604f, 60.9792f,
|
||||
4.27083f, 4.52083f, 4.77083f, 5.02083f, 5.9375f, 6.1875f, 6.4375f, 6.6875f, 7.60417f,
|
||||
7.85417f, 8.10417f, 8.35417f, 29.2708f, 29.5208f, 29.7708f, 30.0208f, 30.9375f, 31.1875f,
|
||||
31.4375f, 31.6875f, 32.6042f, 32.8542f, 33.1042f, 33.3542f, 54.2708f, 54.5208f, 54.7708f,
|
||||
55.0208f, 55.9375f, 56.1875f, 56.4375f, 56.6875f, 57.6042f, 57.8542f, 58.1042f, 58.3542f,
|
||||
6.77083f, 6.77083f, 6.77083f, 6.80208f, 8.4375f, 8.4375f, 8.4375f, 8.46875f, 10.1042f,
|
||||
10.1042f, 10.1042f, 10.1354f, 31.7708f, 31.7708f, 31.7708f, 31.8021f, 33.4375f, 33.4375f,
|
||||
33.4375f, 33.4688f, 35.1042f, 35.1042f, 35.1042f, 35.1354f, 56.7708f, 56.7708f, 56.7708f,
|
||||
56.8021f, 58.4375f, 58.4375f, 58.4375f, 58.4688f, 60.1042f, 60.1042f, 60.1042f, 60.1354f};
|
||||
const auto expected_shape = Shape{num_rois, C, pooled_height, pooled_width};
|
||||
|
||||
EXPECT_EQ(result->get_element_type(), element::f32);
|
||||
EXPECT_EQ(result->get_shape(), expected_shape);
|
||||
ASSERT_TRUE(test::all_close_f(read_vector<float>(result), expected_vec, 6, 0.001));
|
||||
}
|
||||
TEST(op_eval, roi_align_max_pool)
|
||||
{
|
||||
const int N = 1;
|
||||
const int C = 3;
|
||||
const int H = 5;
|
||||
const int W = 5;
|
||||
const int num_rois = 5;
|
||||
const int pooled_height = 3;
|
||||
const int pooled_width = 4;
|
||||
const auto data_shape = Shape{N, C, H, W};
|
||||
const auto rois_shape = Shape{num_rois, 4};
|
||||
|
||||
const auto data = make_shared<op::Parameter>(element::f32, data_shape);
|
||||
const auto rois = make_shared<op::Parameter>(element::f32, rois_shape);
|
||||
const auto batch_indices = make_shared<op::Parameter>(element::i32, Shape{num_rois});
|
||||
|
||||
auto roi_align = make_shared<op::v3::ROIAlign>(
|
||||
data, rois, batch_indices, pooled_height, pooled_width, 2, 1.0f / 16.0f, "max");
|
||||
|
||||
auto f = make_shared<Function>(roi_align, ParameterVector{data, rois, batch_indices});
|
||||
|
||||
std::vector<float> data_vec{0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.,
|
||||
13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
|
||||
26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,
|
||||
39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51.,
|
||||
52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64.,
|
||||
65., 66., 67., 68., 69., 70., 71., 72., 73., 74.};
|
||||
|
||||
std::vector<float> rois_vec{7., 5., 7., 5., -15., -15., -15., -15., -10., 21.,
|
||||
-10., 21., 13., 8., 13., 8., -14., 19., -14., 19.};
|
||||
|
||||
std::vector<int64_t> batch_indices_vec{0, 0, 0, 0, 0};
|
||||
|
||||
auto result = make_shared<HostTensor>();
|
||||
|
||||
ASSERT_TRUE(f->evaluate({result},
|
||||
{make_host_tensor<element::Type_t::f32>(data_shape, data_vec),
|
||||
make_host_tensor<element::Type_t::f32>(rois_shape, rois_vec),
|
||||
make_host_tensor<element::Type_t::i64>(Shape{num_rois})}));
|
||||
|
||||
std::vector<float> expected_vec{
|
||||
2.10938f, 2.95313f, 3.375f, 2.53125f, 3.35938f, 4.70313f, 5.375f, 4.03125f, 3.51563f,
|
||||
4.92188f, 5.625f, 4.21875f, 10.8984f, 15.2578f, 17.4375f, 13.0781f, 17.3568f, 24.2995f,
|
||||
27.7708f, 20.8281f, 18.1641f, 25.4297f, 29.0625f, 21.7969f, 19.6875f, 27.5625f, 31.5f,
|
||||
23.625f, 31.3542f, 43.8958f, 50.1667f, 37.625f, 32.8125f, 45.9375f, 52.5f, 39.375f,
|
||||
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
|
||||
0.f, 0.f, 0.f, 25.f, 25.f, 25.f, 25.f, 25.f, 25.f,
|
||||
25.f, 25.f, 25.f, 25.f, 25.f, 25.f, 50.f, 50.f, 50.f,
|
||||
50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f,
|
||||
5.625f, 5.625f, 5.625f, 4.57031f, 8.95833f, 8.95833f, 8.95833f, 7.27865f, 9.375f,
|
||||
9.375f, 9.375f, 7.61719f, 19.6875f, 19.6875f, 19.6875f, 15.9961f, 31.3542f, 31.3542f,
|
||||
31.3542f, 25.4753f, 32.8125f, 32.8125f, 32.8125f, 26.6602f, 33.75f, 33.75f, 33.75f,
|
||||
27.4219f, 53.75f, 53.75f, 53.75f, 43.6719f, 56.25f, 56.25f, 56.25f, 45.7031f,
|
||||
4.5f, 3.9375f, 2.8125f, 3.9375f, 5.5f, 4.8125f, 3.4375f, 4.8125f, 4.58333f,
|
||||
4.01042f, 2.86458f, 3.9375f, 23.25f, 20.3438f, 14.5313f, 18.f, 28.4167f, 24.86458f,
|
||||
17.76042f, 22.f, 23.25f, 20.3437f, 14.5312f, 18.f, 42.f, 36.75f, 26.25f,
|
||||
32.0625f, 51.3333f, 44.9167f, 32.08333f, 39.1875f, 42.f, 36.75f, 26.25f, 32.0625f,
|
||||
4.375f, 4.375f, 4.375f, 4.375f, 7.70833f, 7.70833f, 7.70833f, 7.70833f, 9.375f,
|
||||
9.375f, 9.375f, 9.375f, 21.875f, 21.875f, 21.875f, 21.875f, 26.9792f, 26.9792f,
|
||||
26.9792f, 26.9792f, 32.8125f, 32.8125f, 32.8125f, 32.8125f, 40.1042f, 40.1042f, 40.1042f,
|
||||
40.1042f, 46.25f, 46.25f, 46.25f, 46.25f, 56.25f, 56.25f, 56.25f, 56.25f};
|
||||
const auto expected_shape = Shape{num_rois, C, pooled_height, pooled_width};
|
||||
|
||||
EXPECT_EQ(result->get_element_type(), element::f32);
|
||||
EXPECT_EQ(result->get_shape(), expected_shape);
|
||||
ASSERT_TRUE(test::all_close_f(read_vector<float>(result), expected_vec, 6, 0.001));
|
||||
}
|
||||
@ -552,6 +552,9 @@ abc_int64
|
||||
# Unsupported primitive of type: SigmoidBackprop
|
||||
sigmoid_bprop_n1c1h4
|
||||
|
||||
# Unsupported primitive of type: ROIAlign
|
||||
IE_CPU.onnx_roi_align_f32
|
||||
|
||||
# [NOT_IMPLEMENTED] Input image format BOOL is not supported yet...
|
||||
select
|
||||
not
|
||||
@ -1201,7 +1204,7 @@ IE_GPU.onnx_dyn_shapes_avg_pool_dyn_shape
|
||||
IE_GPU.onnx_dyn_shapes_max_pool_dyn_shape
|
||||
IE_GPU.onnx_dyn_shapes_global_avg_pool_dyn_shape
|
||||
IE_GPU.onnx_dyn_shapes_global_max_pool_dyn_shape
|
||||
|
||||
IE_GPU.onnx_roi_align_f32
|
||||
IE_GPU.tanh
|
||||
IE_GPU.tan
|
||||
IE_GPU.sum_to_scalar
|
||||
|
||||
Loading…
Reference in New Issue
Block a user