mirror of
https://github.com/saymrwulf/onnxruntime.git
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Implement ROI Align for object detection. (#308)
* Implement ROI Align for object detection. * Fix Mac build * Fix Mac build
This commit is contained in:
Pranav Sharma
GitHub
parent
6b3044ddd3
commit
223773d278
5 changed files with 712 additions and 25 deletions
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@ -25,6 +25,8 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, WordC
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, GatherND);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, MurmurHash3);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, MaxpoolWithMask);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, ROIAlign);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, double, ROIAlign);
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void RegisterContribKernels(std::function<void(KernelCreateInfo&&)> fn) {
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, SampleOp)>());
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@ -48,6 +50,8 @@ void RegisterContribKernels(std::function<void(KernelCreateInfo&&)> fn) {
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fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, GatherND)>());
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fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, MurmurHash3)>());
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, MaxpoolWithMask)>());
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, ROIAlign)>());
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, double, ROIAlign)>());
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}
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} // namespace contrib
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312
onnxruntime/contrib_ops/cpu/roialign.cc
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312
onnxruntime/contrib_ops/cpu/roialign.cc
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@ -0,0 +1,312 @@
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/**
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* Copyright (c) 2016-present, Facebook, Inc.
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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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/* Modifications Copyright (c) Microsoft. */
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#include "roialign.h"
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#include "core/util/math_cpuonly.h"
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#include "core/common/common.h"
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#include "core/framework/tensor.h"
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namespace onnxruntime {
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namespace contrib {
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const int64_t EXPECTED_NUM_ROI_DIMS = 2;
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const int64_t EXPECTED_SECOND_ROI_DIM = 5;
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#define ADD_TYPED_ROIALIGN_OP(data_type) \
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ONNX_CPU_OPERATOR_TYPED_MS_KERNEL( \
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ROIAlign, \
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1, \
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data_type, \
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KernelDefBuilder() \
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.TypeConstraint("T", DataTypeImpl::GetTensorType<data_type>()), \
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ROIAlign<data_type>);
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ADD_TYPED_ROIALIGN_OP(float);
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ADD_TYPED_ROIALIGN_OP(double);
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namespace {
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template <typename T>
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struct PreCalc {
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int64_t pos1;
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int64_t pos2;
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int64_t pos3;
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int64_t pos4;
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T w1;
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T w2;
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T w3;
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T w4;
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};
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template <typename T>
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void pre_calc_for_bilinear_interpolate(
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const int64_t height,
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const int64_t width,
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const int64_t pooled_height,
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const int64_t pooled_width,
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const int64_t iy_upper,
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const int64_t ix_upper,
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T roi_start_h,
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T roi_start_w,
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T bin_size_h,
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T bin_size_w,
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int64_t roi_bin_grid_h,
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int64_t roi_bin_grid_w,
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std::vector<PreCalc<T>>& pre_calc) {
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int64_t pre_calc_index = 0;
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for (int64_t ph = 0; ph < pooled_height; ph++) {
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for (int64_t pw = 0; pw < pooled_width; pw++) {
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for (int64_t iy = 0; iy < iy_upper; iy++) {
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const T yy = roi_start_h + ph * bin_size_h +
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static_cast<T>(iy + .5f) * bin_size_h /
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static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
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for (int64_t ix = 0; ix < ix_upper; ix++) {
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const T xx = roi_start_w + pw * bin_size_w +
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static_cast<T>(ix + .5f) * bin_size_w /
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static_cast<T>(roi_bin_grid_w);
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T x = xx;
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T y = yy;
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// deal with: inverse elements are out of feature map boundary
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if (y < -1.0 || y > height || x < -1.0 || x > width) {
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// empty
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PreCalc<T> pc;
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pc.pos1 = 0;
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pc.pos2 = 0;
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pc.pos3 = 0;
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pc.pos4 = 0;
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pc.w1 = 0;
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pc.w2 = 0;
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pc.w3 = 0;
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pc.w4 = 0;
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pre_calc[pre_calc_index] = pc;
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pre_calc_index += 1;
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continue;
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}
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if (y <= 0) {
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y = 0;
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}
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if (x <= 0) {
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x = 0;
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}
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int64_t y_low = static_cast<int64_t>(y);
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int64_t x_low = static_cast<int64_t>(x);
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int64_t y_high;
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int64_t x_high;
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if (y_low >= height - 1) {
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y_high = y_low = height - 1;
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y = (T)y_low;
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} else {
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y_high = y_low + 1;
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}
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if (x_low >= width - 1) {
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x_high = x_low = width - 1;
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x = (T)x_low;
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} else {
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x_high = x_low + 1;
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}
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T ly = y - y_low;
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T lx = x - x_low;
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T hy = static_cast<T>(1.) - ly, hx = static_cast<T>(1.) - lx;
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T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
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// save weights and indeces
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PreCalc<T> pc;
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pc.pos1 = y_low * width + x_low;
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pc.pos2 = y_low * width + x_high;
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pc.pos3 = y_high * width + x_low;
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pc.pos4 = y_high * width + x_high;
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pc.w1 = w1;
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pc.w2 = w2;
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pc.w3 = w3;
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pc.w4 = w4;
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pre_calc[pre_calc_index] = pc;
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pre_calc_index += 1;
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}
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}
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}
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}
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}
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template <typename T>
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void ROIAlignForward(
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int64_t nthreads,
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const T* bottom_data,
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float spatial_scale,
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int64_t channels,
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int64_t height,
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int64_t width,
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int64_t pooled_height,
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int64_t pooled_width,
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int64_t sampling_ratio,
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const T* bottom_rois,
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int64_t roi_cols,
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T* top_data,
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const std::string& mode) {
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int64_t n_rois = nthreads / channels / pooled_width / pooled_height;
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#ifdef USE_OPENMP
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#pragma omp parallel for
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#endif
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for (int n = 0; n < n_rois; n++) {
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int64_t index_n = n * channels * pooled_width * pooled_height;
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const T* offset_bottom_rois = bottom_rois + n * roi_cols;
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const T roi_batch_ind = offset_bottom_rois[0];
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offset_bottom_rois++;
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// Do not using rounding; this implementation detail is critical
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T roi_start_w = offset_bottom_rois[0] * spatial_scale;
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T roi_start_h = offset_bottom_rois[1] * spatial_scale;
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T roi_end_w = offset_bottom_rois[2] * spatial_scale;
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T roi_end_h = offset_bottom_rois[3] * spatial_scale;
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// Force malformed ROIs to be 1x1
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T roi_width = std::max(roi_end_w - roi_start_w, (T)1.);
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T roi_height = std::max(roi_end_h - roi_start_h, (T)1.);
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T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
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T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
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// We use roi_bin_grid to sample the grid and mimic integral
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int64_t roi_bin_grid_h = (sampling_ratio > 0)
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? sampling_ratio
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: static_cast<int64_t>(ceil(roi_height / pooled_height)); // e.g., = 2
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int64_t roi_bin_grid_w =
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(sampling_ratio > 0) ? sampling_ratio : static_cast<int64_t>(ceil(roi_width / pooled_width));
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// We do average (integral) pooling inside a bin
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const int64_t count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
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// we want to precalculate indices and weights shared by all channels,
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// this is the key point of optimization
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std::vector<PreCalc<T>> pre_calc(
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roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height);
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pre_calc_for_bilinear_interpolate(
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height,
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width,
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pooled_height,
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pooled_width,
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roi_bin_grid_h,
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roi_bin_grid_w,
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roi_start_h,
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roi_start_w,
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bin_size_h,
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bin_size_w,
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roi_bin_grid_h,
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roi_bin_grid_w,
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pre_calc);
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for (int64_t c = 0; c < channels; c++) {
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int64_t index_n_c = index_n + c * pooled_width * pooled_height;
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const T* offset_bottom_data =
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bottom_data + static_cast<int64_t>((roi_batch_ind * channels + c) * height * width);
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int64_t pre_calc_index = 0;
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for (int64_t ph = 0; ph < pooled_height; ph++) {
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for (int64_t pw = 0; pw < pooled_width; pw++) {
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int64_t index = index_n_c + ph * pooled_width + pw;
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T output_val = 0.;
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if (mode == "avg") { // avg pooling
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for (int64_t iy = 0; iy < roi_bin_grid_h; iy++) {
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for (int64_t ix = 0; ix < roi_bin_grid_w; ix++) {
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PreCalc<T> pc = pre_calc[pre_calc_index];
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output_val += pc.w1 * offset_bottom_data[pc.pos1] +
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pc.w2 * offset_bottom_data[pc.pos2] +
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pc.w3 * offset_bottom_data[pc.pos3] +
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pc.w4 * offset_bottom_data[pc.pos4];
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pre_calc_index += 1;
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}
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}
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output_val /= count;
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} else { // max pooling
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bool max_flag = false;
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for (int64_t iy = 0; iy < roi_bin_grid_h; iy++) {
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for (int64_t ix = 0; ix < roi_bin_grid_w; ix++) {
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PreCalc<T> pc = pre_calc[pre_calc_index];
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if (!max_flag) {
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output_val = pc.w1 * offset_bottom_data[pc.pos1];
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max_flag = true;
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} else {
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output_val = std::max(std::max(std::max(output_val, pc.w2 * offset_bottom_data[pc.pos2]),
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pc.w3 * offset_bottom_data[pc.pos3]),
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pc.w4 * offset_bottom_data[pc.pos4]);
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}
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pre_calc_index += 1;
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}
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}
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}
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top_data[index] = output_val;
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} // for pw
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} // for ph
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} // for c
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} // for n
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}
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} // namespace
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template <typename T>
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Status ROIAlign<T>::Compute(OpKernelContext* context) const {
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const Tensor* X_ptr = context->Input<Tensor>(0);
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if (!X_ptr) {
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return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "Null input X ptr");
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}
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const Tensor* rois_ptr = context->Input<Tensor>(1);
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if (!rois_ptr) {
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return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "Null rois_ptr");
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}
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auto& x_dims = X_ptr->Shape();
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auto& rois_dims = rois_ptr->Shape();
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// validate rois_dims
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if (rois_dims.NumDimensions() != EXPECTED_NUM_ROI_DIMS) {
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return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "Number of dimensions for rois should be exactly 2");
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}
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if (rois_dims[1] != EXPECTED_SECOND_ROI_DIM) {
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return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "Second dimension for rois should be exactly 5");
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}
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auto& Y = *context->Output(0, {rois_dims[0], x_dims[1], pooled_h_, pooled_w_});
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int64_t output_size = Y.Shape().Size();
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ROIAlignForward<T>(
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output_size,
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X_ptr->Data<T>(),
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spatial_scale_,
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x_dims[1],
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x_dims[2],
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x_dims[3],
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pooled_h_,
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pooled_w_,
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sampling_ratio_,
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rois_ptr->Data<T>(),
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rois_dims[1],
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Y.template MutableData<T>(),
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mode_);
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return Status::OK();
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}
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} // namespace contrib
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} // namespace onnxruntime
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63
onnxruntime/contrib_ops/cpu/roialign.h
Normal file
63
onnxruntime/contrib_ops/cpu/roialign.h
Normal file
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@ -0,0 +1,63 @@
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#pragma once
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#include "core/framework/op_kernel.h"
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#include <cctype>
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namespace onnxruntime {
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namespace contrib {
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template <typename T>
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class ROIAlign final : public OpKernel {
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public:
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explicit ROIAlign(const OpKernelInfo& info) : OpKernel(info) {
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// mode
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std::string mode_tmp;
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if (info.GetAttr<std::string>("mode", &mode_tmp).IsOK()) {
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mode_ = mode_tmp;
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std::transform(mode_.begin(), mode_.end(), mode_.begin(), [](auto& i) { return static_cast<char>(::tolower(i)); });
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if (mode_ != "avg" && mode_ != "max") {
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ORT_THROW("Invalid mode of value ", mode_, " specified. It should be either avg or max");
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}
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}
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// pooled_h
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int64_t pooled_h_tmp;
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if (info.GetAttr<int64_t>("pooled_h", &pooled_h_tmp).IsOK()) {
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pooled_h_ = pooled_h_tmp;
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}
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// pooled_w
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int64_t pooled_w_tmp;
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if (info.GetAttr<int64_t>("pooled_w", &pooled_w_tmp).IsOK()) {
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pooled_w_ = pooled_w_tmp;
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}
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// sampling_ratio
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int64_t sampling_ratio_tmp;
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if (info.GetAttr<int64_t>("sampling_ratio", &sampling_ratio_tmp).IsOK()) {
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sampling_ratio_ = sampling_ratio_tmp;
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ORT_ENFORCE(sampling_ratio_ >= 0, "Sampling ratio should be >=0, but it was ", sampling_ratio_);
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}
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// spatial_scale
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float spatial_scale_tmp;
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if (info.GetAttr<float>("spatial_scale", &spatial_scale_tmp).IsOK()) {
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spatial_scale_ = spatial_scale_tmp;
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}
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}
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Status Compute(OpKernelContext* context) const override;
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private:
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std::string mode_{"avg"};
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int64_t pooled_h_{1};
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int64_t pooled_w_{1};
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int64_t sampling_ratio_{0};
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float spatial_scale_{1.0f};
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ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(ROIAlign);
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};
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} // namespace contrib
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} // namespace onnxruntime
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@ -18,8 +18,8 @@ void convPoolTypeAndShapeInference(ONNX_NAMESPACE::InferenceContext& ctx, bool u
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namespace onnxruntime {
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namespace contrib {
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using ::ONNX_NAMESPACE::AttributeProto;
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using ::ONNX_NAMESPACE::OPTIONAL;
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using ::ONNX_NAMESPACE::OpSchema;
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using ::ONNX_NAMESPACE::OPTIONAL;
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void RegisterContribSchemas() {
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ONNX_CONTRIB_OPERATOR_SCHEMA(SampleOp)
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@ -80,7 +80,7 @@ Sample echo operator.)DOC");
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.SetDomain(kMSDomain)
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.SinceVersion(1)
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.SetDoc(R"DOC(
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The fused convolution operator schema is the same as Conv besides it includes an attribute
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The fused convolution operator schema is the same as Conv besides it includes an attribute
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activation.)DOC")
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.Attr(
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||||
"auto_pad",
|
||||
|
|
@ -318,33 +318,33 @@ activation.)DOC")
|
|||
}
|
||||
})
|
||||
.SetDoc(R"DOC(
|
||||
This transform extracts n-grams from the input sequence and save them as a vector. Input can
|
||||
be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input.
|
||||
For 2-D input, the output is also a 2-D tensor whose i-th row is the n-gram representation of the i-th input row.
|
||||
More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1].
|
||||
If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor.
|
||||
|
||||
In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original
|
||||
sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips.
|
||||
If the number of skips is 2, we should skip two tokens when scanning through the original sequence.
|
||||
Let's consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2.
|
||||
The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4].
|
||||
If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28]
|
||||
This transform extracts n-grams from the input sequence and save them as a vector. Input can
|
||||
be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input.
|
||||
For 2-D input, the output is also a 2-D tensor whose i-th row is the n-gram representation of the i-th input row.
|
||||
More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1].
|
||||
If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor.
|
||||
|
||||
In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original
|
||||
sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips.
|
||||
If the number of skips is 2, we should skip two tokens when scanning through the original sequence.
|
||||
Let's consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2.
|
||||
The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4].
|
||||
If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28]
|
||||
indexed by [0, 1], [1, 2], [2, 3], [3, 4], respectively.
|
||||
|
||||
The output vector stores the count of each n-gram;
|
||||
Y[i] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping
|
||||
The output vector stores the count of each n-gram;
|
||||
Y[i] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping
|
||||
between index i and the corresponding n-gram. If pool_int64s is [94 , 17 ,17, 36], ngram_indexes is [1, 0],
|
||||
ngram_counts=[0, 0], then the Y[0] (first element in Y) and Y[1] (second element in Y) are the counts of [17, 36] and [94, 17],
|
||||
respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output.
|
||||
Note that we may consider all skips up to S when generating the n-grams.
|
||||
|
||||
The examples used above are true if mode is "TF". If mode is "IDF", all the counts larger than 1 would be truncated to 1 and
|
||||
the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is "TFIDF",
|
||||
this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute.
|
||||
|
||||
Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor.
|
||||
If pool_strings is set, the input must be a string tensor.
|
||||
respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output.
|
||||
Note that we may consider all skips up to S when generating the n-grams.
|
||||
|
||||
The examples used above are true if mode is "TF". If mode is "IDF", all the counts larger than 1 would be truncated to 1 and
|
||||
the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is "TFIDF",
|
||||
this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute.
|
||||
|
||||
Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor.
|
||||
If pool_strings is set, the input must be a string tensor.
|
||||
)DOC");
|
||||
|
||||
// Operators for linear 8 bit quanitzation support.
|
||||
|
|
@ -893,6 +893,61 @@ Example 4:
|
|||
"Constrain to tensor(float).")
|
||||
.SetDoc(R"DOC(The WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.)DOC");
|
||||
|
||||
ONNX_CONTRIB_OPERATOR_SCHEMA(ROIAlign)
|
||||
.SetDomain(kMSDomain)
|
||||
.SinceVersion(1)
|
||||
.Attr(
|
||||
"spatial_scale",
|
||||
"Multiplicative spatial scale factor to translate ROI coordinates "
|
||||
"from their input spatial scale to the scale used when pooling, "
|
||||
"i.e., spatial scale of the input feature map X relative to the "
|
||||
"input image. E.g.; default is 1.0f. ",
|
||||
AttributeProto::FLOAT,
|
||||
1.f)
|
||||
.Attr(
|
||||
"pooled_h",
|
||||
"default 1; Pooled output Y's height.",
|
||||
AttributeProto::INT,
|
||||
static_cast<int64_t>(1))
|
||||
.Attr(
|
||||
"pooled_w",
|
||||
"default 1; Pooled output Y's width.",
|
||||
AttributeProto::INT,
|
||||
static_cast<int64_t>(1))
|
||||
.Attr(
|
||||
"sampling_ratio",
|
||||
"Number of sampling points in the interpolation grid used to compute "
|
||||
"the output value of each pooled output bin. If > 0, then exactly "
|
||||
"sampling_ratio x sampling_ratio grid points are used. If == 0, then "
|
||||
"an adaptive number of grid points are used (computed as "
|
||||
"ceil(roi_width / pooled_w), and likewise for height). Default is 0.",
|
||||
AttributeProto::INT,
|
||||
static_cast<int64_t>(0))
|
||||
.Attr(
|
||||
"mode",
|
||||
"The pooling method. Two modes are supported: 'avg' and 'max'. "
|
||||
"Default is 'avg'.",
|
||||
AttributeProto::STRING,
|
||||
std::string("avg"))
|
||||
.Input(0, "X", "Input data tensor from the previous operator; 4-D feature map of shape (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.", "T")
|
||||
.Input(1, "rois", "RoIs (Regions of Interest2) to pool over; rois is 2-D input of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image.", "T")
|
||||
.Output(0, "Y", "RoI pooled output, 4-D tesnor of shape (num_rois, C, pooled_h, pooled_w). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].", "T")
|
||||
.TypeConstraint(
|
||||
"T",
|
||||
{"tensor(float16)", "tensor(float)", "tensor(double)"},
|
||||
"Constrain to float, float16 and double tensors.")
|
||||
.SetDoc(R"DOC(Region of Interest (RoI) align operation described in the
|
||||
[Mask R-CNN paper](https://arxiv.org/abs/1703.06870).
|
||||
RoIAlign consumes an input tensor X and region of interests (rois)
|
||||
to apply pooling across each RoI; it produces a 4-D tensor of shape
|
||||
(num_rois, C, pooled_h, pooled_w).
|
||||
|
||||
RoIAlign is proposed to avoid the misalignment by removing
|
||||
quantizations while converting from original image into feature
|
||||
map and from feature map into RoI feature; in each ROI bin,
|
||||
the value of the sampled locations are computed directly
|
||||
through bilinear interpolation.)DOC");
|
||||
|
||||
#ifdef MICROSOFT_INTERNAL
|
||||
// register internal ops
|
||||
RegisterInternalSchemas();
|
||||
|
|
|
|||
253
onnxruntime/test/contrib_ops/roialign_test.cc
Normal file
253
onnxruntime/test/contrib_ops/roialign_test.cc
Normal file
|
|
@ -0,0 +1,253 @@
|
|||
// Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
// Licensed under the MIT License.
|
||||
|
||||
#include "gtest/gtest.h"
|
||||
#include "test/providers/provider_test_utils.h"
|
||||
|
||||
namespace onnxruntime {
|
||||
namespace test {
|
||||
|
||||
TEST(ROIAlignTest, AvgModePositive) {
|
||||
OpTester test("ROIAlign", 1, onnxruntime::kMSDomain);
|
||||
test.AddAttribute<int64_t>("pooled_h", 3);
|
||||
test.AddAttribute<int64_t>("pooled_w", 4);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", 2);
|
||||
test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
|
||||
|
||||
const int N = 1;
|
||||
const int C = 3;
|
||||
const int H = 5;
|
||||
const int W = 5;
|
||||
|
||||
std::vector<float> rois{0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.,0.,-14.,19.,-14.,19.};
|
||||
test.AddInput<float>("X", {N, C, H, W}, {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.AddInput<float>("rois", {5, 5}, {0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.,0.,-14.,19.,-14.,19.});
|
||||
test.AddOutput<float>("Y", {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.Run();
|
||||
}
|
||||
|
||||
TEST(ROIAlignTest, MaxModePositive) {
|
||||
OpTester test("ROIAlign", 1, onnxruntime::kMSDomain);
|
||||
test.AddAttribute<std::string>("mode", "max");
|
||||
test.AddAttribute<int64_t>("pooled_h", 3);
|
||||
test.AddAttribute<int64_t>("pooled_w", 4);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", 2);
|
||||
test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
|
||||
|
||||
const int N = 1;
|
||||
const int C = 3;
|
||||
const int H = 5;
|
||||
const int W = 5;
|
||||
|
||||
std::vector<float> rois{0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.,0.,-14.,19.,-14.,19.};
|
||||
test.AddInput<float>("X", {N, C, H, W}, {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.AddInput<float>("rois", {5, 5}, {0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.,0.,-14.,19.,-14.,19.});
|
||||
test.AddOutput<float>("Y", {5,3,3,4}, {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,4.125f,
|
||||
3.20833f,4.8125f,4.58333f,4.01042f,2.86458f,3.9375f,23.25f,20.3438f,14.5313f,18.f,28.4167f,21.3125f,
|
||||
14.6667f,22.f,15.8229f,20.3437f,14.5312f,18.f,42.f,36.75f,26.25f,32.0625f,51.3333f,38.5f,26.125f,39.1875f,
|
||||
28.5833f,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,8.78906f,21.875f,21.875f,21.875f,21.875f,26.9792f,26.9792f,26.9792f,26.9792f,32.8125f,
|
||||
32.8125f,32.8125f,30.7617f,40.1042f,40.1042f,40.1042f,40.1042f,46.25f,46.25f,46.25f,46.25f,56.25f,56.25f,
|
||||
56.25f,52.7344f});
|
||||
|
||||
test.Run();
|
||||
}
|
||||
|
||||
TEST(ROIAlignTest, AvgModeNegativeInvalidMode) {
|
||||
OpTester test("ROIAlign", 1, onnxruntime::kMSDomain);
|
||||
test.AddAttribute<std::string>("mode", "foobar"); // <--
|
||||
test.AddAttribute<int64_t>("pooled_h", 3);
|
||||
test.AddAttribute<int64_t>("pooled_w", 4);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", -2);
|
||||
test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
|
||||
|
||||
const int N = 1;
|
||||
const int C = 3;
|
||||
const int H = 5;
|
||||
const int W = 5;
|
||||
|
||||
std::vector<float> rois{0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.,0.,-14.,19.,-14.,19.};
|
||||
test.AddInput<float>("X", {N, C, H, W}, {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.AddInput<float>("rois", {5, 5}, {0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.,0.,-14.,19.,-14.,19.});
|
||||
test.AddOutput<float>("Y", {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.Run(OpTester::ExpectResult::kExpectFailure, "Invalid mode");
|
||||
}
|
||||
|
||||
TEST(ROIAlignTest, AvgModeNegativeSamplingRatio) {
|
||||
OpTester test("ROIAlign", 1, onnxruntime::kMSDomain);
|
||||
test.AddAttribute<int64_t>("pooled_h", 3);
|
||||
test.AddAttribute<int64_t>("pooled_w", 4);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", -2); // <--
|
||||
test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
|
||||
|
||||
const int N = 1;
|
||||
const int C = 3;
|
||||
const int H = 5;
|
||||
const int W = 5;
|
||||
|
||||
std::vector<float> rois{0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.,0.,-14.,19.,-14.,19.};
|
||||
test.AddInput<float>("X", {N, C, H, W}, {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.AddInput<float>("rois", {5, 5}, {0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.,0.,-14.,19.,-14.,19.});
|
||||
test.AddOutput<float>("Y", {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.Run(OpTester::ExpectResult::kExpectFailure, "Sampling ratio should be >=0");
|
||||
}
|
||||
|
||||
TEST(ROIAlignTest, AvgModeNegativeInvalidNumRoiDims) {
|
||||
OpTester test("ROIAlign", 1, onnxruntime::kMSDomain);
|
||||
test.AddAttribute<int64_t>("pooled_h", 3);
|
||||
test.AddAttribute<int64_t>("pooled_w", 4);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", 2);
|
||||
test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
|
||||
|
||||
const int N = 1;
|
||||
const int C = 3;
|
||||
const int H = 5;
|
||||
const int W = 5;
|
||||
|
||||
std::vector<float> rois{0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.,0.,-14.,19.,-14.,19.};
|
||||
test.AddInput<float>("X", {N, C, H, W}, {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.AddInput<float>("rois", {5, 4, 1}, {0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.}); // <--
|
||||
test.AddOutput<float>("Y", {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.Run(OpTester::ExpectResult::kExpectFailure, "Number of dimensions for rois should be exactly 2");
|
||||
}
|
||||
|
||||
TEST(ROIAlignTest, AvgModeNegativeInvalidSecondRoiDims) {
|
||||
OpTester test("ROIAlign", 1, onnxruntime::kMSDomain);
|
||||
test.AddAttribute<int64_t>("pooled_h", 3);
|
||||
test.AddAttribute<int64_t>("pooled_w", 4);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", 2);
|
||||
test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
|
||||
|
||||
const int N = 1;
|
||||
const int C = 3;
|
||||
const int H = 5;
|
||||
const int W = 5;
|
||||
|
||||
std::vector<float> rois{0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.,0.,-14.,19.,-14.,19.};
|
||||
test.AddInput<float>("X", {N, C, H, W}, {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.AddInput<float>("rois", {5, 4}, {0.,7.,5.,7.,5.,0.,-15.,-15.,-15.,-15.,0.,-10.,21.,-10.,21.,0.,13.,8.,13.,8.}); // <--
|
||||
test.AddOutput<float>("Y", {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.Run(OpTester::ExpectResult::kExpectFailure, "Second dimension for rois should be exactly 5");
|
||||
}
|
||||
} // namespace test
|
||||
} // namespace onnxruntime
|
||||
Loading…
Reference in a new issue