mirror of
https://github.com/saymrwulf/onnxruntime.git
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Modify roialign to conform with the new onnx spec and take it out from contrib ops. (#901)
* support non-tensor types * support non-tensor types. * support non-tensor types. * fix compilation issues * fix compilation issues * Build without mkldnn for release packages. We'll default to MLAS. * Modify roialign to conform with the new onnx spec and take it out from contrib ops.
This commit is contained in:
Pranav Sharma
GitHub
parent
73bc09421c
commit
3ff97de8da
6 changed files with 208 additions and 144 deletions
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@ -18,8 +18,6 @@ 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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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, ReverseSequence);
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// This section includes all opkernel declarations for former experimental ops which have now been removed from onnx.
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@ -63,8 +61,6 @@ void RegisterContribKernels(KernelRegistry& kernel_registry) {
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, GatherND)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, MurmurHash3)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, MaxpoolWithMask)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, ROIAlign)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, double, ROIAlign)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, ReverseSequence)>,
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// These ops were experimental ops in onnx domain which have been removed now. We add them here as
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@ -96,9 +96,9 @@ If scale is not provided, crop the borders as provided.)DOC";
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.Output(0, "output", "Result, has same type as input, with H and W dimensions reduced.", "T")
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.TypeConstraint("T", {"tensor(float16)", "tensor(float)", "tensor(double)"}, "Constrain input and output types to float tensors.");
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static const char* ThresholdedRelu_ver1_doc = R"DOC(
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ThresholdedRelu takes one input data (Tensor<T>) and produces one output data
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(Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise,
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static const char* ThresholdedRelu_ver1_doc = R"DOC(
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ThresholdedRelu takes one input data (Tensor<T>) and produces one output data
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(Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise,
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is applied to the tensor elementwise. )DOC";
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ONNX_CONTRIB_OPERATOR_SCHEMA(ThresholdedRelu)
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@ -1015,61 +1015,6 @@ Example 4:
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"Constrain to tensor(float).")
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.SetDoc(R"DOC(The WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.)DOC");
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ONNX_CONTRIB_OPERATOR_SCHEMA(ROIAlign)
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.SetDomain(kMSDomain)
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.SinceVersion(1)
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.Attr(
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"spatial_scale",
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"Multiplicative spatial scale factor to translate ROI coordinates "
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"from their input spatial scale to the scale used when pooling, "
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"i.e., spatial scale of the input feature map X relative to the "
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"input image. E.g.; default is 1.0f. ",
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AttributeProto::FLOAT,
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1.f)
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.Attr(
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"pooled_h",
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"default 1; Pooled output Y's height.",
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AttributeProto::INT,
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static_cast<int64_t>(1))
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.Attr(
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"pooled_w",
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"default 1; Pooled output Y's width.",
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AttributeProto::INT,
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static_cast<int64_t>(1))
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.Attr(
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"sampling_ratio",
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"Number of sampling points in the interpolation grid used to compute "
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"the output value of each pooled output bin. If > 0, then exactly "
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"sampling_ratio x sampling_ratio grid points are used. If == 0, then "
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"an adaptive number of grid points are used (computed as "
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"ceil(roi_width / pooled_w), and likewise for height). Default is 0.",
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AttributeProto::INT,
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static_cast<int64_t>(0))
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.Attr(
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"mode",
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"The pooling method. Two modes are supported: 'avg' and 'max'. "
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"Default is 'avg'.",
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AttributeProto::STRING,
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std::string("avg"))
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.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")
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.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")
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.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")
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.TypeConstraint(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain to float, float16 and double tensors.")
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.SetDoc(R"DOC(Region of Interest (RoI) align operation described in the
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[Mask R-CNN paper](https://arxiv.org/abs/1703.06870).
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RoIAlign consumes an input tensor X and region of interests (rois)
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to apply pooling across each RoI; it produces a 4-D tensor of shape
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(num_rois, C, pooled_h, pooled_w).
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RoIAlign is proposed to avoid the misalignment by removing
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quantizations while converting from original image into feature
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map and from feature map into RoI feature; in each ROI bin,
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the value of the sampled locations are computed directly
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through bilinear interpolation.)DOC");
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#ifdef MICROSOFT_INTERNAL
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// register internal ops
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RegisterInternalSchemas();
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@ -266,6 +266,8 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, Dropout);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, NonMaxSuppression);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, IsInf);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, float, RoiAlign);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, double, RoiAlign);
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void RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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static const BuildKernelCreateInfoFn function_table[] = {
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@ -521,6 +523,8 @@ void RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, Dropout)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, NonMaxSuppression)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, IsInf)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, float, RoiAlign)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, double, RoiAlign)>,
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};
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for (auto& function_table_entry : function_table) {
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@ -25,18 +25,18 @@
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using namespace onnxruntime::concurrency;
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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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const int64_t EXPECTED_SECOND_ROI_DIM = 4;
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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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#define ADD_TYPED_ROIALIGN_OP(data_type) \
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ONNX_CPU_OPERATOR_TYPED_KERNEL( \
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RoiAlign, \
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10, \
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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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.TypeConstraint("T2", DataTypeImpl::GetTensorType<int64_t>()), \
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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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@ -151,7 +151,7 @@ void pre_calc_for_bilinear_interpolate(
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}
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template <typename T>
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void ROIAlignForward(
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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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@ -162,18 +162,18 @@ void ROIAlignForward(
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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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int64_t num_roi_cols,
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T* top_data,
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const std::string& mode,
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const int64_t* batch_indices_ptr,
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const ThreadPool* ttp) {
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int64_t n_rois = nthreads / channels / pooled_width / pooled_height;
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std::function<void(int32_t)> work_object = [&](int32_t 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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const T* offset_bottom_rois = bottom_rois + n * num_roi_cols;
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const auto roi_batch_ind = batch_indices_ptr[n];
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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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@ -264,53 +264,80 @@ void ROIAlignForward(
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} // for ph
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} // for c
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}; // for n
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const_cast<ThreadPool*>(ttp)->ParallelFor((int32_t)n_rois, work_object);
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const_cast<ThreadPool*>(ttp)->ParallelFor(static_cast<int32_t>(n_rois), work_object);
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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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Status RoiAlign<T>::Compute(OpKernelContext* context) const {
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using namespace onnxruntime::common;
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// X
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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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// rois
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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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// batch indices
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const Tensor* batch_indices_ptr = context->Input<Tensor>(2);
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if (!batch_indices_ptr) {
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return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "Null rois_ptr");
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}
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auto& rois_dims = rois_ptr->Shape();
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const auto& x_dims = X_ptr->Shape();
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const auto& rois_dims = rois_ptr->Shape();
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const auto& batch_indices_dims = batch_indices_ptr->Shape();
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if (batch_indices_dims.NumDimensions() != 1) {
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return Status(ONNXRUNTIME, INVALID_ARGUMENT,
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"Number of dimensions for batch indices should be exactly 1");
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}
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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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return Status(ONNXRUNTIME, INVALID_ARGUMENT,
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"Number of dimensions for rois should be exactly " + std::to_string(EXPECTED_NUM_ROI_DIMS));
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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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return Status(ONNXRUNTIME, INVALID_ARGUMENT,
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"Second dimension for rois should be exactly " + std::to_string(EXPECTED_SECOND_ROI_DIM));
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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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auto num_rois = batch_indices_dims[0];
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auto num_rois_from_rois = rois_dims[0];
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auto num_roi_cols = rois_dims[1];
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// first dimension of batch_indices and rois should match
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if (num_rois != num_rois_from_rois) {
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return Status(ONNXRUNTIME, INVALID_ARGUMENT,
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"First dimension (num_rois) of batch_indices and rois don't match");
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}
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auto& Y = *context->Output(0, {num_rois, x_dims[1], output_height_, output_width_});
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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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RoiAlignForward<T>(
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output_size, // num threads
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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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x_dims[1], // num channels
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x_dims[2], // height
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x_dims[3], // width
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output_height_,
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output_width_,
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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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num_roi_cols,
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Y.template MutableData<T>(),
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mode_,
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batch_indices_ptr->Data<int64_t>(),
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static_cast<OpKernelContextInternal*>(context)->GetOperatorThreadPool());
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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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@ -7,11 +7,10 @@
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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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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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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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@ -22,16 +21,16 @@ class ROIAlign final : public OpKernel {
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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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// output_height
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int64_t output_height_tmp;
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if (info.GetAttr<int64_t>("output_height", &output_height_tmp).IsOK()) {
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output_height_ = output_height_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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// output_width
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int64_t output_width_tmp;
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if (info.GetAttr<int64_t>("output_width", &output_width_tmp).IsOK()) {
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output_width_ = output_width_tmp;
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}
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// sampling_ratio
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@ -52,12 +51,11 @@ class ROIAlign final : public OpKernel {
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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 output_height_{1};
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int64_t output_width_{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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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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@ -7,10 +7,10 @@
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namespace onnxruntime {
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namespace test {
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TEST(ROIAlignTest, AvgModePositive) {
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OpTester test("ROIAlign", 1, onnxruntime::kMSDomain);
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test.AddAttribute<int64_t>("pooled_h", 3);
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test.AddAttribute<int64_t>("pooled_w", 4);
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TEST(RoiAlignTest, AvgModePositive) {
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OpTester test("RoiAlign", 10);
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test.AddAttribute<int64_t>("output_height", 3);
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test.AddAttribute<int64_t>("output_width", 4);
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test.AddAttribute<int64_t>("sampling_ratio", 2);
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test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
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@ -24,7 +24,8 @@ TEST(ROIAlignTest, AvgModePositive) {
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25.,26.,27.,28.,29.,30.,31.,32.,33.,34.,35.,36.,37.,38.,39.,40.,41.,42.,43.,44.,45.,46.,
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47.,48.,49.,50.,51.,52.,53.,54.,55.,56.,57.,58.,59.,60.,61.,62.,63.,64.,65.,66.,67.,68.,
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69.,70.,71.,72.,73.,74.});
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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.});
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test.AddInput<float>("rois", {5, 4}, {7.,5.,7.,5.,-15.,-15.,-15.,-15.,-10.,21.,-10.,21.,13.,8.,13.,8.,-14.,19.,-14.,19.});
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test.AddInput<int64_t>("batch_indices", {5}, {0, 0, 0, 0, 0});
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test.AddOutput<float>("Y", {5,3,3,4}, {2.95833f,3.20833f,3.45833f,3.70833f,4.625f,4.875f,5.125f,5.375f,
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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,
|
||||
|
|
@ -48,11 +49,59 @@ TEST(ROIAlignTest, AvgModePositive) {
|
|||
test.Run();
|
||||
}
|
||||
|
||||
TEST(ROIAlignTest, MaxModePositive) {
|
||||
OpTester test("ROIAlign", 1, onnxruntime::kMSDomain);
|
||||
TEST(RoiAlignTest, OnnxTest) {
|
||||
OpTester test("RoiAlign", 10);
|
||||
test.AddAttribute<int64_t>("output_height", 5);
|
||||
test.AddAttribute<int64_t>("output_width", 5);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", 2);
|
||||
test.AddAttribute<float>("spatial_scale", 1.0f);
|
||||
|
||||
const int N = 1;
|
||||
const int C = 1;
|
||||
const int H = 10;
|
||||
const int W = 10;
|
||||
|
||||
test.AddInput<float>("X", {N, C, H, W}, {
|
||||
0.2764f, 0.7150f, 0.1958f, 0.3416f, 0.4638f, 0.0259f, 0.2963f, 0.6518f, 0.4856f, 0.7250f,
|
||||
0.9637f, 0.0895f, 0.2919f, 0.6753f, 0.0234f, 0.6132f, 0.8085f, 0.5324f, 0.8992f, 0.4467f,
|
||||
0.3265f, 0.8479f, 0.9698f, 0.2471f, 0.9336f, 0.1878f, 0.4766f, 0.4308f, 0.3400f, 0.2162f,
|
||||
0.0206f, 0.1720f, 0.2155f, 0.4394f, 0.0653f, 0.3406f, 0.7724f, 0.3921f, 0.2541f, 0.5799f,
|
||||
0.4062f, 0.2194f, 0.4473f, 0.4687f, 0.7109f, 0.9327f, 0.9815f, 0.6320f, 0.1728f, 0.6119f,
|
||||
0.3097f, 0.1283f, 0.4984f, 0.5068f, 0.4279f, 0.0173f, 0.4388f, 0.0430f, 0.4671f, 0.7119f,
|
||||
0.1011f, 0.8477f, 0.4726f, 0.1777f, 0.9923f, 0.4042f, 0.1869f, 0.7795f, 0.9946f, 0.9689f,
|
||||
0.1366f, 0.3671f, 0.7011f, 0.6234f, 0.9867f, 0.5585f, 0.6985f, 0.5609f, 0.8788f, 0.9928f,
|
||||
0.5697f, 0.8511f, 0.6711f, 0.9406f, 0.8751f, 0.7496f, 0.1650f, 0.1049f, 0.1559f, 0.2514f,
|
||||
0.7012f, 0.4056f, 0.7879f, 0.3461f, 0.0415f, 0.2998f, 0.5094f, 0.3727f, 0.5482f, 0.0502f,});
|
||||
test.AddInput<float>("rois", {3, 4}, {0., 0., 9., 9., 0., 5., 4., 9., 5., 5., 9., 9.});
|
||||
test.AddInput<int64_t>("batch_indices", {3}, {0, 0, 0});
|
||||
test.AddOutput<float>("Y", {3,1,5,5}, {
|
||||
0.4664f, 0.4466f, 0.3405f, 0.5688f, 0.6068f,
|
||||
0.3714f, 0.4296f, 0.3835f, 0.5562f, 0.3510f,
|
||||
0.2768f, 0.4883f, 0.5222f, 0.5528f, 0.4171f,
|
||||
0.4713f, 0.4844f, 0.6904f, 0.4920f, 0.8774f,
|
||||
0.6239f, 0.7125f, 0.6289f, 0.3355f, 0.3495f,
|
||||
|
||||
0.3022f, 0.4305f, 0.4696f, 0.3978f, 0.5423f,
|
||||
0.3656f, 0.7050f, 0.5165f, 0.3172f, 0.7015f,
|
||||
0.2912f, 0.5059f, 0.6476f, 0.6235f, 0.8299f,
|
||||
0.5916f, 0.7389f, 0.7048f, 0.8372f, 0.8893f,
|
||||
0.6227f, 0.6153f, 0.7097f, 0.6154f, 0.4585f,
|
||||
|
||||
0.2384f, 0.3379f, 0.3717f, 0.6100f, 0.7601f,
|
||||
0.3767f, 0.3785f, 0.7147f, 0.9243f, 0.9727f,
|
||||
0.5749f, 0.5826f, 0.5709f, 0.7619f, 0.8770f,
|
||||
0.5355f, 0.2566f, 0.2141f, 0.2796f, 0.3600f,
|
||||
0.4365f, 0.3504f, 0.2887f, 0.3661f, 0.2349f,
|
||||
});
|
||||
|
||||
test.Run();
|
||||
}
|
||||
|
||||
TEST(RoiAlignTest, MaxModePositive) {
|
||||
OpTester test("RoiAlign", 10);
|
||||
test.AddAttribute<std::string>("mode", "max");
|
||||
test.AddAttribute<int64_t>("pooled_h", 3);
|
||||
test.AddAttribute<int64_t>("pooled_w", 4);
|
||||
test.AddAttribute<int64_t>("output_height", 3);
|
||||
test.AddAttribute<int64_t>("output_width", 4);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", 2);
|
||||
test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
|
||||
|
||||
|
|
@ -66,7 +115,8 @@ TEST(ROIAlignTest, MaxModePositive) {
|
|||
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.AddInput<float>("rois", {5, 4}, {7.,5.,7.,5.,-15.,-15.,-15.,-15.,-10.,21.,-10.,21.,13.,8.,13.,8.,-14.,19.,-14.,19.});
|
||||
test.AddInput<int64_t>("batch_indices", {5}, {0, 0, 0, 0, 0});
|
||||
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,
|
||||
|
|
@ -85,11 +135,11 @@ TEST(ROIAlignTest, MaxModePositive) {
|
|||
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(RoiAlignTest, AvgModeNegativeInvalidMode) {
|
||||
OpTester test("RoiAlign", 10);
|
||||
test.AddAttribute<std::string>("mode", "foobar"); // <-- failure condition
|
||||
test.AddAttribute<int64_t>("output_height", 3);
|
||||
test.AddAttribute<int64_t>("output_width", 4);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", -2);
|
||||
test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
|
||||
|
||||
|
|
@ -103,7 +153,8 @@ TEST(ROIAlignTest, AvgModeNegativeInvalidMode) {
|
|||
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.AddInput<float>("rois", {5, 4}, {7.,5.,7.,5.,-15.,-15.,-15.,-15.,-10.,21.,-10.,21.,13.,8.,13.,8.,-14.,19.,-14.,19.});
|
||||
test.AddInput<int64_t>("batch_indices", {5}, {0, 0, 0, 0, 0});
|
||||
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,
|
||||
|
|
@ -127,11 +178,11 @@ TEST(ROIAlignTest, AvgModeNegativeInvalidMode) {
|
|||
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(RoiAlignTest, AvgModeNegativeSamplingRatio) {
|
||||
OpTester test("RoiAlign", 10);
|
||||
test.AddAttribute<int64_t>("output_height", 3);
|
||||
test.AddAttribute<int64_t>("output_width", 4);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", -2); // <-- failure condition
|
||||
test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
|
||||
|
||||
const int N = 1;
|
||||
|
|
@ -139,12 +190,12 @@ TEST(ROIAlignTest, AvgModeNegativeSamplingRatio) {
|
|||
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.AddInput<float>("rois", {5, 4}, {7.,5.,7.,5.,-15.,-15.,-15.,-15.,-10.,21.,-10.,21.,13.,8.,13.,8.,-14.,19.,-14.,19.});
|
||||
test.AddInput<int64_t>("batch_indices", {5}, {0, 0, 0, 0, 0});
|
||||
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,
|
||||
|
|
@ -168,10 +219,10 @@ TEST(ROIAlignTest, AvgModeNegativeSamplingRatio) {
|
|||
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(RoiAlignTest, AvgModeNegativeInvalidNumRoiDims) {
|
||||
OpTester test("RoiAlign", 10);
|
||||
test.AddAttribute<int64_t>("output_height", 3);
|
||||
test.AddAttribute<int64_t>("output_width", 4);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", 2);
|
||||
test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
|
||||
|
||||
|
|
@ -180,12 +231,13 @@ TEST(ROIAlignTest, AvgModeNegativeInvalidNumRoiDims) {
|
|||
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.};
|
||||
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.AddInput<float>("rois", {5, 4, 1}, {7.,5.,7.,5.,-15.,-15.,-15.,-15.,-10.,21.,-10.,21.,13.,8.,13.,8.,-14.,19.,-14.,19.}); // <-- failure condition
|
||||
test.AddInput<int64_t>("batch_indices", {5}, {0, 0, 0, 0, 0});
|
||||
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,
|
||||
|
|
@ -206,13 +258,54 @@ TEST(ROIAlignTest, AvgModeNegativeInvalidNumRoiDims) {
|
|||
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.Run(OpTester::ExpectResult::kExpectFailure, "[ShapeInferenceError] rois input tensor has wrong dimension");
|
||||
}
|
||||
|
||||
TEST(ROIAlignTest, AvgModeNegativeInvalidSecondRoiDims) {
|
||||
OpTester test("ROIAlign", 1, onnxruntime::kMSDomain);
|
||||
test.AddAttribute<int64_t>("pooled_h", 3);
|
||||
test.AddAttribute<int64_t>("pooled_w", 4);
|
||||
TEST(RoiAlignTest, AvgModeNegativeInvalidSecondRoiDims) {
|
||||
OpTester test("RoiAlign", 10);
|
||||
test.AddAttribute<int64_t>("output_height", 3);
|
||||
test.AddAttribute<int64_t>("output_width", 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;
|
||||
|
||||
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, 3}, {7.,5.,7.,5.,-15.,-15.,-15.,-15.,-10.,21.,-10.,21.,13.,8.,13.}); // <-- failure condition
|
||||
test.AddInput<int64_t>("batch_indices", {5}, {0, 0, 0, 0, 0});
|
||||
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 4");
|
||||
}
|
||||
|
||||
TEST(RoiAlignTest, MismatchNumRois) {
|
||||
OpTester test("RoiAlign", 10);
|
||||
test.AddAttribute<int64_t>("output_height", 3);
|
||||
test.AddAttribute<int64_t>("output_width", 4);
|
||||
test.AddAttribute<int64_t>("sampling_ratio", 2);
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||||
test.AddAttribute<float>("spatial_scale", 1.0f / 16.0f);
|
||||
|
||||
|
|
@ -226,7 +319,8 @@ TEST(ROIAlignTest, AvgModeNegativeInvalidSecondRoiDims) {
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|||
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.AddInput<float>("rois", {5, 4}, {7.,5.,7.,5.,-15.,-15.,-15.,-15.,-10.,21.,-10.,21.,13.,8.,13.,8.,-14.,19.,-14.,19.});
|
||||
test.AddInput<int64_t>("batch_indices", {4}, {0, 0, 0, 0}); // <-- failure condition
|
||||
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,
|
||||
|
|
@ -247,7 +341,7 @@ TEST(ROIAlignTest, AvgModeNegativeInvalidSecondRoiDims) {
|
|||
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");
|
||||
test.Run(OpTester::ExpectResult::kExpectFailure, "First dimension (num_rois) of batch_indices and rois don't match");
|
||||
}
|
||||
} // namespace test
|
||||
} // namespace onnxruntime
|
||||
Loading…
Reference in a new issue