Merge pull request #15952 from jerrywgz/fpn_ops
add distribute fpn proposals op, test=developalign_pyramid
jerrywgz
7 years ago
committed by
GitHub
commit
b0e3c02410
@ -0,0 +1,93 @@
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/fluid/operators/detection/distribute_fpn_proposals_op.h"
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namespace paddle {
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namespace operators {
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class DistributeFpnProposalsOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("FpnRois"),
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"Input(FpnRois) shouldn't be null");
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PADDLE_ENFORCE_GE(
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ctx->Outputs("MultiFpnRois").size(), 1UL,
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"Outputs(MultiFpnRois) of DistributeOp should not be empty");
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size_t min_level = static_cast<size_t>(ctx->Attrs().Get<int>("min_level"));
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size_t max_level = static_cast<size_t>(ctx->Attrs().Get<int>("max_level"));
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PADDLE_ENFORCE_GE(max_level, min_level,
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"max_level must not lower than min_level");
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// Set the output shape
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size_t num_out_rois = max_level - min_level + 1;
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std::vector<framework::DDim> outs_dims;
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outs_dims.reserve(num_out_rois);
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for (size_t i = 0; i < num_out_rois; ++i) {
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framework::DDim out_dim = {-1, 4};
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outs_dims.push_back(out_dim);
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}
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ctx->SetOutputsDim("MultiFpnRois", outs_dims);
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ctx->SetOutputDim("RestoreIndex", {1, -1});
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("FpnRois"));
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return framework::OpKernelType(data_type, platform::CPUPlace());
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}
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};
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class DistributeFpnProposalsOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("FpnRois", "(LoDTensor) The rois at all levels in shape (-1, 4)");
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AddOutput("MultiFpnRois", "(LoDTensor) Output with distribute operator")
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.AsDuplicable();
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AddOutput("RestoreIndex",
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"(Tensor) An array of positive number which is "
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"used to restore the order of FpnRois");
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AddAttr<int>("min_level",
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"The lowest level of FPN layer where the"
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" proposals come from");
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AddAttr<int>("max_level",
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"The highest level of FPN layer where the"
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" proposals come from");
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AddAttr<int>("refer_level",
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"The referring level of FPN layer with"
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" specified scale");
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AddAttr<int>("refer_scale",
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"The referring scale of FPN layer with"
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" specified level");
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AddComment(R"DOC(
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This operator distribute all proposals into different fpn level,
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with respect to scale of the proposals, the referring scale and
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the referring level. Besides, to restore the order of proposals,
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we return an array which indicate the original index of rois in
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current proposals.
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(distribute_fpn_proposals, ops::DistributeFpnProposalsOp,
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ops::DistributeFpnProposalsOpMaker,
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paddle::framework::EmptyGradOpMaker);
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REGISTER_OP_CPU_KERNEL(distribute_fpn_proposals,
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ops::DistributeFpnProposalsOpKernel<float>,
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ops::DistributeFpnProposalsOpKernel<double>);
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@ -0,0 +1,221 @@
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include <paddle/fluid/memory/allocation/allocator.h>
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#include "cub/cub.cuh"
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#include "paddle/fluid/memory/memcpy.h"
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#include "paddle/fluid/operators/detection/distribute_fpn_proposals_op.h"
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#include "paddle/fluid/operators/gather.cu.h"
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#include "paddle/fluid/platform/cuda_primitives.h"
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#include "paddle/fluid/platform/for_range.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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static constexpr int kNumCUDAThreads = 512;
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static constexpr int kNumMaxinumNumBlocks = 4096;
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#define CUDA_1D_KERNEL_LOOP(i, n) \
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for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
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i += blockDim.x * gridDim.x)
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int const BBoxSize = 4;
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struct RangeInitFunctor {
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int start_;
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int delta_;
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int* out_;
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__device__ void operator()(size_t i) { out_[i] = start_ + i * delta_; }
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};
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static inline int NumBlocks(const int N) {
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return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads,
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kNumMaxinumNumBlocks);
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}
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static inline void TransLoD(const int* length_lod, const int lod_size,
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int* offset_lod) {
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int offset = 0;
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for (int i = 0; i < lod_size; ++i) {
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offset_lod[i] = offset;
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offset += length_lod[i];
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}
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}
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template <typename T>
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static __device__ inline T RoIArea(const T* box, bool normalized) {
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if (box[2] < box[0] || box[3] < box[1]) {
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// If coordinate values are is invalid
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// (e.g. xmax < xmin or ymax < ymin), return 0.
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return static_cast<T>(0.);
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} else {
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const T w = box[2] - box[0];
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const T h = box[3] - box[1];
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if (normalized) {
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return w * h;
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} else {
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// If coordinate values are not within range [0, 1].
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return (w + 1) * (h + 1);
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}
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}
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}
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template <class T>
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static __global__ void GPUDistFpnProposalsHelper(
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const int nthreads, const T* rois, const int lod_size,
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const int refer_level, const int refer_scale, const int max_level,
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const int min_level, int* roi_batch_id_data, int* sub_lod_list,
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int* target_lvls) {
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CUDA_1D_KERNEL_LOOP(i, nthreads) {
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const T* offset_roi = rois + i * BBoxSize;
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int roi_batch_ind = roi_batch_id_data[i];
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// get the target level of current rois
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T roi_area = RoIArea(offset_roi, false);
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T roi_scale = sqrt(roi_area);
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int tgt_lvl = floor(log2(roi_scale / refer_scale) + refer_level);
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tgt_lvl = min(max_level, max(tgt_lvl, min_level));
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target_lvls[i] = tgt_lvl;
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// compute number of rois in the same batch and same target level
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platform::CudaAtomicAdd(sub_lod_list + tgt_lvl * lod_size + roi_batch_ind,
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1);
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}
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}
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template <typename DeviceContext, typename T>
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class GPUDistributeFpnProposalsOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* fpn_rois = ctx.Input<paddle::framework::LoDTensor>("FpnRois");
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auto multi_fpn_rois = ctx.MultiOutput<LoDTensor>("MultiFpnRois");
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auto* restore_index = ctx.Output<Tensor>("RestoreIndex");
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const int min_level = ctx.Attr<int>("min_level");
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const int max_level = ctx.Attr<int>("max_level");
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const int refer_level = ctx.Attr<int>("refer_level");
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const int refer_scale = ctx.Attr<int>("refer_scale");
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int num_level = max_level - min_level + 1;
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// check that the fpn_rois is not empty
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PADDLE_ENFORCE_EQ(fpn_rois->lod().size(), 1UL,
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"DistributeFpnProposalsOp need 1 level of LoD");
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auto fpn_rois_lod = fpn_rois->lod().back();
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int lod_size = fpn_rois_lod.size() - 1;
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int roi_num = fpn_rois_lod[lod_size];
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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// get batch id by lod in CPU
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Tensor roi_batch_id_list;
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roi_batch_id_list.Resize({roi_num});
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int* roi_batch_id_data =
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roi_batch_id_list.mutable_data<int>(platform::CPUPlace());
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for (int n = 0; n < lod_size; ++n) {
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for (size_t i = fpn_rois_lod[n]; i < fpn_rois_lod[n + 1]; ++i) {
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roi_batch_id_data[i] = n;
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}
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}
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// copy batch id list to GPU
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Tensor roi_batch_id_list_gpu;
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framework::TensorCopySync(roi_batch_id_list, dev_ctx.GetPlace(),
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&roi_batch_id_list_gpu);
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Tensor sub_lod_list;
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sub_lod_list.Resize({num_level, lod_size});
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int* sub_lod_list_data = sub_lod_list.mutable_data<int>(dev_ctx.GetPlace());
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Tensor target_lvls;
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target_lvls.Resize({roi_num});
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int* target_lvls_data = target_lvls.mutable_data<int>(dev_ctx.GetPlace());
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int blocks = NumBlocks(roi_num);
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int threads = kNumCUDAThreads;
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// get target levels and sub_lod list
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GPUDistFpnProposalsHelper<T><<<blocks, threads>>>(
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roi_num, fpn_rois->data<T>(), lod_size, refer_level, refer_scale,
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max_level, min_level, roi_batch_id_list_gpu.data<int>(),
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sub_lod_list_data, target_lvls_data);
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Tensor index_in_t;
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int* idx_in = index_in_t.mutable_data<int>({roi_num}, dev_ctx.GetPlace());
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platform::ForRange<platform::CUDADeviceContext> for_range(dev_ctx, roi_num);
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for_range(RangeInitFunctor{0, 1, idx_in});
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Tensor keys_out_t;
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int* keys_out = keys_out_t.mutable_data<int>({roi_num}, dev_ctx.GetPlace());
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Tensor index_out_t;
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int* idx_out = index_out_t.mutable_data<int>({roi_num}, dev_ctx.GetPlace());
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// Determine temporary device storage requirements
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size_t temp_storage_bytes = 0;
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cub::DeviceRadixSort::SortPairsDescending<int, int>(
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nullptr, temp_storage_bytes, target_lvls_data, keys_out, idx_in,
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idx_out, roi_num);
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// Allocate temporary storage
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auto place = boost::get<platform::CUDAPlace>(dev_ctx.GetPlace());
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auto d_temp_storage = memory::Alloc(place, temp_storage_bytes,
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memory::Allocator::kScratchpad);
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// Run sorting operation
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// sort target level to get corresponding index
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cub::DeviceRadixSort::SortPairsDescending<int, int>(
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d_temp_storage->ptr(), temp_storage_bytes, target_lvls_data, keys_out,
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idx_in, idx_out, roi_num);
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int* restore_idx_data =
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restore_index->mutable_data<int>({roi_num, 1}, dev_ctx.GetPlace());
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// sort current index to get restore index
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cub::DeviceRadixSort::SortPairsDescending<int, int>(
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d_temp_storage->ptr(), temp_storage_bytes, idx_out, keys_out, idx_in,
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restore_idx_data, roi_num);
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Tensor offset_lod;
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int* offset_lod_data =
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offset_lod.mutable_data<int>({lod_size + 1}, dev_ctx.GetPlace());
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for (int i = 0; i < num_level; ++i) {
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Tensor sub_lod = sub_lod_list.Slice(i, i + 1);
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int* sub_lod_data = sub_lod.data<int>();
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// transfer length-based lod to offset-based lod
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TransLoD(sub_lod_data, lod_size + 1, offset_lod_data);
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int sub_rois_num = offset_lod_data[lod_size];
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Tensor sub_idx = index_out_t.Slice(0, sub_rois_num);
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multi_fpn_rois[i]->mutable_data<T>({sub_rois_num, kBoxDim},
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dev_ctx.GetPlace());
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GPUGather<T>(dev_ctx, *fpn_rois, sub_idx, multi_fpn_rois[i]);
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framework::LoD lod;
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std::vector<size_t> offset;
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memory::Copy(platform::CPUPlace(), offset.data(), place, offset_lod_data,
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sizeof(int) * (lod_size + 1), 0);
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lod.emplace_back(offset);
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multi_fpn_rois[i]->set_lod(lod);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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distribute_fpn_proposals,
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ops::GPUDistributeFpnProposalsOpKernel<paddle::platform::CUDADeviceContext,
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float>,
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ops::GPUDistributeFpnProposalsOpKernel<paddle::platform::CUDADeviceContext,
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double>);
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@ -0,0 +1,147 @@
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include <algorithm>
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#include <cmath>
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#include <cstring>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/detail/safe_ref.h"
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#include "paddle/fluid/operators/gather.h"
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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const int kBoxDim = 4;
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template <typename T>
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static inline T BBoxArea(const T* box, bool normalized) {
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if (box[2] < box[0] || box[3] < box[1]) {
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// If coordinate values are is invalid
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// (e.g. xmax < xmin or ymax < ymin), return 0.
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return static_cast<T>(0.);
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} else {
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const T w = box[2] - box[0];
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const T h = box[3] - box[1];
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if (normalized) {
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return w * h;
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} else {
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// If coordinate values are not within range [0, 1].
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return (w + 1) * (h + 1);
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}
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}
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}
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template <typename T>
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class DistributeFpnProposalsOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* fpn_rois = context.Input<paddle::framework::LoDTensor>("FpnRois");
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auto multi_fpn_rois =
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context.MultiOutput<paddle::framework::LoDTensor>("MultiFpnRois");
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auto* restore_index =
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context.Output<paddle::framework::Tensor>("RestoreIndex");
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const int min_level = context.Attr<int>("min_level");
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const int max_level = context.Attr<int>("max_level");
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const int refer_level = context.Attr<int>("refer_level");
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const int refer_scale = context.Attr<int>("refer_scale");
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const int num_level = max_level - min_level + 1;
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// check that the fpn_rois is not empty
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PADDLE_ENFORCE_EQ(fpn_rois->lod().size(), 1UL,
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"DistributeFpnProposalsOp need 1 level of LoD");
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auto fpn_rois_lod = fpn_rois->lod().back();
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int fpn_rois_num = fpn_rois_lod[fpn_rois_lod.size() - 1];
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std::vector<int> target_level;
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// std::vector<int> target_level(fpn_rois_num, -1);
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// record the number of rois in each level
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std::vector<int> num_rois_level(num_level, 0);
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std::vector<int> num_rois_level_integral(num_level + 1, 0);
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for (int i = 0; i < fpn_rois_lod.size() - 1; ++i) {
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Tensor fpn_rois_slice =
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fpn_rois->Slice(fpn_rois_lod[i], fpn_rois_lod[i + 1]);
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const T* rois_data = fpn_rois_slice.data<T>();
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for (int j = 0; j < fpn_rois_slice.dims()[0]; ++j) {
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// get the target level of current rois
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T roi_scale = std::sqrt(BBoxArea(rois_data, false));
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int tgt_lvl =
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std::floor(std::log2(roi_scale / refer_scale) + refer_level);
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tgt_lvl = std::min(max_level, std::max(tgt_lvl, min_level));
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target_level.push_back(tgt_lvl);
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num_rois_level[tgt_lvl - min_level]++;
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rois_data += kBoxDim;
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}
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||||
}
|
||||
// define the output rois
|
||||
// pointer which point to each level fpn rois
|
||||
std::vector<T*> multi_fpn_rois_data(num_level);
|
||||
// lod0 which will record the offset information of each level rois
|
||||
std::vector<std::vector<size_t>> multi_fpn_rois_lod0;
|
||||
for (int i = 0; i < num_level; ++i) {
|
||||
// allocate memory for each level rois
|
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multi_fpn_rois[i]->mutable_data<T>({num_rois_level[i], kBoxDim},
|
||||
context.GetPlace());
|
||||
multi_fpn_rois_data[i] = multi_fpn_rois[i]->data<T>();
|
||||
std::vector<size_t> lod0(1, 0);
|
||||
multi_fpn_rois_lod0.push_back(lod0);
|
||||
// statistic start point for each level rois
|
||||
num_rois_level_integral[i + 1] =
|
||||
num_rois_level_integral[i] + num_rois_level[i];
|
||||
}
|
||||
restore_index->mutable_data<int>({1, fpn_rois_num}, context.GetPlace());
|
||||
int* restore_index_data = restore_index->data<int>();
|
||||
std::vector<int> restore_index_inter(fpn_rois_num, -1);
|
||||
// distribute the rois into different fpn level by target level
|
||||
for (int i = 0; i < fpn_rois_lod.size() - 1; ++i) {
|
||||
Tensor fpn_rois_slice =
|
||||
fpn_rois->Slice(fpn_rois_lod[i], fpn_rois_lod[i + 1]);
|
||||
const T* rois_data = fpn_rois_slice.data<T>();
|
||||
size_t cur_offset = fpn_rois_lod[i];
|
||||
// std::vector<size_t > lod_offset[num_level];
|
||||
for (int j = 0; j < num_level; j++) {
|
||||
multi_fpn_rois_lod0[j].push_back(multi_fpn_rois_lod0[j][i]);
|
||||
}
|
||||
for (int j = 0; j < fpn_rois_slice.dims()[0]; ++j) {
|
||||
int lvl = target_level[cur_offset + j];
|
||||
memcpy(multi_fpn_rois_data[lvl - min_level], rois_data,
|
||||
kBoxDim * sizeof(T));
|
||||
multi_fpn_rois_data[lvl - min_level] += kBoxDim;
|
||||
int index_in_shuffle = num_rois_level_integral[lvl - min_level] +
|
||||
multi_fpn_rois_lod0[lvl - min_level][i + 1];
|
||||
restore_index_inter[index_in_shuffle] = cur_offset + j;
|
||||
multi_fpn_rois_lod0[lvl - min_level][i + 1]++;
|
||||
rois_data += kBoxDim;
|
||||
}
|
||||
}
|
||||
for (int i = 0; i < fpn_rois_num; ++i) {
|
||||
restore_index_data[restore_index_inter[i]] = i;
|
||||
}
|
||||
// merge lod information into LoDTensor
|
||||
for (int i = 0; i < num_level; ++i) {
|
||||
framework::LoD lod;
|
||||
lod.emplace_back(multi_fpn_rois_lod0[i]);
|
||||
multi_fpn_rois[i]->set_lod(lod);
|
||||
}
|
||||
}
|
||||
};
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,117 @@
|
||||
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import unittest
|
||||
import numpy as np
|
||||
import math
|
||||
import sys
|
||||
from op_test import OpTest
|
||||
|
||||
|
||||
class TestDistributeFPNProposalsOp(OpTest):
|
||||
def set_data(self):
|
||||
self.init_test_case()
|
||||
self.make_rois()
|
||||
self.rois_fpn, self.rois_idx_restore = self.calc_rois_distribute()
|
||||
self.inputs = {'FpnRois': (self.rois[:, 1:5], self.rois_lod)}
|
||||
self.attrs = {
|
||||
'max_level': self.roi_max_level,
|
||||
'min_level': self.roi_min_level,
|
||||
'refer_scale': self.canonical_scale,
|
||||
'refer_level': self.canonical_level
|
||||
}
|
||||
output = [('out%d' % i, self.rois_fpn[i])
|
||||
for i in range(len(self.rois_fpn))]
|
||||
self.outputs = {
|
||||
'MultiFpnRois': output,
|
||||
'RestoreIndex': self.rois_idx_restore
|
||||
}
|
||||
|
||||
def init_test_case(self):
|
||||
self.roi_max_level = 5
|
||||
self.roi_min_level = 2
|
||||
self.canonical_scale = 224
|
||||
self.canonical_level = 4
|
||||
self.images_shape = [512, 512]
|
||||
|
||||
def boxes_area(self, boxes):
|
||||
w = (boxes[:, 2] - boxes[:, 0] + 1)
|
||||
h = (boxes[:, 3] - boxes[:, 1] + 1)
|
||||
areas = w * h
|
||||
assert np.all(areas >= 0), 'Negative areas founds'
|
||||
return areas
|
||||
|
||||
def map_rois_to_fpn_levels(self, rois, lvl_min, lvl_max):
|
||||
s = np.sqrt(self.boxes_area(rois))
|
||||
s0 = self.canonical_scale
|
||||
lvl0 = self.canonical_level
|
||||
target_lvls = np.floor(lvl0 + np.log2(s / s0 + 1e-6))
|
||||
target_lvls = np.clip(target_lvls, lvl_min, lvl_max)
|
||||
return target_lvls
|
||||
|
||||
def get_sub_lod(self, sub_lvl):
|
||||
sub_lod = []
|
||||
max_batch_id = sub_lvl[-1]
|
||||
for i in range(max_batch_id.astype(np.int32) + 1):
|
||||
sub_lod.append(np.where(sub_lvl == i)[0].size)
|
||||
return sub_lod
|
||||
|
||||
def add_multilevel_roi(self, rois, target_lvls, lvl_min, lvl_max):
|
||||
rois_idx_order = np.empty((0, ))
|
||||
rois_fpn = []
|
||||
for lvl in range(lvl_min, lvl_max + 1):
|
||||
idx_lvl = np.where(target_lvls == lvl)[0]
|
||||
if len(idx_lvl) == 0:
|
||||
rois_fpn.append((np.empty(shape=(0, 4)), [[0, 0]]))
|
||||
continue
|
||||
sub_lod = self.get_sub_lod(rois[idx_lvl, 0])
|
||||
rois_fpn.append((rois[idx_lvl, 1:], [sub_lod]))
|
||||
rois_idx_order = np.concatenate((rois_idx_order, idx_lvl))
|
||||
rois_idx_restore = np.argsort(rois_idx_order).astype(
|
||||
np.int32, copy=False)
|
||||
return rois_fpn, rois_idx_restore
|
||||
|
||||
def calc_rois_distribute(self):
|
||||
lvl_min = self.roi_min_level
|
||||
lvl_max = self.roi_max_level
|
||||
target_lvls = self.map_rois_to_fpn_levels(self.rois[:, 1:5], lvl_min,
|
||||
lvl_max)
|
||||
rois_fpn, rois_idx_restore = self.add_multilevel_roi(
|
||||
self.rois, target_lvls, lvl_min, lvl_max)
|
||||
return rois_fpn, rois_idx_restore
|
||||
|
||||
def make_rois(self):
|
||||
self.rois_lod = [[100, 200]]
|
||||
rois = []
|
||||
lod = self.rois_lod[0]
|
||||
bno = 0
|
||||
for roi_num in lod:
|
||||
for i in range(roi_num):
|
||||
xywh = np.random.rand(4)
|
||||
xy1 = xywh[0:2] * 20
|
||||
wh = xywh[2:4] * (self.images_shape - xy1)
|
||||
xy2 = xy1 + wh
|
||||
roi = [bno, xy1[0], xy1[1], xy2[0], xy2[1]]
|
||||
rois.append(roi)
|
||||
bno += 1
|
||||
self.rois = np.array(rois).astype("float32")
|
||||
|
||||
def setUp(self):
|
||||
self.op_type = "distribute_fpn_proposals"
|
||||
self.set_data()
|
||||
|
||||
def test_check_output(self):
|
||||
self.check_output()
|
||||
Loading…
Reference in new issue