Merge pull request #6488 from sweetsky0901/detection_output
add Detection output op for SSDdel_some_in_makelist
sweetsky0901
7 years ago
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GitHub
commit
90a33dddad
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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Indicesou 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/operators/detection_output_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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detection_output,
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ops::DetectionOutputKernel<paddle::platform::CUDADeviceContext, float>,
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ops::DetectionOutputKernel<paddle::platform::CUDADeviceContext, double>);
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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Indicesou 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 "paddle/framework/op_registry.h"
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#include "paddle/framework/tensor.h"
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#include "paddle/operators/math/detection_util.h"
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#include "paddle/operators/math/math_function.h"
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#include "paddle/operators/math/softmax.h"
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#include "paddle/operators/strided_memcpy.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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inline void transpose_fun(const framework::ExecutionContext& context,
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const framework::Tensor& src,
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framework::Tensor* dst) {
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int input_nums = src.dims()[0];
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int offset = 0;
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for (int j = 0; j < input_nums; ++j) {
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framework::Tensor in_p_tensor = src.Slice(j, j + 1);
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std::vector<int64_t> shape_vec(
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{in_p_tensor.dims()[0], in_p_tensor.dims()[1], in_p_tensor.dims()[3],
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in_p_tensor.dims()[4], in_p_tensor.dims()[2]});
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framework::DDim shape(framework::make_ddim(shape_vec));
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framework::Tensor in_p_tensor_transpose;
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in_p_tensor_transpose.mutable_data<T>(shape, context.GetPlace());
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std::vector<int> shape_axis({0, 1, 3, 4, 2});
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math::Transpose<DeviceContext, T, 5> trans5;
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trans5(context.template device_context<DeviceContext>(), in_p_tensor,
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&in_p_tensor_transpose, shape_axis);
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auto dst_stride = framework::stride(dst->dims());
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auto src_stride = framework::stride(in_p_tensor_transpose.dims());
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StridedMemcpy<T>(context.device_context(), in_p_tensor_transpose.data<T>(),
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src_stride, in_p_tensor_transpose.dims(), dst_stride,
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dst->data<T>() + offset);
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offset += in_p_tensor_transpose.dims()[4] * src_stride[4];
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}
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}
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template <typename DeviceContext, typename T>
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class DetectionOutputKernel : 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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const framework::Tensor* in_loc = context.Input<framework::Tensor>("Loc");
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const framework::Tensor* in_conf = context.Input<framework::Tensor>("Conf");
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const framework::Tensor* in_priorbox =
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context.Input<framework::Tensor>("PriorBox");
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auto* out = context.Output<framework::Tensor>("Out");
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int num_classes = context.template Attr<int>("num_classes");
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int top_k = context.template Attr<int>("top_k");
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int nms_top_k = context.template Attr<int>("nms_top_k");
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int background_label_id = context.template Attr<int>("background_label_id");
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float nms_threshold = context.template Attr<float>("nms_threshold");
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float confidence_threshold =
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context.template Attr<float>("confidence_threshold");
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size_t batch_size = in_conf->dims()[1];
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int conf_sum_size = in_conf->numel();
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// for softmax
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std::vector<int64_t> conf_shape_softmax_vec(
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{conf_sum_size / num_classes, num_classes});
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framework::DDim conf_shape_softmax(
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framework::make_ddim(conf_shape_softmax_vec));
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// for knchw => nhwc
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std::vector<int64_t> loc_shape_vec({1, in_loc->dims()[1], in_loc->dims()[3],
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in_loc->dims()[4],
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in_loc->dims()[2] * in_loc->dims()[0]});
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std::vector<int64_t> conf_shape_vec(
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{1, in_conf->dims()[1], in_conf->dims()[3], in_conf->dims()[4],
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in_conf->dims()[2] * in_conf->dims()[0]});
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framework::DDim loc_shape(framework::make_ddim(loc_shape_vec));
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framework::DDim conf_shape(framework::make_ddim(conf_shape_vec));
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framework::Tensor loc_tensor;
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framework::Tensor conf_tensor;
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loc_tensor.mutable_data<T>(loc_shape, context.GetPlace());
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conf_tensor.mutable_data<T>(conf_shape, context.GetPlace());
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// for cpu
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framework::Tensor loc_cpu;
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framework::Tensor conf_cpu;
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framework::Tensor priorbox_cpu;
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const T* priorbox_data = in_priorbox->data<T>();
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transpose_fun<DeviceContext, T>(context, *in_loc, &loc_tensor);
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transpose_fun<DeviceContext, T>(context, *in_conf, &conf_tensor);
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conf_tensor.Resize(conf_shape_softmax);
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math::SoftmaxFunctor<DeviceContext, T>()(
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context.template device_context<DeviceContext>(), &conf_tensor,
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&conf_tensor);
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T* loc_data = loc_tensor.data<T>();
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T* conf_data = conf_tensor.data<T>();
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if (platform::is_gpu_place(context.GetPlace())) {
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loc_cpu.mutable_data<T>(loc_tensor.dims(), platform::CPUPlace());
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framework::CopyFrom(loc_tensor, platform::CPUPlace(),
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context.device_context(), &loc_cpu);
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loc_data = loc_cpu.data<T>();
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conf_cpu.mutable_data<T>(conf_tensor.dims(), platform::CPUPlace());
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framework::CopyFrom(conf_tensor, platform::CPUPlace(),
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context.device_context(), &conf_cpu);
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conf_data = conf_cpu.data<T>();
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priorbox_cpu.mutable_data<T>(in_priorbox->dims(), platform::CPUPlace());
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framework::CopyFrom(*in_priorbox, platform::CPUPlace(),
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context.device_context(), &priorbox_cpu);
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priorbox_data = priorbox_cpu.data<T>();
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}
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// get decode bboxes
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size_t num_priors = in_priorbox->numel() / 8;
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std::vector<std::vector<operators::math::BBox<T>>> all_decoded_bboxes;
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for (size_t n = 0; n < batch_size; ++n) {
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std::vector<operators::math::BBox<T>> decoded_bboxes;
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for (size_t i = 0; i < num_priors; ++i) {
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size_t prior_offset = i * 8;
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size_t loc_pred_offset = n * num_priors * 4 + i * 4;
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std::vector<math::BBox<T>> prior_bbox_vec;
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math::GetBBoxFromPriorData<T>(priorbox_data + prior_offset, 1,
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prior_bbox_vec);
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std::vector<std::vector<T>> prior_bbox_var;
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math::GetBBoxVarFromPriorData<T>(priorbox_data + prior_offset, 1,
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prior_bbox_var);
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std::vector<T> loc_pred_data;
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for (size_t j = 0; j < 4; ++j)
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loc_pred_data.push_back(*(loc_data + loc_pred_offset + j));
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math::BBox<T> bbox = math::DecodeBBoxWithVar<T>(
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prior_bbox_vec[0], prior_bbox_var[0], loc_pred_data);
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decoded_bboxes.push_back(bbox);
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}
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all_decoded_bboxes.push_back(decoded_bboxes);
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}
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std::vector<std::map<size_t, std::vector<size_t>>> all_indices;
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int num_kept = math::GetDetectionIndices<T>(
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conf_data, num_priors, num_classes, background_label_id, batch_size,
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confidence_threshold, nms_top_k, nms_threshold, top_k,
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all_decoded_bboxes, &all_indices);
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if (num_kept <= 0) {
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std::vector<int64_t> out_shape_vec({0, 0});
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framework::DDim out_shape(framework::make_ddim(out_shape_vec));
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out->Resize(out_shape);
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return;
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}
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std::vector<int64_t> out_shape_vec({num_kept, 7});
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framework::DDim out_shape(framework::make_ddim(out_shape_vec));
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out->mutable_data<T>(out_shape, context.GetPlace());
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framework::Tensor out_cpu;
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T* out_data = out->data<T>();
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if (platform::is_gpu_place(context.GetPlace())) {
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out_cpu.mutable_data<T>(out->dims(), platform::CPUPlace());
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out_data = out_cpu.data<T>();
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}
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math::GetDetectionOutput<T>(conf_data, num_kept, num_priors, num_classes,
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batch_size, all_indices, all_decoded_bboxes,
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out_data);
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if (platform::is_gpu_place(context.GetPlace())) {
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framework::CopyFrom(out_cpu, platform::CUDAPlace(),
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context.device_context(), out);
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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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File diff suppressed because it is too large
Load Diff
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import unittest
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import numpy as np
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from op_test import OpTest
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class TestUnpoolOp(OpTest):
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def setUp(self):
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self.op_type = "detection_output"
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self.init_test_case()
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#loc.shape ((1, 4, 4, 1, 1))
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#conf.shape ((1, 4, 2, 1, 1))
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loc = np.array([[[[[0.1]], [[0.1]], [[0.1]], [[0.1]]],
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[[[0.1]], [[0.1]], [[0.1]], [[0.1]]],
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[[[0.1]], [[0.1]], [[0.1]], [[0.1]]],
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[[[0.1]], [[0.1]], [[0.1]], [[0.1]]]]])
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conf = np.array([[[[[0.1]], [[0.9]]], [[[0.2]], [[0.8]]],
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[[[0.3]], [[0.7]]], [[[0.4]], [[0.6]]]]])
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priorbox = np.array([
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0.1, 0.1, 0.5, 0.5, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.6, 0.6, 0.1,
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0.1, 0.2, 0.2, 0.3, 0.3, 0.7, 0.7, 0.1, 0.1, 0.2, 0.2, 0.4, 0.4,
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0.8, 0.8, 0.1, 0.1, 0.2, 0.2
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])
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output = np.array([
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0, 1, 0.68997443, 0.099959746, 0.099959746, 0.50804031, 0.50804031
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])
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self.inputs = {
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'Loc': loc.astype('float32'),
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'Conf': conf.astype('float32'),
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'PriorBox': priorbox.astype('float32')
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}
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self.attrs = {
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'num_classes': self.num_classes,
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'top_k': self.top_k,
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'nms_top_k': self.nms_top_k,
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'background_label_id': self.background_label_id,
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'nms_threshold': self.nms_threshold,
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'confidence_threshold': self.confidence_threshold,
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}
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self.outputs = {'Out': output.astype('float32')}
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def test_check_output(self):
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self.check_output()
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def init_test_case(self):
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self.num_classes = 2
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self.top_k = 10
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self.nms_top_k = 20
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self.background_label_id = 0
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self.nms_threshold = 0.01
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self.confidence_threshold = 0.01
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if __name__ == '__main__':
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unittest.main()
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