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Summary: Hi guys, I'd like to build Caffe2 with more supported options in Windows with Microsoft Visual Studios. This is the first pull request. Running scripts/build_windows_shared.bat is able to build Caffe2 with both CMAKE_BUILD_TYPE=Debug and CMAKE_BUILD_TYPE=Release with Visual Studio 14 2015. CUDA is 9.0, cudnn is 7.0.5, glog, gflags and lmdb are supported on my system. Python is 3.5, Detectron works from python interface as well. It was even possible to debug detectron code and step into caffe2_gpu.dll with pdbs built. What is disappointing, that c10/experimental ops don't build with this Visual Studio generator, I added special option INCLUDE_EXPERIMENTAL_C10_OPS (default ON) to deal with it in build_windows_shared.bat. After this pull request the next step is to add Visual Studio 2017 support in the script. Pull Request resolved: https://github.com/pytorch/pytorch/pull/13550 Reviewed By: ezyang Differential Revision: D13042597 Pulled By: orionr fbshipit-source-id: f313f909f599cd582a1d000eff766eef3a9fc4fc
73 lines
2.6 KiB
C++
73 lines
2.6 KiB
C++
// Copyright 2004-present Facebook. All Rights Reserved.
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#ifndef BBOX_TRANSFORM_OP_H_
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#define BBOX_TRANSFORM_OP_H_
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#include "caffe2/core/context.h"
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#include "caffe2/core/logging.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/utils/math.h"
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namespace caffe2 {
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template <typename T, class Context>
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class BBoxTransformOp final : public Operator<Context> {
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public:
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BBoxTransformOp(const OperatorDef& operator_def, Workspace* ws)
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: Operator<Context>(operator_def, ws),
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weights_(this->template GetRepeatedArgument<T>(
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"weights",
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vector<T>{1.0f, 1.0f, 1.0f, 1.0f})),
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apply_scale_(
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this->template GetSingleArgument<bool>("apply_scale", true)),
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correct_transform_coords_(this->template GetSingleArgument<bool>(
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"correct_transform_coords",
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false)),
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rotated_(this->template GetSingleArgument<bool>("rotated", false)),
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angle_bound_on_(
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this->template GetSingleArgument<bool>("angle_bound_on", true)),
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angle_bound_lo_(
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this->template GetSingleArgument<int>("angle_bound_lo", -90)),
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angle_bound_hi_(
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this->template GetSingleArgument<int>("angle_bound_hi", 90)),
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clip_angle_thresh_(
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this->template GetSingleArgument<float>("clip_angle_thresh", 1.0)) {
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CAFFE_ENFORCE_EQ(
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weights_.size(),
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4,
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"weights size " + c10::to_string(weights_.size()) + "must be 4.");
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}
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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bool RunOnDevice() override;
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protected:
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// weights [wx, wy, ww, wh] to apply to the regression target
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vector<T> weights_;
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// Transform the boxes to the scaled image space after applying the bbox
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// deltas.
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// Set to false to match the detectron code, set to true for the keypoint
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// model and for backward compatibility
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bool apply_scale_{true};
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// Correct bounding box transform coordates, see bbox_transform() in boxes.py
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// Set to true to match the detectron code, set to false for backward
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// compatibility
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bool correct_transform_coords_{false};
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// Set for RRPN case to handle rotated boxes. Inputs should be in format
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// [ctr_x, ctr_y, width, height, angle (in degrees)].
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bool rotated_{false};
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// If set, for rotated boxes in RRPN, output angles are normalized to be
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// within [angle_bound_lo, angle_bound_hi].
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bool angle_bound_on_{true};
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int angle_bound_lo_{-90};
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int angle_bound_hi_{90};
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// For RRPN, clip almost horizontal boxes within this threshold of
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// tolerance for backward compatibility. Set to negative value for
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// no clipping.
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float clip_angle_thresh_{1.0};
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};
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} // namespace caffe2
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#endif // BBOX_TRANSFORM_OP_H_
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