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Add Contrib ops

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Dragon/include/operators/misc/proposal_op.h deleted 100644 → 0
// --------------------------------------------------------
// Dragon
// Copyright(c) 2017 SeetaTech
// Written by Ting Pan
// --------------------------------------------------------
#ifndef DRAGON_OPERATORS_MISC_PROPOSAL_OP_H_
#define DRAGON_OPERATORS_MISC_PROPOSAL_OP_H_
#include "core/operator.h"
namespace dragon {
template <class Context>
class ProposalOp final : public Operator<Context> {
public:
ProposalOp(const OperatorDef& op_def, Workspace* ws)
: base_size_(OperatorBase::GetSingleArg<int>("base_size", 16)),
min_size_(OperatorBase::GetSingleArg<int>("min_size", 16)),
feat_stride_(OperatorBase::GetSingleArg<int>("feat_stride", -1)),
pre_nms_topn_(OperatorBase::GetSingleArg<int>("pre_nms_topn", 12000)),
post_nms_topn_(OperatorBase::GetSingleArg<int>("post_nms_topn", 2000)),
nms_thresh_(OperatorBase::GetSingleArg<float>("nms_thresh", (float)0.7)),
Operator<Context>(op_def, ws) { Setup(); }
void Setup();
void RunOnDevice() override;
template <typename T> void RunWithType();
protected:
int min_size_, base_size_, feat_stride_;
int pre_nms_topn_, post_nms_topn_;
float nms_thresh_;
Tensor anchors_, roi_indices_, proposals_, nms_mask_;
};
} // namespace dragon
#endif // DRAGON_OPERATORS_MISC_COMPARE_OP_H_
\ No newline at end of file
Dragon/python/dragon/docs/contents/operators.rst
......@@ -64,6 +64,7 @@ Custom
operators/custom/data_process
operators/custom/vec_mult
========================================= =====================================================================
List Brief
========================================= =====================================================================
......@@ -73,6 +74,19 @@ List Brief
========================================= =====================================================================
Contrib
-------
.. toctree::
:hidden:
operators/contrib/rcnn
========================================= =====================================================================
List Brief
========================================= =====================================================================
`dragon.operators.contrib.rcnn`_ Contrib ops for R-CNN.
========================================= =====================================================================
.. _dragon.operators.data: operators/data.html
......@@ -91,4 +105,6 @@ List Brief
.. _dragon.io: io.html
.. _dragon.operators.custom.minibatch: operators/custom/minibatch.html
.. _dragon.operators.custom.data_process: operators/custom/data_process.html
.. _dragon.operators.custom.vec_mult: operators/custom/vec_mult.html
\ No newline at end of file
.. _dragon.operators.custom.vec_mult: operators/custom/vec_mult.html
.. _dragon.operators.contrib.rcnn: operators/contrib/rcnn.html
Dragon/python/dragon/docs/contents/operators/contrib/rcnn.rst 0 → 100644
============
:mod:`R-CNN`
============
.. toctree::
:hidden:
.. automodule:: dragon.operators.contrib.rcnn.ops
:members:
\ No newline at end of file
Dragon/python/dragon/docs/contents/ops.rst
......@@ -35,8 +35,8 @@ List Brief
`Conv2d`_ 2d Convolution.
`Conv2dTranspose`_ 2d Deconvolution.
`Pool2d`_ 2d Pooling, MAX or AVG.
`ROIPooling`_ ROIPoolin(MAX), introduced by `[Girshick, 2015] <https://arxiv.org/abs/1504.08083>`_.
`ROIAlign`_ ROIAlign(MAX), introduced by `[He et.al, 2017] <https://arxiv.org/abs/1703.06870>`_.
`ROIPooling`_ ROIPooling(MAX), introduced by `[Girshick, 2015] <https://arxiv.org/abs/1504.08083>`_.
`ROIAlign`_ ROIAlign(AVG), introduced by `[He et.al, 2017] <https://arxiv.org/abs/1703.06870>`_.
`LRN`_ Local Response Normalization, introduced by `[Krizhevsky et.al, 2012] <http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks>`_.
`NNResize`_ Resize the image with Nearest-Neighbor method.
`BilinearResize`_ Resize the image with Bi-linear method.
......@@ -167,6 +167,14 @@ List Brief
`Proposal`_ Generate Regional Proposals, introduced by `[Ren et.al, 2015] <https://arxiv.org/abs/1506.01497>`_.
================= ======================================================================
Contrib
-------
================= ======================================================================
List Brief
================= ======================================================================
`Proposal`_ Generate Regional Proposals, introduced by `[Ren et.al, 2015] <https://arxiv.org/abs/1506.01497>`_.
================= ======================================================================
Cast
----
================= ======================================================================
......@@ -279,7 +287,8 @@ List Brief
.. _Accuracy: operators/misc.html#dragon.operators.misc.Accuracy
.. _StopGradient: operators/misc.html#dragon.operators.misc.StopGradient
.. _MovingAverage: operators/misc.html#dragon.operators.misc.MovingAverage
.. _Proposal: operators/misc.html#dragon.operators.misc.Proposal
.. _Proposal: operators/contrib/rcnn.html#dragon.operators.contrib.rcnn.ops.Proposal
.. _FloatToHalf: operators/cast.html#dragon.operators.misc.FloatToHalf
......
Dragon/python/dragon/docs/contents/vm/caffe/layer.rst
......@@ -282,6 +282,8 @@ API Reference
.. _NormalizeParameter.scale_filler: https://github.com/weiliu89/caffe/blob/f5eac041aafbc8b86954bd161710f65e70042ce6/src/caffe/proto/caffe.proto#L1332
.. _NormalizeParameter.channel_shared: https://github.com/weiliu89/caffe/blob/f5eac041aafbc8b86954bd161710f65e70042ce6/src/caffe/proto/caffe.proto#L1334
.. _NormalizeParameter.eps: https://github.com/weiliu89/caffe/blob/f5eac041aafbc8b86954bd161710f65e70042ce6/src/caffe/proto/caffe.proto#L1336
.. _ReductionParameter.operation: https://github.com/BVLC/caffe/blob/effcdb0b62410b2a6a54f18f23cf90733a115673/src/caffe/proto/caffe.proto#L973
.. _ReductionParameter.axis: https://github.com/BVLC/caffe/blob/effcdb0b62410b2a6a54f18f23cf90733a115673/src/caffe/proto/caffe.proto#L988
.. _TileParameter.multiples: https://github.com/neopenx/Dragon/blob/6eeac5fec58ed3d0d79f0b4003471e4a641c72f4/Dragon/python/dragon/vm/caffe/proto/caffe.proto#L1173
.. _ExpandDimsParameter.axis: https://github.com/neopenx/Dragon/blob/6eeac5fec58ed3d0d79f0b4003471e4a641c72f4/Dragon/python/dragon/vm/caffe/proto/caffe.proto#L1480
.. _ProposalParameter.feat_stride: https://github.com/sanghoon/caffe/blob/6068dd04ea93cca9fcee036628fdb3ea95b4ebcd/src/caffe/proto/caffe.proto#L431
......
Dragon/python/dragon/operators/contrib/__init__.py 0 → 100644
# --------------------------------------------------------
# Dragon
# Copyright(c) 2017 SeetaTech
# Written by Ting Pan
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# R-CNN ops
from dragon.operators.contrib.rcnn.ops import *
Dragon/python/dragon/operators/contrib/rcnn/__init__.py 0 → 100644
# --------------------------------------------------------
# Dragon
# Copyright(c) 2017 SeetaTech
# Written by Ting Pan
# --------------------------------------------------------
\ No newline at end of file
Dragon/python/dragon/operators/contrib/rcnn/ops.py 0 → 100644
# --------------------------------------------------------
# Dragon
# Copyright(c) 2017 SeetaTech
# Written by Ting Pan
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from dragon.operators import *
def Proposal(inputs, strides, ratios, scales,
pre_nms_top_n=6000, post_nms_top_n=300,
nms_thresh=0.7, min_size=16,
min_level=2, max_level=5,
canonical_scale=224, canonical_level=4, **kwargs):
"""Generate Regional Proposals, introduced by `[Ren et.al, 2015] <https://arxiv.org/abs/1506.01497>`_.
Multi-Level proposals was introduced by `[Lin et.al, 2017] <https://arxiv.org/abs/1612.03144>`_.
For single level proposals(e.g. C4), the inputs should be: [cls_probs, bbox_deltas, im_info].
For multiple level proposals(e.g. FPN), the inputs should be: [cls_score/Px, ...] + [cls_probs, bbox_deltas, im_info].
Parameters
----------
inputs : list of Tensor
The inputs.
strides : list of int
The strides of anchors.
ratios : list of float
The ratios of anchors.
scales : list of float
The scales of anchors.
pre_nms_top_n : int
The number of anchors before nms.
post_nms_top_n : int
The number of anchors after nms.
nms_thresh : float
The threshold of nms.
min_size : int
The min size of anchors.
min_level : int
Finest level of the FPN pyramid.
max_level : int
Coarsest level of the FPN pyramid.
canonical_scale : int
The baseline scale of mapping policy.
canonical_level : int
Heuristic level of the canonical scale.
Returns
-------
Tensor
The proposals.
"""
CheckInputs(inputs, 3, INT_MAX)
arguments = ParseArguments(locals())
num_levels = (max_level - min_level) + 1
num_levels = 1 if len(inputs) == 3 else num_levels
outputs = Tensor.CreateOperator(nout=num_levels, op_type='Proposal', **arguments)
return outputs
\ No newline at end of file
Dragon/python/dragon/operators/misc.py
......@@ -157,44 +157,4 @@ def MovingAverage(inputs, decay, **kwargs):
output = Tensor.CreateOperator(op_type='MovingAverage',
existing_outputs=variable, **arguments)
return output
def Proposal(inputs, ratios, scales,
base_size=16, min_size=16, feat_stride=16,
pre_nms_topn=12000, post_nms_topn=2000, nms_thresh=0.7, **kwargs):
"""Generate Regional Proposals, introduced by `[Ren et.al, 2015] <https://arxiv.org/abs/1506.01497>`_.
Parameters
----------
inputs : list of Tensor
The inputs, represent [input, anchors, im_info].
ratios : list of float
The ratios of anchors.
scales : list of float
The scales of anchors.
base_size : int
The base size of anchors.
min_size : int
The min size of anchors.
feat_stride : int
The stride of input. Default is ``16`` (The 4th down-samples).
pre_nms_topn : int
The number of anchors before nms.
post_nms_topn : int
The number of anchors after nms.
nms_thresh : float
The threshold of nms.
Returns
=------
Tensor
The proposals.
"""
CheckInputs(inputs, 3)
arguments = ParseArguments(locals())
output = Tensor.CreateOperator(nout=1, op_type='Proposal', **arguments)
return output
\ No newline at end of file
Dragon/python/dragon/operators/ndarray.py
......@@ -272,9 +272,14 @@ def Reduce(inputs, axis=-1, operation='NONE', keep_dims=False, **kwargs):
output = Tensor.CreateOperator(nout=1, op_type='Reduce', **arguments)
if inputs.shape is not None:
if axis == -1: output.shape = [1]
output.shape = inputs.shape[:]
if axis == -1:
if keep_dims:
for i in xrange(len(output.shape)):
output.shape[i] = 1
else: output.shape = [1]
else:
output.shape = inputs.shape[:]
if keep_dims: output.shape[axis] = 1
else: del output.shape[axis]
......
Dragon/python/dragon/operators/vision.py
......@@ -329,7 +329,7 @@ def ROIPooling(inputs, pool_h, pool_w, spatial_scale, **kwargs):
def ROIAlign(inputs, pool_h=0, pool_w=0, spatial_scale=1.0, sampling_ratio=2, **kwargs):
"""Max ROIAlign, introduced by `[He et.al, 2017] <https://arxiv.org/abs/1703.06870>`_.
"""AVG ROIAlign, introduced by `[He et.al, 2017] <https://arxiv.org/abs/1703.06870>`_.
The first dimension of input must be ``1``.
......
Dragon/python/dragon/ops.py
......@@ -21,6 +21,7 @@ from .operators import mpi
from .operators import ndarray
from .operators import norm
from .operators import recurrent
from .operators import contrib
# data
LMDBData = data.LMDBData
......@@ -128,11 +129,13 @@ Template = misc.Template
Accuracy = misc.Accuracy
StopGradient = misc.StopGradient
MovingAverage = misc.MovingAverage
Proposal = misc.Proposal
# cast
FloatToHalf = cast.FloatToHalf
# mpi
MPIBroadcast = mpi.MPIBroadcast
MPIGather = mpi.MPIGather
\ No newline at end of file
MPIGather = mpi.MPIGather
# contrib
Proposal = contrib.Proposal # R-CNN
\ No newline at end of file
Dragon/python/dragon/vm/caffe/layers/common.py
......@@ -272,7 +272,7 @@ class GatherLayer(Layer):
Parameters
----------
axis : int
The axis for gathering. Refer `GatherParameter.axis`_.
The axis for gathering. Refer ``GatherParameter.axis``.
"""
def __init__(self, LayerParameter):
......@@ -623,35 +623,44 @@ class ProposalLayer(Layer):
Parameters
----------
feat_stride : int
The stride of input. Refer `ProposalParameter.feat_stride`_.
base_size : int
The base size of anchors. Refer `ProposalParameter.base_size`_.
min_size : int
The min size of anchors. Refer `ProposalParameter.min_size`_.
ratio : list of float
The ratios of anchors. Refer `ProposalParameter.ratio`_.
stride : list of int
The stride of anchors. Refer ``ProposalParameter.stride``.
scale : list of float
The scales of anchors. Refer `ProposalParameter.scale`_.
pre_nms_topn : int
The num of anchors before nms. Refer `ProposalParameter.pre_nms_topn`_.
post_nms_topn : int
ratio : list of float
The ratios of anchors. Refer `ProposalParameter.ratio`_.
pre_nms_top_n : int
The num of anchors before nms. Refer `ProposalParameter.pre_nms_topn`_.
post_nms_top_n : int
The num of anchors after nms. Refer `ProposalParameter.post_nms_topn`_.
nms_thresh : float
The threshold of nms. Refer `ProposalParameter.nms_thresh`_.
min_size : int
The min size of anchors. Refer `ProposalParameter.min_size`_.
min_level : int
Finest level of the FPN pyramid. Refer ``ProposalParameter.min_level``.
max_level : int
Coarsest level of the FPN pyramid. Refer ``ProposalParameter.max_level``.
canonical_scale : int
The baseline scale of mapping policy. Refer ``ProposalParameter.canonical_scale``.
canonical_level : int
Heuristic level of the canonical scale. Refer ``ProposalParameter.canonical_level``.
"""
def __init__(self, LayerParameter):
super(ProposalLayer, self).__init__(LayerParameter)
param = LayerParameter.proposal_param
self._param = {'base_size': param.base_size,
'min_size': param.min_size,
'feat_stride': param.feat_stride,
'pre_nms_topn': param.pre_nms_topn,
'post_nms_topn': param.post_nms_topn,
'nms_thresh': param.nms_thresh,
self._param = {'strides': param.stride,
'ratios': param.ratio,
'scales': param.scale}
'scales': param.scale,
'pre_nms_top_n': param.pre_nms_top_n,
'post_nms_top_n': param.post_nms_top_n,
'nms_thresh': param.nms_thresh,
'min_size': param.min_size,
'min_level': param.min_level,
'max_level': param.max_level,
'canonical_scale': param.canonical_scale,
'canonical_level': param.canonical_level}
def Setup(self, bottom):
super(ProposalLayer, self).Setup(bottom)
......
Dragon/python/dragon/vm/caffe/proto/caffe.proto
......@@ -1474,14 +1474,17 @@ message ExpandDimsParameter {
}
message ProposalParameter {
optional uint32 feat_stride = 1 [default = 16];
optional uint32 base_size = 2 [default = 16];
optional uint32 min_size = 3 [default = 16];
repeated float ratio = 4;
repeated float scale = 5;
optional uint32 pre_nms_topn = 6 [default = 6000];
optional uint32 post_nms_topn = 7 [default = 300];
optional float nms_thresh = 8 [default = 0.7];
repeated int32 stride = 1;
repeated float ratio = 2;
repeated float scale = 3;
optional uint32 pre_nms_top_n = 4 [default = 6000];
optional uint32 post_nms_top_n = 5 [default = 300];
optional float nms_thresh = 6 [default = 0.7];
optional uint32 min_size = 7 [default = 16];
optional int32 min_level = 8 [default = 2];
optional int32 max_level = 9 [default = 5];
optional int32 canonical_scale = 10 [default = 224];
optional int32 canonical_level = 11 [default = 4];
}
message BatchRenormParameter {
......
Dragon/python/dragon/vm/caffe/proto/caffe_pb2.py
......@@ -19,7 +19,7 @@ _sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='caffe.proto',
package='caffe',
serialized_pb=_b('\n\x0b\x63\x61\x66\x66\x65.proto\x12\x05\x63\x61\x66\x66\x65\"\x1c\n\tBlobShape\x12\x0f\n\x03\x64im\x18\x01 \x03(\x03\x42\x02\x10\x01\"\xcc\x01\n\tBlobProto\x12\x1f\n\x05shape\x18\x07 \x01(\x0b\x32\x10.caffe.BlobShape\x12\x10\n\x04\x64\x61ta\x18\x05 \x03(\x02\x42\x02\x10\x01\x12\x10\n\x04\x64iff\x18\x06 \x03(\x02\x42\x02\x10\x01\x12\x17\n\x0b\x64ouble_data\x18\x08 \x03(\x01\x42\x02\x10\x01\x12\x17\n\x0b\x64ouble_diff\x18\t \x03(\x01\x42\x02\x10\x01\x12\x0e\n\x03num\x18\x01 \x01(\x05:\x01\x30\x12\x13\n\x08\x63hannels\x18\x02 \x01(\x05:\x01\x30\x12\x11\n\x06height\x18\x03 \x01(\x05:\x01\x30\x12\x10\n\x05width\x18\x04 \x01(\x05:\x01\x30\"2\n\x0f\x42lobProtoVector\x12\x1f\n\x05\x62lobs\x18\x01 \x03(\x0b\x32\x10.caffe.BlobProto\"\x81\x01\n\x05\x44\x61tum\x12\x10\n\x08\x63hannels\x18\x01 \x01(\x05\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\r\n\x05width\x18\x03 \x01(\x05\x12\x0c\n\x04\x64\x61ta\x18\x04 \x01(\x0c\x12\r\n\x05label\x18\x05 \x01(\x05\x12\x12\n\nfloat_data\x18\x06 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_sym_db.RegisterFileDescriptor(DESCRIPTOR)
......@@ -40,8 +40,8 @@ _PHASE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=17391,
serialized_end=17419,
serialized_start=17463,
serialized_end=17491,
)
_sym_db.RegisterEnumDescriptor(_PHASE)
......@@ -5624,58 +5624,79 @@ _PROPOSALPARAMETER = _descriptor.Descriptor(
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='feat_stride', full_name='caffe.ProposalParameter.feat_stride', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=16,
name='stride', full_name='caffe.ProposalParameter.stride', index=0,
number=1, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='base_size', full_name='caffe.ProposalParameter.base_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=16,
name='ratio', full_name='caffe.ProposalParameter.ratio', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='min_size', full_name='caffe.ProposalParameter.min_size', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=16,
name='scale', full_name='caffe.ProposalParameter.scale', index=2,
number=3, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='ratio', full_name='caffe.ProposalParameter.ratio', index=3,
number=4, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
name='pre_nms_top_n', full_name='caffe.ProposalParameter.pre_nms_top_n', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=6000,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.ProposalParameter.scale', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
name='post_nms_top_n', full_name='caffe.ProposalParameter.post_nms_top_n', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=300,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pre_nms_topn', full_name='caffe.ProposalParameter.pre_nms_topn', index=5,
number=6, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=6000,
name='nms_thresh', full_name='caffe.ProposalParameter.nms_thresh', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.7,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='post_nms_topn', full_name='caffe.ProposalParameter.post_nms_topn', index=6,
name='min_size', full_name='caffe.ProposalParameter.min_size', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=300,
has_default_value=True, default_value=16,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='nms_thresh', full_name='caffe.ProposalParameter.nms_thresh', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.7,
name='min_level', full_name='caffe.ProposalParameter.min_level', index=7,
number=8, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=2,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='max_level', full_name='caffe.ProposalParameter.max_level', index=8,
number=9, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='canonical_scale', full_name='caffe.ProposalParameter.canonical_scale', index=9,
number=10, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=224,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='canonical_level', full_name='caffe.ProposalParameter.canonical_level', index=10,
number=11, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=4,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
......@@ -5691,7 +5712,7 @@ _PROPOSALPARAMETER = _descriptor.Descriptor(
oneofs=[
],
serialized_start=16818,
serialized_end=17018,
serialized_end=17090,
)
......@@ -5755,8 +5776,8 @@ _BATCHRENORMPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=17021,
serialized_end=17187,
serialized_start=17093,
serialized_end=17259,
)
......@@ -5792,8 +5813,8 @@ _DENSECONCATPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=17189,
serialized_end=17252,
serialized_start=17261,
serialized_end=17324,
)
......@@ -5843,8 +5864,8 @@ _FOCALLOSSPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=17254,
serialized_end=17353,
serialized_start=17326,
serialized_end=17425,
)
......@@ -5873,8 +5894,8 @@ _GATHERPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=17355,
serialized_end=17389,
serialized_start=17427,
serialized_end=17461,
)
_BLOBPROTO.fields_by_name['shape'].message_type = _BLOBSHAPE
......
Dragon/python/setup.py
......@@ -36,7 +36,7 @@ find_packages('dragon')
find_modules()
setup(name = 'dragon',
version='0.2.1.3',
version='0.2.1.4',
description = 'Dragon: A Computation Graph Virtual Machine Based Deep Learning Framework',
url='https://github.com/neopenx/Dragon',
author='Ting Pan',
......
Dragon/src/contrib/rcnn/bbox_utils.cc 0 → 100644
#include "core/context.h"
#include "contrib/rcnn/bbox_utils.h"
namespace dragon {
namespace rcnn {
/******************** Proposal ********************/
template <> void GenerateProposals<float, CPUContext>(const int A,
const int feat_h,
const int feat_w,
const int stride,
const float im_h, const float im_w,
const float min_box_h, const float min_box_w,
const float* scores,
const float* bbox_deltas,
const float* anchors,
float* proposals) {
float* proposal = proposals;
const int K = feat_h * feat_w;
for (int h = 0; h < feat_h; ++h) {
for (int w = 0; w < feat_w; ++w) {
const float x = w * stride;
const float y = h * stride;
// bbox_deltas: [1, A, 4, K]
const float* bbox_delta = bbox_deltas + h * feat_w + w;
// scores: [1, A, K]
const float* score = scores + h * feat_w + w;
for (int a = 0; a < A; ++a) {
const float dx = bbox_delta[(a * 4 + 0) * K];
const float dy = bbox_delta[(a * 4 + 1) * K];
const float d_log_w = bbox_delta[(a * 4 + 2) * K];
const float d_log_h = bbox_delta[(a * 4 + 3) * K];
proposal[0] = x + anchors[a * 4 + 0];
proposal[1] = y + anchors[a * 4 + 1];
proposal[2] = x + anchors[a * 4 + 2];
proposal[3] = y + anchors[a * 4 + 3];
proposal[4] = BBoxTransform<float>(dx, dy,
d_log_w, d_log_h,
im_w, im_h,
min_box_w, min_box_h,
proposal) * score[a * K];
proposal += 5;
}
}
}
}
template <> void GenerateProposals_v2<float, CPUContext>(const int total_anchors,
const float im_h, const float im_w,
const float min_box_h, const float min_box_w,
const float* scores,
const float* bbox_deltas,
float* proposals) {
float* proposal = proposals;
for (int i = 0; i < total_anchors; ++i) {
// bbox_deltas: [1, 4, total_anchors]
// scores: [1, total_anchors]
const float dx = bbox_deltas[i];
const float dy = bbox_deltas[total_anchors + i];
const float d_log_w = bbox_deltas[2 * total_anchors + i];
const float d_log_h = bbox_deltas[3 * total_anchors + i];
proposal[4] = BBoxTransform<float>(dx, dy,
d_log_w, d_log_h,
im_w, im_h,
min_box_w, min_box_h,
proposal) * scores[i];
proposal += 5;
}
}
/******************** NMS ********************/
template <typename T>
T iou(const T A[], const T B[]) {
if (A[0] > B[2] || A[1] > B[3] || A[2] < B[0] || A[3] < B[1]) return 0;
const T x1 = std::max(A[0], B[0]);
const T y1 = std::max(A[1], B[1]);
const T x2 = std::min(A[2], B[2]);
const T y2 = std::min(A[3], B[3]);
const T width = std::max((T)0, x2 - x1 + (T)1);
const T height = std::max((T)0, y2 - y1 + (T)1);
const T area = width * height;
const T A_area = (A[2] - A[0] + (T)1) * (A[3] - A[1] + (T)1);
const T B_area = (B[2] - B[0] + (T)1) * (B[3] - B[1] + (T)1);
return area / (A_area + B_area - area);
}
template <> void NMS<float, CPUContext>(const int num_boxes,
const int max_keeps,
const float thresh,
const float* proposals,
int* roi_indices,
int& num_rois,
Tensor* mask) {
int count = 0;
std::vector<char> is_dead(num_boxes);
for (int i = 0; i < num_boxes; ++i) is_dead[i] = 0;
for (int i = 0; i < num_boxes; ++i) {
if (is_dead[i]) continue;
roi_indices[count++] = i;
if (count == max_keeps) break;
for (int j = i + 1; j < num_boxes; ++j)
if (!is_dead[j] && iou(&proposals[i * 5], &proposals[j * 5]) > thresh) is_dead[j] = 1;
}
num_rois = count;
}
} // namespace rcnn
} // namespace dragon
\ No newline at end of file
Dragon/src/contrib/rcnn/bbox_utils.cu 0 → 100644
#ifdef WITH_CUDA
#include "core/context_cuda.h"
#include "contrib/rcnn/bbox_utils.h"
namespace dragon {
namespace rcnn {
/******************** BBox ********************/
template <typename T>
__device__ int _BBoxTransform(const T dx, const T dy,
const T d_log_w, const T d_log_h,
const T im_w, const T im_h,
const T min_box_w, const T min_box_h,
T* bbox) {
const T w = bbox[2] - bbox[0] + (T)1;
const T h = bbox[3] - bbox[1] + (T)1;
const T ctr_x = bbox[0] + (T)0.5 * w;
const T ctr_y = bbox[1] + (T)0.5 * h;
const T pred_ctr_x = dx * w + ctr_x;
const T pred_ctr_y = dy * h + ctr_y;
const T pred_w = exp(d_log_w) * w;
const T pred_h = exp(d_log_h) * h;
bbox[0] = pred_ctr_x - (T)0.5 * pred_w;
bbox[1] = pred_ctr_y - (T)0.5 * pred_h;
bbox[2] = pred_ctr_x + (T)0.5 * pred_w;
bbox[3] = pred_ctr_y + (T)0.5 * pred_h;
bbox[0] = max((T)0, min(bbox[0], im_w - (T)1));
bbox[1] = max((T)0, min(bbox[1], im_h - (T)1));
bbox[2] = max((T)0, min(bbox[2], im_w - (T)1));
bbox[3] = max((T)0, min(bbox[3], im_h - (T)1));
const T box_w = bbox[2] - bbox[0] + (T)1;
const T box_h = bbox[3] - bbox[1] + (T)1;
return (box_w >= min_box_w) * (box_h >= min_box_h);
}
/******************** Proposal ********************/
template <typename T>
__global__ void _GenerateProposals(const int nthreads,
const int A,
const int feat_h,
const int feat_w,
const int stride,
const float im_h, const float im_w,
const float min_box_h, const float min_box_w,
const T* scores,
const T* bbox_deltas,
const T* anchors,
T* proposals) {
CUDA_KERNEL_LOOP(idx, nthreads) {
const int h = idx / A / feat_w;
const int w = (idx / A) % feat_w;
const int a = idx % A;
const T x = w * stride;
const T y = h * stride;
const T* bbox_delta = bbox_deltas + h * feat_w + w;
const T* score = scores + h * feat_w + w;
const int K = feat_h * feat_w;
const T dx = bbox_delta[(a * 4 + 0) * K];
const T dy = bbox_delta[(a * 4 + 1) * K];
const T d_log_w = bbox_delta[(a * 4 + 2) * K];
const T d_log_h = bbox_delta[(a * 4 + 3) * K];
T* proposal = proposals + idx * 5;
proposal[0] = x + anchors[a * 4 + 0];
proposal[1] = y + anchors[a * 4 + 1];
proposal[2] = x + anchors[a * 4 + 2];
proposal[3] = y + anchors[a * 4 + 3];
proposal[4] = _BBoxTransform(dx, dy,
d_log_w, d_log_h,
im_w, im_h,
min_box_w, min_box_h,
proposal) * score[a * K];
}
}
template <> void GenerateProposals<float, CUDAContext>(const int A,
const int feat_h,
const int feat_w,
const int stride,
const float im_h, const float im_w,
const float min_box_h, const float min_box_w,
const float* scores,
const float* bbox_deltas,
const float* anchors,
float* proposals) {
const int num_proposals = A * feat_h * feat_w;
_GenerateProposals<float> << <GET_BLOCKS(num_proposals), CUDA_NUM_THREADS >> >(num_proposals,
A,
feat_h,
feat_w,
stride,
im_h, im_w,
min_box_h, min_box_w,
scores,
bbox_deltas,
anchors,
proposals);
CUDA_POST_KERNEL_CHECK;
}
template <typename T>
__global__ void _GenerateProposals_v2(const int nthreads,
const float im_h, const float im_w,
const float min_box_h, const float min_box_w,
const T* scores,
const T* bbox_deltas,
T* proposals) {
CUDA_KERNEL_LOOP(idx, nthreads) {
const float dx = bbox_deltas[idx];
const float dy = bbox_deltas[nthreads + idx];
const float d_log_w = bbox_deltas[2 * nthreads + idx];
const float d_log_h = bbox_deltas[3 * nthreads + idx];
T* proposal = proposals + idx * 5;
proposal[4] = _BBoxTransform(dx, dy,
d_log_w, d_log_h,
im_w, im_h,
min_box_w, min_box_h,
proposal) * scores[idx];
}
}
template <> void GenerateProposals_v2<float, CUDAContext>(const int total_anchors,
const float im_h, const float im_w,
const float min_box_h, const float min_box_w,
const float* scores,
const float* bbox_deltas,
float* proposals) {
_GenerateProposals_v2<float> << <GET_BLOCKS(total_anchors), CUDA_NUM_THREADS >> >(total_anchors,
im_h, im_w,
min_box_h, min_box_w,
scores,
bbox_deltas,
proposals);
CUDA_POST_KERNEL_CHECK;
}
/******************** NMS ********************/
#define DIV_THEN_CEIL(x, y) (((x) + (y) - 1) / (y))
#define NMS_BLOCK_SIZE 64
template <typename T>
__device__ T iou(const T* A, const T* B) {
const T x1 = max(A[0], B[0]);
const T y1 = max(A[1], B[1]);
const T x2 = min(A[2], B[2]);
const T y2 = min(A[3], B[3]);
const T width = max((T)0, x2 - x1 + (T)1);
const T height = max((T)0, y2 - y1 + (T)1);
const T area = width * height;
const T A_area = (A[2] - A[0] + (T)1) * (A[3] - A[1] + (T)1);
const T B_area = (B[2] - B[0] + (T)1) * (B[3] - B[1] + (T)1);
return area / (A_area + B_area - area);
}
template <typename T>
__global__ static void nms_mask(const T boxes[],
unsigned long long mask[],
const int num_boxes,
const T nms_thresh) {
const int i_start = blockIdx.x * NMS_BLOCK_SIZE;
const int di_end = min(num_boxes - i_start, NMS_BLOCK_SIZE);
const int j_start = blockIdx.y * NMS_BLOCK_SIZE;
const int dj_end = min(num_boxes - j_start, NMS_BLOCK_SIZE);
__shared__ T boxes_i[NMS_BLOCK_SIZE * 4];
{
const int di = threadIdx.x;
if (di < di_end) {
boxes_i[di * 4 + 0] = boxes[(i_start + di) * 5 + 0];
boxes_i[di * 4 + 1] = boxes[(i_start + di) * 5 + 1];
boxes_i[di * 4 + 2] = boxes[(i_start + di) * 5 + 2];
boxes_i[di * 4 + 3] = boxes[(i_start + di) * 5 + 3];
}
}
__syncthreads();
{
const int dj = threadIdx.x;
if (dj < dj_end) {
const T* const box_j = boxes + (j_start + dj) * 5;
unsigned long long mask_j = 0;
const int di_start = (i_start == j_start) ? (dj + 1) : 0;
for (int di = di_start; di < di_end; ++di) {
const T* const box_i = boxes_i + di * 4;
if (iou(box_j, box_i) > nms_thresh) {
mask_j |= 1ULL << di;
}
}
{
const int num_blocks = DIV_THEN_CEIL(num_boxes, NMS_BLOCK_SIZE);
const int bi = blockIdx.x;
mask[(j_start + dj) * num_blocks + bi] = mask_j;
}
}
}
}
template <typename T>
void _NMS(const int num_boxes,
const int max_keeps,
const float thresh,
const float* proposals,
int* roi_indices,
int& num_rois,
Tensor* mask) {
const int num_blocks = DIV_THEN_CEIL(num_boxes, NMS_BLOCK_SIZE);
{
const dim3 blocks(num_blocks, num_blocks);
vector<TIndex> mask_shape(2);
mask_shape[0] = num_boxes;
mask_shape[1] = num_blocks * sizeof(unsigned long long) / sizeof(int);
mask->Reshape(mask_shape);
nms_mask << <blocks, NMS_BLOCK_SIZE >> >(
proposals, (unsigned long long*)mask->template mutable_data<int, CUDAContext>(),
num_boxes, thresh);
CUDA_POST_KERNEL_CHECK;
}
// discard i-th box if it is significantly overlapped with
// one or more previous (= scored higher) boxes
{
const unsigned long long* p_mask_cpu
= (unsigned long long*)mask->mutable_data<int, CPUContext>();
int num_selected = 0;
vector<unsigned long long> dead_bit(num_blocks);
for (int i = 0; i < num_blocks; ++i) {
dead_bit[i] = 0;
}
for (int i = 0; i < num_boxes; ++i) {
const int nblock = i / NMS_BLOCK_SIZE;
const int inblock = i % NMS_BLOCK_SIZE;
if (!(dead_bit[nblock] & (1ULL << inblock))) {
roi_indices[num_selected++] = i;
const unsigned long long* const mask_i = p_mask_cpu + i * num_blocks;
for (int j = nblock; j < num_blocks; ++j) {
dead_bit[j] |= mask_i[j];
}
if (num_selected == max_keeps) {
break;
}
}
}
num_rois = num_selected;
}
}
template <> void NMS<float, CUDAContext>(const int num_boxes,
const int max_keeps,
const float thresh,
const float* proposals,
int* roi_indices,
int& num_rois,
Tensor* mask) {
_NMS<float>(num_boxes, max_keeps, thresh,
proposals, roi_indices, num_rois, mask);
}
} // namespace rcnn
} // namespace dragon
#endif // WITH_CUDA
\ No newline at end of file
Dragon/src/contrib/rcnn/bbox_utils.h 0 → 100644
// --------------------------------------------------------
// Dragon
// Copyright(c) 2017 SeetaTech
// Written by Ting Pan
// --------------------------------------------------------
#ifndef DRAGON_CONTRIB_RCNN_BBOX_UTILS_H_
#define DRAGON_CONTRIB_RCNN_BBOX_UTILS_H_
#include "core/context.h"
#include "core/operator.h"
namespace dragon {
namespace rcnn {
#define ROUND(x) ((int)((x) + (T)0.5))
/******************** BBox ********************/
template <typename T>
int BBoxTransform(const T dx, const T dy,
const T d_log_w, const T d_log_h,
const T im_w, const T im_h,
const T min_box_w, const T min_box_h,
T* bbox) {
const T w = bbox[2] - bbox[0] + (T)1;
const T h = bbox[3] - bbox[1] + (T)1;
const T ctr_x = bbox[0] + (T)0.5 * w;
const T ctr_y = bbox[1] + (T)0.5 * h;
const T pred_ctr_x = dx * w + ctr_x;
const T pred_ctr_y = dy * h + ctr_y;
const T pred_w = exp(d_log_w) * w;
const T pred_h = exp(d_log_h) * h;
bbox[0] = pred_ctr_x - (T)0.5 * pred_w;
bbox[1] = pred_ctr_y - (T)0.5 * pred_h;
bbox[2] = pred_ctr_x + (T)0.5 * pred_w;
bbox[3] = pred_ctr_y + (T)0.5 * pred_h;
bbox[0] = std::max((T)0, std::min(bbox[0], im_w - (T)1));
bbox[1] = std::max((T)0, std::min(bbox[1], im_h - (T)1));
bbox[2] = std::max((T)0, std::min(bbox[2], im_w - (T)1));
bbox[3] = std::max((T)0, std::min(bbox[3], im_h - (T)1));
const T bbox_w = bbox[2] - bbox[0] + (T)1;
const T bbox_h = bbox[3] - bbox[1] + (T)1;
return (bbox_w >= min_box_w) * (bbox_h >= min_box_h);
}
/******************** Anchor ********************/
template <typename T>
void GenerateAnchors(int base_size,
const int num_ratios,
const int num_scales,
const T* ratios,
const T* scales,
T* anchors) {
const T base_area = (T)(base_size * base_size);
const T center = (T)0.5 * (base_size - (T)1);
T* offset_anchors = anchors;
for (int i = 0; i < num_ratios; ++i) {
const T ratio_w = (T)ROUND(sqrt(base_area / ratios[i]));
const T ratio_h = (T)ROUND(ratio_w * ratios[i]);
for (int j = 0; j < num_scales; ++j) {
const T scale_w = (T)0.5 * (ratio_w * scales[j] - (T)1);
const T scale_h = (T)0.5 * (ratio_h * scales[j] - (T)1);
offset_anchors[0] = center - scale_w;
offset_anchors[1] = center - scale_h;
offset_anchors[2] = center + scale_w;
offset_anchors[3] = center + scale_h;
offset_anchors += 4;
}
}
}
template <typename T>
void GenerateGridAnchors(const int A, const int feat_h, const int feat_w,
const int stride,
const T* anchors,
T* proposals) {
T* proposal = proposals;
for (int a = 0; a < A; ++a) {
for (int h = 0; h < feat_h; ++h) {
for (int w = 0; w < feat_w; ++w) {
const T x = w * stride;
const T y = h * stride;
proposal[0] = x + anchors[a * 4 + 0];
proposal[1] = y + anchors[a * 4 + 1];
proposal[2] = x + anchors[a * 4 + 2];
proposal[3] = y + anchors[a * 4 + 3];
proposal += 5;
}
}
}
}
/******************** Proposal ********************/
template <typename T, class Context>
void GenerateProposals(const int A, const int feat_h, const int feat_w,
const int stride,
const float im_h, const float im_w,
const float min_box_h, const float min_box_w,
const T* scores,
const T* bbox_deltas,
const T* anchors,
T* proposals);
template <typename T, class Context>
void GenerateProposals_v2(const int total_anchors,
const float im_h, const float im_w,
const float min_box_h, const float min_box_w,
const T* scores,
const T* bbox_deltas,
T* proposals);
template <typename T>
void SortProposals(const int start,
const int end,
const int num_top,
T* proposals) {
const T pivot_score = proposals[start * 5 + 4];
int left = start + 1, right = end;
while (left <= right) {
while (left <= end && proposals[left * 5 + 4] >= pivot_score) ++left;
while (right > start && proposals[right * 5 + 4] <= pivot_score) --right;
if (left <= right) {
for (int i = 0; i < 5; ++i)
std::swap(proposals[left * 5 + i], proposals[right * 5 + i]);
++left;
--right;
}
}
if (right > start) {
for (int i = 0; i < 5; ++i)
std::swap(proposals[start * 5 + i], proposals[right * 5 + i]);
}
if (start < right - 1) SortProposals(start, right - 1, num_top, proposals);
if (right + 1 < num_top && right + 1 < end) SortProposals(right + 1, end, num_top, proposals);
}
template <typename T>
void RetrieveRoIs(const int num_rois,
const int roi_batch_ind,
const T* proposals,
const int* roi_indices,
T* rois) {
for (int i = 0; i < num_rois; ++i) {
const T* proposal = proposals + roi_indices[i] * 5;
rois[i * 5 + 0] = roi_batch_ind;
rois[i * 5 + 1] = proposal[0];
rois[i * 5 + 2] = proposal[1];
rois[i * 5 + 3] = proposal[2];
rois[i * 5 + 4] = proposal[3];
}
}
template <typename T>
int roi_level(const int min_level, // e.g. 2
const int max_level, // e.g. 5
const int canonical_level, // e.g. 4
const int canonical_scale, // e.g. 224
T* roi) {
T w = roi[3] - roi[1] + 1;
T h = roi[4] - roi[2] + 1;
// reference the settings of paper
int level = canonical_level + std::log(std::max(std::sqrt(w * h), T(1)) / T(canonical_scale));
return std::min(max_level, std::max(min_level, level));
}
template <typename T>
void CollectRoIs(const int num_rois,
const int min_level,
const int max_level,
const int canonical_level,
const int canonical_scale,
const T* rois,
vector< vector<TIndex> >& roi_bins) {
const T* roi = rois;
for (int i = 0; i < num_rois; ++i) {
int bin_idx = roi_level(min_level, max_level,
canonical_level, canonical_scale,
roi);
bin_idx = std::max(bin_idx - min_level, 0);
roi_bins[bin_idx].push_back(i);
roi += 5;
}
}
template <typename T>
void DistributeRoIs(const vector< vector<TIndex> >& roi_bins,
const T* rois,
vector<T*> outputs) {
for (int i = 0; i < roi_bins.size(); i++) {
auto* y = outputs[i];
if (roi_bins[i].size() == 0) {
// fake a tiny roi to avoid empty roi pooling
y[0] = 0, y[1] = 0, y[2] = 0, y[3] = 1, y[4] = 1;
} else {
for (int j = 0; j < roi_bins[i].size(); ++j) {
const T* roi = rois + roi_bins[i][j] * 5;
for (int k = 0; k < 5; ++k) y[k] = roi[k];
y += 5;
}
}
}
}
/******************** NMS ********************/
template <typename T, class Context>
void NMS(const int num_boxes,
const int max_keeps,
const T thresh,
const T* proposals,
int* roi_indices,
int& num_rois,
Tensor* mask);
} // namespace rcnn
} // namespace dragon
#endif // DRAGON_OPERATORS_CONTRIB_BBOX_UTILS_H_
\ No newline at end of file
Dragon/src/contrib/rcnn/proposal_op.cc 0 → 100644
#include "contrib/rcnn/proposal_op.h"
#include "contrib/rcnn/bbox_utils.h"
namespace dragon {
template <class Context> template <typename T>
void ProposalOp<Context>::RunWithType() {
TIndex total_rois = 0;
auto* im_info = input(-1).template data<T, CPUContext>();
auto* Ydata = output(0)->template mutable_data<T, CPUContext>();
for (int n = 0; n < num_images; ++n) {
const T im_height = im_info[0];
const T im_width = im_info[1];
const T scale = im_info[2];
const T min_box_h = min_size * scale;
const T min_box_w = min_size * scale;
int num_rois = 0;
if (strides.size() == 1) {
// case 1: single stride (Faster R-CNN)
const TIndex feat_height = input(0).dim(2);
const TIndex feat_width = input(0).dim(3);
const TIndex K = feat_height * feat_width;
const TIndex A = ratios.size() * scales.size();
const TIndex num_proposals = K * A;
const TIndex pre_nms_topn = std::min(num_proposals, pre_nms_top_n);
anchors_.Reshape(vector<TIndex>({ A, 4 }));
proposals_.Reshape(vector<TIndex>({ num_proposals, 5 }));
rcnn::GenerateAnchors<T>(strides[0], (int)ratios.size(), (int)scales.size(),
&ratios[0], &scales[0],
anchors_.template mutable_data<T, CPUContext>());
rcnn::GenerateProposals<T, Context>(A, feat_height, feat_width, strides[0],
im_height, im_width, min_box_h, min_box_w,
input(0).template data<T, Context>() + num_proposals,
input(1).template data<T, Context>(),
anchors_.template mutable_data<T, Context>(),
proposals_.template mutable_data<T, Context>());
rcnn::SortProposals(0, num_proposals - 1, pre_nms_top_n,
proposals_.template mutable_data<T, CPUContext>());
rcnn::NMS<T, Context>(pre_nms_topn, post_nms_top_n, nms_thresh,
proposals_.template mutable_data<T, Context>(),
roi_indices_.template mutable_data<int, CPUContext>(),
num_rois,
&nms_mask_);
rcnn::RetrieveRoIs<T>(num_rois, n, proposals_.template mutable_data<T, CPUContext>(),
roi_indices_.template mutable_data<int, CPUContext>(),
Ydata);
} else if (strides.size() > 1) {
// case 2: multiple stride (FPN / Mask R-CNN / RetinaNet)
CHECK_EQ(strides.size(), (int)InputSize() - 3)
<< "\nGiven " << strides.size() << " strides and "
<< InputSize() - 3 << " feature inputs";
CHECK_EQ(strides.size(), scales.size())
<< "\nGiven " << strides.size() << " strides and "
<< scales.size() << " scales";
// cls_probs: [1, 2, total_proposals]
// bbox_deltas: [1, 4, total_proposals]
TIndex total_proposals = input(-3).dim(2), acc_proposals = 0;
const TIndex pre_nms_topn = std::min(total_proposals, pre_nms_top_n);;
proposals_.Reshape(vector<TIndex>({ total_proposals, 5 }));
auto* proposals = proposals_.template mutable_data<T, CPUContext>();
for (int i = 0; i < strides.size(); i++) {
const TIndex feat_height = input(i).dim(2);
const TIndex feat_width = input(i).dim(3);
const TIndex K = feat_height * feat_width;
const TIndex A = ratios.size();
const TIndex num_proposals = K * A;
anchors_.Reshape(vector<TIndex>({ A, 4 }));
rcnn::GenerateAnchors<T>(strides[i], (int)ratios.size(), 1,
&ratios[0], &scales[0],
anchors_.template mutable_data<T, CPUContext>());
rcnn::GenerateGridAnchors<T>(A, feat_height, feat_width, strides[i],
anchors_.template mutable_data<T, CPUContext>(),
proposals);
acc_proposals += num_proposals;
proposals += (num_proposals * 5);
}
CHECK_EQ(acc_proposals, total_proposals)
<< "\nExcepted " << total_proposals << " proposals from the network, "
<< "but generated " << acc_proposals << " proposals.";
rcnn::GenerateProposals_v2<T, Context>(total_proposals, im_height, im_width,
min_box_h, min_box_w,
input(-3).template data<T, Context>() + total_proposals,
input(-2).template data<T, Context>(),
proposals_.template mutable_data<T, Context>());
rcnn::SortProposals(0, total_proposals - 1, pre_nms_top_n,
proposals_.template mutable_data<T, CPUContext>());
rcnn::NMS<T, Context>(pre_nms_topn, post_nms_top_n, nms_thresh,
proposals_.template mutable_data<T, Context>(),
roi_indices_.template mutable_data<int, CPUContext>(),
num_rois,
&nms_mask_);
rcnn::RetrieveRoIs<T>(num_rois, n, proposals_.template mutable_data<T, CPUContext>(),
roi_indices_.template mutable_data<int, CPUContext>(),
Ydata);
} else {
LOG(FATAL) << "There should be given at least one stride for proposals.";
}
total_rois += num_rois;
Ydata += (num_rois * 5);
im_info += 3;
}
output(0)->Reshape(vector<TIndex>({ total_rois, 5 }));
// distribute rois into K bins
if (OutputSize() > 1) {
CHECK_EQ(max_level - min_level + 1, (int)OutputSize())
<< "Excepted " << OutputSize() << " outputs for levels between "
<< "[" << min_level << ", " << max_level << "].";
vector< vector<TIndex> > roi_bins(OutputSize(), vector<TIndex>());
vector<T*> outputs;
Tensor collective_rois;
collective_rois.ReshapeLike(*output(0));
auto* rois = collective_rois.template mutable_data<T, CPUContext>();
CPUContext::template Copy<T, CPUContext, CPUContext>(collective_rois.count(),
rois,
output(0)->template data<T, CPUContext>());
rcnn::CollectRoIs<T>(total_rois, min_level, max_level,
canonical_level, canonical_scale,
rois,
roi_bins);
for (int i = 0; i < OutputSize(); i++) {
output(i)->Reshape(vector<TIndex>({ std::max((int)roi_bins[i].size(), 1), 5 }));
outputs.push_back(output(i)->template mutable_data<T, CPUContext>());
}
rcnn::DistributeRoIs(roi_bins, rois, outputs);
}
}
template <class Context>
void ProposalOp<Context>::RunOnDevice() {
num_images = input(0).dim(0);
CHECK_EQ(input(-1).count(), num_images * 3)
<< "Excepted " << num_images * 3 << " groups image info, "
<< "but got " << input(-1).count() / 3 << ".";
roi_indices_.Reshape(vector<TIndex>(1, post_nms_top_n));
output(0)->Reshape(vector<TIndex>({ num_images * post_nms_top_n, 5 }));
if (TypeMeta::Id<Context>() == TypeMeta::Id<CPUContext>()) {
if (input(0).template IsType<float>()) RunWithType<float>();
else LOG(FATAL) << "Unsupported input types.";
} else if (TypeMeta::Id<Context>() == TypeMeta::Id<CUDAContext>()) {
if (input(0).template IsType<float>()) RunWithType<float>();
else LOG(FATAL) << "Unsupported input types.";
}
}
DEPLOY_CPU(Proposal);
#ifdef WITH_CUDA
DEPLOY_CUDA(Proposal);
#endif
OPERATOR_SCHEMA(Proposal).NumInputs(3, INT_MAX).NumOutputs(1, INT_MAX);
} // namespace dragon
\ No newline at end of file
Dragon/src/contrib/rcnn/proposal_op.h 0 → 100644
// --------------------------------------------------------
// Dragon
// Copyright(c) 2017 SeetaTech
// Written by Ting Pan
// --------------------------------------------------------
#ifndef DRAGON_CONTRIB_RCNN_PROPOSAL_OP_H_
#define DRAGON_CONTRIB_RCNN_PROPOSAL_OP_H_
#include "core/operator.h"
namespace dragon {
template <class Context>
class ProposalOp final : public Operator<Context> {
public:
ProposalOp(const OperatorDef& op_def, Workspace* ws)
: Operator<Context>(op_def, ws),
strides(OperatorBase::GetRepeatedArg<int>("strides")),
ratios(OperatorBase::GetRepeatedArg<float>("ratios")),
scales(OperatorBase::GetRepeatedArg<float>("scales")),
pre_nms_top_n(OperatorBase::GetSingleArg<int>("pre_nms_top_n", 6000)),
post_nms_top_n(OperatorBase::GetSingleArg<int>("post_nms_top_n", 300)),
nms_thresh(OperatorBase::GetSingleArg<float>("nms_thresh", (float)0.7)),
min_size(OperatorBase::GetSingleArg<int>("min_size", 16)),
min_level(OperatorBase::GetSingleArg<int>("min_level", 2)),
max_level(OperatorBase::GetSingleArg<int>("max_level", 5)),
canonical_level(OperatorBase::GetSingleArg<int>("canonical_level", 4)),
canonical_scale(OperatorBase::GetSingleArg<int>("canonical_scale", 224)) {}
void RunOnDevice() override;
template <typename T> void RunWithType();
protected:
vector<int> strides;
vector<float> ratios, scales;
TIndex pre_nms_top_n, post_nms_top_n, min_size, num_images;
TIndex min_level, max_level, canonical_level, canonical_scale;
float nms_thresh;
Tensor anchors_, proposals_, roi_indices_, nms_mask_;
};
} // namespace dragon
#endif // DRAGON_OPERATORS_CONTRIB_RCNN_PROPOSAL_OP_H_
\ No newline at end of file
Dragon/src/operators/misc/proposal_op.cc deleted 100644 → 0
#include "operators/misc/proposal_op.h"
namespace dragon {
#define ROUND(x) ((int)((x) + (T)0.5))
template <typename T>
static void generate_anchors(int base_size,
const T ratios[],
const T scales[],
const int num_ratios,
const int num_scales,
T anchors[]) {
// base box's width & height & center location
const T base_area = (T)(base_size * base_size);
const T center = (T)0.5 * (base_size - (T)1);
// enumerate all transformed boxes
T* p_anchors = anchors;
for (int i = 0; i < num_ratios; ++i) {
// transformed width & height for given ratio factors
const T ratio_w = (T)ROUND(sqrt(base_area / ratios[i]));
const T ratio_h = (T)ROUND(ratio_w * ratios[i]);
for (int j = 0; j < num_scales; ++j) {
// transformed width & height for given scale factors
const T scale_w = (T)0.5 * (ratio_w * scales[j] - (T)1);
const T scale_h = (T)0.5 * (ratio_h * scales[j] - (T)1);
// (x1, y1, x2, y2) for transformed box
p_anchors[0] = center - scale_w;
p_anchors[1] = center - scale_h;
p_anchors[2] = center + scale_w;
p_anchors[3] = center + scale_h;
p_anchors += 4;
} // endfor j
}
}
template <typename T>
static int transform_box(T box[],
const T dx, const T dy,
const T d_log_w, const T d_log_h,
const T img_W, const T img_H,
const T min_box_W, const T min_box_H) {
// width & height of box
const T w = box[2] - box[0] + (T)1;
const T h = box[3] - box[1] + (T)1;
// center location of box
const T ctr_x = box[0] + (T)0.5 * w;
const T ctr_y = box[1] + (T)0.5 * h;
// new center location according to gradient (dx, dy)
const T pred_ctr_x = dx * w + ctr_x;
const T pred_ctr_y = dy * h + ctr_y;
// new width & height according to gradient d(log w), d(log h)
const T pred_w = exp(d_log_w) * w;
const T pred_h = exp(d_log_h) * h;
// update upper-left corner location
box[0] = pred_ctr_x - (T)0.5 * pred_w;
box[1] = pred_ctr_y - (T)0.5 * pred_h;
// update lower-right corner location
box[2] = pred_ctr_x + (T)0.5 * pred_w;
box[3] = pred_ctr_y + (T)0.5 * pred_h;
// adjust new corner locations to be within the image region,
box[0] = std::max((T)0, std::min(box[0], img_W - (T)1));
box[1] = std::max((T)0, std::min(box[1], img_H - (T)1));
box[2] = std::max((T)0, std::min(box[2], img_W - (T)1));
box[3] = std::max((T)0, std::min(box[3], img_H - (T)1));
// recompute new width & height
const T box_w = box[2] - box[0] + (T)1;
const T box_h = box[3] - box[1] + (T)1;
// check if new box's size >= threshold
return (box_w >= min_box_W) * (box_h >= min_box_H);
}
template <typename T>
static void enumerate_proposals_cpu(const T bottom4d[],
const T d_anchor4d[],
const T anchors[],
T proposals[],
const int num_anchors,
const int bottom_H, const int bottom_W,
const T img_H, const T img_W,
const T min_box_H, const T min_box_W,
const int feat_stride) {
T* p_proposal = proposals;
const int bottom_area = bottom_H * bottom_W;
for (int h = 0; h < bottom_H; ++h) {
for (int w = 0; w < bottom_W; ++w) {
const T x = w * feat_stride;
const T y = h * feat_stride;
const T* p_box = d_anchor4d + h * bottom_W + w;
const T* p_score = bottom4d + h * bottom_W + w;
for (int k = 0; k < num_anchors; ++k) {
const T dx = p_box[(k * 4 + 0) * bottom_area];
const T dy = p_box[(k * 4 + 1) * bottom_area];
const T d_log_w = p_box[(k * 4 + 2) * bottom_area];
const T d_log_h = p_box[(k * 4 + 3) * bottom_area];
p_proposal[0] = x + anchors[k * 4 + 0];
p_proposal[1] = y + anchors[k * 4 + 1];
p_proposal[2] = x + anchors[k * 4 + 2];
p_proposal[3] = y + anchors[k * 4 + 3];
p_proposal[4]
= transform_box(p_proposal,
dx, dy, d_log_w, d_log_h,
img_W, img_H, min_box_W, min_box_H)
* p_score[k * bottom_area];
p_proposal += 5;
} // endfor k
} // endfor w
} // endfor h
}
template <typename T>
static void sort_box(T list_cpu[],
const int start,
const int end,
const int num_top) {
const T pivot_score = list_cpu[start * 5 + 4];
int left = start + 1, right = end;
T temp[5];
while (left <= right) {
while (left <= end && list_cpu[left * 5 + 4] >= pivot_score) ++left;
while (right > start && list_cpu[right * 5 + 4] <= pivot_score) --right;
if (left <= right) {
for (int i = 0; i < 5; ++i) {
temp[i] = list_cpu[left * 5 + i];
}
for (int i = 0; i < 5; ++i) {
list_cpu[left * 5 + i] = list_cpu[right * 5 + i];
}
for (int i = 0; i < 5; ++i) {
list_cpu[right * 5 + i] = temp[i];
}
++left;
--right;
}
}
if (right > start) {
for (int i = 0; i < 5; ++i) {
temp[i] = list_cpu[start * 5 + i];
}
for (int i = 0; i < 5; ++i) {
list_cpu[start * 5 + i] = list_cpu[right * 5 + i];
}
for (int i = 0; i < 5; ++i) {
list_cpu[right * 5 + i] = temp[i];
}
}
if (start < right - 1) {
sort_box(list_cpu, start, right - 1, num_top);
}
if (right + 1 < num_top && right + 1 < end) {
sort_box(list_cpu, right + 1, end, num_top);
}
}
template <typename T>
static T iou(const T A[], const T B[]) {
if (A[0] > B[2] || A[1] > B[3] || A[2] < B[0] || A[3] < B[1]) return 0;
// overlapped region (= box)
const T x1 = std::max(A[0], B[0]);
const T y1 = std::max(A[1], B[1]);
const T x2 = std::min(A[2], B[2]);
const T y2 = std::min(A[3], B[3]);
// intersection area
const T width = std::max((T)0, x2 - x1 + (T)1);
const T height = std::max((T)0, y2 - y1 + (T)1);
const T area = width * height;
// area of A, B
const T A_area = (A[2] - A[0] + (T)1) * (A[3] - A[1] + (T)1);
const T B_area = (B[2] - B[0] + (T)1) * (B[3] - B[1] + (T)1);
// IoU
return area / (A_area + B_area - area);
}
template <typename T>
void nms_cpu(const int num_boxes,
const T boxes[],
int index_out[],
int* const num_out,
const int base_index,
const T nms_thresh,
const int max_num_out) {
int count = 0;
std::vector<char> is_dead(num_boxes);
for (int i = 0; i < num_boxes; ++i) is_dead[i] = 0;
for (int i = 0; i < num_boxes; ++i) {
if (is_dead[i]) continue;
index_out[count++] = base_index + i;
if (count == max_num_out) break;
for (int j = i + 1; j < num_boxes; ++j)
if (!is_dead[j] && iou(&boxes[i * 5], &boxes[j * 5]) > nms_thresh) is_dead[j] = 1;
}
*num_out = count;
is_dead.clear();
}
template <typename T>
static void retrieve_rois_cpu(const int num_rois,
const int item_index,
const T proposals[],
const int roi_indices[],
T rois[], T roi_scores[]) {
for (int i = 0; i < num_rois; ++i) {
const T* const proposals_index = proposals + roi_indices[i] * 5;
rois[i * 5 + 0] = item_index;
rois[i * 5 + 1] = proposals_index[0];
rois[i * 5 + 2] = proposals_index[1];
rois[i * 5 + 3] = proposals_index[2];
rois[i * 5 + 4] = proposals_index[3];
if (roi_scores) {
roi_scores[i] = proposals_index[4];
}
}
}
template <class Context> template <typename T>
void ProposalOp<Context>::RunWithType() {
auto* p_bottom_item = input(0).template data<T, CPUContext>();
auto* p_d_anchor_item = input(1).template data<T, CPUContext>();
auto* p_img_info_cpu = input(2).template data<T, CPUContext>();
auto* p_roi_item = output(0)->template mutable_data<T, CPUContext>();
auto* p_score_item = (OutputSize() > 1) ? output(1)->template mutable_data<T, CPUContext>() : NULL;
vector<TIndex> proposals_shape(2), top_shape(2);
proposals_shape[0] = 0; proposals_shape[1] = 5;
top_shape[0] = 0; top_shape[1] = 5;
for (int n = 0; n < input(0).dim(0); ++n) {
// bottom shape: (2 x num_anchors) x H x W
const int bottom_H = input(0).dim(2);
const int bottom_W = input(0).dim(3);
// input image height & width
const T img_H = p_img_info_cpu[0];
const T img_W = p_img_info_cpu[1];
// scale factor for height & width
const T scale_H = p_img_info_cpu[2];
const T scale_W = p_img_info_cpu[3];
// minimum box width & height
const T min_box_H = min_size_ * scale_H;
const T min_box_W = min_size_ * scale_W;
// number of all proposals = num_anchors * H * W
const int num_proposals = anchors_.dim(0) * bottom_H * bottom_W;
// number of top-n proposals before NMS
const int pre_nms_topn = std::min(num_proposals, pre_nms_topn_);
// number of final RoIs
int num_rois = 0;
// enumerate all proposals
// num_proposals = num_anchors * H * W
// (x1, y1, x2, y2, score) for each proposal
// NOTE: for bottom, only foreground scores are passed
proposals_shape[0] = num_proposals;
proposals_.Reshape(proposals_shape);
enumerate_proposals_cpu(p_bottom_item + num_proposals, p_d_anchor_item,
anchors_.template data<T, CPUContext>(), proposals_.mutable_data<T, CPUContext>(),
anchors_.dim(0), bottom_H, bottom_W, img_H, img_W, min_box_H, min_box_W, feat_stride_);
sort_box(proposals_.mutable_data<T, CPUContext>(), 0, num_proposals - 1, pre_nms_topn_);
nms_cpu(pre_nms_topn, proposals_.template data<T, CPUContext>(),
roi_indices_.mutable_data<int, CPUContext>(), &num_rois,
0, nms_thresh_, post_nms_topn_);
retrieve_rois_cpu(
num_rois, n, proposals_.template data<T, CPUContext>(),
roi_indices_.template data<int, CPUContext>(), p_roi_item, p_score_item);
top_shape[0] += num_rois;
}
output(0)->Reshape(top_shape);
if (OutputSize() > 1) {
top_shape.pop_back();
output(1)->Reshape(top_shape);
}
}
template <typename Context>
void ProposalOp<Context>::Setup() {
vector<float> ratios(OperatorBase::GetRepeatedArg<float>("ratios"));
vector<float> scales(OperatorBase::GetRepeatedArg<float>("scales"));
vector<TIndex> anchors_shape(2);
anchors_shape[0] = ratios.size() * scales.size();
anchors_shape[1] = 4;
anchors_.Reshape(anchors_shape);
generate_anchors(base_size_, &ratios[0], &scales[0],
(int)ratios.size(), (int)scales.size(),
anchors_.mutable_data<float, CPUContext>());
vector<TIndex> roi_indices_shape(1);
roi_indices_shape[0] = post_nms_topn_;
roi_indices_.Reshape(roi_indices_shape);
// rois blob : holds R regions of interest, each is a 5 - tuple
// (n, x1, y1, x2, y2) specifying an image batch index n and a
// rectangle(x1, y1, x2, y2)
vector<TIndex> top_shape(2);
top_shape[0] = 1 * post_nms_topn_;
top_shape[1] = 5;
output(0)->Reshape(top_shape);
// scores blob : holds scores for R regions of interest
if (OutputSize() > 1) {
top_shape.pop_back();
output(0)->Reshape(top_shape);
}
}
template <class Context>
void ProposalOp<Context>::RunOnDevice() {
CHECK_EQ(input(0).dim(0), 1) << "Only single item batches are supported.";
if (TypeMeta::Id<Context>() == TypeMeta::Id<CPUContext>()) {
if (input(0).template IsType<float>()) RunWithType<float>();
else LOG(FATAL) << "Unsupported input types.";
} else if (TypeMeta::Id<Context>() == TypeMeta::Id<CUDAContext>()) {
if (input(0).template IsType<float>()) RunWithType<float>();
else LOG(FATAL) << "Unsupported input types.";
}
}
DEPLOY_CPU(Proposal);
#ifdef WITH_CUDA
DEPLOY_CUDA(Proposal);
#endif
OPERATOR_SCHEMA(Proposal).NumInputs(3).NumOutputs(1, 2);
} // namespace dragon
\ No newline at end of file
Dragon/src/operators/misc/proposal_op.cu deleted 100644 → 0
#include "operators/misc/proposal_op.h"
#include "utils/cuda_device.h"
namespace dragon {
template <typename Dtype>
__device__ static int transform_box(Dtype box[],
const Dtype dx,
const Dtype dy,
const Dtype d_log_w,
const Dtype d_log_h,
const Dtype img_W,
const Dtype img_H,
const Dtype min_box_W,
const Dtype min_box_H) {
// width & height of box
const Dtype w = box[2] - box[0] + (Dtype)1;
const Dtype h = box[3] - box[1] + (Dtype)1;
// center location of box
const Dtype ctr_x = box[0] + (Dtype)0.5 * w;
const Dtype ctr_y = box[1] + (Dtype)0.5 * h;
// new center location according to gradient (dx, dy)
const Dtype pred_ctr_x = dx * w + ctr_x;
const Dtype pred_ctr_y = dy * h + ctr_y;
// new width & height according to gradient d(log w), d(log h)
const Dtype pred_w = exp(d_log_w) * w;
const Dtype pred_h = exp(d_log_h) * h;
// update upper-left corner location
box[0] = pred_ctr_x - (Dtype)0.5 * pred_w;
box[1] = pred_ctr_y - (Dtype)0.5 * pred_h;
// update lower-right corner location
box[2] = pred_ctr_x + (Dtype)0.5 * pred_w;
box[3] = pred_ctr_y + (Dtype)0.5 * pred_h;
// adjust new corner locations to be within the image region,
box[0] = max((Dtype)0, min(box[0], img_W - (Dtype)1));
box[1] = max((Dtype)0, min(box[1], img_H - (Dtype)1));
box[2] = max((Dtype)0, min(box[2], img_W - (Dtype)1));
box[3] = max((Dtype)0, min(box[3], img_H - (Dtype)1));
// recompute new width & height
const Dtype box_w = box[2] - box[0] + (Dtype)1;
const Dtype box_h = box[3] - box[1] + (Dtype)1;
// check if new box's size >= threshold
return (box_w >= min_box_W) * (box_h >= min_box_H);
}
template <typename Dtype>
static void sort_box(Dtype* list_cpu, const int start, const int end, const int num_top) {
const Dtype pivot_score = list_cpu[start * 5 + 4];
int left = start + 1, right = end;
Dtype temp[5];
while (left <= right) {
while (left <= end && list_cpu[left * 5 + 4] >= pivot_score) ++left;
while (right > start && list_cpu[right * 5 + 4] <= pivot_score) --right;
if (left <= right) {
for (int i = 0; i < 5; ++i) {
temp[i] = list_cpu[left * 5 + i];
}
for (int i = 0; i < 5; ++i) {
list_cpu[left * 5 + i] = list_cpu[right * 5 + i];
}
for (int i = 0; i < 5; ++i) {
list_cpu[right * 5 + i] = temp[i];
}
++left;
--right;
}
}
if (right > start) {
for (int i = 0; i < 5; ++i) {
temp[i] = list_cpu[start * 5 + i];
}
for (int i = 0; i < 5; ++i) {
list_cpu[start * 5 + i] = list_cpu[right * 5 + i];
}
for (int i = 0; i < 5; ++i) {
list_cpu[right * 5 + i] = temp[i];
}
}
if (start < right - 1) {
sort_box(list_cpu, start, right - 1, num_top);
}
if (right + 1 < num_top && right + 1 < end) {
sort_box(list_cpu, right + 1, end, num_top);
}
}
template <typename Dtype>
__global__ static void enumerate_proposals_gpu(const int nthreads,
const Dtype bottom4d[],
const Dtype d_anchor4d[],
const Dtype anchors[],
Dtype proposals[],
const int num_anchors,
const int bottom_H, const int bottom_W,
const Dtype img_H, const Dtype img_W,
const Dtype min_box_H, const Dtype min_box_W,
const int feat_stride) {
CUDA_KERNEL_LOOP(index, nthreads) {
const int h = index / num_anchors / bottom_W;
const int w = (index / num_anchors) % bottom_W;
const int k = index % num_anchors;
const Dtype x = w * feat_stride;
const Dtype y = h * feat_stride;
const Dtype* p_box = d_anchor4d + h * bottom_W + w;
const Dtype* p_score = bottom4d + h * bottom_W + w;
const int bottom_area = bottom_H * bottom_W;
const Dtype dx = p_box[(k * 4 + 0) * bottom_area];
const Dtype dy = p_box[(k * 4 + 1) * bottom_area];
const Dtype d_log_w = p_box[(k * 4 + 2) * bottom_area];
const Dtype d_log_h = p_box[(k * 4 + 3) * bottom_area];
Dtype* const p_proposal = proposals + index * 5;
p_proposal[0] = x + anchors[k * 4 + 0];
p_proposal[1] = y + anchors[k * 4 + 1];
p_proposal[2] = x + anchors[k * 4 + 2];
p_proposal[3] = y + anchors[k * 4 + 3];
p_proposal[4]
= transform_box(p_proposal,
dx, dy, d_log_w, d_log_h,
img_W, img_H, min_box_W, min_box_H)
* p_score[k * bottom_area];
}
}
template <typename Dtype>
__global__ static void retrieve_rois_gpu(const int nthreads,
const int item_index,
const Dtype proposals[],
const int roi_indices[],
Dtype rois[],
Dtype roi_scores[]) {
CUDA_KERNEL_LOOP(index, nthreads) {
const Dtype* const proposals_index = proposals + roi_indices[index] * 5;
rois[index * 5 + 0] = item_index;
rois[index * 5 + 1] = proposals_index[0];
rois[index * 5 + 2] = proposals_index[1];
rois[index * 5 + 3] = proposals_index[2];
rois[index * 5 + 4] = proposals_index[3];
if (roi_scores) {
roi_scores[index] = proposals_index[4];
}
}
}
template <typename Dtype>
__device__ static Dtype iou(const Dtype A[], const Dtype B[]) {
// overlapped region (= box)
const Dtype x1 = max(A[0], B[0]);
const Dtype y1 = max(A[1], B[1]);
const Dtype x2 = min(A[2], B[2]);
const Dtype y2 = min(A[3], B[3]);
// intersection area
const Dtype width = max((Dtype)0, x2 - x1 + (Dtype)1);
const Dtype height = max((Dtype)0, y2 - y1 + (Dtype)1);
const Dtype area = width * height;
// area of A, B
const Dtype A_area = (A[2] - A[0] + (Dtype)1) * (A[3] - A[1] + (Dtype)1);
const Dtype B_area = (B[2] - B[0] + (Dtype)1) * (B[3] - B[1] + (Dtype)1);
// IoU
return area / (A_area + B_area - area);
}
#define DIV_THEN_CEIL(x, y) (((x) + (y) - 1) / (y))
static const int nms_block_size = 64;
template <typename Dtype>
__global__ static void nms_mask(const Dtype boxes[],
unsigned long long mask[],
const int num_boxes,
const Dtype nms_thresh) {
// block region
// j = j_start + { 0, ..., dj_end - 1 }
// i = i_start + { 0, ..., di_end - 1 }
const int i_start = blockIdx.x * nms_block_size;
const int di_end = min(num_boxes - i_start, nms_block_size);
const int j_start = blockIdx.y * nms_block_size;
const int dj_end = min(num_boxes - j_start, nms_block_size);
// copy all i-th boxes to GPU cache
// i = i_start + { 0, ..., di_end - 1 }
__shared__ Dtype boxes_i[nms_block_size * 4];
{
const int di = threadIdx.x;
if (di < di_end) {
boxes_i[di * 4 + 0] = boxes[(i_start + di) * 5 + 0];
boxes_i[di * 4 + 1] = boxes[(i_start + di) * 5 + 1];
boxes_i[di * 4 + 2] = boxes[(i_start + di) * 5 + 2];
boxes_i[di * 4 + 3] = boxes[(i_start + di) * 5 + 3];
}
}
__syncthreads();
// given j = j_start + dj,
// check whether box i is significantly overlapped with box j
// (i.e., IoU(box j, box i) > threshold)
// for all i = i_start + { 0, ..., di_end - 1 } except for i == j
{
const int dj = threadIdx.x;
if (dj < dj_end) {
// box j
const Dtype* const box_j = boxes + (j_start + dj) * 5;
// mask for significant overlap
// if IoU(box j, box i) > threshold, di-th bit = 1
unsigned long long mask_j = 0;
// check for all i = i_start + { 0, ..., di_end - 1 }
// except for i == j
const int di_start = (i_start == j_start) ? (dj + 1) : 0;
for (int di = di_start; di < di_end; ++di) {
// box i
const Dtype* const box_i = boxes_i + di * 4;
// if IoU(box j, box i) > threshold, di-th bit = 1
if (iou(box_j, box_i) > nms_thresh) {
mask_j |= 1ULL << di;
}
}
// mask: "num_boxes x num_blocks" array
// for mask[j][bi], "di-th bit = 1" means:
// box j is significantly overlapped with box i = i_start + di,
// where i_start = bi * block_size
{
const int num_blocks = DIV_THEN_CEIL(num_boxes, nms_block_size);
const int bi = blockIdx.x;
mask[(j_start + dj) * num_blocks + bi] = mask_j;
}
} // endif dj < dj_end
}
}
template <typename Dtype>
void nms_gpu(const int num_boxes,
const Dtype boxes_gpu[],
Tensor* p_mask,
int index_out_cpu[],
int* const num_out,
const int base_index,
const Dtype nms_thresh,
const int max_num_out) {
const int num_blocks = DIV_THEN_CEIL(num_boxes, nms_block_size);
{
const dim3 blocks(num_blocks, num_blocks);
vector<TIndex> mask_shape(2);
mask_shape[0] = num_boxes;
mask_shape[1] = num_blocks * sizeof(unsigned long long) / sizeof(int);
p_mask->Reshape(mask_shape);
// find all significantly-overlapped pairs of boxes
nms_mask << <blocks, nms_block_size >> >(
boxes_gpu, (unsigned long long*)p_mask->template mutable_data<int, CUDAContext>(),
num_boxes, nms_thresh);
CUDA_POST_KERNEL_CHECK;
}
// discard i-th box if it is significantly overlapped with
// one or more previous (= scored higher) boxes
{
const unsigned long long* p_mask_cpu
= (unsigned long long*)p_mask->mutable_data<int, CPUContext>();
int num_selected = 0;
vector<unsigned long long> dead_bit(num_blocks);
for (int i = 0; i < num_blocks; ++i) {
dead_bit[i] = 0;
}
for (int i = 0; i < num_boxes; ++i) {
const int nblock = i / nms_block_size;
const int inblock = i % nms_block_size;
if (!(dead_bit[nblock] & (1ULL << inblock))) {
index_out_cpu[num_selected++] = base_index + i;
const unsigned long long* const mask_i = p_mask_cpu + i * num_blocks;
for (int j = nblock; j < num_blocks; ++j) {
dead_bit[j] |= mask_i[j];
}
if (num_selected == max_num_out) {
break;
}
}
}
*num_out = num_selected;
}
}
template
void nms_gpu(const int num_boxes,
const float boxes_gpu[],
Tensor* p_mask,
int index_out_cpu[],
int* const num_out,
const int base_index,
const float nms_thresh,
const int max_num_out);
template
void nms_gpu(const int num_boxes,
const double boxes_gpu[],
Tensor* p_mask,
int index_out_cpu[],
int* const num_out,
const int base_index,
const double nms_thresh,
const int max_num_out);
template <class Context> template <typename T>
void ProposalOp<Context>::RunWithType() {
auto* p_bottom_item = this->input(0).template data<T, CUDAContext>();
auto* p_d_anchor_item = this->input(1).template data<T, CUDAContext>();
auto* p_img_info_cpu = this->input(2).template data<T, CPUContext>();
auto* p_roi_item = this->output(0)->template mutable_data<T, CUDAContext>();
auto* p_score_item = (this->OutputSize() > 1) ? this->output(1)->template mutable_data<T, CUDAContext>() : NULL;
vector<TIndex> proposals_shape(2), top_shape(2);
proposals_shape[0] = 0; proposals_shape[1] = 5;
top_shape[0] = 0; top_shape[1] = 5;
for (int n = 0; n < this->input(0).dim(0); ++n) {
// bottom shape: (2 x num_anchors) x H x W
const int bottom_H = this->input(0).dim(2);
const int bottom_W = this->input(0).dim(3);
// input image height & width
const T img_H = p_img_info_cpu[0];
const T img_W = p_img_info_cpu[1];
// scale factor for height & width
const T scale_H = p_img_info_cpu[2];
const T scale_W = p_img_info_cpu[3];
// minimum box width & height
const T min_box_H = min_size_ * scale_H;
const T min_box_W = min_size_ * scale_W;
// number of all proposals = num_anchors * H * W
const int num_proposals = anchors_.dim(0) * bottom_H * bottom_W;
// number of top-n proposals before NMS
const int pre_nms_topn = std::min(num_proposals, pre_nms_topn_);
// number of final RoIs
int num_rois = 0;
// enumerate all proposals
// num_proposals = num_anchors * H * W
// (x1, y1, x2, y2, score) for each proposal
// NOTE: for bottom, only foreground scores are passed
proposals_shape[0] = num_proposals;
proposals_.Reshape(proposals_shape);
enumerate_proposals_gpu<T> << <GET_BLOCKS(num_proposals), CUDA_NUM_THREADS >> >(num_proposals,
p_bottom_item + num_proposals,
p_d_anchor_item,
anchors_.template data<T, CUDAContext>(),
proposals_.template mutable_data<T, CUDAContext>(),
anchors_.dim(0),
bottom_H, bottom_W,
img_H, img_W,
min_box_H, min_box_W,
feat_stride_);
CUDA_POST_KERNEL_CHECK;
sort_box<T>(proposals_.template mutable_data<T, CPUContext>(), 0, num_proposals - 1, pre_nms_topn_);
nms_gpu<T>(pre_nms_topn, proposals_.template data<T, CUDAContext>(),
&nms_mask_,
roi_indices_.template mutable_data<int, CPUContext>(),
&num_rois,
0,
nms_thresh_,
post_nms_topn_);
retrieve_rois_gpu<T> << <GET_BLOCKS(num_rois), CUDA_NUM_THREADS >> >(num_rois,
n,
proposals_.template data<T, CUDAContext>(),
roi_indices_.template data<int, CUDAContext>(),
p_roi_item,
p_score_item);
CUDA_POST_KERNEL_CHECK;
top_shape[0] += num_rois;
}
this->output(0)->Reshape(top_shape);
if (this->OutputSize() > 1) {
top_shape.pop_back();
this->output(1)->Reshape(top_shape);
}
}
template void ProposalOp<CUDAContext>::RunWithType<float>();
}
\ No newline at end of file
Dragon/src/operators/norm/cudnn_batch_norm_op.cc
......@@ -244,23 +244,46 @@ void CuDNNBatchNormGradientOp<Context>::TrainingRunWithType() {
template <class Context> template <typename T>
void CuDNNBatchNormGradientOp<Context>::InferenceRunWithType() {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
auto* dYdata = input(-1).template data<T, Context>();
auto* Sdata = input(3).template data<T, Context>();
auto* hVar_data = input(2).template data<T, Context>();
auto* tVar_data = var->template mutable_data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NMul_data = num_multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
// gradient w.r.t. scale
if (output(1)->name() != "ignore")
LOG(FATAL) << "The gamma should be fixed if using global stats.";
// gradient w.r.t. bias
if (output(2)->name() != "ignore") {
auto* dBdata = output(2)->template mutable_data<T, Context>();
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NC, S,
1.0, dYdata, SMul_data,
0.0, NC_data);
math::Gemv<T, Context>(CblasTrans, N, C,
1.0, NC_data, NMul_data,
1.0, dBdata);
} else if (data_format == "NHWC") {
math::Gemv<T, Context>(CblasTrans, NS, C,
1.0, dYdata, NSMul_data,
1.0, dBdata);
}
}
// gradient w.r.t. x
if (output(0)->name() != "ignore") {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
stddev = ws()->GetBuffer();
stddev->ReshapeLike(input(0));
auto* dYdata = input(-1).template data<T, Context>();
auto* dXdata = output(0)->template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
auto* Sdata = input(3).template data<T, Context>();
auto* hVar_data = input(2).template data<T, Context>();
auto* tVar_data = var->template mutable_data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NMul_data = num_multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
// compute stddev
ctx().template Copy<T, Context, Context>(var->count(), tVar_data, hVar_data);
......
Dragon/src/operators/norm/fused_batch_norm.cc
......@@ -429,21 +429,43 @@ void FusedBatchNormGradientOp<Context>::TrainingRunWithType() {
template <class Context> template <typename T>
void FusedBatchNormGradientOp<Context>::InferenceRunWithType() {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
auto* dYdata = input(-1).template data<T, Context>();
auto* Sdata = input(3).template data<T, Context>();
auto* tVar_data = var->template mutable_data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NMul_data = num_multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
// gradient w.r.t. scale
if (output(1)->name() != "ignore")
LOG(FATAL) << "The gamma should be fixed if using global stats.";
// gradient w.r.t. bias
if (output(2)->name() != "ignore") {
auto* dBdata = output(2)->template mutable_data<T, Context>();
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NC, S,
1.0, dYdata, SMul_data,
0.0, NC_data);
math::Gemv<T, Context>(CblasTrans, N, C,
1.0, NC_data, NMul_data,
1.0, dBdata);
} else if (data_format == "NHWC") {
math::Gemv<T, Context>(CblasTrans, NS, C,
1.0, dYdata, NSMul_data,
1.0, dBdata);
}
}
// gradient w.r.t. x
if (output(0)->name() != "ignore") {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
auto* dYdata = input(-1).template data<T, Context>();
auto* dXdata = output(0)->template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
auto* Sdata = input(3).template data<T, Context>();
auto* hVar_data = input(2).template data<T, Context>();
auto* tVar_data = var->template mutable_data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NMul_data = num_multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
// divide scale by stddev
math::Div<T, Context>(var->count(), Sdata, tVar_data, tVar_data);
......@@ -492,7 +514,9 @@ void FusedBatchNormGradientOp<Context>::Setup() {
// reshape
num_by_chans.Reshape(vector<TIndex>(1, NC));
output(0)->ReshapeLike(input(0));
output(0)->ReshapeLike(input(0)); // dX
output(1)->ReshapeLike(input(3)); // dScale
output(2)->ReshapeLike(input(3)); // dBias
}
template <class Context>
......
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