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GroupNormalization Support

1 parent 18b664b1
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Showing with 1592 additions and 201 deletions
Dragon/include/operators/ndarray/reshape_op.h
......@@ -16,15 +16,16 @@ class ReshapeOp final : public Operator<Context> {
public:
ReshapeOp(const OperatorDef& op_def, Workspace* ws)
: Operator<Context>(op_def, ws),
shape(OperatorBase::GetRepeatedArg<int>("shape")) {
new_shape.resize(shape.size());
shape_like_desc(OperatorBase::GetSingleArg<string>("shape_like", "")) {
GET_ARGUMENTS_WITH_DESC(int, shape);
}
void RunOnDevice() override;
protected:
vector<int> shape;
vector<TIndex> new_shape;
DECLARE_ARGUMENTS_WITH_DESC(int, shape);
string shape_like_desc;
vector<TIndex> require_shape, new_shape;
};
template <class Context>
......@@ -38,6 +39,8 @@ class ReshapeGradientOp final : public Operator<Context> {
void RunOnDevice() override;
};
DEFINE_ARGUMENTS_WITH_DESC(int, ReshapeOp, shape);
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_RESHAPE_OP_H_
\ No newline at end of file
Dragon/include/operators/norm/batch_norm_op.h
......@@ -105,7 +105,7 @@ class FusedBatchNormGradientOp : public Operator<Context> {
: Operator<Context>(op_def, ws),
axis(OperatorBase::GetSingleArg<int>("axis", -1)),
eps(OperatorBase::GetSingleArg<float>("eps", float(1e-3))),
use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)) { }
use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)) {}
void Setup();
......
Dragon/include/operators/norm/group_norm_op.h 0 → 100644
// --------------------------------------------------------
// Dragon
// Copyright(c) 2017 SeetaTech
// Written by Ting Pan
// --------------------------------------------------------
#ifndef DRAGON_OPERATORS_NORM_GROUP_NORM_OP_H_
#define DRAGON_OPERATORS_NORM_GROUP_NORM_OP_H_
#include "core/operator.h"
namespace dragon {
template <class Context>
class GroupNormOp : public Operator<Context> {
public:
GroupNormOp(const OperatorDef& op_def, Workspace* ws)
: Operator<Context>(op_def, ws),
group(OperatorBase::GetSingleArg<int>("group", 32)),
axis(OperatorBase::GetSingleArg<int>("axis", -1)),
momentum(OperatorBase::GetSingleArg<float>("momentum", float(0.9))),
eps(OperatorBase::GetSingleArg<float>("eps", float(1e-3))),
use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)),
mode(OperatorBase::GetSingleArg<string>("mode", "DEFAULT")) {
if (axis != -1)
CHECK_EQ(axis, 1)
<< "\nThe axis can only be set to 1.";
}
void Setup();
void RunOnDevice() override;
template <typename T> void TrainingRunWithType();
template <typename T> void InferenceRunWithType();
protected:
float momentum, eps;
Tensor mean, num_by_chans;
Tensor* multiplier, *num_multiplier, *spatial_multiplier, *cgs_multiplier;
Tensor* stddev, *var;
TIndex group, axis, N, C, S, NG, NC, NS, CGS;
string data_format, mode;
int use_stats;
bool use_global_stats, is_recomputing;
};
template <class Context>
class GroupNormGradientOp final : public Operator<Context> {
public:
GroupNormGradientOp(const OperatorDef& op_def, Workspace *ws)
: Operator<Context>(op_def, ws),
group(OperatorBase::GetSingleArg<int>("group", 32)),
axis(OperatorBase::GetSingleArg<int>("axis", -1)),
use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)) {
if (axis != -1)
CHECK_EQ(axis, 1)
<< "\nThe axis can only be set to 1.";
}
void Setup();
void RunOnDevice() override;
template <typename T> void TrainingRunWithType();
template <typename T> void InferenceRunWithType();
protected:
Tensor num_by_chans;
Tensor* multiplier, *num_multiplier, *spatial_multiplier, *cgs_multiplier;
Tensor* stddev, *var;
TIndex group, axis, N, C, S, NG, NC, NS, CGS;
string data_format;
int use_stats;
bool use_global_stats;
};
template <class Context>
class FusedGroupNormOp : public Operator<Context> {
public:
FusedGroupNormOp(const OperatorDef& op_def, Workspace* ws)
: Operator<Context>(op_def, ws),
group(OperatorBase::GetSingleArg<int>("group", 32)),
axis(OperatorBase::GetSingleArg<int>("axis", -1)),
momentum(OperatorBase::GetSingleArg<float>("momentum", float(0.9))),
eps(OperatorBase::GetSingleArg<float>("eps", float(1e-3))),
use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)) {}
void Setup();
void RunOnDevice() override;
template <typename T> void TrainingRunWithType();
template <typename T> void InferenceRunWithType();
protected:
float momentum, eps;
Tensor num_by_chans;
Tensor* multiplier, *num_multiplier, *spatial_multiplier, *cgs_multiplier;
Tensor* mean, *var, *stddev, *x_norm;
TIndex group, axis, N, C, S, NG, NC, NS, CGS;
string data_format;
int use_stats;
bool use_global_stats, is_recomputing;
};
template <class Context>
class FusedGroupNormGradientOp : public Operator<Context> {
public:
FusedGroupNormGradientOp(const OperatorDef& op_def, Workspace* ws)
: Operator<Context>(op_def, ws),
group(OperatorBase::GetSingleArg<int>("group", 32)),
axis(OperatorBase::GetSingleArg<int>("axis", -1)),
eps(OperatorBase::GetSingleArg<float>("eps", float(1e-3))),
use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)) {}
void Setup();
void ShareGradient() override;
void RunOnDevice() override;
template <typename T> void TrainingRunWithType();
template <typename T> void InferenceRunWithType();
protected:
float eps;
Tensor num_by_chans;
Tensor* multiplier, *num_multiplier, *spatial_multiplier, *cgs_multiplier;
Tensor* mean, *var, *stddev, *x_norm;
TIndex group, axis, N, C, S, NG, NC, NS, CGS;
string data_format;
int use_stats;
bool use_global_stats;
};
} // namespace dragon
#endif // DRAGON_OPERATORS_NORM_GROUP_NORM_OP_H_
\ No newline at end of file
Dragon/python/dragon/docs/contents/operators/norm.rst
......@@ -16,6 +16,14 @@
\sigma_{B}^{2} = \frac{1}{m} \sum_{i=1}^{m}(x_{i} - \mu_{B})^{2} \\
\hat{x}_{i} = \frac{x_{i} - \mu_{B}}{\sqrt{\sigma_{B}^{2} + \epsilon}} \\ y_{i} = \gamma\hat{x}_{i} + \beta \\ \,
.. |groupnorm_function| mathmacro:: \\ \, \\ \mu_{G} = \frac{1}{m} \sum_{i=1}^{m}x_{i} \\
\sigma_{G}^{2} = \frac{1}{m} \sum_{i=1}^{m}(x_{i} - \mu_{G})^{2} \\
\hat{x}_{i} = \frac{x_{i} - \mu_{G}}{\sqrt{\sigma_{G}^{2} + \epsilon}} \\ \,
.. |groupnorm_scale_function| mathmacro:: \\ \, \\ \mu_{G} = \frac{1}{m} \sum_{i=1}^{m}x_{i} \\
\sigma_{G}^{2} = \frac{1}{m} \sum_{i=1}^{m}(x_{i} - \mu_{G})^{2} \\
\hat{x}_{i} = \frac{x_{i} - \mu_{G}}{\sqrt{\sigma_{G}^{2} + \epsilon}} \\ y_{i} = \gamma\hat{x}_{i} + \beta \\ \,
.. |batchrenorm_function| mathmacro:: \\ \, \\ \mu_{B} = \frac{1}{m} \sum_{i=1}^{m}x_{i} \\
\sigma_{B}^{2} = \frac{1}{m} \sum_{i=1}^{m}(x_{i} - \mu_{B})^{2} \\
\hat{x}_{i} = \frac{x_{i} - \mu_{B}}{\sqrt{\sigma_{B}^{2} + \epsilon}} \cdot r + d \\ \,
......
Dragon/python/dragon/docs/contents/ops.rst
......@@ -113,6 +113,8 @@ List Brief
`BatchNorm`_ Batch Normalization, introduced by `[Ioffe & Szegedy, 2015] <https://arxiv.org/abs/1502.03167>`_.
`BatchRenorm`_ Batch Renormalization, introduced by `[Ioffe, 2017] <https://arxiv.org/abs/1702.03275>`_.
`FusedBatchNorm`_ Batch Normalization, with scale procedure after normalization.
`GroupNorm`_ Group Normalization, introduced by `[Wu & He, 2018] <https://arxiv.org/abs/1803.08494>`_.
`FusedGroupNorm`_ Group Normalization, with scale procedure after normalization.
`InstanceNorm`_ Instance Normalization, introduced by `[Ulyanov et.al, 2016] <https://arxiv.org/abs/1607.08022>`_.
`L2Norm`_ L2 Normalization, introduced by `[Liu et.al, 2015] <https://arxiv.org/abs/1506.04579>`_.
================== ======================================================================
......@@ -253,6 +255,8 @@ List Brief
.. _BatchNorm: operators/norm.html#dragon.operators.norm.BatchNorm
.. _BatchRenorm: operators/norm.html#dragon.operators.norm.BatchRenorm
.. _FusedBatchNorm: operators/norm.html#dragon.operators.norm.FusedBatchNorm
.. _GroupNorm: operators/norm.html#dragon.operators.norm.GroupNorm
.. _FusedGroupNorm: operators/norm.html#dragon.operators.norm.FusedGroupNorm
.. _InstanceNorm: operators/norm.html#dragon.operators.norm.InstanceNorm
.. _L2Norm: operators/norm.html#dragon.operators.norm.L2Norm
......
Dragon/python/dragon/docs/contents/vm/caffe/layer.rst
......@@ -73,9 +73,11 @@ List Brief
`ArgMaxLayer`_ The implementation of ``ArgMaxLayer``.
`BatchNormLayer`_ The implementation of ``BatchNormLayer``.
`BatchRenormLayer`_ The implementation of ``BatchRenormLayer``.
`GroupNormLayer`_ The implementation of ``GroupNormLayer``.
`InstanceNormLayer`_ The implementation of ``InstanceNormLayer``.
`ScaleLayer`_ The implementation of ``ScaleLayer``.
`BNLayer`_ The implementation of ``BNLayer``.
`GNLayer`_ The implementation of ``GNLayer``.
`NormalizeLayer`_ The implementation of ``NormalizeLayer``.
`TileLayer`_ The extended implementation of ``TileLayer``.
`ExpandDimsLayer`_ The implementation of ``ExpandDimsLayer``.
......@@ -181,9 +183,11 @@ API Reference
.. _ArgMaxLayer: #dragon.vm.caffe.layers.common.ArgMaxLayer
.. _BatchNormLayer: #dragon.vm.caffe.layers.common.BatchNormLayer
.. _BatchRenormLayer: #dragon.vm.caffe.layers.common.BatchRenormLayer
.. _GroupNormLayer: #dragon.vm.caffe.layers.common.GroupNormLayer
.. _InstanceNormLayer: #dragon.vm.caffe.layers.common.InstanceNormLayer
.. _ScaleLayer: #dragon.vm.caffe.layers.common.ScaleLayer
.. _BNLayer: #dragon.vm.caffe.layers.common.BNLayer
.. _GNLayer: #dragon.vm.caffe.layers.common.GNLayer
.. _NormalizeLayer: #dragon.vm.caffe.layers.common.NormalizeLayer
.. _TileLayer: #dragon.vm.caffe.layers.common.TileLayer
.. _ExpandDimsLayer: #dragon.vm.caffe.layers.common.ExpandDimsLayer
......
Dragon/python/dragon/operators/ndarray.py
......@@ -648,15 +648,21 @@ def Flatten(inputs, axis=0, num_axes=-1, keep_axes=None, **kwargs):
return output
def Reshape(inputs, shape, **kwargs):
def Reshape(inputs, shape, shape_like=None, **kwargs):
"""Reshape the dimensions of input.
``shape`` could be a list of numbers or Tensors.
Set ``shape`` to ``None``, if you want to use ``shape_like``.
Parameters
----------
inputs : Tensor
The input tensor.
shape : list or tuple
shape : list, tuple or None
The new shape.
shape_like: Tensor or None
The tensor for indicating the output shape.
Returns
-------
......@@ -677,12 +683,24 @@ def Reshape(inputs, shape, **kwargs):
CheckInputs(inputs, 1)
arguments = ParseArguments(locals())
if not isinstance(shape, tuple) and not isinstance(shape, list):
raise TypeError('The type of dims must be a tuple or list.')
if shape is not None:
AddArgumentsWithDesc(arguments, shape, 'shape', 'int32', as_target=True)
elif shape_like is not None:
if not isinstance(shape_like, Tensor):
raise TypeError('The shape_like should be a Tensor.')
arguments['shape_like'] = shape_like.name
output = Tensor.CreateOperator(nout=1, op_type='Reshape', **arguments)
if inputs.shape is not None:
possible_to_infer_shape = True
if shape is not None:
for dim in shape:
if isinstance(dim, Tensor):
possible_to_infer_shape = False
if shape_like is not None:
possible_to_infer_shape = False
if possible_to_infer_shape:
output.shape = [1] * len(shape)
for i, s in enumerate(shape):
if s == -1: output.shape[i] = 1
......
Dragon/python/dragon/operators/norm.py
......@@ -165,6 +165,103 @@ def FusedBatchNorm(inputs, axis=-1, momentum=0.9, eps=1e-3, use_stats=-1, **kwar
return output
def GroupNorm(inputs, group=32, axis=-1, momentum=0.9, eps=1e-3,
use_stats=-1, mode='DEFAULT', **kwargs):
"""Group Normalization, introduced by `[Wu & He, 2018] <https://arxiv.org/abs/1803.08494>`_.
It follows the implementation of `Caffe`_, that scale procedure is moved to `ops.Scale(*args, **kwargs)`_.
The number of inputs vary from ``3`` to ``4`` (``DEFAULT`` or ``CAFFE`` mode).
Parameters
----------
inputs : list of Tensor
The inputs, represent [input, mean, var] or [input, mean, var, factor].
group : int
The group size.
axis : int
The channel axis.
momentum : float
The momentum of moving average.
eps : float
The eps.
use_stats : int
Whether to use global stats. Default is ``-1`` (Auto).
mode : str
The moving average mode. ``DEFAULT`` or ``CAFFE``.
Returns
-------
Tensor
The output tensor, calculated as:
|groupnorm_function|
The ``DEFAULT`` moving average of mean/var, calculated as:
|default_moving_average_function|
The ``CAFFE`` moving average of mean/var, calculated as:
|caffe_moving_average_function|
"""
CheckInputs(inputs, 3, 4)
arguments = ParseArguments(locals())
if len(inputs) > 3:
if mode != 'CAFFE':
raise ValueError('Only the CAFFE mode will take 4 inputs.')
output = Tensor.CreateOperator(nout=1, op_type='GroupNorm', **arguments)
if inputs[0].shape is not None:
output.shape = inputs[0].shape[:]
return output
def FusedGroupNorm(inputs, group=32, axis=-1, momentum=0.9, eps=1e-3, use_stats=-1, **kwargs):
"""Group Normalization, with scale procedure after normalization.
Parameters
----------
inputs : list of Tensor
The inputs, represent [input, mean, var, scale, bias].
group : int
The group size.
axis : int
The channel axis.
momentum : float
The momentum of moving average.
eps : float
The eps.
use_stats : int
Whether to use global stats. Default is ``-1`` (Auto).
Returns
-------
Tensor
The output tensor, calculated as:
|groupnorm_scale_function|
The moving average of mean/var, calculated as:
|default_moving_average_function|
"""
CheckInputs(inputs, 5)
arguments = ParseArguments(locals())
output = Tensor.CreateOperator(nout=1, op_type='FusedGroupNorm', **arguments)
if inputs[0].shape is not None:
output.shape = inputs[0].shape[:]
return output
def InstanceNorm(inputs, axis=-1, eps=1e-3, **kwargs):
"""Instance Normalization, introduced by `[Ulyanov et.al, 2016] <https://arxiv.org/abs/1607.08022>`_
......
Dragon/python/dragon/ops.py
......@@ -92,7 +92,9 @@ GramMatrix = math.GramMatrix
# normalization
BatchNorm = norm.BatchNorm
BatchRenorm = norm.BatchRenorm
GroupNorm = norm.GroupNorm
FusedBatchNorm = norm.FusedBatchNorm
FusedGroupNorm = norm.FusedGroupNorm
InstanceNorm = norm.InstanceNorm
L2Norm = norm.L2Norm
......
Dragon/python/dragon/vm/caffe/layers/__init__.py
......@@ -39,6 +39,8 @@ from .common import InnerProductLayer, \
BatchNormLayer, \
BatchRenormLayer,\
BNLayer, \
GroupNormLayer, \
GNLayer, \
ConcatLayer, \
CropLayer, \
PythonLayer, \
......
Dragon/python/dragon/vm/caffe/layers/common.py
......@@ -412,6 +412,47 @@ class BatchRenormLayer(Layer):
return ops.BatchRenorm(bottom + [blob['data'] for blob in self._blobs], **self._param)
class GroupNormLayer(Layer):
"""The implementation of ``GroupNormLayer``.
Parameters
----------
group : int
Refer ``GroupNormParameter.group``.
use_global_stats : boolean
Refer ``GroupNormParameter.use_global_stats``.
moving_average_fraction : float
Refer ``GroupNormParameter.moving_average_fraction``.
eps : float
Refer ``GroupNormParameter.eps``.
"""
def __init__(self, LayerParameter):
super(GroupNormLayer, self).__init__(LayerParameter)
param = LayerParameter.group_norm_param
self._param = {'group': int(param.group),
'use_stats': int(param.use_global_stats)
if param.HasField('use_global_stats') else -1,
'momentum': param.moving_average_fraction,
'eps': param.eps,
'axis': 1,
'mode': 'CAFFE'}
scope = LayerParameter.name
# mean, var, factor are set to 0 in order to do statistics
mean = Tensor(scope + '/param:0').Constant(value=0.0)
var = Tensor(scope + '/param:1').Constant(value=0.0)
factor = Tensor(scope + '/param:2').Constant(value=0.0)
# in dragon, set diff as None will ignore computing grad automatically
# but in bvlc-caffe1, you must set lr_mult = 0 manually
self._blobs.append({'data': mean, 'diff': None})
self._blobs.append({'data': var, 'diff': None})
self._blobs.append({'data': factor, 'diff': None})
def Setup(self, bottom):
super(GroupNormLayer, self).Setup(bottom)
return ops.GroupNorm(bottom + [blob['data'] for blob in self._blobs], **self._param)
class InstanceNormLayer(Layer):
"""
The implementation of ``InstanceNormLayer``.
......@@ -518,6 +559,59 @@ class BNLayer(Layer):
return ops.FusedBatchNorm(bottom + [blob['data'] for blob in self._blobs], **self._param)
class GNLayer(Layer):
"""The implementation of ``GNLayer``.
Parameters
----------
group : int
Refer ``GroupNormParameter.group``.
use_global_stats : boolean
Refer ``GroupNormParameter.use_global_stats``.
moving_average_fraction : float
Refer ``GroupNormParameter.moving_average_fraction``.
eps : float
Refer ``GroupNormParameter.eps``.
filler : FillerParameter
The filler of scale parameter. Refer `ScaleParameter.filler`_.
bias_filler : FillerParameter
The filler of bias parameter. Refer `ScaleParameter.bias_filler`_.
"""
def __init__(self, LayerParameter):
super(GNLayer, self).__init__(LayerParameter)
gn_param = LayerParameter.group_norm_param
scale_param = LayerParameter.scale_param
self._param = {'group': int(gn_param.group),
'use_stats': int(gn_param.use_global_stats)
if gn_param.HasField('use_global_stats') else -1,
'momentum': gn_param.moving_average_fraction,
'eps': gn_param.eps,
'axis': 1}
scope = LayerParameter.name
mean = Tensor(scope + '/param:0').Constant(value=0.0)
var = Tensor(scope + '/param:1').Constant(value=0.0)
scale = Tensor(scope + '/param:2')
scale_diff = Tensor(scope + '/param:2_grad')
bias = Tensor(scope + '/param:3')
bias_diff = Tensor(scope + '/param:3_grad')
if scale_param.HasField('filler'):
self.Fill(scale, scale_param, 'filler')
else: scale.Constant(value=1.0)
self.Fill(bias, scale_param, 'bias_filler')
self.norm_blobs = [{'data': mean, 'diff': None},
{'data': var, 'diff': None}]
self.scale_blobs = [{'data': scale, 'diff': scale_diff},
{'data': bias, 'diff': bias_diff}]
self._blobs.extend(self.norm_blobs)
self._blobs.extend(self.scale_blobs)
def Setup(self, bottom):
super(GNLayer, self).Setup(bottom)
return ops.FusedGroupNorm(bottom + [blob['data'] for blob in self._blobs], **self._param)
class NormalizeLayer(Layer):
"""The implementation of ``NormalizeLayer``.
......
Dragon/python/dragon/vm/caffe/proto/caffe.proto
......@@ -423,6 +423,7 @@ message LayerParameter {
optional DenseConcatParameter dense_concat_param = 163;
optional FocalLossParameter focal_loss_param = 164;
optional GatherParameter gather_param = 165;
optional GroupNormParameter group_norm_param = 166;
}
// Message that stores parameters used to apply transformation
......@@ -1512,3 +1513,17 @@ message GatherParameter {
optional int32 axis = 1 [default = 0];
}
message GroupNormParameter {
// If false, accumulate global mean/variance values via a moving average. If
// true, use those accumulated values instead of computing mean/variance
// across the batch.
optional bool use_global_stats = 1;
// How much does the moving average decay each iteration?
optional float moving_average_fraction = 2 [default = 0.9];
// Small value to add to the variance estimate so that we don't divide by
// zero.
optional float eps = 3 [default = 1e-3];
optional uint32 group = 5 [default = 32]; // The group size
}
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',
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)
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
......@@ -40,8 +40,8 @@ _PHASE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=17463,
serialized_end=17491,
serialized_start=17642,
serialized_end=17670,
)
_sym_db.RegisterEnumDescriptor(_PHASE)
......@@ -209,8 +209,8 @@ _LOSSPARAMETER_NORMALIZATIONMODE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=6525,
serialized_end=6601,
serialized_start=6579,
serialized_end=6655,
)
_sym_db.RegisterEnumDescriptor(_LOSSPARAMETER_NORMALIZATIONMODE)
......@@ -235,8 +235,8 @@ _CONVOLUTIONPARAMETER_ENGINE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=7564,
serialized_end=7607,
serialized_start=7618,
serialized_end=7661,
)
_sym_db.RegisterEnumDescriptor(_CONVOLUTIONPARAMETER_ENGINE)
......@@ -257,8 +257,8 @@ _DATAPARAMETER_DB = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=7925,
serialized_end=7952,
serialized_start=7979,
serialized_end=8006,
)
_sym_db.RegisterEnumDescriptor(_DATAPARAMETER_DB)
......@@ -283,8 +283,8 @@ _ELTWISEPARAMETER_ELTWISEOP = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=8319,
serialized_end=8358,
serialized_start=8373,
serialized_end=8412,
)
_sym_db.RegisterEnumDescriptor(_ELTWISEPARAMETER_ELTWISEOP)
......@@ -305,8 +305,8 @@ _HINGELOSSPARAMETER_NORM = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=8893,
serialized_end=8915,
serialized_start=8947,
serialized_end=8969,
)
_sym_db.RegisterEnumDescriptor(_HINGELOSSPARAMETER_NORM)
......@@ -327,8 +327,8 @@ _LRNPARAMETER_NORMREGION = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=9782,
serialized_end=9835,
serialized_start=9836,
serialized_end=9889,
)
_sym_db.RegisterEnumDescriptor(_LRNPARAMETER_NORMREGION)
......@@ -353,8 +353,8 @@ _LRNPARAMETER_ENGINE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=7564,
serialized_end=7607,
serialized_start=7618,
serialized_end=7661,
)
_sym_db.RegisterEnumDescriptor(_LRNPARAMETER_ENGINE)
......@@ -375,8 +375,8 @@ _MEMORYDATAPARAMETER_DATATYPE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=10036,
serialized_end=10072,
serialized_start=10090,
serialized_end=10126,
)
_sym_db.RegisterEnumDescriptor(_MEMORYDATAPARAMETER_DATATYPE)
......@@ -401,8 +401,8 @@ _POOLINGPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=10560,
serialized_end=10606,
serialized_start=10614,
serialized_end=10660,
)
_sym_db.RegisterEnumDescriptor(_POOLINGPARAMETER_POOLMETHOD)
......@@ -427,8 +427,8 @@ _POOLINGPARAMETER_ENGINE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=7564,
serialized_end=7607,
serialized_start=7618,
serialized_end=7661,
)
_sym_db.RegisterEnumDescriptor(_POOLINGPARAMETER_ENGINE)
......@@ -457,8 +457,8 @@ _REDUCTIONPARAMETER_REDUCTIONOP = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=11042,
serialized_end=11095,
serialized_start=11096,
serialized_end=11149,
)
_sym_db.RegisterEnumDescriptor(_REDUCTIONPARAMETER_REDUCTIONOP)
......@@ -483,8 +483,8 @@ _RELUPARAMETER_ENGINE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=7564,
serialized_end=7607,
serialized_start=7618,
serialized_end=7661,
)
_sym_db.RegisterEnumDescriptor(_RELUPARAMETER_ENGINE)
......@@ -509,8 +509,8 @@ _SIGMOIDPARAMETER_ENGINE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=7564,
serialized_end=7607,
serialized_start=7618,
serialized_end=7661,
)
_sym_db.RegisterEnumDescriptor(_SIGMOIDPARAMETER_ENGINE)
......@@ -535,8 +535,8 @@ _SOFTMAXPARAMETER_ENGINE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=7564,
serialized_end=7607,
serialized_start=7618,
serialized_end=7661,
)
_sym_db.RegisterEnumDescriptor(_SOFTMAXPARAMETER_ENGINE)
......@@ -561,8 +561,8 @@ _TANHPARAMETER_ENGINE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=7564,
serialized_end=7607,
serialized_start=7618,
serialized_end=7661,
)
_sym_db.RegisterEnumDescriptor(_TANHPARAMETER_ENGINE)
......@@ -587,8 +587,8 @@ _SPPPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=10560,
serialized_end=10606,
serialized_start=10614,
serialized_end=10660,
)
_sym_db.RegisterEnumDescriptor(_SPPPARAMETER_POOLMETHOD)
......@@ -613,8 +613,8 @@ _SPPPARAMETER_ENGINE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=7564,
serialized_end=7607,
serialized_start=7618,
serialized_end=7661,
)
_sym_db.RegisterEnumDescriptor(_SPPPARAMETER_ENGINE)
......@@ -787,8 +787,8 @@ _V1LAYERPARAMETER_LAYERTYPE = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=14534,
serialized_end=15134,
serialized_start=14588,
serialized_end=15188,
)
_sym_db.RegisterEnumDescriptor(_V1LAYERPARAMETER_LAYERTYPE)
......@@ -835,8 +835,8 @@ _V0LAYERPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor(
],
containing_type=None,
options=None,
serialized_start=10560,
serialized_end=10606,
serialized_start=10614,
serialized_end=10660,
)
_sym_db.RegisterEnumDescriptor(_V0LAYERPARAMETER_POOLMETHOD)
......@@ -2261,6 +2261,13 @@ _LAYERPARAMETER = _descriptor.Descriptor(
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='group_norm_param', full_name='caffe.LayerParameter.group_norm_param', index=71,
number=166, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
......@@ -2273,7 +2280,7 @@ _LAYERPARAMETER = _descriptor.Descriptor(
oneofs=[
],
serialized_start=2834,
serialized_end=6055,
serialized_end=6109,
)
......@@ -2372,8 +2379,8 @@ _TRANSFORMATIONPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=6058,
serialized_end=6353,
serialized_start=6112,
serialized_end=6407,
)
......@@ -2402,8 +2409,8 @@ _LOSSPARAMETER_EXPANDDIMSPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=6484,
serialized_end=6523,
serialized_start=6538,
serialized_end=6577,
)
_LOSSPARAMETER = _descriptor.Descriptor(
......@@ -2446,8 +2453,8 @@ _LOSSPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=6356,
serialized_end=6601,
serialized_start=6410,
serialized_end=6655,
)
......@@ -2490,8 +2497,8 @@ _ACCURACYPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=6603,
serialized_end=6679,
serialized_start=6657,
serialized_end=6733,
)
......@@ -2534,8 +2541,8 @@ _ARGMAXPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=6681,
serialized_end=6758,
serialized_start=6735,
serialized_end=6812,
)
......@@ -2571,8 +2578,8 @@ _CONCATPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=6760,
serialized_end=6817,
serialized_start=6814,
serialized_end=6871,
)
......@@ -2615,8 +2622,8 @@ _BATCHNORMPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=6819,
serialized_end=6923,
serialized_start=6873,
serialized_end=6977,
)
......@@ -2659,8 +2666,8 @@ _BIASPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=6925,
serialized_end=7018,
serialized_start=6979,
serialized_end=7072,
)
......@@ -2696,8 +2703,8 @@ _CONTRASTIVELOSSPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=7020,
serialized_end=7096,
serialized_start=7074,
serialized_end=7150,
)
......@@ -2846,8 +2853,8 @@ _CONVOLUTIONPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=7099,
serialized_end=7607,
serialized_start=7153,
serialized_end=7661,
)
......@@ -2883,8 +2890,8 @@ _CROPPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=7609,
serialized_end=7657,
serialized_start=7663,
serialized_end=7711,
)
......@@ -2977,8 +2984,8 @@ _DATAPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=7660,
serialized_end=7952,
serialized_start=7714,
serialized_end=8006,
)
......@@ -3014,8 +3021,8 @@ _DROPOUTPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=7954,
serialized_end=8027,
serialized_start=8008,
serialized_end=8081,
)
......@@ -3079,8 +3086,8 @@ _DUMMYDATAPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=8030,
serialized_end=8190,
serialized_start=8084,
serialized_end=8244,
)
......@@ -3124,8 +3131,8 @@ _ELTWISEPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=8193,
serialized_end=8358,
serialized_start=8247,
serialized_end=8412,
)
......@@ -3154,8 +3161,8 @@ _ELUPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=8360,
serialized_end=8392,
serialized_start=8414,
serialized_end=8446,
)
......@@ -3212,8 +3219,8 @@ _EMBEDPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=8395,
serialized_end=8567,
serialized_start=8449,
serialized_end=8621,
)
......@@ -3256,8 +3263,8 @@ _EXPPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=8569,
serialized_end=8637,
serialized_start=8623,
serialized_end=8691,
)
......@@ -3293,8 +3300,8 @@ _FLATTENPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=8639,
serialized_end=8696,
serialized_start=8693,
serialized_end=8750,
)
......@@ -3337,8 +3344,8 @@ _HDF5DATAPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=8698,
serialized_end=8777,
serialized_start=8752,
serialized_end=8831,
)
......@@ -3367,8 +3374,8 @@ _HDF5OUTPUTPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=8779,
serialized_end=8819,
serialized_start=8833,
serialized_end=8873,
)
......@@ -3398,8 +3405,8 @@ _HINGELOSSPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=8821,
serialized_end=8915,
serialized_start=8875,
serialized_end=8969,
)
......@@ -3505,8 +3512,8 @@ _IMAGEDATAPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=8918,
serialized_end=9197,
serialized_start=8972,
serialized_end=9251,
)
......@@ -3535,8 +3542,8 @@ _INFOGAINLOSSPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=9199,
serialized_end=9238,
serialized_start=9253,
serialized_end=9292,
)
......@@ -3600,8 +3607,8 @@ _INNERPRODUCTPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=9241,
serialized_end=9444,
serialized_start=9295,
serialized_end=9498,
)
......@@ -3630,8 +3637,8 @@ _INPUTPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=9446,
serialized_end=9495,
serialized_start=9500,
serialized_end=9549,
)
......@@ -3674,8 +3681,8 @@ _LOGPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=9497,
serialized_end=9565,
serialized_start=9551,
serialized_end=9619,
)
......@@ -3741,8 +3748,8 @@ _LRNPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=9568,
serialized_end=9880,
serialized_start=9622,
serialized_end=9934,
)
......@@ -3800,8 +3807,8 @@ _MEMORYDATAPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=9883,
serialized_end=10072,
serialized_start=9937,
serialized_end=10126,
)
......@@ -3844,8 +3851,8 @@ _MVNPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=10074,
serialized_end=10175,
serialized_start=10128,
serialized_end=10229,
)
......@@ -3874,8 +3881,8 @@ _PARAMETERPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=10177,
serialized_end=10230,
serialized_start=10231,
serialized_end=10284,
)
......@@ -3983,8 +3990,8 @@ _POOLINGPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=10233,
serialized_end=10651,
serialized_start=10287,
serialized_end=10705,
)
......@@ -4027,8 +4034,8 @@ _ROIPOOLINGPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=10653,
serialized_end=10742,
serialized_start=10707,
serialized_end=10796,
)
......@@ -4071,8 +4078,8 @@ _POWERPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=10744,
serialized_end=10814,
serialized_start=10798,
serialized_end=10868,
)
......@@ -4122,8 +4129,8 @@ _PYTHONPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=10816,
serialized_end=10919,
serialized_start=10870,
serialized_end=10973,
)
......@@ -4167,8 +4174,8 @@ _REDUCTIONPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=10922,
serialized_end=11095,
serialized_start=10976,
serialized_end=11149,
)
......@@ -4205,8 +4212,8 @@ _RELUPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=11098,
serialized_end=11239,
serialized_start=11152,
serialized_end=11293,
)
......@@ -4249,8 +4256,8 @@ _RESHAPEPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=11241,
serialized_end=11331,
serialized_start=11295,
serialized_end=11385,
)
......@@ -4307,8 +4314,8 @@ _SCALEPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=11334,
serialized_end=11499,
serialized_start=11388,
serialized_end=11553,
)
......@@ -4338,8 +4345,8 @@ _SIGMOIDPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=11501,
serialized_end=11621,
serialized_start=11555,
serialized_end=11675,
)
......@@ -4382,8 +4389,8 @@ _SLICEPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=11623,
serialized_end=11699,
serialized_start=11677,
serialized_end=11753,
)
......@@ -4420,8 +4427,8 @@ _SOFTMAXPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=11702,
serialized_end=11839,
serialized_start=11756,
serialized_end=11893,
)
......@@ -4451,8 +4458,8 @@ _TANHPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=11841,
serialized_end=11955,
serialized_start=11895,
serialized_end=12009,
)
......@@ -4495,8 +4502,8 @@ _TILEPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=11957,
serialized_end=12041,
serialized_start=12011,
serialized_end=12095,
)
......@@ -4525,8 +4532,8 @@ _THRESHOLDPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=12043,
serialized_end=12085,
serialized_start=12097,
serialized_end=12139,
)
......@@ -4639,8 +4646,8 @@ _WINDOWDATAPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=12088,
serialized_end=12409,
serialized_start=12142,
serialized_end=12463,
)
......@@ -4685,8 +4692,8 @@ _SPPPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=12412,
serialized_end=12647,
serialized_start=12466,
serialized_end=12701,
)
......@@ -5011,8 +5018,8 @@ _V1LAYERPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=12650,
serialized_end=15178,
serialized_start=12704,
serialized_end=15232,
)
......@@ -5301,8 +5308,8 @@ _V0LAYERPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=15181,
serialized_end=16202,
serialized_start=15235,
serialized_end=16256,
)
......@@ -5338,8 +5345,8 @@ _PRELUPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=16204,
serialized_end=16291,
serialized_start=16258,
serialized_end=16345,
)
......@@ -5368,8 +5375,8 @@ _SMOOTHL1LOSSPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=16293,
serialized_end=16334,
serialized_start=16347,
serialized_end=16388,
)
......@@ -5412,8 +5419,8 @@ _MPIPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=16336,
serialized_end=16408,
serialized_start=16390,
serialized_end=16462,
)
......@@ -5442,8 +5449,8 @@ _PERMUTEPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=16410,
serialized_end=16443,
serialized_start=16464,
serialized_end=16497,
)
......@@ -5493,8 +5500,8 @@ _NORMALIZEPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=16446,
serialized_end=16593,
serialized_start=16500,
serialized_end=16647,
)
......@@ -5537,8 +5544,8 @@ _PARALLELPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=16595,
serialized_end=16690,
serialized_start=16649,
serialized_end=16744,
)
......@@ -5581,8 +5588,8 @@ _RESIZEPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=16692,
serialized_end=16774,
serialized_start=16746,
serialized_end=16828,
)
......@@ -5611,8 +5618,8 @@ _EXPANDDIMSPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=6484,
serialized_end=6523,
serialized_start=6538,
serialized_end=6577,
)
......@@ -5711,8 +5718,8 @@ _PROPOSALPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=16818,
serialized_end=17090,
serialized_start=16872,
serialized_end=17144,
)
......@@ -5776,8 +5783,8 @@ _BATCHRENORMPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=17093,
serialized_end=17259,
serialized_start=17147,
serialized_end=17313,
)
......@@ -5813,8 +5820,8 @@ _DENSECONCATPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=17261,
serialized_end=17324,
serialized_start=17315,
serialized_end=17378,
)
......@@ -5864,8 +5871,8 @@ _FOCALLOSSPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=17326,
serialized_end=17425,
serialized_start=17380,
serialized_end=17479,
)
......@@ -5894,8 +5901,59 @@ _GATHERPARAMETER = _descriptor.Descriptor(
extension_ranges=[],
oneofs=[
],
serialized_start=17427,
serialized_end=17461,
serialized_start=17481,
serialized_end=17515,
)
_GROUPNORMPARAMETER = _descriptor.Descriptor(
name='GroupNormParameter',
full_name='caffe.GroupNormParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='use_global_stats', full_name='caffe.GroupNormParameter.use_global_stats', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='moving_average_fraction', full_name='caffe.GroupNormParameter.moving_average_fraction', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.9,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='eps', full_name='caffe.GroupNormParameter.eps', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.001,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='group', full_name='caffe.GroupNormParameter.group', index=3,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=32,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
oneofs=[
],
serialized_start=17517,
serialized_end=17640,
)
_BLOBPROTO.fields_by_name['shape'].message_type = _BLOBSHAPE
......@@ -5986,6 +6044,7 @@ _LAYERPARAMETER.fields_by_name['batch_renorm_param'].message_type = _BATCHRENORM
_LAYERPARAMETER.fields_by_name['dense_concat_param'].message_type = _DENSECONCATPARAMETER
_LAYERPARAMETER.fields_by_name['focal_loss_param'].message_type = _FOCALLOSSPARAMETER
_LAYERPARAMETER.fields_by_name['gather_param'].message_type = _GATHERPARAMETER
_LAYERPARAMETER.fields_by_name['group_norm_param'].message_type = _GROUPNORMPARAMETER
_LOSSPARAMETER_EXPANDDIMSPARAMETER.containing_type = _LOSSPARAMETER
_LOSSPARAMETER.fields_by_name['normalization'].enum_type = _LOSSPARAMETER_NORMALIZATIONMODE
_LOSSPARAMETER_NORMALIZATIONMODE.containing_type = _LOSSPARAMETER
......@@ -6156,6 +6215,7 @@ DESCRIPTOR.message_types_by_name['BatchRenormParameter'] = _BATCHRENORMPARAMETER
DESCRIPTOR.message_types_by_name['DenseConcatParameter'] = _DENSECONCATPARAMETER
DESCRIPTOR.message_types_by_name['FocalLossParameter'] = _FOCALLOSSPARAMETER
DESCRIPTOR.message_types_by_name['GatherParameter'] = _GATHERPARAMETER
DESCRIPTOR.message_types_by_name['GroupNormParameter'] = _GROUPNORMPARAMETER
DESCRIPTOR.enum_types_by_name['Phase'] = _PHASE
BlobShape = _reflection.GeneratedProtocolMessageType('BlobShape', (_message.Message,), dict(
......@@ -6677,6 +6737,13 @@ GatherParameter = _reflection.GeneratedProtocolMessageType('GatherParameter', (_
))
_sym_db.RegisterMessage(GatherParameter)
GroupNormParameter = _reflection.GeneratedProtocolMessageType('GroupNormParameter', (_message.Message,), dict(
DESCRIPTOR = _GROUPNORMPARAMETER,
__module__ = 'caffe_pb2'
# @@protoc_insertion_point(class_scope:caffe.GroupNormParameter)
))
_sym_db.RegisterMessage(GroupNormParameter)
_BLOBSHAPE.fields_by_name['dim'].has_options = True
_BLOBSHAPE.fields_by_name['dim']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))
......
Dragon/python/setup.py
......@@ -36,7 +36,7 @@ find_packages('dragon')
find_modules()
setup(name = 'dragon',
version='0.2.1.12',
version='0.2.1.13',
description = 'Dragon: A Computation Graph Virtual Machine Based Deep Learning Framework',
url='https://github.com/neopenx/Dragon',
author='Ting Pan',
......
Dragon/src/operators/ndarray/reshape_op.cc
#include "operators/ndarray/reshape_op.h"
#include "core/workspace.h"
namespace dragon {
string dim_string(const vector<TIndex>& shape) {
std::stringstream ss;
ss << "(";
for (int i = 0; i < shape.size() - 1; i++) ss << shape[i] << ",";
ss << shape[shape.size() - 1] << ")";
return ss.str();
}
template <class Context>
void ReshapeOp<Context>::RunOnDevice() {
if (shape_desc.size() > 0 || shape_value.size() > 0) {
require_shape.resize(std::max(shape_desc.size(), shape_value.size()));
for (int i = 0; i < require_shape.size(); i++)
require_shape[i] = shape(i);
} else if (shape_like_desc.size() > 0) {
Tensor* shape_like_tensor = ws()->GetTensor(shape_like_desc);
require_shape.resize(shape_like_tensor->ndim());
for (int i = 0; i < require_shape.size(); i++)
require_shape[i] = shape_like_tensor->dim(i);
} else { LOG(FATAL) << "Missing the require shape."; }
vector<TIndex> Xdims = input(0).dims();
new_shape.resize(require_shape.size());
int infer_dim = -1;
TIndex total_count = 1;
for (int i = 0; i < shape.size(); i++) {
for (int i = 0; i < require_shape.size(); i++) {
if (require_shape[i] == 0) {
// handle unchanged dim
if (shape[i] == 0) {
CHECK_LT(i, (int)Xdims.size())
<< "\nDim(" << i << ") is out of the Xdims range of (0, "
<< Xdims.size() << ").";
new_shape[i] = Xdims[i];
}
} else if (require_shape[i] > 0) {
// handle reseted dim
else if (shape[i] > 0) {
new_shape[i] = shape[i];
}
new_shape[i] = require_shape[i];
} else {
// handle inferred dim
else {
CHECK_EQ(infer_dim, -1)
<< "\nDim(" << infer_dim << ") required infer before"
<< "\ncould not infer for dim(" << i << ") both.";
......@@ -35,7 +54,8 @@ void ReshapeOp<Context>::RunOnDevice() {
for (int i = 0; i < new_shape.size(); i++) {
if (new_shape[i] == -1) {
CHECK_EQ(input(0).count() % total_count, 0)
<< "\nCan not change the total size.";
<< "\nCan not change the total size: "
<< input(0).dim_string() << " -> " << dim_string(new_shape);
new_shape[i] = input(0).count() / total_count;
total_count *= new_shape[i];
break;
......@@ -43,7 +63,8 @@ void ReshapeOp<Context>::RunOnDevice() {
}
}
CHECK_EQ(total_count, input(0).count())
<< "\nCan not change the total size.";
<< "\nCan not change the total size."
<< input(0).dim_string() << " -> " << dim_string(new_shape);
output(0)->Reshape(new_shape);
output(0)->Share(input(0));
}
......
Dragon/src/operators/norm/batch_norm_op.cc
......@@ -19,9 +19,9 @@ void BatchNormOp<Context>::TrainingRunWithType() {
auto* tVar_data = var->template mutable_data<T, Context>();
auto* Xdata = input(0).template data<T, Context>();
auto* Ydata = output(0)->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* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
ctx().template Copy<T, Context, Context>(output(0)->count(), Ydata, Xdata);
......@@ -127,9 +127,9 @@ void BatchNormOp<Context>::InferenceRunWithType() {
auto* tVar_data = var->template mutable_data<T, Context>();
auto* Xdata = input(0).template data<T, Context>();
auto* Ydata = output(0)->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* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
ctx().template Copy<T, Context, Context>(input(0).count(), Ydata, Xdata);
......@@ -248,9 +248,9 @@ void BatchNormGradientOp<Context>::TrainingRunWithType() {
auto* dXdata = output(0)->template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_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* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
if (data_format == "NCHW") {
......@@ -337,9 +337,9 @@ void BatchNormGradientOp<Context>::InferenceRunWithType() {
auto* dXdata = output(0)->template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_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* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
if (data_format == "NCHW") {
......
Dragon/src/operators/norm/fused_batch_norm.cc
......@@ -23,9 +23,9 @@ void FusedBatchNormOp<Context>::TrainingRunWithType() {
auto* tVar_data = var->template mutable_data<T, Context>();
auto* Xdata = input(0).template data<T, Context>();
auto* Ydata = output(0)->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* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
ctx().template Copy<T, Context, Context>(output(0)->count(), Ydata, Xdata);
......@@ -153,9 +153,9 @@ void FusedBatchNormOp<Context>::InferenceRunWithType() {
auto* tVar_data = var->template mutable_data<T, Context>();
auto* Xdata = input(0).template data<T, Context>();
auto* Ydata = output(0)->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* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
ctx().template Copy<T, Context, Context>(input(0).count(), Ydata, Xdata);
......@@ -296,9 +296,9 @@ void FusedBatchNormGradientOp<Context>::TrainingRunWithType() {
auto* Std_data = stddev->template mutable_data<T, Context>();
auto* tMean_data = mean->template mutable_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* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
auto* XNorm_data = x_norm->template data<T, Context>();
......@@ -436,9 +436,9 @@ void FusedBatchNormGradientOp<Context>::InferenceRunWithType() {
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* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
// gradient w.r.t. scale
......
Dragon/src/operators/norm/fused_group_norm.cc 0 → 100644
#include "operators/norm/group_norm_op.h"
#include "core/workspace.h"
#include "utils/math_functions.h"
#include "utils/filler.h"
namespace dragon {
template <class Context> template <typename T>
void FusedGroupNormOp<Context>::TrainingRunWithType() {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
INIT_MULTIPLIER(cgs_multiplier, CGS);
TENSOR_FILL(input(1), vector<TIndex>(1, NG)); // history_mean
TENSOR_FILL(input(2), vector<TIndex>(1, NG)); // history_var
TENSOR_FILL(input(3), vector<TIndex>(1, C)); // scale
TENSOR_FILL(input(4), vector<TIndex>(1, C)); // bias
auto* hMean_data = input(1).template mutable_data<T, Context>();
auto* hVar_data = input(2).template mutable_data<T, Context>();
auto* Sdata = input(3).template data<T, Context>();
auto* Bdata = input(4).template data<T, Context>();
auto* tMean_data = mean->template mutable_data<T, Context>();
auto* tVar_data = var->template mutable_data<T, Context>();
auto* Xdata = input(0).template data<T, Context>();
auto* Ydata = output(0)->template mutable_data<T, Context>();
auto* NMul_data = num_multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* CGSMul_data = cgs_multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
ctx().template Copy<T, Context, Context>(output(0)->count(), Ydata, Xdata);
// compute mean
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NG, CGS,
1.0 / CGS, Xdata, CGSMul_data,
0, tMean_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// subtract mean
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
-1.0, tMean_data, CGSMul_data,
1.0, Ydata);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// compute variance
// note that we use VAR(X) = E((X - EX) ^ 2)
math::Square<T, Context>(output(0)->count(), Ydata, Std_data);
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NG, CGS,
1.0 / CGS, Std_data, CGSMul_data,
0.0, tVar_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// compute moving average
if (!is_recomputing) {
// History(X) = (1 - momentum) * Cur(X) + momentum * History(X)
math::Axpby<T, Context>(mean->count(), 1.0 - momentum, tMean_data, momentum, hMean_data);
math::Axpby<T, Context>(var->count(), 1.0 - momentum, tVar_data, momentum, hVar_data);
}
// compute stddev
math::AddScalar<T, Context>(var->count(), eps, tVar_data);
math::Sqrt<T, Context>(var->count(), tVar_data, tVar_data);
// divide by stddev
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tVar_data, CGSMul_data,
0.0, Std_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
math::Div<T, Context>(output(0)->count(), Ydata, Std_data, Ydata);
// store x_norm for backward
auto* XNorm_data = x_norm->template mutable_data<T, Context>();
ctx().template Copy<T, Context, Context>(output(0)->count(), XNorm_data, Ydata);
// scale
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, N, C, 1,
1.0, NMul_data, Sdata,
0.0, NC_data);
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NC, S, 1,
1.0, NC_data, SMul_data,
0.0, Std_data);
} else if (data_format == "NHWC") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NS, C, 1,
1.0, NSMul_data, Sdata,
0.0, Std_data);
}
math::Mul<T, Context>(output(0)->count(), Ydata, Std_data, Ydata);
// shift
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, N, C, 1,
1.0, NMul_data, Bdata,
0.0, NC_data);
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NC, S, 1,
1.0, NC_data, SMul_data,
1.0, Ydata);
} else if (data_format == "NHWC") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NS, C, 1,
1.0, NSMul_data, Bdata,
1.0, Ydata);
}
ws()->ReleaseBuffer(stddev);
}
template <class Context> template <typename T>
void FusedGroupNormOp<Context>::InferenceRunWithType() {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
INIT_MULTIPLIER(cgs_multiplier, CGS);
TENSOR_FILL(input(1), vector<TIndex>(1, NG)); // history_mean
TENSOR_FILL(input(2), vector<TIndex>(1, NG)); // history_var
TENSOR_FILL(input(3), vector<TIndex>(1, C)); // scale
TENSOR_FILL(input(4), vector<TIndex>(1, C)); // bias
auto* hMean_data = input(1).template mutable_data<T, Context>();
auto* hVar_data = input(2).template mutable_data<T, Context>();
auto* Sdata = input(3).template data<T, Context>();
auto* Bdata = input(4).template data<T, Context>();
auto* tMean_data = mean->template mutable_data<T, Context>();
auto* tVar_data = var->template mutable_data<T, Context>();
auto* Xdata = input(0).template data<T, Context>();
auto* Ydata = output(0)->template mutable_data<T, Context>();
auto* NMul_data = num_multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* CGSMul_data = cgs_multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
ctx().template Copy<T, Context, Context>(input(0).count(), Ydata, Xdata);
ctx().template Copy<T, Context, Context>(mean->count(), tMean_data, hMean_data);
ctx().template Copy<T, Context, Context>(var->count(), tVar_data, hVar_data);
// subtract mean
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NG, CGS,
1.0 / CGS, Xdata, CGSMul_data,
0, tMean_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// compute stddev
math::AddScalar<T, Context>(var->count(), eps, tVar_data);
math::Sqrt<T, Context>(var->count(), tVar_data, tVar_data);
// divide by stddev
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tVar_data, CGSMul_data,
0.0, Std_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
math::Div<T, Context>(output(0)->count(), Ydata, Std_data, Ydata);
// scale
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, N, C, 1,
1.0, NMul_data, Sdata,
0.0, NC_data);
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NC, S, 1,
1.0, NC_data, SMul_data,
0.0, Std_data);
} else if (data_format == "NHWC") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NS, C, 1,
1.0, NSMul_data, Sdata,
0.0, Std_data);
}
math::Mul<T, Context>(output(0)->count(), Ydata, Std_data, Ydata);
// shift
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, N, C, 1,
1.0, NMul_data, Bdata,
0.0, NC_data);
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NC, S, 1,
1.0, NC_data, SMul_data,
1.0, Ydata);
} else if (data_format == "NHWC") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NS, C, 1,
1.0, NSMul_data, Bdata,
1.0, Ydata);
}
ws()->ReleaseBuffer(stddev);
}
template <class Context>
void FusedGroupNormOp<Context>::Setup() {
// determine the mode
if (use_stats == -1) use_global_stats = phase() == "TEST" ? true : false;
else use_global_stats = use_stats == 1 ? true : false;
is_recomputing = ws()->GetTensor("/opt/mirror_stage/recompute_flag")
->template data<bool, CPUContext>()[0];
// determine the data format
TIndex channel_axis = axis;
data_format = "NCHW";
if (channel_axis == -1) channel_axis += (int)input(0).ndim();
if (channel_axis + 1 == (int)input(0).ndim()) data_format = "NHWC";
if (input(0).ndim() == 2) data_format = "NCHW";
N = input(0).dim(0);
C = input(0).dim(channel_axis);
CHECK_EQ(C % group, 0) << "\nThe " << C << " channels "
<< "can not be split into " << group << " groups.";
if (group == C && input(0).ndim() == 2) // InstanceNorm
LOG(WARNING) << "The 2d input will output all zeros.";
NC = N * C;
NG = N * group;
S = input(0).count() / NC;
CGS = (C / group) * S;
NS = N * S;
// make resource
mean = ws()->CreateTensor("/mnt/" + anchor() + "/gn_mean");
var = ws()->CreateTensor("/mnt/" + anchor() + "/gn_var");
x_norm = ws()->CreateTensor("/mnt/" + anchor() + "/gn_x_norm");
stddev = ws()->GetBuffer();
stddev->ReshapeLike(input(0));
// reshape
mean->Reshape(vector<TIndex>(1, NG));
var->Reshape(vector<TIndex>(1, NG));
num_by_chans.Reshape(vector<TIndex>(1, NC));
x_norm->ReshapeLike(input(0));
output(0)->ReshapeLike(input(0));
}
template <class Context>
void FusedGroupNormOp<Context>::RunOnDevice() {
Setup();
if (input(0).template IsType<float>()) {
if (use_global_stats) InferenceRunWithType<float>();
else TrainingRunWithType<float>();
}
#ifdef WITH_CUDA_FP16
else if (input(0).template IsType<float16>()) {
if (use_global_stats) InferenceRunWithType<float16>();
else TrainingRunWithType<float16>();
}
#endif
else LOG(FATAL) << "Unsupported input types.";
}
DEPLOY_CPU(FusedGroupNorm);
#ifdef WITH_CUDA
DEPLOY_CUDA(FusedGroupNorm);
#endif
OPERATOR_SCHEMA(FusedGroupNorm).NumInputs(5).NumOutputs(1);
template <class Context> template <typename T>
void FusedGroupNormGradientOp<Context>::TrainingRunWithType() {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
INIT_MULTIPLIER(cgs_multiplier, CGS);
auto* dYdata = input(-1).template data<T, Context>();
auto* dXdata = output(0)->template mutable_data<T, Context>();
auto* Sdata = input(3).template data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
auto* tMean_data = mean->template mutable_data<T, Context>();
auto* tVar_data = var->template mutable_data<T, Context>();
auto* NMul_data = num_multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* CGSMul_data = cgs_multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
auto* XNorm_data = x_norm->template data<T, Context>();
// gradient w.r.t. scale
if (output(1)->name() != "ignore") {
auto* dSdata = output(1)->template mutable_data<T, Context>();
math::Mul<T, Context>(stddev->count(), XNorm_data, dYdata, Std_data);
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NC, S,
1.0, Std_data, SMul_data,
0.0, NC_data);
math::Gemv<T, Context>(CblasTrans, N, C,
1.0, NC_data, NMul_data,
1.0, dSdata);
} else if (data_format == "NHWC") {
math::Gemv<T, Context>(CblasTrans, NS, C,
1.0, Std_data, NSMul_data,
1.0, dSdata);
}
}
// 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") {
// scale * dY
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, N, C, 1,
1.0, NMul_data, Sdata,
0.0, NC_data);
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NC, S, 1,
1.0, NC_data, SMul_data,
0.0, Std_data);
} else if (data_format == "NHWC") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NS, C, 1,
1.0, NSMul_data, Sdata,
0.0, Std_data);
}
math::Mul<T, Context>(stddev->count(), Std_data, dYdata, Std_data);
// sum of x_hat * (dl / dx_hat)
math::Mul<T, Context>(stddev->count(), XNorm_data, Std_data, dXdata);
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NG, CGS,
1.0, dXdata, CGSMul_data,
0.0, tMean_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// x_hat times the sum
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tMean_data, CGSMul_data,
0.0, dXdata);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
math::Mul<T, Context>(stddev->count(), XNorm_data, dXdata, dXdata);
// subtract the average of x_hat times the sum
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NG, CGS,
1.0, Std_data, CGSMul_data,
0.0, tMean_data);
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tMean_data, CGSMul_data,
1.0, dXdata);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
math::Axpby<T, Context>(stddev->count(), 1.0, Std_data, -1.0 / CGS, dXdata);
// multiply with the inverse std
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tVar_data, CGSMul_data,
0.0, Std_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// divide by stddev
math::Div<T, Context>(output(0)->count(), dXdata, Std_data, dXdata);
}
ws()->ReleaseBuffer(stddev);
}
template <class Context> template <typename T>
void FusedGroupNormGradientOp<Context>::InferenceRunWithType() {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
INIT_MULTIPLIER(cgs_multiplier, CGS);
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* NMul_data = num_multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* CGSMul_data = cgs_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") {
auto* dXdata = output(0)->template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
// divide scale by stddev
math::Div<T, Context>(var->count(), Sdata, tVar_data, tVar_data);
// compute dE/dY \cot (scale / std(X))
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tVar_data, CGSMul_data,
0.0, Std_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
math::Mul<T, Context>(output(0)->count(), dYdata, Std_data, dXdata);
}
ws()->ReleaseBuffer(stddev);
}
template <class Context>
void FusedGroupNormGradientOp<Context>::Setup() {
// determine the mode
if (use_stats == -1) use_global_stats = phase() == "TEST" ? true : false;
else use_global_stats = use_stats == 1 ? true : false;
// determine the data format
TIndex channel_axis = axis;
data_format = "NCHW";
if (channel_axis == -1) channel_axis += (int)input(0).ndim();
if (channel_axis + 1 == (int)input(0).ndim()) data_format = "NHWC";
if (input(0).ndim() == 2) data_format = "NCHW";
N = input(0).dim(0);
C = input(0).dim(channel_axis);
CHECK_EQ(C % group, 0) << "\nThe " << C << " channels "
<< "can not be split into " << group << " groups.";
if (group == C && input(0).ndim() == 2) // InstanceNorm
LOG(WARNING) << "The 2d input will output all zeros.";
NC = N * C;
NG = N * group;
S = input(0).count() / NC;
CGS = (C / group) * S;
NS = N * S;
// make resource
mean = ws()->GetTensor("/mnt/" + anchor() + "/gn_mean");
var = ws()->GetTensor("/mnt/" + anchor() + "/gn_var");
x_norm = ws()->GetTensor("/mnt/" + anchor() + "/gn_x_norm");
stddev = ws()->GetBuffer();
stddev->ReshapeLike(input(0));
// reshape
num_by_chans.Reshape(vector<TIndex>(1, NC));
output(0)->ReshapeLike(input(0)); // dX
output(1)->ReshapeLike(input(3)); // dScale
output(2)->ReshapeLike(input(3)); // dBias
}
template <class Context>
void FusedGroupNormGradientOp<Context>::RunOnDevice() {
Setup();
if (input(0).template IsType<float>()) {
if (use_global_stats) InferenceRunWithType<float>();
else TrainingRunWithType<float>();
}
#ifdef WITH_CUDA_FP16
else if (input(0).template IsType<float16>()) {
if (use_global_stats) InferenceRunWithType<float16>();
else TrainingRunWithType<float16>();
}
#endif
else LOG(FATAL) << "Unsupported input types.";
}
template <class Context>
void FusedGroupNormGradientOp<Context>::ShareGradient() {
if (use_global_stats) {
if (output(0)->name() != "ignore") {
Tensor* dX = ws()->GetBuffer("Grad");
ws()->CreateAvatar(output(0), dX);
}
} else {
if (output(0)->name() != "ignore" ||
output(1)->name() != "ignore" ||
output(2)->name() != "ignore") {
Tensor* dX = ws()->GetBuffer("Grad");
ws()->CreateAvatar(output(0), dX);
}
}
}
DEPLOY_CPU(FusedGroupNormGradient);
#ifdef WITH_CUDA
DEPLOY_CUDA(FusedGroupNormGradient);
#endif
OPERATOR_SCHEMA(FusedGroupNormGradient).NumInputs(5).NumOutputs(3);
class GetFusedGroupNormGradient final : public GradientMakerBase {
public:
GRADIENT_MAKER_CTOR(GetFusedGroupNormGradient);
vector<OperatorDef> MakeDefs() override {
return SingleDef(def.type() + "Gradient", "",
vector<string> {I(0), I(1), I(2), I(3), GO(0)},
vector<string> {GI(0), GI(3), GI(4)});
}
};
REGISTER_GRADIENT(FusedGroupNorm, GetFusedGroupNormGradient);
} // namespace dragon
\ No newline at end of file
Dragon/src/operators/norm/group_norm_op.cc 0 → 100644
#include "operators/norm/group_norm_op.h"
#include "core/workspace.h"
#include "utils/math_functions.h"
#include "utils/filler.h"
namespace dragon {
template <class Context> template <typename T>
void GroupNormOp<Context>::TrainingRunWithType() {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
INIT_MULTIPLIER(cgs_multiplier, CGS);
TENSOR_FILL(input(1), vector<TIndex>(1, NG)); // history_mean
TENSOR_FILL(input(2), vector<TIndex>(1, NG)); // history_var
auto* hMean_data = input(1).template mutable_data<T, Context>();
auto* hVar_data = input(2).template mutable_data<T, Context>();
auto* tMean_data = mean.template mutable_data<T, Context>();
auto* tVar_data = var->template mutable_data<T, Context>();
auto* Xdata = input(0).template data<T, Context>();
auto* Ydata = output(0)->template mutable_data<T, Context>();
auto* NMul_data = num_multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* CGSMul_data = cgs_multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
ctx().template Copy<T, Context, Context>(output(0)->count(), Ydata, Xdata);
// compute mean
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NG, CGS,
1.0 / CGS, Xdata, CGSMul_data,
0, tMean_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// subtract mean
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
-1.0, tMean_data, CGSMul_data,
1.0, Ydata);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// compute variance
// note that we use VAR(X) = E((X - EX) ^ 2)
math::Square<T, Context>(output(0)->count(), Ydata, Std_data);
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NG, CGS,
1.0 / CGS, Std_data, CGSMul_data,
0.0, tVar_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// compute moving average
if (!is_recomputing) {
if (mode == "CAFFE") {
CHECK_EQ(InputSize(), 4)
<< "\nThe number of inputs should be 4 if use CAFFE mode.";
TENSOR_FILL(input(3), vector<TIndex>(1, 1));
auto* hFact_data = input(3).template mutable_data<T, CPUContext>();
float factor = dragon_cast<float, T>(hFact_data[0]);
factor *= momentum; factor += 1;
hFact_data[0] = dragon_cast<T, float>(factor);
int m = input(0).count() / C;
float coeff = m > 1 ? float(m) / (m - 1) : 1;
// History(X) = Cur(X) + momentum * History(X)
math::Axpby<T, Context>(mean.count(), 1.0, tMean_data, momentum, hMean_data);
math::Axpby<T, Context>(var->count(), coeff, tVar_data, momentum, hVar_data);
} else {
// History(X) = (1 - momentum) * Cur(X) + momentum * History(X)
math::Axpby<T, Context>(mean.count(), 1.0 - momentum, tMean_data, momentum, hMean_data);
math::Axpby<T, Context>(var->count(), 1.0 - momentum, tVar_data, momentum, hVar_data);
}
}
// compute stddev
math::AddScalar<T, Context>(var->count(), eps, tVar_data);
math::Sqrt<T, Context>(var->count(), tVar_data, tVar_data);
// divide by stddev
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tVar_data, CGSMul_data,
0.0, Std_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
math::Div<T, Context>(output(0)->count(), Ydata, Std_data, Ydata);
ws()->ReleaseBuffer(stddev);
}
template <class Context> template <typename T>
void GroupNormOp<Context>::InferenceRunWithType() {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
INIT_MULTIPLIER(cgs_multiplier, CGS);
TENSOR_FILL(input(1), vector<TIndex>(1, NG)); // history_mean
TENSOR_FILL(input(2), vector<TIndex>(1, NG)); // history_var
auto* hMean_data = input(1).template mutable_data<T, Context>();
auto* hVar_data = input(2).template mutable_data<T, Context>();
auto* tMean_data = mean.template mutable_data<T, Context>();
auto* tVar_data = var->template mutable_data<T, Context>();
auto* Xdata = input(0).template data<T, Context>();
auto* Ydata = output(0)->template mutable_data<T, Context>();
auto* NMul_data = num_multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* CGSMul_data = cgs_multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
auto* Std_data = stddev->template mutable_data<T, Context>();
ctx().template Copy<T, Context, Context>(input(0).count(), Ydata, Xdata);
// scale the mean and variance if necessary
if (mode == "CAFFE") {
CHECK_EQ(InputSize(), 4)
<< "\nThe number of inputs should be 4 if use CAFFE mode.";
TENSOR_FILL(input(3), vector<TIndex>(1, 1));
auto* hFact_data = input(3).template mutable_data<T, CPUContext>();
const float factor = dragon_cast<float, T>(hFact_data[0]);
const float scale = factor == 0 ? 0 : 1.0 / factor;
math::Scale<T, Context>(mean.count(), scale, hMean_data, tMean_data);
math::Scale<T, Context>(var->count(), scale, hVar_data, tVar_data);
} else {
ctx().template Copy<T, Context, Context>(mean.count(), tMean_data, hMean_data);
ctx().template Copy<T, Context, Context>(var->count(), tVar_data, hVar_data);
}
// subtract mean
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NG, CGS,
1.0 / CGS, Xdata, CGSMul_data,
0, tMean_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// compute stddev
math::AddScalar<T, Context>(var->count(), eps, tVar_data);
math::Sqrt<T, Context>(var->count(), tVar_data, tVar_data);
// divide by stddev
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tVar_data, CGSMul_data,
0.0, Std_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
math::Div<T, Context>(output(0)->count(), Ydata, Std_data, Ydata);
ws()->ReleaseBuffer(stddev);
}
template <class Context>
void GroupNormOp<Context>::Setup() {
// determine the mode
if (use_stats == -1) use_global_stats = phase() == "TEST" ? true : false;
else use_global_stats = use_stats == 1 ? true : false;
is_recomputing = ws()->GetTensor("/opt/mirror_stage/recompute_flag")
->template data<bool, CPUContext>()[0];
// determine the data format
TIndex channel_axis = axis;
data_format = "NCHW";
if (channel_axis == -1) channel_axis += (int)input(0).ndim();
if (channel_axis + 1 == (int)input(0).ndim()) data_format = "NHWC";
if (input(0).ndim() == 2) data_format = "NCHW";
N = input(0).dim(0);
C = input(0).dim(channel_axis);
CHECK_EQ(C % group, 0) << "\nThe " << C << " channels "
<< "can not be split into " << group << " groups.";
if (group == C && input(0).ndim() == 2) // InstanceNorm
LOG(WARNING) << "The 2d input will output all zeros.";
NC = N * C;
NG = N * group;
S = input(0).count() / NC;
CGS = (C / group) * S;
NS = N * S;
// make resource
var = ws()->CreateTensor("/mnt/" + anchor() + "/gn_var");
stddev = ws()->GetBuffer();
stddev->ReshapeLike(input(0));
// reshape
mean.Reshape(vector<TIndex>(1, NG));
var->Reshape(vector<TIndex>(1, NG));
num_by_chans.Reshape(vector<TIndex>(1, NC));
output(0)->ReshapeLike(input(0));
}
template <class Context>
void GroupNormOp<Context>::RunOnDevice() {
Setup();
if (input(0).template IsType<float>()) {
if (use_global_stats) InferenceRunWithType<float>();
else TrainingRunWithType<float>();
}
#ifdef WITH_CUDA_FP16
else if (input(0).template IsType<float16>()) {
if (use_global_stats) InferenceRunWithType<float16>();
else TrainingRunWithType<float16>();
}
#endif
else LOG(FATAL) << "Unsupported input types.";
}
DEPLOY_CPU(GroupNorm);
#ifdef WITH_CUDA
DEPLOY_CUDA(GroupNorm);
#endif
OPERATOR_SCHEMA(GroupNorm).NumInputs(3, 4).NumOutputs(1);
template <class Context> template <typename T>
void GroupNormGradientOp<Context>::TrainingRunWithType() {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
INIT_MULTIPLIER(cgs_multiplier, CGS);
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* tVar_data = var->template mutable_data<T, Context>();
auto* NMul_data = num_multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* CGSMul_data = cgs_multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tVar_data, CGSMul_data,
0.0, Std_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
auto* Ydata = input(1).template data<T, Context>();
math::Mul<T, Context>(output(0)->count(), Ydata, dYdata, dXdata);
// sum(dE/dY \cdot Y)
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NG, CGS,
1.0, dXdata, CGSMul_data,
0.0, tVar_data);
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tVar_data, CGSMul_data,
0.0, dXdata);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// sum(dE/dY \cdot Y) \cdot Y
math::Mul<T, Context>(output(0)->count(), Ydata, dXdata, dXdata);
// sum(dE/dY) + sum(dE/dY \cdot Y) \cdot Y
if (data_format == "NCHW") {
math::Gemv<T, Context>(CblasNoTrans, NG, CGS,
1.0, dYdata, CGSMul_data,
0.0, tVar_data);
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tVar_data, CGSMul_data,
1.0, dXdata);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
// dE/dY - mean(dE/dY)- mean(dE/dY \cdot Y) \cdot Y
// = dE/dY - mean(sum(dE/dY) + sum(dE/dY \cdot Y) \cdot Y)
math::Axpby<T, Context>(output(0)->count(), 1.0, dYdata, -1.0 / CGS, dXdata);
// divide by stddev
math::Div<T, Context>(output(0)->count(), dXdata, Std_data, dXdata);
ws()->ReleaseBuffer(stddev);
}
template <class Context> template <typename T>
void GroupNormGradientOp<Context>::InferenceRunWithType() {
INIT_MULTIPLIER(multiplier, NS);
INIT_MULTIPLIER(num_multiplier, N);
INIT_MULTIPLIER(spatial_multiplier, S);
INIT_MULTIPLIER(cgs_multiplier, CGS);
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* tVar_data = var->template mutable_data<T, Context>();
auto* NMul_data = num_multiplier->template data<T, Context>();
auto* SMul_data = spatial_multiplier->template data<T, Context>();
auto* NSMul_data = multiplier->template data<T, Context>();
auto* CGSMul_data = cgs_multiplier->template data<T, Context>();
auto* NC_data = num_by_chans.template mutable_data<T, Context>();
if (data_format == "NCHW") {
math::Gemm<T, Context>(CblasNoTrans, CblasNoTrans, NG, CGS, 1,
1.0, tVar_data, NSMul_data,
0.0, Std_data);
} else if (data_format == "NHWC") {
NOT_IMPLEMENTED;
}
math::Div<T, Context>(output(0)->count(), dYdata, Std_data, dXdata);
ws()->ReleaseBuffer(stddev);
}
template <class Context>
void GroupNormGradientOp<Context>::Setup() {
// determine the mode
if (use_stats == -1) use_global_stats = phase() == "TEST" ? true : false;
else use_global_stats = use_stats == 1 ? true : false;
// determine the data format
TIndex channel_axis = axis;
data_format = "NCHW";
if (channel_axis == -1) channel_axis += (int)input(0).ndim();
if (channel_axis + 1 == (int)input(0).ndim()) data_format = "NHWC";
if (input(0).ndim() == 2) data_format = "NCHW";
N = input(0).dim(0);
C = input(0).dim(channel_axis);
CHECK_EQ(C % group, 0) << "\nThe " << C << " channels "
<< "can not be split into " << group << " groups.";
if (group == C && input(0).ndim() == 2) // InstanceNorm
LOG(WARNING) << "The 2d input will output all zeros.";
NC = N * C;
NG = N * group;
S = input(0).count() / NC;
CGS = (C / group) * S;
NS = N * S;
// make resource
var = ws()->GetTensor("/mnt/" + anchor() + "/gn_var");
stddev = ws()->GetBuffer();
stddev->ReshapeLike(input(0));
// reshape
num_by_chans.Reshape(vector<TIndex>(1, NC));
output(0)->ReshapeLike(input(0));
}
template <class Context>
void GroupNormGradientOp<Context>::RunOnDevice() {
Setup();
if (input(0).template IsType<float>()) {
if (use_global_stats) InferenceRunWithType<float>();
else TrainingRunWithType<float>();
}
#ifdef WITH_CUDA_FP16
else if (input(0).template IsType<float16>()) {
if (use_global_stats) InferenceRunWithType<float16>();
else TrainingRunWithType<float16>();
}
#endif
else LOG(FATAL) << "Unsupported input types.";
}
DEPLOY_CPU(GroupNormGradient);
#ifdef WITH_CUDA
DEPLOY_CUDA(GroupNormGradient);
#endif
OPERATOR_SCHEMA(GroupNormGradient).NumInputs(3).NumOutputs(1);
class GetGroupNormGradient final : public GradientMakerBase {
public:
GRADIENT_MAKER_CTOR(GetGroupNormGradient);
vector<OperatorDef> MakeDefs() override {
return SingleDef(def.type() + "Gradient", "",
vector<string> {I(0), O(0), GO(0)},
vector<string> {GI(0)});
}
};
REGISTER_GRADIENT(GroupNorm, GetGroupNormGradient);
} // namespace dragon
\ No newline at end of file
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