Graph Converters
- class nnabla.experimental.graph_converters.GraphConverter(modifiers=[])[source]
Convert a graph with the modifiers by traversing from output variables.
- convert(o)[source]
- Parameters:
o (list of
nnabla.Variable
) – Output variables.
- class nnabla.experimental.graph_converters.FunctionModifier[source]
Base class of modifiers.
The
modify
method is called for a function with inputs in a graph topological order when you call the GraphConverter(<modifiers>).convert(<root variable>) method.- finish_up()[source]
Finish the very time function modification.
Clean up the internal modifier states.
- Parameters:
None –
- Returns:
None
- get_parameter_scope(v)[source]
Get the parameter name corresponding to v
- Parameters:
v (
nnabla.Variable
) – NNabla Variable Object.- Returns:
Scope name
- Return type:
- modify(f, inputs)[source]
Modify the function.
Implement this method in a sub class to modify a function.
Examples:
class ReLUToLeakyReLUModifier(FunctionModifier): def __init__(self): super(ReLUToLeakyReLUModifier, self).__init__() def modify(self, f, inputs): if f.info.type_name == 'ReLU': x = inputs[0] return F.leaky_relu(x)
This examples is a simple case since the network topological order does not change. In GraphConverter, we expect the modify method is called along the original network topological order not the modified order. In such a complex case, see themodify method of
BatchNormalizationFoldingModifierInner
as a reference.- Parameters:
f (
nnabla.function.Function
) – NNabla function object.inputs (list of
Variable
) – New inputs tof
. This may be modified one or the same as f.inputs.
- Returns:
Variable
or list ofVariable
.
Function Modifiers
- class nnabla.experimental.graph_converters.BatchNormalizationFoldingModifier(opposite=False, channel_last=False)[source]
Single
Convolution -> BatchNormalization
pass is folded into oneConvolution
.If there is a
Convolution -> BatchNormalization
pass, fold the batch normalization parameters to the kernel and bias (if it exists) of the preceding convolution, then skip the batch normalization following the convolution.Supported folding functions:
Convolution
,Deconvolution
,Affine
.Examples:
pred = Model(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.BatchNormalizationFoldingModifier()] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.AddBiasModifier[source]
Add bias to
Convolution
in BatchNormalization folding case if it doesn’t have bias.Supported folding functions:
Convolution
,Deconvolution
,Affine
.Examples:
pred = Model(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.AddBiasModifier()] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.BatchNormalizationFoldingModifierInner(channel_last=False)[source]
Single
Convolution -> BatchNormalization
pass is folded into oneConvolution
.If there is a
Convolution -> BatchNormalization
pass, fold the batch normalization parameters to the kernel and bias (if it exists) of the preceding convolution, then skip the batch normalization following the convolution.Supported folding functions:
Convolution
,Deconvolution
,Affine
.
- class nnabla.experimental.graph_converters.BatchNormalizationFoldingOppositeModifierInner(channel_last=False)[source]
Single
BatchNormalization -> Convolution
pass is folded into oneConvolution
.If there is a
BatchNormalization -> Convolution
pass, fold the batch normalization parameters to the kernel and bias (if it exists) of the preceding convolution, then skip the batch normalization following the convolution.Supported folding functions:
Convolution
,Deconvolution
,Affine
.
- class nnabla.experimental.graph_converters.BatchNormalizationSelfFoldingModifier(name='bn-self-folding')[source]
The parameters of the batch normalization replaced simple scale and bias.
- Parameters:
name (
str
) – Prefix of the parameter scope.
Examples:
pred = Model(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.BatchNormalizationSelfFoldingModifier()] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.FusedBatchNormalizationModifier[source]
Block
BatchNormalization -> Add2 -> Non-Linear
pass is fused into oneFusedBatchNormalization
.If there is a block
BatchNormalization -> Add2 -> Non-Linear
pass, remove all the block functions and replace the whole block toFusedBatchNormalization
.Examples:
pred = Model(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.FusedBatchNormalizationModifier()] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.UnfusedBatchNormalizationModifier[source]
Unfuse
FusedBatchNormalization
toBatchNormalization -> Add2 -> Non-Linear
block.If there is a
FusedBatchNormalization
pass, remove the fused batch normalization and replace it with the blockBatchNormalization -> Add2 -> Non-Linear
.Supported Non-Linear functions:
relu
Examples:
pred = Model(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.UnfusedBatchNormalizationModifier()] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.ChannelLastModifier(inputs, inputs_cl=None)[source]
Convert graph shape from Channel first (NCHW) to Channel last (NHWC) format.
Supported functions:
Convolution
,Deconvolution
,BatchNormalization
,MaxPooling
,AveragePooling
,SumPooling
,Unpooling
,Concatenate
- Parameters:
Examples:
pred = Model(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.ChannelLastModifier(<inputs of pred>)] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.ChannelFirstModifier(inputs, inputs_cf=None)[source]
Convert graph shape from Channel last (NHWC) to Channel first (NCHW) format.
Supported functions:
Convolution
,Deconvolution
,BatchNormalization
,MaxPooling
,AveragePooling
,SumPooling
,Unpooling
,Concatenate
- Parameters:
Examples:
pred = Model(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.ChannelFirstModifier(<inputs of pred>)] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.RemoveFunctionModifier(rm_funcs=[])[source]
Remove specified function layer(s) from a graph.
A convenient converter when one or more functions in an existing graph needs to be removed. This converter remove specified function(s) without recreating a new graph from scratch.
- Parameters:
rm_funcs (list of
str
) – list of function name
Examples:
pred = Model(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.RemoveFunctionModifier(rm_funcs=['BatchNormalization', 'MulScalar'])] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.BatchNormBatchStatModifier[source]
Change
batch_stat
toFalse
. Supported functions:BatchNormalization
,FusedBatchNormalization
,SyncBatchNormalization
.Examples:
pred = Model(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.BatchNormBatchStatModifier()] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.TestModeModifier(rm_funcs=[])[source]
This converter combines BatNormBatchStateModifier and RemoveFunctionModifer. It changes
batch_stat
toFalse
. Supported functions:BatchNormalization
,FusedBatchNormalization
,SyncBatchNormalization
.Functions that specified
rm_funcs
will be removed from a graph.- Parameters:
rm_funcs (list of
str
) – list of function name
Examples:
pred = Model(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.TestModeModifier(rm_funcs=['MulScalar'])] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.IdentityModifier(inputs={}, copy_value=False)[source]
All functions are replaced to the same
new
function.- Parameters:
inputs (
dict
) – Input variable mapping from the original input to another input. Default is the empty dictionary, so the new graph shares the original inputs.
Examples:
pred = Model(...) x = nn.Variable(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.IdentityModifier({x0: x1})] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.NoGradModifier[source]
All functions are replaced to the same
new
function.- Parameters:
inputs (
dict
) – Input variable mapping from the original input to another input. Default is the empty dictionary, so the new graph shares the original inputs.
Examples:
pred = Model(...) x = nn.Variable(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.NoGradModifier()] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)
- class nnabla.experimental.graph_converters.PruningModifier(pruning_threshold, functions_to_prune=('Convolution', 'Deconvolution', 'DepthwiseConvolution', 'DepthwiseDeconvolution', 'Affine'), channel_last=False)[source]
Use
PruningModifier
to prune the small weight value to 0. The pruning is channel-wise. Using the channel-wise L2 norm to represent the degree of sparsity. If the L2 norm less than the threshold provided, all the value of this channel will be set to 0.Supported pruning functions:
Convolution
,Deconvolution
,DepthwiseConvolution
, ‘DepthwiseDeconvolution’, ‘Affine’Examples:
pred = Model(...) import nnabla.experimental.graph_converters as GC modifiers = [GC.PruningModifier(pruning_threshold=0.1)] gc = GC.GraphConverter(modifiers) pred = gc.convert(pred)