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Added SpatialDilatedConvolution handling #40

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Added SpatialDilatedConvolution handling #40

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9 changes: 9 additions & 0 deletions convert_torch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -149,6 +149,11 @@ def lua_recursive_model(module,seq):
n = LambdaReduce(lambda x,y,dim=dim: torch.cat((x,y),dim))
lua_recursive_model(m,n)
add_submodule(seq,n)
elif name == 'SpatialDilatedConvolution':
if not hasattr(m,'groups') or m.groups is None: m.groups=1
n = nn.Conv2d(m.nInputPlane,m.nOutputPlane,(m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),(m.dilationW,m.dilationH),m.groups,bias=(m.bias is not None))
copy_param(m,n)
add_submodule(seq,n)
elif name == 'TorchObject':
print('Not Implement',name,real._typename)
else:
Expand All @@ -168,6 +173,10 @@ def lua_recursive_source(module):
if not hasattr(m,'groups') or m.groups is None: m.groups=1
s += ['nn.Conv2d({},{},{},{},{},{},{},bias={}),#Conv2d'.format(m.nInputPlane,
m.nOutputPlane,(m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),1,m.groups,m.bias is not None)]
elif name == 'SpatialDilatedConvolution':
if not hasattr(m,'groups') or m.groups is None: m.groups=1
s += ['nn.Conv2d({},{},{},{},{},{},{},bias={}),#Conv2d'.format(m.nInputPlane,
m.nOutputPlane,(m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),(m.dilationW,m.dilationH),m.groups,m.bias is not None)]
elif name == 'SpatialBatchNormalization':
s += ['nn.BatchNorm2d({},{},{},{}),#BatchNorm2d'.format(m.running_mean.size(0), m.eps, m.momentum, m.affine)]
elif name == 'VolumetricBatchNormalization':
Expand Down