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from ...vision import *
__all__ = ['xception']
def sep_conv(ni,nf,pad=None,pool=False,act=True):
layers = [nn.ReLU()] if act else []
layers += [
nn.Conv2d(ni,ni,3,1,1,groups=ni,bias=False),
nn.Conv2d(ni,nf,1,bias=False),
nn.BatchNorm2d(nf)
]
if pool: layers.append(nn.MaxPool2d(2))
return nn.Sequential(*layers)
def conv(ni,nf,ks=1,stride=1, pad=None, act=True):
if pad is None: pad=ks//2
layers = [
nn.Conv2d(ni,nf,ks,stride,pad,bias=False),
nn.BatchNorm2d(nf),
]
if act: layers.append(nn.ReLU())
return nn.Sequential(*layers)
class ConvSkip(Module):
def __init__(self,ni,nf=None,act=True):
self.nf,self.ni = nf,ni
if self.nf is None: self.nf = ni
self.conv = conv(ni,nf,stride=2, act=False)
self.m = nn.Sequential(
sep_conv(ni,ni,act=act),
sep_conv(ni,nf,pool=True)
)
def forward(self,x): return self.conv(x) + self.m(x)
def middle_flow(nf):
layers = [sep_conv(nf,nf) for i in range(3)]
return SequentialEx(*layers, MergeLayer())
def xception(c, k=8, n_middle=8):
"Preview version of Xception network. Not tested yet - use at own risk. No pretrained model yet."
layers = [
conv(3, k*4, 3, 2),
conv(k*4, k*8, 3),
ConvSkip(k*8, k*16, act=False),
ConvSkip(k*16, k*32),
ConvSkip(k*32, k*91),
]
for i in range(n_middle): layers.append(middle_flow(k*91))
layers += [
ConvSkip(k*91,k*128),
sep_conv(k*128,k*192,act=False),
sep_conv(k*192,k*256),
nn.ReLU(),
nn.AdaptiveAvgPool2d(1),
Flatten(),
nn.Linear(k*256,c)
]
return nn.Sequential(*layers)
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