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fbrn.py
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fbrn.py
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import torch
import torch.nn as nn
from torch.autograd import Variable
from functools import reduce
class LambdaBase(nn.Sequential):
def __init__(self, fn, *args):
super(LambdaBase, self).__init__(*args)
self.lambda_func = fn
def forward_prepare(self, input):
output = []
for module in self._modules.values():
output.append(module(input))
return output if output else input
class Lambda(LambdaBase):
def forward(self, input):
return self.lambda_func(self.forward_prepare(input))
class LambdaMap(LambdaBase):
def forward(self, input):
return list(map(self.lambda_func,self.forward_prepare(input)))
class LambdaReduce(LambdaBase):
def forward(self, input):
return reduce(self.lambda_func,self.forward_prepare(input))
tmp = nn.Sequential( # Sequential,
nn.Conv2d(3,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Sequential( # Sequential,
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Conv2d(16,16,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(16),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
),
nn.Sequential( # Sequential,
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(16,32,(3, 3),(2, 2),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
),
nn.Sequential( # Sequential,
nn.AvgPool2d((1, 1),(2, 2)),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
Lambda(lambda x: x), # Identity,
Lambda(lambda x: x * 0), #resnet mulconstant,
),
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(32),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
),
nn.Sequential( # Sequential,
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(32,64,(3, 3),(2, 2),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
),
nn.Sequential( # Sequential,
nn.AvgPool2d((1, 1),(2, 2)),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
Lambda(lambda x: x), # Identity,
Lambda(lambda x: x * 0), #resnet mulconstant,
),
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
nn.BatchNorm2d(64),
),
Lambda(lambda x: x), # Identity,
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.ReLU(),
),
),
nn.AvgPool2d((8, 8),(1, 1)),
Lambda(lambda x: x.view(x.size(0),-1)), # View,
nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(64,10)), # Linear,
)