File: //opt/DAIN/MegaDepth/pytorch_DIW_scratch.py
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))
pytorch_DIW_scratch = nn.Sequential( # Sequential,
nn.Conv2d(3,128,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.MaxPool2d((2, 2),(2, 2)),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.MaxPool2d((2, 2),(2, 2)),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(128,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,64,(11, 11),(1, 1),(5, 5)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
),
nn.Sequential( # Sequential,
nn.AvgPool2d((2, 2),(2, 2)),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
),
nn.Sequential( # Sequential,
nn.AvgPool2d((2, 2),(2, 2)),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
nn.UpsamplingNearest2d(scale_factor=2),
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,64,(11, 11),(1, 1),(5, 5)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
nn.UpsamplingNearest2d(scale_factor=2),
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,64,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
),
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(256,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
),
nn.UpsamplingNearest2d(scale_factor=2),
),
nn.Sequential( # Sequential,
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,32,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,32,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,32,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,32,(11, 11),(1, 1),(5, 5)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
),
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,32,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,32,(5, 5),(1, 1),(2, 2)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,32,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
),
),
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(128,16,(1, 1)),
nn.BatchNorm2d(16,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,16,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(16,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,16,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(16,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,32,(1, 1)),
nn.BatchNorm2d(32,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(32,16,(11, 11),(1, 1),(5, 5)),
nn.BatchNorm2d(16,1e-05,0.1,False),
nn.ReLU(),
),
),
nn.UpsamplingNearest2d(scale_factor=2),
),
nn.Sequential( # Sequential,
LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
nn.Sequential( # Sequential,
nn.Conv2d(128,16,(1, 1)),
nn.BatchNorm2d(16,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,16,(3, 3),(1, 1),(1, 1)),
nn.BatchNorm2d(16,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,16,(7, 7),(1, 1),(3, 3)),
nn.BatchNorm2d(16,1e-05,0.1,False),
nn.ReLU(),
),
nn.Sequential( # Sequential,
nn.Conv2d(128,64,(1, 1)),
nn.BatchNorm2d(64,1e-05,0.1,False),
nn.ReLU(),
nn.Conv2d(64,16,(11, 11),(1, 1),(5, 5)),
nn.BatchNorm2d(16,1e-05,0.1,False),
nn.ReLU(),
),
),
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
),
nn.Conv2d(64,1,(3, 3),(1, 1),(1, 1)),
)