densenet_264_k32.py

import torch
import torch.nn as nn
import torch.legacy.nn as lnn

from functools import reduce
from torch.autograd import Variable

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))


densenet_264_k32 = nn.Sequential( # Sequential,
	nn.Conv2d(3,64,(7, 7),(2, 2),(3, 3),1,1,bias=False),
	nn.BatchNorm2d(64),
	nn.ReLU(),
	nn.MaxPool2d((3, 3),(2, 2),(1, 1)),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(64),
			nn.ReLU(),
			nn.Conv2d(64,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(96),
			nn.ReLU(),
			nn.Conv2d(96,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(160),
			nn.ReLU(),
			nn.Conv2d(160,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(192),
			nn.ReLU(),
			nn.Conv2d(192,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(224),
			nn.ReLU(),
			nn.Conv2d(224,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	nn.BatchNorm2d(256),
	nn.ReLU(),
	nn.Conv2d(256,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
	nn.AvgPool2d((2, 2),(2, 2)),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(160),
			nn.ReLU(),
			nn.Conv2d(160,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(192),
			nn.ReLU(),
			nn.Conv2d(192,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(224),
			nn.ReLU(),
			nn.Conv2d(224,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(256),
			nn.ReLU(),
			nn.Conv2d(256,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(288),
			nn.ReLU(),
			nn.Conv2d(288,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(320),
			nn.ReLU(),
			nn.Conv2d(320,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(352),
			nn.ReLU(),
			nn.Conv2d(352,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(384),
			nn.ReLU(),
			nn.Conv2d(384,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(416),
			nn.ReLU(),
			nn.Conv2d(416,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(448),
			nn.ReLU(),
			nn.Conv2d(448,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(480),
			nn.ReLU(),
			nn.Conv2d(480,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	nn.BatchNorm2d(512),
	nn.ReLU(),
	nn.Conv2d(512,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
	nn.AvgPool2d((2, 2),(2, 2)),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(256),
			nn.ReLU(),
			nn.Conv2d(256,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(288),
			nn.ReLU(),
			nn.Conv2d(288,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(320),
			nn.ReLU(),
			nn.Conv2d(320,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(352),
			nn.ReLU(),
			nn.Conv2d(352,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(384),
			nn.ReLU(),
			nn.Conv2d(384,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(416),
			nn.ReLU(),
			nn.Conv2d(416,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(448),
			nn.ReLU(),
			nn.Conv2d(448,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(480),
			nn.ReLU(),
			nn.Conv2d(480,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(512),
			nn.ReLU(),
			nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(544),
			nn.ReLU(),
			nn.Conv2d(544,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(576),
			nn.ReLU(),
			nn.Conv2d(576,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(608),
			nn.ReLU(),
			nn.Conv2d(608,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(640),
			nn.ReLU(),
			nn.Conv2d(640,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(672),
			nn.ReLU(),
			nn.Conv2d(672,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(704),
			nn.ReLU(),
			nn.Conv2d(704,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(736),
			nn.ReLU(),
			nn.Conv2d(736,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(768),
			nn.ReLU(),
			nn.Conv2d(768,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(800),
			nn.ReLU(),
			nn.Conv2d(800,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(832),
			nn.ReLU(),
			nn.Conv2d(832,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(864),
			nn.ReLU(),
			nn.Conv2d(864,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(896),
			nn.ReLU(),
			nn.Conv2d(896,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(928),
			nn.ReLU(),
			nn.Conv2d(928,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(960),
			nn.ReLU(),
			nn.Conv2d(960,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(992),
			nn.ReLU(),
			nn.Conv2d(992,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1024),
			nn.ReLU(),
			nn.Conv2d(1024,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1056),
			nn.ReLU(),
			nn.Conv2d(1056,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1088),
			nn.ReLU(),
			nn.Conv2d(1088,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1120),
			nn.ReLU(),
			nn.Conv2d(1120,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1152),
			nn.ReLU(),
			nn.Conv2d(1152,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1184),
			nn.ReLU(),
			nn.Conv2d(1184,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1216),
			nn.ReLU(),
			nn.Conv2d(1216,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1248),
			nn.ReLU(),
			nn.Conv2d(1248,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1280),
			nn.ReLU(),
			nn.Conv2d(1280,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1312),
			nn.ReLU(),
			nn.Conv2d(1312,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1344),
			nn.ReLU(),
			nn.Conv2d(1344,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1376),
			nn.ReLU(),
			nn.Conv2d(1376,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1408),
			nn.ReLU(),
			nn.Conv2d(1408,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1440),
			nn.ReLU(),
			nn.Conv2d(1440,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1472),
			nn.ReLU(),
			nn.Conv2d(1472,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1504),
			nn.ReLU(),
			nn.Conv2d(1504,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1536),
			nn.ReLU(),
			nn.Conv2d(1536,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1568),
			nn.ReLU(),
			nn.Conv2d(1568,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1600),
			nn.ReLU(),
			nn.Conv2d(1600,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1632),
			nn.ReLU(),
			nn.Conv2d(1632,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1664),
			nn.ReLU(),
			nn.Conv2d(1664,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1696),
			nn.ReLU(),
			nn.Conv2d(1696,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1728),
			nn.ReLU(),
			nn.Conv2d(1728,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1760),
			nn.ReLU(),
			nn.Conv2d(1760,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1792),
			nn.ReLU(),
			nn.Conv2d(1792,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1824),
			nn.ReLU(),
			nn.Conv2d(1824,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1856),
			nn.ReLU(),
			nn.Conv2d(1856,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1888),
			nn.ReLU(),
			nn.Conv2d(1888,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1920),
			nn.ReLU(),
			nn.Conv2d(1920,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1952),
			nn.ReLU(),
			nn.Conv2d(1952,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1984),
			nn.ReLU(),
			nn.Conv2d(1984,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2016),
			nn.ReLU(),
			nn.Conv2d(2016,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2048),
			nn.ReLU(),
			nn.Conv2d(2048,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2080),
			nn.ReLU(),
			nn.Conv2d(2080,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2112),
			nn.ReLU(),
			nn.Conv2d(2112,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2144),
			nn.ReLU(),
			nn.Conv2d(2144,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2176),
			nn.ReLU(),
			nn.Conv2d(2176,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2208),
			nn.ReLU(),
			nn.Conv2d(2208,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2240),
			nn.ReLU(),
			nn.Conv2d(2240,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2272),
			nn.ReLU(),
			nn.Conv2d(2272,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	nn.BatchNorm2d(2304),
	nn.ReLU(),
	nn.Conv2d(2304,1152,(1, 1),(1, 1),(0, 0),1,1,bias=False),
	nn.AvgPool2d((2, 2),(2, 2)),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1152),
			nn.ReLU(),
			nn.Conv2d(1152,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1184),
			nn.ReLU(),
			nn.Conv2d(1184,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1216),
			nn.ReLU(),
			nn.Conv2d(1216,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1248),
			nn.ReLU(),
			nn.Conv2d(1248,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1280),
			nn.ReLU(),
			nn.Conv2d(1280,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1312),
			nn.ReLU(),
			nn.Conv2d(1312,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1344),
			nn.ReLU(),
			nn.Conv2d(1344,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1376),
			nn.ReLU(),
			nn.Conv2d(1376,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1408),
			nn.ReLU(),
			nn.Conv2d(1408,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1440),
			nn.ReLU(),
			nn.Conv2d(1440,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1472),
			nn.ReLU(),
			nn.Conv2d(1472,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1504),
			nn.ReLU(),
			nn.Conv2d(1504,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1536),
			nn.ReLU(),
			nn.Conv2d(1536,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1568),
			nn.ReLU(),
			nn.Conv2d(1568,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1600),
			nn.ReLU(),
			nn.Conv2d(1600,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1632),
			nn.ReLU(),
			nn.Conv2d(1632,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1664),
			nn.ReLU(),
			nn.Conv2d(1664,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1696),
			nn.ReLU(),
			nn.Conv2d(1696,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1728),
			nn.ReLU(),
			nn.Conv2d(1728,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1760),
			nn.ReLU(),
			nn.Conv2d(1760,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1792),
			nn.ReLU(),
			nn.Conv2d(1792,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1824),
			nn.ReLU(),
			nn.Conv2d(1824,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1856),
			nn.ReLU(),
			nn.Conv2d(1856,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1888),
			nn.ReLU(),
			nn.Conv2d(1888,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1920),
			nn.ReLU(),
			nn.Conv2d(1920,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1952),
			nn.ReLU(),
			nn.Conv2d(1952,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(1984),
			nn.ReLU(),
			nn.Conv2d(1984,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2016),
			nn.ReLU(),
			nn.Conv2d(2016,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2048),
			nn.ReLU(),
			nn.Conv2d(2048,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2080),
			nn.ReLU(),
			nn.Conv2d(2080,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2112),
			nn.ReLU(),
			nn.Conv2d(2112,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2144),
			nn.ReLU(),
			nn.Conv2d(2144,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2176),
			nn.ReLU(),
			nn.Conv2d(2176,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2208),
			nn.ReLU(),
			nn.Conv2d(2208,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2240),
			nn.ReLU(),
			nn.Conv2d(2240,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2272),
			nn.ReLU(),
			nn.Conv2d(2272,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2304),
			nn.ReLU(),
			nn.Conv2d(2304,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2336),
			nn.ReLU(),
			nn.Conv2d(2336,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2368),
			nn.ReLU(),
			nn.Conv2d(2368,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2400),
			nn.ReLU(),
			nn.Conv2d(2400,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2432),
			nn.ReLU(),
			nn.Conv2d(2432,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2464),
			nn.ReLU(),
			nn.Conv2d(2464,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2496),
			nn.ReLU(),
			nn.Conv2d(2496,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2528),
			nn.ReLU(),
			nn.Conv2d(2528,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2560),
			nn.ReLU(),
			nn.Conv2d(2560,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2592),
			nn.ReLU(),
			nn.Conv2d(2592,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2624),
			nn.ReLU(),
			nn.Conv2d(2624,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	LambdaReduce(lambda x,y,dim=1: torch.cat((x,y),dim), # Concat,
		Lambda(lambda x: x), # Identity,
		nn.Sequential( # Sequential,
			nn.BatchNorm2d(2656),
			nn.ReLU(),
			nn.Conv2d(2656,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
			nn.BatchNorm2d(128),
			nn.ReLU(),
			nn.Conv2d(128,32,(3, 3),(1, 1),(1, 1),1,1,bias=False),
		),
	),
	nn.BatchNorm2d(2688),
	nn.ReLU(),
	nn.AvgPool2d((7, 7),(7, 7)),
	Lambda(lambda x: x.view(x.size(0),-1)), # Reshape,
	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(2688,1000)), # Linear,
)