vgg_m_1024_torch.py

import torch
import torch.nn as nn
import torch.legacy.nn
from torch.autograd import Variable
from functools import reduce
from LRN import SpatialCrossMapLRN
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))


vgg_m_1024_torch = nn.Sequential( # Sequential,
	nn.Conv2d(3,96,(7, 7),(2, 2)),
	nn.ReLU(),
	Lambda(lambda x,lrn=SpatialCrossMapLRN(*(5, 0.0005, 0.75, 2)): (lrn.forward(x))),
	nn.MaxPool2d((3, 3),(2, 2),(0, 0),ceil_mode=True),
	nn.Conv2d(96,256,(5, 5),(2, 2),(1, 1)),
	nn.ReLU(),
	Lambda(lambda x,lrn=SpatialCrossMapLRN(*(5, 0.0005, 0.75, 2)):(lrn.forward(x))),
	nn.MaxPool2d((3, 3),(2, 2),(0, 0),ceil_mode=True),
	nn.Conv2d(256,512,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.MaxPool2d((3, 3),(2, 2),(0, 0),ceil_mode=True),
	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(18432,4096)), # Linear,
	nn.ReLU(),
	nn.Dropout(0.5),
	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(4096,1024)), # Linear,
	nn.ReLU(),
	nn.Dropout(0.5),
	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(1024,1000)), # Linear,
	nn.Softmax(),
)