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https://github.com/AntoineHX/BU_Stoch_pool.git
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71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
'''LeNet in PyTorch.'''
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class MyLeNet(nn.Module):
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def __init__(self):
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super(MyLeNet, self).__init__()
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self.conv1 = nn.Conv2d(3, 6, 5)
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self.conv2 = nn.Conv2d(6, 16, 5)
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self.fc1 = nn.Linear(16*5*5, 120)
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self.fc2 = nn.Linear(120, 84)
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self.fc3 = nn.Linear(84, 10)
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def savg_pool2d(self,x,size):
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b,c,h,w = x.shape
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selh = torch.LongTensor(h/size,w/size).random_(0, size)
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rngh = torch.arange(0,h,size).long().view(h/size,1).repeat(1,w/size).view(h/size,w/size)
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selx = (selh+rngh).repeat(b,c,1,1)
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selw = torch.LongTensor(h/size,w/size).random_(0, size)
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rngw = torch.arange(0,w,size).long().view(1,h/size).repeat(h/size,1).view(h/size,w/size)
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sely = (selw+rngw).repeat(b,c,1,1)
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bv, cv ,hv, wv = torch.meshgrid([torch.arange(0,b), torch.arange(0,c),torch.arange(0,h/size),torch.arange(0,w/size)])
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#x=x.view(b,c,h*w)
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newx = x[bv,cv, selx, sely]
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#ghdh
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return newx
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def ssoftmax_pool2d(self,x,size,idx):
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b,c,h,w = x.shape
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w = wdataset[idx]
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selh = torch.LongTensor(h/size,w/size).random_(0, size)
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rngh = torch.arange(0,h,size).long().view(h/size,1).repeat(1,w/size).view(h/size,w/size)
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selx = (selh+rngh).repeat(b,c,1,1)
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selw = torch.LongTensor(h/size,w/size).random_(0, size)
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rngw = torch.arange(0,w,size).long().view(1,h/size).repeat(h/size,1).view(h/size,w/size)
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sely = (selw+rngw).repeat(b,c,1,1)
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bv, cv ,hv, wv = torch.meshgrid([torch.arange(0,b), torch.arange(0,c),torch.arange(0,h/size),torch.arange(0,w/size)])
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#x=x.view(b,c,h*w)
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newx = x[bv,cv, selx, sely]
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#ghdh
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return newx
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def mavg_pool2d(self,x,size):
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b,c,h,w = x.shape
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#newx=(x[:,:,0::2,0::2]+x[:,:,1::2,0::2]+x[:,:,0::2,1::2]+x[:,:,1::2,1::2])/4
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newx=(x[:,:,0::2,0::2])
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return newx
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def forward(self, x, stoch=True):
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if self.training==False:
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stoch=False
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out = F.relu(self.conv1(x))
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if stoch:
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out = self.savg_pool2d(out, 2)
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else:
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out = F.avg_pool2d(out, 2)
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out = F.relu(self.conv2(out))
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if stoch:
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out = self.savg_pool2d(out, 2)
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else:
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out = F.avg_pool2d(out, 2)
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out = out.view(out.size(0), -1)
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out = F.relu(self.fc1(out))
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out = F.relu(self.fc2(out))
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out = self.fc3(out)
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return out
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