Initial commit

models/Old/mylenet.py

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