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https://github.com/AntoineHX/BU_Stoch_pool.git
synced 2025-05-04 09:40:46 +02:00
fixed problem with inference with MyLeNetNoceil
This commit is contained in:
Marco Pedersoli
parent
019310e60c
commit
5504ca67ab
4 changed files with 157 additions and 138 deletions
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@ -101,52 +101,6 @@ class MyLeNetMatNormal(nn.Module):#epach 21s
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#out = (self.fc1(out))
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return out
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class MyLeNetMatNormalNoceil(nn.Module):#epoch 136s 16GB
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def __init__(self,k=3):
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super(MyLeNetMatNormalNoceil, self).__init__()
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self.conv1 = SConv2dAvg(3, 200*k, 3, stride=1,padding=1,ceil_mode=False)
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self.conv2 = SConv2dAvg(200*k, 400*k, 3, stride=1,padding=1,ceil_mode=False)
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self.conv3 = SConv2dAvg(400*k, 800*k, 3, stride=1,padding=1,ceil_mode=False)
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self.conv4 = SConv2dAvg(800*k, 1600*k, 3, stride=1,padding=1,ceil_mode=False)
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self.fc1 = nn.Linear(1600*k, 10)
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def forward(self, x, stoch=True):
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out = F.relu(self.conv1(x,stoch=stoch))
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out = F.avg_pool2d(out,2,ceil_mode=True)
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out = F.relu(self.conv2(out,stoch=stoch))
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out = F.avg_pool2d(out,2,ceil_mode=True)
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out = F.relu(self.conv3(out,stoch=stoch))
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out = F.avg_pool2d(out,2,ceil_mode=True)
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out = F.relu(self.conv4(out,stoch=stoch))
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out = F.avg_pool2d(out,4,ceil_mode=True)
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out = out.view(out.size(0), -1 )
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out = self.fc1(out)
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return out
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class MyLeNetMatStochNoceil(nn.Module):#epoch 41s 16BG
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def __init__(self,k=3):
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super(MyLeNetMatStochNoceil, self).__init__()
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self.conv1 = SConv2dAvg(3, 200*k, 3, stride=2,padding=1,ceil_mode=False)
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self.conv2 = SConv2dAvg(200*k, 400*k, 3, stride=2,padding=1,ceil_mode=False)
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self.conv3 = SConv2dAvg(400*k, 800*k, 3, stride=2,padding=1,ceil_mode=False)
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self.conv4 = SConv2dAvg(800*k, 1600*k, 3, stride=4,padding=1,ceil_mode=False)
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self.fc1 = nn.Linear(1600*k, 10)
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def forward(self, x, stoch=True):
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#print('in',x.shape)
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out = F.relu(self.conv1(x,stoch=stoch))
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#print('c1',out.shape)
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out = F.relu(self.conv2(out,stoch=stoch))
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#print('c2', out.shape)
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out = F.relu(self.conv3(out,stoch=stoch))
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#print('c3',out.shape)
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out = F.relu(self.conv4(out,stoch=stoch))
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#print('c4',out.shape)
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out = out.view(out.size(0), -1 )
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out = self.fc1(out)
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return out
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class MyLeNetMatStoch(nn.Module):#epoch 17s
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def __init__(self):
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super(MyLeNetMatStoch, self).__init__()
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@ -169,6 +123,59 @@ class MyLeNetMatStoch(nn.Module):#epoch 17s
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out = out.view(out.size(0), -1 )
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#out = self.fc1(out)
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return out
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class MyLeNetMatNormalNoceil(nn.Module):#epoch 136s 16GB
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def __init__(self,k=3):
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super(MyLeNetMatNormalNoceil, self).__init__()
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self.conv1 = SConv2dAvg(3, 200*k, 3, stride=1,padding=1,ceil_mode=False)
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self.conv2 = SConv2dAvg(200*k, 400*k, 3, stride=1,padding=1,ceil_mode=False)
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self.conv3 = SConv2dAvg(400*k, 800*k, 3, stride=1,padding=1,ceil_mode=False)
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self.conv4 = SConv2dAvg(800*k, 1600*k, 3, stride=1,padding=1,ceil_mode=False)
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self.fc1 = nn.Linear(1600*k, 10)
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def forward(self, x, stoch=True):
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out = F.relu(self.conv1(x,stoch=stoch))
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out = F.avg_pool2d(out,2,ceil_mode=False)
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out = F.relu(self.conv2(out,stoch=stoch))
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out = F.avg_pool2d(out,2,ceil_mode=False)
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out = F.relu(self.conv3(out,stoch=stoch))
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out = F.avg_pool2d(out,2,ceil_mode=False)
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out = F.relu(self.conv4(out,stoch=stoch))
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out = F.avg_pool2d(out,4,ceil_mode=False)
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out = out.view(out.size(0), -1 )
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out = self.fc1(out)
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return out
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class MyLeNetMatStochNoceil(nn.Module):#epoch 41s 16BG
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def __init__(self,k=3):
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super(MyLeNetMatStochNoceil, self).__init__()
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self.conv1 = SConv2dAvg(3, 200*k, 3, stride=2,padding=1,ceil_mode=False)
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self.conv2 = SConv2dAvg(200*k, 400*k, 3, stride=2,padding=1,ceil_mode=False)
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self.conv3 = SConv2dAvg(400*k, 800*k, 3, stride=2,padding=1,ceil_mode=False)
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self.conv4 = SConv2dAvg(800*k, 1600*k, 3, stride=4,padding=1,ceil_mode=False)
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self.fc1 = nn.Linear(1600*k, 10)
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def forward(self, x, stoch=True):
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if stoch:
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out = F.relu(self.conv1(x,stoch=stoch))
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out = F.relu(self.conv2(out,stoch=stoch))
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out = F.relu(self.conv3(out,stoch=stoch))
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out = F.relu(self.conv4(out,stoch=stoch))
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out = out.view(out.size(0), -1 )
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out = self.fc1(out)
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else:
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out = F.relu(self.conv1(x,stride=1))
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out = F.avg_pool2d(out,2,ceil_mode=False)
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out = F.relu(self.conv2(out,stride=1))
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out = F.avg_pool2d(out,2,ceil_mode=False)
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out = F.relu(self.conv3(out,stride=1))
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out = F.avg_pool2d(out,2,ceil_mode=False)
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out = F.relu(self.conv4(out,stride=1))
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out = F.avg_pool2d(out,4,ceil_mode=False)
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out = out.view(out.size(0), -1 )
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out = self.fc1(out)
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return out
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class MyLeNetMatStochBUNoceil(nn.Module):#30.5s 14GB
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def __init__(self,k=3):
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@ -180,34 +187,46 @@ class MyLeNetMatStochBUNoceil(nn.Module):#30.5s 14GB
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self.fc1 = nn.Linear(1600*k, 10)
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def forward(self, x, stoch=True):
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#get sizes
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batch_size = x.shape[0]
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device = x.device
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h0,w0 = x.shape[2],x.shape[3]
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_,_,h1,w1 = self.conv1.get_size(h0,w0)
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_,_,h2,w2 = self.conv2.get_size(h1,w1)
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_,_,h3,w3 = self.conv3.get_size(h2,w2)
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_,_,h4,w4 = self.conv4.get_size(h3,w3)
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# print(h0,w0)
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# print(h1,w1)
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# print(h2,w2)
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# print(h3,w3)
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if stoch:
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#get sizes
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batch_size = x.shape[0]
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device = x.device
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h0,w0 = x.shape[2],x.shape[3]
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_,_,h1,w1 = self.conv1.get_size(h0,w0)
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_,_,h2,w2 = self.conv2.get_size(h1,w1)
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_,_,h3,w3 = self.conv3.get_size(h2,w2)
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_,_,h4,w4 = self.conv4.get_size(h3,w3)
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# print(h0,w0)
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# print(h1,w1)
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# print(h2,w2)
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# print(h3,w3)
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#sample BU
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mask4 = torch.ones(h4,w4).to(x.device)
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# print(mask3.shape)
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index4,mask3 = self.conv4.sample(h3,w3,batch_size,device,mask4)
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index3,mask2 = self.conv3.sample(h2,w2,batch_size,device,mask3)
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index2,mask1 = self.conv2.sample(h1,w1,batch_size,device,mask2)
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index1,mask0 = self.conv1.sample(h0,w0,batch_size,device,mask1)
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##forward
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out = F.relu(self.conv1(x,index1,mask1,stoch=stoch))
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out = F.relu(self.conv2(out,index2,mask2,stoch=stoch))
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out = F.relu(self.conv3(out,index3,mask3,stoch=stoch))
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out = F.relu(self.conv4(out,index4,mask4,stoch=stoch))
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out = out.view(out.size(0), -1 )
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out = self.fc1(out)
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#sample BU
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mask4 = torch.ones(h4,w4).to(x.device)
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# print(mask3.shape)
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index4,mask3 = self.conv4.sample(h3,w3,batch_size,device,mask4)
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index3,mask2 = self.conv3.sample(h2,w2,batch_size,device,mask3)
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index2,mask1 = self.conv2.sample(h1,w1,batch_size,device,mask2)
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index1,mask0 = self.conv1.sample(h0,w0,batch_size,device,mask1)
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##forward
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out = F.relu(self.conv1(x,index1,mask1,stoch=stoch))
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out = F.relu(self.conv2(out,index2,mask2,stoch=stoch))
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out = F.relu(self.conv3(out,index3,mask3,stoch=stoch))
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out = F.relu(self.conv4(out,index4,mask4,stoch=stoch))
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out = out.view(out.size(0), -1 )
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out = self.fc1(out)
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else:
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out = F.relu(self.conv1(x,stride=1))
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out = F.avg_pool2d(out,2,ceil_mode=False)
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out = F.relu(self.conv2(out,stride=1))
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out = F.avg_pool2d(out,2,ceil_mode=False)
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out = F.relu(self.conv3(out,stride=1))
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out = F.avg_pool2d(out,2,ceil_mode=False)
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out = F.relu(self.conv4(out,stride=1))
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out = F.avg_pool2d(out,4,ceil_mode=False)
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out = out.view(out.size(0), -1 )
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out = self.fc1(out)
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return out
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class MyLeNetMatStochBU(nn.Module):#epoch 11s
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