113 lines
3.4 KiB
Python
113 lines
3.4 KiB
Python
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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OUT_CHANNELS = 32
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class N902(nn.Module):
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# 32, 144.878
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# 64, 135.952
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# 128, 128.388
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def __init__(self):
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super(N90, self).__init__()
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# 1 input image channel, 6 output channels, 3x3 square convolution
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# kernel
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self.conv1 = nn.Conv2d(1 , OUT_CHANNELS, 3, padding=1)
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self.conv2 = nn.Conv2d(OUT_CHANNELS, 2 , 3, padding=1)
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = self.conv2(x)
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return x
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class N903(nn.Module):
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# 32, 79.591
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# 64, 69.663
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def __init__(self):
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super(N90, self).__init__()
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# 1 input image channel, 6 output channels, 3x3 square convolution
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# kernel
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self.conv1 = nn.Conv2d(1 , OUT_CHANNELS, 3, padding=1)
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self.conv2 = nn.Conv2d(OUT_CHANNELS, OUT_CHANNELS, 3, padding=1)
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self.conv3 = nn.Conv2d(OUT_CHANNELS, 2 , 3, padding=1)
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = F.relu(self.conv2(x))
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x = self.conv3(x)
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return x
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class N904(nn.Module):
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# 32, 65.503
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# 64, 55.369
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def __init__(self):
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super(N90, self).__init__()
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# 1 input image channel, 6 output channels, 3x3 square convolution
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# kernel
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self.conv1 = nn.Conv2d(1 , OUT_CHANNELS, 3, padding=1)
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self.conv2 = nn.Conv2d(OUT_CHANNELS, OUT_CHANNELS, 3, padding=1)
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self.conv3 = nn.Conv2d(OUT_CHANNELS, OUT_CHANNELS, 3, padding=1)
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self.conv4 = nn.Conv2d(OUT_CHANNELS, 2 , 3, padding=1)
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = F.relu(self.conv2(x))
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x = F.relu(self.conv3(x))
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x = self.conv4(x)
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return x
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class N90(nn.Module):
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# 32, 48.523
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def __init__(self):
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super(N90, self).__init__()
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# 1 input image channel, 6 output channels, 3x3 square convolution
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# kernel
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self.conv1 = nn.Conv2d(1 , OUT_CHANNELS, 3, padding=1)
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self.conv2 = nn.Conv2d(OUT_CHANNELS, OUT_CHANNELS, 3, padding=1)
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self.conv3 = nn.Conv2d(OUT_CHANNELS, OUT_CHANNELS, 3, padding=1)
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self.conv4 = nn.Conv2d(OUT_CHANNELS, OUT_CHANNELS, 3, padding=1)
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self.conv5 = nn.Conv2d(OUT_CHANNELS, 2 , 3, padding=1)
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = F.relu(self.conv2(x))
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x = F.relu(self.conv3(x))
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x = F.relu(self.conv4(x))
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x = self.conv5(x)
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return x
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class N906(nn.Module):
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# 32, 43.330
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def __init__(self):
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super(N90, self).__init__()
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# 1 input image channel, 6 output channels, 3x3 square convolution
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# kernel
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self.conv1 = nn.Conv2d(1 , OUT_CHANNELS, 3, padding=1)
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self.conv2 = nn.Conv2d(OUT_CHANNELS, OUT_CHANNELS, 3, padding=1)
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self.conv3 = nn.Conv2d(OUT_CHANNELS, OUT_CHANNELS, 3, padding=1)
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self.conv4 = nn.Conv2d(OUT_CHANNELS, OUT_CHANNELS, 3, padding=1)
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self.conv5 = nn.Conv2d(OUT_CHANNELS, OUT_CHANNELS, 3, padding=1)
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self.conv6 = nn.Conv2d(OUT_CHANNELS, 2 , 3, padding=1)
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = F.relu(self.conv2(x))
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x = F.relu(self.conv3(x))
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x = F.relu(self.conv4(x))
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x = F.relu(self.conv5(x))
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x = self.conv6(x)
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return x
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# net = N90_100()
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# print(net)
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