192 lines
5.3 KiB
Python
192 lines
5.3 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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from torch.nn import Module
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from torch.nn import Sequential
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from torch.nn import Conv2d, BatchNorm2d
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from torch.nn import Flatten
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from torch.nn import Linear
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from torch.nn import MaxPool2d
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from torch.nn import ReLU
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class ConvNet2(Module):
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def __init__(self,
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in_channels,
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h=32,
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w=32,
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hidden=2048,
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class_num=10,
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use_bn=True,
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dropout=.0):
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super(ConvNet2, self).__init__()
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self.conv1 = Conv2d(in_channels, 32, 5, padding=2)
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self.conv2 = Conv2d(32, 64, 5, padding=2)
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self.use_bn = use_bn
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if use_bn:
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self.bn1 = BatchNorm2d(32)
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self.bn2 = BatchNorm2d(64)
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self.fc1 = Linear((h // 2 // 2) * (w // 2 // 2) * 64, hidden)
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self.fc2 = Linear(hidden, class_num)
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self.relu = ReLU(inplace=True)
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self.maxpool = MaxPool2d(2)
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self.dropout = dropout
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def forward(self, x):
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x = self.bn1(self.conv1(x)) if self.use_bn else self.conv1(x)
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x = self.maxpool(self.relu(x))
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x = self.bn2(self.conv2(x)) if self.use_bn else self.conv2(x)
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x = self.maxpool(self.relu(x))
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x = Flatten()(x)
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x = F.dropout(x, p=self.dropout, training=self.training)
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x = self.relu(self.fc1(x))
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x = F.dropout(x, p=self.dropout, training=self.training)
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x = self.fc2(x)
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return x
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class ConvNet5(Module):
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def __init__(self,
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in_channels,
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h=32,
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w=32,
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hidden=2048,
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class_num=10,
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dropout=.0):
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super(ConvNet5, self).__init__()
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self.conv1 = Conv2d(in_channels, 32, 5, padding=2)
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self.bn1 = BatchNorm2d(32)
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self.conv2 = Conv2d(32, 64, 5, padding=2)
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self.bn2 = BatchNorm2d(64)
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self.conv3 = Conv2d(64, 64, 5, padding=2)
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self.bn3 = BatchNorm2d(64)
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self.conv4 = Conv2d(64, 128, 5, padding=2)
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self.bn4 = BatchNorm2d(128)
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self.conv5 = Conv2d(128, 128, 5, padding=2)
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self.bn5 = BatchNorm2d(128)
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self.relu = ReLU(inplace=True)
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self.maxpool = MaxPool2d(2)
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self.fc1 = Linear(
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(h // 2 // 2 // 2 // 2 // 2) * (w // 2 // 2 // 2 // 2 // 2) * 128,
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hidden)
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self.fc2 = Linear(hidden, class_num)
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self.dropout = dropout
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def forward(self, x):
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x = self.relu(self.bn1(self.conv1(x)))
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x = self.maxpool(x)
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x = self.relu(self.bn2(self.conv2(x)))
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x = self.maxpool(x)
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x = self.relu(self.bn3(self.conv3(x)))
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x = self.maxpool(x)
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x = self.relu(self.bn4(self.conv4(x)))
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x = self.maxpool(x)
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x = self.relu(self.bn5(self.conv5(x)))
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x = self.maxpool(x)
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x = Flatten()(x)
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x = F.dropout(x, p=self.dropout, training=self.training)
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x = self.relu(self.fc1(x))
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x = F.dropout(x, p=self.dropout, training=self.training)
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x = self.fc2(x)
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return x
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class VGG11(Module):
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def __init__(self,
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in_channels,
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h=32,
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w=32,
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hidden=128,
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class_num=10,
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dropout=.0):
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super(VGG11, self).__init__()
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self.conv1 = Conv2d(in_channels, 64, 3, padding=1)
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self.bn1 = BatchNorm2d(64)
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self.conv2 = Conv2d(64, 128, 3, padding=1)
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self.bn2 = BatchNorm2d(128)
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self.conv3 = Conv2d(128, 256, 3, padding=1)
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self.bn3 = BatchNorm2d(256)
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self.conv4 = Conv2d(256, 256, 3, padding=1)
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self.bn4 = BatchNorm2d(256)
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self.conv5 = Conv2d(256, 512, 3, padding=1)
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self.bn5 = BatchNorm2d(512)
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self.conv6 = Conv2d(512, 512, 3, padding=1)
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self.bn6 = BatchNorm2d(512)
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self.conv7 = Conv2d(512, 512, 3, padding=1)
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self.bn7 = BatchNorm2d(512)
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self.conv8 = Conv2d(512, 512, 3, padding=1)
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self.bn8 = BatchNorm2d(512)
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self.relu = ReLU(inplace=True)
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self.maxpool = MaxPool2d(2)
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self.fc1 = Linear(
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(h // 2 // 2 // 2 // 2 // 2) * (w // 2 // 2 // 2 // 2 // 2) * 512,
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hidden)
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self.fc2 = Linear(hidden, hidden)
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self.fc3 = Linear(hidden, class_num)
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self.dropout = dropout
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def forward(self, x):
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x = self.relu(self.bn1(self.conv1(x)))
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x = self.maxpool(x)
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x = self.relu(self.bn2(self.conv2(x)))
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x = self.maxpool(x)
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x = self.relu(self.bn3(self.conv3(x)))
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x = self.maxpool(x)
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x = self.relu(self.bn4(self.conv4(x)))
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x = self.maxpool(x)
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x = self.relu(self.bn5(self.conv5(x)))
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x = self.maxpool(x)
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x = self.relu(self.bn6(self.conv6(x)))
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x = self.maxpool(x)
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x = self.relu(self.bn7(self.conv7(x)))
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x = self.maxpool(x)
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x = self.relu(self.bn8(self.conv8(x)))
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x = self.maxpool(x)
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x = Flatten()(x)
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x = F.dropout(x, p=self.dropout, training=self.training)
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x = self.relu(self.fc1(x))
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x = F.dropout(x, p=self.dropout, training=self.training)
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x = self.relu(self.fc2(x))
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x = F.dropout(x, p=self.dropout, training=self.training)
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x = self.fc3(x)
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return x
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