200 lines
6.7 KiB
Python
200 lines
6.7 KiB
Python
"""This file is part of https://github.com/mit-han-lab/dlg.
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MIT License
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Copyright (c) 2019 Ildoo Kim
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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"""
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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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from torch.autograd import grad
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import torchvision
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from torchvision import models, datasets, transforms
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def weights_init(m):
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if hasattr(m, "weight"):
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m.weight.data.uniform_(-0.5, 0.5)
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if hasattr(m, "bias"):
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m.bias.data.uniform_(-0.5, 0.5)
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class LeNet(nn.Module):
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def __init__(self):
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super(LeNet, self).__init__()
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act = nn.Sigmoid
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self.body = nn.Sequential(
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nn.Conv2d(3, 12, kernel_size=5, padding=5 // 2, stride=2),
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act(),
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nn.Conv2d(12, 12, kernel_size=5, padding=5 // 2, stride=2),
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act(),
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nn.Conv2d(12, 12, kernel_size=5, padding=5 // 2, stride=1),
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act(),
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)
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self.fc = nn.Sequential(nn.Linear(768, 100))
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def forward(self, x):
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out = self.body(x)
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out = out.view(out.size(0), -1)
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# print(out.size())
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out = self.fc(out)
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return out
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'''ResNet in PyTorch.
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For Pre-activation ResNet, see 'preact_resnet.py'.
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Reference:
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[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
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Deep Residual Learning for Image Recognition. arXiv:1512.03385
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'''
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, in_planes, planes, stride=1):
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super(BasicBlock, self).__init__()
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self.conv1 = nn.Conv2d(in_planes,
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planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes,
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planes,
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kernel_size=3,
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stride=1,
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padding=1,
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bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_planes != self.expansion * planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes,
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self.expansion * planes,
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kernel_size=1,
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stride=stride,
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bias=False), nn.BatchNorm2d(self.expansion * planes))
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def forward(self, x):
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out = F.Sigmoid(self.bn1(self.conv1(x)))
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out = self.bn2(self.conv2(out))
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out += self.shortcut(x)
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out = F.Sigmoid(out)
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return out
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, in_planes, planes, stride=1):
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super(Bottleneck, self).__init__()
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self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes,
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planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes,
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self.expansion * planes,
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kernel_size=1,
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bias=False)
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self.bn3 = nn.BatchNorm2d(self.expansion * planes)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_planes != self.expansion * planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes,
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self.expansion * planes,
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kernel_size=1,
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stride=stride,
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bias=False), nn.BatchNorm2d(self.expansion * planes))
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def forward(self, x):
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out = F.Sigmoid(self.bn1(self.conv1(x)))
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out = F.Sigmoid(self.bn2(self.conv2(out)))
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out = self.bn3(self.conv3(out))
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out += self.shortcut(x)
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out = F.Sigmoid(out)
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return out
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class ResNet(nn.Module):
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def __init__(self, block, num_blocks, num_classes=10):
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super(ResNet, self).__init__()
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self.in_planes = 64
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self.conv1 = nn.Conv2d(3,
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64,
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kernel_size=3,
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stride=1,
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padding=1,
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bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
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self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=1)
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self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=1)
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self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=1)
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self.linear = nn.Linear(512 * block.expansion, num_classes)
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def _make_layer(self, block, planes, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_planes, planes, stride))
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self.in_planes = planes * block.expansion
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return nn.Sequential(*layers)
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def forward(self, x):
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out = F.Sigmoid(self.bn1(self.conv1(x)))
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out = self.layer1(out)
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out = self.layer2(out)
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out = self.layer3(out)
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out = self.layer4(out)
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out = F.avg_pool2d(out, 4)
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out = out.view(out.size(0), -1)
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out = self.linear(out)
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return out
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def ResNet18():
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return ResNet(BasicBlock, [2, 2, 2, 2])
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def ResNet34():
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return ResNet(BasicBlock, [3, 4, 6, 3])
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def ResNet50():
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return ResNet(Bottleneck, [3, 4, 6, 3])
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def ResNet101():
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return ResNet(Bottleneck, [3, 4, 23, 3])
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def ResNet152():
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return ResNet(Bottleneck, [3, 8, 36, 3])
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