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models.py
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models.py
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import torch
import torch.nn as nn
# import torch.nn.functional as F
import torchvision.models as models
class client_model(nn.Module):
def __init__(self, name, n_cls, args=True):
super(client_model, self).__init__()
self.name = name
self.n_cls = n_cls
if self.name == 'Linear':
[self.n_dim, self.n_out] = args
self.fc = nn.Linear(self.n_dim, self.n_out)
if self.name == 'mnist_2NN':
# self.n_cls = 10
self.fc1 = nn.Linear(1 * 28 * 28, 200)
self.fc2 = nn.Linear(200, 200)
self.fc3 = nn.Linear(200, self.n_cls)
if self.name == 'emnist_NN':
# self.n_cls = 10
self.fc1 = nn.Linear(1 * 28 * 28, 100)
self.fc2 = nn.Linear(100, 100)
self.fc3 = nn.Linear(100, self.n_cls)
if self.name == 'LeNet':
# self.n_cls = 10
self.conv1 = nn.Conv2d(in_channels=3, out_channels=64 , kernel_size=5)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=5)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64*5*5, 384)
self.fc2 = nn.Linear(384, 192)
self.fc3 = nn.Linear(192, self.n_cls)
if self.name == 'ResNet18':
resnet18 = models.resnet18()
resnet18.fc = nn.Linear(512, self.n_cls)
# Change BN to GN
resnet18.bn1 = nn.GroupNorm(num_groups = 2, num_channels = 64)
resnet18.layer1[0].bn1 = nn.GroupNorm(num_groups = 2, num_channels = 64)
resnet18.layer1[0].bn2 = nn.GroupNorm(num_groups = 2, num_channels = 64)
resnet18.layer1[1].bn1 = nn.GroupNorm(num_groups = 2, num_channels = 64)
resnet18.layer1[1].bn2 = nn.GroupNorm(num_groups = 2, num_channels = 64)
resnet18.layer2[0].bn1 = nn.GroupNorm(num_groups = 2, num_channels = 128)
resnet18.layer2[0].bn2 = nn.GroupNorm(num_groups = 2, num_channels = 128)
resnet18.layer2[0].downsample[1] = nn.GroupNorm(num_groups = 2, num_channels = 128)
resnet18.layer2[1].bn1 = nn.GroupNorm(num_groups = 2, num_channels = 128)
resnet18.layer2[1].bn2 = nn.GroupNorm(num_groups = 2, num_channels = 128)
resnet18.layer3[0].bn1 = nn.GroupNorm(num_groups = 2, num_channels = 256)
resnet18.layer3[0].bn2 = nn.GroupNorm(num_groups = 2, num_channels = 256)
resnet18.layer3[0].downsample[1] = nn.GroupNorm(num_groups = 2, num_channels = 256)
resnet18.layer3[1].bn1 = nn.GroupNorm(num_groups = 2, num_channels = 256)
resnet18.layer3[1].bn2 = nn.GroupNorm(num_groups = 2, num_channels = 256)
resnet18.layer4[0].bn1 = nn.GroupNorm(num_groups = 2, num_channels = 512)
resnet18.layer4[0].bn2 = nn.GroupNorm(num_groups = 2, num_channels = 512)
resnet18.layer4[0].downsample[1] = nn.GroupNorm(num_groups = 2, num_channels = 512)
resnet18.layer4[1].bn1 = nn.GroupNorm(num_groups = 2, num_channels = 512)
resnet18.layer4[1].bn2 = nn.GroupNorm(num_groups = 2, num_channels = 512)
assert len(dict(resnet18.named_parameters()).keys()) == len(resnet18.state_dict().keys()), 'More BN layers are there...'
self.model = resnet18
def forward(self, x):
if self.name == 'Linear':
x = self.fc(x)
if self.name == 'mnist_2NN':
x = x.view(-1, 1 * 28 * 28)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
if self.name == 'emnist_NN':
x = x.view(-1, 1 * 28 * 28)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
if self.name == 'LeNet':
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 64*5*5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
if self.name == 'ResNet18':
x = self.model(x)
return x