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models.py
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254 lines (209 loc) · 7.68 KB
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import collections
import torch
import torch.nn as nn
from torchvision.models import resnet18
class MnistNet(nn.Module):
def __init__(self, n_channels=1):
super(MnistNet, self).__init__()
self.features = []
self.initial = None
self.classifier = []
self.layers = collections.OrderedDict()
self.conv1 = nn.Conv2d(
in_channels=n_channels,
out_channels=8,
kernel_size=5
)
self.features.append(self.conv1)
self.layers['conv1'] = self.conv1
self.ReLU1 = nn.ReLU(False)
self.features.append(self.ReLU1)
self.layers['ReLU1'] = self.ReLU1
self.pool1 = nn.MaxPool2d(2, 2)
self.features.append(self.pool1)
self.layers['pool1'] = self.pool1
self.conv2 = nn.Conv2d(
in_channels=8,
out_channels=16,
kernel_size=5
)
self.features.append(self.conv2)
self.layers['conv2'] = self.conv2
self.ReLU2 = nn.ReLU(False)
self.features.append(self.ReLU2)
self.layers['ReLU2'] = self.ReLU2
self.pool2 = nn.MaxPool2d(2, 2)
self.features.append(self.pool2)
self.layers['pool2'] = self.pool2
self.feature_dims = 16 * 4 * 4
self.fc1 = nn.Linear(self.feature_dims, 120)
self.classifier.append(self.fc1)
self.layers['fc1'] = self.fc1
self.fc1act = nn.ReLU(False)
self.classifier.append(self.fc1act)
self.layers['fc1act'] = self.fc1act
self.fc2 = nn.Linear(120, 84)
self.classifier.append(self.fc2)
self.layers['fc2'] = self.fc2
self.fc2act = nn.ReLU(False)
self.classifier.append(self.fc2act)
self.layers['fc2act'] = self.fc2act
self.fc3 = nn.Linear(84, 10)
self.classifier.append(self.fc3)
self.layers['fc3'] = self.fc3
self.initial_params = [param.clone().detach().data for param in self.parameters()]
def forward(self, x, start=0, end=10):
if start <= 5: # start in self.features
for idx, layer in enumerate(self.features[start:]):
x = layer(x)
if idx == end:
return x
x = x.view(-1, self.feature_dims)
for idx, layer in enumerate(self.classifier):
x = layer(x)
if idx + 6 == end:
return x
else:
if start == 6:
x = x.view(-1, self.feature_dims)
for idx, layer in enumerate(self.classifier):
if idx >= start - 6:
x = layer(x)
if idx + 6 == end:
return x
def get_params(self, end=10):
params = []
for layer in list(self.layers.values())[:end+1]:
params += list(layer.parameters())
return params
def restore_initial_params(self):
for param, initial in zip(self.parameters(), self.initial_params):
param.data = initial.requires_grad_(True)
class CifarNet(nn.Module):
def __init__(self, n_channels=3):
super(CifarNet, self).__init__()
self.features = []
self.initial = None
self.classifier = []
self.layers = collections.OrderedDict()
self.conv11 = nn.Conv2d(
in_channels=n_channels,
out_channels=64,
kernel_size=3,
padding=1
)
self.features.append(self.conv11)
self.layers['conv11'] = self.conv11
self.ReLU11 = nn.ReLU(True)
self.features.append(self.ReLU11)
self.layers['ReLU11'] = self.ReLU11
self.conv12 = nn.Conv2d(
in_channels=64,
out_channels=64,
kernel_size=3,
padding=1
)
self.features.append(self.conv12)
self.layers['conv12'] = self.conv12
self.ReLU12 = nn.ReLU(True)
self.features.append(self.ReLU12)
self.layers['ReLU12'] = self.ReLU12
self.pool1 = nn.MaxPool2d(2, 2)
self.features.append(self.pool1)
self.layers['pool1'] = self.pool1
self.conv21 = nn.Conv2d(
in_channels=64,
out_channels=128,
kernel_size=3,
padding=1
)
self.features.append(self.conv21)
self.layers['conv21'] = self.conv21
self.ReLU21 = nn.ReLU(True)
self.features.append(self.ReLU21)
self.layers['ReLU21'] = self.ReLU21
self.conv22 = nn.Conv2d(
in_channels=128,
out_channels=128,
kernel_size=3,
padding=1
)
self.features.append(self.conv22)
self.layers['conv22'] = self.conv22
self.ReLU22 = nn.ReLU(True)
self.features.append(self.ReLU22)
self.layers['ReLU22'] = self.ReLU22
self.pool2 = nn.MaxPool2d(2, 2)
self.features.append(self.pool2)
self.layers['pool2'] = self.pool2
self.conv31 = nn.Conv2d(
in_channels=128,
out_channels=128,
kernel_size=3,
padding=1
)
self.features.append(self.conv31)
self.layers['conv31'] = self.conv31
self.ReLU31 = nn.ReLU(True)
self.features.append(self.ReLU31)
self.layers['ReLU31'] = self.ReLU31
self.conv32 = nn.Conv2d(
in_channels=128,
out_channels=128,
kernel_size=3,
padding=1
)
self.features.append(self.conv32)
self.layers['conv32'] = self.conv32
self.ReLU32 = nn.ReLU(True)
self.features.append(self.ReLU32)
self.layers['ReLU32'] = self.ReLU32
self.pool3 = nn.MaxPool2d(2, 2)
self.features.append(self.pool3)
self.layers['pool3'] = self.pool3
self.feature_dims = 4 * 4 * 128
self.fc1 = nn.Linear(self.feature_dims, 512)
self.classifier.append(self.fc1)
self.layers['fc1'] = self.fc1
self.fc1act = nn.Sigmoid()
self.classifier.append(self.fc1act)
self.layers['fc1act'] = self.fc1act
self.fc2 = nn.Linear(512, 10)
self.classifier.append(self.fc2)
self.layers['fc2'] = self.fc2
self.initial_params = [param.data for param in self.parameters()]
def forward(self, x, start=0, end=17):
if start <= len(self.features)-1: # start in self.features
for idx, layer in enumerate(self.features[start:]):
x = layer(x)
if idx == end:
return x
x = x.view(-1, self.feature_dims)
for idx, layer in enumerate(self.classifier):
x = layer(x)
if idx + 15 == end:
return x
else:
if start == 15:
x = x.view(-1, self.feature_dims)
for idx, layer in enumerate(self.classifier):
if idx >= start - 15:
x = layer(x)
if idx + 15 == end:
return x
def get_params(self, end=17):
params = []
for layer in list(self.layers.values())[:end+1]:
params += list(layer.parameters())
return params
def restore_initial_params(self):
for param, initial in zip(self.parameters(), self.initial_params):
param.data = initial
class ResNetMNIST(nn.Module):
def __init__(self):
super().__init__()
self.model = resnet18(num_classes=10)
self.model.conv1 = nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(2, 2), padding=(3, 3), bias=False)
self.loss = nn.CrossEntropyLoss()
def forward(self, x):
return self.model(x)