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models.py
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models.py
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import torch
import torch.nn as nn
import operator
import functools
import math
class VGG(nn.Module):
'''
VGG model
'''
def __init__(self, architecture, num_classes=10, input_dims=[3, 32, 32]):
super(VGG, self).__init__()
self.architecture = architecture
self.num_classes = num_classes
self.input_dims = input_dims
self.convs = self.conv()
self.fcs = self.fc()
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
m.bias.data.zero_()
def conv(self):
layers = []
input_channels = self.input_dims[0]
for value in self.architecture:
if type(value) == int:
layers += [nn.Conv2d(in_channels=input_channels, out_channels=value, kernel_size=3, padding=1),
nn.BatchNorm2d(value), nn.ReLU(inplace=True)]
input_channels = value
else:
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
return nn.Sequential(*layers)
def fc(self):
features_size = functools.reduce(operator.mul, list(self.convs(torch.rand(1, *self.input_dims)).shape))
return nn.Sequential(nn.Dropout(p=0.5),
nn.Linear(features_size, 512),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(512, 512),
nn.ReLU(inplace=True),
nn.Linear(512, self.num_classes)
)
def forward(self, x):
x = self.convs(x)
x = x.view(x.size(0), -1)
x = self.fcs(x)
return x
# Used inside ResNet
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super().__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=10):
super().__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, num_classes)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes, 1, stride, bias=False),
nn.BatchNorm2d(planes),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
# model = ResNet(BasicBlock,[3,4,6,3])