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custom_models_cifar_vgg.py
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custom_models_cifar_vgg.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 25 22:55:07 2020
@modified by: tibrayev
"""
import torch
import torch.nn as nn
class customizable_VGG(nn.Module):
def __init__(self, features, num_classes=100, fc1=512, fc2=512, init_weights=True):
super(customizable_VGG, self).__init__()
self.features = features
self.classifier = nn.Sequential(
nn.Linear(512, fc1),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(fc1, fc2),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(fc2, num_classes),
)
if init_weights:
self._initialize_weights()
def forward(self, x, with_latent = False):
x = self.features(x)
features = torch.flatten(x, 1)
outputs = self.classifier(features)
if with_latent:
return outputs, features
else:
return outputs
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
def make_layers(cfg, batch_norm=False):
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)
cfgs = {
'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
def vgg11(**kwargs):
model = customizable_VGG(make_layers(cfgs['A'], batch_norm=False), **kwargs)
print("Requested model is modified to suit CIFAR10/100 image resolutions of 3x32x32!")
return model
def vgg11_bn(**kwargs):
model = customizable_VGG(make_layers(cfgs['A'], batch_norm=True), **kwargs)
print("Requested model is modified to suit CIFAR10/100 image resolutions of 3x32x32!")
return model
def vgg13(**kwargs):
model = customizable_VGG(make_layers(cfgs['B'], batch_norm=False), **kwargs)
print("Requested model is modified to suit CIFAR10/100 image resolutions of 3x32x32!")
return model
def vgg13_bn(**kwargs):
model = customizable_VGG(make_layers(cfgs['B'], batch_norm=True), **kwargs)
print("Requested model is modified to suit CIFAR10/100 image resolutions of 3x32x32!")
return model
def vgg16(**kwargs):
model = customizable_VGG(make_layers(cfgs['D'], batch_norm=False), **kwargs)
print("Requested model is modified to suit CIFAR10/100 image resolutions of 3x32x32!")
return model
def vgg16_bn(**kwargs):
model = customizable_VGG(make_layers(cfgs['D'], batch_norm=True), **kwargs)
print("Requested model is modified to suit CIFAR10/100 image resolutions of 3x32x32!")
return model
def vgg19(**kwargs):
model = customizable_VGG(make_layers(cfgs['E'], batch_norm=False), **kwargs)
print("Requested model is modified to suit CIFAR10/100 image resolutions of 3x32x32!")
return model
def vgg19_bn(**kwargs):
model = customizable_VGG(make_layers(cfgs['E'], batch_norm=True), **kwargs)
print("Requested model is modified to suit CIFAR10/100 image resolutions of 3x32x32!")
return model