Module models.vgg_cifar

VGG11/13/16/19 in Pytorch.

Expand source code
#!/usr/bin/env python3

"""
VGG11/13/16/19 in Pytorch.
"""
import torch.nn as nn


cfg = {
    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}


class VGG(nn.Module):
    def __init__(self, vgg_name, num_classes=10):
        super(VGG, self).__init__()
        self.features = self._make_layers(cfg[vgg_name])
        self.classifier = nn.Linear(512, num_classes)

    def forward(self, x):
        out = self.features(x)
        out = out.view(out.size(0), -1)
        out = self.classifier(out)
        return out

    def _make_layers(self, cfg):
        layers = []
        in_channels = 3
        for x in cfg:
            if x == 'M':
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            else:
                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
                           nn.BatchNorm2d(x),
                           nn.ReLU(inplace=True)]
                in_channels = x
        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
        return nn.Sequential(*layers)


# TODO: Any way we can actually have an useful pretrained argument here?
def vgg11(pretrained=False, num_classes=10):
    return VGG('VGG11', num_classes=num_classes)


def vgg13(pretrained=False, num_classes=10):
    return VGG('VGG13', num_classes=num_classes)


def vgg16(pretrained=False, num_classes=10):
    return VGG('VGG16', num_classes=num_classes)


def vgg19(pretrained=False, num_classes=10):
    return VGG('VGG19', num_classes=num_classes)

Functions

def vgg11(pretrained=False, num_classes=10)
Expand source code
def vgg11(pretrained=False, num_classes=10):
    return VGG('VGG11', num_classes=num_classes)
def vgg13(pretrained=False, num_classes=10)
Expand source code
def vgg13(pretrained=False, num_classes=10):
    return VGG('VGG13', num_classes=num_classes)
def vgg16(pretrained=False, num_classes=10)
Expand source code
def vgg16(pretrained=False, num_classes=10):
    return VGG('VGG16', num_classes=num_classes)
def vgg19(pretrained=False, num_classes=10)
Expand source code
def vgg19(pretrained=False, num_classes=10):
    return VGG('VGG19', num_classes=num_classes)

Classes

class VGG (vgg_name, num_classes=10)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Expand source code
class VGG(nn.Module):
    def __init__(self, vgg_name, num_classes=10):
        super(VGG, self).__init__()
        self.features = self._make_layers(cfg[vgg_name])
        self.classifier = nn.Linear(512, num_classes)

    def forward(self, x):
        out = self.features(x)
        out = out.view(out.size(0), -1)
        out = self.classifier(out)
        return out

    def _make_layers(self, cfg):
        layers = []
        in_channels = 3
        for x in cfg:
            if x == 'M':
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            else:
                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
                           nn.BatchNorm2d(x),
                           nn.ReLU(inplace=True)]
                in_channels = x
        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
        return nn.Sequential(*layers)

Ancestors

  • torch.nn.modules.module.Module

Class variables

var dump_patches : bool
var training : bool

Methods

def forward(self, x) ‑> Callable[..., Any]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Expand source code
def forward(self, x):
    out = self.features(x)
    out = out.view(out.size(0), -1)
    out = self.classifier(out)
    return out