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