Module models.wideresnet_cifar
Expand source code
#!/usr/bin/env python3
# Import PyTorch root package import torch
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)
self.relu2 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
padding=1, bias=False)
self.droprate = dropRate
self.equalInOut = (in_planes == out_planes)
self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
padding=0, bias=False) or None
def forward(self, x):
if not self.equalInOut:
x = self.relu1(self.bn1(x))
else:
out = self.relu1(self.bn1(x))
out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
if self.droprate > 0:
out = F.dropout(out, p=self.droprate, training=self.training)
out = self.conv2(out)
return torch.add(x if self.equalInOut else self.convShortcut(x), out)
class NetworkBlock(nn.Module):
def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0):
super(NetworkBlock, self).__init__()
self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate)
def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate):
layers = []
for i in range(int(nb_layers)):
layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate))
return nn.Sequential(*layers)
def forward(self, x):
return self.layer(x)
class WideResNet(nn.Module):
def __init__(self, depth, widen_factor, num_classes=10, dropRate=0.0):
super(WideResNet, self).__init__()
nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor]
assert((depth - 4) % 6 == 0)
n = (depth - 4) / 6
block = BasicBlock
# 1st conv before any network block
self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1,
padding=1, bias=False)
# 1st block
self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
# 2nd block
self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
# 3rd block
self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
# global average pooling and classifier
self.bn1 = nn.BatchNorm2d(nChannels[3])
self.relu = nn.ReLU(inplace=True)
self.fc = nn.Linear(nChannels[3], num_classes)
self.nChannels = nChannels[3]
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.bias.data.zero_()
def forward(self, x):
batch_size = x.shape[0]
out = self.conv1(x)
out = self.block1(out)
out = self.block2(out)
out = self.block3(out)
out = self.relu(self.bn1(out))
out = F.avg_pool2d(out, 8)
out = out.view(batch_size, -1)
return self.fc(out)
# TODO: Any way we can actually have an useful pretrained argument here?
def WideResNet_28_2(pretrained=False, num_classes=10):
return WideResNet(28, 2, num_classes=num_classes)
def WideResNet_28_4(pretrained=False, num_classes=10):
return WideResNet(28, 4, num_classes=num_classes)
def WideResNet_28_8(pretrained=False, num_classes=10):
return WideResNet(28, 8, num_classes=num_classes)
Functions
def WideResNet_28_2(pretrained=False, num_classes=10)
-
Expand source code
def WideResNet_28_2(pretrained=False, num_classes=10): return WideResNet(28, 2, num_classes=num_classes)
def WideResNet_28_4(pretrained=False, num_classes=10)
-
Expand source code
def WideResNet_28_4(pretrained=False, num_classes=10): return WideResNet(28, 4, num_classes=num_classes)
def WideResNet_28_8(pretrained=False, num_classes=10)
-
Expand source code
def WideResNet_28_8(pretrained=False, num_classes=10): return WideResNet(28, 8, num_classes=num_classes)
Classes
class BasicBlock (in_planes, out_planes, stride, dropRate=0.0)
-
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 BasicBlock(nn.Module): def __init__(self, in_planes, out_planes, stride, dropRate=0.0): super(BasicBlock, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.relu1 = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_planes) self.relu2 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False) self.droprate = dropRate self.equalInOut = (in_planes == out_planes) self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False) or None def forward(self, x): if not self.equalInOut: x = self.relu1(self.bn1(x)) else: out = self.relu1(self.bn1(x)) out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x))) if self.droprate > 0: out = F.dropout(out, p=self.droprate, training=self.training) out = self.conv2(out) return torch.add(x if self.equalInOut else self.convShortcut(x), out)
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): if not self.equalInOut: x = self.relu1(self.bn1(x)) else: out = self.relu1(self.bn1(x)) out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x))) if self.droprate > 0: out = F.dropout(out, p=self.droprate, training=self.training) out = self.conv2(out) return torch.add(x if self.equalInOut else self.convShortcut(x), out)
class NetworkBlock (nb_layers, in_planes, out_planes, block, stride, dropRate=0.0)
-
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 NetworkBlock(nn.Module): def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0): super(NetworkBlock, self).__init__() self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate) def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate): layers = [] for i in range(int(nb_layers)): layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate)) return nn.Sequential(*layers) def forward(self, x): return self.layer(x)
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): return self.layer(x)
class WideResNet (depth, widen_factor, num_classes=10, dropRate=0.0)
-
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 WideResNet(nn.Module): def __init__(self, depth, widen_factor, num_classes=10, dropRate=0.0): super(WideResNet, self).__init__() nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor] assert((depth - 4) % 6 == 0) n = (depth - 4) / 6 block = BasicBlock # 1st conv before any network block self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1, padding=1, bias=False) # 1st block self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate) # 2nd block self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate) # 3rd block self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate) # global average pooling and classifier self.bn1 = nn.BatchNorm2d(nChannels[3]) self.relu = nn.ReLU(inplace=True) self.fc = nn.Linear(nChannels[3], num_classes) self.nChannels = nChannels[3] for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.bias.data.zero_() def forward(self, x): batch_size = x.shape[0] out = self.conv1(x) out = self.block1(out) out = self.block2(out) out = self.block3(out) out = self.relu(self.bn1(out)) out = F.avg_pool2d(out, 8) out = out.view(batch_size, -1) return self.fc(out)
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): batch_size = x.shape[0] out = self.conv1(x) out = self.block1(out) out = self.block2(out) out = self.block3(out) out = self.relu(self.bn1(out)) out = F.avg_pool2d(out, 8) out = out.view(batch_size, -1) return self.fc(out)