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resnet.py
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resnet.py
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import torch.nn as nn
from utils.builder import get_builder
from args import args
# BasicBlock {{{
class BasicBlock(nn.Module):
M = 2
expansion = 1
def __init__(self, builder, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = builder.conv3x3(inplanes, planes, stride)
self.bn1 = builder.batchnorm(planes)
self.relu = builder.activation()
self.conv2 = builder.conv3x3(planes, planes)
self.bn2 = builder.batchnorm(planes, last_bn=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
if self.bn1 is not None:
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
if self.bn2 is not None:
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
# BasicBlock }}}
# Bottleneck {{{
class Bottleneck(nn.Module):
M = 3
expansion = 4
def __init__(self, builder, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = builder.conv1x1(inplanes, planes)
self.bn1 = builder.batchnorm(planes)
self.conv2 = builder.conv3x3(planes, planes, stride=stride)
self.bn2 = builder.batchnorm(planes)
self.conv3 = builder.conv1x1(planes, planes * self.expansion)
self.bn3 = builder.batchnorm(planes * self.expansion, last_bn=True)
self.relu = builder.activation()
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
# Bottleneck }}}
# ResNet {{{
class ResNet(nn.Module):
def __init__(self, builder, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
if args.first_layer_dense:
print("FIRST LAYER DENSE!!!!")
self.conv1 = nn.Conv2d(
3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
)
else:
self.conv1 = builder.conv7x7(3, 64, stride=2, first_layer=True)
self.bn1 = builder.batchnorm(64)
self.relu = builder.activation()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(builder, block, 64, layers[0])
self.layer2 = self._make_layer(builder, block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(builder, block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(builder, block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d(1)
# self.fc = nn.Linear(512 * block.expansion, num_classes)
if args.last_layer_dense:
self.fc = nn.Conv2d(512 * block.expansion, args.num_classes, 1)
else:
self.fc = builder.conv1x1(512 * block.expansion, num_classes)
def _make_layer(self, builder, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
dconv = builder.conv1x1(
self.inplanes, planes * block.expansion, stride=stride
)
dbn = builder.batchnorm(planes * block.expansion)
if dbn is not None:
downsample = nn.Sequential(dconv, dbn)
else:
downsample = dconv
layers = []
layers.append(block(builder, self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(builder, self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
if self.bn1 is not None:
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 = self.fc(x)
x = x.view(x.size(0), -1)
return x
# ResNet }}}
def ResNet18(pretrained=False):
# TODO: pretrained
return ResNet(get_builder(), BasicBlock, [2, 2, 2, 2], 1000)
def ResNet50(pretrained=False):
# TODO: pretrained
return ResNet(get_builder(), Bottleneck, [3, 4, 6, 3], 1000)