"""`Facebook implementation <https://github.com/facebook/fb.resnet.torch>`_ of ResNet"""
# Source: https://github.com/Cadene/pretrained-models.pytorch/blob/0819c4f43a70fcd40234b03ff02f87599cd8ace6/pretrainedmodels/models/fbresnet.py
# Note this is the version with adaptive capabilities so it can accept differently-sized images
import torch.nn as nn
import torch.nn.functional as F
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['FBResNet', 'FBResNet18', 'FBResNet34', 'FBResNet50', 'FBResNet101', 'fbresnet152']
pretrained_settings = {
'fbresnet152': {
'imagenet': {
# 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/fbresnet152-2e20f6b4.pth', # old version?
'url': 'http://pretorched-x.csail.mit.edu/models/fbresnet152-3ade0e00.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225],
'num_classes': 1000
}
}
}
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=True)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
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)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=True)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=True)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=True)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
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
[docs]class FBResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
# Special attributs
self.input_space = None
self.input_size = (299, 299, 3)
self.mean = None
self.std = None
super(FBResNet, self).__init__()
# Modules
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=True)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.last_linear = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=True),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def features(self, input_):
x = self.conv1(input_)
self.conv1_input = x.clone()
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)
return x
def logits(self, features):
adaptiveAvgPoolWidth = features.shape[2]
x = F.avg_pool2d(features, kernel_size=adaptiveAvgPoolWidth)
x = x.view(x.size(0), -1)
x = self.last_linear(x)
return x
def forward(self, input_):
x = self.features(input_)
x = self.logits(x)
return x
[docs]def FBResNet18(num_classes=1000):
"""Constructs a ResNet-18 model.
Args:
num_classes
"""
model = FBResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes)
return model
[docs]def FBResNet34(num_classes=1000):
"""Constructs a ResNet-34 model.
Args:
num_classes
"""
model = FBResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes)
return model
[docs]def FBResNet50(num_classes=1000):
"""Constructs a ResNet-50 model.
Args:
num_classes
"""
model = FBResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes)
return model
[docs]def FBResNet101(num_classes=1000):
"""Constructs a ResNet-101 model.
Args:
num_classes
"""
model = FBResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes)
return model
[docs]def fbresnet152(num_classes=1000, pretrained='imagenet'):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = FBResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes)
if pretrained is not None:
settings = pretrained_settings['fbresnet152'][pretrained]
if num_classes != settings['num_classes']:
raise AssertionError("num_classes should be {}, but is {}".format(settings['num_classes'], num_classes))
model.load_state_dict(model_zoo.load_url(settings['url']))
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
return model