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imagenet_models.py
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"""ResNet and BagNet implementations.
original codes
- https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
- https://github.com/wielandbrendel/bag-of-local-features-models/blob/master/bagnets/pytorchnet.py
"""
import torch
import torch.nn as nn
import math
from torch.utils.model_zoo import load_url as load_state_dict_from_url
MODEL_URLS = {
'bagnet9': 'https://bitbucket.org/wielandbrendel/bag-of-feature-pretrained-models/raw/249e8fa82c0913623a807d9d35eeab9da7dcc2a8/bagnet8-34f4ccd2.pth.tar',
'bagnet17': 'https://bitbucket.org/wielandbrendel/bag-of-feature-pretrained-models/raw/249e8fa82c0913623a807d9d35eeab9da7dcc2a8/bagnet16-105524de.pth.tar',
'bagnet33': 'https://bitbucket.org/wielandbrendel/bag-of-feature-pretrained-models/raw/249e8fa82c0913623a807d9d35eeab9da7dcc2a8/bagnet32-2ddd53ed.pth.tar',
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}
class BasicBlock_(nn.Module):
expansion = 1
__constants__ = ['downsample']
def __init__(self, inplanes, planes, stride=1, downsample=None, kernel_size=1):
super(BasicBlock_, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=kernel_size, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = 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:
identity = self.downsample(x)
if identity.size(-1) != out.size(-1):
diff = identity.size(-1) - out.size(-1)
identity = identity[:, :, :-diff, :-diff]
out += identity
out = self.relu(out)
return out
class Bottleneck_(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, kernel_size=1):
super(Bottleneck_, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=kernel_size, stride=stride,
padding=0, bias=False) # changed padding from (kernel_size - 1) // 2
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
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)
if residual.size(-1) != out.size(-1):
diff = residual.size(-1) - out.size(-1)
residual = residual[:, :, :-diff, :-diff]
out += residual
out = self.relu(out)
return out
class BagNetDeep(nn.Module):
def __init__(self, block, layers, strides=[2, 2, 2, 1], kernel3=[0, 0, 0, 0], num_classes=1000,
feature_pos='post', avg_pool=True):
super(BagNetDeep, self).__init__()
self.inplanes = 64
self.feature_pos = feature_pos
self.conv1 = nn.Conv2d(3, 64, kernel_size=1, stride=1, padding=0, bias=False)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(64, momentum=0.001)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], kernel3=kernel3[0], prefix='layer1')
self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], kernel3=kernel3[1], prefix='layer2')
self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], kernel3=kernel3[2], prefix='layer3')
self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], kernel3=kernel3[3], prefix='layer4')
self.avgpool = nn.AvgPool2d(1, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
self.avg_pool = avg_pool
self.block = block
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, kernel3=0, prefix=''):
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=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
kernel = 1 if kernel3 == 0 else 3
layers.append(block(self.inplanes, planes, stride, downsample, kernel_size=kernel))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
kernel = 1 if kernel3 <= i else 3
layers.append(block(self.inplanes, planes, kernel_size=kernel))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x_ = nn.AvgPool2d(x.size()[2], stride=1)(x)
x = x_.view(x_.size(0), -1)
x = self.fc(x)
return x, x_
def bagnet18(feature_pos='post', num_classes=1000, rf=43):
model = BagNetDeep(BasicBlock_, [2, 2, 2, 2], strides=[2, 2, 2, 1], kernel3=[1, 0, 0, 0],
num_classes=num_classes, feature_pos=feature_pos)
return model
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
__constants__ = ['downsample']
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = 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:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
__constants__ = ['downsample']
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = 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:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, feature_pos='post', zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None, rf=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.feature_pos = feature_pos
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = norm_layer(self.inplanes)
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,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
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, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
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 = torch.flatten(x_, 1)
x = self.fc(x)
return x, x_
def _resnet(arch, block, layers, pretrained, progress, rf, num_classes, feature_pos, **kwargs):
model = ResNet(block, layers, rf=rf, num_classes=num_classes, feature_pos=feature_pos, **kwargs)
if pretrained:
state_dict = load_state_dict_from_url(MODEL_URLS[arch],
progress=progress)
model.load_state_dict(state_dict, strict=False)
return model
def resnet18(feature_pos='post', num_classes=1000, rf=43, pretrained=False, progress=True, **kwargs):
r"""ResNet-18 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, rf, num_classes, feature_pos,
**kwargs)