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import json
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=(3, 3), stride=(stride, stride), padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=(1, 1), stride=(stride, stride), bias=False)
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):
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
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = conv1x1(planes, planes * self.expansion)
self.bn3 = nn.BatchNorm2d(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 fc_block(nn.Module):
def __init__(self, inplanes, planes, drop_rate=0.15):
super(fc_block, self).__init__()
self.fc = nn.Linear(inplanes, planes)
self.bn = nn.BatchNorm1d(planes)
if drop_rate > 0:
self.dropout = nn.Dropout(drop_rate)
self.relu = nn.ReLU(inplace=True)
self.drop_rate = drop_rate
def forward(self, x):
x = self.fc(x)
x = self.bn(x)
if self.drop_rate > 0:
x = self.dropout(x)
x = self.relu(x)
return x
class ResNet(nn.Module):
def __init__(self,
block,
layers,
attr_file,
zero_init_residual=False,
dropout_rate=0):
super(ResNet, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=3, bias=False)
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.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.stem = fc_block(512 * block.expansion, 512, dropout_rate)
# Construct classifier heads according to the number of values of each attribute
self.attr_file = attr_file
with open(self.attr_file, 'r') as f:
attr_f = json.load(f)
self.attr_info = attr_f['attr_info']
for idx, (key, val) in enumerate(self.attr_info.items()):
num_val = int(len(val["value"]))
setattr(self, 'classifier' + str(key).zfill(2) + val["name"],
nn.Sequential(fc_block(512, 256, dropout_rate), nn.Linear(256, num_val)))
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.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):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(conv1x1(self.inplanes, planes * block.expansion, stride),
nn.BatchNorm2d(planes * block.expansion))
layers = [block(self.inplanes, planes, stride, downsample)]
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
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 = x.view(x.size(0), -1)
x = self.stem(x)
predictions = {}
for idx, (key, val) in enumerate(self.attr_info.items()):
classifier = getattr(self, 'classifier' + str(key).zfill(2) + val["name"])
predictions.update({val["name"]: classifier(x)})
return predictions
def celeba_attr_predictor(attr_file, pretrained='models/pretrained/celeba_attributes/predictor_1024.pth.tar'):
model = ResNet(Bottleneck, [3, 4, 6, 3], attr_file=attr_file)
init_pretrained_weights(model, 'https://download.pytorch.org/models/resnet50-19c8e357.pth')
model.load_state_dict(torch.load(pretrained)['state_dict'], strict=True)
return model
def init_pretrained_weights(model, model_url):
"""Initialize model with pretrained weights. Layers that don't match with pretrained layers in name or size are kept
unchanged.
"""
pretrain_dict = model_zoo.load_url(model_url)
model_dict = model.state_dict()
pretrain_dict = {
k: v
for k, v in pretrain_dict.items()
if k in model_dict and model_dict[k].size() == v.size()
}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
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