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resnet.py
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import torch.nn as nn
import math, torch
import torch.utils.model_zoo as model_zoo
from torch.nn import init
from NonLocalBlock1D import NonLocalBlock1D
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=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
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)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, train=True):
self.inplanes = 64
super(ResNet, self).__init__()
self.istrain = train
self.frames = 16
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=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=1)
self.avgpool = nn.AvgPool2d((16,8), stride=1)
self.num_features = 128
self.feat = nn.Linear(512 * block.expansion, self.num_features)
self.feat_bn = nn.BatchNorm1d(self.num_features*4)
self.drop = nn.Dropout(0.5)
self.classifier = nn.Linear(4*self.num_features, 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_()
elif isinstance(m, nn.BatchNorm1d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
init.constant_(m.bias, 0)
init.kaiming_normal_(self.feat.weight, mode='fan_out')
init.constant_(self.feat.bias, 0)
self.feat1 = nn.Conv2d(1, 128, kernel_size=(3,128), stride=1, dilation=(1,1), padding=(1,0), bias=False)
self.feat2 = nn.Conv2d(1, 128, kernel_size=(3,128), stride=1, dilation=(2,1), padding=(2,0), bias=False)
self.feat3 = nn.Conv2d(1, 128, kernel_size=(3,128), stride=1, dilation=(3,1), padding=(3,0), bias=False)
init.normal_(self.feat1.weight, std=0.001)
init.normal_(self.feat2.weight, std=0.001)
init.normal_(self.feat3.weight, std=0.001)
self.Nonlocal_block0 = NonLocalBlock1D(128*4)
# self.Nonlocal_block1 = NonLocalBlock1D(128)
# self.Nonlocal_block2 = NonLocalBlock1D(128)
# self.Nonlocal_block3 = NonLocalBlock1D(128)
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=False),
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 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.feat(x)
x = x.view(x.size(0)/self.frames, self.frames, -1)
x0 = torch.transpose(x, 1, 2)
x = x.unsqueeze(dim=1)
x1 = self.feat1(x).squeeze(dim=3)
x2 = self.feat2(x).squeeze(dim=3)
x3 = self.feat3(x).squeeze(dim=3)
#print x0.size(), x1.size(), x2.size(), x3.size()
x = torch.cat((x0, x1, x2, x3), dim=1)
#print x.size()
x = self.Nonlocal_block0(x).mean(dim=2)
if self.istrain:
x = self.feat_bn(x)
x = self.relu(x)
x = self.drop(x)
x = self.classifier(x)
return x#0+x1+x2+x3
def resnet50(pretrained='True', num_classes=1000, train=True):
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes, train)
#if pretrained:
# model.load_state_dict('resnet50-19c8e357.pth')
weight = torch.load(pretrained)
static = model.state_dict()
for name, param in weight.items():
if name not in static:
print 'not load weight ', name
continue
if isinstance(param, nn.Parameter):
print 'load weight ', name, type(param)
param = param.data
static[name].copy_(param)
#model.load_state_dict(weight)
new_param = []
for name, param in static.items():
if name not in weight:
print 'new param ', name
new_param.append(name)
#model.load_state_dict(weight)
return model, new_param