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update7.py
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update7.py
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import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn.parameter imporimport torch.nn.functional as F
t Parameter
import torch
from torch.nn import init
from torch.autograd import Variable
from collections import OrderedDict
import math
import time
__all__ = ['update7_resnet18', 'update7_resnet34', 'update7_resnet50', 'update7_resnet101', 'update7_resnet152']
class ParamFree(nn.Module):
def __init__(self, channel, reduction=16):
super(ParamFree, self).__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.gap2 = nn.AdaptiveAvgPool2d(2)
self.gap4 = nn.AdaptiveAvgPool2d(4)
self.sig = nn.Sigmoid()
self.batch_norm = nn.BatchNorm2d(channel)
self.fc = nn.Sequential(
nn.Conv2d(4,2,1,bias=False),
nn.ReLU(inplace=True),
nn.Conv2d(2, 4, 1, bias=False)
)
def forward(self, x):
b, c, h, w = x.size()
gap = self.batch_norm(F.interpolate(self.gap(x), size=([h, w]))).unsqueeze(dim=1)
gap2 = self.batch_norm(F.interpolate(self.gap2(x), size=[h, w])).unsqueeze(dim=1)
gap4 = self.batch_norm(F.interpolate(self.gap4(x), size=[h, w])).unsqueeze(dim=1)
x = x.unsqueeze(dim=1)
all_out = torch.cat([x, gap, gap2, gap4], dim=1)
all_gap = all_out.mean(dim=-1, keepdim=False).mean(dim=-1, keepdim=True)
weight_gap = self.fc(all_gap).unsqueeze(dim=-1)
weight_gap = weight_gap.softmax(dim=1)
out = weight_gap*all_out
out = out.sum(dim=1, keepdim=False)
return out
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=False)
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
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
self.pf = ParamFree(planes)
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.pf(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.pf = ParamFree(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)
out = self.pf(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=365, zero_init_residual=False):
super(ResNet, self).__init__()
self.inplanes = 64
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=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.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 = []
layers.append(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.fc(x)
return x
def update7_resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
return model
def update7_resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
return model
def update7_resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
return model
def update7_resnet101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
return model
def update7_resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
return model
def demo():
st = time.perf_counter()
for i in range(1):
net = update7_resnet50(num_classes=1000)
y = net(torch.ones(2, 3, 224, 224))
print(y.size())
print("CPU time: {}".format(time.perf_counter() - st))
def demo2():
st = time.perf_counter()
for i in range(100):
net = adaptive_resnet50(num_classes=1000).cuda()
y = net(torch.randn(2, 3, 224, 224).cuda())
print(y.size())
print("Allocated: {}".format(torch.cuda.memory_allocated()))
print("GPU time: {}".format(time.perf_counter() - st))
# demo()
# demo2()