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- # Code adapted from: https://github.com/Cadene/pretrained-models.pytorch
- import math
- from collections import OrderedDict
- from itertools import chain
-
- import torch.nn as nn
- from torch.utils import model_zoo
-
- from utils import Flatten
-
-
- class SEModule(nn.Module):
- def __init__(self, channels, reduction):
- super(SEModule, self).__init__()
- self.avg_pool = nn.AdaptiveAvgPool2d(1)
- self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, padding=0)
- self.relu = nn.ReLU(inplace=True)
- self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0)
- self.sigmoid = nn.Sigmoid()
-
- def forward(self, x):
- module_input = x
- x = self.avg_pool(x)
- x = self.fc1(x)
- x = self.relu(x)
- x = self.fc2(x)
- x = self.sigmoid(x)
- return module_input * x
-
-
- class SEResNeXtBottleneck(nn.Module):
- """
- ResNeXt bottleneck type C with a Squeeze-and-Excitation module.
- """
- expansion = 4
-
- def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None, base_width=4):
- super(SEResNeXtBottleneck, self).__init__()
- width = math.floor(planes * (base_width / 64)) * groups
- self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False, stride=1)
- self.bn1 = nn.BatchNorm2d(width)
- self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
- self.bn2 = nn.BatchNorm2d(width)
- self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * 4)
- self.relu = nn.ReLU(inplace=True)
- self.se_module = SEModule(planes * 4, reduction=reduction)
- 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 = self.se_module(out) + residual
- out = self.relu(out)
-
- return out
-
-
- class SENet(nn.Module):
- def __init__(self, block, layers, groups, reduction, inplanes=128,
- downsample_kernel_size=3, downsample_padding=1):
- super(SENet, self).__init__()
- self.inplanes = inplanes
-
- layer0_modules = [
- ('conv1', nn.Conv2d(3, inplanes, kernel_size=7, stride=2, padding=3, bias=False)),
- ('bn1', nn.BatchNorm2d(inplanes)),
- ('relu1', nn.ReLU(inplace=True)),
- # To preserve compatibility with Caffe weights `ceil_mode=True`
- # is used instead of `padding=1`.
- ('pool', nn.MaxPool2d(3, stride=2, ceil_mode=True))
- ]
- self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
- self.layer1 = self._make_layer(
- block,
- planes=64,
- blocks=layers[0],
- groups=groups,
- reduction=reduction,
- downsample_kernel_size=1,
- downsample_padding=0
- )
- self.layer2 = self._make_layer(
- block,
- planes=128,
- blocks=layers[1],
- stride=2,
- groups=groups,
- reduction=reduction,
- downsample_kernel_size=downsample_kernel_size,
- downsample_padding=downsample_padding
- )
- self.layer3 = self._make_layer(
- block,
- planes=256,
- blocks=layers[2],
- stride=2,
- groups=groups,
- reduction=reduction,
- downsample_kernel_size=downsample_kernel_size,
- downsample_padding=downsample_padding
- )
- self.layer4 = self._make_layer(
- block,
- planes=512,
- blocks=layers[3],
- stride=2,
- groups=groups,
- reduction=reduction,
- downsample_kernel_size=downsample_kernel_size,
- downsample_padding=downsample_padding
- )
- self.cls = nn.Sequential(
- nn.AdaptiveAvgPool2d(1),
- Flatten(),
- nn.Linear(512 * block.expansion, 1)
- )
-
- def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
- downsample_kernel_size=1, downsample_padding=0):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=downsample_kernel_size, stride=stride,
- padding=downsample_padding, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- layers = [block(self.inplanes, planes, groups, reduction, stride, downsample)]
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes, groups, reduction))
-
- return nn.Sequential(*layers)
-
- def paramgroup01(self):
- return chain(
- self.layer0.parameters(),
- self.layer1.parameters(),
- )
-
- def paramgroup234(self):
- return chain(
- self.layer2.parameters(),
- self.layer3.parameters(),
- self.layer4.parameters(),
- )
-
- def parameters_classifier(self):
- return self.cls.parameters()
-
- def forward(self, x):
- x = self.layer0(x)
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
- c = self.cls(x)
- return c
-
-
- def get_model():
- model = SENet(SEResNeXtBottleneck, [3, 4, 6, 3], groups=32, reduction=16, inplanes=64,
- downsample_kernel_size=1, downsample_padding=0)
- checkpoint = model_zoo.load_url('http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth')
- model.load_state_dict(checkpoint, strict=False)
- return model
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