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465 lines
17 KiB
465 lines
17 KiB
"""
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SEResNet implementation from Cadene's pretrained models
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https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
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Additional credit to https://github.com/creafz
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Original model: https://github.com/hujie-frank/SENet
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ResNet code gently borrowed from
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https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
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FIXME I'm deprecating this model and moving them to ResNet as I don't want to maintain duplicate
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support for extras like dilation, switchable BN/activations, feature extraction, etc that don't exist here.
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"""
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import math
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from collections import OrderedDict
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .helpers import build_model_with_cfg
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from .layers import create_classifier
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from .registry import register_model
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__all__ = ['SENet']
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def _cfg(url='', **kwargs):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bilinear',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'layer0.conv1', 'classifier': 'last_linear',
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**kwargs
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}
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default_cfgs = {
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'legacy_senet154':
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_cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth'),
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'legacy_seresnet18': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet18-4bb0ce65.pth',
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interpolation='bicubic'),
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'legacy_seresnet34': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet34-a4004e63.pth'),
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'legacy_seresnet50': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet50-ce0d4300.pth'),
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'legacy_seresnet101': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet101-7e38fcc6.pth'),
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'legacy_seresnet152': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet152-d17c99b7.pth'),
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'legacy_seresnext26_32x4d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pth',
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interpolation='bicubic'),
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'legacy_seresnext50_32x4d':
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_cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth'),
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'legacy_seresnext101_32x4d':
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_cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth'),
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}
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def _weight_init(m):
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1.)
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nn.init.constant_(m.bias, 0.)
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class SEModule(nn.Module):
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def __init__(self, channels, reduction):
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super(SEModule, self).__init__()
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self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1)
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self.relu = nn.ReLU(inplace=True)
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self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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module_input = x
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x = x.mean((2, 3), keepdim=True)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.sigmoid(x)
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return module_input * x
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class Bottleneck(nn.Module):
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"""
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Base class for bottlenecks that implements `forward()` method.
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"""
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def forward(self, x):
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shortcut = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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shortcut = self.downsample(x)
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out = self.se_module(out) + shortcut
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out = self.relu(out)
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return out
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class SEBottleneck(Bottleneck):
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"""
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Bottleneck for SENet154.
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"""
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expansion = 4
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def __init__(self, inplanes, planes, groups, reduction, stride=1,
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downsample=None):
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super(SEBottleneck, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes * 2)
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self.conv2 = nn.Conv2d(
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planes * 2, planes * 4, kernel_size=3, stride=stride,
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padding=1, groups=groups, bias=False)
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self.bn2 = nn.BatchNorm2d(planes * 4)
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self.conv3 = nn.Conv2d(
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planes * 4, planes * 4, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * 4)
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self.relu = nn.ReLU(inplace=True)
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self.se_module = SEModule(planes * 4, reduction=reduction)
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self.downsample = downsample
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self.stride = stride
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class SEResNetBottleneck(Bottleneck):
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"""
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ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe
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implementation and uses `stride=stride` in `conv1` and not in `conv2`
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(the latter is used in the torchvision implementation of ResNet).
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"""
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expansion = 4
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def __init__(self, inplanes, planes, groups, reduction, stride=1,
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downsample=None):
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super(SEResNetBottleneck, self).__init__()
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self.conv1 = nn.Conv2d(
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inplanes, planes, kernel_size=1, bias=False, stride=stride)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(
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planes, planes, kernel_size=3, padding=1, groups=groups, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * 4)
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self.relu = nn.ReLU(inplace=True)
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self.se_module = SEModule(planes * 4, reduction=reduction)
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self.downsample = downsample
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self.stride = stride
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class SEResNeXtBottleneck(Bottleneck):
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"""
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ResNeXt bottleneck type C with a Squeeze-and-Excitation module.
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"""
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expansion = 4
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def __init__(self, inplanes, planes, groups, reduction, stride=1,
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downsample=None, base_width=4):
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super(SEResNeXtBottleneck, self).__init__()
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width = math.floor(planes * (base_width / 64)) * groups
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self.conv1 = nn.Conv2d(
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inplanes, width, kernel_size=1, bias=False, stride=1)
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self.bn1 = nn.BatchNorm2d(width)
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self.conv2 = nn.Conv2d(
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width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
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self.bn2 = nn.BatchNorm2d(width)
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self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * 4)
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self.relu = nn.ReLU(inplace=True)
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self.se_module = SEModule(planes * 4, reduction=reduction)
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self.downsample = downsample
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self.stride = stride
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class SEResNetBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None):
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super(SEResNetBlock, self).__init__()
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self.conv1 = nn.Conv2d(
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inplanes, planes, kernel_size=3, padding=1, stride=stride, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(
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planes, planes, kernel_size=3, padding=1, groups=groups, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.relu = nn.ReLU(inplace=True)
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self.se_module = SEModule(planes, reduction=reduction)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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shortcut = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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if self.downsample is not None:
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shortcut = self.downsample(x)
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out = self.se_module(out) + shortcut
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out = self.relu(out)
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return out
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class SENet(nn.Module):
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def __init__(self, block, layers, groups, reduction, drop_rate=0.2,
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in_chans=3, inplanes=64, input_3x3=False, downsample_kernel_size=1,
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downsample_padding=0, num_classes=1000, global_pool='avg'):
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"""
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Parameters
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----------
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block (nn.Module): Bottleneck class.
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- For SENet154: SEBottleneck
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- For SE-ResNet models: SEResNetBottleneck
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- For SE-ResNeXt models: SEResNeXtBottleneck
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layers (list of ints): Number of residual blocks for 4 layers of the
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network (layer1...layer4).
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groups (int): Number of groups for the 3x3 convolution in each
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bottleneck block.
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- For SENet154: 64
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- For SE-ResNet models: 1
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- For SE-ResNeXt models: 32
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reduction (int): Reduction ratio for Squeeze-and-Excitation modules.
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- For all models: 16
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dropout_p (float or None): Drop probability for the Dropout layer.
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If `None` the Dropout layer is not used.
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- For SENet154: 0.2
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- For SE-ResNet models: None
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- For SE-ResNeXt models: None
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inplanes (int): Number of input channels for layer1.
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- For SENet154: 128
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- For SE-ResNet models: 64
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- For SE-ResNeXt models: 64
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input_3x3 (bool): If `True`, use three 3x3 convolutions instead of
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a single 7x7 convolution in layer0.
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- For SENet154: True
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- For SE-ResNet models: False
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- For SE-ResNeXt models: False
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downsample_kernel_size (int): Kernel size for downsampling convolutions
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in layer2, layer3 and layer4.
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- For SENet154: 3
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- For SE-ResNet models: 1
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- For SE-ResNeXt models: 1
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downsample_padding (int): Padding for downsampling convolutions in
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layer2, layer3 and layer4.
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- For SENet154: 1
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- For SE-ResNet models: 0
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- For SE-ResNeXt models: 0
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num_classes (int): Number of outputs in `last_linear` layer.
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- For all models: 1000
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"""
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super(SENet, self).__init__()
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self.inplanes = inplanes
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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if input_3x3:
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layer0_modules = [
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('conv1', nn.Conv2d(in_chans, 64, 3, stride=2, padding=1, bias=False)),
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('bn1', nn.BatchNorm2d(64)),
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('relu1', nn.ReLU(inplace=True)),
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('conv2', nn.Conv2d(64, 64, 3, stride=1, padding=1, bias=False)),
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('bn2', nn.BatchNorm2d(64)),
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('relu2', nn.ReLU(inplace=True)),
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('conv3', nn.Conv2d(64, inplanes, 3, stride=1, padding=1, bias=False)),
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('bn3', nn.BatchNorm2d(inplanes)),
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('relu3', nn.ReLU(inplace=True)),
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]
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else:
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layer0_modules = [
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('conv1', nn.Conv2d(
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in_chans, inplanes, kernel_size=7, stride=2, padding=3, bias=False)),
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('bn1', nn.BatchNorm2d(inplanes)),
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('relu1', nn.ReLU(inplace=True)),
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]
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self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
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# To preserve compatibility with Caffe weights `ceil_mode=True` is used instead of `padding=1`.
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self.pool0 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
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self.feature_info = [dict(num_chs=inplanes, reduction=2, module='layer0')]
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self.layer1 = self._make_layer(
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block,
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planes=64,
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blocks=layers[0],
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groups=groups,
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reduction=reduction,
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downsample_kernel_size=1,
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downsample_padding=0
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)
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self.feature_info += [dict(num_chs=64 * block.expansion, reduction=4, module='layer1')]
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self.layer2 = self._make_layer(
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block,
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planes=128,
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blocks=layers[1],
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stride=2,
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groups=groups,
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reduction=reduction,
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downsample_kernel_size=downsample_kernel_size,
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downsample_padding=downsample_padding
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)
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self.feature_info += [dict(num_chs=128 * block.expansion, reduction=8, module='layer2')]
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self.layer3 = self._make_layer(
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block,
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planes=256,
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blocks=layers[2],
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stride=2,
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groups=groups,
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reduction=reduction,
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downsample_kernel_size=downsample_kernel_size,
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downsample_padding=downsample_padding
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)
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self.feature_info += [dict(num_chs=256 * block.expansion, reduction=16, module='layer3')]
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self.layer4 = self._make_layer(
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block,
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planes=512,
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blocks=layers[3],
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stride=2,
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groups=groups,
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reduction=reduction,
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downsample_kernel_size=downsample_kernel_size,
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downsample_padding=downsample_padding
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)
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self.feature_info += [dict(num_chs=512 * block.expansion, reduction=32, module='layer4')]
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self.num_features = 512 * block.expansion
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self.global_pool, self.last_linear = create_classifier(
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self.num_features, self.num_classes, pool_type=global_pool)
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for m in self.modules():
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_weight_init(m)
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def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
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downsample_kernel_size=1, downsample_padding=0):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(
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self.inplanes, planes * block.expansion, kernel_size=downsample_kernel_size,
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stride=stride, padding=downsample_padding, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = [block(self.inplanes, planes, groups, reduction, stride, downsample)]
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, groups, reduction))
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return nn.Sequential(*layers)
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def get_classifier(self):
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return self.last_linear
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def reset_classifier(self, num_classes, global_pool='avg'):
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self.num_classes = num_classes
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self.global_pool, self.last_linear = create_classifier(
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self.num_features, self.num_classes, pool_type=global_pool)
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def forward_features(self, x):
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x = self.layer0(x)
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x = self.pool0(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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return x
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def logits(self, x):
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x = self.global_pool(x)
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if self.drop_rate > 0.:
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x = F.dropout(x, p=self.drop_rate, training=self.training)
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x = self.last_linear(x)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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x = self.logits(x)
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return x
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def _create_senet(variant, pretrained=False, **kwargs):
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return build_model_with_cfg(SENet, variant, pretrained, **kwargs)
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@register_model
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def legacy_seresnet18(pretrained=False, **kwargs):
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model_args = dict(
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block=SEResNetBlock, layers=[2, 2, 2, 2], groups=1, reduction=16, **kwargs)
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return _create_senet('legacy_seresnet18', pretrained, **model_args)
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@register_model
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def legacy_seresnet34(pretrained=False, **kwargs):
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model_args = dict(
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block=SEResNetBlock, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs)
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return _create_senet('legacy_seresnet34', pretrained, **model_args)
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@register_model
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def legacy_seresnet50(pretrained=False, **kwargs):
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model_args = dict(
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block=SEResNetBottleneck, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs)
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return _create_senet('legacy_seresnet50', pretrained, **model_args)
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@register_model
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def legacy_seresnet101(pretrained=False, **kwargs):
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model_args = dict(
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block=SEResNetBottleneck, layers=[3, 4, 23, 3], groups=1, reduction=16, **kwargs)
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return _create_senet('legacy_seresnet101', pretrained, **model_args)
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@register_model
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def legacy_seresnet152(pretrained=False, **kwargs):
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model_args = dict(
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block=SEResNetBottleneck, layers=[3, 8, 36, 3], groups=1, reduction=16, **kwargs)
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return _create_senet('legacy_seresnet152', pretrained, **model_args)
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@register_model
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def legacy_senet154(pretrained=False, **kwargs):
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model_args = dict(
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block=SEBottleneck, layers=[3, 8, 36, 3], groups=64, reduction=16,
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downsample_kernel_size=3, downsample_padding=1, inplanes=128, input_3x3=True, **kwargs)
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return _create_senet('legacy_senet154', pretrained, **model_args)
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@register_model
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def legacy_seresnext26_32x4d(pretrained=False, **kwargs):
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model_args = dict(
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block=SEResNeXtBottleneck, layers=[2, 2, 2, 2], groups=32, reduction=16, **kwargs)
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return _create_senet('legacy_seresnext26_32x4d', pretrained, **model_args)
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|
|
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@register_model
|
|
def legacy_seresnext50_32x4d(pretrained=False, **kwargs):
|
|
model_args = dict(
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|
block=SEResNeXtBottleneck, layers=[3, 4, 6, 3], groups=32, reduction=16, **kwargs)
|
|
return _create_senet('legacy_seresnext50_32x4d', pretrained, **model_args)
|
|
|
|
|
|
@register_model
|
|
def legacy_seresnext101_32x4d(pretrained=False, **kwargs):
|
|
model_args = dict(
|
|
block=SEResNeXtBottleneck, layers=[3, 4, 23, 3], groups=32, reduction=16, **kwargs)
|
|
return _create_senet('legacy_seresnext101_32x4d', pretrained, **model_args)
|