""" SEResNet implementation from Cadene's pretrained models https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py Additional credit to https://github.com/creafz Original model: https://github.com/hujie-frank/SENet ResNet code gently borrowed from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py FIXME I'm deprecating this model and moving them to ResNet as I don't want to maintain duplicate support for extras like dilation, switchable BN/activations, feature extraction, etc that don't exist here. """ import math from collections import OrderedDict import torch import torch.nn as nn import torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .helpers import build_model_with_cfg from .layers import create_classifier from .registry import register_model __all__ = ['SENet'] def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), 'crop_pct': 0.875, 'interpolation': 'bilinear', 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'first_conv': 'layer0.conv1', 'classifier': 'last_linear', **kwargs } default_cfgs = { 'legacy_senet154': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_senet154-e9eb9fe6.pth'), 'legacy_seresnet18': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet18-4bb0ce65.pth', interpolation='bicubic'), 'legacy_seresnet34': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet34-a4004e63.pth'), 'legacy_seresnet50': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet50-ce0d4300.pth'), 'legacy_seresnet101': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet101-7e38fcc6.pth'), 'legacy_seresnet152': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet152-d17c99b7.pth'), 'legacy_seresnext26_32x4d': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pth', interpolation='bicubic'), 'legacy_seresnext50_32x4d': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_se_resnext50_32x4d-f3651bad.pth'), 'legacy_seresnext101_32x4d': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_se_resnext101_32x4d-37725eac.pth'), } def _weight_init(m): 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.) class SEModule(nn.Module): def __init__(self, channels, reduction): super(SEModule, self).__init__() self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1) self.sigmoid = nn.Sigmoid() def forward(self, x): module_input = x x = x.mean((2, 3), keepdim=True) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.sigmoid(x) return module_input * x class Bottleneck(nn.Module): """ Base class for bottlenecks that implements `forward()` method. """ def forward(self, x): shortcut = 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: shortcut = self.downsample(x) out = self.se_module(out) + shortcut out = self.relu(out) return out class SEBottleneck(Bottleneck): """ Bottleneck for SENet154. """ expansion = 4 def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None): super(SEBottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes * 2) self.conv2 = nn.Conv2d( planes * 2, planes * 4, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False) self.bn2 = nn.BatchNorm2d(planes * 4) self.conv3 = nn.Conv2d(planes * 4, 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 class SEResNetBottleneck(Bottleneck): """ ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe implementation and uses `stride=stride` in `conv1` and not in `conv2` (the latter is used in the torchvision implementation of ResNet). """ expansion = 4 def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None): super(SEResNetBottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False, stride=stride) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, groups=groups, 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.se_module = SEModule(planes * 4, reduction=reduction) self.downsample = downsample self.stride = stride class SEResNeXtBottleneck(Bottleneck): """ 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 class SEResNetBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None): super(SEResNetBlock, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1, stride=stride, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, groups=groups, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.se_module = SEModule(planes, reduction=reduction) self.downsample = downsample self.stride = stride def forward(self, x): shortcut = 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) if self.downsample is not None: shortcut = self.downsample(x) out = self.se_module(out) + shortcut out = self.relu(out) return out class SENet(nn.Module): def __init__( self, block, layers, groups, reduction, drop_rate=0.2, in_chans=3, inplanes=64, input_3x3=False, downsample_kernel_size=1, downsample_padding=0, num_classes=1000, global_pool='avg'): """ Parameters ---------- block (nn.Module): Bottleneck class. - For SENet154: SEBottleneck - For SE-ResNet models: SEResNetBottleneck - For SE-ResNeXt models: SEResNeXtBottleneck layers (list of ints): Number of residual blocks for 4 layers of the network (layer1...layer4). groups (int): Number of groups for the 3x3 convolution in each bottleneck block. - For SENet154: 64 - For SE-ResNet models: 1 - For SE-ResNeXt models: 32 reduction (int): Reduction ratio for Squeeze-and-Excitation modules. - For all models: 16 dropout_p (float or None): Drop probability for the Dropout layer. If `None` the Dropout layer is not used. - For SENet154: 0.2 - For SE-ResNet models: None - For SE-ResNeXt models: None inplanes (int): Number of input channels for layer1. - For SENet154: 128 - For SE-ResNet models: 64 - For SE-ResNeXt models: 64 input_3x3 (bool): If `True`, use three 3x3 convolutions instead of a single 7x7 convolution in layer0. - For SENet154: True - For SE-ResNet models: False - For SE-ResNeXt models: False downsample_kernel_size (int): Kernel size for downsampling convolutions in layer2, layer3 and layer4. - For SENet154: 3 - For SE-ResNet models: 1 - For SE-ResNeXt models: 1 downsample_padding (int): Padding for downsampling convolutions in layer2, layer3 and layer4. - For SENet154: 1 - For SE-ResNet models: 0 - For SE-ResNeXt models: 0 num_classes (int): Number of outputs in `last_linear` layer. - For all models: 1000 """ super(SENet, self).__init__() self.inplanes = inplanes self.num_classes = num_classes self.drop_rate = drop_rate if input_3x3: layer0_modules = [ ('conv1', nn.Conv2d(in_chans, 64, 3, stride=2, padding=1, bias=False)), ('bn1', nn.BatchNorm2d(64)), ('relu1', nn.ReLU(inplace=True)), ('conv2', nn.Conv2d(64, 64, 3, stride=1, padding=1, bias=False)), ('bn2', nn.BatchNorm2d(64)), ('relu2', nn.ReLU(inplace=True)), ('conv3', nn.Conv2d(64, inplanes, 3, stride=1, padding=1, bias=False)), ('bn3', nn.BatchNorm2d(inplanes)), ('relu3', nn.ReLU(inplace=True)), ] else: layer0_modules = [ ('conv1', nn.Conv2d( in_chans, inplanes, kernel_size=7, stride=2, padding=3, bias=False)), ('bn1', nn.BatchNorm2d(inplanes)), ('relu1', nn.ReLU(inplace=True)), ] self.layer0 = nn.Sequential(OrderedDict(layer0_modules)) # To preserve compatibility with Caffe weights `ceil_mode=True` is used instead of `padding=1`. self.pool0 = nn.MaxPool2d(3, stride=2, ceil_mode=True) self.feature_info = [dict(num_chs=inplanes, reduction=2, module='layer0')] self.layer1 = self._make_layer( block, planes=64, blocks=layers[0], groups=groups, reduction=reduction, downsample_kernel_size=1, downsample_padding=0 ) self.feature_info += [dict(num_chs=64 * block.expansion, reduction=4, module='layer1')] 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.feature_info += [dict(num_chs=128 * block.expansion, reduction=8, module='layer2')] 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.feature_info += [dict(num_chs=256 * block.expansion, reduction=16, module='layer3')] 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.feature_info += [dict(num_chs=512 * block.expansion, reduction=32, module='layer4')] self.num_features = 512 * block.expansion self.global_pool, self.last_linear = create_classifier( self.num_features, self.num_classes, pool_type=global_pool) for m in self.modules(): _weight_init(m) 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) @torch.jit.ignore def group_matcher(self, coarse=False): matcher = dict(stem=r'^layer0', blocks=r'^layer(\d+)' if coarse else r'^layer(\d+)\.(\d+)') return matcher @torch.jit.ignore def set_grad_checkpointing(self, enable=True): assert not enable, 'gradient checkpointing not supported' @torch.jit.ignore def get_classifier(self): return self.last_linear def reset_classifier(self, num_classes, global_pool='avg'): self.num_classes = num_classes self.global_pool, self.last_linear = create_classifier( self.num_features, self.num_classes, pool_type=global_pool) def forward_features(self, x): x = self.layer0(x) x = self.pool0(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) return x def forward_head(self, x, pre_logits: bool = False): x = self.global_pool(x) if self.drop_rate > 0.: x = F.dropout(x, p=self.drop_rate, training=self.training) return x if pre_logits else self.last_linear(x) def forward(self, x): x = self.forward_features(x) x = self.forward_head(x) return x def _create_senet(variant, pretrained=False, **kwargs): return build_model_with_cfg(SENet, variant, pretrained, **kwargs) @register_model def legacy_seresnet18(pretrained=False, **kwargs): model_args = dict( block=SEResNetBlock, layers=[2, 2, 2, 2], groups=1, reduction=16, **kwargs) return _create_senet('legacy_seresnet18', pretrained, **model_args) @register_model def legacy_seresnet34(pretrained=False, **kwargs): model_args = dict( block=SEResNetBlock, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs) return _create_senet('legacy_seresnet34', pretrained, **model_args) @register_model def legacy_seresnet50(pretrained=False, **kwargs): model_args = dict( block=SEResNetBottleneck, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs) return _create_senet('legacy_seresnet50', pretrained, **model_args) @register_model def legacy_seresnet101(pretrained=False, **kwargs): model_args = dict( block=SEResNetBottleneck, layers=[3, 4, 23, 3], groups=1, reduction=16, **kwargs) return _create_senet('legacy_seresnet101', pretrained, **model_args) @register_model def legacy_seresnet152(pretrained=False, **kwargs): model_args = dict( block=SEResNetBottleneck, layers=[3, 8, 36, 3], groups=1, reduction=16, **kwargs) return _create_senet('legacy_seresnet152', pretrained, **model_args) @register_model def legacy_senet154(pretrained=False, **kwargs): model_args = dict( block=SEBottleneck, layers=[3, 8, 36, 3], groups=64, reduction=16, downsample_kernel_size=3, downsample_padding=1, inplanes=128, input_3x3=True, **kwargs) return _create_senet('legacy_senet154', pretrained, **model_args) @register_model def legacy_seresnext26_32x4d(pretrained=False, **kwargs): model_args = dict( block=SEResNeXtBottleneck, layers=[2, 2, 2, 2], groups=32, reduction=16, **kwargs) return _create_senet('legacy_seresnext26_32x4d', pretrained, **model_args) @register_model def legacy_seresnext50_32x4d(pretrained=False, **kwargs): model_args = dict( 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)