"""PyTorch ResNet This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with additional dropout and dynamic global avg/max pool. ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman """ import math import torch import torch.nn as nn import torch.nn.functional as F from .registry import register_model from .helpers import load_pretrained from .adaptive_avgmax_pool import SelectAdaptivePool2d from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD __all__ = ['ResNet', 'BasicBlock', 'Bottleneck'] # model_registry will add each entrypoint fn to this 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': 'conv1', 'classifier': 'fc', **kwargs } default_cfgs = { 'resnet18': _cfg(url='https://download.pytorch.org/models/resnet18-5c106cde.pth'), 'resnet34': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.pth'), 'resnet26': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26-9aa10e23.pth', interpolation='bicubic'), 'resnet26d': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26d-69e92c46.pth', interpolation='bicubic'), 'resnet50': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50_ram-a26f946b.pth', interpolation='bicubic'), 'resnet50d': _cfg( url='', interpolation='bicubic'), 'resnet101': _cfg(url='https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'), 'resnet152': _cfg(url='https://download.pytorch.org/models/resnet152-b121ed2d.pth'), 'tv_resnet34': _cfg(url='https://download.pytorch.org/models/resnet34-333f7ec4.pth'), 'tv_resnet50': _cfg(url='https://download.pytorch.org/models/resnet50-19c8e357.pth'), 'wide_resnet50_2': _cfg(url='https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth'), 'wide_resnet101_2': _cfg(url='https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth'), 'resnext50_32x4d': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50_32x4d-068914d1.pth', interpolation='bicubic'), 'resnext50d_32x4d': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50d_32x4d-103e99f8.pth', interpolation='bicubic'), 'resnext101_32x4d': _cfg(url=''), 'resnext101_32x8d': _cfg(url='https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth'), 'resnext101_64x4d': _cfg(url=''), 'tv_resnext50_32x4d': _cfg(url='https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth'), 'ig_resnext101_32x8d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth'), 'ig_resnext101_32x16d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth'), 'ig_resnext101_32x32d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth'), 'ig_resnext101_32x48d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth'), 'ssl_resnet18': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth'), 'ssl_resnet50': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth'), 'ssl_resnext50_32x4d': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth'), 'ssl_resnext101_32x4d': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth'), 'ssl_resnext101_32x8d': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth'), 'ssl_resnext101_32x16d': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth'), 'swsl_resnet18': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth'), 'swsl_resnet50': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth'), 'swsl_resnext50_32x4d': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth'), 'swsl_resnext101_32x4d': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth'), 'swsl_resnext101_32x8d': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth'), 'swsl_resnext101_32x16d': _cfg( url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth'), 'seresnext26d_32x4d': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26d_32x4d-80fa48a3.pth', interpolation='bicubic'), 'seresnext26t_32x4d': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26t_32x4d-361bc1c4.pth', interpolation='bicubic'), 'seresnext26tn_32x4d': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26tn_32x4d-569cb627.pth', interpolation='bicubic'), } def _get_padding(kernel_size, stride, dilation=1): padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2 return padding class SEModule(nn.Module): def __init__(self, channels, reduction_channels): super(SEModule, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc1 = nn.Conv2d( channels, reduction_channels, kernel_size=1, padding=0, bias=True) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Conv2d( reduction_channels, channels, kernel_size=1, padding=0, bias=True) def forward(self, x): x_se = self.avg_pool(x) x_se = self.fc1(x_se) x_se = self.relu(x_se) x_se = self.fc2(x_se) return x * x_se.sigmoid() class BasicBlock(nn.Module): __constants__ = ['se', 'downsample'] # for pre 1.4 torchscript compat expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64, use_se=False, reduce_first=1, dilation=1, previous_dilation=1, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d): super(BasicBlock, self).__init__() assert cardinality == 1, 'BasicBlock only supports cardinality of 1' assert base_width == 64, 'BasicBlock doest not support changing base width' first_planes = planes // reduce_first outplanes = planes * self.expansion self.conv1 = nn.Conv2d( inplanes, first_planes, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, bias=False) self.bn1 = norm_layer(first_planes) self.act1 = act_layer(inplace=True) self.conv2 = nn.Conv2d( first_planes, outplanes, kernel_size=3, padding=previous_dilation, dilation=previous_dilation, bias=False) self.bn2 = norm_layer(outplanes) self.se = SEModule(outplanes, planes // 4) if use_se else None self.act2 = act_layer(inplace=True) self.downsample = downsample self.stride = stride self.dilation = dilation def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.act1(out) out = self.conv2(out) out = self.bn2(out) if self.se is not None: out = self.se(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.act2(out) return out class Bottleneck(nn.Module): __constants__ = ['se', 'downsample'] # for pre 1.4 torchscript compat expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64, use_se=False, reduce_first=1, dilation=1, previous_dilation=1, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d): super(Bottleneck, self).__init__() width = int(math.floor(planes * (base_width / 64)) * cardinality) first_planes = width // reduce_first outplanes = planes * self.expansion self.conv1 = nn.Conv2d(inplanes, first_planes, kernel_size=1, bias=False) self.bn1 = norm_layer(first_planes) self.act1 = act_layer(inplace=True) self.conv2 = nn.Conv2d( first_planes, width, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, groups=cardinality, bias=False) self.bn2 = norm_layer(width) self.act2 = act_layer(inplace=True) self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False) self.bn3 = norm_layer(outplanes) self.se = SEModule(outplanes, planes // 4) if use_se else None self.act3 = act_layer(inplace=True) self.downsample = downsample self.stride = stride self.dilation = dilation def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.act1(out) out = self.conv2(out) out = self.bn2(out) out = self.act2(out) out = self.conv3(out) out = self.bn3(out) if self.se is not None: out = self.se(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.act3(out) return out class ResNet(nn.Module): """ResNet / ResNeXt / SE-ResNeXt / SE-Net This class implements all variants of ResNet, ResNeXt, SE-ResNeXt, and SENet that * have > 1 stride in the 3x3 conv layer of bottleneck * have conv-bn-act ordering This ResNet impl supports a number of stem and downsample options based on the v1c, v1d, v1e, and v1s variants included in the MXNet Gluon ResNetV1b model. The C and D variants are also discussed in the 'Bag of Tricks' paper: https://arxiv.org/pdf/1812.01187. The B variant is equivalent to torchvision default. ResNet variants (the same modifications can be used in SE/ResNeXt models as well): * normal, b - 7x7 stem, stem_width = 64, same as torchvision ResNet, NVIDIA ResNet 'v1.5', Gluon v1b * c - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64) * d - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64), average pool in downsample * e - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128), average pool in downsample * s - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128) * t - 3 layer deep 3x3 stem, stem width = 32 (24, 48, 64), average pool in downsample * tn - 3 layer deep 3x3 stem, stem width = 32 (24, 32, 64), average pool in downsample ResNeXt * normal - 7x7 stem, stem_width = 64, standard cardinality and base widths * same c,d, e, s variants as ResNet can be enabled SE-ResNeXt * normal - 7x7 stem, stem_width = 64 * same c, d, e, s variants as ResNet can be enabled SENet-154 - 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64, reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block Parameters ---------- block : Block Class for the residual block. Options are BasicBlockGl, BottleneckGl. layers : list of int Numbers of layers in each block num_classes : int, default 1000 Number of classification classes. in_chans : int, default 3 Number of input (color) channels. use_se : bool, default False Enable Squeeze-Excitation module in blocks cardinality : int, default 1 Number of convolution groups for 3x3 conv in Bottleneck. base_width : int, default 64 Factor determining bottleneck channels. `planes * base_width / 64 * cardinality` stem_width : int, default 64 Number of channels in stem convolutions stem_type : str, default '' The type of stem: * '', default - a single 7x7 conv with a width of stem_width * 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2 * 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width//4 * 6, stem_width * 2 * 'deep_tiered_narrow' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2 block_reduce_first: int, default 1 Reduction factor for first convolution output width of residual blocks, 1 for all archs except senets, where 2 down_kernel_size: int, default 1 Kernel size of residual block downsampling path, 1x1 for most archs, 3x3 for senets avg_down : bool, default False Whether to use average pooling for projection skip connection between stages/downsample. output_stride : int, default 32 Set the output stride of the network, 32, 16, or 8. Typically used in segmentation. act_layer : class, activation layer norm_layer : class, normalization layer drop_rate : float, default 0. Dropout probability before classifier, for training global_pool : str, default 'avg' Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' """ def __init__(self, block, layers, num_classes=1000, in_chans=3, use_se=False, cardinality=1, base_width=64, stem_width=64, stem_type='', block_reduce_first=1, down_kernel_size=1, avg_down=False, output_stride=32, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, drop_rate=0.0, global_pool='avg', zero_init_last_bn=True, block_args=None): block_args = block_args or dict() self.num_classes = num_classes deep_stem = 'deep' in stem_type self.inplanes = stem_width * 2 if deep_stem else 64 self.cardinality = cardinality self.base_width = base_width self.drop_rate = drop_rate self.expansion = block.expansion super(ResNet, self).__init__() # Stem if deep_stem: stem_chs_1 = stem_chs_2 = stem_width if 'tiered' in stem_type: stem_chs_1 = 3 * (stem_width // 4) stem_chs_2 = stem_width if 'narrow' in stem_type else 6 * (stem_width // 4) self.conv1 = nn.Sequential(*[ nn.Conv2d(in_chans, stem_chs_1, 3, stride=2, padding=1, bias=False), norm_layer(stem_chs_1), act_layer(inplace=True), nn.Conv2d(stem_chs_1, stem_chs_2, 3, stride=1, padding=1, bias=False), norm_layer(stem_chs_2), act_layer(inplace=True), nn.Conv2d(stem_chs_2, self.inplanes, 3, stride=1, padding=1, bias=False)]) else: self.conv1 = nn.Conv2d(in_chans, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = norm_layer(self.inplanes) self.act1 = act_layer(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # Feature Blocks channels, strides, dilations = [64, 128, 256, 512], [1, 2, 2, 2], [1] * 4 if output_stride == 16: strides[3] = 1 dilations[3] = 2 elif output_stride == 8: strides[2:4] = [1, 1] dilations[2:4] = [2, 4] else: assert output_stride == 32 llargs = list(zip(channels, layers, strides, dilations)) lkwargs = dict( use_se=use_se, reduce_first=block_reduce_first, act_layer=act_layer, norm_layer=norm_layer, avg_down=avg_down, down_kernel_size=down_kernel_size, **block_args) self.layer1 = self._make_layer(block, *llargs[0], **lkwargs) self.layer2 = self._make_layer(block, *llargs[1], **lkwargs) self.layer3 = self._make_layer(block, *llargs[2], **lkwargs) self.layer4 = self._make_layer(block, *llargs[3], **lkwargs) # Head (Pooling and Classifier) self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) self.num_features = 512 * block.expansion self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes) last_bn_name = 'bn3' if 'Bottle' in block.__name__ else 'bn2' for n, m in self.named_modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): if zero_init_last_bn and 'layer' in n and last_bn_name in n: # Initialize weight/gamma of last BN in each residual block to zero nn.init.constant_(m.weight, 0.) else: nn.init.constant_(m.weight, 1.) nn.init.constant_(m.bias, 0.) def _make_layer(self, block, planes, blocks, stride=1, dilation=1, reduce_first=1, use_se=False, avg_down=False, down_kernel_size=1, **kwargs): norm_layer = kwargs.get('norm_layer') downsample = None down_kernel_size = 1 if stride == 1 and dilation == 1 else down_kernel_size if stride != 1 or self.inplanes != planes * block.expansion: downsample_padding = _get_padding(down_kernel_size, stride) downsample_layers = [] conv_stride = stride if avg_down: avg_stride = stride if dilation == 1 else 1 conv_stride = 1 downsample_layers = [nn.AvgPool2d(avg_stride, avg_stride, ceil_mode=True, count_include_pad=False)] downsample_layers += [ nn.Conv2d(self.inplanes, planes * block.expansion, down_kernel_size, stride=conv_stride, padding=downsample_padding, bias=False), norm_layer(planes * block.expansion)] downsample = nn.Sequential(*downsample_layers) first_dilation = 1 if dilation in (1, 2) else 2 bkwargs = dict( cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first, use_se=use_se, **kwargs) layers = [block( self.inplanes, planes, stride, downsample, dilation=first_dilation, previous_dilation=dilation, **bkwargs)] self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block( self.inplanes, planes, dilation=dilation, previous_dilation=dilation, **bkwargs)) return nn.Sequential(*layers) def get_classifier(self): return self.fc def reset_classifier(self, num_classes, global_pool='avg'): self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) self.num_classes = num_classes del self.fc self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes) if num_classes else None def forward_features(self, x): x = self.conv1(x) x = self.bn1(x) x = self.act1(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) return x def forward(self, x): x = self.forward_features(x) x = self.global_pool(x).flatten(1) if self.drop_rate > 0.: x = F.dropout(x, p=self.drop_rate, training=self.training) x = self.fc(x) return x @register_model def resnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-18 model. """ default_cfg = default_cfgs['resnet18'] model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def resnet34(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-34 model. """ default_cfg = default_cfgs['resnet34'] model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def resnet26(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-26 model. """ default_cfg = default_cfgs['resnet26'] model = ResNet(Bottleneck, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def resnet26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-26 v1d model. This is technically a 28 layer ResNet, sticking with 'd' modifier from Gluon for now. """ default_cfg = default_cfgs['resnet26d'] model = ResNet( Bottleneck, [2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def resnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-50 model. """ default_cfg = default_cfgs['resnet50'] model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def resnet50d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-50-D model. """ default_cfg = default_cfgs['resnet50d'] model = ResNet( Bottleneck, [3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def resnet101(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-101 model. """ default_cfg = default_cfgs['resnet101'] model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def resnet152(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-152 model. """ default_cfg = default_cfgs['resnet152'] model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def tv_resnet34(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-34 model with original Torchvision weights. """ model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfgs['tv_resnet34'] if pretrained: load_pretrained(model, model.default_cfg, num_classes, in_chans) return model @register_model def tv_resnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNet-50 model with original Torchvision weights. """ model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfgs['tv_resnet50'] if pretrained: load_pretrained(model, model.default_cfg, num_classes, in_chans) return model @register_model def wide_resnet50_2(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a Wide ResNet-50-2 model. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. """ model = ResNet( Bottleneck, [3, 4, 6, 3], base_width=128, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfgs['wide_resnet50_2'] if pretrained: load_pretrained(model, model.default_cfg, num_classes, in_chans) return model @register_model def wide_resnet101_2(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a Wide ResNet-101-2 model. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same. """ model = ResNet( Bottleneck, [3, 4, 23, 3], base_width=128, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfgs['wide_resnet101_2'] if pretrained: load_pretrained(model, model.default_cfg, num_classes, in_chans) return model @register_model def resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt50-32x4d model. """ default_cfg = default_cfgs['resnext50_32x4d'] model = ResNet( Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def resnext50d_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt50d-32x4d model. ResNext50 w/ deep stem & avg pool downsample """ default_cfg = default_cfgs['resnext50d_32x4d'] model = ResNet( Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, stem_width=32, stem_type='deep', avg_down=True, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def resnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt-101 32x4d model. """ default_cfg = default_cfgs['resnext101_32x4d'] model = ResNet( Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def resnext101_32x8d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt-101 32x8d model. """ default_cfg = default_cfgs['resnext101_32x8d'] model = ResNet( Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=8, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def resnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt101-64x4d model. """ default_cfg = default_cfgs['resnext101_32x4d'] model = ResNet( Bottleneck, [3, 4, 23, 3], cardinality=64, base_width=4, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def tv_resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a ResNeXt50-32x4d model with original Torchvision weights. """ default_cfg = default_cfgs['tv_resnext50_32x4d'] model = ResNet( Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def ig_resnext101_32x8d(pretrained=True, **kwargs): """Constructs a ResNeXt-101 32x8 model pre-trained on weakly-supervised data and finetuned on ImageNet from Figure 5 in `"Exploring the Limits of Weakly Supervised Pretraining" `_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ """ model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=8, **kwargs) model.default_cfg = default_cfgs['ig_resnext101_32x8d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def ig_resnext101_32x16d(pretrained=True, **kwargs): """Constructs a ResNeXt-101 32x16 model pre-trained on weakly-supervised data and finetuned on ImageNet from Figure 5 in `"Exploring the Limits of Weakly Supervised Pretraining" `_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ """ model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=16, **kwargs) model.default_cfg = default_cfgs['ig_resnext101_32x16d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def ig_resnext101_32x32d(pretrained=True, **kwargs): """Constructs a ResNeXt-101 32x32 model pre-trained on weakly-supervised data and finetuned on ImageNet from Figure 5 in `"Exploring the Limits of Weakly Supervised Pretraining" `_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ """ model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=32, **kwargs) model.default_cfg = default_cfgs['ig_resnext101_32x32d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def ig_resnext101_32x48d(pretrained=True, **kwargs): """Constructs a ResNeXt-101 32x48 model pre-trained on weakly-supervised data and finetuned on ImageNet from Figure 5 in `"Exploring the Limits of Weakly Supervised Pretraining" `_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ """ model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=48, **kwargs) model.default_cfg = default_cfgs['ig_resnext101_32x48d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def ssl_resnet18(pretrained=True, **kwargs): """Constructs a semi-supervised ResNet-18 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) model.default_cfg = default_cfgs['ssl_resnet18'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def ssl_resnet50(pretrained=True, **kwargs): """Constructs a semi-supervised ResNet-50 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) model.default_cfg = default_cfgs['ssl_resnet50'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def ssl_resnext50_32x4d(pretrained=True, **kwargs): """Constructs a semi-supervised ResNeXt-50 32x4 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, **kwargs) model.default_cfg = default_cfgs['ssl_resnext50_32x4d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def ssl_resnext101_32x4d(pretrained=True, **kwargs): """Constructs a semi-supervised ResNeXt-101 32x4 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4, **kwargs) model.default_cfg = default_cfgs['ssl_resnext101_32x4d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def ssl_resnext101_32x8d(pretrained=True, **kwargs): """Constructs a semi-supervised ResNeXt-101 32x8 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=8, **kwargs) model.default_cfg = default_cfgs['ssl_resnext101_32x8d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def ssl_resnext101_32x16d(pretrained=True, **kwargs): """Constructs a semi-supervised ResNeXt-101 32x16 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=16, **kwargs) model.default_cfg = default_cfgs['ssl_resnext101_32x16d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def swsl_resnet18(pretrained=True, **kwargs): """Constructs a semi-weakly supervised Resnet-18 model pre-trained on 1B weakly supervised image dataset and finetuned on ImageNet. `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) model.default_cfg = default_cfgs['swsl_resnet18'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def swsl_resnet50(pretrained=True, **kwargs): """Constructs a semi-weakly supervised ResNet-50 model pre-trained on 1B weakly supervised image dataset and finetuned on ImageNet. `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) model.default_cfg = default_cfgs['swsl_resnet50'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def swsl_resnext50_32x4d(pretrained=True, **kwargs): """Constructs a semi-weakly supervised ResNeXt-50 32x4 model pre-trained on 1B weakly supervised image dataset and finetuned on ImageNet. `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, **kwargs) model.default_cfg = default_cfgs['swsl_resnext50_32x4d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def swsl_resnext101_32x4d(pretrained=True, **kwargs): """Constructs a semi-weakly supervised ResNeXt-101 32x4 model pre-trained on 1B weakly supervised image dataset and finetuned on ImageNet. `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4, **kwargs) model.default_cfg = default_cfgs['swsl_resnext101_32x4d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def swsl_resnext101_32x8d(pretrained=True, **kwargs): """Constructs a semi-weakly supervised ResNeXt-101 32x8 model pre-trained on 1B weakly supervised image dataset and finetuned on ImageNet. `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=8, **kwargs) model.default_cfg = default_cfgs['swsl_resnext101_32x8d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def swsl_resnext101_32x16d(pretrained=True, **kwargs): """Constructs a semi-weakly supervised ResNeXt-101 32x16 model pre-trained on 1B weakly supervised image dataset and finetuned on ImageNet. `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=16, **kwargs) model.default_cfg = default_cfgs['swsl_resnext101_32x16d'] if pretrained: load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3)) return model @register_model def seresnext26d_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a SE-ResNeXt-26-D model. This is technically a 28 layer ResNet, using the 'D' modifier from Gluon / bag-of-tricks for combination of deep stem and avg_pool in downsample. """ default_cfg = default_cfgs['seresnext26d_32x4d'] model = ResNet( Bottleneck, [2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32, stem_type='deep', avg_down=True, use_se=True, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def seresnext26t_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a SE-ResNet-26-T model. This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 48, 64 channels in the deep stem. """ default_cfg = default_cfgs['seresnext26t_32x4d'] model = ResNet( Bottleneck, [2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32, stem_type='deep_tiered', avg_down=True, use_se=True, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model @register_model def seresnext26tn_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): """Constructs a SE-ResNeXt-26-TN model. This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem. The channel number of the middle stem conv is narrower than the 'T' variant. """ default_cfg = default_cfgs['seresnext26tn_32x4d'] model = ResNet( Bottleneck, [2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32, stem_type='deep_tiered_narrow', avg_down=True, use_se=True, num_classes=num_classes, in_chans=in_chans, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained(model, default_cfg, num_classes, in_chans) return model