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"""PyTorch ResNet
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This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
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additional dropout and dynamic global avg/max pool.
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ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
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Copyright 2020 Ross Wightman
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"""
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import math
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from functools import partial
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import torch
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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 DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, create_attn, get_attn, create_classifier
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from .registry import register_model
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__all__ = ['ResNet', 'BasicBlock', 'Bottleneck'] # model_registry will add each entrypoint fn to this
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'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': 'conv1', 'classifier': 'fc',
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**kwargs
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}
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default_cfgs = {
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# ResNet and Wide ResNet
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'resnet18': _cfg(url='https://download.pytorch.org/models/resnet18-5c106cde.pth'),
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'resnet18d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet18d_ra2-48a79e06.pth',
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interpolation='bicubic', first_conv='conv1.0'),
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'resnet34': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.pth'),
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'resnet34d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34d_ra2-f8dcfcaf.pth',
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interpolation='bicubic', first_conv='conv1.0'),
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'resnet26': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26-9aa10e23.pth',
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interpolation='bicubic'),
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'resnet26d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26d-69e92c46.pth',
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interpolation='bicubic', first_conv='conv1.0'),
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'resnet50': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50_ram-a26f946b.pth',
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interpolation='bicubic'),
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'resnet50d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50d_ra2-464e36ba.pth',
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interpolation='bicubic', first_conv='conv1.0'),
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'resnet50t': _cfg(
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url='',
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interpolation='bicubic', first_conv='conv1.0'),
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'resnet101': _cfg(url='', interpolation='bicubic'),
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'resnet101d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=1.0, test_input_size=(3, 320, 320)),
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'resnet152': _cfg(url='', interpolation='bicubic'),
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'resnet152d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=1.0, test_input_size=(3, 320, 320)),
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'resnet200': _cfg(url='', interpolation='bicubic'),
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'resnet200d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=1.0, test_input_size=(3, 320, 320)),
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'tv_resnet34': _cfg(url='https://download.pytorch.org/models/resnet34-333f7ec4.pth'),
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'tv_resnet50': _cfg(url='https://download.pytorch.org/models/resnet50-19c8e357.pth'),
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'tv_resnet101': _cfg(url='https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'),
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'tv_resnet152': _cfg(url='https://download.pytorch.org/models/resnet152-b121ed2d.pth'),
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'wide_resnet50_2': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/wide_resnet50_racm-8234f177.pth',
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interpolation='bicubic'),
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'wide_resnet101_2': _cfg(url='https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth'),
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# ResNeXt
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'resnext50_32x4d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50_32x4d_ra-d733960d.pth',
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interpolation='bicubic'),
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'resnext50d_32x4d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50d_32x4d-103e99f8.pth',
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interpolation='bicubic',
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first_conv='conv1.0'),
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'resnext101_32x4d': _cfg(url=''),
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'resnext101_32x8d': _cfg(url='https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth'),
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'resnext101_64x4d': _cfg(url=''),
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'tv_resnext50_32x4d': _cfg(url='https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth'),
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# ResNeXt models - Weakly Supervised Pretraining on Instagram Hashtags
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# from https://github.com/facebookresearch/WSL-Images
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# Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
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'ig_resnext101_32x8d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth'),
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'ig_resnext101_32x16d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth'),
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'ig_resnext101_32x32d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth'),
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'ig_resnext101_32x48d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth'),
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# Semi-Supervised ResNe*t models from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models
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# Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
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'ssl_resnet18': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth'),
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'ssl_resnet50': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth'),
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'ssl_resnext50_32x4d': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth'),
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'ssl_resnext101_32x4d': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth'),
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'ssl_resnext101_32x8d': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth'),
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'ssl_resnext101_32x16d': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth'),
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# Semi-Weakly Supervised ResNe*t models from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models
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# Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
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'swsl_resnet18': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth'),
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'swsl_resnet50': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth'),
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'swsl_resnext50_32x4d': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth'),
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'swsl_resnext101_32x4d': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth'),
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'swsl_resnext101_32x8d': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth'),
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'swsl_resnext101_32x16d': _cfg(
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url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth'),
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# Squeeze-Excitation ResNets, to eventually replace the models in senet.py
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'seresnet18': _cfg(
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url='',
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interpolation='bicubic'),
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'seresnet34': _cfg(
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url='',
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interpolation='bicubic'),
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'seresnet50': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet50_ra_224-8efdb4bb.pth',
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interpolation='bicubic'),
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'seresnet50t': _cfg(
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url='',
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interpolation='bicubic',
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first_conv='conv1.0'),
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'seresnet101': _cfg(
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url='',
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interpolation='bicubic'),
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'seresnet152': _cfg(
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url='',
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interpolation='bicubic'),
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'seresnet152d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=1.0, test_input_size=(3, 320, 320)
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),
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'seresnet200d': _cfg(
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url='',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
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'seresnet269d': _cfg(
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url='',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
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# Squeeze-Excitation ResNeXts, to eventually replace the models in senet.py
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'seresnext26d_32x4d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26d_32x4d-80fa48a3.pth',
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interpolation='bicubic',
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first_conv='conv1.0'),
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'seresnext26t_32x4d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26tn_32x4d-569cb627.pth',
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interpolation='bicubic',
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first_conv='conv1.0'),
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'seresnext50_32x4d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext50_32x4d_racm-a304a460.pth',
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interpolation='bicubic'),
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'seresnext101_32x4d': _cfg(
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url='',
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interpolation='bicubic'),
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'seresnext101_32x8d': _cfg(
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url='',
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interpolation='bicubic'),
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'senet154': _cfg(
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url='',
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interpolation='bicubic',
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first_conv='conv1.0'),
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# Efficient Channel Attention ResNets
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'ecaresnet26t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet26t_ra2-46609757.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=0.95, test_input_size=(3, 320, 320)),
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'ecaresnetlight': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNetLight_4f34b35b.pth',
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interpolation='bicubic'),
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'ecaresnet50d': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet50D_833caf58.pth',
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interpolation='bicubic',
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first_conv='conv1.0'),
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'ecaresnet50d_pruned': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45899/outputs/ECAResNet50D_P_9c67f710.pth',
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interpolation='bicubic',
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first_conv='conv1.0'),
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'ecaresnet50t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet50t_ra2-f7ac63c4.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=0.95, test_input_size=(3, 320, 320)),
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'ecaresnet101d': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet101D_281c5844.pth',
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interpolation='bicubic', first_conv='conv1.0'),
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'ecaresnet101d_pruned': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth',
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interpolation='bicubic',
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first_conv='conv1.0'),
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'ecaresnet200d': _cfg(
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url='',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
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'ecaresnet269d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet269d_320_ra2-7baa55cb.pth',
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), pool_size=(10, 10),
|
|
|
|
|
crop_pct=1.0, test_input_size=(3, 352, 352)),
|
|
|
|
|
|
|
|
|
|
# Efficient Channel Attention ResNeXts
|
|
|
|
|
'ecaresnext26t_32x4d': _cfg(
|
|
|
|
|
url='',
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0'),
|
|
|
|
|
'ecaresnext50t_32x4d': _cfg(
|
|
|
|
|
url='',
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0'),
|
|
|
|
|
|
|
|
|
|
# ResNets with anti-aliasing blur pool
|
|
|
|
|
'resnetblur18': _cfg(
|
|
|
|
|
interpolation='bicubic'),
|
|
|
|
|
'resnetblur50': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnetblur50-84f4748f.pth',
|
|
|
|
|
interpolation='bicubic'),
|
|
|
|
|
|
|
|
|
|
# ResNet-RS models
|
|
|
|
|
'resnetrs50': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs50-7c9728e2.pth',
|
|
|
|
|
input_size=(3, 160, 160), pool_size=(4, 4), crop_pct=0.91, test_input_size=(3, 224, 224),
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0'),
|
|
|
|
|
'resnetrs101': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs101-3e4bb55c.pth',
|
|
|
|
|
input_size=(3, 192, 192), pool_size=(6, 6), crop_pct=0.94, test_input_size=(3, 288, 288),
|
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|
|
|
interpolation='bicubic', first_conv='conv1.0'),
|
|
|
|
|
'resnetrs152': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs152-b1efe56d.pth',
|
|
|
|
|
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320),
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0'),
|
|
|
|
|
'resnetrs200': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs200-b455b791.pth',
|
|
|
|
|
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320),
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0'),
|
|
|
|
|
'resnetrs270': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs270-cafcfbc7.pth',
|
|
|
|
|
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320),
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0'),
|
|
|
|
|
'resnetrs350': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs350-06d9bfac.pth',
|
|
|
|
|
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0, test_input_size=(3, 384, 384),
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0'),
|
|
|
|
|
'resnetrs420': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs420-d26764a5.pth',
|
|
|
|
|
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0, test_input_size=(3, 416, 416),
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0'),
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_padding(kernel_size, stride, dilation=1):
|
|
|
|
|
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
|
|
|
|
|
return padding
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class BasicBlock(nn.Module):
|
|
|
|
|
expansion = 1
|
|
|
|
|
|
|
|
|
|
def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
|
|
|
|
|
reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
|
|
|
|
|
attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
|
|
|
|
|
super(BasicBlock, self).__init__()
|
|
|
|
|
|
|
|
|
|
assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
|
|
|
|
|
assert base_width == 64, 'BasicBlock does not support changing base width'
|
|
|
|
|
first_planes = planes // reduce_first
|
|
|
|
|
outplanes = planes * self.expansion
|
|
|
|
|
first_dilation = first_dilation or dilation
|
|
|
|
|
use_aa = aa_layer is not None and (stride == 2 or first_dilation != dilation)
|
|
|
|
|
|
|
|
|
|
self.conv1 = nn.Conv2d(
|
|
|
|
|
inplanes, first_planes, kernel_size=3, stride=1 if use_aa else stride, padding=first_dilation,
|
|
|
|
|
dilation=first_dilation, bias=False)
|
|
|
|
|
self.bn1 = norm_layer(first_planes)
|
|
|
|
|
self.act1 = act_layer(inplace=True)
|
|
|
|
|
self.aa = aa_layer(channels=first_planes, stride=stride) if use_aa else None
|
|
|
|
|
|
|
|
|
|
self.conv2 = nn.Conv2d(
|
|
|
|
|
first_planes, outplanes, kernel_size=3, padding=dilation, dilation=dilation, bias=False)
|
|
|
|
|
self.bn2 = norm_layer(outplanes)
|
|
|
|
|
|
|
|
|
|
self.se = create_attn(attn_layer, outplanes)
|
|
|
|
|
|
|
|
|
|
self.act2 = act_layer(inplace=True)
|
|
|
|
|
self.downsample = downsample
|
|
|
|
|
self.stride = stride
|
|
|
|
|
self.dilation = dilation
|
|
|
|
|
self.drop_block = drop_block
|
|
|
|
|
self.drop_path = drop_path
|
|
|
|
|
|
|
|
|
|
def zero_init_last_bn(self):
|
|
|
|
|
nn.init.zeros_(self.bn2.weight)
|
|
|
|
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
|
residual = x
|
|
|
|
|
|
|
|
|
|
x = self.conv1(x)
|
|
|
|
|
x = self.bn1(x)
|
|
|
|
|
if self.drop_block is not None:
|
|
|
|
|
x = self.drop_block(x)
|
|
|
|
|
x = self.act1(x)
|
|
|
|
|
if self.aa is not None:
|
|
|
|
|
x = self.aa(x)
|
|
|
|
|
|
|
|
|
|
x = self.conv2(x)
|
|
|
|
|
x = self.bn2(x)
|
|
|
|
|
if self.drop_block is not None:
|
|
|
|
|
x = self.drop_block(x)
|
|
|
|
|
|
|
|
|
|
if self.se is not None:
|
|
|
|
|
x = self.se(x)
|
|
|
|
|
|
|
|
|
|
if self.drop_path is not None:
|
|
|
|
|
x = self.drop_path(x)
|
|
|
|
|
|
|
|
|
|
if self.downsample is not None:
|
|
|
|
|
residual = self.downsample(residual)
|
|
|
|
|
x += residual
|
|
|
|
|
x = self.act2(x)
|
|
|
|
|
|
|
|
|
|
return x
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Bottleneck(nn.Module):
|
|
|
|
|
expansion = 4
|
|
|
|
|
|
|
|
|
|
def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
|
|
|
|
|
reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
|
|
|
|
|
attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
|
|
|
|
|
super(Bottleneck, self).__init__()
|
|
|
|
|
|
|
|
|
|
width = int(math.floor(planes * (base_width / 64)) * cardinality)
|
|
|
|
|
first_planes = width // reduce_first
|
|
|
|
|
outplanes = planes * self.expansion
|
|
|
|
|
first_dilation = first_dilation or dilation
|
|
|
|
|
use_aa = aa_layer is not None and (stride == 2 or first_dilation != dilation)
|
|
|
|
|
|
|
|
|
|
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=1 if use_aa else stride,
|
|
|
|
|
padding=first_dilation, dilation=first_dilation, groups=cardinality, bias=False)
|
|
|
|
|
self.bn2 = norm_layer(width)
|
|
|
|
|
self.act2 = act_layer(inplace=True)
|
|
|
|
|
self.aa = aa_layer(channels=width, stride=stride) if use_aa else None
|
|
|
|
|
|
|
|
|
|
self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
|
|
|
|
|
self.bn3 = norm_layer(outplanes)
|
|
|
|
|
|
|
|
|
|
self.se = create_attn(attn_layer, outplanes)
|
|
|
|
|
|
|
|
|
|
self.act3 = act_layer(inplace=True)
|
|
|
|
|
self.downsample = downsample
|
|
|
|
|
self.stride = stride
|
|
|
|
|
self.dilation = dilation
|
|
|
|
|
self.drop_block = drop_block
|
|
|
|
|
self.drop_path = drop_path
|
|
|
|
|
|
|
|
|
|
def zero_init_last_bn(self):
|
|
|
|
|
nn.init.zeros_(self.bn3.weight)
|
|
|
|
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
|
residual = x
|
|
|
|
|
|
|
|
|
|
x = self.conv1(x)
|
|
|
|
|
x = self.bn1(x)
|
|
|
|
|
if self.drop_block is not None:
|
|
|
|
|
x = self.drop_block(x)
|
|
|
|
|
x = self.act1(x)
|
|
|
|
|
|
|
|
|
|
x = self.conv2(x)
|
|
|
|
|
x = self.bn2(x)
|
|
|
|
|
if self.drop_block is not None:
|
|
|
|
|
x = self.drop_block(x)
|
|
|
|
|
x = self.act2(x)
|
|
|
|
|
if self.aa is not None:
|
|
|
|
|
x = self.aa(x)
|
|
|
|
|
|
|
|
|
|
x = self.conv3(x)
|
|
|
|
|
x = self.bn3(x)
|
|
|
|
|
if self.drop_block is not None:
|
|
|
|
|
x = self.drop_block(x)
|
|
|
|
|
|
|
|
|
|
if self.se is not None:
|
|
|
|
|
x = self.se(x)
|
|
|
|
|
|
|
|
|
|
if self.drop_path is not None:
|
|
|
|
|
x = self.drop_path(x)
|
|
|
|
|
|
|
|
|
|
if self.downsample is not None:
|
|
|
|
|
residual = self.downsample(residual)
|
|
|
|
|
x += residual
|
|
|
|
|
x = self.act3(x)
|
|
|
|
|
|
|
|
|
|
return x
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def downsample_conv(
|
|
|
|
|
in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None):
|
|
|
|
|
norm_layer = norm_layer or nn.BatchNorm2d
|
|
|
|
|
kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size
|
|
|
|
|
first_dilation = (first_dilation or dilation) if kernel_size > 1 else 1
|
|
|
|
|
p = get_padding(kernel_size, stride, first_dilation)
|
|
|
|
|
|
|
|
|
|
return nn.Sequential(*[
|
|
|
|
|
nn.Conv2d(
|
|
|
|
|
in_channels, out_channels, kernel_size, stride=stride, padding=p, dilation=first_dilation, bias=False),
|
|
|
|
|
norm_layer(out_channels)
|
|
|
|
|
])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def downsample_avg(
|
|
|
|
|
in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None):
|
|
|
|
|
norm_layer = norm_layer or nn.BatchNorm2d
|
|
|
|
|
avg_stride = stride if dilation == 1 else 1
|
|
|
|
|
if stride == 1 and dilation == 1:
|
|
|
|
|
pool = nn.Identity()
|
|
|
|
|
else:
|
|
|
|
|
avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
|
|
|
|
|
pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
|
|
|
|
|
|
|
|
|
|
return nn.Sequential(*[
|
|
|
|
|
pool,
|
|
|
|
|
nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False),
|
|
|
|
|
norm_layer(out_channels)
|
|
|
|
|
])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def drop_blocks(drop_block_rate=0.):
|
|
|
|
|
return [
|
|
|
|
|
None, None,
|
|
|
|
|
DropBlock2d(drop_block_rate, 5, 0.25) if drop_block_rate else None,
|
|
|
|
|
DropBlock2d(drop_block_rate, 3, 1.00) if drop_block_rate else None]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def make_blocks(
|
|
|
|
|
block_fn, channels, block_repeats, inplanes, reduce_first=1, output_stride=32,
|
|
|
|
|
down_kernel_size=1, avg_down=False, drop_block_rate=0., drop_path_rate=0., **kwargs):
|
|
|
|
|
stages = []
|
|
|
|
|
feature_info = []
|
|
|
|
|
net_num_blocks = sum(block_repeats)
|
|
|
|
|
net_block_idx = 0
|
|
|
|
|
net_stride = 4
|
|
|
|
|
dilation = prev_dilation = 1
|
|
|
|
|
for stage_idx, (planes, num_blocks, db) in enumerate(zip(channels, block_repeats, drop_blocks(drop_block_rate))):
|
|
|
|
|
stage_name = f'layer{stage_idx + 1}' # never liked this name, but weight compat requires it
|
|
|
|
|
stride = 1 if stage_idx == 0 else 2
|
|
|
|
|
if net_stride >= output_stride:
|
|
|
|
|
dilation *= stride
|
|
|
|
|
stride = 1
|
|
|
|
|
else:
|
|
|
|
|
net_stride *= stride
|
|
|
|
|
|
|
|
|
|
downsample = None
|
|
|
|
|
if stride != 1 or inplanes != planes * block_fn.expansion:
|
|
|
|
|
down_kwargs = dict(
|
|
|
|
|
in_channels=inplanes, out_channels=planes * block_fn.expansion, kernel_size=down_kernel_size,
|
|
|
|
|
stride=stride, dilation=dilation, first_dilation=prev_dilation, norm_layer=kwargs.get('norm_layer'))
|
|
|
|
|
downsample = downsample_avg(**down_kwargs) if avg_down else downsample_conv(**down_kwargs)
|
|
|
|
|
|
|
|
|
|
block_kwargs = dict(reduce_first=reduce_first, dilation=dilation, drop_block=db, **kwargs)
|
|
|
|
|
blocks = []
|
|
|
|
|
for block_idx in range(num_blocks):
|
|
|
|
|
downsample = downsample if block_idx == 0 else None
|
|
|
|
|
stride = stride if block_idx == 0 else 1
|
|
|
|
|
block_dpr = drop_path_rate * net_block_idx / (net_num_blocks - 1) # stochastic depth linear decay rule
|
|
|
|
|
blocks.append(block_fn(
|
|
|
|
|
inplanes, planes, stride, downsample, first_dilation=prev_dilation,
|
|
|
|
|
drop_path=DropPath(block_dpr) if block_dpr > 0. else None, **block_kwargs))
|
|
|
|
|
prev_dilation = dilation
|
|
|
|
|
inplanes = planes * block_fn.expansion
|
|
|
|
|
net_block_idx += 1
|
|
|
|
|
|
|
|
|
|
stages.append((stage_name, nn.Sequential(*blocks)))
|
|
|
|
|
feature_info.append(dict(num_chs=inplanes, reduction=net_stride, module=stage_name))
|
|
|
|
|
|
|
|
|
|
return stages, feature_info
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class ResNet(nn.Module):
|
|
|
|
|
"""ResNet / ResNeXt / SE-ResNeXt / SE-Net
|
|
|
|
|
|
|
|
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This class implements all variants of ResNet, ResNeXt, SE-ResNeXt, and SENet that
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* have > 1 stride in the 3x3 conv layer of bottleneck
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* have conv-bn-act ordering
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This ResNet impl supports a number of stem and downsample options based on the v1c, v1d, v1e, and v1s
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variants included in the MXNet Gluon ResNetV1b model. The C and D variants are also discussed in the
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'Bag of Tricks' paper: https://arxiv.org/pdf/1812.01187. The B variant is equivalent to torchvision default.
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ResNet variants (the same modifications can be used in SE/ResNeXt models as well):
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* normal, b - 7x7 stem, stem_width = 64, same as torchvision ResNet, NVIDIA ResNet 'v1.5', Gluon v1b
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* c - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64)
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* d - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64), average pool in downsample
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* e - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128), average pool in downsample
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* s - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128)
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* t - 3 layer deep 3x3 stem, stem width = 32 (24, 48, 64), average pool in downsample
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* tn - 3 layer deep 3x3 stem, stem width = 32 (24, 32, 64), average pool in downsample
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ResNeXt
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* normal - 7x7 stem, stem_width = 64, standard cardinality and base widths
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* same c,d, e, s variants as ResNet can be enabled
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SE-ResNeXt
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* normal - 7x7 stem, stem_width = 64
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* same c, d, e, s variants as ResNet can be enabled
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SENet-154 - 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64,
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reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block
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Parameters
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----------
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block : Block
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Class for the residual block. Options are BasicBlockGl, BottleneckGl.
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layers : list of int
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Numbers of layers in each block
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num_classes : int, default 1000
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Number of classification classes.
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in_chans : int, default 3
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Number of input (color) channels.
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cardinality : int, default 1
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Number of convolution groups for 3x3 conv in Bottleneck.
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base_width : int, default 64
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Factor determining bottleneck channels. `planes * base_width / 64 * cardinality`
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stem_width : int, default 64
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Number of channels in stem convolutions
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stem_type : str, default ''
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The type of stem:
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* '', default - a single 7x7 conv with a width of stem_width
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* 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2
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* 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2
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block_reduce_first: int, default 1
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Reduction factor for first convolution output width of residual blocks,
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1 for all archs except senets, where 2
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down_kernel_size: int, default 1
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Kernel size of residual block downsampling path, 1x1 for most archs, 3x3 for senets
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avg_down : bool, default False
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Whether to use average pooling for projection skip connection between stages/downsample.
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output_stride : int, default 32
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Set the output stride of the network, 32, 16, or 8. Typically used in segmentation.
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act_layer : nn.Module, activation layer
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norm_layer : nn.Module, normalization layer
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aa_layer : nn.Module, anti-aliasing layer
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drop_rate : float, default 0.
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Dropout probability before classifier, for training
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global_pool : str, default 'avg'
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Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax'
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"""
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def __init__(self, block, layers, num_classes=1000, in_chans=3,
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cardinality=1, base_width=64, stem_width=64, stem_type='', replace_stem_pool=False,
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output_stride=32, block_reduce_first=1, down_kernel_size=1, avg_down=False,
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act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_rate=0.0, drop_path_rate=0.,
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drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None):
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block_args = block_args or dict()
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assert output_stride in (8, 16, 32)
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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super(ResNet, self).__init__()
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# Stem
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deep_stem = 'deep' in stem_type
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inplanes = stem_width * 2 if deep_stem else 64
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if deep_stem:
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stem_chs = (stem_width, stem_width)
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if 'tiered' in stem_type:
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stem_chs = (3 * (stem_width // 4), stem_width)
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self.conv1 = nn.Sequential(*[
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nn.Conv2d(in_chans, stem_chs[0], 3, stride=2, padding=1, bias=False),
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norm_layer(stem_chs[0]),
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act_layer(inplace=True),
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nn.Conv2d(stem_chs[0], stem_chs[1], 3, stride=1, padding=1, bias=False),
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norm_layer(stem_chs[1]),
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act_layer(inplace=True),
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nn.Conv2d(stem_chs[1], inplanes, 3, stride=1, padding=1, bias=False)])
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else:
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self.conv1 = nn.Conv2d(in_chans, inplanes, kernel_size=7, stride=2, padding=3, bias=False)
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self.bn1 = norm_layer(inplanes)
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self.act1 = act_layer(inplace=True)
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self.feature_info = [dict(num_chs=inplanes, reduction=2, module='act1')]
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# Stem Pooling
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if replace_stem_pool:
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self.maxpool = nn.Sequential(*filter(None, [
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nn.Conv2d(inplanes, inplanes, 3, stride=1 if aa_layer else 2, padding=1, bias=False),
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aa_layer(channels=inplanes, stride=2) if aa_layer else None,
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norm_layer(inplanes),
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act_layer(inplace=True)
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]))
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else:
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if aa_layer is not None:
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self.maxpool = nn.Sequential(*[
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nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
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aa_layer(channels=inplanes, stride=2)])
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else:
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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# Feature Blocks
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channels = [64, 128, 256, 512]
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stage_modules, stage_feature_info = make_blocks(
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block, channels, layers, inplanes, cardinality=cardinality, base_width=base_width,
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output_stride=output_stride, reduce_first=block_reduce_first, avg_down=avg_down,
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down_kernel_size=down_kernel_size, act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer,
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drop_block_rate=drop_block_rate, drop_path_rate=drop_path_rate, **block_args)
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for stage in stage_modules:
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self.add_module(*stage) # layer1, layer2, etc
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self.feature_info.extend(stage_feature_info)
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# Head (Pooling and Classifier)
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self.num_features = 512 * block.expansion
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self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
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for n, m in self.named_modules():
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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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if zero_init_last_bn:
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for m in self.modules():
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if hasattr(m, 'zero_init_last_bn'):
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m.zero_init_last_bn()
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def get_classifier(self):
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return self.fc
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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.fc = create_classifier(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.conv1(x)
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x = self.bn1(x)
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x = self.act1(x)
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x = self.maxpool(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 forward(self, x):
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x = self.forward_features(x)
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x = self.global_pool(x)
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if self.drop_rate:
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x = F.dropout(x, p=float(self.drop_rate), training=self.training)
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x = self.fc(x)
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return x
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def _create_resnet(variant, pretrained=False, **kwargs):
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return build_model_with_cfg(
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ResNet, variant, pretrained,
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default_cfg=default_cfgs[variant],
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**kwargs)
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@register_model
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def resnet18(pretrained=False, **kwargs):
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"""Constructs a ResNet-18 model.
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"""
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model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], **kwargs)
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return _create_resnet('resnet18', pretrained, **model_args)
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@register_model
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def resnet18d(pretrained=False, **kwargs):
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"""Constructs a ResNet-18-D model.
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"""
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model_args = dict(
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block=BasicBlock, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('resnet18d', pretrained, **model_args)
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@register_model
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def resnet34(pretrained=False, **kwargs):
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"""Constructs a ResNet-34 model.
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"""
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model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], **kwargs)
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return _create_resnet('resnet34', pretrained, **model_args)
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@register_model
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def resnet34d(pretrained=False, **kwargs):
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"""Constructs a ResNet-34-D model.
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"""
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model_args = dict(
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block=BasicBlock, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('resnet34d', pretrained, **model_args)
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@register_model
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def resnet26(pretrained=False, **kwargs):
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"""Constructs a ResNet-26 model.
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"""
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model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], **kwargs)
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return _create_resnet('resnet26', pretrained, **model_args)
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@register_model
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def resnet26d(pretrained=False, **kwargs):
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"""Constructs a ResNet-26-D model.
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"""
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model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('resnet26d', pretrained, **model_args)
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@register_model
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def resnet50(pretrained=False, **kwargs):
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"""Constructs a ResNet-50 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs)
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return _create_resnet('resnet50', pretrained, **model_args)
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@register_model
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def resnet50d(pretrained=False, **kwargs):
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"""Constructs a ResNet-50-D model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('resnet50d', pretrained, **model_args)
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@register_model
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def resnet50t(pretrained=False, **kwargs):
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"""Constructs a ResNet-50-T model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered', avg_down=True, **kwargs)
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return _create_resnet('resnet50t', pretrained, **model_args)
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@register_model
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def resnet101(pretrained=False, **kwargs):
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"""Constructs a ResNet-101 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], **kwargs)
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return _create_resnet('resnet101', pretrained, **model_args)
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@register_model
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def resnet101d(pretrained=False, **kwargs):
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"""Constructs a ResNet-101-D model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('resnet101d', pretrained, **model_args)
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@register_model
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def resnet152(pretrained=False, **kwargs):
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"""Constructs a ResNet-152 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], **kwargs)
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return _create_resnet('resnet152', pretrained, **model_args)
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@register_model
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def resnet152d(pretrained=False, **kwargs):
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"""Constructs a ResNet-152-D model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('resnet152d', pretrained, **model_args)
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@register_model
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def resnet200(pretrained=False, **kwargs):
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"""Constructs a ResNet-200 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 24, 36, 3], **kwargs)
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|
return _create_resnet('resnet200', pretrained, **model_args)
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@register_model
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def resnet200d(pretrained=False, **kwargs):
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|
"""Constructs a ResNet-200-D model.
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|
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|
"""
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|
model_args = dict(
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|
block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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|
return _create_resnet('resnet200d', pretrained, **model_args)
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|
@register_model
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|
|
def tv_resnet34(pretrained=False, **kwargs):
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|
"""Constructs a ResNet-34 model with original Torchvision weights.
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|
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|
"""
|
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|
model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], **kwargs)
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|
return _create_resnet('tv_resnet34', pretrained, **model_args)
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|
@register_model
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|
|
def tv_resnet50(pretrained=False, **kwargs):
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|
"""Constructs a ResNet-50 model with original Torchvision weights.
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|
"""
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|
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs)
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|
return _create_resnet('tv_resnet50', pretrained, **model_args)
|
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|
@register_model
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|
def tv_resnet101(pretrained=False, **kwargs):
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|
"""Constructs a ResNet-101 model w/ Torchvision pretrained weights.
|
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|
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|
"""
|
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|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], **kwargs)
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|
return _create_resnet('tv_resnet101', pretrained, **model_args)
|
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|
@register_model
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|
|
def tv_resnet152(pretrained=False, **kwargs):
|
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|
|
"""Constructs a ResNet-152 model w/ Torchvision pretrained weights.
|
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|
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|
"""
|
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|
|
model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], **kwargs)
|
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|
return _create_resnet('tv_resnet152', pretrained, **model_args)
|
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|
@register_model
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|
|
|
def wide_resnet50_2(pretrained=False, **kwargs):
|
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|
|
"""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_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], base_width=128, **kwargs)
|
|
|
|
|
return _create_resnet('wide_resnet50_2', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def wide_resnet101_2(pretrained=False, **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_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], base_width=128, **kwargs)
|
|
|
|
|
return _create_resnet('wide_resnet101_2', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def resnext50_32x4d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNeXt50-32x4d model.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs)
|
|
|
|
|
return _create_resnet('resnext50_32x4d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def resnext50d_32x4d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNeXt50d-32x4d model. ResNext50 w/ deep stem & avg pool downsample
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4,
|
|
|
|
|
stem_width=32, stem_type='deep', avg_down=True, **kwargs)
|
|
|
|
|
return _create_resnet('resnext50d_32x4d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def resnext101_32x4d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNeXt-101 32x4d model.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, **kwargs)
|
|
|
|
|
return _create_resnet('resnext101_32x4d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def resnext101_32x8d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNeXt-101 32x8d model.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8, **kwargs)
|
|
|
|
|
return _create_resnet('resnext101_32x8d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def resnext101_64x4d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNeXt101-64x4d model.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4, **kwargs)
|
|
|
|
|
return _create_resnet('resnext101_64x4d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tv_resnext50_32x4d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNeXt50-32x4d model with original Torchvision weights.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs)
|
|
|
|
|
return _create_resnet('tv_resnext50_32x4d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1805.00932>`_
|
|
|
|
|
Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8, **kwargs)
|
|
|
|
|
return _create_resnet('ig_resnext101_32x8d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1805.00932>`_
|
|
|
|
|
Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16, **kwargs)
|
|
|
|
|
return _create_resnet('ig_resnext101_32x16d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1805.00932>`_
|
|
|
|
|
Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=32, **kwargs)
|
|
|
|
|
return _create_resnet('ig_resnext101_32x32d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1805.00932>`_
|
|
|
|
|
Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=48, **kwargs)
|
|
|
|
|
return _create_resnet('ig_resnext101_32x48d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1905.00546>`_
|
|
|
|
|
Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], **kwargs)
|
|
|
|
|
return _create_resnet('ssl_resnet18', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1905.00546>`_
|
|
|
|
|
Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs)
|
|
|
|
|
return _create_resnet('ssl_resnet50', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1905.00546>`_
|
|
|
|
|
Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs)
|
|
|
|
|
return _create_resnet('ssl_resnext50_32x4d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1905.00546>`_
|
|
|
|
|
Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, **kwargs)
|
|
|
|
|
return _create_resnet('ssl_resnext101_32x4d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1905.00546>`_
|
|
|
|
|
Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8, **kwargs)
|
|
|
|
|
return _create_resnet('ssl_resnext101_32x8d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1905.00546>`_
|
|
|
|
|
Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16, **kwargs)
|
|
|
|
|
return _create_resnet('ssl_resnext101_32x16d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1905.00546>`_
|
|
|
|
|
Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], **kwargs)
|
|
|
|
|
return _create_resnet('swsl_resnet18', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1905.00546>`_
|
|
|
|
|
Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs)
|
|
|
|
|
return _create_resnet('swsl_resnet50', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1905.00546>`_
|
|
|
|
|
Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs)
|
|
|
|
|
return _create_resnet('swsl_resnext50_32x4d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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" <https://arxiv.org/abs/1905.00546>`_
|
|
|
|
|
Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, **kwargs)
|
|
|
|
|
return _create_resnet('swsl_resnext101_32x4d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
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def swsl_resnext101_32x8d(pretrained=True, **kwargs):
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"""Constructs a semi-weakly supervised ResNeXt-101 32x8 model pre-trained on 1B weakly supervised
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|
image dataset and finetuned on ImageNet.
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|
`"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
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Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
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|
"""
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|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8, **kwargs)
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return _create_resnet('swsl_resnext101_32x8d', pretrained, **model_args)
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@register_model
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|
def swsl_resnext101_32x16d(pretrained=True, **kwargs):
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|
"""Constructs a semi-weakly supervised ResNeXt-101 32x16 model pre-trained on 1B weakly supervised
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|
image dataset and finetuned on ImageNet.
|
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|
`"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
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|
Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
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|
"""
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|
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16, **kwargs)
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|
return _create_resnet('swsl_resnext101_32x16d', pretrained, **model_args)
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@register_model
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|
def ecaresnet26t(pretrained=False, **kwargs):
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|
"""Constructs an ECA-ResNeXt-26-T model.
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|
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
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|
in the deep stem and ECA attn.
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|
"""
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|
model_args = dict(
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|
block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32,
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|
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs)
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|
return _create_resnet('ecaresnet26t', pretrained, **model_args)
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@register_model
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|
def ecaresnet50d(pretrained=False, **kwargs):
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|
"""Constructs a ResNet-50-D model with eca.
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|
"""
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|
model_args = dict(
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|
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True,
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|
block_args=dict(attn_layer='eca'), **kwargs)
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|
return _create_resnet('ecaresnet50d', pretrained, **model_args)
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@register_model
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|
def resnetrs50(pretrained=False, **kwargs):
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|
"""Constructs a ResNet-RS-50 model.
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|
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
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|
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
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|
"""
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|
attn_layer = partial(get_attn('se'), reduction_ratio=0.25)
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|
model_args = dict(
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|
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
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|
avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs)
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|
return _create_resnet('resnetrs50', pretrained, **model_args)
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|
@register_model
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|
def resnetrs101(pretrained=False, **kwargs):
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|
"""Constructs a ResNet-RS-101 model.
|
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|
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
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|
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
|
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|
"""
|
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|
attn_layer = partial(get_attn('se'), reduction_ratio=0.25)
|
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|
model_args = dict(
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|
block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
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|
avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs)
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|
return _create_resnet('resnetrs101', pretrained, **model_args)
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|
@register_model
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|
def resnetrs152(pretrained=False, **kwargs):
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|
"""Constructs a ResNet-RS-152 model.
|
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|
|
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
|
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|
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
|
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|
|
|
"""
|
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|
|
attn_layer = partial(get_attn('se'), reduction_ratio=0.25)
|
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|
|
|
model_args = dict(
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|
|
block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
|
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|
|
avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs)
|
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|
|
return _create_resnet('resnetrs152', pretrained, **model_args)
|
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|
@register_model
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|
|
|
def resnetrs200(pretrained=False, **kwargs):
|
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|
|
"""Constructs a ResNet-RS-200 model.
|
|
|
|
|
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
|
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|
|
|
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
|
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|
|
|
"""
|
|
|
|
|
attn_layer = partial(get_attn('se'), reduction_ratio=0.25)
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
|
|
|
|
|
avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs)
|
|
|
|
|
return _create_resnet('resnetrs200', pretrained, **model_args)
|
|
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|
|
|
|
|
|
|
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|
|
@register_model
|
|
|
|
|
def resnetrs270(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-RS-270 model.
|
|
|
|
|
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
|
|
|
|
|
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
|
|
|
|
|
"""
|
|
|
|
|
attn_layer = partial(get_attn('se'), reduction_ratio=0.25)
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[4, 29, 53, 4], stem_width=32, stem_type='deep', replace_stem_pool=True,
|
|
|
|
|
avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs)
|
|
|
|
|
return _create_resnet('resnetrs270', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
@register_model
|
|
|
|
|
def resnetrs350(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-RS-350 model.
|
|
|
|
|
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
|
|
|
|
|
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
|
|
|
|
|
"""
|
|
|
|
|
attn_layer = partial(get_attn('se'), reduction_ratio=0.25)
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[4, 36, 72, 4], stem_width=32, stem_type='deep', replace_stem_pool=True,
|
|
|
|
|
avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs)
|
|
|
|
|
return _create_resnet('resnetrs350', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def resnetrs420(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-RS-420 model
|
|
|
|
|
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
|
|
|
|
|
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
|
|
|
|
|
"""
|
|
|
|
|
attn_layer = partial(get_attn('se'), reduction_ratio=0.25)
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[4, 44, 87, 4], stem_width=32, stem_type='deep', replace_stem_pool=True,
|
|
|
|
|
avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs)
|
|
|
|
|
return _create_resnet('resnetrs420', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnet50d_pruned(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-50-D model pruned with eca.
|
|
|
|
|
The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True,
|
|
|
|
|
block_args=dict(attn_layer='eca'), **kwargs)
|
|
|
|
|
return _create_resnet('ecaresnet50d_pruned', pretrained, pruned=True, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnet50t(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs an ECA-ResNet-50-T model.
|
|
|
|
|
Like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem and ECA attn.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32,
|
|
|
|
|
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs)
|
|
|
|
|
return _create_resnet('ecaresnet50t', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnetlight(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-50-D light model with eca.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[1, 1, 11, 3], stem_width=32, avg_down=True,
|
|
|
|
|
block_args=dict(attn_layer='eca'), **kwargs)
|
|
|
|
|
return _create_resnet('ecaresnetlight', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnet101d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-101-D model with eca.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True,
|
|
|
|
|
block_args=dict(attn_layer='eca'), **kwargs)
|
|
|
|
|
return _create_resnet('ecaresnet101d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnet101d_pruned(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-101-D model pruned with eca.
|
|
|
|
|
The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True,
|
|
|
|
|
block_args=dict(attn_layer='eca'), **kwargs)
|
|
|
|
|
return _create_resnet('ecaresnet101d_pruned', pretrained, pruned=True, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnet200d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-200-D model with ECA.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True,
|
|
|
|
|
block_args=dict(attn_layer='eca'), **kwargs)
|
|
|
|
|
return _create_resnet('ecaresnet200d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnet269d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-269-D model with ECA.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[3, 30, 48, 8], stem_width=32, stem_type='deep', avg_down=True,
|
|
|
|
|
block_args=dict(attn_layer='eca'), **kwargs)
|
|
|
|
|
return _create_resnet('ecaresnet269d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnext26t_32x4d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs an ECA-ResNeXt-26-T 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. This model replaces SE module with the ECA module
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
|
|
|
|
|
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs)
|
|
|
|
|
return _create_resnet('ecaresnext26t_32x4d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnext50t_32x4d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs an ECA-ResNeXt-50-T 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. This model replaces SE module with the ECA module
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(
|
|
|
|
|
block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
|
|
|
|
|
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs)
|
|
|
|
|
return _create_resnet('ecaresnext50t_32x4d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def resnetblur18(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-18 model with blur anti-aliasing
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], aa_layer=BlurPool2d, **kwargs)
|
|
|
|
|
return _create_resnet('resnetblur18', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def resnetblur50(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-50 model with blur anti-aliasing
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=BlurPool2d, **kwargs)
|
|
|
|
|
return _create_resnet('resnetblur50', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def seresnet18(pretrained=False, **kwargs):
|
|
|
|
|
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], block_args=dict(attn_layer='se'), **kwargs)
|
|
|
|
|
return _create_resnet('seresnet18', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def seresnet34(pretrained=False, **kwargs):
|
|
|
|
|
model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], block_args=dict(attn_layer='se'), **kwargs)
|
|
|
|
|
return _create_resnet('seresnet34', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def seresnet50(pretrained=False, **kwargs):
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], block_args=dict(attn_layer='se'), **kwargs)
|
|
|
|
|
return _create_resnet('seresnet50', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def seresnet50t(pretrained=False, **kwargs):
|
|
|
|
|
model_args = dict(
|
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block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered', avg_down=True,
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block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('seresnet50t', pretrained, **model_args)
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@register_model
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def seresnet101(pretrained=False, **kwargs):
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model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('seresnet101', pretrained, **model_args)
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@register_model
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def seresnet152(pretrained=False, **kwargs):
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model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('seresnet152', pretrained, **model_args)
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@register_model
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def seresnet152d(pretrained=False, **kwargs):
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model_args = dict(
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block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True,
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block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('seresnet152d', pretrained, **model_args)
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@register_model
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def seresnet200d(pretrained=False, **kwargs):
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"""Constructs a ResNet-200-D model with SE attn.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True,
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block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('seresnet200d', pretrained, **model_args)
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@register_model
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def seresnet269d(pretrained=False, **kwargs):
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"""Constructs a ResNet-269-D model with SE attn.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 30, 48, 8], stem_width=32, stem_type='deep', avg_down=True,
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block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('seresnet269d', pretrained, **model_args)
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@register_model
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def seresnext26d_32x4d(pretrained=False, **kwargs):
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"""Constructs a SE-ResNeXt-26-D model.`
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This is technically a 28 layer ResNet, using the 'D' modifier from Gluon / bag-of-tricks for
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combination of deep stem and avg_pool in downsample.
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"""
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model_args = dict(
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block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
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stem_type='deep', avg_down=True, block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('seresnext26d_32x4d', pretrained, **model_args)
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@register_model
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def seresnext26t_32x4d(pretrained=False, **kwargs):
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"""Constructs a SE-ResNet-26-T model.
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This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
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in the deep stem.
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"""
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model_args = dict(
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block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
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stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('seresnext26t_32x4d', pretrained, **model_args)
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@register_model
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def seresnext26tn_32x4d(pretrained=False, **kwargs):
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"""Constructs a SE-ResNeXt-26-T model.
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NOTE I deprecated previous 't' model defs and replaced 't' with 'tn', this was the only tn model of note
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so keeping this def for backwards compat with any uses out there. Old 't' model is lost.
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"""
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return seresnext26t_32x4d(pretrained=pretrained, **kwargs)
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@register_model
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def seresnext50_32x4d(pretrained=False, **kwargs):
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4,
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block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('seresnext50_32x4d', pretrained, **model_args)
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@register_model
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def seresnext101_32x4d(pretrained=False, **kwargs):
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4,
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block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('seresnext101_32x4d', pretrained, **model_args)
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@register_model
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def seresnext101_32x8d(pretrained=False, **kwargs):
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8,
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block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('seresnext101_32x8d', pretrained, **model_args)
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@register_model
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def senet154(pretrained=False, **kwargs):
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model_args = dict(
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block=Bottleneck, layers=[3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep',
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down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer='se'), **kwargs)
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return _create_resnet('senet154', pretrained, **model_args)
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