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resnet.py
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resnet.py
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"""PyTorch ResNet
This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
additional dropout and dynamic global avg/max pool.
ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
Copyright 2019, Ross Wightman
"""
import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, GroupNorm, create_attn, get_attn, \
get_act_layer, get_norm_layer, create_classifier
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, model_entrypoint
__all__ = ['ResNet', 'BasicBlock', 'Bottleneck'] # model_registry will add each entrypoint fn to this
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv1', 'classifier': 'fc',
**kwargs
}
default_cfgs = {
# ResNet and Wide ResNet
'resnet10t': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet10t_176_c3-f3215ab1.pth',
input_size=(3, 176, 176), pool_size=(6, 6),
test_crop_pct=0.95, test_input_size=(3, 224, 224),
first_conv='conv1.0'),
'resnet14t': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet14t_176_c3-c4ed2c37.pth',
input_size=(3, 176, 176), pool_size=(6, 6),
test_crop_pct=0.95, test_input_size=(3, 224, 224),
first_conv='conv1.0'),
'resnet18': _cfg(url='https://download.pytorch.org/models/resnet18-5c106cde.pth'),
'resnet18d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet18d_ra2-48a79e06.pth',
interpolation='bicubic', first_conv='conv1.0'),
'resnet34': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.pth'),
'resnet34d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34d_ra2-f8dcfcaf.pth',
interpolation='bicubic', first_conv='conv1.0'),
'resnet26': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26-9aa10e23.pth',
interpolation='bicubic'),
'resnet26d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26d-69e92c46.pth',
interpolation='bicubic', first_conv='conv1.0'),
'resnet26t': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet26t_256_ra2-6f6fa748.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.94),
'resnet50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth',
interpolation='bicubic', crop_pct=0.95),
'resnet50d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50d_ra2-464e36ba.pth',
interpolation='bicubic', first_conv='conv1.0'),
'resnet50t': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0'),
'resnet101': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet101_a1h-36d3f2aa.pth',
interpolation='bicubic', crop_pct=0.95),
'resnet101d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=1.0, test_input_size=(3, 320, 320)),
'resnet152': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet152_a1h-dc400468.pth',
interpolation='bicubic', crop_pct=0.95),
'resnet152d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=1.0, test_input_size=(3, 320, 320)),
'resnet200': _cfg(url='', interpolation='bicubic'),
'resnet200d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=1.0, test_input_size=(3, 320, 320)),
'tv_resnet34': _cfg(url='https://download.pytorch.org/models/resnet34-333f7ec4.pth'),
'tv_resnet50': _cfg(url='https://download.pytorch.org/models/resnet50-19c8e357.pth'),
'tv_resnet101': _cfg(url='https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'),
'tv_resnet152': _cfg(url='https://download.pytorch.org/models/resnet152-b121ed2d.pth'),
'wide_resnet50_2': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/wide_resnet50_racm-8234f177.pth',
interpolation='bicubic'),
'wide_resnet101_2': _cfg(url='https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth'),
# ResNets w/ alternative norm layers
'resnet50_gn': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_gn_a1h2-8fe6c4d0.pth',
crop_pct=0.94, interpolation='bicubic'),
# ResNeXt
'resnext50_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnext50_32x4d_a1h-0146ab0a.pth',
interpolation='bicubic', crop_pct=0.95),
'resnext50d_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50d_32x4d-103e99f8.pth',
interpolation='bicubic',
first_conv='conv1.0'),
'resnext101_32x4d': _cfg(url=''),
'resnext101_32x8d': _cfg(url='https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth'),
'resnext101_64x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/resnext101_64x4d_c-0d0e0cc0.pth',
interpolation='bicubic', crop_pct=1.0, test_input_size=(3, 288, 288)),
'tv_resnext50_32x4d': _cfg(url='https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth'),
# ResNeXt models - Weakly Supervised Pretraining on Instagram Hashtags
# from https://github.com/facebookresearch/WSL-Images
# Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
'ig_resnext101_32x8d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth'),
'ig_resnext101_32x16d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth'),
'ig_resnext101_32x32d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth'),
'ig_resnext101_32x48d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth'),
# Semi-Supervised ResNe*t models from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models
# Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
'ssl_resnet18': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth'),
'ssl_resnet50': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth'),
'ssl_resnext50_32x4d': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth'),
'ssl_resnext101_32x4d': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth'),
'ssl_resnext101_32x8d': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth'),
'ssl_resnext101_32x16d': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth'),
# Semi-Weakly Supervised ResNe*t models from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models
# Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
'swsl_resnet18': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth'),
'swsl_resnet50': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth'),
'swsl_resnext50_32x4d': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth'),
'swsl_resnext101_32x4d': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth'),
'swsl_resnext101_32x8d': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth'),
'swsl_resnext101_32x16d': _cfg(
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth'),
# Efficient Channel Attention ResNets
'ecaresnet26t': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet26t_ra2-46609757.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=0.95, test_input_size=(3, 320, 320)),
'ecaresnetlight': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnetlight-75a9c627.pth',
interpolation='bicubic'),
'ecaresnet50d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet50d-93c81e3b.pth',
interpolation='bicubic',
first_conv='conv1.0'),
'ecaresnet50d_pruned': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet50d_p-e4fa23c2.pth',
interpolation='bicubic',
first_conv='conv1.0'),
'ecaresnet50t': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet50t_ra2-f7ac63c4.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=0.95, test_input_size=(3, 320, 320)),
'ecaresnet101d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet101d-153dad65.pth',
interpolation='bicubic', first_conv='conv1.0'),
'ecaresnet101d_pruned': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet101d_p-9e74cb91.pth',
interpolation='bicubic',
first_conv='conv1.0'),
'ecaresnet200d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
'ecaresnet269d': _cfg(
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'),
# Squeeze-Excitation ResNets, to eventually replace the models in senet.py
'seresnet18': _cfg(
url='',
interpolation='bicubic'),
'seresnet34': _cfg(
url='',
interpolation='bicubic'),
'seresnet50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet50_ra_224-8efdb4bb.pth',
interpolation='bicubic'),
'seresnet50t': _cfg(
url='',
interpolation='bicubic',
first_conv='conv1.0'),
'seresnet101': _cfg(
url='',
interpolation='bicubic'),
'seresnet152': _cfg(
url='',
interpolation='bicubic'),
'seresnet152d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=1.0, test_input_size=(3, 320, 320)
),
'seresnet200d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
'seresnet269d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
# Squeeze-Excitation ResNeXts, to eventually replace the models in senet.py
'seresnext26d_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26d_32x4d-80fa48a3.pth',
interpolation='bicubic',
first_conv='conv1.0'),
'seresnext26t_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26tn_32x4d-569cb627.pth',
interpolation='bicubic',
first_conv='conv1.0'),
'seresnext50_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext50_32x4d_racm-a304a460.pth',
interpolation='bicubic'),
'seresnext101_32x4d': _cfg(
url='',
interpolation='bicubic'),
'seresnext101_32x8d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnext101_32x8d_ah-e6bc4c0a.pth',
interpolation='bicubic', test_input_size=(3, 288, 288), crop_pct=1.0),
'seresnext101d_32x8d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnext101d_32x8d_ah-191d7b94.pth',
interpolation='bicubic', first_conv='conv1.0', test_input_size=(3, 288, 288), crop_pct=1.0),
'senet154': _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'),
'resnetblur50d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0'),
'resnetblur101d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0'),
'resnetaa50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnetaa50_a1h-4cf422b3.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0, interpolation='bicubic'),
'resnetaa50d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0'),
'resnetaa101d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0'),
'seresnetaa50d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0'),
'seresnextaa101d_32x8d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnextaa101d_32x8d_ah-83c8ae12.pth',
interpolation='bicubic', first_conv='conv1.0', test_input_size=(3, 288, 288), crop_pct=1.0),
# ResNet-RS models
'resnetrs50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs50_ema-6b53758b.pth',
input_size=(3, 160, 160), pool_size=(5, 5), 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_i192_ema-1509bbf6.pth',
input_size=(3, 192, 192), pool_size=(6, 6), crop_pct=0.94, test_input_size=(3, 288, 288),
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs152': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs152_i256_ema-a9aff7f9.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-tpu-weights/resnetrs200_c-6b698b88.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_ema-b40e674c.pth',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 352, 352),
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs350': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs350_i256_ema-5a1aa8f1.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_ema-972dee69.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
def create_aa(aa_layer, channels, stride=2, enable=True):
if not aa_layer or not enable:
return nn.Identity()
return aa_layer(stride) if issubclass(aa_layer, nn.AvgPool2d) else aa_layer(channels=channels, stride=stride)
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.drop_block = drop_block() if drop_block is not None else nn.Identity()
self.act1 = act_layer(inplace=True)
self.aa = create_aa(aa_layer, channels=first_planes, stride=stride, enable=use_aa)
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_path = drop_path
def zero_init_last(self):
if getattr(self.bn2, 'weight', None) is not None:
nn.init.zeros_(self.bn2.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.bn1(x)
x = self.drop_block(x)
x = self.act1(x)
x = self.aa(x)
x = self.conv2(x)
x = self.bn2(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:
shortcut = self.downsample(shortcut)
x += shortcut
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.drop_block = drop_block() if drop_block is not None else nn.Identity()
self.act2 = act_layer(inplace=True)
self.aa = create_aa(aa_layer, channels=width, stride=stride, enable=use_aa)
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_path = drop_path
def zero_init_last(self):
if getattr(self.bn3, 'weight', None) is not None:
nn.init.zeros_(self.bn3.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.drop_block(x)
x = self.act2(x)
x = self.aa(x)
x = self.conv3(x)
x = self.bn3(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:
shortcut = self.downsample(shortcut)
x += shortcut
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_prob=0.):
return [
None, None,
partial(DropBlock2d, drop_prob=drop_prob, block_size=5, gamma_scale=0.25) if drop_prob else None,
partial(DropBlock2d, drop_prob=drop_prob, block_size=3, gamma_scale=1.00) if drop_prob 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
This class implements all variants of ResNet, ResNeXt, SE-ResNeXt, and SENet that
* have > 1 stride in the 3x3 conv layer of bottleneck
* have conv-bn-act ordering
This ResNet impl supports a number of stem and downsample options based on the v1c, v1d, v1e, and v1s
variants included in the MXNet Gluon ResNetV1b model. The C and D variants are also discussed in the
'Bag of Tricks' paper: https://arxiv.org/pdf/1812.01187. The B variant is equivalent to torchvision default.
ResNet variants (the same modifications can be used in SE/ResNeXt models as well):
* normal, b - 7x7 stem, stem_width = 64, same as torchvision ResNet, NVIDIA ResNet 'v1.5', Gluon v1b
* c - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64)
* d - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64), average pool in downsample
* e - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128), average pool in downsample
* s - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128)
* t - 3 layer deep 3x3 stem, stem width = 32 (24, 48, 64), average pool in downsample
* tn - 3 layer deep 3x3 stem, stem width = 32 (24, 32, 64), average pool in downsample
ResNeXt
* normal - 7x7 stem, stem_width = 64, standard cardinality and base widths
* same c,d, e, s variants as ResNet can be enabled
SE-ResNeXt
* normal - 7x7 stem, stem_width = 64
* same c, d, e, s variants as ResNet can be enabled
SENet-154 - 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64,
reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block
"""
def __init__(
self,
block,
layers,
num_classes=1000,
in_chans=3,
output_stride=32,
global_pool='avg',
cardinality=1,
base_width=64,
stem_width=64,
stem_type='',
replace_stem_pool=False,
block_reduce_first=1,
down_kernel_size=1,
avg_down=False,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
aa_layer=None,
drop_rate=0.0,
drop_path_rate=0.,
drop_block_rate=0.,
zero_init_last=True,
block_args=None,
):
"""
Args:
block (nn.Module): class for the residual block. Options are BasicBlock, Bottleneck.
layers (List[int]) : number of layers in each block
num_classes (int): number of classification classes (default 1000)
in_chans (int): number of input (color) channels. (default 3)
output_stride (int): output stride of the network, 32, 16, or 8. (default 32)
global_pool (str): Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' (default 'avg')
cardinality (int): number of convolution groups for 3x3 conv in Bottleneck. (default 1)
base_width (int): bottleneck channels factor. `planes * base_width / 64 * cardinality` (default 64)
stem_width (int): number of channels in stem convolutions (default 64)
stem_type (str): The type of stem (default ''):
* '', default - a single 7x7 conv with a width of stem_width
* 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2
* 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2
replace_stem_pool (bool): replace stem max-pooling layer with a 3x3 stride-2 convolution
block_reduce_first (int): Reduction factor for first convolution output width of residual blocks,
1 for all archs except senets, where 2 (default 1)
down_kernel_size (int): kernel size of residual block downsample path,
1x1 for most, 3x3 for senets (default: 1)
avg_down (bool): use avg pooling for projection skip connection between stages/downsample (default False)
act_layer (str, nn.Module): activation layer
norm_layer (str, nn.Module): normalization layer
aa_layer (nn.Module): anti-aliasing layer
drop_rate (float): Dropout probability before classifier, for training (default 0.)
drop_path_rate (float): Stochastic depth drop-path rate (default 0.)
drop_block_rate (float): Drop block rate (default 0.)
zero_init_last (bool): zero-init the last weight in residual path (usually last BN affine weight)
block_args (dict): Extra kwargs to pass through to block module
"""
super(ResNet, self).__init__()
block_args = block_args or dict()
assert output_stride in (8, 16, 32)
self.num_classes = num_classes
self.drop_rate = drop_rate
self.grad_checkpointing = False
act_layer = get_act_layer(act_layer)
norm_layer = get_norm_layer(norm_layer)
# Stem
deep_stem = 'deep' in stem_type
inplanes = stem_width * 2 if deep_stem else 64
if deep_stem:
stem_chs = (stem_width, stem_width)
if 'tiered' in stem_type:
stem_chs = (3 * (stem_width // 4), stem_width)
self.conv1 = nn.Sequential(*[
nn.Conv2d(in_chans, stem_chs[0], 3, stride=2, padding=1, bias=False),
norm_layer(stem_chs[0]),
act_layer(inplace=True),
nn.Conv2d(stem_chs[0], stem_chs[1], 3, stride=1, padding=1, bias=False),
norm_layer(stem_chs[1]),
act_layer(inplace=True),
nn.Conv2d(stem_chs[1], inplanes, 3, stride=1, padding=1, bias=False)])
else:
self.conv1 = nn.Conv2d(in_chans, inplanes, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = norm_layer(inplanes)
self.act1 = act_layer(inplace=True)
self.feature_info = [dict(num_chs=inplanes, reduction=2, module='act1')]
# Stem pooling. The name 'maxpool' remains for weight compatibility.
if replace_stem_pool:
self.maxpool = nn.Sequential(*filter(None, [
nn.Conv2d(inplanes, inplanes, 3, stride=1 if aa_layer else 2, padding=1, bias=False),
create_aa(aa_layer, channels=inplanes, stride=2) if aa_layer is not None else None,
norm_layer(inplanes),
act_layer(inplace=True)
]))
else:
if aa_layer is not None:
if issubclass(aa_layer, nn.AvgPool2d):
self.maxpool = aa_layer(2)
else:
self.maxpool = nn.Sequential(*[
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
aa_layer(channels=inplanes, stride=2)])
else:
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# Feature Blocks
channels = [64, 128, 256, 512]
stage_modules, stage_feature_info = make_blocks(
block,
channels,
layers,
inplanes,
cardinality=cardinality,
base_width=base_width,
output_stride=output_stride,
reduce_first=block_reduce_first,
avg_down=avg_down,
down_kernel_size=down_kernel_size,
act_layer=act_layer,
norm_layer=norm_layer,
aa_layer=aa_layer,
drop_block_rate=drop_block_rate,
drop_path_rate=drop_path_rate,
**block_args,
)
for stage in stage_modules:
self.add_module(*stage) # layer1, layer2, etc
self.feature_info.extend(stage_feature_info)
# Head (Pooling and Classifier)
self.num_features = 512 * block.expansion
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
self.init_weights(zero_init_last=zero_init_last)
@staticmethod
def from_pretrained(model_name: str, load_weights=True, **kwargs) -> 'ResNet':
entry_fn = model_entrypoint(model_name, 'resnet')
return entry_fn(pretrained=not load_weights, **kwargs)
@torch.jit.ignore
def init_weights(self, zero_init_last=True):
for n, m in self.named_modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if zero_init_last:
for m in self.modules():
if hasattr(m, 'zero_init_last'):
m.zero_init_last()
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(stem=r'^conv1|bn1|maxpool', blocks=r'^layer(\d+)' if coarse else r'^layer(\d+)\.(\d+)')
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self, name_only=False):
return 'fc' if name_only else self.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.maxpool(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq([self.layer1, self.layer2, self.layer3, self.layer4], x, flatten=True)
else:
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
if self.drop_rate:
x = F.dropout(x, p=float(self.drop_rate), training=self.training)
return x if pre_logits else self.fc(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_resnet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(ResNet, variant, pretrained, **kwargs)
@register_model
def resnet10t(pretrained=False, **kwargs):
"""Constructs a ResNet-10-T model.
"""
model_args = dict(block=BasicBlock, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True)
return _create_resnet('resnet10t', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet14t(pretrained=False, **kwargs):
"""Constructs a ResNet-14-T model.
"""
model_args = dict(block=Bottleneck, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True)
return _create_resnet('resnet14t', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
"""
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2])
return _create_resnet('resnet18', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet18d(pretrained=False, **kwargs):
"""Constructs a ResNet-18-D model.
"""
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet18d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
"""
model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3])
return _create_resnet('resnet34', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet34d(pretrained=False, **kwargs):
"""Constructs a ResNet-34-D model.
"""
model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet34d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet26(pretrained=False, **kwargs):
"""Constructs a ResNet-26 model.
"""
model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2])
return _create_resnet('resnet26', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet26t(pretrained=False, **kwargs):
"""Constructs a ResNet-26-T model.
"""
model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep_tiered', avg_down=True)
return _create_resnet('resnet26t', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet26d(pretrained=False, **kwargs):
"""Constructs a ResNet-26-D model.
"""
model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet26d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs)
return _create_resnet('resnet50', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet50d(pretrained=False, **kwargs) -> ResNet:
"""Constructs a ResNet-50-D model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet50d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet50t(pretrained=False, **kwargs):
"""Constructs a ResNet-50-T model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered', avg_down=True)
return _create_resnet('resnet50t', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3])
return _create_resnet('resnet101', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet101d(pretrained=False, **kwargs):
"""Constructs a ResNet-101-D model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet101d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
"""
model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3])
return _create_resnet('resnet152', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet152d(pretrained=False, **kwargs):
"""Constructs a ResNet-152-D model.
"""
model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet152d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet200(pretrained=False, **kwargs):
"""Constructs a ResNet-200 model.
"""
model_args = dict(block=Bottleneck, layers=[3, 24, 36, 3])
return _create_resnet('resnet200', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet200d(pretrained=False, **kwargs):
"""Constructs a ResNet-200-D model.
"""
model_args = dict(block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet200d', pretrained, **dict(model_args, **kwargs))
@register_model
def tv_resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model with original Torchvision weights.
"""
model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3])
return _create_resnet('tv_resnet34', pretrained, **dict(model_args, **kwargs))
@register_model
def tv_resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model with original Torchvision weights.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs)