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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 numpy as np
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, LayerNorm2d
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 softball(nn.Module):
def __init__(self, radius2=None, inplace=True):
super(softball, self).__init__()
self.radius2 = radius2 if radius2 is not None else None
def forward(self, x):
if self.radius2 is None:
self.radius2 = x.size()[1]
norm = torch.sqrt(1 + (x*x).sum(1, keepdim=True) / self.radius2)
return x / norm
class hardball(nn.Module):
def __init__(self, radius2=None):
super(hardball, self).__init__()
self.radius = np.sqrt(radius2) if radius2 is not None else None
def forward(self, x):
norm = torch.sqrt((x*x).sum(1, keepdim=True))
if self.radius is None:
self.radius = np.sqrt(x.size()[1])
return torch.where(norm > self.radius, self.radius * x / norm, x)
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): # original
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
class Bottleneck1(nn.Module): # original
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(Bottleneck1, self).__init__()
# k = 4 if planes <= 256 else 2
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*2, 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=first_planes, 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 = hardball(radius2=outplanes) if downsample is not None else None
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)
C = x.size()[1]
x = x[:, :C//2, :, :] * x[:, C//2:, :, :]
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
if self.act3 is not None:
x = self.act3(x)
return x
class ConvBN(nn.Module):
def __init__(self, conv, bn):
super(ConvBN, self).__init__()
self.conv = conv
self.bn = bn
self.fused_weight = None
self.fused_bias = None
def forward(self, x):
if self.training:
x = self.conv(x)
x = self.bn(x)
else:
if self.fused_weight is not None and self.fused_bias is not None:
x = F.conv2d(x, self.fused_weight, self.fused_bias,
self.conv.stride, self.conv.padding,
self.conv.dilation, self.conv.groups)
else:
x = self.conv(x)
x = self.bn(x)
return x
def fuse_bn(self):
if self.training:
raise RuntimeError("Call fuse_bn only in eval mode")
# Calculate the fused weight and bias
w = self.conv.weight
mean = self.bn.running_mean
var = torch.sqrt(self.bn.running_var + self.bn.eps)
gamma = self.bn.weight
beta = self.bn.bias
self.fused_weight = w * (gamma / var).reshape(-1, 1, 1, 1)
self.fused_bias = beta - (gamma * mean / var)
class PDEBlock(nn.Module): # quasilinear hyperbolic system
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(PDEBlock, self).__init__()
k = 4 if inplanes <= 128 else 2
width = inplanes * k
outplanes = inplanes if downsample is None else inplanes * 2
first_dilation = first_dilation or dilation
use_aa = aa_layer is not None and (stride == 2 or first_dilation != dilation)
self.conv1 = ConvBN(
nn.Conv2d(inplanes, width*2, kernel_size=1, stride=1,
dilation=first_dilation, groups=1, bias=False),
norm_layer(width*2))
self.conv2 = nn.Conv2d(width, width*2, kernel_size=3, stride=stride,
padding=1, dilation=first_dilation, groups=width, bias=False)
self.bn2 = norm_layer(width*2)
self.conv3 = ConvBN(
nn.Conv2d(width*2, outplanes, kernel_size=1, groups=1, bias=False),
norm_layer(outplanes))
self.skip = ConvBN(
nn.Conv2d(inplanes, outplanes, kernel_size=1, stride=stride,
dilation=first_dilation, groups=1, bias=False),
norm_layer(outplanes)) if downsample is not None else nn.Identity()
self.act3 = hardball(radius2=outplanes) if downsample is not None else None
def zero_init_last(self):
if getattr(self.conv3.bn, 'weight', None) is not None:
nn.init.zeros_(self.conv3.bn.weight)
def conv_forward(self, x):
conv = self.conv2
k = conv.in_channels
C = x.size()[1] // k
kernel = conv.weight.repeat(C, 1, 1, 1)
bias = conv.bias.repeat(C) if conv.bias is not None else None
return F.conv2d(x, kernel, bias, conv.stride,
conv.padding, conv.dilation, C * k)
def forward(self, x):
x0 = self.skip(x)
x = self.conv1(x)
C = x.size()[1]
x = x[:, :C//2, :, :] * x[:, C//2:, :, :]
x = self.conv2(x)
x = self.bn2(x)
x = self.conv3(x)
x += x0
if self.act3 is not None:
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)
# self.act = nn.Hardtanh(max_val=5, min_val=-5, inplace=True)