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896 lines
37 KiB
896 lines
37 KiB
""" Normalization Free Nets. NFNet, NF-RegNet, NF-ResNet (pre-activation) Models
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Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
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- https://arxiv.org/abs/2101.08692
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Paper: `High-Performance Large-Scale Image Recognition Without Normalization`
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- https://arxiv.org/abs/2102.06171
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Official Deepmind JAX code: https://github.com/deepmind/deepmind-research/tree/master/nfnets
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Status:
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* These models are a work in progress, experiments ongoing.
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* Pretrained weights for two models so far, more to come.
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* Model details updated to closer match official JAX code now that it's released
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* NF-ResNet, NF-RegNet-B, and NFNet-F models supported
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Hacked together by / copyright Ross Wightman, 2021.
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"""
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from collections import OrderedDict
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from dataclasses import dataclass
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from functools import partial
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from typing import Tuple, Optional
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import torch
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import torch.nn as nn
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.layers import ClassifierHead, DropPath, AvgPool2dSame, ScaledStdConv2d, ScaledStdConv2dSame, \
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get_act_layer, get_act_fn, get_attn, make_divisible
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from ._builder import build_model_with_cfg
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from ._features_fx import register_notrace_module
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from ._manipulate import checkpoint_seq
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from ._registry import register_model
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__all__ = ['NormFreeNet', 'NfCfg'] # model_registry will add each entrypoint fn to this
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def _dcfg(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': 0.9, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'stem.conv1', 'classifier': 'head.fc',
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**kwargs
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}
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default_cfgs = dict(
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dm_nfnet_f0=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f0-604f9c3a.pth',
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pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), crop_pct=.9),
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dm_nfnet_f1=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f1-fc540f82.pth',
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pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 320, 320), crop_pct=0.91),
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dm_nfnet_f2=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f2-89875923.pth',
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pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 352, 352), crop_pct=0.92),
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dm_nfnet_f3=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f3-d74ab3aa.pth',
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pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 416, 416), crop_pct=0.94),
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dm_nfnet_f4=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f4-0ac5b10b.pth',
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pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 512, 512), crop_pct=0.951),
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dm_nfnet_f5=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f5-ecb20ab1.pth',
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pool_size=(13, 13), input_size=(3, 416, 416), test_input_size=(3, 544, 544), crop_pct=0.954),
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dm_nfnet_f6=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f6-e0f12116.pth',
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pool_size=(14, 14), input_size=(3, 448, 448), test_input_size=(3, 576, 576), crop_pct=0.956),
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nfnet_f0=_dcfg(
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url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256)),
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nfnet_f1=_dcfg(
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url='', pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 320, 320)),
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nfnet_f2=_dcfg(
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url='', pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 352, 352)),
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nfnet_f3=_dcfg(
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url='', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 416, 416)),
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nfnet_f4=_dcfg(
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url='', pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 512, 512)),
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nfnet_f5=_dcfg(
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url='', pool_size=(13, 13), input_size=(3, 416, 416), test_input_size=(3, 544, 544)),
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nfnet_f6=_dcfg(
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url='', pool_size=(14, 14), input_size=(3, 448, 448), test_input_size=(3, 576, 576)),
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nfnet_f7=_dcfg(
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url='', pool_size=(15, 15), input_size=(3, 480, 480), test_input_size=(3, 608, 608)),
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nfnet_l0=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nfnet_l0_ra2-45c6688d.pth',
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pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 288, 288), crop_pct=1.0),
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eca_nfnet_l0=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecanfnet_l0_ra2-e3e9ac50.pth',
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hf_hub_id='timm/eca_nfnet_l0',
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pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 288, 288), crop_pct=1.0),
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eca_nfnet_l1=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecanfnet_l1_ra2-7dce93cd.pth',
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pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 320, 320), crop_pct=1.0),
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eca_nfnet_l2=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecanfnet_l2_ra3-da781a61.pth',
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pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 384, 384), crop_pct=1.0),
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eca_nfnet_l3=_dcfg(
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url='',
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pool_size=(11, 11), input_size=(3, 352, 352), test_input_size=(3, 448, 448), crop_pct=1.0),
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nf_regnet_b0=_dcfg(
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url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv'),
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nf_regnet_b1=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nf_regnet_b1_256_ra2-ad85cfef.pth',
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pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 288, 288), first_conv='stem.conv'), # NOT to paper spec
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nf_regnet_b2=_dcfg(
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url='', pool_size=(8, 8), input_size=(3, 240, 240), test_input_size=(3, 272, 272), first_conv='stem.conv'),
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nf_regnet_b3=_dcfg(
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url='', pool_size=(9, 9), input_size=(3, 288, 288), test_input_size=(3, 320, 320), first_conv='stem.conv'),
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nf_regnet_b4=_dcfg(
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url='', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 384, 384), first_conv='stem.conv'),
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nf_regnet_b5=_dcfg(
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url='', pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 456, 456), first_conv='stem.conv'),
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nf_resnet26=_dcfg(url='', first_conv='stem.conv'),
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nf_resnet50=_dcfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nf_resnet50_ra2-9f236009.pth',
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pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 288, 288), crop_pct=0.94, first_conv='stem.conv'),
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nf_resnet101=_dcfg(url='', first_conv='stem.conv'),
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nf_seresnet26=_dcfg(url='', first_conv='stem.conv'),
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nf_seresnet50=_dcfg(url='', first_conv='stem.conv'),
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nf_seresnet101=_dcfg(url='', first_conv='stem.conv'),
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nf_ecaresnet26=_dcfg(url='', first_conv='stem.conv'),
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nf_ecaresnet50=_dcfg(url='', first_conv='stem.conv'),
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nf_ecaresnet101=_dcfg(url='', first_conv='stem.conv'),
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)
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@dataclass
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class NfCfg:
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depths: Tuple[int, int, int, int]
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channels: Tuple[int, int, int, int]
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alpha: float = 0.2
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stem_type: str = '3x3'
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stem_chs: Optional[int] = None
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group_size: Optional[int] = None
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attn_layer: Optional[str] = None
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attn_kwargs: dict = None
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attn_gain: float = 2.0 # NF correction gain to apply if attn layer is used
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width_factor: float = 1.0
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bottle_ratio: float = 0.5
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num_features: int = 0 # num out_channels for final conv, no final_conv if 0
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ch_div: int = 8 # round channels % 8 == 0 to keep tensor-core use optimal
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reg: bool = False # enables EfficientNet-like options used in RegNet variants, expand from in_chs, se in middle
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extra_conv: bool = False # extra 3x3 bottleneck convolution for NFNet models
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gamma_in_act: bool = False
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same_padding: bool = False
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std_conv_eps: float = 1e-5
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skipinit: bool = False # disabled by default, non-trivial performance impact
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zero_init_fc: bool = False
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act_layer: str = 'silu'
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def _nfres_cfg(
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depths, channels=(256, 512, 1024, 2048), group_size=None, act_layer='relu', attn_layer=None, attn_kwargs=None):
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attn_kwargs = attn_kwargs or {}
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cfg = NfCfg(
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depths=depths, channels=channels, stem_type='7x7_pool', stem_chs=64, bottle_ratio=0.25,
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group_size=group_size, act_layer=act_layer, attn_layer=attn_layer, attn_kwargs=attn_kwargs)
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return cfg
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def _nfreg_cfg(depths, channels=(48, 104, 208, 440)):
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num_features = 1280 * channels[-1] // 440
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attn_kwargs = dict(rd_ratio=0.5)
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cfg = NfCfg(
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depths=depths, channels=channels, stem_type='3x3', group_size=8, width_factor=0.75, bottle_ratio=2.25,
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num_features=num_features, reg=True, attn_layer='se', attn_kwargs=attn_kwargs)
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return cfg
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def _nfnet_cfg(
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depths, channels=(256, 512, 1536, 1536), group_size=128, bottle_ratio=0.5, feat_mult=2.,
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act_layer='gelu', attn_layer='se', attn_kwargs=None):
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num_features = int(channels[-1] * feat_mult)
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attn_kwargs = attn_kwargs if attn_kwargs is not None else dict(rd_ratio=0.5)
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cfg = NfCfg(
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depths=depths, channels=channels, stem_type='deep_quad', stem_chs=128, group_size=group_size,
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bottle_ratio=bottle_ratio, extra_conv=True, num_features=num_features, act_layer=act_layer,
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attn_layer=attn_layer, attn_kwargs=attn_kwargs)
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return cfg
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def _dm_nfnet_cfg(depths, channels=(256, 512, 1536, 1536), act_layer='gelu', skipinit=True):
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cfg = NfCfg(
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depths=depths, channels=channels, stem_type='deep_quad', stem_chs=128, group_size=128,
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bottle_ratio=0.5, extra_conv=True, gamma_in_act=True, same_padding=True, skipinit=skipinit,
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num_features=int(channels[-1] * 2.0), act_layer=act_layer, attn_layer='se', attn_kwargs=dict(rd_ratio=0.5))
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return cfg
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model_cfgs = dict(
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# NFNet-F models w/ GELU compatible with DeepMind weights
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dm_nfnet_f0=_dm_nfnet_cfg(depths=(1, 2, 6, 3)),
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dm_nfnet_f1=_dm_nfnet_cfg(depths=(2, 4, 12, 6)),
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dm_nfnet_f2=_dm_nfnet_cfg(depths=(3, 6, 18, 9)),
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dm_nfnet_f3=_dm_nfnet_cfg(depths=(4, 8, 24, 12)),
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dm_nfnet_f4=_dm_nfnet_cfg(depths=(5, 10, 30, 15)),
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dm_nfnet_f5=_dm_nfnet_cfg(depths=(6, 12, 36, 18)),
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dm_nfnet_f6=_dm_nfnet_cfg(depths=(7, 14, 42, 21)),
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# NFNet-F models w/ GELU
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nfnet_f0=_nfnet_cfg(depths=(1, 2, 6, 3)),
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nfnet_f1=_nfnet_cfg(depths=(2, 4, 12, 6)),
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nfnet_f2=_nfnet_cfg(depths=(3, 6, 18, 9)),
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nfnet_f3=_nfnet_cfg(depths=(4, 8, 24, 12)),
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nfnet_f4=_nfnet_cfg(depths=(5, 10, 30, 15)),
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nfnet_f5=_nfnet_cfg(depths=(6, 12, 36, 18)),
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nfnet_f6=_nfnet_cfg(depths=(7, 14, 42, 21)),
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nfnet_f7=_nfnet_cfg(depths=(8, 16, 48, 24)),
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# Experimental 'light' versions of NFNet-F that are little leaner
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nfnet_l0=_nfnet_cfg(
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depths=(1, 2, 6, 3), feat_mult=1.5, group_size=64, bottle_ratio=0.25,
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attn_kwargs=dict(rd_ratio=0.25, rd_divisor=8), act_layer='silu'),
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eca_nfnet_l0=_nfnet_cfg(
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depths=(1, 2, 6, 3), feat_mult=1.5, group_size=64, bottle_ratio=0.25,
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attn_layer='eca', attn_kwargs=dict(), act_layer='silu'),
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eca_nfnet_l1=_nfnet_cfg(
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depths=(2, 4, 12, 6), feat_mult=2, group_size=64, bottle_ratio=0.25,
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attn_layer='eca', attn_kwargs=dict(), act_layer='silu'),
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eca_nfnet_l2=_nfnet_cfg(
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depths=(3, 6, 18, 9), feat_mult=2, group_size=64, bottle_ratio=0.25,
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attn_layer='eca', attn_kwargs=dict(), act_layer='silu'),
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eca_nfnet_l3=_nfnet_cfg(
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depths=(4, 8, 24, 12), feat_mult=2, group_size=64, bottle_ratio=0.25,
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attn_layer='eca', attn_kwargs=dict(), act_layer='silu'),
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# EffNet influenced RegNet defs.
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# NOTE: These aren't quite the official ver, ch_div=1 must be set for exact ch counts. I round to ch_div=8.
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nf_regnet_b0=_nfreg_cfg(depths=(1, 3, 6, 6)),
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nf_regnet_b1=_nfreg_cfg(depths=(2, 4, 7, 7)),
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nf_regnet_b2=_nfreg_cfg(depths=(2, 4, 8, 8), channels=(56, 112, 232, 488)),
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nf_regnet_b3=_nfreg_cfg(depths=(2, 5, 9, 9), channels=(56, 128, 248, 528)),
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nf_regnet_b4=_nfreg_cfg(depths=(2, 6, 11, 11), channels=(64, 144, 288, 616)),
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nf_regnet_b5=_nfreg_cfg(depths=(3, 7, 14, 14), channels=(80, 168, 336, 704)),
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# FIXME add B6-B8
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# ResNet (preact, D style deep stem/avg down) defs
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nf_resnet26=_nfres_cfg(depths=(2, 2, 2, 2)),
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nf_resnet50=_nfres_cfg(depths=(3, 4, 6, 3)),
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nf_resnet101=_nfres_cfg(depths=(3, 4, 23, 3)),
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nf_seresnet26=_nfres_cfg(depths=(2, 2, 2, 2), attn_layer='se', attn_kwargs=dict(rd_ratio=1/16)),
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nf_seresnet50=_nfres_cfg(depths=(3, 4, 6, 3), attn_layer='se', attn_kwargs=dict(rd_ratio=1/16)),
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nf_seresnet101=_nfres_cfg(depths=(3, 4, 23, 3), attn_layer='se', attn_kwargs=dict(rd_ratio=1/16)),
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nf_ecaresnet26=_nfres_cfg(depths=(2, 2, 2, 2), attn_layer='eca', attn_kwargs=dict()),
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nf_ecaresnet50=_nfres_cfg(depths=(3, 4, 6, 3), attn_layer='eca', attn_kwargs=dict()),
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nf_ecaresnet101=_nfres_cfg(depths=(3, 4, 23, 3), attn_layer='eca', attn_kwargs=dict()),
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)
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class GammaAct(nn.Module):
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def __init__(self, act_type='relu', gamma: float = 1.0, inplace=False):
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super().__init__()
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self.act_fn = get_act_fn(act_type)
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self.gamma = gamma
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self.inplace = inplace
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def forward(self, x):
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return self.act_fn(x, inplace=self.inplace).mul_(self.gamma)
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def act_with_gamma(act_type, gamma: float = 1.):
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def _create(inplace=False):
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return GammaAct(act_type, gamma=gamma, inplace=inplace)
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return _create
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class DownsampleAvg(nn.Module):
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def __init__(
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self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None, conv_layer=ScaledStdConv2d):
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""" AvgPool Downsampling as in 'D' ResNet variants. Support for dilation."""
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super(DownsampleAvg, self).__init__()
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avg_stride = stride if dilation == 1 else 1
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if stride > 1 or dilation > 1:
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avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
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self.pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
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else:
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self.pool = nn.Identity()
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self.conv = conv_layer(in_chs, out_chs, 1, stride=1)
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def forward(self, x):
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return self.conv(self.pool(x))
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@register_notrace_module # reason: mul_ causes FX to drop a relevant node. https://github.com/pytorch/pytorch/issues/68301
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class NormFreeBlock(nn.Module):
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"""Normalization-Free pre-activation block.
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"""
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def __init__(
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self, in_chs, out_chs=None, stride=1, dilation=1, first_dilation=None,
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alpha=1.0, beta=1.0, bottle_ratio=0.25, group_size=None, ch_div=1, reg=True, extra_conv=False,
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skipinit=False, attn_layer=None, attn_gain=2.0, act_layer=None, conv_layer=None, drop_path_rate=0.):
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super().__init__()
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first_dilation = first_dilation or dilation
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out_chs = out_chs or in_chs
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# RegNet variants scale bottleneck from in_chs, otherwise scale from out_chs like ResNet
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mid_chs = make_divisible(in_chs * bottle_ratio if reg else out_chs * bottle_ratio, ch_div)
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groups = 1 if not group_size else mid_chs // group_size
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if group_size and group_size % ch_div == 0:
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mid_chs = group_size * groups # correct mid_chs if group_size divisible by ch_div, otherwise error
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self.alpha = alpha
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self.beta = beta
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self.attn_gain = attn_gain
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if in_chs != out_chs or stride != 1 or dilation != first_dilation:
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self.downsample = DownsampleAvg(
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in_chs, out_chs, stride=stride, dilation=dilation, first_dilation=first_dilation, conv_layer=conv_layer)
|
|
else:
|
|
self.downsample = None
|
|
|
|
self.act1 = act_layer()
|
|
self.conv1 = conv_layer(in_chs, mid_chs, 1)
|
|
self.act2 = act_layer(inplace=True)
|
|
self.conv2 = conv_layer(mid_chs, mid_chs, 3, stride=stride, dilation=first_dilation, groups=groups)
|
|
if extra_conv:
|
|
self.act2b = act_layer(inplace=True)
|
|
self.conv2b = conv_layer(mid_chs, mid_chs, 3, stride=1, dilation=dilation, groups=groups)
|
|
else:
|
|
self.act2b = None
|
|
self.conv2b = None
|
|
if reg and attn_layer is not None:
|
|
self.attn = attn_layer(mid_chs) # RegNet blocks apply attn btw conv2 & 3
|
|
else:
|
|
self.attn = None
|
|
self.act3 = act_layer()
|
|
self.conv3 = conv_layer(mid_chs, out_chs, 1, gain_init=1. if skipinit else 0.)
|
|
if not reg and attn_layer is not None:
|
|
self.attn_last = attn_layer(out_chs) # ResNet blocks apply attn after conv3
|
|
else:
|
|
self.attn_last = None
|
|
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
|
|
self.skipinit_gain = nn.Parameter(torch.tensor(0.)) if skipinit else None
|
|
|
|
def forward(self, x):
|
|
out = self.act1(x) * self.beta
|
|
|
|
# shortcut branch
|
|
shortcut = x
|
|
if self.downsample is not None:
|
|
shortcut = self.downsample(out)
|
|
|
|
# residual branch
|
|
out = self.conv1(out)
|
|
out = self.conv2(self.act2(out))
|
|
if self.conv2b is not None:
|
|
out = self.conv2b(self.act2b(out))
|
|
if self.attn is not None:
|
|
out = self.attn_gain * self.attn(out)
|
|
out = self.conv3(self.act3(out))
|
|
if self.attn_last is not None:
|
|
out = self.attn_gain * self.attn_last(out)
|
|
out = self.drop_path(out)
|
|
|
|
if self.skipinit_gain is not None:
|
|
out.mul_(self.skipinit_gain) # this slows things down more than expected, TBD
|
|
out = out * self.alpha + shortcut
|
|
return out
|
|
|
|
|
|
def create_stem(in_chs, out_chs, stem_type='', conv_layer=None, act_layer=None, preact_feature=True):
|
|
stem_stride = 2
|
|
stem_feature = dict(num_chs=out_chs, reduction=2, module='stem.conv')
|
|
stem = OrderedDict()
|
|
assert stem_type in ('', 'deep', 'deep_tiered', 'deep_quad', '3x3', '7x7', 'deep_pool', '3x3_pool', '7x7_pool')
|
|
if 'deep' in stem_type:
|
|
if 'quad' in stem_type:
|
|
# 4 deep conv stack as in NFNet-F models
|
|
assert not 'pool' in stem_type
|
|
stem_chs = (out_chs // 8, out_chs // 4, out_chs // 2, out_chs)
|
|
strides = (2, 1, 1, 2)
|
|
stem_stride = 4
|
|
stem_feature = dict(num_chs=out_chs // 2, reduction=2, module='stem.conv3')
|
|
else:
|
|
if 'tiered' in stem_type:
|
|
stem_chs = (3 * out_chs // 8, out_chs // 2, out_chs) # 'T' resnets in resnet.py
|
|
else:
|
|
stem_chs = (out_chs // 2, out_chs // 2, out_chs) # 'D' ResNets
|
|
strides = (2, 1, 1)
|
|
stem_feature = dict(num_chs=out_chs // 2, reduction=2, module='stem.conv2')
|
|
last_idx = len(stem_chs) - 1
|
|
for i, (c, s) in enumerate(zip(stem_chs, strides)):
|
|
stem[f'conv{i + 1}'] = conv_layer(in_chs, c, kernel_size=3, stride=s)
|
|
if i != last_idx:
|
|
stem[f'act{i + 2}'] = act_layer(inplace=True)
|
|
in_chs = c
|
|
elif '3x3' in stem_type:
|
|
# 3x3 stem conv as in RegNet
|
|
stem['conv'] = conv_layer(in_chs, out_chs, kernel_size=3, stride=2)
|
|
else:
|
|
# 7x7 stem conv as in ResNet
|
|
stem['conv'] = conv_layer(in_chs, out_chs, kernel_size=7, stride=2)
|
|
|
|
if 'pool' in stem_type:
|
|
stem['pool'] = nn.MaxPool2d(3, stride=2, padding=1)
|
|
stem_stride = 4
|
|
|
|
return nn.Sequential(stem), stem_stride, stem_feature
|
|
|
|
|
|
# from https://github.com/deepmind/deepmind-research/tree/master/nfnets
|
|
_nonlin_gamma = dict(
|
|
identity=1.0,
|
|
celu=1.270926833152771,
|
|
elu=1.2716004848480225,
|
|
gelu=1.7015043497085571,
|
|
leaky_relu=1.70590341091156,
|
|
log_sigmoid=1.9193484783172607,
|
|
log_softmax=1.0002083778381348,
|
|
relu=1.7139588594436646,
|
|
relu6=1.7131484746932983,
|
|
selu=1.0008515119552612,
|
|
sigmoid=4.803835391998291,
|
|
silu=1.7881293296813965,
|
|
softsign=2.338853120803833,
|
|
softplus=1.9203323125839233,
|
|
tanh=1.5939117670059204,
|
|
)
|
|
|
|
|
|
class NormFreeNet(nn.Module):
|
|
""" Normalization-Free Network
|
|
|
|
As described in :
|
|
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
|
|
- https://arxiv.org/abs/2101.08692
|
|
and
|
|
`High-Performance Large-Scale Image Recognition Without Normalization` - https://arxiv.org/abs/2102.06171
|
|
|
|
This model aims to cover both the NFRegNet-Bx models as detailed in the paper's code snippets and
|
|
the (preact) ResNet models described earlier in the paper.
|
|
|
|
There are a few differences:
|
|
* channels are rounded to be divisible by 8 by default (keep tensor core kernels happy),
|
|
this changes channel dim and param counts slightly from the paper models
|
|
* activation correcting gamma constants are moved into the ScaledStdConv as it has less performance
|
|
impact in PyTorch when done with the weight scaling there. This likely wasn't a concern in the JAX impl.
|
|
* a config option `gamma_in_act` can be enabled to not apply gamma in StdConv as described above, but
|
|
apply it in each activation. This is slightly slower, numerically different, but matches official impl.
|
|
* skipinit is disabled by default, it seems to have a rather drastic impact on GPU memory use and throughput
|
|
for what it is/does. Approx 8-10% throughput loss.
|
|
"""
|
|
def __init__(
|
|
self, cfg: NfCfg, num_classes=1000, in_chans=3, global_pool='avg', output_stride=32,
|
|
drop_rate=0., drop_path_rate=0.
|
|
):
|
|
super().__init__()
|
|
self.num_classes = num_classes
|
|
self.drop_rate = drop_rate
|
|
self.grad_checkpointing = False
|
|
|
|
assert cfg.act_layer in _nonlin_gamma, f"Please add non-linearity constants for activation ({cfg.act_layer})."
|
|
conv_layer = ScaledStdConv2dSame if cfg.same_padding else ScaledStdConv2d
|
|
if cfg.gamma_in_act:
|
|
act_layer = act_with_gamma(cfg.act_layer, gamma=_nonlin_gamma[cfg.act_layer])
|
|
conv_layer = partial(conv_layer, eps=cfg.std_conv_eps)
|
|
else:
|
|
act_layer = get_act_layer(cfg.act_layer)
|
|
conv_layer = partial(conv_layer, gamma=_nonlin_gamma[cfg.act_layer], eps=cfg.std_conv_eps)
|
|
attn_layer = partial(get_attn(cfg.attn_layer), **cfg.attn_kwargs) if cfg.attn_layer else None
|
|
|
|
stem_chs = make_divisible((cfg.stem_chs or cfg.channels[0]) * cfg.width_factor, cfg.ch_div)
|
|
self.stem, stem_stride, stem_feat = create_stem(
|
|
in_chans, stem_chs, cfg.stem_type, conv_layer=conv_layer, act_layer=act_layer)
|
|
|
|
self.feature_info = [stem_feat]
|
|
drop_path_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(cfg.depths)).split(cfg.depths)]
|
|
prev_chs = stem_chs
|
|
net_stride = stem_stride
|
|
dilation = 1
|
|
expected_var = 1.0
|
|
stages = []
|
|
for stage_idx, stage_depth in enumerate(cfg.depths):
|
|
stride = 1 if stage_idx == 0 and stem_stride > 2 else 2
|
|
if net_stride >= output_stride and stride > 1:
|
|
dilation *= stride
|
|
stride = 1
|
|
net_stride *= stride
|
|
first_dilation = 1 if dilation in (1, 2) else 2
|
|
|
|
blocks = []
|
|
for block_idx in range(cfg.depths[stage_idx]):
|
|
first_block = block_idx == 0 and stage_idx == 0
|
|
out_chs = make_divisible(cfg.channels[stage_idx] * cfg.width_factor, cfg.ch_div)
|
|
blocks += [NormFreeBlock(
|
|
in_chs=prev_chs, out_chs=out_chs,
|
|
alpha=cfg.alpha,
|
|
beta=1. / expected_var ** 0.5,
|
|
stride=stride if block_idx == 0 else 1,
|
|
dilation=dilation,
|
|
first_dilation=first_dilation,
|
|
group_size=cfg.group_size,
|
|
bottle_ratio=1. if cfg.reg and first_block else cfg.bottle_ratio,
|
|
ch_div=cfg.ch_div,
|
|
reg=cfg.reg,
|
|
extra_conv=cfg.extra_conv,
|
|
skipinit=cfg.skipinit,
|
|
attn_layer=attn_layer,
|
|
attn_gain=cfg.attn_gain,
|
|
act_layer=act_layer,
|
|
conv_layer=conv_layer,
|
|
drop_path_rate=drop_path_rates[stage_idx][block_idx],
|
|
)]
|
|
if block_idx == 0:
|
|
expected_var = 1. # expected var is reset after first block of each stage
|
|
expected_var += cfg.alpha ** 2 # Even if reset occurs, increment expected variance
|
|
first_dilation = dilation
|
|
prev_chs = out_chs
|
|
self.feature_info += [dict(num_chs=prev_chs, reduction=net_stride, module=f'stages.{stage_idx}')]
|
|
stages += [nn.Sequential(*blocks)]
|
|
self.stages = nn.Sequential(*stages)
|
|
|
|
if cfg.num_features:
|
|
# The paper NFRegNet models have an EfficientNet-like final head convolution.
|
|
self.num_features = make_divisible(cfg.width_factor * cfg.num_features, cfg.ch_div)
|
|
self.final_conv = conv_layer(prev_chs, self.num_features, 1)
|
|
self.feature_info[-1] = dict(num_chs=self.num_features, reduction=net_stride, module=f'final_conv')
|
|
else:
|
|
self.num_features = prev_chs
|
|
self.final_conv = nn.Identity()
|
|
self.final_act = act_layer(inplace=cfg.num_features > 0)
|
|
|
|
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
|
|
|
|
for n, m in self.named_modules():
|
|
if 'fc' in n and isinstance(m, nn.Linear):
|
|
if cfg.zero_init_fc:
|
|
nn.init.zeros_(m.weight)
|
|
else:
|
|
nn.init.normal_(m.weight, 0., .01)
|
|
if m.bias is not None:
|
|
nn.init.zeros_(m.bias)
|
|
elif isinstance(m, nn.Conv2d):
|
|
nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='linear')
|
|
if m.bias is not None:
|
|
nn.init.zeros_(m.bias)
|
|
|
|
@torch.jit.ignore
|
|
def group_matcher(self, coarse=False):
|
|
matcher = dict(
|
|
stem=r'^stem',
|
|
blocks=[
|
|
(r'^stages\.(\d+)' if coarse else r'^stages\.(\d+)\.(\d+)', None),
|
|
(r'^final_conv', (99999,))
|
|
]
|
|
)
|
|
return matcher
|
|
|
|
@torch.jit.ignore
|
|
def set_grad_checkpointing(self, enable=True):
|
|
self.grad_checkpointing = enable
|
|
|
|
@torch.jit.ignore
|
|
def get_classifier(self):
|
|
return self.head.fc
|
|
|
|
def reset_classifier(self, num_classes, global_pool='avg'):
|
|
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
|
|
|
|
def forward_features(self, x):
|
|
x = self.stem(x)
|
|
if self.grad_checkpointing and not torch.jit.is_scripting():
|
|
x = checkpoint_seq(self.stages, x)
|
|
else:
|
|
x = self.stages(x)
|
|
x = self.final_conv(x)
|
|
x = self.final_act(x)
|
|
return x
|
|
|
|
def forward_head(self, x):
|
|
return self.head(x)
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
x = self.forward_head(x)
|
|
return x
|
|
|
|
|
|
def _create_normfreenet(variant, pretrained=False, **kwargs):
|
|
model_cfg = model_cfgs[variant]
|
|
feature_cfg = dict(flatten_sequential=True)
|
|
return build_model_with_cfg(
|
|
NormFreeNet, variant, pretrained,
|
|
model_cfg=model_cfg,
|
|
feature_cfg=feature_cfg,
|
|
**kwargs)
|
|
|
|
|
|
@register_model
|
|
def dm_nfnet_f0(pretrained=False, **kwargs):
|
|
""" NFNet-F0 (DeepMind weight compatible)
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('dm_nfnet_f0', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def dm_nfnet_f1(pretrained=False, **kwargs):
|
|
""" NFNet-F1 (DeepMind weight compatible)
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('dm_nfnet_f1', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def dm_nfnet_f2(pretrained=False, **kwargs):
|
|
""" NFNet-F2 (DeepMind weight compatible)
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('dm_nfnet_f2', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def dm_nfnet_f3(pretrained=False, **kwargs):
|
|
""" NFNet-F3 (DeepMind weight compatible)
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('dm_nfnet_f3', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def dm_nfnet_f4(pretrained=False, **kwargs):
|
|
""" NFNet-F4 (DeepMind weight compatible)
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('dm_nfnet_f4', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def dm_nfnet_f5(pretrained=False, **kwargs):
|
|
""" NFNet-F5 (DeepMind weight compatible)
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('dm_nfnet_f5', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def dm_nfnet_f6(pretrained=False, **kwargs):
|
|
""" NFNet-F6 (DeepMind weight compatible)
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('dm_nfnet_f6', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nfnet_f0(pretrained=False, **kwargs):
|
|
""" NFNet-F0
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('nfnet_f0', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nfnet_f1(pretrained=False, **kwargs):
|
|
""" NFNet-F1
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('nfnet_f1', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nfnet_f2(pretrained=False, **kwargs):
|
|
""" NFNet-F2
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('nfnet_f2', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nfnet_f3(pretrained=False, **kwargs):
|
|
""" NFNet-F3
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('nfnet_f3', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nfnet_f4(pretrained=False, **kwargs):
|
|
""" NFNet-F4
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('nfnet_f4', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nfnet_f5(pretrained=False, **kwargs):
|
|
""" NFNet-F5
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('nfnet_f5', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nfnet_f6(pretrained=False, **kwargs):
|
|
""" NFNet-F6
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('nfnet_f6', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nfnet_f7(pretrained=False, **kwargs):
|
|
""" NFNet-F7
|
|
`High-Performance Large-Scale Image Recognition Without Normalization`
|
|
- https://arxiv.org/abs/2102.06171
|
|
"""
|
|
return _create_normfreenet('nfnet_f7', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nfnet_l0(pretrained=False, **kwargs):
|
|
""" NFNet-L0b w/ SiLU
|
|
My experimental 'light' model w/ F0 repeats, 1.5x final_conv mult, 64 group_size, .25 bottleneck & SE ratio
|
|
"""
|
|
return _create_normfreenet('nfnet_l0', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def eca_nfnet_l0(pretrained=False, **kwargs):
|
|
""" ECA-NFNet-L0 w/ SiLU
|
|
My experimental 'light' model w/ F0 repeats, 1.5x final_conv mult, 64 group_size, .25 bottleneck & ECA attn
|
|
"""
|
|
return _create_normfreenet('eca_nfnet_l0', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def eca_nfnet_l1(pretrained=False, **kwargs):
|
|
""" ECA-NFNet-L1 w/ SiLU
|
|
My experimental 'light' model w/ F1 repeats, 2.0x final_conv mult, 64 group_size, .25 bottleneck & ECA attn
|
|
"""
|
|
return _create_normfreenet('eca_nfnet_l1', pretrained=pretrained, **kwargs)
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@register_model
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def eca_nfnet_l2(pretrained=False, **kwargs):
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""" ECA-NFNet-L2 w/ SiLU
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My experimental 'light' model w/ F2 repeats, 2.0x final_conv mult, 64 group_size, .25 bottleneck & ECA attn
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"""
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return _create_normfreenet('eca_nfnet_l2', pretrained=pretrained, **kwargs)
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@register_model
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def eca_nfnet_l3(pretrained=False, **kwargs):
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""" ECA-NFNet-L3 w/ SiLU
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My experimental 'light' model w/ F3 repeats, 2.0x final_conv mult, 64 group_size, .25 bottleneck & ECA attn
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"""
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return _create_normfreenet('eca_nfnet_l3', pretrained=pretrained, **kwargs)
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@register_model
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def nf_regnet_b0(pretrained=False, **kwargs):
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""" Normalization-Free RegNet-B0
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`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
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- https://arxiv.org/abs/2101.08692
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"""
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return _create_normfreenet('nf_regnet_b0', pretrained=pretrained, **kwargs)
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@register_model
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def nf_regnet_b1(pretrained=False, **kwargs):
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""" Normalization-Free RegNet-B1
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`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
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- https://arxiv.org/abs/2101.08692
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"""
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return _create_normfreenet('nf_regnet_b1', pretrained=pretrained, **kwargs)
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@register_model
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def nf_regnet_b2(pretrained=False, **kwargs):
|
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""" Normalization-Free RegNet-B2
|
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`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
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|
- https://arxiv.org/abs/2101.08692
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"""
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return _create_normfreenet('nf_regnet_b2', pretrained=pretrained, **kwargs)
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@register_model
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def nf_regnet_b3(pretrained=False, **kwargs):
|
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""" Normalization-Free RegNet-B3
|
|
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
|
|
- https://arxiv.org/abs/2101.08692
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"""
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return _create_normfreenet('nf_regnet_b3', pretrained=pretrained, **kwargs)
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@register_model
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def nf_regnet_b4(pretrained=False, **kwargs):
|
|
""" Normalization-Free RegNet-B4
|
|
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
|
|
- https://arxiv.org/abs/2101.08692
|
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"""
|
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return _create_normfreenet('nf_regnet_b4', pretrained=pretrained, **kwargs)
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@register_model
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|
def nf_regnet_b5(pretrained=False, **kwargs):
|
|
""" Normalization-Free RegNet-B5
|
|
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
|
|
- https://arxiv.org/abs/2101.08692
|
|
"""
|
|
return _create_normfreenet('nf_regnet_b5', pretrained=pretrained, **kwargs)
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@register_model
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|
def nf_resnet26(pretrained=False, **kwargs):
|
|
""" Normalization-Free ResNet-26
|
|
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
|
|
- https://arxiv.org/abs/2101.08692
|
|
"""
|
|
return _create_normfreenet('nf_resnet26', pretrained=pretrained, **kwargs)
|
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|
|
@register_model
|
|
def nf_resnet50(pretrained=False, **kwargs):
|
|
""" Normalization-Free ResNet-50
|
|
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
|
|
- https://arxiv.org/abs/2101.08692
|
|
"""
|
|
return _create_normfreenet('nf_resnet50', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nf_resnet101(pretrained=False, **kwargs):
|
|
""" Normalization-Free ResNet-101
|
|
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
|
|
- https://arxiv.org/abs/2101.08692
|
|
"""
|
|
return _create_normfreenet('nf_resnet101', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nf_seresnet26(pretrained=False, **kwargs):
|
|
""" Normalization-Free SE-ResNet26
|
|
"""
|
|
return _create_normfreenet('nf_seresnet26', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nf_seresnet50(pretrained=False, **kwargs):
|
|
""" Normalization-Free SE-ResNet50
|
|
"""
|
|
return _create_normfreenet('nf_seresnet50', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nf_seresnet101(pretrained=False, **kwargs):
|
|
""" Normalization-Free SE-ResNet101
|
|
"""
|
|
return _create_normfreenet('nf_seresnet101', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nf_ecaresnet26(pretrained=False, **kwargs):
|
|
""" Normalization-Free ECA-ResNet26
|
|
"""
|
|
return _create_normfreenet('nf_ecaresnet26', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nf_ecaresnet50(pretrained=False, **kwargs):
|
|
""" Normalization-Free ECA-ResNet50
|
|
"""
|
|
return _create_normfreenet('nf_ecaresnet50', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def nf_ecaresnet101(pretrained=False, **kwargs):
|
|
""" Normalization-Free ECA-ResNet101
|
|
"""
|
|
return _create_normfreenet('nf_ecaresnet101', pretrained=pretrained, **kwargs)
|