regnet.py multi-weight conversion, new ImageNet-12k pretrain/ft from timm for y_120 and y_160, also new tv v2, swag, & seer weights for push to Hf hub.
Ross Wightman
parent
c78319adce
commit
e7ef8335bf
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@ -1,16 +1,26 @@
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"""RegNet
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"""RegNet X, Y, Z, and more
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Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
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Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
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Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877
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Original Impl: None
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Based on original PyTorch impl linked above, but re-wrote to use my own blocks (adapted from ResNet here)
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and cleaned up with more descriptive variable names.
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Weights from original impl have been modified
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Weights from original pycls impl have been modified:
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* first layer from BGR -> RGB as most PyTorch models are
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* removed training specific dict entries from checkpoints and keep model state_dict only
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* remap names to match the ones here
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Supports weight loading from torchvision and classy-vision (incl VISSL SEER)
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A number of custom timm model definitions additions including:
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* stochastic depth, gradient checkpointing, layer-decay, configurable dilation
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* a pre-activation 'V' variant
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* only known RegNet-Z model definitions with pretrained weights
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import math
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@ -24,10 +34,10 @@ 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, AvgPool2dSame, ConvNormAct, SEModule, DropPath, GroupNormAct
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from timm.layers import get_act_layer, get_norm_act_layer, create_conv2d
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from timm.layers import get_act_layer, get_norm_act_layer, create_conv2d, make_divisible
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from ._builder import build_model_with_cfg
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from ._manipulate import checkpoint_seq, named_apply
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from ._registry import register_model
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from ._registry import generate_default_cfgs, register_model, register_model_deprecations
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__all__ = ['RegNet', 'RegNetCfg'] # model_registry will add each entrypoint fn to this
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@ -41,6 +51,7 @@ class RegNetCfg:
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group_size: int = 24
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bottle_ratio: float = 1.
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se_ratio: float = 0.
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group_min_ratio: float = 0.
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stem_width: int = 32
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downsample: Optional[str] = 'conv1x1'
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linear_out: bool = False
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@ -50,178 +61,79 @@ class RegNetCfg:
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norm_layer: Union[str, Callable] = 'batchnorm'
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# Model FLOPS = three trailing digits * 10^8
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model_cfgs = dict(
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# RegNet-X
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regnetx_002=RegNetCfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13),
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regnetx_004=RegNetCfg(w0=24, wa=24.48, wm=2.54, group_size=16, depth=22),
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regnetx_006=RegNetCfg(w0=48, wa=36.97, wm=2.24, group_size=24, depth=16),
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regnetx_008=RegNetCfg(w0=56, wa=35.73, wm=2.28, group_size=16, depth=16),
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regnetx_016=RegNetCfg(w0=80, wa=34.01, wm=2.25, group_size=24, depth=18),
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regnetx_032=RegNetCfg(w0=88, wa=26.31, wm=2.25, group_size=48, depth=25),
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regnetx_040=RegNetCfg(w0=96, wa=38.65, wm=2.43, group_size=40, depth=23),
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regnetx_064=RegNetCfg(w0=184, wa=60.83, wm=2.07, group_size=56, depth=17),
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regnetx_080=RegNetCfg(w0=80, wa=49.56, wm=2.88, group_size=120, depth=23),
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regnetx_120=RegNetCfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19),
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regnetx_160=RegNetCfg(w0=216, wa=55.59, wm=2.1, group_size=128, depth=22),
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regnetx_320=RegNetCfg(w0=320, wa=69.86, wm=2.0, group_size=168, depth=23),
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# RegNet-Y
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regnety_002=RegNetCfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13, se_ratio=0.25),
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regnety_004=RegNetCfg(w0=48, wa=27.89, wm=2.09, group_size=8, depth=16, se_ratio=0.25),
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regnety_006=RegNetCfg(w0=48, wa=32.54, wm=2.32, group_size=16, depth=15, se_ratio=0.25),
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regnety_008=RegNetCfg(w0=56, wa=38.84, wm=2.4, group_size=16, depth=14, se_ratio=0.25),
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regnety_016=RegNetCfg(w0=48, wa=20.71, wm=2.65, group_size=24, depth=27, se_ratio=0.25),
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regnety_032=RegNetCfg(w0=80, wa=42.63, wm=2.66, group_size=24, depth=21, se_ratio=0.25),
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regnety_040=RegNetCfg(w0=96, wa=31.41, wm=2.24, group_size=64, depth=22, se_ratio=0.25),
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regnety_064=RegNetCfg(w0=112, wa=33.22, wm=2.27, group_size=72, depth=25, se_ratio=0.25),
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regnety_080=RegNetCfg(w0=192, wa=76.82, wm=2.19, group_size=56, depth=17, se_ratio=0.25),
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regnety_120=RegNetCfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19, se_ratio=0.25),
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regnety_160=RegNetCfg(w0=200, wa=106.23, wm=2.48, group_size=112, depth=18, se_ratio=0.25),
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regnety_320=RegNetCfg(w0=232, wa=115.89, wm=2.53, group_size=232, depth=20, se_ratio=0.25),
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regnety_640=RegNetCfg(w0=352, wa=147.48, wm=2.4, group_size=328, depth=20, se_ratio=0.25),
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regnety_1280=RegNetCfg(w0=456, wa=160.83, wm=2.52, group_size=264, depth=27, se_ratio=0.25),
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regnety_2560=RegNetCfg(w0=640, wa=124.47, wm=2.04, group_size=848, depth=27, se_ratio=0.25),
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# Experimental
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regnety_040s_gn=RegNetCfg(
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w0=96, wa=31.41, wm=2.24, group_size=64, depth=22, se_ratio=0.25,
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act_layer='silu', norm_layer=partial(GroupNormAct, group_size=16)),
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# regnetv = 'preact regnet y'
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regnetv_040=RegNetCfg(
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depth=22, w0=96, wa=31.41, wm=2.24, group_size=64, se_ratio=0.25, preact=True, act_layer='silu'),
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regnetv_064=RegNetCfg(
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depth=25, w0=112, wa=33.22, wm=2.27, group_size=72, se_ratio=0.25, preact=True, act_layer='silu',
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downsample='avg'),
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# RegNet-Z (unverified)
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regnetz_005=RegNetCfg(
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depth=21, w0=16, wa=10.7, wm=2.51, group_size=4, bottle_ratio=4.0, se_ratio=0.25,
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downsample=None, linear_out=True, num_features=1024, act_layer='silu',
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),
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regnetz_040=RegNetCfg(
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depth=28, w0=48, wa=14.5, wm=2.226, group_size=8, bottle_ratio=4.0, se_ratio=0.25,
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downsample=None, linear_out=True, num_features=0, act_layer='silu',
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),
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regnetz_040h=RegNetCfg(
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depth=28, w0=48, wa=14.5, wm=2.226, group_size=8, bottle_ratio=4.0, se_ratio=0.25,
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downsample=None, linear_out=True, num_features=1536, act_layer='silu',
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),
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)
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def _cfg(url='', **kwargs):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'stem.conv', 'classifier': 'head.fc',
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**kwargs
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}
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default_cfgs = dict(
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regnetx_002=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth'),
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regnetx_004=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth'),
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regnetx_006=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth'),
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regnetx_008=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth'),
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regnetx_016=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth'),
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regnetx_032=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth'),
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regnetx_040=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth'),
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regnetx_064=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth'),
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regnetx_080=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth'),
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regnetx_120=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth'),
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regnetx_160=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth'),
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regnetx_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth'),
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regnety_002=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth'),
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regnety_004=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth'),
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regnety_006=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth'),
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regnety_008=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth'),
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regnety_016=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth'),
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regnety_032=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth',
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crop_pct=1.0, test_input_size=(3, 288, 288)),
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regnety_040=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/regnety_040_ra3-670e1166.pth',
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crop_pct=1.0, test_input_size=(3, 288, 288)),
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regnety_064=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/regnety_064_ra3-aa26dc7d.pth',
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crop_pct=1.0, test_input_size=(3, 288, 288)),
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regnety_080=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/regnety_080_ra3-1fdc4344.pth',
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crop_pct=1.0, test_input_size=(3, 288, 288)),
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regnety_120=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth'),
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regnety_160=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/regnety_160-a5fe301d.pth', # from Facebook DeiT GitHub repository
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crop_pct=1.0, test_input_size=(3, 288, 288)),
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regnety_320=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'
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),
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regnety_640=_cfg(url=''),
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regnety_1280=_cfg(url=''),
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regnety_2560=_cfg(url=''),
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regnety_040s_gn=_cfg(url=''),
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regnetv_040=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetv_040_ra3-c248f51f.pth',
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first_conv='stem', crop_pct=1.0, test_input_size=(3, 288, 288)),
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regnetv_064=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetv_064_ra3-530616c2.pth',
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first_conv='stem', crop_pct=1.0, test_input_size=(3, 288, 288)),
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regnetz_005=_cfg(url=''),
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regnetz_040=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetz_040_ra3-9007edf5.pth',
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input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320)),
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regnetz_040h=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetz_040h_ra3-f594343b.pth',
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input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320)),
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)
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def quantize_float(f, q):
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"""Converts a float to closest non-zero int divisible by q."""
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"""Converts a float to the closest non-zero int divisible by q."""
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return int(round(f / q) * q)
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def adjust_widths_groups_comp(widths, bottle_ratios, groups):
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def adjust_widths_groups_comp(widths, bottle_ratios, groups, min_ratio=0.):
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"""Adjusts the compatibility of widths and groups."""
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bottleneck_widths = [int(w * b) for w, b in zip(widths, bottle_ratios)]
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groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_widths)]
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bottleneck_widths = [quantize_float(w_bot, g) for w_bot, g in zip(bottleneck_widths, groups)]
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if min_ratio:
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# torchvision uses a different rounding scheme for ensuring bottleneck widths divisible by group widths
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bottleneck_widths = [make_divisible(w_bot, g, min_ratio) for w_bot, g in zip(bottleneck_widths, groups)]
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else:
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bottleneck_widths = [quantize_float(w_bot, g) for w_bot, g in zip(bottleneck_widths, groups)]
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widths = [int(w_bot / b) for w_bot, b in zip(bottleneck_widths, bottle_ratios)]
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return widths, groups
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def generate_regnet(width_slope, width_initial, width_mult, depth, group_size, q=8):
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def generate_regnet(width_slope, width_initial, width_mult, depth, group_size, quant=8):
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"""Generates per block widths from RegNet parameters."""
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assert width_slope >= 0 and width_initial > 0 and width_mult > 1 and width_initial % q == 0
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assert width_slope >= 0 and width_initial > 0 and width_mult > 1 and width_initial % quant == 0
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# TODO dWr scaling?
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# depth = int(depth * (scale ** 0.1))
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# width_scale = scale ** 0.4 # dWr scale, exp 0.8 / 2, applied to both group and layer widths
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widths_cont = np.arange(depth) * width_slope + width_initial
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width_exps = np.round(np.log(widths_cont / width_initial) / np.log(width_mult))
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widths = width_initial * np.power(width_mult, width_exps)
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widths = np.round(np.divide(widths, q)) * q
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widths = np.round(np.divide(width_initial * np.power(width_mult, width_exps), quant)) * quant
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num_stages, max_stage = len(np.unique(widths)), width_exps.max() + 1
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groups = np.array([group_size for _ in range(num_stages)])
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return widths.astype(int).tolist(), num_stages, groups.astype(int).tolist()
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def downsample_conv(in_chs, out_chs, kernel_size=1, stride=1, dilation=1, norm_layer=None, preact=False):
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def downsample_conv(
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in_chs,
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out_chs,
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kernel_size=1,
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stride=1,
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dilation=1,
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norm_layer=None,
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preact=False,
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):
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norm_layer = norm_layer or nn.BatchNorm2d
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kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size
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dilation = dilation if kernel_size > 1 else 1
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if preact:
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return create_conv2d(in_chs, out_chs, kernel_size, stride=stride, dilation=dilation)
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return create_conv2d(
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in_chs,
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out_chs,
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kernel_size,
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stride=stride,
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dilation=dilation,
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)
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else:
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return ConvNormAct(
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in_chs, out_chs, kernel_size, stride=stride, dilation=dilation, norm_layer=norm_layer, apply_act=False)
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in_chs,
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out_chs,
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kernel_size,
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stride=stride,
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dilation=dilation,
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norm_layer=norm_layer,
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apply_act=False,
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)
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def downsample_avg(in_chs, out_chs, kernel_size=1, stride=1, dilation=1, norm_layer=None, preact=False):
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def downsample_avg(
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in_chs,
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out_chs,
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kernel_size=1,
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stride=1,
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dilation=1,
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norm_layer=None,
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preact=False,
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):
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""" AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment."""
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norm_layer = norm_layer or nn.BatchNorm2d
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avg_stride = stride if dilation == 1 else 1
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@ -290,8 +202,15 @@ class Bottleneck(nn.Module):
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cargs = dict(act_layer=act_layer, norm_layer=norm_layer)
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self.conv1 = ConvNormAct(in_chs, bottleneck_chs, kernel_size=1, **cargs)
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self.conv2 = ConvNormAct(
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bottleneck_chs, bottleneck_chs, kernel_size=3, stride=stride, dilation=dilation[0],
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groups=groups, drop_layer=drop_block, **cargs)
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bottleneck_chs,
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bottleneck_chs,
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kernel_size=3,
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stride=stride,
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dilation=dilation[0],
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groups=groups,
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drop_layer=drop_block,
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**cargs,
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)
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if se_ratio:
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||||
se_channels = int(round(in_chs * se_ratio))
|
||||
self.se = SEModule(bottleneck_chs, rd_channels=se_channels, act_layer=act_layer)
|
||||
|
|
@ -299,7 +218,15 @@ class Bottleneck(nn.Module):
|
|||
self.se = nn.Identity()
|
||||
self.conv3 = ConvNormAct(bottleneck_chs, out_chs, kernel_size=1, apply_act=False, **cargs)
|
||||
self.act3 = nn.Identity() if linear_out else act_layer()
|
||||
self.downsample = create_shortcut(downsample, in_chs, out_chs, 1, stride, dilation, norm_layer=norm_layer)
|
||||
self.downsample = create_shortcut(
|
||||
downsample,
|
||||
in_chs,
|
||||
out_chs,
|
||||
kernel_size=1,
|
||||
stride=stride,
|
||||
dilation=dilation,
|
||||
norm_layer=norm_layer,
|
||||
)
|
||||
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
|
||||
|
||||
def zero_init_last(self):
|
||||
|
|
@ -351,7 +278,13 @@ class PreBottleneck(nn.Module):
|
|||
self.conv1 = create_conv2d(in_chs, bottleneck_chs, kernel_size=1)
|
||||
self.norm2 = norm_act_layer(bottleneck_chs)
|
||||
self.conv2 = create_conv2d(
|
||||
bottleneck_chs, bottleneck_chs, kernel_size=3, stride=stride, dilation=dilation[0], groups=groups)
|
||||
bottleneck_chs,
|
||||
bottleneck_chs,
|
||||
kernel_size=3,
|
||||
stride=stride,
|
||||
dilation=dilation[0],
|
||||
groups=groups,
|
||||
)
|
||||
if se_ratio:
|
||||
se_channels = int(round(in_chs * se_ratio))
|
||||
self.se = SEModule(bottleneck_chs, rd_channels=se_channels, act_layer=act_layer)
|
||||
|
|
@ -359,7 +292,15 @@ class PreBottleneck(nn.Module):
|
|||
self.se = nn.Identity()
|
||||
self.norm3 = norm_act_layer(bottleneck_chs)
|
||||
self.conv3 = create_conv2d(bottleneck_chs, out_chs, kernel_size=1)
|
||||
self.downsample = create_shortcut(downsample, in_chs, out_chs, 1, stride, dilation, preact=True)
|
||||
self.downsample = create_shortcut(
|
||||
downsample,
|
||||
in_chs,
|
||||
out_chs,
|
||||
kernel_size=1,
|
||||
stride=stride,
|
||||
dilation=dilation,
|
||||
preact=True,
|
||||
)
|
||||
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
|
||||
|
||||
def zero_init_last(self):
|
||||
|
|
@ -406,7 +347,8 @@ class RegStage(nn.Module):
|
|||
dpr = drop_path_rates[i] if drop_path_rates is not None else 0.
|
||||
name = "b{}".format(i + 1)
|
||||
self.add_module(
|
||||
name, block_fn(
|
||||
name,
|
||||
block_fn(
|
||||
block_in_chs,
|
||||
out_chs,
|
||||
stride=block_stride,
|
||||
|
|
@ -477,12 +419,23 @@ class RegNet(nn.Module):
|
|||
prev_width = stem_width
|
||||
curr_stride = 2
|
||||
per_stage_args, common_args = self._get_stage_args(
|
||||
cfg, output_stride=output_stride, drop_path_rate=drop_path_rate)
|
||||
cfg,
|
||||
output_stride=output_stride,
|
||||
drop_path_rate=drop_path_rate,
|
||||
)
|
||||
assert len(per_stage_args) == 4
|
||||
block_fn = PreBottleneck if cfg.preact else Bottleneck
|
||||
for i, stage_args in enumerate(per_stage_args):
|
||||
stage_name = "s{}".format(i + 1)
|
||||
self.add_module(stage_name, RegStage(in_chs=prev_width, block_fn=block_fn, **stage_args, **common_args))
|
||||
self.add_module(
|
||||
stage_name,
|
||||
RegStage(
|
||||
in_chs=prev_width,
|
||||
block_fn=block_fn,
|
||||
**stage_args,
|
||||
**common_args,
|
||||
)
|
||||
)
|
||||
prev_width = stage_args['out_chs']
|
||||
curr_stride *= stage_args['stride']
|
||||
self.feature_info += [dict(num_chs=prev_width, reduction=curr_stride, module=stage_name)]
|
||||
|
|
@ -496,7 +449,11 @@ class RegNet(nn.Module):
|
|||
self.final_conv = get_act_layer(cfg.act_layer)() if final_act else nn.Identity()
|
||||
self.num_features = prev_width
|
||||
self.head = ClassifierHead(
|
||||
in_features=self.num_features, num_classes=num_classes, pool_type=global_pool, drop_rate=drop_rate)
|
||||
in_features=self.num_features,
|
||||
num_classes=num_classes,
|
||||
pool_type=global_pool,
|
||||
drop_rate=drop_rate,
|
||||
)
|
||||
|
||||
named_apply(partial(_init_weights, zero_init_last=zero_init_last), self)
|
||||
|
||||
|
|
@ -523,11 +480,13 @@ class RegNet(nn.Module):
|
|||
stage_dpr = np.split(np.linspace(0, drop_path_rate, sum(stage_depths)), np.cumsum(stage_depths[:-1]))
|
||||
|
||||
# Adjust the compatibility of ws and gws
|
||||
stage_widths, stage_gs = adjust_widths_groups_comp(stage_widths, stage_br, stage_gs)
|
||||
stage_widths, stage_gs = adjust_widths_groups_comp(
|
||||
stage_widths, stage_br, stage_gs, min_ratio=cfg.group_min_ratio)
|
||||
arg_names = ['out_chs', 'stride', 'dilation', 'depth', 'bottle_ratio', 'group_size', 'drop_path_rates']
|
||||
per_stage_args = [
|
||||
dict(zip(arg_names, params)) for params in
|
||||
zip(stage_widths, stage_strides, stage_dilations, stage_depths, stage_br, stage_gs, stage_dpr)]
|
||||
zip(stage_widths, stage_strides, stage_dilations, stage_depths, stage_br, stage_gs, stage_dpr)
|
||||
]
|
||||
common_args = dict(
|
||||
downsample=cfg.downsample,
|
||||
se_ratio=cfg.se_ratio,
|
||||
|
|
@ -554,7 +513,7 @@ class RegNet(nn.Module):
|
|||
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)
|
||||
self.head.reset(num_classes, pool_type=global_pool)
|
||||
|
||||
def forward_features(self, x):
|
||||
x = self.stem(x)
|
||||
|
|
@ -590,7 +549,25 @@ def _init_weights(module, name='', zero_init_last=False):
|
|||
|
||||
|
||||
def _filter_fn(state_dict):
|
||||
state_dict = state_dict.get('model', state_dict)
|
||||
replaces = [
|
||||
('f.a.0', 'conv1.conv'),
|
||||
('f.a.1', 'conv1.bn'),
|
||||
('f.b.0', 'conv2.conv'),
|
||||
('f.b.1', 'conv2.bn'),
|
||||
('f.final_bn', 'conv3.bn'),
|
||||
('f.se.excitation.0', 'se.fc1'),
|
||||
('f.se.excitation.2', 'se.fc2'),
|
||||
('f.se', 'se'),
|
||||
('f.c.0', 'conv3.conv'),
|
||||
('f.c.1', 'conv3.bn'),
|
||||
('f.c', 'conv3.conv'),
|
||||
('proj.0', 'downsample.conv'),
|
||||
('proj.1', 'downsample.bn'),
|
||||
('proj', 'downsample.conv'),
|
||||
]
|
||||
if 'classy_state_dict' in state_dict:
|
||||
# classy-vision & vissl (SEER) weights
|
||||
import re
|
||||
state_dict = state_dict['classy_state_dict']['base_model']['model']
|
||||
out = {}
|
||||
|
|
@ -601,15 +578,8 @@ def _filter_fn(state_dict):
|
|||
r'^_feature_blocks.res\d.block(\d)-(\d+)',
|
||||
lambda x: f's{int(x.group(1))}.b{int(x.group(2)) + 1}', k)
|
||||
k = re.sub(r's(\d)\.b(\d+)\.bn', r's\1.b\2.downsample.bn', k)
|
||||
k = k.replace('proj', 'downsample.conv')
|
||||
k = k.replace('f.a.0', 'conv1.conv')
|
||||
k = k.replace('f.a.1', 'conv1.bn')
|
||||
k = k.replace('f.b.0', 'conv2.conv')
|
||||
k = k.replace('f.b.1', 'conv2.bn')
|
||||
k = k.replace('f.c', 'conv3.conv')
|
||||
k = k.replace('f.final_bn', 'conv3.bn')
|
||||
k = k.replace('f.se.excitation.0', 'se.fc1')
|
||||
k = k.replace('f.se.excitation.2', 'se.fc2')
|
||||
for s, r in replaces:
|
||||
k = k.replace(s, r)
|
||||
out[k] = v
|
||||
for k, v in state_dict['heads'].items():
|
||||
if 'projection_head' in k or 'prototypes' in k:
|
||||
|
|
@ -617,13 +587,89 @@ def _filter_fn(state_dict):
|
|||
k = k.replace('0.clf.0', 'head.fc')
|
||||
out[k] = v
|
||||
return out
|
||||
|
||||
if 'model' in state_dict:
|
||||
# For DeiT trained regnety_160 pretraiend model
|
||||
state_dict = state_dict['model']
|
||||
if 'stem.0.weight' in state_dict:
|
||||
# torchvision weights
|
||||
import re
|
||||
out = {}
|
||||
for k, v in state_dict.items():
|
||||
k = k.replace('stem.0', 'stem.conv')
|
||||
k = k.replace('stem.1', 'stem.bn')
|
||||
k = re.sub(
|
||||
r'trunk_output.block(\d)\.block(\d+)\-(\d+)',
|
||||
lambda x: f's{int(x.group(1))}.b{int(x.group(3)) + 1}', k)
|
||||
for s, r in replaces:
|
||||
k = k.replace(s, r)
|
||||
k = k.replace('fc.', 'head.fc.')
|
||||
out[k] = v
|
||||
return out
|
||||
return state_dict
|
||||
|
||||
|
||||
# Model FLOPS = three trailing digits * 10^8
|
||||
model_cfgs = dict(
|
||||
# RegNet-X
|
||||
regnetx_002=RegNetCfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13),
|
||||
regnetx_004=RegNetCfg(w0=24, wa=24.48, wm=2.54, group_size=16, depth=22),
|
||||
regnetx_004_tv=RegNetCfg(w0=24, wa=24.48, wm=2.54, group_size=16, depth=22, group_min_ratio=0.9),
|
||||
regnetx_006=RegNetCfg(w0=48, wa=36.97, wm=2.24, group_size=24, depth=16),
|
||||
regnetx_008=RegNetCfg(w0=56, wa=35.73, wm=2.28, group_size=16, depth=16),
|
||||
regnetx_016=RegNetCfg(w0=80, wa=34.01, wm=2.25, group_size=24, depth=18),
|
||||
regnetx_032=RegNetCfg(w0=88, wa=26.31, wm=2.25, group_size=48, depth=25),
|
||||
regnetx_040=RegNetCfg(w0=96, wa=38.65, wm=2.43, group_size=40, depth=23),
|
||||
regnetx_064=RegNetCfg(w0=184, wa=60.83, wm=2.07, group_size=56, depth=17),
|
||||
regnetx_080=RegNetCfg(w0=80, wa=49.56, wm=2.88, group_size=120, depth=23),
|
||||
regnetx_120=RegNetCfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19),
|
||||
regnetx_160=RegNetCfg(w0=216, wa=55.59, wm=2.1, group_size=128, depth=22),
|
||||
regnetx_320=RegNetCfg(w0=320, wa=69.86, wm=2.0, group_size=168, depth=23),
|
||||
|
||||
# RegNet-Y
|
||||
regnety_002=RegNetCfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13, se_ratio=0.25),
|
||||
regnety_004=RegNetCfg(w0=48, wa=27.89, wm=2.09, group_size=8, depth=16, se_ratio=0.25),
|
||||
regnety_006=RegNetCfg(w0=48, wa=32.54, wm=2.32, group_size=16, depth=15, se_ratio=0.25),
|
||||
regnety_008=RegNetCfg(w0=56, wa=38.84, wm=2.4, group_size=16, depth=14, se_ratio=0.25),
|
||||
regnety_008_tv=RegNetCfg(w0=56, wa=38.84, wm=2.4, group_size=16, depth=14, se_ratio=0.25, group_min_ratio=0.9),
|
||||
regnety_016=RegNetCfg(w0=48, wa=20.71, wm=2.65, group_size=24, depth=27, se_ratio=0.25),
|
||||
regnety_032=RegNetCfg(w0=80, wa=42.63, wm=2.66, group_size=24, depth=21, se_ratio=0.25),
|
||||
regnety_040=RegNetCfg(w0=96, wa=31.41, wm=2.24, group_size=64, depth=22, se_ratio=0.25),
|
||||
regnety_064=RegNetCfg(w0=112, wa=33.22, wm=2.27, group_size=72, depth=25, se_ratio=0.25),
|
||||
regnety_080=RegNetCfg(w0=192, wa=76.82, wm=2.19, group_size=56, depth=17, se_ratio=0.25),
|
||||
regnety_080_tv=RegNetCfg(w0=192, wa=76.82, wm=2.19, group_size=56, depth=17, se_ratio=0.25, group_min_ratio=0.9),
|
||||
regnety_120=RegNetCfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19, se_ratio=0.25),
|
||||
regnety_160=RegNetCfg(w0=200, wa=106.23, wm=2.48, group_size=112, depth=18, se_ratio=0.25),
|
||||
regnety_320=RegNetCfg(w0=232, wa=115.89, wm=2.53, group_size=232, depth=20, se_ratio=0.25),
|
||||
regnety_640=RegNetCfg(w0=352, wa=147.48, wm=2.4, group_size=328, depth=20, se_ratio=0.25),
|
||||
regnety_1280=RegNetCfg(w0=456, wa=160.83, wm=2.52, group_size=264, depth=27, se_ratio=0.25),
|
||||
regnety_2560=RegNetCfg(w0=640, wa=230.83, wm=2.53, group_size=373, depth=27, se_ratio=0.25),
|
||||
#regnety_2560=RegNetCfg(w0=640, wa=124.47, wm=2.04, group_size=848, depth=27, se_ratio=0.25),
|
||||
|
||||
# Experimental
|
||||
regnety_040_sgn=RegNetCfg(
|
||||
w0=96, wa=31.41, wm=2.24, group_size=64, depth=22, se_ratio=0.25,
|
||||
act_layer='silu', norm_layer=partial(GroupNormAct, group_size=16)),
|
||||
|
||||
# regnetv = 'preact regnet y'
|
||||
regnetv_040=RegNetCfg(
|
||||
depth=22, w0=96, wa=31.41, wm=2.24, group_size=64, se_ratio=0.25, preact=True, act_layer='silu'),
|
||||
regnetv_064=RegNetCfg(
|
||||
depth=25, w0=112, wa=33.22, wm=2.27, group_size=72, se_ratio=0.25, preact=True, act_layer='silu',
|
||||
downsample='avg'),
|
||||
|
||||
# RegNet-Z (unverified)
|
||||
regnetz_005=RegNetCfg(
|
||||
depth=21, w0=16, wa=10.7, wm=2.51, group_size=4, bottle_ratio=4.0, se_ratio=0.25,
|
||||
downsample=None, linear_out=True, num_features=1024, act_layer='silu',
|
||||
),
|
||||
regnetz_040=RegNetCfg(
|
||||
depth=28, w0=48, wa=14.5, wm=2.226, group_size=8, bottle_ratio=4.0, se_ratio=0.25,
|
||||
downsample=None, linear_out=True, num_features=0, act_layer='silu',
|
||||
),
|
||||
regnetz_040_h=RegNetCfg(
|
||||
depth=28, w0=48, wa=14.5, wm=2.226, group_size=8, bottle_ratio=4.0, se_ratio=0.25,
|
||||
downsample=None, linear_out=True, num_features=1536, act_layer='silu',
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def _create_regnet(variant, pretrained, **kwargs):
|
||||
return build_model_with_cfg(
|
||||
RegNet, variant, pretrained,
|
||||
|
|
@ -632,6 +678,220 @@ def _create_regnet(variant, pretrained, **kwargs):
|
|||
**kwargs)
|
||||
|
||||
|
||||
def _cfg(url='', **kwargs):
|
||||
return {
|
||||
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
|
||||
'test_input_size': (3, 288, 288), 'crop_pct': 0.95, 'test_crop_pct': 1.0,
|
||||
'interpolation': 'bicubic', 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
||||
'first_conv': 'stem.conv', 'classifier': 'head.fc',
|
||||
**kwargs
|
||||
}
|
||||
|
||||
|
||||
def _cfgpyc(url='', **kwargs):
|
||||
return {
|
||||
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
|
||||
'crop_pct': 0.875, 'interpolation': 'bicubic',
|
||||
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
||||
'first_conv': 'stem.conv', 'classifier': 'head.fc',
|
||||
'license': 'mit', 'origin_url': 'https://github.com/facebookresearch/pycls', **kwargs
|
||||
}
|
||||
|
||||
|
||||
def _cfgtv2(url='', **kwargs):
|
||||
return {
|
||||
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
|
||||
'crop_pct': 0.965, 'interpolation': 'bicubic',
|
||||
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
||||
'first_conv': 'stem.conv', 'classifier': 'head.fc',
|
||||
'license': 'bsd-3-clause', 'origin_url': 'https://github.com/pytorch/vision', **kwargs
|
||||
}
|
||||
|
||||
|
||||
default_cfgs = generate_default_cfgs({
|
||||
# timm trained models
|
||||
'regnety_032.ra_in1k': _cfg(
|
||||
hf_hub_id='timm/',
|
||||
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth'),
|
||||
'regnety_040.ra3_in1k': _cfg(
|
||||
hf_hub_id='timm/',
|
||||
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnety_040_ra3-670e1166.pth'),
|
||||
'regnety_064.ra3_in1k': _cfg(
|
||||
hf_hub_id='timm/',
|
||||
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnety_064_ra3-aa26dc7d.pth'),
|
||||
'regnety_080.ra3_in1k': _cfg(
|
||||
hf_hub_id='timm/',
|
||||
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnety_080_ra3-1fdc4344.pth'),
|
||||
'regnety_120.sw_in12k_ft_in1k': _cfg(hf_hub_id='timm/'),
|
||||
'regnety_160.sw_in12k_ft_in1k': _cfg(hf_hub_id='timm/'),
|
||||
'regnety_160.lion_in12k_ft_in1k': _cfg(hf_hub_id='timm/'),
|
||||
|
||||
# timm in12k pretrain
|
||||
'regnety_120.sw_in12k': _cfg(
|
||||
hf_hub_id='timm/',
|
||||
num_classes=11821),
|
||||
'regnety_160.sw_in12k': _cfg(
|
||||
hf_hub_id='timm/',
|
||||
num_classes=11821),
|
||||
|
||||
# timm custom arch (v and z guess) + trained models
|
||||
'regnety_040_sgn.untrained': _cfg(url=''),
|
||||
'regnetv_040.ra3_in1k': _cfg(
|
||||
hf_hub_id='timm/',
|
||||
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetv_040_ra3-c248f51f.pth',
|
||||
first_conv='stem'),
|
||||
'regnetv_064.ra3_in1k': _cfg(
|
||||
hf_hub_id='timm/',
|
||||
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetv_064_ra3-530616c2.pth',
|
||||
first_conv='stem'),
|
||||
|
||||
'regnetz_005.untrained': _cfg(url=''),
|
||||
'regnetz_040.ra3_in1k': _cfg(
|
||||
hf_hub_id='timm/',
|
||||
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetz_040_ra3-9007edf5.pth',
|
||||
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320)),
|
||||
'regnetz_040_h.ra3_in1k': _cfg(
|
||||
hf_hub_id='timm/',
|
||||
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetz_040h_ra3-f594343b.pth',
|
||||
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320)),
|
||||
|
||||
# used in DeiT for distillation (from Facebook DeiT GitHub repository)
|
||||
'regnety_160.deit_in1k': _cfg(
|
||||
hf_hub_id='timm/', url='https://dl.fbaipublicfiles.com/deit/regnety_160-a5fe301d.pth'),
|
||||
|
||||
'regnetx_004_tv.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_x_400mf-62229a5f.pth'),
|
||||
'regnetx_008.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_x_800mf-94a99ebd.pth'),
|
||||
'regnetx_016.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_x_1_6gf-a12f2b72.pth'),
|
||||
'regnetx_032.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_x_3_2gf-7071aa85.pth'),
|
||||
'regnetx_080.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_x_8gf-2b70d774.pth'),
|
||||
'regnetx_160.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_x_16gf-ba3796d7.pth'),
|
||||
'regnetx_320.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_x_32gf-6eb8fdc6.pth'),
|
||||
|
||||
'regnety_004.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_400mf-e6988f5f.pth'),
|
||||
'regnety_008_tv.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_800mf-58fc7688.pth'),
|
||||
'regnety_016.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_1_6gf-0d7bc02a.pth'),
|
||||
'regnety_032.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_3_2gf-9180c971.pth'),
|
||||
'regnety_080_tv.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_8gf-dc2b1b54.pth'),
|
||||
'regnety_160.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_16gf-3e4a00f9.pth'),
|
||||
'regnety_320.tv2_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_32gf-8db6d4b5.pth'),
|
||||
|
||||
'regnety_160.swag_ft_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_16gf_swag-43afe44d.pth', license='cc-by-nc-4.0',
|
||||
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
|
||||
'regnety_320.swag_ft_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_32gf_swag-04fdfa75.pth', license='cc-by-nc-4.0',
|
||||
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
|
||||
'regnety_1280.swag_ft_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_128gf_swag-c8ce3e52.pth', license='cc-by-nc-4.0',
|
||||
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
|
||||
|
||||
'regnety_160.swag_lc_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_16gf_lc_swag-f3ec0043.pth', license='cc-by-nc-4.0'),
|
||||
'regnety_320.swag_lc_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_32gf_lc_swag-e1583746.pth', license='cc-by-nc-4.0'),
|
||||
'regnety_1280.swag_lc_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://download.pytorch.org/models/regnet_y_128gf_lc_swag-cbe8ce12.pth', license='cc-by-nc-4.0'),
|
||||
|
||||
'regnety_320.seer_ft_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
license='other', origin_url='https://github.com/facebookresearch/vissl',
|
||||
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch',
|
||||
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
|
||||
'regnety_640.seer_ft_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
license='other', origin_url='https://github.com/facebookresearch/vissl',
|
||||
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch',
|
||||
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
|
||||
'regnety_1280.seer_ft_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
license='other', origin_url='https://github.com/facebookresearch/vissl',
|
||||
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch',
|
||||
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
|
||||
'regnety_2560.seer_ft_in1k': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
license='other', origin_url='https://github.com/facebookresearch/vissl',
|
||||
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet256_finetuned_in1k_model_final_checkpoint_phase38.torch',
|
||||
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
|
||||
|
||||
'regnety_320.seer': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch',
|
||||
num_classes=0, license='other', origin_url='https://github.com/facebookresearch/vissl'),
|
||||
'regnety_640.seer': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch',
|
||||
num_classes=0, license='other', origin_url='https://github.com/facebookresearch/vissl'),
|
||||
'regnety_1280.seer': _cfgtv2(
|
||||
hf_hub_id='timm/',
|
||||
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch',
|
||||
num_classes=0, license='other', origin_url='https://github.com/facebookresearch/vissl'),
|
||||
# FIXME invalid weight <-> model match, mistake on their end
|
||||
#'regnety_2560.seer': _cfgtv2(
|
||||
# url='https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_cosine_rg256gf_noBNhead_wd1e5_fairstore_bs16_node64_sinkhorn10_proto16k_apex_syncBN64_warmup8k/model_final_checkpoint_phase0.torch',
|
||||
# num_classes=0, license='other', origin_url='https://github.com/facebookresearch/vissl'),
|
||||
|
||||
'regnetx_002.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnetx_004.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnetx_006.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnetx_008.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnetx_016.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnetx_032.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnetx_040.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnetx_064.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnetx_080.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnetx_120.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnetx_160.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnetx_320.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
|
||||
'regnety_002.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnety_004.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnety_006.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnety_008.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnety_016.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnety_032.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnety_040.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnety_064.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnety_080.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnety_120.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnety_160.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
'regnety_320.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
|
||||
})
|
||||
|
||||
|
||||
@register_model
|
||||
def regnetx_002(pretrained=False, **kwargs):
|
||||
"""RegNetX-200MF"""
|
||||
|
|
@ -644,6 +904,12 @@ def regnetx_004(pretrained=False, **kwargs):
|
|||
return _create_regnet('regnetx_004', pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def regnetx_004_tv(pretrained=False, **kwargs):
|
||||
"""RegNetX-400MF w/ torchvision group rounding"""
|
||||
return _create_regnet('regnetx_004_tv', pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def regnetx_006(pretrained=False, **kwargs):
|
||||
"""RegNetX-600MF"""
|
||||
|
|
@ -728,6 +994,12 @@ def regnety_008(pretrained=False, **kwargs):
|
|||
return _create_regnet('regnety_008', pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def regnety_008_tv(pretrained=False, **kwargs):
|
||||
"""RegNetY-800MF w/ torchvision group rounding"""
|
||||
return _create_regnet('regnety_008_tv', pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def regnety_016(pretrained=False, **kwargs):
|
||||
"""RegNetY-1.6GF"""
|
||||
|
|
@ -758,6 +1030,12 @@ def regnety_080(pretrained=False, **kwargs):
|
|||
return _create_regnet('regnety_080', pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def regnety_080_tv(pretrained=False, **kwargs):
|
||||
"""RegNetY-8.0GF w/ torchvision group rounding"""
|
||||
return _create_regnet('regnety_080_tv', pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def regnety_120(pretrained=False, **kwargs):
|
||||
"""RegNetY-12GF"""
|
||||
|
|
@ -795,20 +1073,20 @@ def regnety_2560(pretrained=False, **kwargs):
|
|||
|
||||
|
||||
@register_model
|
||||
def regnety_040s_gn(pretrained=False, **kwargs):
|
||||
def regnety_040_sgn(pretrained=False, **kwargs):
|
||||
"""RegNetY-4.0GF w/ GroupNorm """
|
||||
return _create_regnet('regnety_040s_gn', pretrained, **kwargs)
|
||||
return _create_regnet('regnety_040_sgn', pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def regnetv_040(pretrained=False, **kwargs):
|
||||
""""""
|
||||
"""RegNetV-4.0GF (pre-activation)"""
|
||||
return _create_regnet('regnetv_040', pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def regnetv_064(pretrained=False, **kwargs):
|
||||
""""""
|
||||
"""RegNetV-6.4GF (pre-activation)"""
|
||||
return _create_regnet('regnetv_064', pretrained, **kwargs)
|
||||
|
||||
|
||||
|
|
@ -831,9 +1109,14 @@ def regnetz_040(pretrained=False, **kwargs):
|
|||
|
||||
|
||||
@register_model
|
||||
def regnetz_040h(pretrained=False, **kwargs):
|
||||
def regnetz_040_h(pretrained=False, **kwargs):
|
||||
"""RegNetZ-4.0GF
|
||||
NOTE: config found in https://github.com/facebookresearch/ClassyVision/blob/main/classy_vision/models/regnet.py
|
||||
but it's not clear it is equivalent to paper model as not detailed in the paper.
|
||||
"""
|
||||
return _create_regnet('regnetz_040h', pretrained, zero_init_last=False, **kwargs)
|
||||
return _create_regnet('regnetz_040_h', pretrained, zero_init_last=False, **kwargs)
|
||||
|
||||
|
||||
register_model_deprecations(__name__, {
|
||||
'regnetz_040h': 'regnetz_040_h',
|
||||
})
|
||||
Loading…
Reference in New Issue