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443 lines
18 KiB
443 lines
18 KiB
""" Bring-Your-Own-Attention Network
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A flexible network w/ dataclass based config for stacking NN blocks including
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self-attention (or similar) layers.
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Currently used to implement experimental variants of:
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* Bottleneck Transformers
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* Lambda ResNets
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* HaloNets
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Consider all of the models definitions here as experimental WIP and likely to change.
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Hacked together by / copyright Ross Wightman, 2021.
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"""
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from ._builder import build_model_with_cfg
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from ._registry import register_model
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from .byobnet import ByoBlockCfg, ByoModelCfg, ByobNet, interleave_blocks
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__all__ = []
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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': 0.95, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'stem.conv1.conv', 'classifier': 'head.fc',
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'fixed_input_size': False, 'min_input_size': (3, 224, 224),
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**kwargs
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}
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default_cfgs = {
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# GPU-Efficient (ResNet) weights
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'botnet26t_256': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/botnet26t_c1_256-167a0e9f.pth',
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fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
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'sebotnet33ts_256': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/sebotnet33ts_a1h2_256-957e3c3e.pth',
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fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.94),
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'botnet50ts_256': _cfg(
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url='',
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fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
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'eca_botnext26ts_256': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_botnext26ts_c_256-95a898f6.pth',
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fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
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'halonet_h1': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
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'halonet26t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/halonet26t_a1h_256-3083328c.pth',
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input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
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'sehalonet33ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/sehalonet33ts_256-87e053f9.pth',
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input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
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'halonet50ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/halonet50ts_a1h2_256-f3a3daee.pth',
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input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
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'eca_halonext26ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_halonext26ts_c_256-06906299.pth',
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input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
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'lambda_resnet26t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet26t_c_256-e5a5c857.pth',
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min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.94),
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'lambda_resnet50ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet50ts_a1h_256-b87370f7.pth',
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min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
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'lambda_resnet26rpt_256': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet26rpt_c_256-ab00292d.pth',
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fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.94),
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'haloregnetz_b': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/haloregnetz_c_raa_256-c8ad7616.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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first_conv='stem.conv', input_size=(3, 224, 224), pool_size=(7, 7), min_input_size=(3, 224, 224), crop_pct=0.94),
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'lamhalobotnet50ts_256': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lamhalobotnet50ts_a1h2_256-fe3d9445.pth',
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fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
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'halo2botnet50ts_256': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/halo2botnet50ts_a1h2_256-fd9c11a3.pth',
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fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
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}
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model_cfgs = dict(
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botnet26t=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
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ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=0, br=0.25),
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ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='maxpool',
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fixed_input_size=True,
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self_attn_layer='bottleneck',
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self_attn_kwargs=dict()
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),
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sebotnet33ts=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), every=[2], d=3, c=512, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), every=[2], d=3, c=1024, s=2, gs=0, br=0.25),
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ByoBlockCfg('self_attn', d=2, c=1536, s=2, gs=0, br=0.333),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='',
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act_layer='silu',
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num_features=1280,
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attn_layer='se',
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self_attn_layer='bottleneck',
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self_attn_kwargs=dict()
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),
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botnet50ts=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), every=4, d=4, c=512, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='maxpool',
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act_layer='silu',
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fixed_input_size=True,
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self_attn_layer='bottleneck',
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self_attn_kwargs=dict()
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),
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eca_botnext26ts=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
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ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=16, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=16, br=0.25),
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ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=16, br=0.25),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='maxpool',
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fixed_input_size=True,
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act_layer='silu',
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attn_layer='eca',
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self_attn_layer='bottleneck',
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self_attn_kwargs=dict(dim_head=16)
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),
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halonet_h1=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='self_attn', d=3, c=64, s=1, gs=0, br=1.0),
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ByoBlockCfg(type='self_attn', d=3, c=128, s=2, gs=0, br=1.0),
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ByoBlockCfg(type='self_attn', d=10, c=256, s=2, gs=0, br=1.0),
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ByoBlockCfg(type='self_attn', d=3, c=512, s=2, gs=0, br=1.0),
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),
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stem_chs=64,
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stem_type='7x7',
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stem_pool='maxpool',
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self_attn_layer='halo',
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self_attn_kwargs=dict(block_size=8, halo_size=3),
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),
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halonet26t=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
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ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=0, br=0.25),
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ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='maxpool',
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self_attn_layer='halo',
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self_attn_kwargs=dict(block_size=8, halo_size=2)
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),
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sehalonet33ts=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), every=[2], d=3, c=512, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), every=[2], d=3, c=1024, s=2, gs=0, br=0.25),
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ByoBlockCfg('self_attn', d=2, c=1536, s=2, gs=0, br=0.333),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='',
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act_layer='silu',
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num_features=1280,
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attn_layer='se',
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self_attn_layer='halo',
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self_attn_kwargs=dict(block_size=8, halo_size=3)
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),
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halonet50ts=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
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interleave_blocks(
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types=('bottle', 'self_attn'), every=4, d=4, c=512, s=2, gs=0, br=0.25,
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self_attn_layer='halo', self_attn_kwargs=dict(block_size=8, halo_size=3, num_heads=4)),
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interleave_blocks(types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='maxpool',
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act_layer='silu',
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self_attn_layer='halo',
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self_attn_kwargs=dict(block_size=8, halo_size=3)
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),
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eca_halonext26ts=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
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ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=16, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=16, br=0.25),
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ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=16, br=0.25),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='maxpool',
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act_layer='silu',
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attn_layer='eca',
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self_attn_layer='halo',
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self_attn_kwargs=dict(block_size=8, halo_size=2, dim_head=16)
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),
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lambda_resnet26t=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
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ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=0, br=0.25),
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ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='maxpool',
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self_attn_layer='lambda',
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self_attn_kwargs=dict(r=9)
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),
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lambda_resnet50ts=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), every=4, d=4, c=512, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='maxpool',
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act_layer='silu',
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self_attn_layer='lambda',
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self_attn_kwargs=dict(r=9)
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),
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lambda_resnet26rpt_256=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
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ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=0, br=0.25),
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ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='maxpool',
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self_attn_layer='lambda',
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self_attn_kwargs=dict(r=None)
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),
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# experimental
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haloregnetz_b=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=3),
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ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=3),
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interleave_blocks(types=('bottle', 'self_attn'), every=3, d=12, c=192, s=2, gs=16, br=3),
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ByoBlockCfg('self_attn', d=2, c=288, s=2, gs=16, br=3),
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),
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stem_chs=32,
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stem_pool='',
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downsample='',
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num_features=1536,
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act_layer='silu',
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attn_layer='se',
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attn_kwargs=dict(rd_ratio=0.25),
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block_kwargs=dict(bottle_in=True, linear_out=True),
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self_attn_layer='halo',
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self_attn_kwargs=dict(block_size=7, halo_size=2, qk_ratio=0.33)
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),
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# experimental
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lamhalobotnet50ts=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
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interleave_blocks(
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types=('bottle', 'self_attn'), d=4, c=512, s=2, gs=0, br=0.25,
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self_attn_layer='lambda', self_attn_kwargs=dict(r=13)),
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interleave_blocks(
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types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25,
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self_attn_layer='halo', self_attn_kwargs=dict(halo_size=3)),
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interleave_blocks(
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types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25,
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self_attn_layer='bottleneck', self_attn_kwargs=dict()),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='',
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act_layer='silu',
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),
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halo2botnet50ts=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
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interleave_blocks(
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types=('bottle', 'self_attn'), d=4, c=512, s=2, gs=0, br=0.25,
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self_attn_layer='halo', self_attn_kwargs=dict(halo_size=3)),
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interleave_blocks(
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types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25,
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self_attn_layer='halo', self_attn_kwargs=dict(halo_size=3)),
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interleave_blocks(
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types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25,
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self_attn_layer='bottleneck', self_attn_kwargs=dict()),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='',
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act_layer='silu',
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),
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)
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def _create_byoanet(variant, cfg_variant=None, pretrained=False, **kwargs):
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return build_model_with_cfg(
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ByobNet, variant, pretrained,
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model_cfg=model_cfgs[variant] if not cfg_variant else model_cfgs[cfg_variant],
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feature_cfg=dict(flatten_sequential=True),
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**kwargs)
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@register_model
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def botnet26t_256(pretrained=False, **kwargs):
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""" Bottleneck Transformer w/ ResNet26-T backbone.
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"""
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kwargs.setdefault('img_size', 256)
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return _create_byoanet('botnet26t_256', 'botnet26t', pretrained=pretrained, **kwargs)
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@register_model
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def sebotnet33ts_256(pretrained=False, **kwargs):
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""" Bottleneck Transformer w/ a ResNet33-t backbone, SE attn for non Halo blocks, SiLU,
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"""
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return _create_byoanet('sebotnet33ts_256', 'sebotnet33ts', pretrained=pretrained, **kwargs)
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@register_model
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def botnet50ts_256(pretrained=False, **kwargs):
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""" Bottleneck Transformer w/ ResNet50-T backbone, silu act.
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"""
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kwargs.setdefault('img_size', 256)
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return _create_byoanet('botnet50ts_256', 'botnet50ts', pretrained=pretrained, **kwargs)
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@register_model
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def eca_botnext26ts_256(pretrained=False, **kwargs):
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""" Bottleneck Transformer w/ ResNet26-T backbone, silu act.
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"""
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kwargs.setdefault('img_size', 256)
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return _create_byoanet('eca_botnext26ts_256', 'eca_botnext26ts', pretrained=pretrained, **kwargs)
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@register_model
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def halonet_h1(pretrained=False, **kwargs):
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""" HaloNet-H1. Halo attention in all stages as per the paper.
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NOTE: This runs very slowly!
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"""
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return _create_byoanet('halonet_h1', pretrained=pretrained, **kwargs)
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@register_model
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def halonet26t(pretrained=False, **kwargs):
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""" HaloNet w/ a ResNet26-t backbone. Halo attention in final two stages
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"""
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return _create_byoanet('halonet26t', pretrained=pretrained, **kwargs)
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@register_model
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def sehalonet33ts(pretrained=False, **kwargs):
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""" HaloNet w/ a ResNet33-t backbone, SE attn for non Halo blocks, SiLU, 1-2 Halo in stage 2,3,4.
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"""
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return _create_byoanet('sehalonet33ts', pretrained=pretrained, **kwargs)
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@register_model
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def halonet50ts(pretrained=False, **kwargs):
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""" HaloNet w/ a ResNet50-t backbone, silu act. Halo attention in final two stages
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|
"""
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|
return _create_byoanet('halonet50ts', pretrained=pretrained, **kwargs)
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@register_model
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|
def eca_halonext26ts(pretrained=False, **kwargs):
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|
""" HaloNet w/ a ResNet26-t backbone, silu act. Halo attention in final two stages
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|
"""
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|
return _create_byoanet('eca_halonext26ts', pretrained=pretrained, **kwargs)
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@register_model
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def lambda_resnet26t(pretrained=False, **kwargs):
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""" Lambda-ResNet-26-T. Lambda layers w/ conv pos in last two stages.
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|
"""
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|
return _create_byoanet('lambda_resnet26t', pretrained=pretrained, **kwargs)
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@register_model
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|
def lambda_resnet50ts(pretrained=False, **kwargs):
|
|
""" Lambda-ResNet-50-TS. SiLU act. Lambda layers w/ conv pos in last two stages.
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|
"""
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|
return _create_byoanet('lambda_resnet50ts', pretrained=pretrained, **kwargs)
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|
|
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|
@register_model
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|
def lambda_resnet26rpt_256(pretrained=False, **kwargs):
|
|
""" Lambda-ResNet-26-R-T. Lambda layers w/ rel pos embed in last two stages.
|
|
"""
|
|
kwargs.setdefault('img_size', 256)
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|
return _create_byoanet('lambda_resnet26rpt_256', pretrained=pretrained, **kwargs)
|
|
|
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|
|
@register_model
|
|
def haloregnetz_b(pretrained=False, **kwargs):
|
|
""" Halo + RegNetZ
|
|
"""
|
|
return _create_byoanet('haloregnetz_b', pretrained=pretrained, **kwargs)
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|
|
|
|
|
@register_model
|
|
def lamhalobotnet50ts_256(pretrained=False, **kwargs):
|
|
""" Combo Attention (Lambda + Halo + Bot) Network
|
|
"""
|
|
return _create_byoanet('lamhalobotnet50ts_256', 'lamhalobotnet50ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def halo2botnet50ts_256(pretrained=False, **kwargs):
|
|
""" Combo Attention (Halo + Halo + Bot) Network
|
|
"""
|
|
return _create_byoanet('halo2botnet50ts_256', 'halo2botnet50ts', pretrained=pretrained, **kwargs)
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