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Add RegNetZ-D8 (83.5 @ 256, 84 @ 320) and RegNetZ-E8 (84.5 @ 256, 85 @ 320) weights. Update names of existing RegZ models to include group size.

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pull/989/head
Ross Wightman 3 years ago
parent 05092e2fbe
commit 734b2244fe
1 changed files with 72 additions and 15 deletions
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87
timm/models/byobnet.py
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@ -35,7 +35,7 @@ import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, named_apply
from .layers import ClassifierHead, ConvBnAct, BatchNormAct2d, DropPath, AvgPool2dSame, \
create_conv2d, get_act_layer, convert_norm_act, get_attn, make_divisible, to_2tuple
create_conv2d, get_act_layer, convert_norm_act, get_attn, make_divisible, to_2tuple, EvoNormSample2d
from .registry import register_model
__all__ = ['ByobNet', 'ByoModelCfg', 'ByoBlockCfg', 'create_byob_stem', 'create_block']
@ -136,20 +136,26 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext50ts_256-3e0f515e.pth'),
# experimental models, likely to change ot be removed
'regnetz_b': _cfgr(
'regnetz_b16': _cfgr(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_b_raa-677d9606.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 224, 224), pool_size=(7, 7), test_input_size=(3, 288, 288), first_conv='stem.conv', crop_pct=0.94),
'regnetz_c': _cfgr(
'regnetz_c16': _cfgr(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_c_rab2_256-a54bf36a.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), first_conv='stem.conv', crop_pct=0.94),
'regnetz_d': _cfgr(
'regnetz_d32': _cfgr(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_d_rab_256-b8073a89.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), crop_pct=0.95),
'regnetz_d8': _cfgr(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_d8_bh-afc03c55.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), crop_pct=1.0),
'regnetz_e8': _cfgr(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_e8_bh-aace8e6e.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), crop_pct=1.0),
'regnetz_d8_evob': _cfgr(
url='',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), crop_pct=0.95),
'regnetz_e8': _cfgr(
'regnetz_d8_evos': _cfgr(
url='',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), crop_pct=0.95),
}
@ -506,7 +512,7 @@ model_cfgs = dict(
),
# experimental models, closer to a RegNetZ than a ResNet. Similar to EfficientNets but w/ groups instead of DW
regnetz_b=ByoModelCfg(
regnetz_b16=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=3),
ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=3),
@ -522,7 +528,7 @@ model_cfgs = dict(
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
regnetz_c=ByoModelCfg(
regnetz_c16=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=4),
ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=4),
@ -538,7 +544,7 @@ model_cfgs = dict(
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
regnetz_d=ByoModelCfg(
regnetz_d32=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=32, br=4),
ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=32, br=4),
@ -589,8 +595,45 @@ model_cfgs = dict(
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
)
# experimental EvoNorm configs
regnetz_d8_evob=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=8, br=4),
ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=8, br=4),
ByoBlockCfg(type='bottle', d=12, c=256, s=2, gs=8, br=4),
ByoBlockCfg(type='bottle', d=3, c=384, s=2, gs=8, br=4),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
downsample='',
num_features=1792,
act_layer='silu',
norm_layer='evonormbatch',
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
regnetz_d8_evos=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=8, br=4),
ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=8, br=4),
ByoBlockCfg(type='bottle', d=12, c=256, s=2, gs=8, br=4),
ByoBlockCfg(type='bottle', d=3, c=384, s=2, gs=8, br=4),
),
stem_chs=64,
stem_type='deep',
stem_pool='',
downsample='',
num_features=1792,
act_layer='silu',
norm_layer=partial(EvoNormSample2d, groups=32),
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
)
@register_model
def gernet_l(pretrained=False, **kwargs):
@ -779,24 +822,24 @@ def gcresnext50ts(pretrained=False, **kwargs):
@register_model
def regnetz_b(pretrained=False, **kwargs):
def regnetz_b16(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('regnetz_b', pretrained=pretrained, **kwargs)
return _create_byobnet('regnetz_b16', pretrained=pretrained, **kwargs)
@register_model
def regnetz_c(pretrained=False, **kwargs):
def regnetz_c16(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('regnetz_c', pretrained=pretrained, **kwargs)
return _create_byobnet('regnetz_c16', pretrained=pretrained, **kwargs)
@register_model
def regnetz_d(pretrained=False, **kwargs):
def regnetz_d32(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('regnetz_d', pretrained=pretrained, **kwargs)
return _create_byobnet('regnetz_d32', pretrained=pretrained, **kwargs)
@register_model
@ -813,6 +856,20 @@ def regnetz_e8(pretrained=False, **kwargs):
return _create_byobnet('regnetz_e8', pretrained=pretrained, **kwargs)
@register_model
def regnetz_d8_evob(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('regnetz_d8_evob', pretrained=pretrained, **kwargs)
@register_model
def regnetz_d8_evos(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('regnetz_d8_evos', pretrained=pretrained, **kwargs)
def expand_blocks_cfg(stage_blocks_cfg: Union[ByoBlockCfg, Sequence[ByoBlockCfg]]) -> List[ByoBlockCfg]:
if not isinstance(stage_blocks_cfg, Sequence):
stage_blocks_cfg = (stage_blocks_cfg,)

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