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Update maxvit_tiny_256 weight to better iter, add coatnet / maxvit / maxxvit model defs for future runs

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pull/804/merge
Ross Wightman 2 years ago
parent de40f66536
commit fa8c84eede
1 changed files with 134 additions and 5 deletions
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139
timm/models/maxxvit.py
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@ -82,6 +82,7 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_1_rw_224_sw-5cae1ea8.pth'
),
'coatnet_2_rw_224': _cfg(url=''),
'coatnet_3_rw_224': _cfg(url=''),
# Highly experimental configs
'coatnet_bn_0_rw_224': _cfg(
@ -94,6 +95,8 @@ default_cfgs = {
'coatnet_rmlp_0_rw_224': _cfg(url=''),
'coatnet_rmlp_1_rw_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_rmlp_1_rw_224_sw-9051e6c3.pth'),
'coatnet_rmlp_2_rw_224': _cfg(url=''),
'coatnet_rmlp_3_rw_224': _cfg(url=''),
'coatnet_nano_cc_224': _cfg(url=''),
'coatnext_nano_rw_224': _cfg(url=''),
@ -122,10 +125,19 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_nano_rw_256_sw-c17bb0d6.pth',
input_size=(3, 256, 256), pool_size=(8, 8)),
'maxvit_rmlp_tiny_rw_256': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_tiny_rw_256_sw-2da819a5.pth',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_tiny_rw_256_sw-bbef0ff5.pth',
input_size=(3, 256, 256), pool_size=(8, 8)),
'maxvit_rmlp_small_rw_224': _cfg(
url=''),
'maxvit_rmlp_small_rw_256': _cfg(
url='',
input_size=(3, 256, 256), pool_size=(8, 8)),
'maxvit_tiny_pm_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
'maxxvit_nano_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
'maxxvit_tiny_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
'maxxvit_small_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
# Trying to be like the MaxViT paper configs
'maxvit_tiny_224': _cfg(url=''),
@ -182,7 +194,7 @@ class MaxxVitConvCfg:
attn_layer: str = 'se'
attn_act_layer: str = 'silu'
attn_ratio: float = 0.25
init_values: Optional[float] = 1e-5 # for ConvNeXt block
init_values: Optional[float] = 1e-6 # for ConvNeXt block, ignored by MBConv
act_layer: str = 'gelu'
norm_layer: str = ''
norm_layer_cl: str = ''
@ -218,10 +230,12 @@ def _rw_coat_cfg(
pool_type='avg2',
conv_output_bias=False,
conv_attn_early=False,
conv_attn_act_layer='relu',
conv_norm_layer='',
transformer_shortcut_bias=True,
transformer_norm_layer='layernorm2d',
transformer_norm_layer_cl='layernorm',
init_values=None,
rel_pos_type='bias',
rel_pos_dim=512,
):
@ -246,7 +260,7 @@ def _rw_coat_cfg(
expand_output=False,
output_bias=conv_output_bias,
attn_early=conv_attn_early,
attn_act_layer='relu',
attn_act_layer=conv_attn_act_layer,
act_layer='silu',
norm_layer=conv_norm_layer,
),
@ -254,6 +268,7 @@ def _rw_coat_cfg(
expand_first=False,
shortcut_bias=transformer_shortcut_bias,
pool_type=pool_type,
init_values=init_values,
norm_layer=transformer_norm_layer,
norm_layer_cl=transformer_norm_layer_cl,
rel_pos_type=rel_pos_type,
@ -272,6 +287,7 @@ def _rw_max_cfg(
transformer_norm_layer_cl='layernorm',
window_size=None,
dim_head=32,
init_values=None,
rel_pos_type='bias',
rel_pos_dim=512,
):
@ -296,6 +312,7 @@ def _rw_max_cfg(
pool_type=pool_type,
dim_head=dim_head,
window_size=window_size,
init_values=init_values,
norm_layer=transformer_norm_layer,
norm_layer_cl=transformer_norm_layer_cl,
rel_pos_type=rel_pos_type,
@ -312,7 +329,8 @@ def _next_cfg(
transformer_norm_layer='layernorm2d',
transformer_norm_layer_cl='layernorm',
window_size=None,
rel_pos_type='bias',
init_values=1e-6,
rel_pos_type='mlp', # MLP by default for maxxvit
rel_pos_dim=512,
):
# For experimental models with convnext instead of mbconv
@ -322,6 +340,7 @@ def _next_cfg(
stride_mode=stride_mode,
pool_type=pool_type,
expand_output=False,
init_values=init_values,
norm_layer=conv_norm_layer,
norm_layer_cl=conv_norm_layer_cl,
),
@ -329,6 +348,7 @@ def _next_cfg(
expand_first=False,
pool_type=pool_type,
window_size=window_size,
init_values=init_values,
norm_layer=transformer_norm_layer,
norm_layer_cl=transformer_norm_layer_cl,
rel_pos_type=rel_pos_type,
@ -381,7 +401,21 @@ model_cfgs = dict(
embed_dim=(128, 256, 512, 1024),
depths=(2, 6, 14, 2),
stem_width=(64, 128),
**_rw_coat_cfg(stride_mode='dw'),
**_rw_coat_cfg(
stride_mode='dw',
conv_attn_act_layer='silu',
init_values=1e-6,
),
),
coatnet_3_rw_224=MaxxVitCfg(
embed_dim=(192, 384, 768, 1536),
depths=(2, 6, 14, 2),
stem_width=(96, 192),
**_rw_coat_cfg(
stride_mode='dw',
conv_attn_act_layer='silu',
init_values=1e-6,
),
),
# Highly experimental configs
@ -428,6 +462,29 @@ model_cfgs = dict(
rel_pos_dim=384, # was supposed to be 512, woops
),
),
coatnet_rmlp_2_rw_224=MaxxVitCfg(
embed_dim=(128, 256, 512, 1024),
depths=(2, 6, 14, 2),
stem_width=(64, 128),
**_rw_coat_cfg(
stride_mode='dw',
conv_attn_act_layer='silu',
init_values=1e-6,
rel_pos_type='mlp'
),
),
coatnet_rmlp_3_rw_224=MaxxVitCfg(
embed_dim=(192, 384, 768, 1536),
depths=(2, 6, 14, 2),
stem_width=(96, 192),
**_rw_coat_cfg(
stride_mode='dw',
conv_attn_act_layer='silu',
init_values=1e-6,
rel_pos_type='mlp'
),
),
coatnet_nano_cc_224=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(3, 4, 6, 3),
@ -504,6 +561,7 @@ model_cfgs = dict(
stem_width=(32, 64),
**_rw_max_cfg(),
),
maxvit_rmlp_pico_rw_256=MaxxVitCfg(
embed_dim=(32, 64, 128, 256),
depths=(2, 2, 5, 2),
@ -525,6 +583,27 @@ model_cfgs = dict(
stem_width=(32, 64),
**_rw_max_cfg(rel_pos_type='mlp'),
),
maxvit_rmlp_small_rw_224=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=(32, 64),
**_rw_max_cfg(
rel_pos_type='mlp',
init_values=1e-6,
),
),
maxvit_rmlp_small_rw_256=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=(32, 64),
**_rw_max_cfg(
rel_pos_type='mlp',
init_values=1e-6,
),
),
maxvit_tiny_pm_256=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(2, 2, 5, 2),
@ -532,6 +611,7 @@ model_cfgs = dict(
stem_width=(32, 64),
**_rw_max_cfg(),
),
maxxvit_nano_rw_256=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(1, 2, 3, 1),
@ -540,6 +620,20 @@ model_cfgs = dict(
weight_init='normal',
**_next_cfg(),
),
maxxvit_tiny_rw_256=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=(32, 64),
**_next_cfg(),
),
maxxvit_small_rw_256=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=(48, 96),
**_next_cfg(),
),
# Trying to be like the MaxViT paper configs
maxvit_tiny_224=MaxxVitCfg(
@ -1641,6 +1735,11 @@ def coatnet_2_rw_224(pretrained=False, **kwargs):
return _create_maxxvit('coatnet_2_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_3_rw_224(pretrained=False, **kwargs):
return _create_maxxvit('coatnet_3_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_bn_0_rw_224(pretrained=False, **kwargs):
return _create_maxxvit('coatnet_bn_0_rw_224', pretrained=pretrained, **kwargs)
@ -1661,6 +1760,16 @@ def coatnet_rmlp_1_rw_224(pretrained=False, **kwargs):
return _create_maxxvit('coatnet_rmlp_1_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_rmlp_2_rw_224(pretrained=False, **kwargs):
return _create_maxxvit('coatnet_rmlp_2_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_rmlp_3_rw_224(pretrained=False, **kwargs):
return _create_maxxvit('coatnet_rmlp_3_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_nano_cc_224(pretrained=False, **kwargs):
return _create_maxxvit('coatnet_nano_cc_224', pretrained=pretrained, **kwargs)
@ -1736,6 +1845,16 @@ def maxvit_rmlp_tiny_rw_256(pretrained=False, **kwargs):
return _create_maxxvit('maxvit_rmlp_tiny_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxvit_rmlp_small_rw_224(pretrained=False, **kwargs):
return _create_maxxvit('maxvit_rmlp_small_rw_224', pretrained=pretrained, **kwargs)
@register_model
def maxvit_rmlp_small_rw_256(pretrained=False, **kwargs):
return _create_maxxvit('maxvit_rmlp_small_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxvit_tiny_pm_256(pretrained=False, **kwargs):
return _create_maxxvit('maxvit_tiny_pm_256', pretrained=pretrained, **kwargs)
@ -1746,6 +1865,16 @@ def maxxvit_nano_rw_256(pretrained=False, **kwargs):
return _create_maxxvit('maxxvit_nano_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxxvit_tiny_rw_256(pretrained=False, **kwargs):
return _create_maxxvit('maxxvit_tiny_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxxvit_small_rw_256(pretrained=False, **kwargs):
return _create_maxxvit('maxxvit_small_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxvit_tiny_224(pretrained=False, **kwargs):
return _create_maxxvit('maxvit_tiny_224', pretrained=pretrained, **kwargs)

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