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Add more maxxvit weights includ ConvNeXt conv block based experiments.

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pull/1493/head
Ross Wightman 2 years ago
parent 145c4d8e46
commit 9914f744dc
2 changed files with 46 additions and 18 deletions
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15
README.md
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@ -21,6 +21,21 @@ And a big thanks to all GitHub sponsors who helped with some of my costs before
## What's New
### Oct 10, 2022
* More weights in `maxxvit` series, incl first ConvNeXt block based `coatnext` and `maxxvit` experiments:
* `coatnext_nano_rw_224` - 82.0 @ 224 (G) -- (uses ConvNeXt conv block, no BatchNorm)
* `maxxvit_nano_rw_256` - 83.0 @ 256, 83.7 @ 320 (G) (uses ConvNeXt conv block, no BN)
* `maxvit_rmlp_small_rw_224` - 84.5 @ 224, 85.1 @ 320 (G)
* `maxxvit_small_rw_256` - 84.6 @ 256, 84.9 @ 288 (G) -- could be trained better, hparams need tuning (uses ConvNeXt block, no BN)
* `coatnet_rmlp_2_rw_224` - 84.6 @ 224, 85 @ 320 (T)
### Sept 23, 2022
* LAION-2B CLIP image towers supported as pretrained backbones for fine-tune or features (no classifier)
* vit_base_patch32_224_clip_laion2b
* vit_large_patch14_224_clip_laion2b
* vit_huge_patch14_224_clip_laion2b
* vit_giant_patch14_224_clip_laion2b
### Sept 7, 2022
* Hugging Face [`timm` docs](https://huggingface.co/docs/hub/timm) home now exists, look for more here in the future
* Add BEiT-v2 weights for base and large 224x224 models from https://github.com/microsoft/unilm/tree/master/beit2

49
timm/models/maxxvit.py
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@ -95,10 +95,13 @@ 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_2_rw_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_rmlp_2_rw_224_sw-5ccfac55.pth'),
'coatnet_rmlp_3_rw_224': _cfg(url=''),
'coatnet_nano_cc_224': _cfg(url=''),
'coatnext_nano_rw_224': _cfg(url=''),
'coatnext_nano_rw_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnext_nano_rw_224_ad-22cb71c2.pth',
crop_pct=0.9),
# Trying to be like the CoAtNet paper configs
'coatnet_0_224': _cfg(url=''),
@ -128,16 +131,22 @@ default_cfgs = {
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=''),
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_small_rw_224_sw-6ef0ae4f.pth',
crop_pct=0.9,
),
'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)),
'maxxvit_rmlp_nano_rw_256': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxxvit_rmlp_nano_rw_256_sw-0325d459.pth',
input_size=(3, 256, 256), pool_size=(8, 8)),
'maxxvit_rmlp_tiny_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
'maxxvit_rmlp_small_rw_256': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxxvit_rmlp_small_rw_256_sw-37e217ff.pth',
input_size=(3, 256, 256), pool_size=(8, 8)),
# Trying to be like the MaxViT paper configs
'maxvit_tiny_224': _cfg(url=''),
@ -334,13 +343,14 @@ def _next_cfg(
rel_pos_dim=512,
):
# For experimental models with convnext instead of mbconv
init_values = to_2tuple(init_values)
return dict(
conv_cfg=MaxxVitConvCfg(
block_type='convnext',
stride_mode=stride_mode,
pool_type=pool_type,
expand_output=False,
init_values=init_values,
init_values=init_values[0],
norm_layer=conv_norm_layer,
norm_layer_cl=conv_norm_layer_cl,
),
@ -348,7 +358,7 @@ def _next_cfg(
expand_first=False,
pool_type=pool_type,
window_size=window_size,
init_values=init_values,
init_values=init_values[1],
norm_layer=transformer_norm_layer,
norm_layer_cl=transformer_norm_layer_cl,
rel_pos_type=rel_pos_type,
@ -497,7 +507,10 @@ model_cfgs = dict(
depths=(3, 4, 6, 3),
stem_width=(32, 64),
weight_init='normal',
**_next_cfg(),
**_next_cfg(
rel_pos_type='bias',
init_values=(1e-5, None)
),
),
# Trying to be like the CoAtNet paper configs
@ -612,7 +625,7 @@ model_cfgs = dict(
**_rw_max_cfg(),
),
maxxvit_nano_rw_256=MaxxVitCfg(
maxxvit_rmlp_nano_rw_256=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(1, 2, 3, 1),
block_type=('M',) * 4,
@ -620,14 +633,14 @@ model_cfgs = dict(
weight_init='normal',
**_next_cfg(),
),
maxxvit_tiny_rw_256=MaxxVitCfg(
maxxvit_rmlp_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(
maxxvit_rmlp_small_rw_256=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
@ -1861,18 +1874,18 @@ def maxvit_tiny_pm_256(pretrained=False, **kwargs):
@register_model
def maxxvit_nano_rw_256(pretrained=False, **kwargs):
return _create_maxxvit('maxxvit_nano_rw_256', pretrained=pretrained, **kwargs)
def maxxvit_rmlp_nano_rw_256(pretrained=False, **kwargs):
return _create_maxxvit('maxxvit_rmlp_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)
def maxxvit_rmlp_tiny_rw_256(pretrained=False, **kwargs):
return _create_maxxvit('maxxvit_rmlp_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)
def maxxvit_rmlp_small_rw_256(pretrained=False, **kwargs):
return _create_maxxvit('maxxvit_rmlp_small_rw_256', pretrained=pretrained, **kwargs)
@register_model

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