mirrors/pytorch-image-models
1
0
Fork 0
mirror of https://github.com/huggingface/pytorch-image-models.git synced 2025-06-03 15:01:08 +08:00
Code Issues Projects Releases Wiki Activity

Update experimental vit model configs

Browse Source
This commit is contained in:
Ross Wightman 2024-02-10 16:05:58 -08:00
parent 7d3c2dc993
commit 87fec3dc14
1 changed files with 31 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

35
timm/models/vision_transformer.py
View File

@ -1723,7 +1723,12 @@ default_cfgs = {
input_size=(3, 256, 256)), input_size=(3, 256, 256)),
'vit_medium_patch16_reg4_gap_256': _cfg( 'vit_medium_patch16_reg4_gap_256': _cfg(
input_size=(3, 256, 256)), input_size=(3, 256, 256)),
'vit_base_patch16_reg8_gap_256': _cfg(input_size=(3, 256, 256)), 'vit_base_patch16_reg4_gap_256': _cfg(
input_size=(3, 256, 256)),
'vit_so150m_patch16_reg4_gap_256': _cfg(
input_size=(3, 256, 256)),
'vit_so150m_patch16_reg4_map_256': _cfg(
input_size=(3, 256, 256)),
} }
_quick_gelu_cfgs = [ _quick_gelu_cfgs = [
@ -2623,13 +2628,35 @@ def vit_medium_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> Visio
@register_model @register_model
def vit_base_patch16_reg8_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer: def vit_base_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict( model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False, patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False,
no_embed_class=True, global_pool='avg', reg_tokens=8, no_embed_class=True, global_pool='avg', reg_tokens=4,
) )
model = _create_vision_transformer( model = _create_vision_transformer(
'vit_base_patch16_reg8_gap_256', pretrained=pretrained, **dict(model_args, **kwargs)) 'vit_base_patch16_reg4_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_so150m_patch16_reg4_map_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=896, depth=18, num_heads=14, mlp_ratio=2.572,
class_token=False, reg_tokens=4, global_pool='map',
)
model = _create_vision_transformer(
'vit_so150m_patch16_reg4_map_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_so150m_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=896, depth=18, num_heads=14, mlp_ratio=2.572,
class_token=False, reg_tokens=4, global_pool='avg', fc_norm=False,
)
model = _create_vision_transformer(
'vit_so150m_patch16_reg4_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model return model