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

Upload weights to hub, tweak crop_pct, comment out SAM EfficientViTs for now (v2 weights comming)

Browse Source
This commit is contained in:
Ross Wightman 2023-11-21 14:05:04 -08:00
parent 3775e4984f
commit c6b0c98963
1 changed files with 62 additions and 33 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

95
timm/models/efficientvit_mit.py
View File

@ -947,31 +947,56 @@ default_cfgs = generate_default_cfgs({
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0,
),
'efficientvit_l1.r224_in1k': _cfg(
# hf_hub_id='timm/',
hf_hub_id='timm/',
crop_pct=1.0,
),
'efficientvit_l2.r224_in1k': _cfg(
# hf_hub_id='timm/',
hf_hub_id='timm/',
crop_pct=1.0,
),
'efficientvit_l2.r256_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0,
),
'efficientvit_l2.r288_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0,
),
'efficientvit_l2.r384_in1k': _cfg(
# hf_hub_id='timm/',
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
),
'efficientvit_l3.r224_in1k': _cfg(
# hf_hub_id='timm/',
hf_hub_id='timm/',
crop_pct=1.0,
),
'efficientvit_l3.r256_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0,
),
'efficientvit_l3.r320_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0,
),
'efficientvit_l3.r384_in1k': _cfg(
# hf_hub_id='timm/',
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
),
'efficientvit_l0_sam.sam': _cfg(
# hf_hub_id='timm/',
),
'efficientvit_l1_sam.sam': _cfg(
# hf_hub_id='timm/',
),
'efficientvit_l2_sam.sam': _cfg(
# hf_hub_id='timm/',
),
# 'efficientvit_l0_sam.sam': _cfg(
# # hf_hub_id='timm/',
# input_size=(3, 512, 512), crop_pct=1.0,
# num_classes=0,
# ),
# 'efficientvit_l1_sam.sam': _cfg(
# # hf_hub_id='timm/',
# input_size=(3, 512, 512), crop_pct=1.0,
# num_classes=0,
# ),
# 'efficientvit_l2_sam.sam': _cfg(
# # hf_hub_id='timm/',f
# input_size=(3, 512, 512), crop_pct=1.0,
# num_classes=0,
# ),
})
@ -1048,22 +1073,26 @@ def efficientvit_l3(pretrained=False, **kwargs):
return _create_efficientvit_large('efficientvit_l3', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l0_sam(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 1, 1, 4, 4), head_dim=32, num_classes=0, norm_eps=1e-6) # only backbone for segment-anything-model weights
return _create_efficientvit_large('efficientvit_l0_sam', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l1_sam(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 1, 1, 6, 6), head_dim=32, num_classes=0, norm_eps=1e-6) # only backbone for segment-anything-model weights
return _create_efficientvit_large('efficientvit_l1_sam', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l2_sam(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 2, 2, 8, 8), head_dim=32, num_classes=0, norm_eps=1e-6) # only backbone for segment-anything-model weights
return _create_efficientvit_large('efficientvit_l2_sam', pretrained=pretrained, **dict(model_args, **kwargs))
# FIXME will wait for v2 SAM models which are pending
# @register_model
# def efficientvit_l0_sam(pretrained=False, **kwargs):
# # only backbone for segment-anything-model weights
# model_args = dict(
# widths=(32, 64, 128, 256, 512), depths=(1, 1, 1, 4, 4), head_dim=32, num_classes=0, norm_eps=1e-6)
# return _create_efficientvit_large('efficientvit_l0_sam', pretrained=pretrained, **dict(model_args, **kwargs))
#
#
# @register_model
# def efficientvit_l1_sam(pretrained=False, **kwargs):
# # only backbone for segment-anything-model weights
# model_args = dict(
# widths=(32, 64, 128, 256, 512), depths=(1, 1, 1, 6, 6), head_dim=32, num_classes=0, norm_eps=1e-6)
# return _create_efficientvit_large('efficientvit_l1_sam', pretrained=pretrained, **dict(model_args, **kwargs))
#
#
# @register_model
# def efficientvit_l2_sam(pretrained=False, **kwargs):
# # only backbone for segment-anything-model weights
# model_args = dict(
# widths=(32, 64, 128, 256, 512), depths=(1, 2, 2, 8, 8), head_dim=32, num_classes=0, norm_eps=1e-6)
# return _create_efficientvit_large('efficientvit_l2_sam', pretrained=pretrained, **dict(model_args, **kwargs))