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Add gc_efficientnetv2_rw_t weights (global context instead of SE attn). Add TF XL weights even though the fine-tuned ones don't validate that well. Change default arg for GlobalContext to use scal (mul) mode.

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This commit is contained in:
Ross Wightman 2021-08-07 16:45:29 -07:00
parent bd56946676
commit 01cb46a9a5
3 changed files with 5 additions and 30 deletions
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2
tests/test_models.py
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@ -25,7 +25,7 @@ if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system():
# GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
EXCLUDE_FILTERS = [
'*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*50x3_bitm',
'*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*',
'*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*',
'*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*']
else:
EXCLUDE_FILTERS = []

31
timm/models/efficientnet.py
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@ -92,11 +92,6 @@ default_cfgs = {
interpolation='bilinear'),
# NOTE experimenting with alternate attention
'eca_efficientnet_b0': _cfg(
url=''),
'gc_efficientnet_b0': _cfg(
url=''),
'efficientnet_b0': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0_ra-3dd342df.pth'),
'efficientnet_b1': _cfg(
@ -169,7 +164,7 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_t_agc-3620981a.pth',
input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0),
'gc_efficientnetv2_rw_t': _cfg(
url='',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gc_efficientnetv2_rw_t_agc-927a0bde.pth',
input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0),
'efficientnetv2_rw_s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2_288-a6477665.pth',
@ -362,7 +357,7 @@ default_cfgs = {
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
'tf_efficientnetv2_xl_in21ft1k': _cfg(
url='',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_xl_in21ft1k-06c35c48.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0),
@ -379,7 +374,7 @@ default_cfgs = {
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
'tf_efficientnetv2_xl_in21k': _cfg(
url='',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_xl_in21k-fd7e8abf.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0),
@ -1276,26 +1271,6 @@ def efficientnet_b0(pretrained=False, **kwargs):
return model
@register_model
def eca_efficientnet_b0(pretrained=False, **kwargs):
""" EfficientNet-B0 w/ ECA attn """
# NOTE experimental config
model = _gen_efficientnet(
'eca_efficientnet_b0', se_layer='ecam', channel_multiplier=1.0, depth_multiplier=1.0,
pretrained=pretrained, **kwargs)
return model
@register_model
def gc_efficientnet_b0(pretrained=False, **kwargs):
""" EfficientNet-B0 w/ GlobalContext """
# NOTE experminetal config
model = _gen_efficientnet(
'gc_efficientnet_b0', se_layer='gc', channel_multiplier=1.0, depth_multiplier=1.0,
pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b1(pretrained=False, **kwargs):
""" EfficientNet-B1 """

2
timm/models/layers/global_context.py
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@ -18,7 +18,7 @@ from .norm import LayerNorm2d
class GlobalContext(nn.Module):
def __init__(self, channels, use_attn=True, fuse_add=True, fuse_scale=False, init_last_zero=False,
def __init__(self, channels, use_attn=True, fuse_add=False, fuse_scale=True, init_last_zero=False,
rd_ratio=1./8, rd_channels=None, rd_divisor=1, act_layer=nn.ReLU, gate_layer='sigmoid'):
super(GlobalContext, self).__init__()
act_layer = get_act_layer(act_layer)