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Fix some checkpoint / model str regressions

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This commit is contained in:
Ross Wightman 2020-07-29 19:43:01 -07:00
parent ecdeb470f2
commit d72ddafe56
4 changed files with 14 additions and 11 deletions
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9
tests/test_models.py
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@ -86,6 +86,15 @@ def test_model_default_cfgs(model_name, batch_size):
assert any([k.startswith(first_conv) for k in state_dict.keys()]), f'{first_conv} not in model params'
if 'GITHUB_ACTIONS' not in os.environ:
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models())
@pytest.mark.parametrize('batch_size', [1])
def test_model_load_pretrained(model_name, batch_size):
"""Run a single forward pass with each model"""
create_model(model_name, pretrained=True)
EXCLUDE_JIT_FILTERS = [
'*iabn*', 'tresnet*', # models using inplace abn unlikely to ever be scriptable
'dla*', 'hrnet*', # hopefully fix at some point

4
timm/models/cspnet.py
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@ -433,7 +433,7 @@ def cspresnext50(pretrained=False, **kwargs):
@register_model
def cspresnext50_iabn(pretrained=False, **kwargs):
norm_layer = get_norm_act_layer('iabn')
return _create_cspnet('cspresnext50', pretrained=pretrained, norm_layer=norm_layer, **kwargs)
return _create_cspnet('cspresnext50_iabn', pretrained=pretrained, norm_layer=norm_layer, **kwargs)
@register_model
@ -444,7 +444,7 @@ def cspdarknet53(pretrained=False, **kwargs):
@register_model
def cspdarknet53_iabn(pretrained=False, **kwargs):
norm_layer = get_norm_act_layer('iabn')
return _create_cspnet('cspdarknet53', pretrained=pretrained, block_fn=DarkBlock, norm_layer=norm_layer, **kwargs)
return _create_cspnet('cspdarknet53_iabn', pretrained=pretrained, block_fn=DarkBlock, norm_layer=norm_layer, **kwargs)
@register_model

4
timm/models/res2net.py
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@ -189,7 +189,7 @@ def res2net50_48w_2s(pretrained=False, **kwargs):
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=48, block_args=dict(scale=2), **kwargs)
return _create_res2net('res2net50_26w_8s', pretrained, **model_args)
return _create_res2net('res2net50_48w_2s', pretrained, **model_args)
@register_model
@ -200,7 +200,7 @@ def res2net50_14w_8s(pretrained=False, **kwargs):
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=14, block_args=dict(scale=8), **kwargs)
return _create_res2net('res2net50_26w_8s', pretrained, **model_args)
return _create_res2net('res2net50_14w_8s', pretrained, **model_args)
@register_model

8
timm/models/resnet.py
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@ -624,7 +624,7 @@ def resnet26d(pretrained=False, **kwargs):
"""Constructs a ResNet-26 v1d model.
This is technically a 28 layer ResNet, sticking with 'd' modifier from Gluon for now.
"""
model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_type='deep', avg_down=True, **kwargs)
model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnet26d', pretrained, **model_args)
@ -1129,9 +1129,3 @@ def senet154(pretrained=False, **kwargs):
block=Bottleneck, layers=[3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep',
down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer='se'), **kwargs)
return _create_resnet('senet154', pretrained, **model_args)
@register_model
def eseresnet50(pretrained=False, **kwargs):
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], block_args=dict(attn_layer='ese'), **kwargs)
return _create_resnet('seresnet50', pretrained, **model_args)