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fix bug in ci for efficientvits

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方曦 2023-08-17 14:40:17 +08:00
parent a56e2bbf19
commit 00f670fa69
3 changed files with 7 additions and 7 deletions
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2
tests/test_models.py
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@ -41,7 +41,7 @@ NON_STD_FILTERS = [
'vit_*', 'tnt_*', 'pit_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*', 'vit_*', 'tnt_*', 'pit_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit*', 'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit*',
'poolformer_*', 'volo_*', 'sequencer2d_*', 'pvt_v2*', 'mvitv2*', 'gcvit*', 'efficientformer*', 'poolformer_*', 'volo_*', 'sequencer2d_*', 'pvt_v2*', 'mvitv2*', 'gcvit*', 'efficientformer*',
'eva_*', 'flexivit*', 'eva02*', 'samvit_*' 'eva_*', 'flexivit*', 'eva02*', 'samvit_*', 'efficientvit_m*'
] ]
NUM_NON_STD = len(NON_STD_FILTERS) NUM_NON_STD = len(NON_STD_FILTERS)

6
timm/models/efficientvit_mit.py
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@ -661,14 +661,14 @@ def efficientvit_b0(pretrained=False, **kwargs):
@register_model @register_model
def efficientvit_b1(pretrained=False, **kwargs): def efficientvit_b1(pretrained=False, **kwargs):
model_args = dict(width_list=[16, 32, 64, 128, 256], depth_list=[1, 2, 3, 3, 4], dim=16, head_width_list=[1536, 1600]) model_args = dict(width_list=[16, 32, 64, 128, 256], depth_list=[1, 2, 3, 3, 4], dim=16, head_width_list=[1536, 1600])
return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs)) return _create_efficientvit('efficientvit_b1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model @register_model
def efficientvit_b2(pretrained=False, **kwargs): def efficientvit_b2(pretrained=False, **kwargs):
model_args = dict(width_list=[24, 48, 96, 192, 384], depth_list=[1, 3, 4, 4, 6], dim=32, head_width_list=[2304, 2560]) model_args = dict(width_list=[24, 48, 96, 192, 384], depth_list=[1, 3, 4, 4, 6], dim=32, head_width_list=[2304, 2560])
return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs)) return _create_efficientvit('efficientvit_b2', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model @register_model
def efficientvit_b3(pretrained=False, **kwargs): def efficientvit_b3(pretrained=False, **kwargs):
model_args = dict(width_list=[32, 64, 128, 256, 512], depth_list=[1, 4, 6, 6, 9], dim=32, head_width_list=[2304, 2560]) model_args = dict(width_list=[32, 64, 128, 256, 512], depth_list=[1, 4, 6, 6, 9], dim=32, head_width_list=[2304, 2560])
return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs)) return _create_efficientvit('efficientvit_b3', pretrained=pretrained, **dict(model_args, **kwargs))

6
timm/models/efficientvit_msra.py
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@ -373,8 +373,8 @@ class EfficientViTMSRA(nn.Module):
self.stages = nn.Sequential(*stages) self.stages = nn.Sequential(*stages)
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool, flatten=True, input_fmt='NCHW') self.global_pool = SelectAdaptivePool2d(pool_type=global_pool, flatten=True, input_fmt='NCHW')
self.out_dims = embed_dim[-1] self.num_features = embed_dim[-1]
self.head = BNLinear(self.out_dims, num_classes) if num_classes > 0 else torch.nn.Identity() self.head = BNLinear(self.num_features, num_classes) if num_classes > 0 else torch.nn.Identity()
@torch.jit.ignore @torch.jit.ignore
def group_matcher(self, coarse=False): def group_matcher(self, coarse=False):
@ -396,7 +396,7 @@ class EfficientViTMSRA(nn.Module):
self.num_classes = num_classes self.num_classes = num_classes
if global_pool is not None: if global_pool is not None:
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool, flatten=True, input_fmt='NCHW') self.global_pool = SelectAdaptivePool2d(pool_type=global_pool, flatten=True, input_fmt='NCHW')
self.head = BNLinear(self.out_dims, num_classes) if num_classes > 0 else torch.nn.Identity() self.head = BNLinear(self.num_features, num_classes) if num_classes > 0 else torch.nn.Identity()
def forward_features(self, x): def forward_features(self, x):
x = self.patch_embed(x) x = self.patch_embed(x)