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add test_models

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Ryan 2025-05-03 23:40:37 +08:00
parent 77788f4f92
commit 91e6e1737e
3 changed files with 12 additions and 14 deletions
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5
tests/test_models.py
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@ -53,13 +53,14 @@ FEAT_INTER_FILTERS = [
'vision_transformer', 'vision_transformer_sam', 'vision_transformer_hybrid', 'vision_transformer_relpos', 'vision_transformer', 'vision_transformer_sam', 'vision_transformer_hybrid', 'vision_transformer_relpos',
'beit', 'mvitv2', 'eva', 'cait', 'xcit', 'volo', 'twins', 'deit', 'swin_transformer', 'swin_transformer_v2', 'beit', 'mvitv2', 'eva', 'cait', 'xcit', 'volo', 'twins', 'deit', 'swin_transformer', 'swin_transformer_v2',
'swin_transformer_v2_cr', 'maxxvit', 'efficientnet', 'mobilenetv3', 'levit', 'efficientformer', 'resnet', 'swin_transformer_v2_cr', 'maxxvit', 'efficientnet', 'mobilenetv3', 'levit', 'efficientformer', 'resnet',
'regnet', 'byobnet', 'byoanet', 'mlp_mixer', 'hiera', 'fastvit', 'hieradet_sam2', 'aimv2*' 'regnet', 'byobnet', 'byoanet', 'mlp_mixer', 'hiera', 'fastvit', 'hieradet_sam2', 'aimv2*', 'swiftformer',
'starnet', 'shvit',
] ]
# transformer / hybrid models don't support full set of spatial / feature APIs and/or have spatial output. # transformer / hybrid models don't support full set of spatial / feature APIs and/or have spatial output.
NON_STD_FILTERS = [ 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*', 'xcit_*', 'crossvit_*', 'beit*', 'aimv2*', 'convit_*', 'levit*', 'visformer*', 'deit*', 'xcit_*', 'crossvit_*', 'beit*', 'aimv2*', 'swiftformer_*',
'poolformer_*', 'volo_*', 'sequencer2d_*', 'mvitv2*', 'gcvit*', 'efficientformer*', 'sam_hiera*', 'poolformer_*', 'volo_*', 'sequencer2d_*', 'mvitv2*', 'gcvit*', 'efficientformer*', 'sam_hiera*',
'eva_*', 'flexivit*', 'eva02*', 'samvit_*', 'efficientvit_m*', 'tiny_vit_*', 'hiera_*', 'vitamin*', 'test_vit*', 'eva_*', 'flexivit*', 'eva02*', 'samvit_*', 'efficientvit_m*', 'tiny_vit_*', 'hiera_*', 'vitamin*', 'test_vit*',
] ]

13
timm/models/shvit.py
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@ -235,7 +235,7 @@ class StageBlock(nn.Module):
PatchMerging(prev_dim, dim, act_layer), PatchMerging(prev_dim, dim, act_layer),
Residule(Conv2d_BN(dim, dim, 3, 1, 1, groups=dim)), Residule(Conv2d_BN(dim, dim, 3, 1, 1, groups=dim)),
Residule(FFN(dim, int(dim * 2), act_layer)), Residule(FFN(dim, int(dim * 2), act_layer)),
) if prev_dim is not None else nn.Identity() ) if prev_dim != dim else nn.Identity()
self.block = nn.Sequential(*[ self.block = nn.Sequential(*[
BasicBlock(dim, qk_dim, pdim, type, norm_layer, act_layer) for _ in range(depth) BasicBlock(dim, qk_dim, pdim, type, norm_layer, act_layer) for _ in range(depth)
@ -269,19 +269,20 @@ class SHViT(nn.Module):
self.feature_info = [] self.feature_info = []
# Patch embedding # Patch embedding
stem_chs = embed_dim[0]
self.patch_embed = nn.Sequential( self.patch_embed = nn.Sequential(
Conv2d_BN(in_chans, embed_dim[0] // 8, 3, 2, 1), Conv2d_BN(in_chans, stem_chs // 8, 3, 2, 1),
act_layer(), act_layer(),
Conv2d_BN(embed_dim[0] // 8, embed_dim[0] // 4, 3, 2, 1), Conv2d_BN(stem_chs // 8, stem_chs // 4, 3, 2, 1),
act_layer(), act_layer(),
Conv2d_BN(embed_dim[0] // 4, embed_dim[0] // 2, 3, 2, 1), Conv2d_BN(stem_chs // 4, stem_chs // 2, 3, 2, 1),
act_layer(), act_layer(),
Conv2d_BN(embed_dim[0] // 2, embed_dim[0], 3, 2, 1) Conv2d_BN(stem_chs // 2, stem_chs, 3, 2, 1)
) )
# Build SHViT blocks # Build SHViT blocks
blocks = [] blocks = []
prev_chs = None prev_chs = stem_chs
for i in range(len(embed_dim)): for i in range(len(embed_dim)):
blocks.append(StageBlock( blocks.append(StageBlock(
prev_dim=prev_chs, prev_dim=prev_chs,

8
timm/models/swiftformer.py
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@ -497,11 +497,7 @@ class SwiftFormer(nn.Module):
x = self.norm(x) x = self.norm(x)
return x return x
def forward_head( def forward_head(self, x: torch.Tensor, pre_logits: bool = False):
self,
x: torch.Tensor,
pre_logits: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
if self.global_pool == 'avg': if self.global_pool == 'avg':
x = x.mean(dim=(2, 3)) x = x.mean(dim=(2, 3))
x = self.head_drop(x) x = self.head_drop(x)
@ -515,7 +511,7 @@ class SwiftFormer(nn.Module):
# during standard train/finetune, inference average the classifier predictions # during standard train/finetune, inference average the classifier predictions
return (x + x_dist) / 2 return (x + x_dist) / 2
def forward(self, x: torch.Tensor) -> Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]: def forward(self, x: torch.Tensor):
x = self.forward_features(x) x = self.forward_features(x)
x = self.forward_head(x) x = self.forward_head(x)
return x return x