Fix C&P patch_size error, and order of op patch_size arg resolution bug. Remove a test vit model.

pull/450/head
Ross Wightman 4 years ago
parent 0706d05d52
commit 17cdee7354

@ -202,7 +202,6 @@ default_cfgs = {
'vit_deit_tiny_patch16_224': _cfg( 'vit_deit_tiny_patch16_224': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth'), url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth'),
'vit_deit_tiny_patch16_224_in21k': _cfg(num_classes=21843), 'vit_deit_tiny_patch16_224_in21k': _cfg(num_classes=21843),
'vit_deit_tiny_patch16_224_in21k_norep': _cfg(num_classes=21843),
'vit_deit_tiny_patch16_384': _cfg(input_size=(3, 384, 384)), 'vit_deit_tiny_patch16_384': _cfg(input_size=(3, 384, 384)),
'vit_deit_small_patch16_224': _cfg( 'vit_deit_small_patch16_224': _cfg(
@ -399,7 +398,7 @@ class VisionTransformer(nn.Module):
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
act_layer = act_layer or nn.GELU act_layer = act_layer or nn.GELU
patch_size = patch_size or 1 if hybrid_backbone is not None else 16 patch_size = patch_size or (1 if hybrid_backbone is not None else 16)
if hybrid_backbone is not None: if hybrid_backbone is not None:
self.patch_embed = HybridEmbed( self.patch_embed = HybridEmbed(
@ -1099,14 +1098,6 @@ def vit_deit_tiny_patch16_224(pretrained=False, **kwargs):
return model return model
@register_model
def vit_deit_tiny_patch16_224_in21k_norep(pretrained=False, **kwargs):
""" DeiT-tiny model"""
model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
model = _create_vision_transformer('vit_deit_tiny_patch16_224_in21k_norep', pretrained=pretrained, **model_kwargs)
return model
@register_model @register_model
def vit_deit_tiny_patch16_224_in21k(pretrained=False, **kwargs): def vit_deit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
""" DeiT-tiny model""" """ DeiT-tiny model"""
@ -1155,7 +1146,7 @@ def vit_deit_small_patch32_224(pretrained=False, **kwargs):
""" DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877). """ DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
ImageNet-1k weights from https://github.com/facebookresearch/deit. ImageNet-1k weights from https://github.com/facebookresearch/deit.
""" """
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs) model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_deit_small_patch32_224', pretrained=pretrained, **model_kwargs) model = _create_vision_transformer('vit_deit_small_patch32_224', pretrained=pretrained, **model_kwargs)
return model return model
@ -1163,7 +1154,7 @@ def vit_deit_small_patch32_224(pretrained=False, **kwargs):
@register_model @register_model
def vit_deit_small_patch32_224_in21k(pretrained=False, **kwargs): def vit_deit_small_patch32_224_in21k(pretrained=False, **kwargs):
""" DeiT-small """ """ DeiT-small """
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, representation_size=384, **kwargs) model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, representation_size=384, **kwargs)
model = _create_vision_transformer('vit_deit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs) model = _create_vision_transformer('vit_deit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
return model return model
@ -1171,7 +1162,7 @@ def vit_deit_small_patch32_224_in21k(pretrained=False, **kwargs):
@register_model @register_model
def vit_deit_small_patch32_384(pretrained=False, **kwargs): def vit_deit_small_patch32_384(pretrained=False, **kwargs):
""" DeiT-small """ """ DeiT-small """
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs) model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_deit_small_patch32_384', pretrained=pretrained, **model_kwargs) model = _create_vision_transformer('vit_deit_small_patch32_384', pretrained=pretrained, **model_kwargs)
return model return model

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