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# Copyright (c) 2023-2024, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from torch import nn
from timm.models import register_model
from timm.models.vision_transformer import VisionTransformer, _create_vision_transformer, Mlp
@register_model
def vit_tiny_patch14_224(pretrained=False, **kwargs) -> VisionTransformer:
""" ViT-Tiny (Vit-Ti/16)
"""
model_args = dict(patch_size=14, embed_dim=192, depth=12, num_heads=3)
model = _create_vision_transformer('vit_tiny_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_patch14_224(pretrained=False, **kwargs) -> VisionTransformer:
""" ViT-Small (ViT-S/16)
"""
model_args = dict(patch_size=14, embed_dim=384, depth=12, num_heads=6)
model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch14_224(pretrained=False, **kwargs) -> VisionTransformer:
""" ViT-Base (ViT-B/14) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
"""
model_args = dict(patch_size=14, embed_dim=768, depth=12, num_heads=12)
model = _create_vision_transformer('vit_base_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_huge_patch16_224(pretrained=False, **kwargs) -> VisionTransformer:
""" ViT-Huge model (ViT-H/16) from original paper (https://arxiv.org/abs/2010.11929).
"""
model_args = dict(patch_size=16, embed_dim=1280, depth=32, num_heads=16)
if pretrained:
# There is no pretrained version of ViT-H/16, but we can adapt a ViT-H/14 for this purpose
model = _create_vision_transformer('vit_huge_patch14_clip_336', pretrained=True, **dict(model_args, pre_norm=True, **kwargs))
else:
model = _create_vision_transformer('vit_huge_patch16_224', pretrained=False, **dict(model_args, **kwargs))
return model
@register_model
def vit_huge_patch16_224_mlpnorm(pretrained=False, **kwargs) -> VisionTransformer:
""" ViT-Huge model (ViT-H/16) from original paper (https://arxiv.org/abs/2010.11929).
"""
model = vit_huge_patch16_224(pretrained=pretrained, **kwargs)
for m in model.modules():
if isinstance(m, Mlp) and not isinstance(m.norm, nn.LayerNorm):
m.norm = nn.LayerNorm(m.fc1.out_features)
return model
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