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timm_vit.py
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import torch
import torchvision
from torch import Tensor
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
import timm
from timm.models.vision_transformer import VisionTransformer, checkpoint_filter_fn
from timm.models.helpers import build_model_with_cfg, resolve_pretrained_cfg
class VisionTransformer_nohead(timm.models.vision_transformer.VisionTransformer):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.final_feat_dim = self.embed_dim
def forward(self, x: Tensor) -> Tensor:
x = self.forward_features(x)
# forward_head(self, x, pre_logits: bool = False)
# return x if pre_logits els self.head(x)
x = self.forward_head(x, True)
return x
def _create_vision_transformer(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
pretrained_cfg = resolve_pretrained_cfg(variant, pretrained_cfg=kwargs.pop('pretrained_cfg', None))
model = build_model_with_cfg(
VisionTransformer_nohead, variant, pretrained,
pretrained_cfg=pretrained_cfg,
pretrained_filter_fn=checkpoint_filter_fn,
pretrained_custom_load='npz' in pretrained_cfg['url'],
**kwargs)
return model
# exclude models for 384 image size
def vit_small_patch16_224(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/16)
NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
"""
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs)
return model
def vit_small_patch32_224(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/32)
"""
model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_small_patch32_224', pretrained=pretrained, **model_kwargs)
return model
def vit_base_patch8_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/8) 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_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_base_patch8_224', pretrained=pretrained, **model_kwargs)
return model
def vit_base_patch16_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) 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_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **model_kwargs)
return model
def vit_base_patch32_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k, source https://github.com/google-research/vision_transformer.
"""
model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **model_kwargs)
return model
def vit_large_patch16_224(pretrained=False, **kwargs):
""" ViT-Large model (ViT-L/16) 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_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **model_kwargs)
return model
def vit_large_patch32_224(pretrained=False, **kwargs):
""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
"""
model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **model_kwargs)
return model
# SSL pretrained models by DINO
def vit_small_patch8_224_dino(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
"""
model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_small_patch8_224_dino', pretrained=pretrained, **model_kwargs)
return model
def vit_small_patch16_224_dino(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/16) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
"""
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_small_patch16_224_dino', pretrained=pretrained, **model_kwargs)
return model
def vit_base_patch8_224_dino(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
"""
model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_base_patch8_224_dino', pretrained=pretrained, **model_kwargs)
return model
def vit_base_patch16_224_dino(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) /w DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
"""
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_base_patch16_224_dino', pretrained=pretrained, **model_kwargs)
return model