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""" Vision Transformer (ViT) in PyTorch
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A PyTorch implement of Vision Transformers as described in:
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'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
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- https://arxiv.org/abs/2010.11929
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`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
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- https://arxiv.org/abs/2106.TODO
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The official jax code is released and available at https://github.com/google-research/vision_transformer
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DeiT model defs and weights from https://github.com/facebookresearch/deit,
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paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
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Acknowledgments:
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* The paper authors for releasing code and weights, thanks!
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* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
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for some einops/einsum fun
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* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
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* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
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Hacked together by / Copyright 2021 Ross Wightman
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"""
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import math
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import logging
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from functools import partial
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from collections import OrderedDict
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from copy import deepcopy
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from .helpers import build_model_with_cfg, named_apply, adapt_input_conv
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from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_
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from .registry import register_model
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_logger = logging.getLogger(__name__)
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'patch_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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# patch models (weights from official Google JAX impl)
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'vit_tiny_patch16_224': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
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'vit_tiny_patch16_384': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
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input_size=(3, 384, 384), crop_pct=1.0),
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'vit_small_patch32_224': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
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'vit_small_patch32_384': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
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input_size=(3, 384, 384), crop_pct=1.0),
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'vit_small_patch16_224': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
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'vit_small_patch16_384': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
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input_size=(3, 384, 384), crop_pct=1.0),
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'vit_base_patch32_224': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
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'vit_base_patch32_384': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
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input_size=(3, 384, 384), crop_pct=1.0),
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'vit_base_patch16_224': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
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'vit_base_patch16_384': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
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input_size=(3, 384, 384), crop_pct=1.0),
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'vit_large_patch32_224': _cfg(
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url='', # no official model weights for this combo, only for in21k
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),
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'vit_large_patch32_384': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
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input_size=(3, 384, 384), crop_pct=1.0),
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'vit_large_patch16_224': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
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'vit_large_patch16_384': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
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input_size=(3, 384, 384), crop_pct=1.0),
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# patch models, imagenet21k (weights from official Google JAX impl)
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'vit_tiny_patch16_224_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
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num_classes=21843),
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'vit_small_patch32_224_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
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num_classes=21843),
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'vit_small_patch16_224_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
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num_classes=21843),
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'vit_base_patch32_224_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0.npz',
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num_classes=21843),
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'vit_base_patch16_224_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
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num_classes=21843),
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'vit_large_patch32_224_in21k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',
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num_classes=21843),
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'vit_large_patch16_224_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1.npz',
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num_classes=21843),
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'vit_huge_patch14_224_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/imagenet21k/ViT-H_14.npz',
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hf_hub='timm/vit_huge_patch14_224_in21k',
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num_classes=21843),
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# deit models (FB weights)
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'deit_tiny_patch16_224': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'deit_small_patch16_224': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'deit_base_patch16_224': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'deit_base_patch16_384': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(3, 384, 384), crop_pct=1.0),
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'deit_tiny_distilled_patch16_224': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
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'deit_small_distilled_patch16_224': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
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'deit_base_distilled_patch16_224': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
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'deit_base_distilled_patch16_384': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(3, 384, 384), crop_pct=1.0,
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classifier=('head', 'head_dist')),
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# ViT ImageNet-21K-P pretraining by MILL
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'vit_base_patch16_224_miil_in21k': _cfg(
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url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/vit_base_patch16_224_in21k_miil.pth',
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mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
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),
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'vit_base_patch16_224_miil': _cfg(
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url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm'
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'/vit_base_patch16_224_1k_miil_84_4.pth',
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mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
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),
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}
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class Attention(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = head_dim ** -0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class Block(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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def forward(self, x):
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x = x + self.drop_path(self.attn(self.norm1(x)))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class VisionTransformer(nn.Module):
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""" Vision Transformer
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A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
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- https://arxiv.org/abs/2010.11929
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Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
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- https://arxiv.org/abs/2012.12877
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
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num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False,
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drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
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act_layer=None, weight_init=''):
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"""
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Args:
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img_size (int, tuple): input image size
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patch_size (int, tuple): patch size
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in_chans (int): number of input channels
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num_classes (int): number of classes for classification head
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embed_dim (int): embedding dimension
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depth (int): depth of transformer
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num_heads (int): number of attention heads
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim
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qkv_bias (bool): enable bias for qkv if True
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representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
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distilled (bool): model includes a distillation token and head as in DeiT models
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drop_rate (float): dropout rate
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attn_drop_rate (float): attention dropout rate
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drop_path_rate (float): stochastic depth rate
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embed_layer (nn.Module): patch embedding layer
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norm_layer: (nn.Module): normalization layer
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weight_init: (str): weight init scheme
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"""
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super().__init__()
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self.num_classes = num_classes
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self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
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self.num_tokens = 2 if distilled else 1
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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act_layer = act_layer or nn.GELU
|
|
|
|
|
|
|
|
self.patch_embed = embed_layer(
|
|
|
|
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
|
|
|
|
num_patches = self.patch_embed.num_patches
|
|
|
|
|
|
|
|
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
|
|
|
self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
|
|
|
|
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
|
|
|
|
self.pos_drop = nn.Dropout(p=drop_rate)
|
|
|
|
|
|
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
|
|
|
self.blocks = nn.Sequential(*[
|
|
|
|
Block(
|
|
|
|
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
|
|
|
|
attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
|
|
|
|
for i in range(depth)])
|
|
|
|
self.norm = norm_layer(embed_dim)
|
|
|
|
|
|
|
|
# Representation layer
|
|
|
|
if representation_size and not distilled:
|
|
|
|
self.num_features = representation_size
|
|
|
|
self.pre_logits = nn.Sequential(OrderedDict([
|
|
|
|
('fc', nn.Linear(embed_dim, representation_size)),
|
|
|
|
('act', nn.Tanh())
|
|
|
|
]))
|
|
|
|
else:
|
|
|
|
self.pre_logits = nn.Identity()
|
|
|
|
|
|
|
|
# Classifier head(s)
|
|
|
|
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
self.head_dist = None
|
|
|
|
if distilled:
|
|
|
|
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
|
|
|
|
self.init_weights(weight_init)
|
|
|
|
|
|
|
|
def init_weights(self, mode=''):
|
|
|
|
assert mode in ('jax', 'jax_nlhb', 'nlhb', '')
|
|
|
|
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
|
|
|
|
trunc_normal_(self.pos_embed, std=.02)
|
|
|
|
if self.dist_token is not None:
|
|
|
|
trunc_normal_(self.dist_token, std=.02)
|
|
|
|
if mode.startswith('jax'):
|
|
|
|
# leave cls token as zeros to match jax impl
|
|
|
|
named_apply(partial(_init_vit_weights, head_bias=head_bias, jax_impl=True), self)
|
|
|
|
else:
|
|
|
|
trunc_normal_(self.cls_token, std=.02)
|
|
|
|
self.apply(_init_vit_weights)
|
|
|
|
|
|
|
|
def _init_weights(self, m):
|
|
|
|
# this fn left here for compat with downstream users
|
|
|
|
_init_vit_weights(m)
|
|
|
|
|
|
|
|
@torch.jit.ignore()
|
|
|
|
def load_pretrained(self, checkpoint_path, prefix=''):
|
|
|
|
_load_weights(self, checkpoint_path, prefix)
|
|
|
|
|
|
|
|
@torch.jit.ignore
|
|
|
|
def no_weight_decay(self):
|
|
|
|
return {'pos_embed', 'cls_token', 'dist_token'}
|
|
|
|
|
|
|
|
def get_classifier(self):
|
|
|
|
if self.dist_token is None:
|
|
|
|
return self.head
|
|
|
|
else:
|
|
|
|
return self.head, self.head_dist
|
|
|
|
|
|
|
|
def reset_classifier(self, num_classes, global_pool=''):
|
|
|
|
self.num_classes = num_classes
|
|
|
|
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
if self.num_tokens == 2:
|
|
|
|
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
|
|
|
|
def forward_features(self, x):
|
|
|
|
x = self.patch_embed(x)
|
|
|
|
cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
|
|
|
if self.dist_token is None:
|
|
|
|
x = torch.cat((cls_token, x), dim=1)
|
|
|
|
else:
|
|
|
|
x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
|
|
|
|
x = self.pos_drop(x + self.pos_embed)
|
|
|
|
x = self.blocks(x)
|
|
|
|
x = self.norm(x)
|
|
|
|
if self.dist_token is None:
|
|
|
|
return self.pre_logits(x[:, 0])
|
|
|
|
else:
|
|
|
|
return x[:, 0], x[:, 1]
|
|
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
x = self.forward_features(x)
|
|
|
|
if self.head_dist is not None:
|
|
|
|
x, x_dist = self.head(x[0]), self.head_dist(x[1]) # x must be a tuple
|
|
|
|
if self.training and not torch.jit.is_scripting():
|
|
|
|
# during inference, return the average of both classifier predictions
|
|
|
|
return x, x_dist
|
|
|
|
else:
|
|
|
|
return (x + x_dist) / 2
|
|
|
|
else:
|
|
|
|
x = self.head(x)
|
|
|
|
return x
|
|
|
|
|
|
|
|
|
|
|
|
def _init_vit_weights(module: nn.Module, name: str = '', head_bias: float = 0., jax_impl: bool = False):
|
|
|
|
""" ViT weight initialization
|
|
|
|
* When called without n, head_bias, jax_impl args it will behave exactly the same
|
|
|
|
as my original init for compatibility with prev hparam / downstream use cases (ie DeiT).
|
|
|
|
* When called w/ valid n (module name) and jax_impl=True, will (hopefully) match JAX impl
|
|
|
|
"""
|
|
|
|
if isinstance(module, nn.Linear):
|
|
|
|
if name.startswith('head'):
|
|
|
|
nn.init.zeros_(module.weight)
|
|
|
|
nn.init.constant_(module.bias, head_bias)
|
|
|
|
elif name.startswith('pre_logits'):
|
|
|
|
lecun_normal_(module.weight)
|
|
|
|
nn.init.zeros_(module.bias)
|
|
|
|
else:
|
|
|
|
if jax_impl:
|
|
|
|
nn.init.xavier_uniform_(module.weight)
|
|
|
|
if module.bias is not None:
|
|
|
|
if 'mlp' in name:
|
|
|
|
nn.init.normal_(module.bias, std=1e-6)
|
|
|
|
else:
|
|
|
|
nn.init.zeros_(module.bias)
|
|
|
|
else:
|
|
|
|
trunc_normal_(module.weight, std=.02)
|
|
|
|
if module.bias is not None:
|
|
|
|
nn.init.zeros_(module.bias)
|
|
|
|
elif jax_impl and isinstance(module, nn.Conv2d):
|
|
|
|
# NOTE conv was left to pytorch default in my original init
|
|
|
|
lecun_normal_(module.weight)
|
|
|
|
if module.bias is not None:
|
|
|
|
nn.init.zeros_(module.bias)
|
|
|
|
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
|
|
|
|
nn.init.zeros_(module.bias)
|
|
|
|
nn.init.ones_(module.weight)
|
|
|
|
|
|
|
|
|
|
|
|
@torch.no_grad()
|
|
|
|
def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
|
|
|
|
""" Load weights from .npz checkpoints for official Google Brain Flax implementation
|
|
|
|
"""
|
|
|
|
import numpy as np
|
|
|
|
|
|
|
|
def _n2p(w, t=True):
|
|
|
|
if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
|
|
|
|
w = w.flatten()
|
|
|
|
if t:
|
|
|
|
if w.ndim == 4:
|
|
|
|
w = w.transpose([3, 2, 0, 1])
|
|
|
|
elif w.ndim == 3:
|
|
|
|
w = w.transpose([2, 0, 1])
|
|
|
|
elif w.ndim == 2:
|
|
|
|
w = w.transpose([1, 0])
|
|
|
|
return torch.from_numpy(w)
|
|
|
|
|
|
|
|
w = np.load(checkpoint_path)
|
|
|
|
if not prefix and 'opt/target/embedding/kernel' in w:
|
|
|
|
prefix = 'opt/target/'
|
|
|
|
|
|
|
|
if hasattr(model.patch_embed, 'backbone'):
|
|
|
|
# hybrid
|
|
|
|
backbone = model.patch_embed.backbone
|
|
|
|
stem_only = not hasattr(backbone, 'stem')
|
|
|
|
stem = backbone if stem_only else backbone.stem
|
|
|
|
stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
|
|
|
|
stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
|
|
|
|
stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
|
|
|
|
if not stem_only:
|
|
|
|
for i, stage in enumerate(backbone.stages):
|
|
|
|
for j, block in enumerate(stage.blocks):
|
|
|
|
bp = f'{prefix}block{i + 1}/unit{j + 1}/'
|
|
|
|
for r in range(3):
|
|
|
|
getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
|
|
|
|
getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
|
|
|
|
getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
|
|
|
|
if block.downsample is not None:
|
|
|
|
block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
|
|
|
|
block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
|
|
|
|
block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
|
|
|
|
embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
|
|
|
|
else:
|
|
|
|
embed_conv_w = adapt_input_conv(
|
|
|
|
model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
|
|
|
|
model.patch_embed.proj.weight.copy_(embed_conv_w)
|
|
|
|
model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
|
|
|
|
model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
|
|
|
|
pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
|
|
|
|
if pos_embed_w.shape != model.pos_embed.shape:
|
|
|
|
pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights
|
|
|
|
pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
|
|
|
|
model.pos_embed.copy_(pos_embed_w)
|
|
|
|
model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
|
|
|
|
model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
|
|
|
|
if model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
|
|
|
|
model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
|
|
|
|
model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
|
|
|
|
for i, block in enumerate(model.blocks.children()):
|
|
|
|
block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
|
|
|
|
mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
|
|
|
|
block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
|
|
|
|
block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
|
|
|
|
block.attn.qkv.weight.copy_(torch.cat([
|
|
|
|
_n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
|
|
|
|
block.attn.qkv.bias.copy_(torch.cat([
|
|
|
|
_n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
|
|
|
|
block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
|
|
|
|
block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
|
|
|
|
for r in range(2):
|
|
|
|
getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
|
|
|
|
getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
|
|
|
|
block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
|
|
|
|
block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))
|
|
|
|
|
|
|
|
|
|
|
|
def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
|
|
|
|
# Rescale the grid of position embeddings when loading from state_dict. Adapted from
|
|
|
|
# https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
|
|
|
|
_logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
|
|
|
|
ntok_new = posemb_new.shape[1]
|
|
|
|
if num_tokens:
|
|
|
|
posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:]
|
|
|
|
ntok_new -= num_tokens
|
|
|
|
else:
|
|
|
|
posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
|
|
|
|
gs_old = int(math.sqrt(len(posemb_grid)))
|
|
|
|
if not len(gs_new): # backwards compatibility
|
|
|
|
gs_new = [int(math.sqrt(ntok_new))] * 2
|
|
|
|
assert len(gs_new) >= 2
|
|
|
|
_logger.info('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new)
|
|
|
|
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
|
|
|
|
posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bilinear')
|
|
|
|
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
|
|
|
|
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
|
|
|
|
return posemb
|
|
|
|
|
|
|
|
|
|
|
|
def checkpoint_filter_fn(state_dict, model):
|
|
|
|
""" convert patch embedding weight from manual patchify + linear proj to conv"""
|
|
|
|
out_dict = {}
|
|
|
|
if 'model' in state_dict:
|
|
|
|
# For deit models
|
|
|
|
state_dict = state_dict['model']
|
|
|
|
for k, v in state_dict.items():
|
|
|
|
if 'patch_embed.proj.weight' in k and len(v.shape) < 4:
|
|
|
|
# For old models that I trained prior to conv based patchification
|
|
|
|
O, I, H, W = model.patch_embed.proj.weight.shape
|
|
|
|
v = v.reshape(O, -1, H, W)
|
|
|
|
elif k == 'pos_embed' and v.shape != model.pos_embed.shape:
|
|
|
|
# To resize pos embedding when using model at different size from pretrained weights
|
|
|
|
v = resize_pos_embed(
|
|
|
|
v, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
|
|
|
|
out_dict[k] = v
|
|
|
|
return out_dict
|
|
|
|
|
|
|
|
|
|
|
|
def _create_vision_transformer(variant, pretrained=False, default_cfg=None, **kwargs):
|
|
|
|
default_cfg = default_cfg or default_cfgs[variant]
|
|
|
|
if kwargs.get('features_only', None):
|
|
|
|
raise RuntimeError('features_only not implemented for Vision Transformer models.')
|
|
|
|
|
|
|
|
# NOTE this extra code to support handling of repr size for in21k pretrained models
|
|
|
|
default_num_classes = default_cfg['num_classes']
|
|
|
|
num_classes = kwargs.get('num_classes', default_num_classes)
|
|
|
|
repr_size = kwargs.pop('representation_size', None)
|
|
|
|
if repr_size is not None and num_classes != default_num_classes:
|
|
|
|
# Remove representation layer if fine-tuning. This may not always be the desired action,
|
|
|
|
# but I feel better than doing nothing by default for fine-tuning. Perhaps a better interface?
|
|
|
|
_logger.warning("Removing representation layer for fine-tuning.")
|
|
|
|
repr_size = None
|
|
|
|
|
|
|
|
model = build_model_with_cfg(
|
|
|
|
VisionTransformer, variant, pretrained,
|
|
|
|
default_cfg=default_cfg,
|
|
|
|
representation_size=repr_size,
|
|
|
|
pretrained_filter_fn=checkpoint_filter_fn,
|
|
|
|
pretrained_custom_load='npz' in default_cfg['url'],
|
|
|
|
**kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_tiny_patch16_224(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Tiny (Vit-Ti/16)
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
|
|
|
|
model = _create_vision_transformer('vit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_tiny_patch16_384(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Tiny (Vit-Ti/16) @ 384x384.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
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model = _create_vision_transformer('vit_tiny_patch16_384', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_small_patch32_224(pretrained=False, **kwargs):
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""" ViT-Small (ViT-S/32)
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|
"""
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model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
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model = _create_vision_transformer('vit_small_patch32_224', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_small_patch32_384(pretrained=False, **kwargs):
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""" ViT-Small (ViT-S/32) at 384x384.
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|
"""
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|
model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
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model = _create_vision_transformer('vit_small_patch32_384', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_small_patch16_224(pretrained=False, **kwargs):
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|
""" ViT-Small (ViT-S/16)
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|
NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
|
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|
"""
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|
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
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|
model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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|
def vit_small_patch16_384(pretrained=False, **kwargs):
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|
""" ViT-Small (ViT-S/16)
|
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|
NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
|
|
|
|
"""
|
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|
|
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
|
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|
model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **model_kwargs)
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|
return model
|
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|
@register_model
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|
def vit_base_patch32_224(pretrained=False, **kwargs):
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|
""" ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
|
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|
|
"""
|
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|
|
model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
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|
model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **model_kwargs)
|
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|
return model
|
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|
@register_model
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|
|
def vit_base_patch32_384(pretrained=False, **kwargs):
|
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|
|
""" ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
|
|
|
|
ImageNet-1k weights fine-tuned from in21k @ 384x384, 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_384', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
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|
|
@register_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
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_base_patch16_384(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
|
|
|
|
ImageNet-1k weights fine-tuned from in21k @ 384x384, 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_384', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_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
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_large_patch32_384(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
|
|
|
|
ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
|
|
|
|
model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_large_patch16_224(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Large model (ViT-L/32) 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
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_large_patch16_384(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 @ 384x384, 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_384', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Tiny (Vit-Ti/16).
|
|
|
|
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
|
|
|
|
model = _create_vision_transformer('vit_tiny_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_small_patch32_224_in21k(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Small (ViT-S/16)
|
|
|
|
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
|
|
|
|
model = _create_vision_transformer('vit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_small_patch16_224_in21k(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Small (ViT-S/16)
|
|
|
|
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
|
|
|
|
model = _create_vision_transformer('vit_small_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
|
|
|
|
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(
|
|
|
|
patch_size=32, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
|
|
|
|
model = _create_vision_transformer('vit_base_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
|
|
|
|
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(
|
|
|
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
|
|
|
|
model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
|
|
|
|
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(
|
|
|
|
patch_size=32, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
|
|
|
|
model = _create_vision_transformer('vit_large_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
|
|
|
|
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(
|
|
|
|
patch_size=16, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
|
|
|
|
model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
|
|
|
|
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
|
|
|
NOTE: converted weights not currently available, too large for github release hosting.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(
|
|
|
|
patch_size=14, embed_dim=1280, depth=32, num_heads=16, representation_size=1280, **kwargs)
|
|
|
|
model = _create_vision_transformer('vit_huge_patch14_224_in21k', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def deit_tiny_patch16_224(pretrained=False, **kwargs):
|
|
|
|
""" DeiT-tiny model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
|
|
|
|
ImageNet-1k weights from https://github.com/facebookresearch/deit.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
|
|
|
|
model = _create_vision_transformer('deit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def deit_small_patch16_224(pretrained=False, **kwargs):
|
|
|
|
""" DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
|
|
|
|
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 = _create_vision_transformer('deit_small_patch16_224', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def deit_base_patch16_224(pretrained=False, **kwargs):
|
|
|
|
""" DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
|
|
|
|
ImageNet-1k weights from https://github.com/facebookresearch/deit.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
|
|
|
model = _create_vision_transformer('deit_base_patch16_224', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def deit_base_patch16_384(pretrained=False, **kwargs):
|
|
|
|
""" DeiT base model @ 384x384 from paper (https://arxiv.org/abs/2012.12877).
|
|
|
|
ImageNet-1k weights from https://github.com/facebookresearch/deit.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
|
|
|
model = _create_vision_transformer('deit_base_patch16_384', pretrained=pretrained, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def deit_tiny_distilled_patch16_224(pretrained=False, **kwargs):
|
|
|
|
""" DeiT-tiny distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
|
|
|
|
ImageNet-1k weights from https://github.com/facebookresearch/deit.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
|
|
|
|
model = _create_vision_transformer(
|
|
|
|
'deit_tiny_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def deit_small_distilled_patch16_224(pretrained=False, **kwargs):
|
|
|
|
""" DeiT-small distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
|
|
|
|
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 = _create_vision_transformer(
|
|
|
|
'deit_small_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def deit_base_distilled_patch16_224(pretrained=False, **kwargs):
|
|
|
|
""" DeiT-base distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
|
|
|
|
ImageNet-1k weights from https://github.com/facebookresearch/deit.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
|
|
|
model = _create_vision_transformer(
|
|
|
|
'deit_base_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def deit_base_distilled_patch16_384(pretrained=False, **kwargs):
|
|
|
|
""" DeiT-base distilled model @ 384x384 from paper (https://arxiv.org/abs/2012.12877).
|
|
|
|
ImageNet-1k weights from https://github.com/facebookresearch/deit.
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
|
|
|
model = _create_vision_transformer(
|
|
|
|
'deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
def vit_base_patch16_224_miil_in21k(pretrained=False, **kwargs):
|
|
|
|
""" ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
|
|
|
|
Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
|
|
|
|
"""
|
|
|
|
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
|
|
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model = _create_vision_transformer('vit_base_patch16_224_miil_in21k', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_base_patch16_224_miil(pretrained=False, **kwargs):
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""" ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
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Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
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"""
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model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
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model = _create_vision_transformer('vit_base_patch16_224_miil', pretrained=pretrained, **model_kwargs)
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return model
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