""" Vision Transformer (ViT) in PyTorch A PyTorch implement of Vision Transformers as described in 'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929 The official jax code is released and available at https://github.com/google-research/vision_transformer Acknowledgments: * The paper authors for releasing code and weights, thanks! * I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out for some einops/einsum fun * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT * Bert reference code checks against Huggingface Transformers and Tensorflow Bert DeiT model defs and weights from https://github.com/facebookresearch/deit, paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 Hacked together by / Copyright 2020 Ross Wightman """ import math import logging from functools import partial from collections import OrderedDict import torch import torch.nn as nn import torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .helpers import load_pretrained from .layers import StdConv2dSame, StdConv2d, DropPath, to_2tuple, trunc_normal_ from .resnet import resnet26d, resnet50d from .resnetv2 import ResNetV2, create_resnetv2_stem from .registry import register_model _logger = logging.getLogger(__name__) def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, 'crop_pct': .9, 'interpolation': 'bicubic', 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'first_conv': 'patch_embed.proj', 'classifier': 'head', **kwargs } default_cfgs = { # patch models (my experiments) 'vit_small_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth', ), # patch models (weights ported from official Google JAX impl) 'vit_base_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), ), 'vit_base_patch32_224': _cfg( url='', # no official model weights for this combo, only for in21k mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_base_patch16_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0), 'vit_base_patch32_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0), 'vit_large_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch32_224': _cfg( url='', # no official model weights for this combo, only for in21k mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch16_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0), 'vit_large_patch32_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0), # patch models, imagenet21k (weights ported from official Google JAX impl) 'vit_base_patch16_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch16_224_in21k-e5005f0a.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_base_patch32_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch32_224_in21k-8db57226.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch16_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch16_224_in21k-606da67d.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch32_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_huge_patch14_224_in21k': _cfg( url='', # FIXME I have weights for this but > 2GB limit for github release binaries num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), # hybrid in-21k models (weights ported from official Google JAX impl where they exist) 'vit_base_r50_s16_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.9, first_conv='patch_embed.backbone.stem.conv'), # hybrid in-1k models (weights ported from official Google JAX impl where they exist) 'vit_tiny_r_s16_p8_224': _cfg( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_tiny_r_s16_p8_224_in21k': _cfg( num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_tiny_r_s16_p8_384': _cfg( input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r_s16_p8_224': _cfg( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r_s16_p8_224_in21k': _cfg( num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r_s16_p8_384': _cfg( input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r20_s16_p2_224': _cfg( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r20_s16_p2_224_in21k': _cfg( inum_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r20_s16_p2_384': _cfg( input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r20_s16_224': _cfg( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r20_s16_224_in21k': _cfg( num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r20_s16_384': _cfg( input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r26_s32_224': _cfg( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r26_s32_224_in21k': _cfg( num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_small_r26_s32_384': _cfg( input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_base_r20_s16_224': _cfg( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_base_r20_s16_224_in21k': _cfg( num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_base_r20_s16_384': _cfg( input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_base_r26_s32_224': _cfg( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_base_r26_s32_224_in21k': _cfg( num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_base_r26_s32_384': _cfg( input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_base_r50_s16_224': _cfg( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_base_r50_s16_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_large_r50_s32_224': _cfg( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_large_r50_s32_224_in21k': _cfg( num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), 'vit_large_r50_s32_384': _cfg( input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), # hybrid models (my experiments) 'vit_small_resnet26d_224': _cfg(), 'vit_small_resnet50d_s16_224': _cfg(), 'vit_base_resnet26d_224': _cfg(), 'vit_base_resnet50d_224': _cfg(), # deit models (FB weights) 'vit_deit_tiny_patch16_224': _cfg( 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_384': _cfg(input_size=(3, 384, 384)), 'vit_deit_small_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'), 'vit_deit_small_patch16_224_in21k': _cfg(num_classes=21843), 'vit_deit_small_patch16_384': _cfg(input_size=(3, 384, 384)), 'vit_deit_small_patch32_224': _cfg(), 'vit_deit_small_patch32_224_in21k': _cfg(num_classes=21843), 'vit_deit_small_patch32_384': _cfg(input_size=(3, 384, 384)), 'vit_deit_base_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',), 'vit_deit_base_patch16_384': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth', input_size=(3, 384, 384), crop_pct=1.0), 'vit_deit_tiny_distilled_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth'), 'vit_deit_small_distilled_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth'), 'vit_deit_base_distilled_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth', ), 'vit_deit_base_distilled_patch16_384': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth', input_size=(3, 384, 384), crop_pct=1.0), } class Mlp(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights self.scale = qk_scale or head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def forward(self, x): x = x + self.drop_path(self.attn(self.norm1(x))) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class PatchEmbed(nn.Module): """ Image to Patch Embedding """ def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) self.img_size = img_size self.patch_size = patch_size self.num_patches = num_patches self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, x): B, C, H, W = x.shape # FIXME look at relaxing size constraints assert H == self.img_size[0] and W == self.img_size[1], \ f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." x = self.proj(x).flatten(2).transpose(1, 2) return x class HybridEmbed(nn.Module): """ CNN Feature Map Embedding Extract feature map from CNN, flatten, project to embedding dim. """ def __init__(self, backbone, img_size=224, patch_size=1, feature_size=None, in_chans=3, embed_dim=768): super().__init__() assert isinstance(backbone, nn.Module) img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) self.img_size = img_size self.patch_size = patch_size self.backbone = backbone if feature_size is None: with torch.no_grad(): # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature # map for all networks, the feature metadata has reliable channel and stride info, but using # stride to calc feature dim requires info about padding of each stage that isn't captured. training = backbone.training if training: backbone.eval() o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1])) if isinstance(o, (list, tuple)): o = o[-1] # last feature if backbone outputs list/tuple of features feature_size = o.shape[-2:] feature_dim = o.shape[1] backbone.train(training) else: feature_size = to_2tuple(feature_size) if hasattr(self.backbone, 'feature_info'): feature_dim = self.backbone.feature_info.channels()[-1] else: feature_dim = self.backbone.num_features assert feature_size[0] % patch_size[0] == 0 and feature_size[1] % patch_size[1] == 0 self.num_patches = feature_size[0] // patch_size[0] * feature_size[1] // patch_size[1] self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, x): x = self.backbone(x) if isinstance(x, (list, tuple)): x = x[-1] # last feature if backbone outputs list/tuple of features x = self.proj(x).flatten(2).transpose(1, 2) return x class VisionTransformer(nn.Module): """ Vision Transformer A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 """ def __init__(self, img_size=224, patch_size=None, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., hybrid_backbone=None, norm_layer=None, act_layer=None): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True qk_scale (float): override default qk scale of head_dim ** -0.5 if set representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate hybrid_backbone (nn.Module): CNN backbone to use in-place of PatchEmbed module norm_layer: (nn.Module): normalization layer """ super().__init__() self.num_classes = num_classes 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) act_layer = act_layer or nn.GELU patch_size = patch_size or (1 if hybrid_backbone is not None else 16) if hybrid_backbone is not None: self.patch_embed = HybridEmbed( hybrid_backbone, img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) else: self.patch_embed = PatchEmbed( 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.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, 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.ModuleList([ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, 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: 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 self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity() trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token'} def get_classifier(self): return self.head 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() def forward_features(self, x): B = x.shape[0] x = self.patch_embed(x) cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks x = torch.cat((cls_tokens, x), dim=1) x = x + self.pos_embed x = self.pos_drop(x) for blk in self.blocks: x = blk(x) x = self.norm(x)[:, 0] x = self.pre_logits(x) return x def forward(self, x): x = self.forward_features(x) x = self.head(x) return x class DistilledVisionTransformer(VisionTransformer): """ Vision Transformer with distillation token. Paper: `Training data-efficient image transformers & distillation through attention` - https://arxiv.org/abs/2012.12877 This impl of distilled ViT is taken from https://github.com/facebookresearch/deit """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.dist_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) num_patches = self.patch_embed.num_patches self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, self.embed_dim)) self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if self.num_classes > 0 else nn.Identity() trunc_normal_(self.dist_token, std=.02) trunc_normal_(self.pos_embed, std=.02) self.head_dist.apply(self._init_weights) def forward_features(self, x): B = x.shape[0] x = self.patch_embed(x) cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks dist_token = self.dist_token.expand(B, -1, -1) x = torch.cat((cls_tokens, dist_token, x), dim=1) x = x + self.pos_embed x = self.pos_drop(x) for blk in self.blocks: x = blk(x) x = self.norm(x) return x[:, 0], x[:, 1] def forward(self, x): x, x_dist = self.forward_features(x) x = self.head(x) x_dist = self.head_dist(x_dist) if self.training: return x, x_dist else: # during inference, return the average of both classifier predictions return (x + x_dist) / 2 def resize_pos_embed(posemb, posemb_new, token='class'): # 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 token: assert token in ('class', 'distill') token_idx = 2 if token == 'distill' else 1 posemb_tok, posemb_grid = posemb[:, :token_idx], posemb[0, token_idx:] ntok_new -= 1 else: posemb_tok, posemb_grid = posemb[:, :0], posemb[0] gs_old = int(math.sqrt(len(posemb_grid))) gs_new = int(math.sqrt(ntok_new)) _logger.info('Position embedding grid-size from %s to %s', 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, gs_new), mode='bilinear') posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -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) out_dict[k] = v return out_dict def _create_vision_transformer(variant, pretrained=False, distilled=False, **kwargs): default_cfg = default_cfgs[variant] default_num_classes = default_cfg['num_classes'] default_img_size = default_cfg['input_size'][-1] num_classes = kwargs.pop('num_classes', default_num_classes) img_size = kwargs.pop('img_size', default_img_size) 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_cls = DistilledVisionTransformer if distilled else VisionTransformer model = model_cls(img_size=img_size, num_classes=num_classes, representation_size=repr_size, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained( model, num_classes=num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=partial(checkpoint_filter_fn, model=model)) return model @register_model def vit_small_patch16_224(pretrained=False, **kwargs): """ My custom 'small' ViT model. embed_dim=768, depth=8, num_heads=8, mlp_ratio=3. NOTE: * this differs from the DeiT based 'small' definitions with embed_dim=384, depth=12, num_heads=6 * this model does not have a bias for QKV (unlike the official ViT and DeiT models) """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3., qkv_bias=False, norm_layer=nn.LayerNorm, **kwargs) if pretrained: # NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model model_kwargs.setdefault('qk_scale', 768 ** -0.5) model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs) return model @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_patch32_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights. """ 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 @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_base_patch32_384(pretrained=False, **kwargs): """ 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 @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_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_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_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_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_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_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_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_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 def _resnetv2(layers=(3, 4, 9), **kwargs): """ ResNet-V2 backbone helper""" padding_same = kwargs.get('padding_same', True) if padding_same: stem_type = 'same' conv_layer = StdConv2dSame else: stem_type = '' conv_layer = StdConv2d if len(layers): backbone = ResNetV2( layers=layers, num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3), preact=False, stem_type=stem_type, conv_layer=conv_layer) else: backbone = create_resnetv2_stem( kwargs.get('in_chans', 3), stem_type=stem_type, preact=False, conv_layer=conv_layer) return backbone @register_model def vit_base_r50_s16_224_in21k(pretrained=False, **kwargs): """ R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929). ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer. """ backbone = _resnetv2(layers=(3, 4, 9), **kwargs) model_kwargs = dict( embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, representation_size=768, **kwargs) model = _create_vision_transformer('vit_base_r50_s16_224_in21k', pretrained=pretrained, **model_kwargs) return model @register_model def vit_tiny_r_s16_p8_224(pretrained=False, **kwargs): """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224. """ backbone = _resnetv2(layers=(), **kwargs) model_kwargs = dict( patch_size=8, embed_dim=192, depth=12, num_heads=3, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_tiny_r_s16_p8_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_tiny_r_s16_p8_224_in21k(pretrained=False, **kwargs): """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224. """ backbone = _resnetv2(layers=(), **kwargs) model_kwargs = dict( patch_size=8, embed_dim=192, depth=12, num_heads=3, representation_size=192, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_tiny_r_s16_p8_224_in21k', pretrained=pretrained, **model_kwargs) return model @register_model def vit_tiny_r_s16_p8_384(pretrained=False, **kwargs): """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224. """ backbone = _resnetv2(layers=(), **kwargs) model_kwargs = dict( patch_size=8, embed_dim=192, depth=12, num_heads=3, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_tiny_r_s16_p8_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r_s16_p8_224(pretrained=False, **kwargs): """ R+ViT-S/S16 w/ 8x8 patch hybrid @ 224 x 224. """ backbone = _resnetv2(layers=(), **kwargs) model_kwargs = dict( patch_size=8, embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r_s16_p8_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r_s16_p8_224_in21k(pretrained=False, **kwargs): """ R+ViT-S/S16 w/ 8x8 patch hybrid @ 224 x 224. """ backbone = _resnetv2(layers=(), **kwargs) model_kwargs = dict( patch_size=8, embed_dim=384, depth=12, num_heads=6, representation_size=384, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r_s16_p8_224_in21k', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r_s16_p8_384(pretrained=False, **kwargs): """ R+ViT-S/S16 w/ 8x8 patch hybrid @ 224 x 224. """ backbone = _resnetv2(layers=(), **kwargs) model_kwargs = dict( patch_size=8, embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r_s16_p8_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r20_s16_p2_224(pretrained=False, **kwargs): """ R52+ViT-S/S16 w/ 2x2 patch hybrid @ 224 x 224. """ backbone = _resnetv2((2, 4), **kwargs) model_kwargs = dict( patch_size=2, embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r20_s16_p2_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r20_s16_p2_224_in21k(pretrained=False, **kwargs): """ R52+ViT-S/S16 w/ 2x2 patch hybrid @ 224 x 224. """ backbone = _resnetv2((2, 4), **kwargs) model_kwargs = dict( patch_size=2, embed_dim=384, depth=12, num_heads=6, representation_size=384, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r20_s16_p2_224_in21k', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r20_s16_p2_384(pretrained=False, **kwargs): """ R20+ViT-S/S16 w/ 2x2 Patch hybrid @ 384x384. """ backbone = _resnetv2((2, 4), **kwargs) model_kwargs = dict( embed_dim=384, patch_size=2, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r20_s16_p2_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r20_s16_224(pretrained=False, **kwargs): """ R20+ViT-S/S16 hybrid. """ backbone = _resnetv2((2, 2, 2), **kwargs) model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r20_s16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r20_s16_224_in21k(pretrained=False, **kwargs): """ R20+ViT-S/S16 hybrid. """ backbone = _resnetv2((2, 2, 2), **kwargs) model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, representation_size=384, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r20_s16_224_in21k', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r20_s16_384(pretrained=False, **kwargs): """ R20+ViT-S/S16 hybrid @ 384x384. """ backbone = _resnetv2((2, 2, 2), **kwargs) model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r20_s16_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r26_s32_224(pretrained=False, **kwargs): """ R26+ViT-S/S32 hybrid. """ backbone = _resnetv2((2, 2, 2, 2), **kwargs) model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r26_s32_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r26_s32_224_in21k(pretrained=False, **kwargs): """ R26+ViT-S/S32 hybrid. """ backbone = _resnetv2((2, 2, 2, 2), **kwargs) model_kwargs = dict( embed_dim=384, depth=12, num_heads=6, representation_size=384, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r26_s32_224_in21k', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_r26_s32_384(pretrained=False, **kwargs): """ R26+ViT-S/S32 hybrid @ 384x384. """ backbone = _resnetv2((2, 2, 2, 2), **kwargs) model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_r26_s32_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_base_r20_s16_224(pretrained=False, **kwargs): """ R20+ViT-B/S16 hybrid. """ backbone = _resnetv2((2, 2, 2), **kwargs) model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_base_r20_s16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_base_r20_s16_224_in21k(pretrained=False, **kwargs): """ R20+ViT-B/S16 hybrid. """ backbone = _resnetv2((2, 2, 2), **kwargs) model_kwargs = dict( embed_dim=768, depth=12, num_heads=12, representation_size=768, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_base_r20_s16_224_in21k', pretrained=pretrained, **model_kwargs) return model @register_model def vit_base_r20_s16_384(pretrained=False, **kwargs): """ R20+ViT-B/S16 hybrid. """ backbone = _resnetv2((2, 2, 2), **kwargs) model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_base_r20_s16_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_base_r26_s32_224(pretrained=False, **kwargs): """ R26+ViT-B/S32 hybrid. """ backbone = _resnetv2((2, 2, 2, 2), **kwargs) model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_base_r26_s32_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_base_r26_s32_224_in21k(pretrained=False, **kwargs): """ R26+ViT-B/S32 hybrid. """ backbone = _resnetv2((2, 2, 2, 2), **kwargs) model_kwargs = dict( embed_dim=768, depth=12, num_heads=12, representation_size=768, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_base_r26_s32_224_in21k', pretrained=pretrained, **model_kwargs) return model @register_model def vit_base_r26_s32_384(pretrained=False, **kwargs): """ R26+ViT-B/S32 hybrid. """ backbone = _resnetv2((2, 2, 2, 2), **kwargs) model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_base_r26_s32_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_base_r50_s16_224(pretrained=False, **kwargs): """ R50+ViT-B/S16 hybrid from original paper (https://arxiv.org/abs/2010.11929). """ backbone = _resnetv2((3, 4, 9), **kwargs) model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_base_r50_s16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_base_r50_s16_384(pretrained=False, **kwargs): """ R50+ViT-B/16 hybrid 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. """ backbone = _resnetv2((3, 4, 9), **kwargs) model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_base_r50_s16_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_large_r50_s32_224(pretrained=False, **kwargs): """ R50+ViT-L/S32 hybrid. """ backbone = _resnetv2((3, 4, 6, 3), **kwargs) model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_large_r50_s32_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_large_r50_s32_224_in21k(pretrained=False, **kwargs): """ R50+ViT-L/S32 hybrid. """ backbone = _resnetv2((3, 4, 6, 3), **kwargs) model_kwargs = dict( embed_dim=768, depth=12, num_heads=12, representation_size=768, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_large_r50_s32_224_in21k', pretrained=pretrained, **model_kwargs) return model @register_model def vit_large_r50_s32_384(pretrained=False, **kwargs): """ R50+ViT-L/S32 hybrid. """ backbone = _resnetv2((3, 4, 6, 3), **kwargs) model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_large_r50_s32_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_resnet26d_224(pretrained=False, **kwargs): """ Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights. """ backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4]) model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_resnet26d_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_small_resnet50d_s16_224(pretrained=False, **kwargs): """ Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights. """ backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[3]) model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_small_resnet50d_s16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_base_resnet26d_224(pretrained=False, **kwargs): """ Custom ViT base hybrid w/ ResNet26D stride 32. No pretrained weights. """ backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4]) model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_base_resnet26d_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_base_resnet50d_224(pretrained=False, **kwargs): """ Custom ViT base hybrid w/ ResNet50D stride 32. No pretrained weights. """ backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4]) model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs) model = _create_vision_transformer('vit_base_resnet50d_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_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('vit_deit_tiny_patch16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_deit_tiny_patch16_224_in21k(pretrained=False, **kwargs): """ DeiT-tiny model""" model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, representation_size=192, **kwargs) model = _create_vision_transformer('vit_deit_tiny_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model @register_model def vit_deit_tiny_patch16_384(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_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_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('vit_deit_small_patch16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_deit_small_patch16_224_in21k(pretrained=False, **kwargs): """ DeiT-small """ model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, representation_size=384, **kwargs) model = _create_vision_transformer('vit_deit_small_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model @register_model def vit_deit_small_patch16_384(pretrained=False, **kwargs): """ DeiT-small """ model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs) model = _create_vision_transformer('vit_deit_small_patch16_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_deit_small_patch32_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=32, embed_dim=384, depth=12, num_heads=6, **kwargs) model = _create_vision_transformer('vit_deit_small_patch32_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_deit_small_patch32_224_in21k(pretrained=False, **kwargs): """ DeiT-small """ 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) return model @register_model def vit_deit_small_patch32_384(pretrained=False, **kwargs): """ DeiT-small """ 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) return model @register_model def vit_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('vit_deit_base_patch16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_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('vit_deit_base_patch16_384', pretrained=pretrained, **model_kwargs) return model @register_model def vit_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( 'vit_deit_tiny_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs) return model @register_model def vit_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( 'vit_deit_small_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs) return model @register_model def vit_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( 'vit_deit_base_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs) return model @register_model def vit_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( 'vit_deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs) return model