# Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # References: # https://github.com/facebookresearch/dino/blob/main/vision_transformer.py # https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py from functools import partial import math import logging from typing import Sequence, Tuple, Union, Callable, Dict, Optional, Any, List from torch import Tensor import torch import torch.nn as nn import torch.utils.checkpoint from torch.nn.init import trunc_normal_ import os import warnings logger = logging.getLogger("dinov2") from xformers.ops import memory_efficient_attention, unbind, fmha, scaled_index_add, index_select_cat from xformers.ops import SwiGLU XFORMERS_AVAILABLE = True warnings.warn("xFormers is available (Attention)") def drop_path(x, drop_prob: float = 0.0, training: bool = False): if drop_prob == 0.0 or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = x.new_empty(shape).bernoulli_(keep_prob) if keep_prob > 0.0: random_tensor.div_(keep_prob) output = x * random_tensor return output class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) class LayerScale(nn.Module): def __init__( self, dim: int, init_values: Union[float, Tensor] = 1e-5, inplace: bool = False, ) -> None: super().__init__() self.inplace = inplace self.gamma = nn.Parameter(init_values * torch.ones(dim)) def forward(self, x: Tensor) -> Tensor: return x.mul_(self.gamma) if self.inplace else x * self.gamma class Attention(nn.Module): def __init__( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bias: bool = True, attn_drop: float = 0.0, proj_drop: float = 0.0, ) -> None: super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = 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, bias=proj_bias) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x: Tensor) -> Tensor: 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] * self.scale, qkv[1], qkv[2] attn = q @ k.transpose(-2, -1) 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 MemEffAttention(Attention): def forward(self, x: Tensor, attn_bias=None) -> Tensor: if not XFORMERS_AVAILABLE: if attn_bias is not None: raise AssertionError("xFormers is required for using nested tensors") return super().forward(x) B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) q, k, v = unbind(qkv, 2) x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) x = x.reshape([B, N, C]) x = self.proj(x) x = self.proj_drop(x) return x class SwiGLUFFN(nn.Module): def __init__( self, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None, act_layer: Callable[..., nn.Module] = None, drop: float = 0.0, bias: bool = True, ) -> None: super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias) self.w3 = nn.Linear(hidden_features, out_features, bias=bias) def forward(self, x: Tensor) -> Tensor: x12 = self.w12(x) x1, x2 = x12.chunk(2, dim=-1) hidden = F.silu(x1) * x2 return self.w3(hidden) class SwiGLUFFNFused(SwiGLU): def __init__( self, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None, act_layer: Callable[..., nn.Module] = None, drop: float = 0.0, bias: bool = True, ) -> None: out_features = out_features or in_features hidden_features = hidden_features or in_features hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 super().__init__( in_features=in_features, hidden_features=hidden_features, out_features=out_features, bias=bias, ) def make_2tuple(x): if isinstance(x, tuple): assert len(x) == 2 return x assert isinstance(x, int) return (x, x) class PatchEmbed(nn.Module): """ 2D image to patch embedding: (B,C,H,W) -> (B,N,D) Args: img_size: Image size. patch_size: Patch token size. in_chans: Number of input image channels. embed_dim: Number of linear projection output channels. norm_layer: Normalization layer. """ def __init__( self, img_size: Union[int, Tuple[int, int]] = 224, patch_size: Union[int, Tuple[int, int]] = 16, in_chans: int = 3, embed_dim: int = 768, norm_layer: Optional[Callable] = None, flatten_embedding: bool = True, ) -> None: super().__init__() image_HW = make_2tuple(img_size) patch_HW = make_2tuple(patch_size) patch_grid_size = ( image_HW[0] // patch_HW[0], image_HW[1] // patch_HW[1], ) self.img_size = image_HW self.patch_size = patch_HW self.patches_resolution = patch_grid_size self.num_patches = patch_grid_size[0] * patch_grid_size[1] self.in_chans = in_chans self.embed_dim = embed_dim self.flatten_embedding = flatten_embedding self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x: Tensor) -> Tensor: _, _, H, W = x.shape patch_H, patch_W = self.patch_size assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}" assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}" x = self.proj(x) # B C H W H, W = x.size(2), x.size(3) x = x.flatten(2).transpose(1, 2) # B HW C x = self.norm(x) if not self.flatten_embedding: x = x.reshape(-1, H, W, self.embed_dim) # B H W C return x def flops(self) -> float: Ho, Wo = self.patches_resolution flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) if self.norm is not None: flops += Ho * Wo * self.embed_dim return flops class Mlp(nn.Module): def __init__( self, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None, act_layer: Callable[..., nn.Module] = nn.GELU, drop: float = 0.0, bias: bool = True, ) -> None: super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features, bias=bias) self.drop = nn.Dropout(drop) def forward(self, x: Tensor) -> Tensor: x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class Basic_Block(nn.Module): def __init__( self, dim: int, num_heads: int, mlp_ratio: float = 4.0, qkv_bias: bool = False, proj_bias: bool = True, ffn_bias: bool = True, drop: float = 0.0, attn_drop: float = 0.0, init_values=None, drop_path: float = 0.0, act_layer: Callable[..., nn.Module] = nn.GELU, norm_layer: Callable[..., nn.Module] = nn.LayerNorm, attn_class: Callable[..., nn.Module] = Attention, ffn_layer: Callable[..., nn.Module] = Mlp, ) -> None: super().__init__() # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}") self.norm1 = norm_layer(dim) self.attn = attn_class( dim, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, attn_drop=attn_drop, proj_drop=drop, ) self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = ffn_layer( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, bias=ffn_bias, ) self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.sample_drop_ratio = drop_path def forward(self, x: Tensor) -> Tensor: def attn_residual_func(x: Tensor) -> Tensor: return self.ls1(self.attn(self.norm1(x))) def ffn_residual_func(x: Tensor) -> Tensor: return self.ls2(self.mlp(self.norm2(x))) if self.training and self.sample_drop_ratio > 0.1: # the overhead is compensated only for a drop path rate larger than 0.1 x = drop_add_residual_stochastic_depth( x, residual_func=attn_residual_func, sample_drop_ratio=self.sample_drop_ratio, ) x = drop_add_residual_stochastic_depth( x, residual_func=ffn_residual_func, sample_drop_ratio=self.sample_drop_ratio, ) elif self.training and self.sample_drop_ratio > 0.0: x = x + self.drop_path1(attn_residual_func(x)) x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2 else: x = x + attn_residual_func(x) x = x + ffn_residual_func(x) return x def drop_add_residual_stochastic_depth( x: Tensor, residual_func: Callable[[Tensor], Tensor], sample_drop_ratio: float = 0.0, ) -> Tensor: # 1) extract subset using permutation b, n, d = x.shape sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) brange = (torch.randperm(b, device=x.device))[:sample_subset_size] x_subset = x[brange] # 2) apply residual_func to get residual residual = residual_func(x_subset) x_flat = x.flatten(1) residual = residual.flatten(1) residual_scale_factor = b / sample_subset_size # 3) add the residual x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) return x_plus_residual.view_as(x) def get_branges_scales(x, sample_drop_ratio=0.0): b, n, d = x.shape sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) brange = (torch.randperm(b, device=x.device))[:sample_subset_size] residual_scale_factor = b / sample_subset_size return brange, residual_scale_factor def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None): if scaling_vector is None: x_flat = x.flatten(1) residual = residual.flatten(1) x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) else: x_plus_residual = scaled_index_add( x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor ) return x_plus_residual attn_bias_cache: Dict[Tuple, Any] = {} def get_attn_bias_and_cat(x_list, branges=None): """ this will perform the index select, cat the tensors, and provide the attn_bias from cache """ batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list] all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list)) if all_shapes not in attn_bias_cache.keys(): seqlens = [] for b, x in zip(batch_sizes, x_list): for _ in range(b): seqlens.append(x.shape[1]) attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens) attn_bias._batch_sizes = batch_sizes attn_bias_cache[all_shapes] = attn_bias if branges is not None: cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1]) else: tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list) cat_tensors = torch.cat(tensors_bs1, dim=1) return attn_bias_cache[all_shapes], cat_tensors def drop_add_residual_stochastic_depth_list( x_list: List[Tensor], residual_func: Callable[[Tensor, Any], Tensor], sample_drop_ratio: float = 0.0, scaling_vector=None, ) -> Tensor: # 1) generate random set of indices for dropping samples in the batch branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list] branges = [s[0] for s in branges_scales] residual_scale_factors = [s[1] for s in branges_scales] # 2) get attention bias and index+concat the tensors attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges) # 3) apply residual_func to get residual, and split the result residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore outputs = [] for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors): outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x)) return outputs class Block(Basic_Block): def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]: """ x_list contains a list of tensors to nest together and run """ assert isinstance(self.attn, MemEffAttention) if self.training and self.sample_drop_ratio > 0.0: def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: return self.attn(self.norm1(x), attn_bias=attn_bias) def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: return self.mlp(self.norm2(x)) x_list = drop_add_residual_stochastic_depth_list( x_list, residual_func=attn_residual_func, sample_drop_ratio=self.sample_drop_ratio, scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None, ) x_list = drop_add_residual_stochastic_depth_list( x_list, residual_func=ffn_residual_func, sample_drop_ratio=self.sample_drop_ratio, scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None, ) return x_list else: def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias)) def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: return self.ls2(self.mlp(self.norm2(x))) attn_bias, x = get_attn_bias_and_cat(x_list) x = x + attn_residual_func(x, attn_bias=attn_bias) x = x + ffn_residual_func(x) return attn_bias.split(x) def forward(self, x_or_x_list): if isinstance(x_or_x_list, Tensor): return super().forward(x_or_x_list) elif isinstance(x_or_x_list, list): if not XFORMERS_AVAILABLE: raise AssertionError("xFormers is required for using nested tensors") return self.forward_nested(x_or_x_list) else: raise AssertionError def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module: if not depth_first and include_root: fn(module=module, name=name) for child_name, child_module in module.named_children(): child_name = ".".join((name, child_name)) if name else child_name named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True) if depth_first and include_root: fn(module=module, name=name) return module class BlockChunk(nn.ModuleList): def forward(self, x): for b in self: x = b(x) return x class DinoVisionTransformer(nn.Module): def __init__( self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=True, ffn_bias=True, proj_bias=True, drop_path_rate=0.0, drop_path_uniform=False, init_values=None, # for layerscale: None or 0 => no layerscale embed_layer=PatchEmbed, act_layer=nn.GELU, block_fn=Block, ffn_layer="mlp", block_chunks=1, num_register_tokens=0, interpolate_antialias=False, interpolate_offset=0.1, ): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels 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 proj_bias (bool): enable bias for proj in attn if True ffn_bias (bool): enable bias for ffn if True drop_path_rate (float): stochastic depth rate drop_path_uniform (bool): apply uniform drop rate across blocks weight_init (str): weight init scheme init_values (float): layer-scale init values embed_layer (nn.Module): patch embedding layer act_layer (nn.Module): MLP activation layer block_fn (nn.Module): transformer block class ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity" block_chunks: (int) split block sequence into block_chunks units for FSDP wrap num_register_tokens: (int) number of extra cls tokens (so-called "registers") interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings """ super().__init__() norm_layer = partial(nn.LayerNorm, eps=1e-6) self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 1 self.n_blocks = depth self.num_heads = num_heads self.patch_size = patch_size self.num_register_tokens = num_register_tokens self.interpolate_antialias = interpolate_antialias self.interpolate_offset = interpolate_offset 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.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) assert num_register_tokens >= 0 self.register_tokens = ( nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None ) if drop_path_uniform is True: dpr = [drop_path_rate] * depth else: dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule if ffn_layer == "mlp": logger.info("using MLP layer as FFN") ffn_layer = Mlp elif ffn_layer == "swiglufused" or ffn_layer == "swiglu": logger.info("using SwiGLU layer as FFN") ffn_layer = SwiGLUFFNFused elif ffn_layer == "identity": logger.info("using Identity layer as FFN") def f(*args, **kwargs): return nn.Identity() ffn_layer = f else: raise NotImplementedError blocks_list = [ block_fn( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, proj_bias=proj_bias, ffn_bias=ffn_bias, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer, ffn_layer=ffn_layer, init_values=init_values, ) for i in range(depth) ] if block_chunks > 0: self.chunked_blocks = True chunked_blocks = [] chunksize = depth // block_chunks for i in range(0, depth, chunksize): # this is to keep the block index consistent if we chunk the block list chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize]) self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks]) else: self.chunked_blocks = False self.blocks = nn.ModuleList(blocks_list) self.norm = norm_layer(embed_dim) self.head = nn.Identity() self.mask_token = nn.Parameter(torch.zeros(1, embed_dim)) self.init_weights() def init_weights(self): trunc_normal_(self.pos_embed, std=0.02) nn.init.normal_(self.cls_token, std=1e-6) if self.register_tokens is not None: nn.init.normal_(self.register_tokens, std=1e-6) named_apply(init_weights_vit_timm, self) def interpolate_pos_encoding(self, x, w, h): previous_dtype = x.dtype npatch = x.shape[1] - 1 N = self.pos_embed.shape[1] - 1 if npatch == N and w == h: return self.pos_embed pos_embed = self.pos_embed.float() class_pos_embed = pos_embed[:, 0] patch_pos_embed = pos_embed[:, 1:] dim = x.shape[-1] w0 = w // self.patch_size h0 = h // self.patch_size # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset sqrt_N = math.sqrt(N) sx, sy = float(w0) / sqrt_N, float(h0) / sqrt_N patch_pos_embed = nn.functional.interpolate( patch_pos_embed.reshape(1, int(sqrt_N), int(sqrt_N), dim).permute(0, 3, 1, 2), scale_factor=(sx, sy), mode="bicubic", antialias=self.interpolate_antialias, ) assert int(w0) == patch_pos_embed.shape[-2] assert int(h0) == patch_pos_embed.shape[-1] patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype) def prepare_tokens_with_masks(self, x, masks=None): B, nc, w, h = x.shape x = self.patch_embed(x) if masks is not None: x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x) x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) x = x + self.interpolate_pos_encoding(x, w, h) if self.register_tokens is not None: x = torch.cat( ( x[:, :1], self.register_tokens.expand(x.shape[0], -1, -1), x[:, 1:], ), dim=1, ) return x def forward_features_list(self, x_list, masks_list): x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)] for blk in self.blocks: x = blk(x) all_x = x output = [] for x, masks in zip(all_x, masks_list): x_norm = self.norm(x) output.append( { "x_norm_clstoken": x_norm[:, 0], "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1], "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :], "x_prenorm": x, "masks": masks, } ) return output def forward_features(self, x, masks=None): if isinstance(x, list): return self.forward_features_list(x, masks) x = self.prepare_tokens_with_masks(x, masks) for blk in self.blocks: x = blk(x) x_norm = self.norm(x) return { "x_norm_clstoken": x_norm[:, 0], "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1], "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :], "x_prenorm": x, "masks": masks, } def _get_intermediate_layers_not_chunked(self, x, n=1): x = self.prepare_tokens_with_masks(x) # If n is an int, take the n last blocks. If it's a list, take them output, total_block_len = [], len(self.blocks) blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n for i, blk in enumerate(self.blocks): x = blk(x) if i in blocks_to_take: output.append(x) assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" return output def _get_intermediate_layers_chunked(self, x, n=1): x = self.prepare_tokens_with_masks(x) output, i, total_block_len = [], 0, len(self.blocks[-1]) # If n is an int, take the n last blocks. If it's a list, take them blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n for block_chunk in self.blocks: for blk in block_chunk[i:]: # Passing the nn.Identity() x = blk(x) if i in blocks_to_take: output.append(x) i += 1 assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" return output def get_intermediate_layers( self, x: torch.Tensor, n: Union[int, Sequence] = 1, # Layers or n last layers to take reshape: bool = False, return_class_token: bool = False, norm=True, ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]: if self.chunked_blocks: outputs = self._get_intermediate_layers_chunked(x, n) else: outputs = self._get_intermediate_layers_not_chunked(x, n) if norm: outputs = [self.norm(out) for out in outputs] class_tokens = [out[:, 0] for out in outputs] outputs = [out[:, 1:] for out in outputs] if reshape: B, _, w, h = x.shape outputs = [ out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous() for out in outputs ] if return_class_token: return tuple(zip(outputs, class_tokens)) return tuple(outputs) def forward(self, *args, is_training=False, **kwargs): ret = self.forward_features(*args, **kwargs) if is_training: return ret else: return self.head(ret["x_norm_clstoken"]) def init_weights_vit_timm(module: nn.Module, name: str = ""): """ViT weight initialization, original timm impl (for reproducibility)""" if isinstance(module, nn.Linear): trunc_normal_(module.weight, std=0.02) if module.bias is not None: nn.init.zeros_(module.bias) def vit_tiny(patch_size=16, num_register_tokens=0, **kwargs): model = DinoVisionTransformer( patch_size=patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, block_fn=partial(Block, attn_class=MemEffAttention), num_register_tokens=num_register_tokens, **kwargs, ) return model def vit_small(patch_size=16, num_register_tokens=0, **kwargs): model = DinoVisionTransformer( patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, block_fn=partial(Block, attn_class=MemEffAttention), num_register_tokens=num_register_tokens, **kwargs, ) return model def vit_base(patch_size=16, num_register_tokens=0, **kwargs): model = DinoVisionTransformer( patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, block_fn=partial(Block, attn_class=MemEffAttention), num_register_tokens=num_register_tokens, **kwargs, ) return model def vit_large(patch_size=16, num_register_tokens=0, **kwargs): model = DinoVisionTransformer( patch_size=patch_size, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, block_fn=partial(Block, attn_class=MemEffAttention), num_register_tokens=num_register_tokens, **kwargs, ) return model def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs): """ Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64 """ model = DinoVisionTransformer( patch_size=patch_size, embed_dim=1536, depth=40, num_heads=24, mlp_ratio=4, block_fn=partial(Block, attn_class=MemEffAttention), num_register_tokens=num_register_tokens, **kwargs, ) return model if __name__ == "__main__": import argparse from fvcore.nn import FlopCountAnalysis, parameter_count_table with torch.no_grad(): model = vit_base(img_size=224, patch_size=14, init_values=1.0, ffn_layer='mlp', block_chunks=0, num_register_tokens=0, interpolate_antialias=False, interpolate_offset=0.1) for name, param in model.named_parameters(): print(name, param) # print(parameter_count_table(model)) # tensor = torch.rand(1, 3, 224, 224) # flops = FlopCountAnalysis(model, tensor) # print("FLOPs: ", flops.total()/1e9)