""" Vision Transformer (ViT) in PyTorch A PyTorch implement of Vision Transformers as described in: 'Exploring Plain Vision Transformer Backbones for Object Detection' - https://arxiv.org/abs/2203.16527 'Segment Anything Model (SAM)' - https://github.com/facebookresearch/segment-anything/ """ import logging from functools import partial from typing import Callable, List, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD from timm.layers import PatchEmbed, Mlp, DropPath, PatchDropout, LayerNorm2d, ClassifierHead, NormMlpClassifierHead, \ Format, resample_abs_pos_embed_nhwc, RotaryEmbeddingCat, apply_rot_embed_cat, to_2tuple, use_fused_attn from torch.jit import Final from ._builder import build_model_with_cfg from ._features import feature_take_indices from ._features_fx import register_notrace_function from ._manipulate import checkpoint_seq from ._registry import generate_default_cfgs, register_model # model_registry will add each entrypoint fn to this __all__ = ['VisionTransformerSAM'] _logger = logging.getLogger(__name__) def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: """ Get relative positional embeddings according to the relative positions of query and key sizes. Args: q_size (int): size of query q. k_size (int): size of key k. rel_pos (Tensor): relative position embeddings (L, C). Returns: Extracted positional embeddings according to relative positions. """ max_rel_dist = int(2 * max(q_size, k_size) - 1) # Interpolate rel pos if needed. if rel_pos.shape[0] != max_rel_dist: # Interpolate rel pos. rel_pos_resized = F.interpolate( rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), size=max_rel_dist, mode="linear", ) rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) else: rel_pos_resized = rel_pos # Scale the coords with short length if shapes for q and k are different. q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) return rel_pos_resized[relative_coords.long()] register_notrace_function(get_rel_pos) def get_decomposed_rel_pos_bias( q: torch.Tensor, rel_pos_h: torch.Tensor, rel_pos_w: torch.Tensor, q_size: Tuple[int, int], k_size: Tuple[int, int], ) -> torch.Tensor: """ Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py Args: q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. q_size (Tuple): spatial sequence size of query q with (q_h, q_w). k_size (Tuple): spatial sequence size of key k with (k_h, k_w). Returns: bias (Tensor): attention bias to add to attention map """ q_h, q_w = q_size k_h, k_w = k_size Rh = get_rel_pos(q_h, k_h, rel_pos_h) Rw = get_rel_pos(q_w, k_w, rel_pos_w) B, _, dim = q.shape r_q = q.reshape(B, q_h, q_w, dim) rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) attn_bias = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] return attn_bias.reshape(-1, q_h * q_w, k_h * k_w) class Attention(nn.Module): fused_attn: Final[bool] def __init__( self, dim, num_heads=8, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., norm_layer=nn.LayerNorm, use_rel_pos: bool = False, input_size: Optional[Tuple[int, int]] = None, rope: Optional[nn.Module] = None, ): super().__init__() assert dim % num_heads == 0, 'dim should be divisible by num_heads' self.num_heads = num_heads self.head_dim = dim // num_heads self.scale = self.head_dim ** -0.5 self.fused_attn = use_fused_attn() self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity() self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity() self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.use_rel_pos = use_rel_pos if self.use_rel_pos: assert rope is None assert ( input_size is not None ), "Input size must be provided if using relative positional encoding." # initialize relative positional embeddings self.rel_pos_h = nn.Parameter(torch.zeros( 2 * input_size[0] - 1, self.head_dim)) self.rel_pos_w = nn.Parameter(torch.zeros( 2 * input_size[1] - 1, self.head_dim)) self.rope = rope def forward(self, x): B, H, W, _ = x.shape N = H * W x = x.reshape(B, N, -1) qkv = self.qkv(x).view(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # qkv with shape (3, B, nHead, H * W, C) q, k, v = qkv.reshape(3, B * self.num_heads, N, -1).unbind(0) # q, k, v with shape (B * nHead, H * W, C) q, k = self.q_norm(q), self.k_norm(k) if self.use_rel_pos: attn_bias = get_decomposed_rel_pos_bias(q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)) else: attn_bias = None if self.rope is not None: rope = self.rope.get_embed() q = apply_rot_embed_cat(q, rope).type_as(v) k = apply_rot_embed_cat(k, rope).type_as(v) if self.fused_attn: x = torch.nn.functional.scaled_dot_product_attention( q, k, v, attn_mask=attn_bias, dropout_p=self.attn_drop.p if self.training else 0., ) else: q = q * self.scale attn = q @ k.transpose(-2, -1) if attn_bias is not None: attn = attn + attn_bias attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = attn @ v x = x.view(B, self.num_heads, N, -1).transpose(1, 2).reshape(B, N, -1) x = self.proj(x) x = x.view(B, H, W, -1) return x class LayerScale(nn.Module): def __init__(self, dim, init_values=1e-5, inplace=False): super().__init__() self.inplace = inplace self.gamma = nn.Parameter(init_values * torch.ones(dim)) def forward(self, x): return x.mul_(self.gamma) if self.inplace else x * self.gamma class Block(nn.Module): def __init__( self, dim, num_heads, mlp_ratio=4., qkv_bias=True, qk_norm=False, proj_drop=0., attn_drop=0., init_values=None, drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, mlp_layer=Mlp, use_rel_pos=False, window_size=0, input_size=None, rope=None, ): super().__init__() self.window_size = window_size self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_norm=qk_norm, attn_drop=attn_drop, proj_drop=proj_drop, norm_layer=norm_layer, use_rel_pos=use_rel_pos, input_size=input_size if window_size == 0 else (window_size, window_size), rope=rope, ) self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) self.mlp = mlp_layer( in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=proj_drop, ) self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity() def forward(self, x): B, H, W, _ = x.shape shortcut = x x = self.norm1(x) # Window partition pad_hw: Optional[Tuple[int, int]] = None if self.window_size > 0: x, pad_hw = window_partition(x, self.window_size) x = self.drop_path1(self.ls1(self.attn(x))) # Reverse window partition if self.window_size > 0: x = window_unpartition(x, self.window_size, (H, W), pad_hw) x = shortcut + x x = x.reshape(B, H * W, -1) # MLP is faster for N, L, C tensor x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x)))) x = x.reshape(B, H, W, -1) return x def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]: """ Partition into non-overlapping windows with padding if needed. Args: x (tensor): input tokens with [B, H, W, C]. window_size (int): window size. Returns: windows: windows after partition with [B * num_windows, window_size, window_size, C]. (Hp, Wp): padded height and width before partition """ B, H, W, C = x.shape pad_h = (window_size - H % window_size) % window_size pad_w = (window_size - W % window_size) % window_size x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) Hp, Wp = H + pad_h, W + pad_w x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows, (Hp, Wp) def window_unpartition( windows: torch.Tensor, window_size: int, hw: Tuple[int, int], pad_hw: Optional[Tuple[int, int]] = None, ) -> torch.Tensor: """ Window unpartition into original sequences and removing padding. Args: windows (tensor): input tokens with [B * num_windows, window_size, window_size, C]. window_size (int): window size. pad_hw (Tuple): padded height and width (Hp, Wp). hw (Tuple): original height and width (H, W) before padding. Returns: x: unpartitioned sequences with [B, H, W, C]. """ Hp, Wp = pad_hw if pad_hw is not None else hw H, W = hw B = windows.shape[0] // (Hp * Wp // window_size // window_size) x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) x = x[:, :H, :W, :].contiguous() return x class VisionTransformerSAM(nn.Module): """ Vision Transformer for Segment-Anything Model(SAM) A PyTorch impl of : `Exploring Plain Vision Transformer Backbones for Object Detection` or `Segment Anything Model (SAM)` - https://arxiv.org/abs/2010.11929 """ def __init__( self, img_size: int = 1024, patch_size: int = 16, in_chans: int = 3, num_classes: int = 768, embed_dim: int = 768, depth: int = 12, num_heads: int = 12, mlp_ratio: float = 4., qkv_bias: bool = True, qk_norm: bool = False, init_values: Optional[float] = None, pre_norm: bool = False, drop_rate: float = 0., pos_drop_rate: float = 0., patch_drop_rate: float = 0., proj_drop_rate: float = 0., attn_drop_rate: float = 0., drop_path_rate: float = 0., weight_init: str = '', embed_layer: Callable = partial(PatchEmbed, output_fmt=Format.NHWC, strict_img_size=False), norm_layer: Optional[Callable] = nn.LayerNorm, act_layer: Optional[Callable] = nn.GELU, block_fn: Callable = Block, mlp_layer: Callable = Mlp, use_abs_pos: bool = True, use_rel_pos: bool = False, use_rope: bool = False, window_size: int = 14, global_attn_indexes: Tuple[int, ...] = (), neck_chans: int = 256, global_pool: str = 'avg', head_hidden_size: Optional[int] = None, ref_feat_shape: Optional[Tuple[Tuple[int, int], Tuple[int, int]]] = None ): """ Args: img_size: Input image size. patch_size: Patch size. in_chans: Number of image input channels. num_classes: Mumber of classes for classification head. global_pool: Type of global pooling for final sequence (default: 'token'). embed_dim: Transformer embedding dimension. depth: Depth of transformer. num_heads: Number of attention heads. mlp_ratio: Ratio of mlp hidden dim to embedding dim. qkv_bias: Enable bias for qkv projections if True. init_values: Layer-scale init values (layer-scale enabled if not None). drop_rate: Head dropout rate. pos_drop_rate: Position embedding dropout rate. attn_drop_rate: Attention dropout rate. drop_path_rate: Stochastic depth rate. weight_init: Weight initialization scheme. embed_layer: Patch embedding layer. norm_layer: Normalization layer. act_layer: MLP activation layer. block_fn: Transformer block layer. use_abs_pos: If True, use absolute positional embeddings. use_rel_pos: If True, add relative positional embeddings to the attention map. use_rope: If True, add rotary position embeddings to q/k in attention block. window_size: Window size for window attention blocks. If 0, not use window attention. global_attn_indexes: Indexes for blocks using global attention. Used when window_size > 0. global_pool: Global pooling type. head_hidden_size: If set, use NormMlpHead ref_feat_shape: Tuple of reference feature shapes for ROPE, (global, local) """ super().__init__() norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.num_classes = num_classes self.global_pool = global_pool self.num_features = self.head_hidden_size = self.embed_dim = embed_dim # for consistency with other models self.grad_checkpointing = False self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, bias=not pre_norm, # disable bias if pre-norm is used ) grid_size = self.patch_embed.grid_size r = self.patch_embed.feat_ratio() if hasattr(self.patch_embed, 'feat_ratio') else patch_size if use_abs_pos: # Initialize absolute positional embedding with pretrain image size. self.pos_embed = nn.Parameter(torch.zeros(1, grid_size[0], grid_size[1], embed_dim)) else: self.pos_embed = None self.pos_drop = nn.Dropout(p=pos_drop_rate) if patch_drop_rate > 0: self.patch_drop = PatchDropout( patch_drop_rate, num_prefix_tokens=0, ) else: self.patch_drop = nn.Identity() self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity() if use_rope: assert not use_rel_pos, "ROPE and relative pos embeddings should not be enabled at same time" if ref_feat_shape is not None: assert len(ref_feat_shape) == 2 ref_feat_shape_global = to_2tuple(ref_feat_shape[0]) ref_feat_shape_window = to_2tuple(ref_feat_shape[1]) else: ref_feat_shape_global = ref_feat_shape_window = None self.rope_global = RotaryEmbeddingCat( embed_dim // num_heads, in_pixels=False, feat_shape=grid_size, ref_feat_shape=ref_feat_shape_global, ) self.rope_window = RotaryEmbeddingCat( embed_dim // num_heads, in_pixels=False, feat_shape=to_2tuple(window_size), ref_feat_shape=ref_feat_shape_window, ) else: self.rope_global = None self.rope_window = None # stochastic depth decay rule dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] self.blocks = nn.Sequential(*[ block_fn( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_norm=qk_norm, init_values=init_values, proj_drop=proj_drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer, mlp_layer=mlp_layer, use_rel_pos=use_rel_pos, window_size=window_size if i not in global_attn_indexes else 0, input_size=grid_size, rope=self.rope_window if i not in global_attn_indexes else self.rope_global, ) for i in range(depth)]) self.feature_info = [ dict(module=f'blocks.{i}', num_chs=embed_dim, reduction=r) for i in range(depth)] if neck_chans: self.neck = nn.Sequential( nn.Conv2d( embed_dim, neck_chans, kernel_size=1, bias=False, ), LayerNorm2d(neck_chans), nn.Conv2d( neck_chans, neck_chans, kernel_size=3, padding=1, bias=False, ), LayerNorm2d(neck_chans), ) self.num_features = neck_chans else: if head_hidden_size: self.neck = nn.Identity() else: # should have a final norm with standard ClassifierHead self.neck = LayerNorm2d(embed_dim) neck_chans = embed_dim # Classifier Head if head_hidden_size: self.head = NormMlpClassifierHead( neck_chans, num_classes, hidden_size=head_hidden_size, pool_type=global_pool, drop_rate=drop_rate, ) else: self.head = ClassifierHead( neck_chans, num_classes, pool_type=global_pool, drop_rate=drop_rate, ) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'dist_token'} @torch.jit.ignore def group_matcher(self, coarse=False): return dict( stem=r'^pos_embed|patch_embed', # stem and embed blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))] ) @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.grad_checkpointing = enable @torch.jit.ignore def get_classifier(self) -> nn.Module: return self.head def reset_classifier(self, num_classes=0, global_pool=None): self.head.reset(num_classes, global_pool) def forward_intermediates( self, x: torch.Tensor, indices: Optional[Union[int, List[int], Tuple[int]]] = None, norm: bool = False, stop_early: bool = False, output_fmt: str = 'NCHW', intermediates_only: bool = False, ) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]: """ Forward features that returns intermediates. Args: x: Input image tensor indices: Take last n blocks if int, all if None, select matching indices if sequence norm: Apply norm layer to all intermediates stop_early: Stop iterating over blocks when last desired intermediate hit output_fmt: Shape of intermediate feature outputs intermediates_only: Only return intermediate features Returns: """ assert output_fmt == 'NCHW', 'Output shape for ViT-SAM must be NCHW.' intermediates = [] take_indices, max_index = feature_take_indices(len(self.blocks), indices) # forward pass, collect intermediates x = self.patch_embed(x) if self.pos_embed is not None: # dynamically resize abs pos embedding if needed x = x + resample_abs_pos_embed_nhwc(self.pos_embed, x.shape[1:3]) x = self.pos_drop(x) x = self.patch_drop(x) x = self.norm_pre(x) if torch.jit.is_scripting() or not stop_early: # can't slice blocks in torchscript blocks = self.blocks else: blocks = self.blocks[:max_index + 1] for i, blk in enumerate(blocks): x = blk(x) if i in take_indices: # make output BCHW if norm: # norm is intertwined with neck convs so apply both, changes the dim # FIXME only apply to final? Need experiments intermediates.append(self.neck(x.permute(0, 3, 1, 2))) else: intermediates.append(x.permute(0, 3, 1, 2)) if intermediates_only: return intermediates x = self.neck(x.permute(0, 3, 1, 2)) return x, intermediates def prune_intermediate_layers( self, indices: Optional[Union[int, List[int], Tuple[int]]] = None, prune_norm: bool = False, prune_head: bool = True, ): """ Prune layers not required for specified intermediates. """ take_indices, max_index = feature_take_indices(len(self.blocks), indices) self.blocks = self.blocks[:max_index + 1] # truncate blocks if prune_norm: # neck is being treated as equivalent to final norm here self.neck = nn.Identity() if prune_head: self.reset_classifier(0, '') return take_indices def forward_features(self, x): x = self.patch_embed(x) if self.pos_embed is not None: # dynamically resize abs pos embedding if needed x = x + resample_abs_pos_embed_nhwc(self.pos_embed, x.shape[1:3]) x = self.pos_drop(x) x = self.patch_drop(x) x = self.norm_pre(x) if self.grad_checkpointing and not torch.jit.is_scripting(): x = checkpoint_seq(self.blocks, x) else: x = self.blocks(x) x = self.neck(x.permute(0, 3, 1, 2)) return x def forward_head(self, x, pre_logits: bool = False): return self.head(x, pre_logits=True) if pre_logits else self.head(x) def forward(self, x): x = self.forward_features(x) x = self.forward_head(x) return x def checkpoint_filter_fn( state_dict, model, ): """ Remap SAM checkpoints -> timm """ sam_checkpoint = 'image_encoder.patch_embed.proj.weight' in state_dict out_dict = {} for k, v in state_dict.items(): if k.startswith('image_encoder.'): k = k[14:] k = k.replace('mlp.lin', 'mlp.fc') else: if sam_checkpoint: continue out_dict[k] = v return out_dict def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 1024, 1024), 'pool_size': None, 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, 'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD, 'first_conv': 'patch_embed.proj', 'classifier': 'head.fc', **kwargs } default_cfgs = generate_default_cfgs({ # Segment-Anyhing Model (SAM) pretrained - https://github.com/facebookresearch/segment-anything (no classifier head, for fine-tune/features only) 'samvit_base_patch16.sa1b': _cfg( url='https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth', hf_hub_id='timm/', license='apache-2.0', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0, input_size=(3, 1024, 1024), crop_pct=1.0), 'samvit_large_patch16.sa1b': _cfg( url='https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth', hf_hub_id='timm/', license='apache-2.0', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0, input_size=(3, 1024, 1024), crop_pct=1.0), 'samvit_huge_patch16.sa1b': _cfg( url='https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth', hf_hub_id='timm/', license='apache-2.0', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0, input_size=(3, 1024, 1024), crop_pct=1.0), 'samvit_base_patch16_224': _cfg( mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=1000, input_size=(3, 224, 224), crop_pct=0.9), }) def _create_vision_transformer(variant, pretrained=False, **kwargs): out_indices = kwargs.pop('out_indices', 3) return build_model_with_cfg( VisionTransformerSAM, variant, pretrained, pretrained_filter_fn=checkpoint_filter_fn, feature_cfg=dict(out_indices=out_indices, feature_cls='getter'), **kwargs, ) @register_model def samvit_base_patch16(pretrained=False, **kwargs) -> VisionTransformerSAM: """ ViT-B/16 for Segment-Anything """ model_args = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, global_attn_indexes=[2, 5, 8, 11], window_size=14, use_rel_pos=True, img_size=1024, ) model = _create_vision_transformer( 'samvit_base_patch16', pretrained=pretrained, **dict(model_args, **kwargs)) return model @register_model def samvit_large_patch16(pretrained=False, **kwargs) -> VisionTransformerSAM: """ ViT-L/16 for Segment-Anything """ model_args = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, global_attn_indexes=[5, 11, 17, 23], window_size=14, use_rel_pos=True, img_size=1024, ) model = _create_vision_transformer( 'samvit_large_patch16', pretrained=pretrained, **dict(model_args, **kwargs)) return model @register_model def samvit_huge_patch16(pretrained=False, **kwargs) -> VisionTransformerSAM: """ ViT-H/16 for Segment-Anything """ model_args = dict( patch_size=16, embed_dim=1280, depth=32, num_heads=16, global_attn_indexes=[7, 15, 23, 31], window_size=14, use_rel_pos=True, img_size=1024, ) model = _create_vision_transformer( 'samvit_huge_patch16', pretrained=pretrained, **dict(model_args, **kwargs)) return model @register_model def samvit_base_patch16_224(pretrained=False, **kwargs) -> VisionTransformerSAM: """ ViT-B/16 based on samvit arch """ model_args = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, global_attn_indexes=[2, 5, 8, 11], window_size=14, use_rel_pos=True, use_abs_pos=False, img_size=224, neck_chans=None, ) model = _create_vision_transformer( 'samvit_base_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs)) return model