diff --git a/timm/models/__init__.py b/timm/models/__init__.py index c24f136f..8f56b5f1 100644 --- a/timm/models/__init__.py +++ b/timm/models/__init__.py @@ -60,6 +60,7 @@ from .visformer import * from .vision_transformer import * from .vision_transformer_hybrid import * from .vision_transformer_relpos import * +from .vision_transformer_sam import * from .volo import * from .vovnet import * from .xception import * diff --git a/timm/models/vision_transformer_sam.py b/timm/models/vision_transformer_sam.py new file mode 100644 index 00000000..98c0096d --- /dev/null +++ b/timm/models/vision_transformer_sam.py @@ -0,0 +1,610 @@ +""" 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, Optional, Tuple + +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, Format +from ._builder import build_model_with_cfg +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__) + + +class Attention(nn.Module): + + 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, + rel_pos_zero_init: bool = True, + input_size: Optional[Tuple[int, int]] = 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.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 ( + 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)) + + def forward(self, x): + B, H, W, _ = x.shape + # qkv with shape (3, B, nHead, H * W, C) + qkv = self.qkv(x).reshape( + B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + # q, k, v with shape (B * nHead, H * W, C) + q, k, v = qkv.unbind(0) + q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0) + q, k = self.q_norm(q), self.k_norm(k) + + attn = (q * self.scale) @ k.transpose(-2, -1) + + if self.use_rel_pos: + attn = add_decomposed_rel_pos( + attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + x = (attn @ v).view(B, self.num_heads, H, W, - + 1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1) + x = self.proj(x) + + 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, + ): + 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), + ) + 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): + shortcut = x + x = self.norm1(x) + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + 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, pad_hw, (H, W)) + + x = shortcut + x + x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x)))) + + 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 + if pad_h > 0 or pad_w > 0: + 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, pad_hw: Tuple[int, int], hw: Tuple[int, int] +) -> 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 + 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) + + if Hp > H or Wp > W: + x = x[:, :H, :W, :].contiguous() + return x + + +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()] + + +def add_decomposed_rel_pos( + attn: torch.Tensor, + 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: + attn (Tensor): attention map. + 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: + attn (Tensor): attention map with added relative positional embeddings. + """ + 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 = ( + attn.view(B, q_h, q_w, k_h, k_w) + + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + ).view(B, q_h * q_w, k_h * k_w) + + return attn + + +class VisionTransformerSAM(nn.Module): + """ Vision Transformer for vitsam or 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), + 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, + window_size: int = 14, + global_attn_indexes: Tuple[int, ...] = (), + neck_chans: int = 256, + global_pool: str = 'avg', + ): + """ + 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. + 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. + """ + 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 + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + 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 + ) + if use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + self.pos_embed = nn.Parameter( + torch.zeros(1, img_size // patch_size, + img_size // patch_size, embed_dim) + ) + else: + self.pos_embed = 0. + 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() + + # 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=(img_size // patch_size, img_size // patch_size), + ) + 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), + ) + else: + self.neck = nn.Identity() + neck_chans = embed_dim + + # Classifier Head + 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): + return self.head + + def reset_classifier(self, num_classes=0, global_pool=None): + self.head = self.head.reset(num_classes, global_pool) if num_classes > 0 else nn.Identity() + + def _pos_embed(self, x): + x = x + self.pos_embed + return self.pos_drop(x) + + def forward_features(self, x): + x = self.patch_embed(x) + x = self._pos_embed(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 """ + out_dict = {} + for k, v in state_dict.items(): + if 'image_encoder.' in k: + new_k = k.replace('image_encoder.', '') + new_k = new_k.replace('mlp.lin', 'mlp.fc') + out_dict[new_k] = v + return state_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', + **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) + 'vitsam_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), + 'vitsam_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), + 'vitsam_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), +}) + + +def _create_vision_transformer(variant, pretrained=False, **kwargs): + if kwargs.get('features_only', None): + raise RuntimeError( + 'features_only not implemented for Vision Transformer models.') + + return build_model_with_cfg( + VisionTransformerSAM, + variant, + pretrained, + pretrained_filter_fn=checkpoint_filter_fn, + **kwargs, + ) + + +@register_model +def vitsam_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( + 'vitsam_base_patch16', pretrained=pretrained, **dict(model_args, **kwargs)) + return model + + +@register_model +def vitsam_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( + 'vitsam_large_patch16', pretrained=pretrained, **dict(model_args, **kwargs)) + return model + + +@register_model +def vitsam_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( + 'vitsam_huge_patch16', pretrained=pretrained, **dict(model_args, **kwargs)) + return model + +# TODO: +# support any input size, now only 1024 x 1024 (pretrained)