diff --git a/timm/models/vision_transformer_sam.py b/timm/models/vision_transformer_sam.py index 9beb7b01..53c49b07 100644 --- a/timm/models/vision_transformer_sam.py +++ b/timm/models/vision_transformer_sam.py @@ -17,13 +17,15 @@ import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint +from torch.jit import Final 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 + Format, resample_abs_pos_embed_nhwc, RotaryEmbeddingCat, apply_rot_embed_cat, to_2tuple, use_fused_attn from ._builder import build_model_with_cfg from ._manipulate import checkpoint_seq from ._registry import generate_default_cfgs, register_model +from ._features_fx import register_notrace_function # model_registry will add each entrypoint fn to this __all__ = ['VisionTransformerSAM'] @@ -32,191 +34,6 @@ __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 = self.qkv(x).reshape( - B, H * W, 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, H * W, -1).unbind(0) - # q, k, v with shape (B * nHead, H * W, C) - q, k = self.q_norm(q), self.k_norm(k) - q = q * self.scale - attn = q @ 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 @@ -249,9 +66,10 @@ def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor return rel_pos_resized[relative_coords.long()] +register_notrace_function(get_rel_pos) -def add_decomposed_rel_pos( - attn: torch.Tensor, + +def get_decomposed_rel_pos_bias( q: torch.Tensor, rel_pos_h: torch.Tensor, rel_pos_w: torch.Tensor, @@ -262,7 +80,6 @@ def add_decomposed_rel_pos( 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. @@ -270,7 +87,7 @@ def add_decomposed_rel_pos( k_size (Tuple): spatial sequence size of key k with (k_h, k_w). Returns: - attn (Tensor): attention map with added relative positional embeddings. + bias (Tensor): attention bias to add to attention map """ q_h, q_w = q_size k_h, k_w = k_size @@ -282,12 +99,220 @@ def add_decomposed_rel_pos( 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) + attn_bias = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + return attn_bias.reshape(-1, q_h * q_w, k_h * k_w) - return attn + +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, + ) + 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): @@ -326,11 +351,13 @@ class VisionTransformerSAM(nn.Module): 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 + head_hidden_size: Optional[int] = None, + ref_feat_shape: Optional[Tuple[Tuple[int, int], Tuple[int, int]]] = None ): """ Args: @@ -356,10 +383,12 @@ class VisionTransformerSAM(nn.Module): 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) @@ -394,6 +423,30 @@ class VisionTransformerSAM(nn.Module): 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(*[ @@ -413,6 +466,7 @@ class VisionTransformerSAM(nn.Module): 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)]) @@ -436,7 +490,11 @@ class VisionTransformerSAM(nn.Module): ) self.num_features = neck_chans else: - self.neck = nn.Identity() + 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 @@ -526,7 +584,7 @@ def _cfg(url='', **kwargs): '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', + 'first_conv': 'patch_embed.proj', 'classifier': 'head.fc', **kwargs } @@ -552,6 +610,10 @@ default_cfgs = generate_default_cfgs({ 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), }) @@ -606,3 +668,17 @@ def samvit_huge_patch16(pretrained=False, **kwargs) -> VisionTransformerSAM: 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 +