timm/models/vision_transformer_sam.py

""" 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, NormMlpClassifierHead,\
    Format, resample_abs_pos_embed_nhwc
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 = 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
        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 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,
            window_size: int = 14,
            global_attn_indexes: Tuple[int, ...] = (),
            neck_chans: int = 256,
            global_pool: str = 'avg',
            head_hidden_size: Optional[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.
            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
        """
        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
        )
        grid_size = self.patch_embed.grid_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()

        # 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,
            )
            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
        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):
        return self.head

    def reset_classifier(self, num_classes=0, global_pool=None):
        self.head.reset(num_classes, global_pool)

    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',
        **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),
})


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 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