dinov2/eval/segmentation_m2f/models/backbones/vit.py

# 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.

"""Vision Transformer (ViT) in PyTorch.

A PyTorch implement of Vision Transformers as described in:

'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
    - https://arxiv.org/abs/2010.11929

`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
    - https://arxiv.org/abs/2106.10270

The official jax code is released and available at https://github.com/google-research/vision_transformer

DeiT model defs and weights from https://github.com/facebookresearch/deit,
paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877

Acknowledgments:
* The paper authors for releasing code and weights, thanks!
* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
for some einops/einsum fun
* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
* Bert reference code checks against Huggingface Transformers and Tensorflow Bert

Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
from functools import partial
from itertools import repeat
from typing import Callable, Optional

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.runner import BaseModule, load_checkpoint
from mmseg.ops import resize
from mmseg.utils import get_root_logger
from torch import Tensor

from .drop_path import DropPath


def to_2tuple(x):
    return tuple(repeat(x, 2))


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 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,
    ) -> None:
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        swiglu_hidden_features = int(2 * hidden_features / 3)
        align_as = 8
        swiglu_hidden_features = (swiglu_hidden_features + align_as - 1) // align_as * align_as
        self.w1 = nn.Linear(in_features, swiglu_hidden_features)
        self.w2 = nn.Linear(in_features, swiglu_hidden_features)
        self.w3 = nn.Linear(swiglu_hidden_features, out_features)

    def forward(self, x: Tensor) -> Tensor:
        x1 = self.w1(x)
        x2 = self.w2(x)
        hidden = F.silu(x1) * x2
        return self.w3(hidden)


class PatchEmbed(nn.Module):
    """2D Image to Patch Embedding."""

    def __init__(
        self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, bias=True
    ):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.flatten = flatten

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        x = self.proj(x)
        _, _, H, W = x.shape
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
        x = self.norm(x)
        return x, H, W


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0):
        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)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x, H, W):
        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.unbind(0)  # make torchscript happy (cannot use tensor as tuple)

        attn = (q @ k.transpose(-2, -1)) * self.scale
        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(nn.Module):
    def __init__(
        self,
        dim: int,
        num_heads: int = 8,
        qkv_bias: bool = False,
        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)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x: Tensor, H, W) -> Tensor:
        from xformers.ops import memory_efficient_attention, unbind

        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)
        x = x.reshape([B, N, C])

        x = self.proj(x)
        x = self.proj_drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size
    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image
    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowedAttention(nn.Module):
    def __init__(
        self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0, window_size=14, pad_mode="constant"
    ):
        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)
        self.proj_drop = nn.Dropout(proj_drop)
        self.window_size = window_size
        self.pad_mode = pad_mode

    def forward(self, x, H, W):
        B, N, C = x.shape
        N_ = self.window_size * self.window_size
        H_ = math.ceil(H / self.window_size) * self.window_size
        W_ = math.ceil(W / self.window_size) * self.window_size

        qkv = self.qkv(x)  # [B, N, C]
        qkv = qkv.transpose(1, 2).reshape(B, C * 3, H, W)  # [B, C, H, W]
        qkv = F.pad(qkv, [0, W_ - W, 0, H_ - H], mode=self.pad_mode)

        qkv = F.unfold(
            qkv, kernel_size=(self.window_size, self.window_size), stride=(self.window_size, self.window_size)
        )
        B, C_kw_kw, L = qkv.shape  # L - the num of windows
        qkv = qkv.reshape(B, C * 3, N_, L).permute(0, 3, 2, 1)  # [B, L, N_, C]
        qkv = qkv.reshape(B, L, N_, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5)
        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)

        # q,k,v [B, L, num_head, N_, C/num_head]
        attn = (q @ k.transpose(-2, -1)) * self.scale  # [B, L, num_head, N_, N_]
        # if self.mask:
        #     attn = attn * mask
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)  # [B, L, num_head, N_, N_]
        # attn @ v = [B, L, num_head, N_, C/num_head]
        x = (attn @ v).permute(0, 2, 4, 3, 1).reshape(B, C_kw_kw // 3, L)

        x = F.fold(
            x,
            output_size=(H_, W_),
            kernel_size=(self.window_size, self.window_size),
            stride=(self.window_size, self.window_size),
        )  # [B, C, H_, W_]
        x = x[:, :, :H, :W].reshape(B, C, N).transpose(-1, -2)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


# class WindowedAttention(nn.Module):
#     def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., window_size=14, pad_mode="constant"):
#         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)
#         self.proj_drop = nn.Dropout(proj_drop)
#         self.window_size = window_size
#         self.pad_mode = pad_mode
#
#     def forward(self, x, H, W):
#         B, N, C = x.shape
#
#         N_ = self.window_size * self.window_size
#         H_ = math.ceil(H / self.window_size) * self.window_size
#         W_ = math.ceil(W / self.window_size) * self.window_size
#         x = x.view(B, H, W, C)
#         x = F.pad(x, [0, 0, 0, W_ - W, 0, H_- H], mode=self.pad_mode)
#
#         x = window_partition(x, window_size=self.window_size)# nW*B, window_size, window_size, C
#         x = x.view(-1, N_, C)
#
#         qkv = self.qkv(x).view(-1, N_, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
#         q, k, v = qkv.unbind(0)   # make torchscript happy (cannot use tensor as tuple)
#         attn = (q @ k.transpose(-2, -1)) * self.scale # [B, L, num_head, N_, N_]
#         attn = attn.softmax(dim=-1)
#         attn = self.attn_drop(attn) # [B, L, num_head, N_, N_]
#         x = (attn @ v).transpose(1, 2).reshape(-1, self.window_size, self.window_size, C)
#
#         x = window_reverse(x, self.window_size, H_, W_)
#         x = x[:, :H, :W, :].reshape(B, N, C).contiguous()
#         x = self.proj(x)
#         x = self.proj_drop(x)
#         return x


class Block(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        windowed=False,
        window_size=14,
        pad_mode="constant",
        layer_scale=False,
        with_cp=False,
        ffn_layer=Mlp,
        memeff=False,
    ):
        super().__init__()
        self.with_cp = with_cp
        self.norm1 = norm_layer(dim)
        if windowed:
            self.attn = WindowedAttention(
                dim,
                num_heads=num_heads,
                qkv_bias=qkv_bias,
                attn_drop=attn_drop,
                proj_drop=drop,
                window_size=window_size,
                pad_mode=pad_mode,
            )
        elif memeff:
            self.attn = MemEffAttention(
                dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop
            )
        else:
            self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = 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)
        self.layer_scale = layer_scale
        if layer_scale:
            self.gamma1 = nn.Parameter(torch.ones((dim)), requires_grad=True)
            self.gamma2 = nn.Parameter(torch.ones((dim)), requires_grad=True)

    def forward(self, x, H, W):
        def _inner_forward(x):
            if self.layer_scale:
                x = x + self.drop_path(self.gamma1 * self.attn(self.norm1(x), H, W))
                x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x)))
            else:
                x = x + self.drop_path(self.attn(self.norm1(x), H, W))
                x = x + self.drop_path(self.mlp(self.norm2(x)))
            return x

        if self.with_cp and x.requires_grad:
            x = cp.checkpoint(_inner_forward, x)
        else:
            x = _inner_forward(x)

        return x


class TIMMVisionTransformer(BaseModule):
    """Vision Transformer.

    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
        - https://arxiv.org/abs/2010.11929

    Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
        - https://arxiv.org/abs/2012.12877
    """

    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_chans=3,
        num_classes=1000,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        layer_scale=True,
        embed_layer=PatchEmbed,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        act_layer=nn.GELU,
        window_attn=False,
        window_size=14,
        pretrained=None,
        with_cp=False,
        pre_norm=False,
        ffn_type="mlp",
        memeff=False,
    ):
        """
        Args:
            img_size (int, tuple): input image size
            patch_size (int, tuple): patch size
            in_chans (int): number of input channels
            num_classes (int): number of classes for classification head
            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
            drop_rate (float): dropout rate
            attn_drop_rate (float): attention dropout rate
            drop_path_rate (float): stochastic depth rate
            embed_layer (nn.Module): patch embedding layer
            norm_layer: (nn.Module): normalization layer
            pretrained: (str): pretrained path
        """
        super().__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        self.num_tokens = 1
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
        act_layer = act_layer or nn.GELU
        self.norm_layer = norm_layer
        self.act_layer = act_layer
        self.pretrain_size = img_size
        self.drop_path_rate = drop_path_rate
        self.drop_rate = drop_rate
        self.patch_size = patch_size

        window_attn = [window_attn] * depth if not isinstance(window_attn, list) else window_attn
        window_size = [window_size] * depth if not isinstance(window_size, list) else window_size
        logging.info("window attention:", window_attn)
        logging.info("window size:", window_size)
        logging.info("layer scale:", layer_scale)

        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
        )
        num_patches = self.patch_embed.num_patches

        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        ffn_types = {"mlp": Mlp, "swiglu": SwiGLUFFN}

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        self.blocks = nn.Sequential(
            *[
                Block(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[i],
                    norm_layer=norm_layer,
                    act_layer=act_layer,
                    windowed=window_attn[i],
                    window_size=window_size[i],
                    layer_scale=layer_scale,
                    with_cp=with_cp,
                    ffn_layer=ffn_types[ffn_type],
                    memeff=memeff,
                )
                for i in range(depth)
            ]
        )

        # self.norm = norm_layer(embed_dim)
        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        # For CLIP
        if pre_norm:
            norm_pre = norm_layer(embed_dim)
            self.norm_pre = norm_pre
        else:
            self.norm_pre = nn.Identity()
        self.init_weights(pretrained)

    def init_weights(self, pretrained=None):
        if isinstance(pretrained, str):
            logger = get_root_logger()
            load_checkpoint(self, pretrained, map_location="cpu", strict=False, logger=logger)

    def forward_features(self, x):
        x, H, W = self.patch_embed(x)
        cls_token = self.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
        x = torch.cat((cls_token, x), dim=1)
        x = self.pos_drop(x + self.pos_embed)

        # For CLIP
        x = self.norm_pre(x)

        for blk in self.blocks:
            x = blk(x, H, W)
        x = self.norm(x)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        return x

    @staticmethod
    def resize_pos_embed(pos_embed, input_shpae, pos_shape, mode):
        """Resize pos_embed weights.

        Resize pos_embed using bicubic interpolate method.
        Args:
            pos_embed (torch.Tensor): Position embedding weights.
            input_shpae (tuple): Tuple for (downsampled input image height,
                downsampled input image width).
            pos_shape (tuple): The resolution of downsampled origin training
                image.
            mode (str): Algorithm used for upsampling:
                ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` |
                ``'trilinear'``. Default: ``'nearest'``
        Return:
            torch.Tensor: The resized pos_embed of shape [B, L_new, C]
        """
        assert pos_embed.ndim == 3, "shape of pos_embed must be [B, L, C]"
        pos_h, pos_w = pos_shape
        # keep dim for easy deployment
        cls_token_weight = pos_embed[:, 0:1]
        pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w) :]
        pos_embed_weight = pos_embed_weight.reshape(1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2)
        pos_embed_weight = resize(pos_embed_weight, size=input_shpae, align_corners=False, mode=mode)
        pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2)
        pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1)
        return pos_embed