pytorch-image-models/timm/layers/pos_embed_rel.py

""" Relative position embedding modules and functions

Hacked together by / Copyright 2022 Ross Wightman
"""
import math
from typing import Optional, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F

from .mlp import Mlp
from .weight_init import trunc_normal_


def gen_relative_position_index(
        q_size: Tuple[int, int],
        k_size: Tuple[int, int] = None,
        class_token: bool = False) -> torch.Tensor:
    # Adapted with significant modifications from Swin / BeiT codebases
    # get pair-wise relative position index for each token inside the window
    q_coords = torch.stack(torch.meshgrid([torch.arange(q_size[0]), torch.arange(q_size[1])])).flatten(1)  # 2, Wh, Ww
    if k_size is None:
        k_coords = q_coords
        k_size = q_size
    else:
        # different q vs k sizes is a WIP
        k_coords = torch.stack(torch.meshgrid([torch.arange(k_size[0]), torch.arange(k_size[1])])).flatten(1)
    relative_coords = q_coords[:, :, None] - k_coords[:, None, :]  # 2, Wh*Ww, Wh*Ww
    relative_coords = relative_coords.permute(1, 2, 0)  # Wh*Ww, Wh*Ww, 2
    _, relative_position_index = torch.unique(relative_coords.view(-1, 2), return_inverse=True, dim=0)

    if class_token:
        # handle cls to token & token 2 cls & cls to cls as per beit for rel pos bias
        # NOTE not intended or tested with MLP log-coords
        max_size = (max(q_size[0], k_size[0]), max(q_size[1], k_size[1]))
        num_relative_distance = (2 * max_size[0] - 1) * (2 * max_size[1] - 1) + 3
        relative_position_index = F.pad(relative_position_index, [1, 0, 1, 0])
        relative_position_index[0, 0:] = num_relative_distance - 3
        relative_position_index[0:, 0] = num_relative_distance - 2
        relative_position_index[0, 0] = num_relative_distance - 1

    return relative_position_index.contiguous()


class RelPosBias(nn.Module):
    """ Relative Position Bias
    Adapted from Swin-V1 relative position bias impl, modularized.
    """

    def __init__(self, window_size, num_heads, prefix_tokens=0):
        super().__init__()
        assert prefix_tokens <= 1
        self.window_size = window_size
        self.window_area = window_size[0] * window_size[1]
        self.bias_shape = (self.window_area + prefix_tokens,) * 2 + (num_heads,)

        num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * prefix_tokens
        self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads))
        self.register_buffer(
            "relative_position_index",
            gen_relative_position_index(self.window_size, class_token=prefix_tokens > 0),
            persistent=False,
        )

        self.init_weights()

    def init_weights(self):
        trunc_normal_(self.relative_position_bias_table, std=.02)

    def get_bias(self) -> torch.Tensor:
        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)]
        # win_h * win_w, win_h * win_w, num_heads
        relative_position_bias = relative_position_bias.view(self.bias_shape).permute(2, 0, 1)
        return relative_position_bias.unsqueeze(0).contiguous()

    def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
        return attn + self.get_bias()


def gen_relative_log_coords(
        win_size: Tuple[int, int],
        pretrained_win_size: Tuple[int, int] = (0, 0),
        mode='swin',
):
    assert mode in ('swin', 'cr', 'rw')
    # as per official swin-v2 impl, supporting timm specific 'cr' and 'rw' log coords as well
    relative_coords_h = torch.arange(-(win_size[0] - 1), win_size[0], dtype=torch.float32)
    relative_coords_w = torch.arange(-(win_size[1] - 1), win_size[1], dtype=torch.float32)
    relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w]))
    relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous()  # 2*Wh-1, 2*Ww-1, 2
    if mode == 'swin':
        if pretrained_win_size[0] > 0:
            relative_coords_table[:, :, 0] /= (pretrained_win_size[0] - 1)
            relative_coords_table[:, :, 1] /= (pretrained_win_size[1] - 1)
        else:
            relative_coords_table[:, :, 0] /= (win_size[0] - 1)
            relative_coords_table[:, :, 1] /= (win_size[1] - 1)
        relative_coords_table *= 8  # normalize to -8, 8
        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
            1.0 + relative_coords_table.abs()) / math.log2(8)
    else:
        if mode == 'rw':
            # cr w/ window size normalization -> [-1,1] log coords
            relative_coords_table[:, :, 0] /= (win_size[0] - 1)
            relative_coords_table[:, :, 1] /= (win_size[1] - 1)
            relative_coords_table *= 8  # scale to -8, 8
            relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
                1.0 + relative_coords_table.abs())
            relative_coords_table /= math.log2(9)   # -> [-1, 1]
        else:
            # mode == 'cr'
            relative_coords_table = torch.sign(relative_coords_table) * torch.log(
                1.0 + relative_coords_table.abs())

    return relative_coords_table


class RelPosMlp(nn.Module):
    """ Log-Coordinate Relative Position MLP
    Based on ideas presented in Swin-V2 paper (https://arxiv.org/abs/2111.09883)

    This impl covers the 'swin' implementation as well as two timm specific modes ('cr', and 'rw')
    """
    def __init__(
            self,
            window_size,
            num_heads=8,
            hidden_dim=128,
            prefix_tokens=0,
            mode='cr',
            pretrained_window_size=(0, 0)
    ):
        super().__init__()
        self.window_size = window_size
        self.window_area = self.window_size[0] * self.window_size[1]
        self.prefix_tokens = prefix_tokens
        self.num_heads = num_heads
        self.bias_shape = (self.window_area,) * 2 + (num_heads,)
        if mode == 'swin':
            self.bias_act = nn.Sigmoid()
            self.bias_gain = 16
            mlp_bias = (True, False)
        elif mode == 'rw':
            self.bias_act = nn.Tanh()
            self.bias_gain = 4
            mlp_bias = True
        else:
            self.bias_act = nn.Identity()
            self.bias_gain = None
            mlp_bias = True

        self.mlp = Mlp(
            2,  # x, y
            hidden_features=hidden_dim,
            out_features=num_heads,
            act_layer=nn.ReLU,
            bias=mlp_bias,
            drop=(0.125, 0.)
        )

        self.register_buffer(
            "relative_position_index",
            gen_relative_position_index(window_size),
            persistent=False)

        # get relative_coords_table
        self.register_buffer(
            "rel_coords_log",
            gen_relative_log_coords(window_size, pretrained_window_size, mode=mode),
            persistent=False)

    def get_bias(self) -> torch.Tensor:
        relative_position_bias = self.mlp(self.rel_coords_log)
        if self.relative_position_index is not None:
            relative_position_bias = relative_position_bias.view(-1, self.num_heads)[
                self.relative_position_index.view(-1)]  # Wh*Ww,Wh*Ww,nH
            relative_position_bias = relative_position_bias.view(self.bias_shape)
        relative_position_bias = relative_position_bias.permute(2, 0, 1)
        relative_position_bias = self.bias_act(relative_position_bias)
        if self.bias_gain is not None:
            relative_position_bias = self.bias_gain * relative_position_bias
        if self.prefix_tokens:
            relative_position_bias = F.pad(relative_position_bias, [self.prefix_tokens, 0, self.prefix_tokens, 0])
        return relative_position_bias.unsqueeze(0).contiguous()

    def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
        return attn + self.get_bias()


def generate_lookup_tensor(
        length: int,
        max_relative_position: Optional[int] = None,
):
    """Generate a one_hot lookup tensor to reindex embeddings along one dimension.

    Args:
        length: the length to reindex to.
        max_relative_position: the maximum relative position to consider.
            Relative position embeddings for distances above this threshold
            are zeroed out.
    Returns:
        a lookup Tensor of size [length, length, vocab_size] that satisfies
            ret[n,m,v] = 1{m - n + max_relative_position = v}.
    """
    if max_relative_position is None:
        max_relative_position = length - 1
    # Return the cached lookup tensor, otherwise compute it and cache it.
    vocab_size = 2 * max_relative_position + 1
    ret = torch.zeros(length, length, vocab_size)
    for i in range(length):
        for x in range(length):
            v = x - i + max_relative_position
            if abs(x - i) > max_relative_position:
                continue
            ret[i, x, v] = 1
    return ret


def reindex_2d_einsum_lookup(
        relative_position_tensor,
        height: int,
        width: int,
        height_lookup: torch.Tensor,
        width_lookup: torch.Tensor,
) -> torch.Tensor:
    """Reindex 2d relative position bias with 2 independent einsum lookups.

    Adapted from:
     https://github.com/google-research/maxvit/blob/2e06a7f1f70c76e64cd3dabe5cd1b8c1a23c9fb7/maxvit/models/attention_utils.py

    Args:
        relative_position_tensor: tensor of shape
            [..., vocab_height, vocab_width, ...].
        height: height to reindex to.
        width: width to reindex to.
        height_lookup: one-hot height lookup
        width_lookup: one-hot width lookup
    Returns:
        reindexed_tensor: a Tensor of shape
            [..., height * width, height * width, ...]
    """
    reindexed_tensor = torch.einsum('nhw,ixh->nixw', relative_position_tensor, height_lookup)
    reindexed_tensor = torch.einsum('nixw,jyw->nijxy', reindexed_tensor, width_lookup)
    area = height * width
    return reindexed_tensor.reshape(relative_position_tensor.shape[0], area, area)


class RelPosBiasTf(nn.Module):
    """ Relative Position Bias Impl (Compatible with Tensorflow MaxViT models)
    Adapted from:
     https://github.com/google-research/maxvit/blob/2e06a7f1f70c76e64cd3dabe5cd1b8c1a23c9fb7/maxvit/models/attention_utils.py
    """
    def __init__(self, window_size, num_heads, prefix_tokens=0):
        super().__init__()
        assert prefix_tokens <= 1
        self.window_size = window_size
        self.window_area = window_size[0] * window_size[1]
        self.num_heads = num_heads

        vocab_height = 2 * window_size[0] - 1
        vocab_width = 2 * window_size[1] - 1
        self.bias_shape = (self.num_heads, vocab_height, vocab_width)
        self.relative_position_bias_table = nn.Parameter(torch.zeros(self.bias_shape))
        self.register_buffer('height_lookup', generate_lookup_tensor(window_size[0]), persistent=False)
        self.register_buffer('width_lookup', generate_lookup_tensor(window_size[1]), persistent=False)
        self.init_weights()

    def init_weights(self):
        nn.init.normal_(self.relative_position_bias_table, std=.02)

    def get_bias(self) -> torch.Tensor:
        # FIXME change to not use one-hot/einsum?
        return reindex_2d_einsum_lookup(
            self.relative_position_bias_table,
            self.window_size[0],
            self.window_size[1],
            self.height_lookup,
            self.width_lookup
        )

    def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
        return attn + self.get_bias()