AutoAndroidController/py/venv/Lib/site-packages/timm/layers/attention_pool2d.py

""" Attention Pool 2D

Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.

Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py

Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import Optional, Union, Tuple

import torch
import torch.nn as nn

from .config import use_fused_attn
from .helpers import to_2tuple
from .pos_embed import resample_abs_pos_embed
from .pos_embed_sincos import apply_rot_embed, RotaryEmbedding
from .weight_init import trunc_normal_


class RotAttentionPool2d(nn.Module):
    """ Attention based 2D feature pooling w/ rotary (relative) pos embedding.
    This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.

    Adapted from the AttentionPool2d in CLIP w/ rotary embedding instead of learned embed.
    https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py

    NOTE: While this impl does not require a fixed feature size, performance at differeing resolutions from
    train varies widely and falls off dramatically. I'm not sure if there is a way around this... -RW
    """
    fused_attn: torch.jit.Final[bool]

    def __init__(
            self,
            in_features: int,
            out_features: Optional[int] = None,
            ref_feat_size: Union[int, Tuple[int, int]] = 7,
            embed_dim: Optional[int] = None,
            head_dim: Optional[int] = 64,
            num_heads: Optional[int] = None,
            qkv_bias: bool = True,
            qkv_separate: bool = False,
            pool_type: str = 'token',
            class_token: bool = False,
            drop_rate: float = 0.,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        assert pool_type in ('', 'token')
        self.embed_dim = embed_dim = embed_dim or in_features
        self.in_features = in_features
        self.out_features = out_features or in_features
        ref_feat_size = to_2tuple(ref_feat_size)
        if num_heads is not None:
            assert embed_dim % num_heads == 0
            head_dim = embed_dim // num_heads
        else:
            assert embed_dim % head_dim == 0
            num_heads = embed_dim // head_dim
        self.num_heads = num_heads
        self.head_dim = head_dim
        self.pool_type = pool_type.lower()
        self.scale = self.head_dim ** -0.5
        self.fused_attn = use_fused_attn()

        if class_token:
            self.cls_token = nn.Parameter(torch.zeros(1, embed_dim, **dd))
        else:
            self.cls_token = None

        if qkv_separate:
            self.q = nn.Linear(in_features, embed_dim, bias=qkv_bias, **dd)
            self.k = nn.Linear(in_features, embed_dim, bias=qkv_bias, **dd)
            self.v = nn.Linear(in_features, embed_dim, bias=qkv_bias, **dd)
            self.qkv = None
        else:
            self.qkv = nn.Linear(in_features, embed_dim * 3, bias=qkv_bias, **dd)
        self.drop = nn.Dropout(drop_rate)
        self.proj = nn.Linear(embed_dim, self.out_features, **dd)
        self.pos_embed = RotaryEmbedding(self.head_dim, in_pixels=False, ref_feat_shape=ref_feat_size, **dd)

    def init_weights(self, zero_init_last: bool = False):
        if self.qkv is None:
            in_features = self.q.in_features
            trunc_normal_(self.q.weight, std=in_features ** -0.5)
            nn.init.zeros_(self.q.bias)
            trunc_normal_(self.k.weight, std=in_features ** -0.5)
            nn.init.zeros_(self.k.bias)
            trunc_normal_(self.v.weight, std=in_features ** -0.5)
            nn.init.zeros_(self.v.bias)
        else:
            in_features = self.qkv.in_features
            trunc_normal_(self.qkv.weight, std=in_features ** -0.5)
            nn.init.zeros_(self.qkv.bias)

    def reset(self, num_classes: Optional[int] = None, pool_type: Optional[str] = None):
        # NOTE: this module is being used as a head, so need compatible reset()
        if pool_type is not None:
            assert pool_type in ('', 'token')
            self.pool_type = pool_type
        if num_classes is not None:
            self.proj = nn.Linear(self.in_features, num_classes) if num_classes > 0 else nn.Identity()
            self.out_features = num_classes if num_classes > 0 else self.embed_dim

    def _pool(self, x: torch.Tensor, H: int, W: int) -> torch.Tensor:
        if self.pool_type == 'token':
            x = x[:, 0]
        else:
            # if not pooled, return spatial output without token
            x = x[:, 1:].reshape(x.shape[0], H, W, -1).permute(0, 3, 1, 2)
        return x

    def forward(self, x, pre_logits: bool = False):
        B, _, H, W = x.shape
        N = H * W
        x = x.flatten(2).transpose(1, 2)
        if self.cls_token is None:
            x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
        else:
            x = torch.cat([self.cls_token.expand(x.shape[0], -1, -1), x], dim=1)
        if self.qkv is None:
            q = self.q(x).reshape(B, N + 1, self.num_heads, self.head_dim).transpose(1, 2)
            k = self.k(x).reshape(B, N + 1, self.num_heads, self.head_dim).transpose(1, 2)
            v = self.v(x).reshape(B, N + 1, self.num_heads, self.head_dim).transpose(1, 2)
        else:
            x = self.qkv(x).reshape(B, N + 1, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
            q, k, v = x.unbind(0)

        rse, rce = self.pos_embed.get_embed((H, W))
        q = torch.cat([q[:, :, :1, :], apply_rot_embed(q[:, :, 1:, :], rse, rce)], dim=2).type_as(v)
        k = torch.cat([k[:, :, :1, :], apply_rot_embed(k[:, :, 1:, :], rse, rce)], dim=2).type_as(v)

        if self.fused_attn:
            x = nn.functional.scaled_dot_product_attention(q, k, v)
        else:
            q = q * self.scale
            attn = q @ k.transpose(-2, -1)
            attn = attn.softmax(dim=-1)
            x = attn @ v
        x = x.transpose(1, 2).reshape(B, N + 1, -1)
        x = self.drop(x)
        if pre_logits:
            x = self._pool(x, H, W)
            return x
        x = self.proj(x)
        x = self._pool(x, H, W)
        return x


class AttentionPool2d(nn.Module):
    """ Attention based 2D feature pooling w/ learned (absolute) pos embedding.
    This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.

    It was based on impl in CLIP by OpenAI
    https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py

    NOTE: This requires feature size upon construction and well prevent adaptive sizing of the network.
    """
    fused_attn: torch.jit.Final[bool]

    def __init__(
            self,
            in_features: int,
            feat_size: Union[int, Tuple[int, int]] = 7,
            out_features: Optional[int] = None,
            embed_dim: Optional[int] = None,
            head_dim: Optional[int] = 64,
            num_heads: Optional[int] = None,
            qkv_bias: bool = True,
            qkv_separate: bool = False,
            pool_type: str = 'token',
            class_token: bool = False,
            drop_rate: float = 0.,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        assert pool_type in ('', 'token')
        self.embed_dim = embed_dim = embed_dim or in_features
        self.in_features = in_features
        self.out_features = out_features or in_features
        if num_heads is not None:
            assert embed_dim % num_heads == 0
            head_dim = embed_dim // num_heads
        else:
            assert embed_dim % head_dim == 0
            num_heads = embed_dim // head_dim
        self.feat_size = to_2tuple(feat_size)
        self.seq_len = self.feat_size[0] * self.feat_size[1]
        self.num_heads = num_heads
        self.head_dim = head_dim
        self.pool_type = pool_type
        self.scale = self.head_dim ** -0.5
        self.fused_attn = use_fused_attn()

        if class_token:
            self.cls_token = nn.Parameter(torch.zeros(1, embed_dim, **dd))
        else:
            self.cls_token = None

        if qkv_separate:
            self.q = nn.Linear(in_features, embed_dim, bias=qkv_bias, **dd)
            self.k = nn.Linear(in_features, embed_dim, bias=qkv_bias, **dd)
            self.v = nn.Linear(in_features, embed_dim, bias=qkv_bias, **dd)
            self.qkv = None
        else:
            self.q = self.k = self.v = None
            self.qkv = nn.Linear(in_features, embed_dim * 3, bias=qkv_bias, **dd)
        self.drop = nn.Dropout(drop_rate)
        self.proj = nn.Linear(embed_dim, self.out_features, **dd)
        self.pos_embed = nn.Parameter(torch.zeros(self.seq_len + 1, in_features, **dd))

        self.init_weights()

    def init_weights(self, zero_init_last: bool = False):
        if self.qkv is None:
            in_features = self.q.in_features
            trunc_normal_(self.q.weight, std=in_features ** -0.5)
            nn.init.zeros_(self.q.bias)
            trunc_normal_(self.k.weight, std=in_features ** -0.5)
            nn.init.zeros_(self.k.bias)
            trunc_normal_(self.v.weight, std=in_features ** -0.5)
            nn.init.zeros_(self.v.bias)
        else:
            in_features = self.qkv.in_features
            trunc_normal_(self.qkv.weight, std=in_features ** -0.5)
            nn.init.zeros_(self.qkv.bias)
        trunc_normal_(self.pos_embed, std=in_features ** -0.5)

    def reset(self, num_classes: Optional[int] = None, pool_type: Optional[str] = None):
        # NOTE: this module is being used as a head, so need compatible reset()
        if pool_type is not None:
            assert pool_type in ('', 'token')
            self.pool_type = pool_type
        if num_classes is not None:
            self.proj = nn.Linear(self.in_features, num_classes) if num_classes > 0 else nn.Identity()
            self.out_features = num_classes if num_classes > 0 else self.embed_dim

    def _pool(self, x: torch.Tensor, H: int, W: int) -> torch.Tensor:
        if self.pool_type == 'token':
            x = x[:, 0]
        else:
            # if not pooled, return spatial output without token
            x = x[:, 1:].reshape(x.shape[0], H, W, -1).permute(0, 3, 1, 2)
        return x

    def forward(self, x, pre_logits: bool = False):
        B, _, H, W = x.shape
        N = H * W
        x = x.flatten(2).transpose(1, 2)
        if self.cls_token is None:
            x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
        else:
            x = torch.cat([self.cls_token.expand(x.shape[0], -1, -1), x], dim=1)
        pos_embed = resample_abs_pos_embed(self.pos_embed.unsqueeze(0), (H, W), num_prefix_tokens=1)
        x = x + pos_embed

        if self.qkv is None:
            q = self.q(x).reshape(B, N + 1, self.num_heads, self.head_dim).transpose(1, 2)
            k = self.k(x).reshape(B, N + 1, self.num_heads, self.head_dim).transpose(1, 2)
            v = self.v(x).reshape(B, N + 1, self.num_heads, self.head_dim).transpose(1, 2)
        else:
            x = self.qkv(x).reshape(B, -1, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
            q, k, v = x.unbind(0)

        if self.fused_attn:
            x = nn.functional.scaled_dot_product_attention(q, k, v)
        else:
            q = q * self.scale
            attn = q @ k.transpose(-2, -1)
            attn = attn.softmax(dim=-1)
            x = attn @ v
        x = x.transpose(1, 2).reshape(B, N + 1, -1)
        x = self.drop(x)
        if pre_logits:
            x = self._pool(x, H, W)
            return x
        x = self.proj(x)
        x = self._pool(x, H, W)
        return x