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| """ Bottleneck Self Attention (Bottleneck Transformers) | |
| Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605 | |
| @misc{2101.11605, | |
| Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vaswani}, | |
| Title = {Bottleneck Transformers for Visual Recognition}, | |
| Year = {2021}, | |
| } | |
| Based on ref gist at: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2 | |
| This impl is a WIP but given that it is based on the ref gist likely not too far off. | |
| Hacked together by / Copyright 2021 Ross Wightman | |
| """ | |
| from typing import List | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from .helpers import to_2tuple | |
| from .weight_init import trunc_normal_ | |
| def rel_logits_1d(q, rel_k, permute_mask: List[int]): | |
| """ Compute relative logits along one dimension | |
| As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2 | |
| Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925 | |
| Args: | |
| q: (batch, heads, height, width, dim) | |
| rel_k: (2 * width - 1, dim) | |
| permute_mask: permute output dim according to this | |
| """ | |
| B, H, W, dim = q.shape | |
| x = (q @ rel_k.transpose(-1, -2)) | |
| x = x.reshape(-1, W, 2 * W -1) | |
| # pad to shift from relative to absolute indexing | |
| x_pad = F.pad(x, [0, 1]).flatten(1) | |
| x_pad = F.pad(x_pad, [0, W - 1]) | |
| # reshape and slice out the padded elements | |
| x_pad = x_pad.reshape(-1, W + 1, 2 * W - 1) | |
| x = x_pad[:, :W, W - 1:] | |
| # reshape and tile | |
| x = x.reshape(B, H, 1, W, W).expand(-1, -1, H, -1, -1) | |
| return x.permute(permute_mask) | |
| class PosEmbedRel(nn.Module): | |
| """ Relative Position Embedding | |
| As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2 | |
| Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925 | |
| """ | |
| def __init__(self, feat_size, dim_head, scale): | |
| super().__init__() | |
| self.height, self.width = to_2tuple(feat_size) | |
| self.dim_head = dim_head | |
| self.scale = scale | |
| self.height_rel = nn.Parameter(torch.randn(self.height * 2 - 1, dim_head) * self.scale) | |
| self.width_rel = nn.Parameter(torch.randn(self.width * 2 - 1, dim_head) * self.scale) | |
| def forward(self, q): | |
| B, num_heads, HW, _ = q.shape | |
| # relative logits in width dimension. | |
| q = q.reshape(B * num_heads, self.height, self.width, -1) | |
| rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4)) | |
| # relative logits in height dimension. | |
| q = q.transpose(1, 2) | |
| rel_logits_h = rel_logits_1d(q, self.height_rel, permute_mask=(0, 3, 1, 4, 2)) | |
| rel_logits = rel_logits_h + rel_logits_w | |
| rel_logits = rel_logits.reshape(B, num_heads, HW, HW) | |
| return rel_logits | |
| class BottleneckAttn(nn.Module): | |
| """ Bottleneck Attention | |
| Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605 | |
| """ | |
| def __init__(self, dim, dim_out=None, feat_size=None, stride=1, num_heads=4, qkv_bias=False): | |
| super().__init__() | |
| assert feat_size is not None, 'A concrete feature size matching expected input (H, W) is required' | |
| dim_out = dim_out or dim | |
| assert dim_out % num_heads == 0 | |
| self.num_heads = num_heads | |
| self.dim_out = dim_out | |
| self.dim_head = dim_out // num_heads | |
| self.scale = self.dim_head ** -0.5 | |
| self.qkv = nn.Conv2d(dim, self.dim_out * 3, 1, bias=qkv_bias) | |
| # NOTE I'm only supporting relative pos embedding for now | |
| self.pos_embed = PosEmbedRel(feat_size, dim_head=self.dim_head, scale=self.scale) | |
| self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity() | |
| def reset_parameters(self): | |
| trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5) | |
| trunc_normal_(self.pos_embed.height_rel, std=self.scale) | |
| trunc_normal_(self.pos_embed.width_rel, std=self.scale) | |
| def forward(self, x): | |
| B, C, H, W = x.shape | |
| assert H == self.pos_embed.height and W == self.pos_embed.width | |
| x = self.qkv(x) # B, 3 * num_heads * dim_head, H, W | |
| x = x.reshape(B, -1, self.dim_head, H * W).transpose(-1, -2) | |
| q, k, v = torch.split(x, self.num_heads, dim=1) | |
| attn_logits = (q @ k.transpose(-1, -2)) * self.scale | |
| attn_logits = attn_logits + self.pos_embed(q) # B, num_heads, H * W, H * W | |
| attn_out = attn_logits.softmax(dim = -1) | |
| attn_out = (attn_out @ v).transpose(1, 2).reshape(B, self.dim_out, H, W) # B, dim_out, H, W | |
| attn_out = self.pool(attn_out) | |
| return attn_out | |