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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, make_divisible | |
| from .weight_init import trunc_normal_ | |
| from .trace_utils import _assert | |
| 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.height_rel = nn.Parameter(torch.randn(self.height * 2 - 1, dim_head) * scale) | |
| self.width_rel = nn.Parameter(torch.randn(self.width * 2 - 1, dim_head) * scale) | |
| def forward(self, q): | |
| B, HW, _ = q.shape | |
| # relative logits in width dimension. | |
| q = q.reshape(B, 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, HW, HW) | |
| return rel_logits | |
| class BottleneckAttn(nn.Module): | |
| """ Bottleneck Attention | |
| Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605 | |
| The internal dimensions of the attention module are controlled by the interaction of several arguments. | |
| * the output dimension of the module is specified by dim_out, which falls back to input dim if not set | |
| * the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim | |
| * the query and key (qk) dimensions are determined by | |
| * num_heads * dim_head if dim_head is not None | |
| * num_heads * (dim_out * attn_ratio // num_heads) if dim_head is None | |
| * as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not used | |
| Args: | |
| dim (int): input dimension to the module | |
| dim_out (int): output dimension of the module, same as dim if not set | |
| stride (int): output stride of the module, avg pool used if stride == 2 (default: 1). | |
| num_heads (int): parallel attention heads (default: 4) | |
| dim_head (int): dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set | |
| qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0) | |
| qkv_bias (bool): add bias to q, k, and v projections | |
| scale_pos_embed (bool): scale the position embedding as well as Q @ K | |
| """ | |
| def __init__( | |
| self, dim, dim_out=None, feat_size=None, stride=1, num_heads=4, dim_head=None, | |
| qk_ratio=1.0, qkv_bias=False, scale_pos_embed=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_head_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads | |
| self.dim_head_v = dim_out // self.num_heads | |
| self.dim_out_qk = num_heads * self.dim_head_qk | |
| self.dim_out_v = num_heads * self.dim_head_v | |
| self.scale = self.dim_head_qk ** -0.5 | |
| self.scale_pos_embed = scale_pos_embed | |
| self.qkv = nn.Conv2d(dim, self.dim_out_qk * 2 + self.dim_out_v, 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_qk, scale=self.scale) | |
| self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity() | |
| self.reset_parameters() | |
| def reset_parameters(self): | |
| trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5) # fan-in | |
| 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, '') | |
| _assert(W == self.pos_embed.width, '') | |
| x = self.qkv(x) # B, (2 * dim_head_qk + dim_head_v) * num_heads, H, W | |
| # NOTE head vs channel split ordering in qkv projection was decided before I allowed qk to differ from v | |
| # So, this is more verbose than if heads were before qkv splits, but throughput is not impacted. | |
| q, k, v = torch.split(x, [self.dim_out_qk, self.dim_out_qk, self.dim_out_v], dim=1) | |
| q = q.reshape(B * self.num_heads, self.dim_head_qk, -1).transpose(-1, -2) | |
| k = k.reshape(B * self.num_heads, self.dim_head_qk, -1) # no transpose, for q @ k | |
| v = v.reshape(B * self.num_heads, self.dim_head_v, -1).transpose(-1, -2) | |
| if self.scale_pos_embed: | |
| attn = (q @ k + self.pos_embed(q)) * self.scale # B * num_heads, H * W, H * W | |
| else: | |
| attn = (q @ k) * self.scale + self.pos_embed(q) | |
| attn = attn.softmax(dim=-1) | |
| out = (attn @ v).transpose(-1, -2).reshape(B, self.dim_out_v, H, W) # B, dim_out, H, W | |
| out = self.pool(out) | |
| return out | |