260 lines
7.8 KiB
Python
260 lines
7.8 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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from __future__ import division
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from __future__ import print_function
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import math
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from functools import partial
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from typing import Optional, Text
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import xlayers
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DEFAULT_NET_CONFIG = dict(
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d_feat=6,
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embed_dim=64,
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depth=5,
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num_heads=4,
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mlp_ratio=4.0,
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qkv_bias=True,
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pos_drop=0.0,
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mlp_drop_rate=0.0,
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attn_drop_rate=0.0,
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drop_path_rate=0.0,
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)
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# Real Model
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class Attention(nn.Module):
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def __init__(
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self,
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dim,
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num_heads=8,
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qkv_bias=False,
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qk_scale=None,
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attn_drop=0.0,
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proj_drop=0.0,
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):
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super(Attention, self).__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or math.sqrt(head_dim)
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x):
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B, N, C = x.shape
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qkv = (
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self.qkv(x)
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.reshape(B, N, 3, self.num_heads, C // self.num_heads)
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.permute(2, 0, 3, 1, 4)
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)
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q, k, v = (
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qkv[0],
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qkv[1],
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qkv[2],
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) # make torchscript happy (cannot use tensor as tuple)
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class Block(nn.Module):
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def __init__(
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self,
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dim,
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num_heads,
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mlp_ratio=4.0,
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qkv_bias=False,
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qk_scale=None,
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attn_drop=0.0,
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mlp_drop=0.0,
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drop_path=0.0,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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):
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super(Block, self).__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim,
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=attn_drop,
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proj_drop=mlp_drop,
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)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = (
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xlayers.DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
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)
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = xlayers.MLP(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=mlp_drop,
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)
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def forward(self, x):
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x = x + self.drop_path(self.attn(self.norm1(x)))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class SimpleEmbed(nn.Module):
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def __init__(self, d_feat, embed_dim):
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super(SimpleEmbed, self).__init__()
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self.d_feat = d_feat
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self.embed_dim = embed_dim
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self.proj = nn.Linear(d_feat, embed_dim)
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def forward(self, x):
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x = x.reshape(len(x), self.d_feat, -1) # [N, F*T] -> [N, F, T]
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x = x.permute(0, 2, 1) # [N, F, T] -> [N, T, F]
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out = self.proj(x) * math.sqrt(self.embed_dim)
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return out
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class TransformerModel(nn.Module):
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def __init__(
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self,
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d_feat: int = 6,
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embed_dim: int = 64,
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depth: int = 4,
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num_heads: int = 4,
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mlp_ratio: float = 4.0,
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qkv_bias: bool = True,
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qk_scale: Optional[float] = None,
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pos_drop: float = 0.0,
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mlp_drop_rate: float = 0.0,
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attn_drop_rate: float = 0.0,
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drop_path_rate: float = 0.0,
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norm_layer: Optional[nn.Module] = None,
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max_seq_len: int = 65,
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):
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"""
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Args:
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d_feat (int, tuple): input image size
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embed_dim (int): embedding dimension
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depth (int): depth of transformer
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num_heads (int): number of attention heads
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim
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qkv_bias (bool): enable bias for qkv if True
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qk_scale (float): override default qk scale of head_dim ** -0.5 if set
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pos_drop (float): dropout rate for the positional embedding
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mlp_drop_rate (float): the dropout rate for MLP layers in a block
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attn_drop_rate (float): attention dropout rate
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drop_path_rate (float): stochastic depth rate
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norm_layer: (nn.Module): normalization layer
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"""
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super(TransformerModel, self).__init__()
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self.embed_dim = embed_dim
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self.num_features = embed_dim
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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self.input_embed = SimpleEmbed(d_feat, embed_dim=embed_dim)
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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self.pos_embed = xlayers.PositionalEncoder(
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d_model=embed_dim, max_seq_len=max_seq_len, dropout=pos_drop
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)
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dpr = [
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x.item() for x in torch.linspace(0, drop_path_rate, depth)
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] # stochastic depth decay rule
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self.blocks = nn.ModuleList(
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[
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Block(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=attn_drop_rate,
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mlp_drop=mlp_drop_rate,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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)
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for i in range(depth)
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]
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)
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self.norm = norm_layer(embed_dim)
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# regression head
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self.head = nn.Linear(self.num_features, 1)
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xlayers.trunc_normal_(self.cls_token, std=0.02)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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xlayers.trunc_normal_(m.weight, std=0.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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def forward_features(self, x):
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batch, flatten_size = x.shape
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feats = self.input_embed(x) # batch * 60 * 64
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cls_tokens = self.cls_token.expand(
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batch, -1, -1
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) # stole cls_tokens impl from Phil Wang, thanks
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feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
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feats_w_tp = self.pos_embed(feats_w_ct)
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xfeats = feats_w_tp
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for block in self.blocks:
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xfeats = block(xfeats)
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xfeats = self.norm(xfeats)[:, 0]
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return xfeats
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def forward(self, x):
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feats = self.forward_features(x)
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predicts = self.head(feats).squeeze(-1)
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return predicts
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def get_transformer(config):
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if not isinstance(config, dict):
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raise ValueError("Invalid Configuration: {:}".format(config))
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name = config.get("name", "basic")
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if name == "basic":
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model = TransformerModel(
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d_feat=config.get("d_feat"),
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embed_dim=config.get("embed_dim"),
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depth=config.get("depth"),
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num_heads=config.get("num_heads"),
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mlp_ratio=config.get("mlp_ratio"),
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qkv_bias=config.get("qkv_bias"),
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qk_scale=config.get("qkv_scale"),
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pos_drop=config.get("pos_drop"),
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mlp_drop_rate=config.get("mlp_drop_rate"),
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attn_drop_rate=config.get("attn_drop_rate"),
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drop_path_rate=config.get("drop_path_rate"),
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norm_layer=config.get("norm_layer", None),
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)
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else:
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raise ValueError("Unknown model name: {:}".format(name))
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return model
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