wehub-resource-sync
172 lines
6.5 KiB
Python
172 lines
6.5 KiB
Python
import math
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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 torch.utils.checkpoint as checkpoint
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from timm.models.layers import DropPath, Mlp, PatchEmbed, \
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trunc_normal_ as __call_trunc_normal_
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def trunc_normal_(tensor, mean=0., std=1.):
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__call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std)
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class Attention(nn.Module):
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def __init__(
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self, dim, num_heads=8, qkv_bias=False,
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attn_drop=0., proj_drop=0.
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):
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super().__init__()
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assert dim % num_heads == 0, 'dim should be divisible by num_heads'
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = head_dim ** -0.5
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# Disable bias for k: https://github.com/microsoft/unilm/issues/510
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self.qkv = nn.Linear(dim, dim * 3, bias=False)
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if qkv_bias:
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self.q_bias = nn.Parameter(torch.zeros(dim))
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self.v_bias = nn.Parameter(torch.zeros(dim))
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else:
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self.q_bias = None
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self.v_bias = None
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self.qk_float = False
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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, is_causal=False, attn_mask=None):
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B, N, C = x.shape
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qkv_bias = None
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if self.q_bias is not None:
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qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
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qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
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qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) (B, H, N, C)
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x = F.scaled_dot_product_attention(
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q, k, v,
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attn_mask=attn_mask,
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is_causal=is_causal,
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dropout_p=self.attn_drop.p,
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)
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x = x.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.,
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qkv_bias=False,
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drop=0.,
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attn_drop=0.,
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drop_path=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().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim, num_heads=num_heads, qkv_bias=qkv_bias,
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attn_drop=attn_drop, proj_drop=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_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
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self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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def forward(self, x, attn_mask=None, is_causal=False):
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x = x + self.drop_path1(self.attn(self.norm1(x), attn_mask=attn_mask, is_causal=is_causal))
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x = x + self.drop_path2(self.mlp(self.norm2(x)))
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return x
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class VisionTransformer(nn.Module):
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""" Vision Transformer with support for patch or hybrid CNN input stage
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"""
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def __init__(
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self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, depth=12,
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num_heads=12, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0.,
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drop_path_rate=0., norm_layer=nn.LayerNorm, use_checkpoint=False, use_cls=True,
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):
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super().__init__()
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self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
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self.patch_embed = PatchEmbed(
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img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
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num_patches = self.patch_embed.num_patches
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self.num_heads = num_heads
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if use_cls:
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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else:
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self.cls_token = None
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self.decode_tokens = num_patches + (1 if use_cls else 0)
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self.pos_embed = nn.Parameter(torch.zeros(1, self.decode_tokens, embed_dim))
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self.pos_drop = nn.Dropout(p=drop_rate)
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self.use_checkpoint = use_checkpoint
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
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self.blocks = nn.ModuleList([
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Block(
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer
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) for i in range(depth)])
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self.fc_norm = norm_layer(embed_dim)
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trunc_normal_(self.pos_embed, std=.02)
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if use_cls:
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trunc_normal_(self.cls_token, std=.02)
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self.apply(self._init_weights)
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self.fix_init_weight()
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self.num_patches = num_patches
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def fix_init_weight(self):
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def rescale(param, layer_id):
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param.div_(math.sqrt(2.0 * layer_id))
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for layer_id, layer in enumerate(self.blocks):
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rescale(layer.attn.proj.weight.data, layer_id + 1)
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rescale(layer.mlp.fc2.weight.data, layer_id + 1)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.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, return_patch_tokens=False, **kwargs):
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x = self.patch_embed(x)
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if self.cls_token is not None:
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batch_size, seq_len, _ = x.size()
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cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
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x = torch.cat((cls_tokens, x), dim=1)
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x = x + self.pos_embed
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x = self.pos_drop(x)
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for blk in self.blocks:
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if self.use_checkpoint:
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x = checkpoint.checkpoint(blk, x)
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else:
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x = blk(x)
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x = self.fc_norm(x)
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return x[:, 1:] if return_patch_tokens else x
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def forward(self, x, **kwargs):
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x = self.forward_features(x, **kwargs)
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return x
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