Ross Wightman
3 years ago
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b7cb8d0337
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""" Relative Position Vision Transformer (ViT) in PyTorch
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Hacked together by / Copyright 2022, Ross Wightman
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"""
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import math
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import logging
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from functools import partial
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from collections import OrderedDict
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from typing import Optional, Tuple
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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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from torch.utils.checkpoint import checkpoint
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from .helpers import build_model_with_cfg, resolve_pretrained_cfg, named_apply
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from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_, to_2tuple
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from .registry import register_model
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_logger = logging.getLogger(__name__)
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'patch_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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'vit_relpos_base_patch32_plus_rpn_256': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_replos_base_patch32_plus_rpn_256-sw-dd486f51.pth',
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input_size=(3, 256, 256)),
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'vit_relpos_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240)),
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'vit_relpos_base_patch16_rpn_224': _cfg(url=''),
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'vit_relpos_base_patch16_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_224-sw-49049aed.pth'),
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}
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def gen_relative_position_index(win_size: Tuple[int, int], class_token: int = 0) -> torch.Tensor:
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# cut and paste w/ modifications from swin / beit codebase
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# cls to token & token 2 cls & cls to cls
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# get pair-wise relative position index for each token inside the window
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window_area = win_size[0] * win_size[1]
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coords = torch.stack(torch.meshgrid([torch.arange(win_size[0]), torch.arange(win_size[1])])).flatten(1) # 2, Wh, Ww
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relative_coords = coords[:, :, None] - coords[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += win_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += win_size[1] - 1
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relative_coords[:, :, 0] *= 2 * win_size[1] - 1
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if class_token:
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num_relative_distance = (2 * win_size[0] - 1) * (2 * win_size[1] - 1) + 3
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relative_position_index = torch.zeros(size=(window_area + 1,) * 2, dtype=relative_coords.dtype)
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relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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relative_position_index[0, 0:] = num_relative_distance - 3
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relative_position_index[0:, 0] = num_relative_distance - 2
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relative_position_index[0, 0] = num_relative_distance - 1
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else:
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relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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return relative_position_index
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def gen_relative_position_log(win_size: Tuple[int, int]) -> torch.Tensor:
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"""Method initializes the pair-wise relative positions to compute the positional biases."""
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coordinates = torch.stack(torch.meshgrid([torch.arange(win_size[0]), torch.arange(win_size[1])])).flatten(1)
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relative_coords = coordinates[:, :, None] - coordinates[:, None, :]
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relative_coords = relative_coords.permute(1, 2, 0).float()
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relative_coordinates_log = torch.sign(relative_coords) * torch.log(1.0 + relative_coords.abs())
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return relative_coordinates_log
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class RelPosMlp(nn.Module):
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# based on timm swin-v2 impl
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def __init__(self, window_size, num_heads=8, hidden_dim=32, class_token=False):
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super().__init__()
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self.window_size = window_size
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self.window_area = self.window_size[0] * self.window_size[1]
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self.class_token = 1 if class_token else 0
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self.num_heads = num_heads
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self.mlp = Mlp(
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2, # x, y
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hidden_features=min(128, hidden_dim * num_heads),
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out_features=num_heads,
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act_layer=nn.ReLU,
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drop=(0.125, 0.)
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)
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self.register_buffer(
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'rel_coords_log',
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gen_relative_position_log(window_size),
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persistent=False
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)
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def get_bias(self) -> torch.Tensor:
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relative_position_bias = self.mlp(self.rel_coords_log).permute(2, 0, 1).unsqueeze(0)
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if self.class_token:
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relative_position_bias = F.pad(relative_position_bias, [self.class_token, 0, self.class_token, 0])
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return relative_position_bias
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def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
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return attn + self.get_bias()
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class RelPosBias(nn.Module):
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def __init__(self, window_size, num_heads, class_token=False):
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super().__init__()
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self.window_size = window_size
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self.window_area = window_size[0] * window_size[1]
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self.class_token = 1 if class_token else 0
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self.bias_shape = (self.window_area + self.class_token,) * 2 + (num_heads,)
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num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * self.class_token
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self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads))
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self.register_buffer(
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"relative_position_index",
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gen_relative_position_index(self.window_size, class_token=self.class_token),
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persistent=False,
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)
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self.init_weights()
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def init_weights(self):
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trunc_normal_(self.relative_position_bias_table, std=.02)
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def get_bias(self) -> torch.Tensor:
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relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.bias_shape) # win_h * win_w, win_h * win_w, num_heads
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
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return relative_position_bias
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def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
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return attn + self.get_bias()
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class RelPosAttention(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False, rel_pos_cls=None, attn_drop=0., proj_drop=0.):
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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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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.rel_pos = rel_pos_cls(num_heads=num_heads) if rel_pos_cls else None
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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, shared_rel_pos: Optional[torch.Tensor] = None):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
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attn = (q @ k.transpose(-2, -1)) * self.scale
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if self.rel_pos is not None:
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attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos)
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elif shared_rel_pos is not None:
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attn = attn + shared_rel_pos
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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 LayerScale(nn.Module):
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def __init__(self, dim, init_values=1e-5, inplace=False):
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super().__init__()
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self.inplace = inplace
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self.gamma = nn.Parameter(init_values * torch.ones(dim))
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def forward(self, x):
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return x.mul_(self.gamma) if self.inplace else x * self.gamma
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class RelPosBlock(nn.Module):
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def __init__(
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self, dim, num_heads, mlp_ratio=4., qkv_bias=False, rel_pos_cls=None, init_values=None,
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drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = RelPosAttention(
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dim, num_heads, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls, attn_drop=attn_drop, proj_drop=drop)
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self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
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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.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
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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, shared_rel_pos: Optional[torch.Tensor] = None):
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x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x), shared_rel_pos=shared_rel_pos)))
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x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
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return x
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class ResPostRelPosBlock(nn.Module):
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def __init__(
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self, dim, num_heads, mlp_ratio=4., qkv_bias=False, rel_pos_cls=None, init_values=None,
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drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
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super().__init__()
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self.init_values = init_values
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self.attn = RelPosAttention(
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dim, num_heads, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls, attn_drop=attn_drop, proj_drop=drop)
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self.norm1 = norm_layer(dim)
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self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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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.norm2 = norm_layer(dim)
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self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.init_weights()
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def init_weights(self):
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# NOTE this init overrides that base model init with specific changes for the block type
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if self.init_values is not None:
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nn.init.constant_(self.norm1.weight, self.init_values)
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nn.init.constant_(self.norm2.weight, self.init_values)
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def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
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x = x + self.drop_path1(self.norm1(self.attn(x, shared_rel_pos=shared_rel_pos)))
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x = x + self.drop_path2(self.norm2(self.mlp(x)))
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return x
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class VisionTransformerRelPos(nn.Module):
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""" Vision Transformer w/ Relative Position Bias
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"""
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def __init__(
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self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='avg',
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embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=None,
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drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip', class_token=False,
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rel_pos_type='mlp', shared_rel_pos=False, fc_norm=False,
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embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=RelPosBlock):
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"""
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Args:
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img_size (int, tuple): input image size
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patch_size (int, tuple): patch size
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in_chans (int): number of input channels
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num_classes (int): number of classes for classification head
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global_pool (str): type of global pooling for final sequence (default: 'token')
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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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init_values: (float): layer-scale init values
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drop_rate (float): dropout rate
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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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weight_init (str): weight init scheme
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class_token (bool): use class token (default: False)
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rel_pos_ty pe (str): type of relative position
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shared_rel_pos (bool): share relative pos across all blocks
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fc_norm (bool): use pre classifier norm
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embed_layer (nn.Module): patch embedding layer
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norm_layer: (nn.Module): normalization layer
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act_layer: (nn.Module): MLP activation layer
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"""
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super().__init__()
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assert global_pool in ('', 'avg', 'token')
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assert class_token or global_pool != 'token'
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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act_layer = act_layer or nn.GELU
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self.num_classes = num_classes
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self.global_pool = global_pool
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self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
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self.num_tokens = 1 if class_token else 0
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self.grad_checkpointing = False
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self.patch_embed = embed_layer(
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img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
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feat_size = self.patch_embed.grid_size
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rel_pos_cls = RelPosMlp if rel_pos_type == 'mlp' else RelPosBias
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rel_pos_cls = partial(rel_pos_cls, window_size=feat_size, class_token=class_token)
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self.shared_rel_pos = None
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if shared_rel_pos:
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self.shared_rel_pos = rel_pos_cls(num_heads=num_heads)
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# NOTE shared rel pos currently mutually exclusive w/ per-block, but could support both...
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rel_pos_cls = None
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self.cls_token = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim)) if self.num_tokens else None
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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_fn(
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls,
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init_values=init_values, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i],
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norm_layer=norm_layer, act_layer=act_layer)
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for i in range(depth)])
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self.norm = norm_layer(embed_dim) if not fc_norm else nn.Identity()
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||||||
|
|
||||||
|
# Classifier Head
|
||||||
|
self.fc_norm = norm_layer(embed_dim) if fc_norm else nn.Identity()
|
||||||
|
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
||||||
|
|
||||||
|
if weight_init != 'skip':
|
||||||
|
self.init_weights(weight_init)
|
||||||
|
|
||||||
|
def init_weights(self, mode=''):
|
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|
assert mode in ('jax', 'moco', '')
|
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|
if self.cls_token is not None:
|
||||||
|
nn.init.normal_(self.cls_token, std=1e-6)
|
||||||
|
# FIXME weight init scheme using PyTorch defaults curently
|
||||||
|
#named_apply(get_init_weights_vit(mode, head_bias), self)
|
||||||
|
|
||||||
|
@torch.jit.ignore
|
||||||
|
def no_weight_decay(self):
|
||||||
|
return {'cls_token'}
|
||||||
|
|
||||||
|
@torch.jit.ignore
|
||||||
|
def group_matcher(self, coarse=False):
|
||||||
|
return dict(
|
||||||
|
stem=r'^cls_token|patch_embed', # stem and embed
|
||||||
|
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
|
||||||
|
)
|
||||||
|
|
||||||
|
@torch.jit.ignore
|
||||||
|
def set_grad_checkpointing(self, enable=True):
|
||||||
|
self.grad_checkpointing = enable
|
||||||
|
|
||||||
|
@torch.jit.ignore
|
||||||
|
def get_classifier(self):
|
||||||
|
return self.head
|
||||||
|
|
||||||
|
def reset_classifier(self, num_classes: int, global_pool=None):
|
||||||
|
self.num_classes = num_classes
|
||||||
|
if global_pool is not None:
|
||||||
|
assert global_pool in ('', 'avg', 'token')
|
||||||
|
self.global_pool = global_pool
|
||||||
|
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
||||||
|
|
||||||
|
def forward_features(self, x):
|
||||||
|
x = self.patch_embed(x)
|
||||||
|
if self.cls_token is not None:
|
||||||
|
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
|
||||||
|
|
||||||
|
shared_rel_pos = self.shared_rel_pos.get_bias() if self.shared_rel_pos is not None else None
|
||||||
|
for blk in self.blocks:
|
||||||
|
if self.grad_checkpointing and not torch.jit.is_scripting():
|
||||||
|
x = checkpoint(blk, x, shared_rel_pos=shared_rel_pos)
|
||||||
|
else:
|
||||||
|
x = blk(x, shared_rel_pos=shared_rel_pos)
|
||||||
|
x = self.norm(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
def forward_head(self, x, pre_logits: bool = False):
|
||||||
|
if self.global_pool:
|
||||||
|
x = x[:, self.num_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
|
||||||
|
x = self.fc_norm(x)
|
||||||
|
return x if pre_logits else self.head(x)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x = self.forward_features(x)
|
||||||
|
x = self.forward_head(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def _create_vision_transformer_relpos(variant, pretrained=False, **kwargs):
|
||||||
|
if kwargs.get('features_only', None):
|
||||||
|
raise RuntimeError('features_only not implemented for Vision Transformer models.')
|
||||||
|
|
||||||
|
model = build_model_with_cfg(VisionTransformerRelPos, variant, pretrained, **kwargs)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def vit_relpos_base_patch32_plus_rpn_256(pretrained=False, **kwargs):
|
||||||
|
""" ViT-Base (ViT-B/32+) w/ relative log-coord position and residual post-norm, no class token
|
||||||
|
"""
|
||||||
|
model_kwargs = dict(
|
||||||
|
patch_size=32, embed_dim=896, depth=12, num_heads=14, init_values=1e-5,
|
||||||
|
block_fn=ResPostRelPosBlock, **kwargs)
|
||||||
|
model = _create_vision_transformer_relpos(
|
||||||
|
'vit_relpos_base_patch32_plus_rpn_256', pretrained=pretrained, **model_kwargs)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def vit_relpos_base_patch16_plus_240(pretrained=False, **kwargs):
|
||||||
|
""" ViT-Base (ViT-B/16+) w/ relative log-coord position, no class token
|
||||||
|
"""
|
||||||
|
model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
|
||||||
|
model = _create_vision_transformer_relpos('vit_relpos_base_patch16_plus_240', pretrained=pretrained, **model_kwargs)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def vit_relpos_base_patch16_224(pretrained=False, **kwargs):
|
||||||
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
|
||||||
|
"""
|
||||||
|
model_kwargs = dict(
|
||||||
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5,
|
||||||
|
fc_norm=True, **kwargs)
|
||||||
|
model = _create_vision_transformer_relpos('vit_relpos_base_patch16_224', pretrained=pretrained, **model_kwargs)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def vit_relpos_base_patch16_rpn_224(pretrained=False, **kwargs):
|
||||||
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
|
||||||
|
"""
|
||||||
|
model_kwargs = dict(
|
||||||
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5,
|
||||||
|
block_fn=ResPostRelPosBlock, **kwargs)
|
||||||
|
model = _create_vision_transformer_relpos('vit_relpos_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs)
|
||||||
|
return model
|
||||||
Loading…
Reference in new issue