Source code for mmpretrain.models.backbones.hivit
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
from mmcv.cnn.bricks import DropPath
from mmengine.model.weight_init import trunc_normal_
from mmpretrain.registry import MODELS
from ..utils import build_norm_layer, to_2tuple
from .base_backbone import BaseBackbone
class Mlp(nn.Module):
"""MLP block.
Args:
in_features (int): Number of input dims.
hidden_features (int): Number of hidden dims.
out_feature (int): Number of out dims.
act_layer: MLP activation layer.
drop (float): MLP dropout rate.
"""
def __init__(self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
"""Attention.
Args:
input size (int): Input size.
dim (int): Number of input dims.
num_heads (int): Number of attention heads.
qkv_bias (bool): Enable bias for qkv projections if True.
qk_scale (float): The number of divider after q@k. Default to None.
attn_drop (float): The drop out rate for attention output weights.
Defaults to 0.
proj_drop (float): Probability of an element to be zeroed
after the feed forward layer. Defaults to 0.
rpe (bool): If True, add relative position embedding to
the patch embedding.
"""
def __init__(self,
input_size,
dim,
num_heads,
qkv_bias=True,
qk_scale=None,
attn_drop=0.,
proj_drop=0.,
rpe=True):
super().__init__()
self.input_size = input_size
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim**-0.5
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
torch.zeros((2 * input_size - 1) *
(2 * input_size - 1), num_heads)) if rpe else None
if rpe:
coords_h = torch.arange(input_size)
coords_w = torch.arange(input_size)
coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
coords_flatten = torch.flatten(coords, 1)
relative_coords = coords_flatten[:, :,
None] - coords_flatten[:, None, :]
relative_coords = relative_coords.permute(1, 2, 0).contiguous()
relative_coords[:, :, 0] += input_size - 1
relative_coords[:, :, 1] += input_size - 1
relative_coords[:, :, 0] *= 2 * input_size - 1
relative_position_index = relative_coords.sum(-1)
self.register_buffer('relative_position_index',
relative_position_index)
trunc_normal_(self.relative_position_bias_table, std=.02)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x, rpe_index=None, mask=None):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[
2] # make torchscript happy (cannot use tensor as tuple)
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
if rpe_index is not None:
rpe_index = self.relative_position_index.view(-1)
S = int(math.sqrt(rpe_index.size(-1)))
relative_position_bias = self.relative_position_bias_table[
rpe_index].view(-1, S, S, self.num_heads)
relative_position_bias = relative_position_bias.permute(
0, 3, 1, 2).contiguous()
attn = attn + relative_position_bias
if mask is not None:
mask = mask.bool()
attn = attn.masked_fill(~mask[:, None, None, :], float('-inf'))
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class BlockWithRPE(nn.Module):
"""HiViT block.
Args:
input_size (int): Input size.
dim (int): Number of input dims.
num_heads (int): Number of attention heads.
mlp_ratio (int): Ratio of MLP hidden dim to embedding dim.
qkv_bias (bool): Enable bias for qkv projections if True.
qk_scale (float): The number of divider after q@k. Default to None.
drop (float): Probability of an element to be zeroed
after the feed forward layer. Defaults to 0.
attn_drop (float): The drop out rate for attention output weights.
Defaults to 0.
drop_path (float): Stochastic depth rate. Defaults to 0.
rpe (bool): If True, add relative position embedding to
the patch embedding.
layer_scale_init_value (float): Layer-scale init values. Defaults to 0.
act_layer: MLP activation layer.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
"""
def __init__(self,
input_size,
dim,
num_heads=0.,
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
drop=0.,
attn_drop=0.,
drop_path=0.,
rpe=True,
layer_scale_init_value=0.0,
act_layer=nn.GELU,
norm_cfg=dict(type='LN')):
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.mlp_ratio = mlp_ratio
with_attn = num_heads > 0.
self.norm1 = build_norm_layer(norm_cfg, dim) if with_attn else None
self.attn = Attention(
input_size,
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop,
rpe=rpe,
) if with_attn else None
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = build_norm_layer(norm_cfg, dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop)
if layer_scale_init_value > 0:
self.gamma_1 = nn.Parameter(
layer_scale_init_value * torch.ones(
(dim)), requires_grad=True) if with_attn else None
self.gamma_2 = nn.Parameter(
layer_scale_init_value * torch.ones((dim)), requires_grad=True)
else:
self.gamma_1, self.gamma_2 = None, None
def forward(self, x, rpe_index=None, mask=None):
if self.attn is not None:
if self.gamma_1 is not None:
x = x + self.drop_path(
self.gamma_1 * self.attn(self.norm1(x), rpe_index, mask))
else:
x = x + self.drop_path(
self.attn(self.norm1(x), rpe_index, mask))
if self.gamma_2 is not None:
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
else:
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class PatchEmbed(nn.Module):
"""PatchEmbed for HiViT.
Args:
img_size (int): Input image size.
patch_size (int): Patch size. Defaults to 16.
inner_patches (int): Inner patch. Defaults to 4.
in_chans (int): Number of image input channels.
embed_dim (int): Transformer embedding dimension.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
kernel_size (int): Kernel size.
pad_size (int): Pad size.
"""
def __init__(self,
img_size=224,
patch_size=16,
inner_patches=4,
in_chans=3,
embed_dim=128,
norm_cfg=None,
kernel_size=None,
pad_size=None):
super().__init__()
img_size = to_2tuple(img_size) if not isinstance(img_size,
tuple) else img_size
patch_size = to_2tuple(patch_size)
patches_resolution = [
img_size[0] // patch_size[0], img_size[1] // patch_size[1]
]
self.img_size = img_size
self.patch_size = patch_size
self.inner_patches = inner_patches
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
conv_size = [size // inner_patches for size in patch_size]
kernel_size = kernel_size or conv_size
pad_size = pad_size or 0
self.proj = nn.Conv2d(
in_chans,
embed_dim,
kernel_size=kernel_size,
stride=conv_size,
padding=pad_size)
if norm_cfg is not None:
self.norm = build_norm_layer(norm_cfg, embed_dim)
else:
self.norm = None
def forward(self, x):
B, C, H, W = x.shape
patches_resolution = (H // self.patch_size[0], W // self.patch_size[1])
num_patches = patches_resolution[0] * patches_resolution[1]
x = self.proj(x).view(
B,
-1,
patches_resolution[0],
self.inner_patches,
patches_resolution[1],
self.inner_patches,
).permute(0, 2, 4, 3, 5, 1).reshape(B, num_patches, self.inner_patches,
self.inner_patches, -1)
if self.norm is not None:
x = self.norm(x)
return x
class PatchMerge(nn.Module):
"""PatchMerge for HiViT.
Args:
dim (int): Number of input channels.
norm_cfg (dict): Config dict for normalization layer.
"""
def __init__(self, dim, norm_cfg):
super().__init__()
self.norm = build_norm_layer(norm_cfg, dim * 4)
self.reduction = nn.Linear(dim * 4, dim * 2, bias=False)
def forward(self, x, *args, **kwargs):
is_main_stage = len(x.shape) == 3
if is_main_stage:
B, N, C = x.shape
S = int(math.sqrt(N))
x = x.reshape(B, S // 2, 2, S // 2, 2, C) \
.permute(0, 1, 3, 2, 4, 5) \
.reshape(B, -1, 2, 2, C)
x0 = x[..., 0::2, 0::2, :]
x1 = x[..., 1::2, 0::2, :]
x2 = x[..., 0::2, 1::2, :]
x3 = x[..., 1::2, 1::2, :]
x = torch.cat([x0, x1, x2, x3], dim=-1)
x = self.norm(x)
x = self.reduction(x)
if is_main_stage:
x = x[:, :, 0, 0, :]
return x
[docs]@MODELS.register_module()
class HiViT(BaseBackbone):
"""HiViT.
A PyTorch implement of: `HiViT: A Simple and More Efficient Design
of Hierarchical Vision Transformer <https://arxiv.org/abs/2205.14949>`_.
Args:
arch (str | dict): Swin Transformer architecture. If use string, choose
from 'tiny', 'small', and'base'. If use dict, it should
have below keys:
- **embed_dims** (int): The dimensions of embedding.
- **depths** (List[int]): The number of blocks in each stage.
- **num_heads** (int): The number of heads in attention
modules of each stage.
Defaults to 'tiny'.
img_size (int): Input image size.
patch_size (int): Patch size. Defaults to 16.
inner_patches (int): Inner patch. Defaults to 4.
in_chans (int): Number of image input channels.
embed_dim (int): Transformer embedding dimension.
depths (list[int]): Number of successive HiViT blocks.
num_heads (int): Number of attention heads.
stem_mlp_ratio (int): Ratio of MLP hidden dim to embedding dim
in the first two stages.
mlp_ratio (int): Ratio of MLP hidden dim to embedding dim in
the last stage.
qkv_bias (bool): Enable bias for qkv projections if True.
qk_scale (float): The number of divider after q@k. Default to None.
drop_rate (float): Probability of an element to be zeroed
after the feed forward layer. Defaults to 0.
attn_drop_rate (float): The drop out rate for attention output weights.
Defaults to 0.
drop_path_rate (float): Stochastic depth rate. Defaults to 0.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
ape (bool): If True, add absolute position embedding to
the patch embedding.
rpe (bool): If True, add relative position embedding to
the patch embedding.
patch_norm (bool): If True, use norm_cfg for normalization layer.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters. Defaults to -1.
kernel_size (int): Kernel size.
pad_size (int): Pad size.
layer_scale_init_value (float): Layer-scale init values. Defaults to 0.
init_cfg (dict, optional): The extra config for initialization.
Defaults to None.
"""
arch_zoo = {
**dict.fromkeys(['t', 'tiny'],
{'embed_dims': 384,
'depths': [1, 1, 10],
'num_heads': 6}),
**dict.fromkeys(['s', 'small'],
{'embed_dims': 384,
'depths': [2, 2, 20],
'num_heads': 6}),
**dict.fromkeys(['b', 'base'],
{'embed_dims': 512,
'depths': [2, 2, 24],
'num_heads': 8}),
**dict.fromkeys(['l', 'large'],
{'embed_dims': 768,
'depths': [2, 2, 40],
'num_heads': 12}),
} # yapf: disable
num_extra_tokens = 0
def __init__(self,
arch='base',
img_size=224,
patch_size=16,
inner_patches=4,
in_chans=3,
stem_mlp_ratio=3.,
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.0,
norm_cfg=dict(type='LN'),
out_indices=[23],
ape=True,
rpe=False,
patch_norm=True,
frozen_stages=-1,
kernel_size=None,
pad_size=None,
layer_scale_init_value=0.0,
init_cfg=None):
super(HiViT, self).__init__(init_cfg=init_cfg)
if isinstance(arch, str):
arch = arch.lower()
assert arch in set(self.arch_zoo), \
f'Arch {arch} is not in default archs {set(self.arch_zoo)}'
self.arch_settings = self.arch_zoo[arch]
else:
essential_keys = {'embed_dims', 'depths', 'num_heads'}
assert isinstance(arch, dict) and set(arch) == essential_keys, \
f'Custom arch needs a dict with keys {essential_keys}'
self.arch_settings = arch
self.embed_dims = self.arch_settings['embed_dims']
self.depths = self.arch_settings['depths']
self.num_heads = self.arch_settings['num_heads']
self.num_stages = len(self.depths)
self.ape = ape
self.rpe = rpe
self.patch_size = patch_size
self.num_features = self.embed_dims
self.mlp_ratio = mlp_ratio
self.num_main_blocks = self.depths[-1]
self.out_indices = out_indices
self.out_indices[-1] = self.depths[-1] - 1
img_size = to_2tuple(img_size) if not isinstance(img_size,
tuple) else img_size
embed_dim = self.embed_dims // 2**(self.num_stages - 1)
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
inner_patches=inner_patches,
in_chans=in_chans,
embed_dim=embed_dim,
norm_cfg=norm_cfg if patch_norm else None,
kernel_size=kernel_size,
pad_size=pad_size)
num_patches = self.patch_embed.num_patches
Hp, Wp = self.patch_embed.patches_resolution
if rpe:
assert Hp == Wp, 'If you use relative position, make sure H == W '
'of input size'
# absolute position embedding
if ape:
self.pos_embed = nn.Parameter(
torch.zeros(1, num_patches, self.num_features))
trunc_normal_(self.pos_embed, std=.02)
if rpe:
# get pair-wise relative position index for each token inside the
# window
coords_h = torch.arange(Hp)
coords_w = torch.arange(Wp)
coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
coords_flatten = torch.flatten(coords, 1)
relative_coords = coords_flatten[:, :,
None] - coords_flatten[:, None, :]
relative_coords = relative_coords.permute(1, 2, 0).contiguous()
relative_coords[:, :, 0] += Hp - 1
relative_coords[:, :, 1] += Wp - 1
relative_coords[:, :, 0] *= 2 * Wp - 1
relative_position_index = relative_coords.sum(-1)
self.register_buffer('relative_position_index',
relative_position_index)
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth
dpr = iter(
x.item()
for x in torch.linspace(0, drop_path_rate,
sum(self.depths) + sum(self.depths[:-1])))
# build blocks
self.blocks = nn.ModuleList()
for stage_i, stage_depth in enumerate(self.depths):
is_main_stage = embed_dim == self.num_features
nhead = self.num_heads if is_main_stage else 0
ratio = mlp_ratio if is_main_stage else stem_mlp_ratio
# every block not in main stage includes two mlp blocks
stage_depth = stage_depth if is_main_stage else stage_depth * 2
for _ in range(stage_depth):
self.blocks.append(
BlockWithRPE(
Hp,
embed_dim,
nhead,
ratio,
qkv_bias,
qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=next(dpr),
rpe=rpe,
norm_cfg=norm_cfg,
layer_scale_init_value=layer_scale_init_value,
))
if stage_i + 1 < self.num_stages:
self.blocks.append(PatchMerge(embed_dim, norm_cfg))
embed_dim *= 2
self.frozen_stages = frozen_stages
if self.frozen_stages > 0:
self._freeze_stages()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def interpolate_pos_encoding(self, x, h, w):
npatch = x.shape[1]
N = self.pos_embed.shape[1]
if npatch == N and w == h:
return self.pos_embed
patch_pos_embed = self.pos_embed
dim = x.shape[-1]
w0 = w // self.patch_size
h0 = h // self.patch_size
# we add a small number to avoid floating point error in interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
w0, h0 = w0 + 0.1, h0 + 0.1
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)),
dim).permute(0, 3, 1, 2),
scale_factor=(h0 / math.sqrt(N), w0 / math.sqrt(N)),
mode='bicubic',
)
assert int(h0) == patch_pos_embed.shape[-2] and int(
w0) == patch_pos_embed.shape[-1]
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return patch_pos_embed
def forward(self, x):
B, C, H, W = x.shape
Hp, Wp = H // self.patch_size, W // self.patch_size
x = self.patch_embed(x)
outs = []
for i, blk in enumerate(self.blocks[:-self.num_main_blocks]):
x = blk(x)
if i in self.out_indices:
x = x.reshape(B, Hp, Wp, *x.shape[-3:]).permute(
0, 5, 1, 3, 2, 4).reshape(B, -1, Hp * x.shape[-3],
Wp * x.shape[-2]).contiguous()
outs.append(x)
x = x[..., 0, 0, :]
if self.ape:
x = x + self.interpolate_pos_encoding(x, H, W)
x = self.pos_drop(x)
rpe_index = True if self.rpe else None
for i, blk in enumerate(self.blocks[-self.num_main_blocks:]):
x = blk(x, rpe_index)
if i in self.out_indices:
x = x.transpose(1, 2).view(B, -1, Hp, Wp).contiguous()
outs.append(x)
return tuple(outs)
def _freeze_stages(self):
# freeze position embedding
if self.pos_embed is not None:
self.pos_embed.requires_grad = False
# set dropout to eval model
self.pos_drop.eval()
# freeze patch embedding
self.patch_embed.eval()
for param in self.patch_embed.parameters():
param.requires_grad = False
# freeze layers
for i in range(1, self.frozen_stages + 1):
m = self.blocks[i - 1]
m.eval()
for param in m.parameters():
param.requires_grad = False
# freeze the last layer norm
for param in self.fc_norm.parameters():
param.requires_grad = False
[docs] def get_layer_depth(self, param_name: str, prefix: str = ''):
"""Get the layer-wise depth of a parameter.
Args:
param_name (str): The name of the parameter.
prefix (str): The prefix for the parameter.
Defaults to an empty string.
Returns:
Tuple[int, int]: The layer-wise depth and the num of layers.
Note:
The first depth is the stem module (``layer_depth=0``), and the
last depth is the subsequent module (``layer_depth=num_layers-1``)
"""
self.num_layers = len(self.blocks)
num_layers = self.num_layers + 2
if not param_name.startswith(prefix):
# For subsequent module like head
return num_layers - 1, num_layers
param_name = param_name[len(prefix):]
if param_name in 'pos_embed':
layer_depth = 0
elif param_name.startswith('patch_embed'):
layer_depth = 0
elif param_name.startswith('layers'):
layer_id = int(param_name.split('.')[1])
layer_depth = layer_id + 1
else:
layer_depth = num_layers - 1
return layer_depth, num_layers