Source code for mmpretrain.models.backbones.vit_sam
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
from mmengine.model import BaseModule, ModuleList
from mmengine.model.weight_init import trunc_normal_
from mmpretrain.registry import MODELS
from ..utils import LayerNorm2d, build_norm_layer, resize_pos_embed, to_2tuple
from .base_backbone import BaseBackbone
def window_partition(x: torch.Tensor,
window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
"""Partition into non-overlapping windows with padding if needed.
Borrowed from https://github.com/facebookresearch/segment-anything/
Args:
x (torch.Tensor): Input tokens with [B, H, W, C].
window_size (int): Window size.
Returns:
Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]
- ``windows``: Windows after partition with
[B * num_windows, window_size, window_size, C].
- ``(Hp, Wp)``: Padded height and width before partition
"""
B, H, W, C = x.shape
pad_h = (window_size - H % window_size) % window_size
pad_w = (window_size - W % window_size) % window_size
if pad_h > 0 or pad_w > 0:
x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
Hp, Wp = H + pad_h, W + pad_w
x = x.view(B, Hp // window_size, window_size, Wp // window_size,
window_size, C)
windows = x.permute(0, 1, 3, 2, 4,
5).contiguous().view(-1, window_size, window_size, C)
return windows, (Hp, Wp)
def window_unpartition(windows: torch.Tensor, window_size: int,
pad_hw: Tuple[int, int],
hw: Tuple[int, int]) -> torch.Tensor:
"""Window unpartition into original sequences and removing padding.
Borrowed from https://github.com/facebookresearch/segment-anything/
Args:
x (torch.Tensor): Input tokens with
[B * num_windows, window_size, window_size, C].
window_size (int): Window size.
pad_hw (tuple): Padded height and width (Hp, Wp).
hw (tuple): Original height and width (H, W) before padding.
Returns:
torch.Tensor: Unpartitioned sequences with [B, H, W, C].
"""
Hp, Wp = pad_hw
H, W = hw
B = windows.shape[0] // (Hp * Wp // window_size // window_size)
x = windows.view(B, Hp // window_size, Wp // window_size, window_size,
window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
if Hp > H or Wp > W:
x = x[:, :H, :W, :].contiguous()
return x
def get_rel_pos(q_size: int, k_size: int,
rel_pos: torch.Tensor) -> torch.Tensor:
"""Get relative positional embeddings according to the relative positions
of query and key sizes.
Borrowed from https://github.com/facebookresearch/segment-anything/
Args:
q_size (int): Size of query q.
k_size (int): Size of key k.
rel_pos (torch.Tensor): Relative position embeddings (L, C).
Returns:
torch.Tensor: Extracted positional embeddings according to relative
positions.
"""
max_rel_dist = int(2 * max(q_size, k_size) - 1)
# Interpolate rel pos if needed.
if rel_pos.shape[0] != max_rel_dist:
# Interpolate rel pos.
rel_pos_resized = F.interpolate(
rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
size=max_rel_dist,
mode='linear',
)
rel_pos_resized = rel_pos_resized.reshape(-1,
max_rel_dist).permute(1, 0)
else:
rel_pos_resized = rel_pos
# Scale the coords with short length if shapes for q and k are different.
q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
relative_coords = (q_coords -
k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
return rel_pos_resized[relative_coords.long()]
def add_decomposed_rel_pos(
attn: torch.Tensor,
q: torch.Tensor,
rel_pos_h: torch.Tensor,
rel_pos_w: torch.Tensor,
q_size: Tuple[int, int],
k_size: Tuple[int, int],
) -> torch.Tensor:
"""Borrowed from https://github.com/facebookresearch/segment-anything/
Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py
Args:
attn (torch.Tensor): Attention map.
q (torch.Tensor): Query q in the attention layer with shape
(B, q_h * q_w, C).
rel_pos_h (torch.Tensor): Relative position embeddings (Lh, C) for
height axis.
rel_pos_w (torch.Tensor): Relative position embeddings (Lw, C) for
width axis.
q_size (tuple): Spatial sequence size of query q with (q_h, q_w).
k_size (tuple): Spatial sequence size of key k with (k_h, k_w).
Returns:
torch.Tensor: Attention map with added relative positional embeddings.
"""
q_h, q_w = q_size
k_h, k_w = k_size
Rh = get_rel_pos(q_h, k_h, rel_pos_h)
Rw = get_rel_pos(q_w, k_w, rel_pos_w)
B, _, dim = q.shape
r_q = q.reshape(B, q_h, q_w, dim)
rel_h = torch.einsum('bhwc,hkc->bhwk', r_q, Rh)
rel_w = torch.einsum('bhwc,wkc->bhwk', r_q, Rw)
attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] +
rel_w[:, :, :, None, :]).view(B, q_h * q_w, k_h * k_w)
return attn
class Attention(nn.Module):
"""Multi-head Attention block with relative position embeddings.
Borrowed from https://github.com/facebookresearch/segment-anything/
Args:
embed_dims (int): The embedding dimension.
num_heads (int): Parallel attention heads.
qkv_bias (bool): If True, add a learnable bias to q, k, v.
Defaults to True.
use_rel_pos (bool):Whether to use relative position embedding.
Defaults to False.
input_size (int, optional): Input resolution for calculating the
relative positional parameter size. Defaults to None.
"""
def __init__(
self,
embed_dims: int,
num_heads: int = 8,
qkv_bias: bool = True,
use_rel_pos: bool = False,
input_size: Optional[Tuple[int, int]] = None,
) -> None:
super().__init__()
self.num_heads = num_heads
head_embed_dims = embed_dims // num_heads
self.scale = head_embed_dims**-0.5
self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
self.proj = nn.Linear(embed_dims, embed_dims)
self.use_rel_pos = use_rel_pos
if self.use_rel_pos:
assert (input_size is not None), \
'Input size must be provided if using relative position embed.'
# initialize relative positional embeddings
self.rel_pos_h = nn.Parameter(
torch.zeros(2 * input_size[0] - 1, head_embed_dims))
self.rel_pos_w = nn.Parameter(
torch.zeros(2 * input_size[1] - 1, head_embed_dims))
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, H, W, _ = x.shape
# qkv with shape (3, B, nHead, H * W, C)
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads,
-1).permute(2, 0, 3, 1, 4)
# q, k, v with shape (B * nHead, H * W, C)
q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
attn = (q * self.scale) @ k.transpose(-2, -1)
if self.use_rel_pos:
attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h,
self.rel_pos_w, (H, W), (H, W))
attn = attn.softmax(dim=-1)
x = (attn @ v).view(B, self.num_heads, H, W,
-1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
x = self.proj(x)
return x
class TransformerEncoderLayer(BaseModule):
"""Encoder layer with window attention in Vision Transformer.
Args:
embed_dims (int): The feature dimension
num_heads (int): Parallel attention heads
feedforward_channels (int): The hidden dimension for FFNs
drop_rate (float): Probability of an element to be zeroed
after the feed forward layer. Defaults to 0.
drop_path_rate (float): Stochastic depth rate. Defaults to 0.
num_fcs (int): The number of fully-connected layers for FFNs.
Defaults to 2.
qkv_bias (bool): enable bias for qkv if True. Defaults to True.
act_cfg (dict): The activation config for FFNs.
Defaults to ``dict(type='GELU')``.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
use_rel_pos (bool):Whether to use relative position embedding.
Defaults to False.
window_size (int): Window size for window attention. Defaults to 0.
input_size (int, optional): Input resolution for calculating the
relative positional parameter size. Defaults to None.
init_cfg (dict, optional): Initialization config dict.
Defaults to None.
"""
def __init__(self,
embed_dims: int,
num_heads: int,
feedforward_channels: int,
drop_rate: float = 0.,
drop_path_rate: float = 0.,
num_fcs: int = 2,
qkv_bias: bool = True,
act_cfg: dict = dict(type='GELU'),
norm_cfg: dict = dict(type='LN'),
use_rel_pos: bool = False,
window_size: int = 0,
input_size: Optional[Tuple[int, int]] = None,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.embed_dims = embed_dims
self.window_size = window_size
self.ln1 = build_norm_layer(norm_cfg, self.embed_dims)
self.attn = Attention(
embed_dims=embed_dims,
num_heads=num_heads,
qkv_bias=qkv_bias,
use_rel_pos=use_rel_pos,
input_size=input_size if window_size == 0 else
(window_size, window_size),
)
self.ln2 = build_norm_layer(norm_cfg, self.embed_dims)
self.ffn = FFN(
embed_dims=embed_dims,
feedforward_channels=feedforward_channels,
num_fcs=num_fcs,
ffn_drop=drop_rate,
dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
act_cfg=act_cfg)
@property
def norm1(self):
return self.ln1
@property
def norm2(self):
return self.ln2
def forward(self, x):
shortcut = x
x = self.ln1(x)
# Window partition
if self.window_size > 0:
H, W = x.shape[1], x.shape[2]
x, pad_hw = window_partition(x, self.window_size)
x = self.attn(x)
# Reverse window partition
if self.window_size > 0:
x = window_unpartition(x, self.window_size, pad_hw, (H, W))
x = shortcut + x
x = self.ffn(self.ln2(x), identity=x)
return x
[docs]@MODELS.register_module()
class ViTSAM(BaseBackbone):
"""Vision Transformer as image encoder used in SAM.
A PyTorch implement of backbone: `Segment Anything
<https://arxiv.org/abs/2304.02643>`_
Args:
arch (str | dict): Vision Transformer architecture. If use string,
choose from 'base', 'large', 'huge'. If use dict, it should have
below keys:
- **embed_dims** (int): The dimensions of embedding.
- **num_layers** (int): The number of transformer encoder layers.
- **num_heads** (int): The number of heads in attention modules.
- **feedforward_channels** (int): The hidden dimensions in
feedforward modules.
- **global_attn_indexes** (int): The index of layers with global
attention.
Defaults to 'base'.
img_size (int | tuple): The expected input image shape. Because we
support dynamic input shape, just set the argument to the most
common input image shape. Defaults to 224.
patch_size (int | tuple): The patch size in patch embedding.
Defaults to 16.
in_channels (int): The num of input channels. Defaults to 3.
out_channels (int): The num of output channels, if equal to 0, the
channel reduction layer is disabled. Defaults to 256.
out_indices (Sequence | int): Output from which stages.
Defaults to -1, means the last stage.
out_type (str): The type of output features. Please choose from
- ``"raw"`` or ``"featmap"``: The feature map tensor from the
patch tokens with shape (B, C, H, W).
- ``"avg_featmap"``: The global averaged feature map tensor
with shape (B, C).
Defaults to ``"raw"``.
drop_rate (float): Probability of an element to be zeroed.
Defaults to 0.
drop_path_rate (float): stochastic depth rate. Defaults to 0.
qkv_bias (bool): Whether to add bias for qkv in attention modules.
Defaults to True.
use_abs_pos (bool): Whether to use absolute position embedding.
Defaults to True.
use_rel_pos (bool):Whether to use relative position embedding.
Defaults to True.
window_size (int): Window size for window attention. Defaults to 14.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters. Defaults to -1.
interpolate_mode (str): Select the interpolate mode for position
embeding vector resize. Defaults to "bicubic".
patch_cfg (dict): Configs of patch embeding. Defaults to an empty dict.
layer_cfgs (Sequence | dict): Configs of each transformer layer in
encoder. Defaults to an empty dict.
init_cfg (dict, optional): Initialization config dict.
Defaults to None.
"""
arch_zoo = {
**dict.fromkeys(
['b', 'base'], {
'embed_dims': 768,
'num_layers': 12,
'num_heads': 12,
'feedforward_channels': 3072,
'global_attn_indexes': [2, 5, 8, 11]
}),
**dict.fromkeys(
['l', 'large'], {
'embed_dims': 1024,
'num_layers': 24,
'num_heads': 16,
'feedforward_channels': 4096,
'global_attn_indexes': [5, 11, 17, 23]
}),
**dict.fromkeys(
['h', 'huge'], {
'embed_dims': 1280,
'num_layers': 32,
'num_heads': 16,
'feedforward_channels': 5120,
'global_attn_indexes': [7, 15, 23, 31]
}),
}
OUT_TYPES = {'raw', 'featmap', 'avg_featmap'}
def __init__(self,
arch: str = 'base',
img_size: int = 224,
patch_size: int = 16,
in_channels: int = 3,
out_channels: int = 256,
out_indices: int = -1,
out_type: str = 'raw',
drop_rate: float = 0.,
drop_path_rate: float = 0.,
qkv_bias: bool = True,
use_abs_pos: bool = True,
use_rel_pos: bool = True,
window_size: int = 14,
norm_cfg: dict = dict(type='LN', eps=1e-6),
frozen_stages: int = -1,
interpolate_mode: str = 'bicubic',
patch_cfg: dict = dict(),
layer_cfgs: dict = dict(),
init_cfg: Optional[dict] = None):
super().__init__(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', 'num_layers', 'num_heads', 'feedforward_channels'
}
assert isinstance(arch, dict) and essential_keys <= set(arch), \
f'Custom arch needs a dict with keys {essential_keys}'
self.arch_settings = arch
self.embed_dims = self.arch_settings['embed_dims']
self.num_layers = self.arch_settings['num_layers']
self.global_attn_indexes = self.arch_settings['global_attn_indexes']
self.img_size = to_2tuple(img_size)
# Set patch embedding
_patch_cfg = dict(
in_channels=in_channels,
input_size=img_size,
embed_dims=self.embed_dims,
conv_type='Conv2d',
kernel_size=patch_size,
stride=patch_size,
)
_patch_cfg.update(patch_cfg)
self.patch_embed = PatchEmbed(**_patch_cfg)
self.patch_resolution = self.patch_embed.init_out_size
# Set out type
if out_type not in self.OUT_TYPES:
raise ValueError(f'Unsupported `out_type` {out_type}, please '
f'choose from {self.OUT_TYPES}')
self.out_type = out_type
self.use_abs_pos = use_abs_pos
self.interpolate_mode = interpolate_mode
if use_abs_pos:
# Set position embedding
self.pos_embed = nn.Parameter(
torch.zeros(1, *self.patch_resolution, self.embed_dims))
self.drop_after_pos = nn.Dropout(p=drop_rate)
self._register_load_state_dict_pre_hook(self._prepare_pos_embed)
if use_rel_pos:
self._register_load_state_dict_pre_hook(
self._prepare_relative_position)
if isinstance(out_indices, int):
out_indices = [out_indices]
assert isinstance(out_indices, Sequence), \
f'"out_indices" must by a sequence or int, ' \
f'get {type(out_indices)} instead.'
for i, index in enumerate(out_indices):
if index < 0:
out_indices[i] = self.num_layers + index
assert 0 <= out_indices[i] <= self.num_layers, \
f'Invalid out_indices {index}'
self.out_indices = out_indices
# stochastic depth decay rule
dpr = np.linspace(0, drop_path_rate, self.num_layers)
self.layers = ModuleList()
if isinstance(layer_cfgs, dict):
layer_cfgs = [layer_cfgs] * self.num_layers
for i in range(self.num_layers):
_layer_cfg = dict(
embed_dims=self.embed_dims,
num_heads=self.arch_settings['num_heads'],
feedforward_channels=self.
arch_settings['feedforward_channels'],
drop_rate=drop_rate,
drop_path_rate=dpr[i],
qkv_bias=qkv_bias,
window_size=window_size
if i not in self.global_attn_indexes else 0,
input_size=self.patch_resolution,
use_rel_pos=use_rel_pos,
norm_cfg=norm_cfg)
_layer_cfg.update(layer_cfgs[i])
self.layers.append(TransformerEncoderLayer(**_layer_cfg))
self.out_channels = out_channels
if self.out_channels > 0:
self.channel_reduction = nn.Sequential(
nn.Conv2d(
self.embed_dims,
out_channels,
kernel_size=1,
bias=False,
),
LayerNorm2d(out_channels, eps=1e-6),
nn.Conv2d(
out_channels,
out_channels,
kernel_size=3,
padding=1,
bias=False,
),
LayerNorm2d(out_channels, eps=1e-6),
)
# freeze stages only when self.frozen_stages > 0
self.frozen_stages = frozen_stages
if self.frozen_stages > 0:
self._freeze_stages()
def init_weights(self):
super().init_weights()
if not (isinstance(self.init_cfg, dict)
and self.init_cfg['type'] == 'Pretrained'):
if self.pos_embed is not None:
trunc_normal_(self.pos_embed, std=0.02)
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.drop_after_pos.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.layers[i - 1]
m.eval()
for param in m.parameters():
param.requires_grad = False
# freeze channel_reduction module
if self.frozen_stages == self.num_layers and self.out_channels > 0:
m = self.channel_reduction
m.eval()
for param in m.parameters():
param.requires_grad = False
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]:
B = x.shape[0]
x, patch_resolution = self.patch_embed(x)
x = x.view(B, patch_resolution[0], patch_resolution[1],
self.embed_dims)
if self.use_abs_pos:
# 'resize_pos_embed' only supports 'pos_embed' with ndim==3, but
# in ViTSAM, the 'pos_embed' has 4 dimensions (1, H, W, C), so it
# is flattened. Besides, ViTSAM doesn't have any extra token.
resized_pos_embed = resize_pos_embed(
self.pos_embed.flatten(1, 2),
self.patch_resolution,
patch_resolution,
mode=self.interpolate_mode,
num_extra_tokens=0)
x = x + resized_pos_embed.view(1, *patch_resolution,
self.embed_dims)
x = self.drop_after_pos(x)
outs = []
for i, layer in enumerate(self.layers):
x = layer(x)
if i in self.out_indices:
# (B, H, W, C) -> (B, C, H, W)
x_reshape = x.permute(0, 3, 1, 2)
if self.out_channels > 0:
x_reshape = self.channel_reduction(x_reshape)
outs.append(self._format_output(x_reshape))
return tuple(outs)
def _format_output(self, x) -> torch.Tensor:
if self.out_type == 'raw' or self.out_type == 'featmap':
return x
elif self.out_type == 'avg_featmap':
# (B, C, H, W) -> (B, C, N) -> (B, N, C)
x = x.flatten(2).permute(0, 2, 1)
return x.mean(dim=1)
def _prepare_pos_embed(self, state_dict, prefix, *args, **kwargs):
name = prefix + 'pos_embed'
if name not in state_dict.keys():
return
ckpt_pos_embed_shape = state_dict[name].shape
if self.pos_embed.shape != ckpt_pos_embed_shape:
from mmengine.logging import MMLogger
logger = MMLogger.get_current_instance()
logger.info(
f'Resize the pos_embed shape from {ckpt_pos_embed_shape} '
f'to {self.pos_embed.shape}.')
ckpt_pos_embed_shape = ckpt_pos_embed_shape[1:3]
pos_embed_shape = self.patch_embed.init_out_size
flattened_pos_embed = state_dict[name].flatten(1, 2)
resized_pos_embed = resize_pos_embed(flattened_pos_embed,
ckpt_pos_embed_shape,
pos_embed_shape,
self.interpolate_mode, 0)
state_dict[name] = resized_pos_embed.view(1, *pos_embed_shape,
self.embed_dims)
def _prepare_relative_position(self, state_dict, prefix, *args, **kwargs):
state_dict_model = self.state_dict()
all_keys = list(state_dict_model.keys())
for key in all_keys:
if 'rel_pos_' in key:
ckpt_key = prefix + key
if ckpt_key not in state_dict:
continue
relative_position_pretrained = state_dict[ckpt_key]
relative_position_current = state_dict_model[key]
L1, _ = relative_position_pretrained.size()
L2, _ = relative_position_current.size()
if L1 != L2:
new_rel_pos = F.interpolate(
relative_position_pretrained.reshape(1, L1,
-1).permute(
0, 2, 1),
size=L2,
mode='linear',
)
new_rel_pos = new_rel_pos.reshape(-1, L2).permute(1, 0)
from mmengine.logging import MMLogger
logger = MMLogger.get_current_instance()
logger.info(f'Resize the {ckpt_key} from '
f'{state_dict[ckpt_key].shape} to '
f'{new_rel_pos.shape}')
state_dict[ckpt_key] = new_rel_pos
[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``)
"""
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 ('cls_token', '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