Source code for mmpretrain.models.backbones.twins
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Conv2d, build_norm_layer
from mmcv.cnn.bricks.drop import build_dropout
from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
from mmengine.model import BaseModule, ModuleList
from mmengine.model.weight_init import (constant_init, normal_init,
trunc_normal_init)
from torch.nn.modules.batchnorm import _BatchNorm
from mmpretrain.registry import MODELS
from ..utils import ConditionalPositionEncoding, MultiheadAttention
class GlobalSubsampledAttention(MultiheadAttention):
"""Global Sub-sampled Attention (GSA) module.
Args:
embed_dims (int): The embedding dimension.
num_heads (int): Parallel attention heads.
input_dims (int, optional): The input dimension, and if None,
use ``embed_dims``. Defaults to None.
attn_drop (float): Dropout rate of the dropout layer after the
attention calculation of query and key. Defaults to 0.
proj_drop (float): Dropout rate of the dropout layer after the
output projection. Defaults to 0.
dropout_layer (dict): The dropout config before adding the shortcut.
Defaults to ``dict(type='Dropout', drop_prob=0.)``.
qkv_bias (bool): If True, add a learnable bias to q, k, v.
Defaults to True.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='LN').
qk_scale (float, optional): Override default qk scale of
``head_dim ** -0.5`` if set. Defaults to None.
proj_bias (bool) If True, add a learnable bias to output projection.
Defaults to True.
v_shortcut (bool): Add a shortcut from value to output. It's usually
used if ``input_dims`` is different from ``embed_dims``.
Defaults to False.
sr_ratio (float): The ratio of spatial reduction in attention modules.
Defaults to 1.
init_cfg (dict, optional): The Config for initialization.
Defaults to None.
"""
def __init__(self,
embed_dims,
num_heads,
norm_cfg=dict(type='LN'),
qkv_bias=True,
sr_ratio=1,
**kwargs):
super(GlobalSubsampledAttention,
self).__init__(embed_dims, num_heads, **kwargs)
self.qkv_bias = qkv_bias
self.q = nn.Linear(self.input_dims, embed_dims, bias=qkv_bias)
self.kv = nn.Linear(self.input_dims, embed_dims * 2, bias=qkv_bias)
# remove self.qkv, here split into self.q, self.kv
delattr(self, 'qkv')
self.sr_ratio = sr_ratio
if sr_ratio > 1:
# use a conv as the spatial-reduction operation, the kernel_size
# and stride in conv are equal to the sr_ratio.
self.sr = Conv2d(
in_channels=embed_dims,
out_channels=embed_dims,
kernel_size=sr_ratio,
stride=sr_ratio)
# The ret[0] of build_norm_layer is norm name.
self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
def forward(self, x, hw_shape):
B, N, C = x.shape
H, W = hw_shape
assert H * W == N, 'The product of h and w of hw_shape must be N, ' \
'which is the 2nd dim number of the input Tensor x.'
q = self.q(x).reshape(B, N, self.num_heads,
C // self.num_heads).permute(0, 2, 1, 3)
if self.sr_ratio > 1:
x = x.permute(0, 2, 1).reshape(B, C, *hw_shape) # BNC_2_BCHW
x = self.sr(x)
x = x.reshape(B, C, -1).permute(0, 2, 1) # BCHW_2_BNC
x = self.norm(x)
kv = self.kv(x).reshape(B, -1, 2, self.num_heads,
self.head_dims).permute(2, 0, 3, 1, 4)
k, v = kv[0], kv[1]
attn_drop = self.attn_drop if self.training else 0.
x = self.scaled_dot_product_attention(q, k, v, dropout_p=attn_drop)
x = x.transpose(1, 2).reshape(B, N, self.embed_dims)
x = self.proj(x)
x = self.out_drop(self.proj_drop(x))
if self.v_shortcut:
x = v.squeeze(1) + x
return x
class GSAEncoderLayer(BaseModule):
"""Implements one encoder layer with GlobalSubsampledAttention(GSA).
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. Default: 0.0.
attn_drop_rate (float): The drop out rate for attention layer.
Default: 0.0.
drop_path_rate (float): Stochastic depth rate. Default 0.0.
num_fcs (int): The number of fully-connected layers for FFNs.
Default: 2.
qkv_bias (bool): Enable bias for qkv if True. Default: True
act_cfg (dict): The activation config for FFNs.
Default: dict(type='GELU').
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='LN').
sr_ratio (float): The ratio of spatial reduction in attention modules.
Defaults to 1.
init_cfg (dict, optional): The Config for initialization.
Defaults to None.
"""
def __init__(self,
embed_dims,
num_heads,
feedforward_channels,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
num_fcs=2,
qkv_bias=True,
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='LN'),
sr_ratio=1.,
init_cfg=None):
super(GSAEncoderLayer, self).__init__(init_cfg=init_cfg)
self.norm1 = build_norm_layer(norm_cfg, embed_dims, postfix=1)[1]
self.attn = GlobalSubsampledAttention(
embed_dims=embed_dims,
num_heads=num_heads,
attn_drop=attn_drop_rate,
proj_drop=drop_rate,
dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
qkv_bias=qkv_bias,
norm_cfg=norm_cfg,
sr_ratio=sr_ratio)
self.norm2 = build_norm_layer(norm_cfg, embed_dims, postfix=2)[1]
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,
add_identity=False)
self.drop_path = build_dropout(
dict(type='DropPath', drop_prob=drop_path_rate)
) if drop_path_rate > 0. else nn.Identity()
def forward(self, x, hw_shape):
x = x + self.drop_path(self.attn(self.norm1(x), hw_shape))
x = x + self.drop_path(self.ffn(self.norm2(x)))
return x
class LocallyGroupedSelfAttention(BaseModule):
"""Locally-grouped Self Attention (LSA) module.
Args:
embed_dims (int): Number of input channels.
num_heads (int): Number of attention heads. Default: 8
qkv_bias (bool, optional): If True, add a learnable bias to q, k, v.
Default: False.
qk_scale (float | None, optional): Override default qk scale of
head_dim ** -0.5 if set. Default: None.
attn_drop_rate (float, optional): Dropout ratio of attention weight.
Default: 0.0
proj_drop_rate (float, optional): Dropout ratio of output. Default: 0.
window_size(int): Window size of LSA. Default: 1.
init_cfg (dict, optional): The Config for initialization.
Defaults to None.
"""
def __init__(self,
embed_dims,
num_heads=8,
qkv_bias=False,
qk_scale=None,
attn_drop_rate=0.,
proj_drop_rate=0.,
window_size=1,
init_cfg=None):
super(LocallyGroupedSelfAttention, self).__init__(init_cfg=init_cfg)
assert embed_dims % num_heads == 0, \
f'dim {embed_dims} should be divided by num_heads {num_heads}'
self.embed_dims = embed_dims
self.num_heads = num_heads
head_dim = embed_dims // num_heads
self.scale = qk_scale or head_dim**-0.5
self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop_rate)
self.proj = nn.Linear(embed_dims, embed_dims)
self.proj_drop = nn.Dropout(proj_drop_rate)
self.window_size = window_size
def forward(self, x, hw_shape):
B, N, C = x.shape
H, W = hw_shape
x = x.view(B, H, W, C)
# pad feature maps to multiples of Local-groups
pad_l = pad_t = 0
pad_r = (self.window_size - W % self.window_size) % self.window_size
pad_b = (self.window_size - H % self.window_size) % self.window_size
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
# calculate attention mask for LSA
Hp, Wp = x.shape[1:-1]
_h, _w = Hp // self.window_size, Wp // self.window_size
mask = torch.zeros((1, Hp, Wp), device=x.device)
mask[:, -pad_b:, :].fill_(1)
mask[:, :, -pad_r:].fill_(1)
# [B, _h, _w, window_size, window_size, C]
x = x.reshape(B, _h, self.window_size, _w, self.window_size,
C).transpose(2, 3)
mask = mask.reshape(1, _h, self.window_size, _w,
self.window_size).transpose(2, 3).reshape(
1, _h * _w,
self.window_size * self.window_size)
# [1, _h*_w, window_size*window_size, window_size*window_size]
attn_mask = mask.unsqueeze(2) - mask.unsqueeze(3)
attn_mask = attn_mask.masked_fill(attn_mask != 0,
float(-1000.0)).masked_fill(
attn_mask == 0, float(0.0))
# [3, B, _w*_h, nhead, window_size*window_size, dim]
qkv = self.qkv(x).reshape(B, _h * _w,
self.window_size * self.window_size, 3,
self.num_heads, C // self.num_heads).permute(
3, 0, 1, 4, 2, 5)
q, k, v = qkv[0], qkv[1], qkv[2]
# [B, _h*_w, n_head, window_size*window_size, window_size*window_size]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn + attn_mask.unsqueeze(2)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.window_size,
self.window_size, C)
x = attn.transpose(2, 3).reshape(B, _h * self.window_size,
_w * self.window_size, C)
if pad_r > 0 or pad_b > 0:
x = x[:, :H, :W, :].contiguous()
x = x.reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LSAEncoderLayer(BaseModule):
"""Implements one encoder layer with LocallyGroupedSelfAttention(LSA).
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. Default: 0.0.
attn_drop_rate (float, optional): Dropout ratio of attention weight.
Default: 0.0
drop_path_rate (float): Stochastic depth rate. Default 0.0.
num_fcs (int): The number of fully-connected layers for FFNs.
Default: 2.
qkv_bias (bool): Enable bias for qkv if True. Default: True
qk_scale (float | None, optional): Override default qk scale of
head_dim ** -0.5 if set. Default: None.
act_cfg (dict): The activation config for FFNs.
Default: dict(type='GELU').
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='LN').
window_size (int): Window size of LSA. Default: 1.
init_cfg (dict, optional): The Config for initialization.
Defaults to None.
"""
def __init__(self,
embed_dims,
num_heads,
feedforward_channels,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
num_fcs=2,
qkv_bias=True,
qk_scale=None,
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='LN'),
window_size=1,
init_cfg=None):
super(LSAEncoderLayer, self).__init__(init_cfg=init_cfg)
self.norm1 = build_norm_layer(norm_cfg, embed_dims, postfix=1)[1]
self.attn = LocallyGroupedSelfAttention(embed_dims, num_heads,
qkv_bias, qk_scale,
attn_drop_rate, drop_rate,
window_size)
self.norm2 = build_norm_layer(norm_cfg, embed_dims, postfix=2)[1]
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,
add_identity=False)
self.drop_path = build_dropout(
dict(type='DropPath', drop_prob=drop_path_rate)
) if drop_path_rate > 0. else nn.Identity()
def forward(self, x, hw_shape):
x = x + self.drop_path(self.attn(self.norm1(x), hw_shape))
x = x + self.drop_path(self.ffn(self.norm2(x)))
return x
[docs]@MODELS.register_module()
class PCPVT(BaseModule):
"""The backbone of Twins-PCPVT.
This backbone is the implementation of `Twins: Revisiting the Design
of Spatial Attention in Vision Transformers
<https://arxiv.org/abs/1512.03385>`_.
Args:
arch (dict, str): PCPVT architecture, a str value in arch zoo or a
detailed configuration dict with 7 keys, and the length of all the
values in dict should be the same:
- depths (List[int]): The number of encoder layers in each stage.
- embed_dims (List[int]): Embedding dimension in each stage.
- patch_sizes (List[int]): The patch sizes in each stage.
- num_heads (List[int]): Numbers of attention head in each stage.
- strides (List[int]): The strides in each stage.
- mlp_ratios (List[int]): The ratios of mlp in each stage.
- sr_ratios (List[int]): The ratios of GSA-encoder layers in each
stage.
in_channels (int): Number of input channels. Defaults to 3.
out_indices (tuple[int]): Output from which stages.
Defaults to ``(3, )``.
qkv_bias (bool): Enable bias for qkv if True. Defaults to False.
drop_rate (float): Probability of an element to be zeroed.
Defaults to 0.
attn_drop_rate (float): The drop out rate for attention layer.
Defaults to 0.0
drop_path_rate (float): Stochastic depth rate. Defaults to 0.0.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
norm_after_stage(bool, List[bool]): Add extra norm after each stage.
Defaults to False.
init_cfg (dict, optional): The Config for initialization.
Defaults to None.
Examples:
>>> from mmpretrain.models import PCPVT
>>> import torch
>>> pcpvt_cfg = {'arch': "small",
>>> 'norm_after_stage': [False, False, False, True]}
>>> model = PCPVT(**pcpvt_cfg)
>>> x = torch.rand(1, 3, 224, 224)
>>> outputs = model(x)
>>> print(outputs[-1].shape)
torch.Size([1, 512, 7, 7])
>>> pcpvt_cfg['norm_after_stage'] = [True, True, True, True]
>>> pcpvt_cfg['out_indices'] = (0, 1, 2, 3)
>>> model = PCPVT(**pcpvt_cfg)
>>> outputs = model(x)
>>> for feat in outputs:
>>> print(feat.shape)
torch.Size([1, 64, 56, 56])
torch.Size([1, 128, 28, 28])
torch.Size([1, 320, 14, 14])
torch.Size([1, 512, 7, 7])
"""
arch_zoo = {
**dict.fromkeys(['s', 'small'],
{'embed_dims': [64, 128, 320, 512],
'depths': [3, 4, 6, 3],
'num_heads': [1, 2, 5, 8],
'patch_sizes': [4, 2, 2, 2],
'strides': [4, 2, 2, 2],
'mlp_ratios': [8, 8, 4, 4],
'sr_ratios': [8, 4, 2, 1]}),
**dict.fromkeys(['b', 'base'],
{'embed_dims': [64, 128, 320, 512],
'depths': [3, 4, 18, 3],
'num_heads': [1, 2, 5, 8],
'patch_sizes': [4, 2, 2, 2],
'strides': [4, 2, 2, 2],
'mlp_ratios': [8, 8, 4, 4],
'sr_ratios': [8, 4, 2, 1]}),
**dict.fromkeys(['l', 'large'],
{'embed_dims': [64, 128, 320, 512],
'depths': [3, 8, 27, 3],
'num_heads': [1, 2, 5, 8],
'patch_sizes': [4, 2, 2, 2],
'strides': [4, 2, 2, 2],
'mlp_ratios': [8, 8, 4, 4],
'sr_ratios': [8, 4, 2, 1]}),
} # yapf: disable
essential_keys = {
'embed_dims', 'depths', 'num_heads', 'patch_sizes', 'strides',
'mlp_ratios', 'sr_ratios'
}
def __init__(self,
arch,
in_channels=3,
out_indices=(3, ),
qkv_bias=False,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_cfg=dict(type='LN'),
norm_after_stage=False,
init_cfg=None):
super(PCPVT, 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:
assert isinstance(arch, dict) and (
set(arch) == self.essential_keys
), f'Custom arch needs a dict with keys {self.essential_keys}.'
self.arch_settings = arch
self.depths = self.arch_settings['depths']
self.embed_dims = self.arch_settings['embed_dims']
self.patch_sizes = self.arch_settings['patch_sizes']
self.strides = self.arch_settings['strides']
self.mlp_ratios = self.arch_settings['mlp_ratios']
self.num_heads = self.arch_settings['num_heads']
self.sr_ratios = self.arch_settings['sr_ratios']
self.num_extra_tokens = 0 # there is no cls-token in Twins
self.num_stage = len(self.depths)
for key, value in self.arch_settings.items():
assert isinstance(value, list) and len(value) == self.num_stage, (
'Length of setting item in arch dict must be type of list and'
' have the same length.')
# patch_embeds
self.patch_embeds = ModuleList()
self.position_encoding_drops = ModuleList()
self.stages = ModuleList()
for i in range(self.num_stage):
# use in_channels of the model in the first stage
if i == 0:
stage_in_channels = in_channels
else:
stage_in_channels = self.embed_dims[i - 1]
self.patch_embeds.append(
PatchEmbed(
in_channels=stage_in_channels,
embed_dims=self.embed_dims[i],
conv_type='Conv2d',
kernel_size=self.patch_sizes[i],
stride=self.strides[i],
padding='corner',
norm_cfg=dict(type='LN')))
self.position_encoding_drops.append(nn.Dropout(p=drop_rate))
# PEGs
self.position_encodings = ModuleList([
ConditionalPositionEncoding(embed_dim, embed_dim)
for embed_dim in self.embed_dims
])
# stochastic depth
total_depth = sum(self.depths)
self.dpr = [
x.item() for x in torch.linspace(0, drop_path_rate, total_depth)
] # stochastic depth decay rule
cur = 0
for k in range(len(self.depths)):
_block = ModuleList([
GSAEncoderLayer(
embed_dims=self.embed_dims[k],
num_heads=self.num_heads[k],
feedforward_channels=self.mlp_ratios[k] *
self.embed_dims[k],
attn_drop_rate=attn_drop_rate,
drop_rate=drop_rate,
drop_path_rate=self.dpr[cur + i],
num_fcs=2,
qkv_bias=qkv_bias,
act_cfg=dict(type='GELU'),
norm_cfg=norm_cfg,
sr_ratio=self.sr_ratios[k]) for i in range(self.depths[k])
])
self.stages.append(_block)
cur += self.depths[k]
self.out_indices = out_indices
assert isinstance(norm_after_stage, (bool, list))
if isinstance(norm_after_stage, bool):
self.norm_after_stage = [norm_after_stage] * self.num_stage
else:
self.norm_after_stage = norm_after_stage
assert len(self.norm_after_stage) == self.num_stage, \
(f'Number of norm_after_stage({len(self.norm_after_stage)}) should'
f' be equal to the number of stages({self.num_stage}).')
for i, has_norm in enumerate(self.norm_after_stage):
assert isinstance(has_norm, bool), 'norm_after_stage should be ' \
'bool or List[bool].'
if has_norm and norm_cfg is not None:
norm_layer = build_norm_layer(norm_cfg, self.embed_dims[i])[1]
else:
norm_layer = nn.Identity()
self.add_module(f'norm_after_stage{i}', norm_layer)
def init_weights(self):
if self.init_cfg is not None:
super(PCPVT, self).init_weights()
else:
for m in self.modules():
if isinstance(m, nn.Linear):
trunc_normal_init(m, std=.02, bias=0.)
elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
constant_init(m, val=1.0, bias=0.)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[
1] * m.out_channels
fan_out //= m.groups
normal_init(
m, mean=0, std=math.sqrt(2.0 / fan_out), bias=0)
def forward(self, x):
outputs = list()
b = x.shape[0]
for i in range(self.num_stage):
x, hw_shape = self.patch_embeds[i](x)
h, w = hw_shape
x = self.position_encoding_drops[i](x)
for j, blk in enumerate(self.stages[i]):
x = blk(x, hw_shape)
if j == 0:
x = self.position_encodings[i](x, hw_shape)
norm_layer = getattr(self, f'norm_after_stage{i}')
x = norm_layer(x)
x = x.reshape(b, h, w, -1).permute(0, 3, 1, 2).contiguous()
if i in self.out_indices:
outputs.append(x)
return tuple(outputs)
[docs]@MODELS.register_module()
class SVT(PCPVT):
"""The backbone of Twins-SVT.
This backbone is the implementation of `Twins: Revisiting the Design
of Spatial Attention in Vision Transformers
<https://arxiv.org/abs/1512.03385>`_.
Args:
arch (dict, str): SVT architecture, a str value in arch zoo or a
detailed configuration dict with 8 keys, and the length of all the
values in dict should be the same:
- depths (List[int]): The number of encoder layers in each stage.
- embed_dims (List[int]): Embedding dimension in each stage.
- patch_sizes (List[int]): The patch sizes in each stage.
- num_heads (List[int]): Numbers of attention head in each stage.
- strides (List[int]): The strides in each stage.
- mlp_ratios (List[int]): The ratios of mlp in each stage.
- sr_ratios (List[int]): The ratios of GSA-encoder layers in each
stage.
- windiow_sizes (List[int]): The window sizes in LSA-encoder layers
in each stage.
in_channels (int): Number of input channels. Defaults to 3.
out_indices (tuple[int]): Output from which stages.
Defaults to (3, ).
qkv_bias (bool): Enable bias for qkv if True. Defaults to False.
drop_rate (float): Dropout rate. Defaults to 0.
attn_drop_rate (float): Dropout ratio of attention weight.
Defaults to 0.0
drop_path_rate (float): Stochastic depth rate. Defaults to 0.2.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
norm_after_stage(bool, List[bool]): Add extra norm after each stage.
Defaults to False.
init_cfg (dict, optional): The Config for initialization.
Defaults to None.
Examples:
>>> from mmpretrain.models import SVT
>>> import torch
>>> svt_cfg = {'arch': "small",
>>> 'norm_after_stage': [False, False, False, True]}
>>> model = SVT(**svt_cfg)
>>> x = torch.rand(1, 3, 224, 224)
>>> outputs = model(x)
>>> print(outputs[-1].shape)
torch.Size([1, 512, 7, 7])
>>> svt_cfg["out_indices"] = (0, 1, 2, 3)
>>> svt_cfg["norm_after_stage"] = [True, True, True, True]
>>> model = SVT(**svt_cfg)
>>> output = model(x)
>>> for feat in output:
>>> print(feat.shape)
torch.Size([1, 64, 56, 56])
torch.Size([1, 128, 28, 28])
torch.Size([1, 320, 14, 14])
torch.Size([1, 512, 7, 7])
"""
arch_zoo = {
**dict.fromkeys(['s', 'small'],
{'embed_dims': [64, 128, 256, 512],
'depths': [2, 2, 10, 4],
'num_heads': [2, 4, 8, 16],
'patch_sizes': [4, 2, 2, 2],
'strides': [4, 2, 2, 2],
'mlp_ratios': [4, 4, 4, 4],
'sr_ratios': [8, 4, 2, 1],
'window_sizes': [7, 7, 7, 7]}),
**dict.fromkeys(['b', 'base'],
{'embed_dims': [96, 192, 384, 768],
'depths': [2, 2, 18, 2],
'num_heads': [3, 6, 12, 24],
'patch_sizes': [4, 2, 2, 2],
'strides': [4, 2, 2, 2],
'mlp_ratios': [4, 4, 4, 4],
'sr_ratios': [8, 4, 2, 1],
'window_sizes': [7, 7, 7, 7]}),
**dict.fromkeys(['l', 'large'],
{'embed_dims': [128, 256, 512, 1024],
'depths': [2, 2, 18, 2],
'num_heads': [4, 8, 16, 32],
'patch_sizes': [4, 2, 2, 2],
'strides': [4, 2, 2, 2],
'mlp_ratios': [4, 4, 4, 4],
'sr_ratios': [8, 4, 2, 1],
'window_sizes': [7, 7, 7, 7]}),
} # yapf: disable
essential_keys = {
'embed_dims', 'depths', 'num_heads', 'patch_sizes', 'strides',
'mlp_ratios', 'sr_ratios', 'window_sizes'
}
def __init__(self,
arch,
in_channels=3,
out_indices=(3, ),
qkv_bias=False,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.0,
norm_cfg=dict(type='LN'),
norm_after_stage=False,
init_cfg=None):
super(SVT, self).__init__(arch, in_channels, out_indices, qkv_bias,
drop_rate, attn_drop_rate, drop_path_rate,
norm_cfg, norm_after_stage, init_cfg)
self.window_sizes = self.arch_settings['window_sizes']
for k in range(self.num_stage):
for i in range(self.depths[k]):
# in even-numbered layers of each stage, replace GSA with LSA
if i % 2 == 0:
ffn_channels = self.mlp_ratios[k] * self.embed_dims[k]
self.stages[k][i] = \
LSAEncoderLayer(
embed_dims=self.embed_dims[k],
num_heads=self.num_heads[k],
feedforward_channels=ffn_channels,
drop_rate=drop_rate,
norm_cfg=norm_cfg,
attn_drop_rate=attn_drop_rate,
drop_path_rate=self.dpr[sum(self.depths[:k])+i],
qkv_bias=qkv_bias,
window_size=self.window_sizes[k])