780 lines
29 KiB
Python
780 lines
29 KiB
Python
import warnings
|
|
from copy import deepcopy
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from mmcv.cnn import build_norm_layer, trunc_normal_init
|
|
from mmcv.cnn.bricks.registry import ATTENTION
|
|
from mmcv.cnn.bricks.transformer import FFN, build_dropout
|
|
from mmcv.cnn.utils.weight_init import constant_init
|
|
from mmcv.runner import _load_checkpoint
|
|
from mmcv.runner.base_module import BaseModule, ModuleList
|
|
from torch.nn.modules.linear import Linear
|
|
from torch.nn.modules.normalization import LayerNorm
|
|
from torch.nn.modules.utils import _pair as to_2tuple
|
|
|
|
from ...utils import get_root_logger
|
|
from ..builder import BACKBONES
|
|
from ..utils import PatchEmbed, swin_convert
|
|
|
|
|
|
class PatchMerging(BaseModule):
|
|
"""Merge patch feature map.
|
|
|
|
This layer use nn.Unfold to group feature map by kernel_size, and use norm
|
|
and linear layer to embed grouped feature map.
|
|
Args:
|
|
in_channels (int): The num of input channels.
|
|
out_channels (int): The num of output channels.
|
|
stride (int | tuple): the stride of the sliding length in the
|
|
unfold layer. Defaults: 2. (Default to be equal with kernel_size).
|
|
bias (bool, optional): Whether to add bias in linear layer or not.
|
|
Defaults: False.
|
|
norm_cfg (dict, optional): Config dict for normalization layer.
|
|
Defaults: dict(type='LN').
|
|
init_cfg (dict, optional): The extra config for initialization.
|
|
Defaults: None.
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
stride=2,
|
|
bias=False,
|
|
norm_cfg=dict(type='LN'),
|
|
init_cfg=None):
|
|
super().__init__(init_cfg)
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.stride = stride
|
|
|
|
self.sampler = nn.Unfold(
|
|
kernel_size=stride, dilation=1, padding=0, stride=stride)
|
|
|
|
sample_dim = stride**2 * in_channels
|
|
|
|
if norm_cfg is not None:
|
|
self.norm = build_norm_layer(norm_cfg, sample_dim)[1]
|
|
else:
|
|
self.norm = None
|
|
|
|
self.reduction = nn.Linear(sample_dim, out_channels, bias=bias)
|
|
|
|
def forward(self, x, hw_shape):
|
|
"""
|
|
x: x.shape -> [B, H*W, C]
|
|
hw_shape: (H, W)
|
|
"""
|
|
B, L, C = x.shape
|
|
H, W = hw_shape
|
|
assert L == H * W, 'input feature has wrong size'
|
|
|
|
x = x.view(B, H, W, C).permute([0, 3, 1, 2]) # B, C, H, W
|
|
|
|
# stride is fixed to be equal to kernel_size.
|
|
if (H % self.stride != 0) or (W % self.stride != 0):
|
|
x = F.pad(x, (0, W % self.stride, 0, H % self.stride))
|
|
|
|
# Use nn.Unfold to merge patch. About 25% faster than original method,
|
|
# but need to modify pretrained model for compatibility
|
|
x = self.sampler(x) # B, 4*C, H/2*W/2
|
|
x = x.transpose(1, 2) # B, H/2*W/2, 4*C
|
|
|
|
x = self.norm(x) if self.norm else x
|
|
x = self.reduction(x)
|
|
|
|
down_hw_shape = (H + 1) // 2, (W + 1) // 2
|
|
return x, down_hw_shape
|
|
|
|
|
|
@ATTENTION.register_module()
|
|
class WindowMSA(BaseModule):
|
|
"""Window based multi-head self-attention (W-MSA) module with relative
|
|
position bias.
|
|
|
|
Args:
|
|
embed_dims (int): Number of input channels.
|
|
window_size (tuple[int]): The height and width of the window.
|
|
num_heads (int): Number of attention heads.
|
|
qkv_bias (bool, optional): If True, add a learnable bias to q, k, v.
|
|
Default: True.
|
|
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: 0.0
|
|
init_cfg (dict | None, optional): The Config for initialization.
|
|
Default: None.
|
|
"""
|
|
|
|
def __init__(self,
|
|
embed_dims,
|
|
num_heads,
|
|
window_size,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
attn_drop_rate=0.,
|
|
proj_drop_rate=0.,
|
|
init_cfg=None):
|
|
|
|
super().__init__()
|
|
self.embed_dims = embed_dims
|
|
self.window_size = window_size # Wh, Ww
|
|
self.num_heads = num_heads
|
|
head_embed_dims = embed_dims // num_heads
|
|
self.scale = qk_scale or head_embed_dims**-0.5
|
|
self.init_cfg = init_cfg
|
|
|
|
# define a parameter table of relative position bias
|
|
self.relative_position_bias_table = nn.Parameter(
|
|
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
|
|
num_heads)) # 2*Wh-1 * 2*Ww-1, nH
|
|
|
|
# About 2x faster than original impl
|
|
Wh, Ww = self.window_size
|
|
rel_index_coords = self.double_step_seq(2 * Ww - 1, Wh, 1, Ww)
|
|
rel_position_index = rel_index_coords + rel_index_coords.T
|
|
rel_position_index = rel_position_index.flip(1).contiguous()
|
|
self.register_buffer('relative_position_index', rel_position_index)
|
|
|
|
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.softmax = nn.Softmax(dim=-1)
|
|
|
|
def init_weights(self):
|
|
trunc_normal_init(self.relative_position_bias_table, std=0.02)
|
|
|
|
def forward(self, x, mask=None):
|
|
"""
|
|
Args:
|
|
|
|
x (tensor): input features with shape of (num_windows*B, N, C)
|
|
mask (tensor | None, Optional): mask with shape of (num_windows,
|
|
Wh*Ww, Wh*Ww), value should be between (-inf, 0].
|
|
"""
|
|
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))
|
|
|
|
relative_position_bias = self.relative_position_bias_table[
|
|
self.relative_position_index.view(-1)].view(
|
|
self.window_size[0] * self.window_size[1],
|
|
self.window_size[0] * self.window_size[1],
|
|
-1) # Wh*Ww,Wh*Ww,nH
|
|
relative_position_bias = relative_position_bias.permute(
|
|
2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
|
attn = attn + relative_position_bias.unsqueeze(0)
|
|
|
|
if mask is not None:
|
|
nW = mask.shape[0]
|
|
attn = attn.view(B // nW, nW, self.num_heads, N,
|
|
N) + mask.unsqueeze(1).unsqueeze(0)
|
|
attn = attn.view(-1, self.num_heads, N, N)
|
|
attn = self.softmax(attn)
|
|
else:
|
|
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
|
|
|
|
@staticmethod
|
|
def double_step_seq(step1, len1, step2, len2):
|
|
seq1 = torch.arange(0, step1 * len1, step1)
|
|
seq2 = torch.arange(0, step2 * len2, step2)
|
|
return (seq1[:, None] + seq2[None, :]).reshape(1, -1)
|
|
|
|
|
|
@ATTENTION.register_module()
|
|
class ShiftWindowMSA(BaseModule):
|
|
"""Shift Window Multihead Self-Attention Module.
|
|
|
|
Args:
|
|
embed_dims (int): Number of input channels.
|
|
num_heads (int): Number of attention heads.
|
|
window_size (int): The height and width of the window.
|
|
shift_size (int, optional): The shift step of each window towards
|
|
right-bottom. If zero, act as regular window-msa. Defaults to 0.
|
|
qkv_bias (bool, optional): If True, add a learnable bias to q, k, v.
|
|
Default: True
|
|
qk_scale (float | None, optional): Override default qk scale of
|
|
head_dim ** -0.5 if set. Defaults: None.
|
|
attn_drop_rate (float, optional): Dropout ratio of attention weight.
|
|
Defaults: 0.
|
|
proj_drop_rate (float, optional): Dropout ratio of output.
|
|
Defaults: 0.
|
|
dropout_layer (dict, optional): The dropout_layer used before output.
|
|
Defaults: dict(type='DropPath', drop_prob=0.).
|
|
init_cfg (dict, optional): The extra config for initialization.
|
|
Default: None.
|
|
"""
|
|
|
|
def __init__(self,
|
|
embed_dims,
|
|
num_heads,
|
|
window_size,
|
|
shift_size=0,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
attn_drop_rate=0,
|
|
proj_drop_rate=0,
|
|
dropout_layer=dict(type='DropPath', drop_prob=0.),
|
|
init_cfg=None):
|
|
super().__init__(init_cfg)
|
|
|
|
self.window_size = window_size
|
|
self.shift_size = shift_size
|
|
assert 0 <= self.shift_size < self.window_size
|
|
|
|
self.w_msa = WindowMSA(
|
|
embed_dims=embed_dims,
|
|
num_heads=num_heads,
|
|
window_size=to_2tuple(window_size),
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
attn_drop_rate=attn_drop_rate,
|
|
proj_drop_rate=proj_drop_rate,
|
|
init_cfg=None)
|
|
|
|
self.drop = build_dropout(dropout_layer)
|
|
|
|
def forward(self, query, hw_shape):
|
|
B, L, C = query.shape
|
|
H, W = hw_shape
|
|
assert L == H * W, 'input feature has wrong size'
|
|
query = query.view(B, H, W, C)
|
|
|
|
# pad feature maps to multiples of window size
|
|
pad_r = (self.window_size - W % self.window_size) % self.window_size
|
|
pad_b = (self.window_size - H % self.window_size) % self.window_size
|
|
query = F.pad(query, (0, 0, 0, pad_r, 0, pad_b))
|
|
H_pad, W_pad = query.shape[1], query.shape[2]
|
|
|
|
# cyclic shift
|
|
if self.shift_size > 0:
|
|
shifted_query = torch.roll(
|
|
query,
|
|
shifts=(-self.shift_size, -self.shift_size),
|
|
dims=(1, 2))
|
|
|
|
# calculate attention mask for SW-MSA
|
|
img_mask = torch.zeros((1, H_pad, W_pad, 1),
|
|
device=query.device) # 1 H W 1
|
|
h_slices = (slice(0, -self.window_size),
|
|
slice(-self.window_size,
|
|
-self.shift_size), slice(-self.shift_size, None))
|
|
w_slices = (slice(0, -self.window_size),
|
|
slice(-self.window_size,
|
|
-self.shift_size), slice(-self.shift_size, None))
|
|
cnt = 0
|
|
for h in h_slices:
|
|
for w in w_slices:
|
|
img_mask[:, h, w, :] = cnt
|
|
cnt += 1
|
|
|
|
# nW, window_size, window_size, 1
|
|
mask_windows = self.window_partition(img_mask)
|
|
mask_windows = mask_windows.view(
|
|
-1, self.window_size * self.window_size)
|
|
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
|
attn_mask = attn_mask.masked_fill(attn_mask != 0,
|
|
float(-100.0)).masked_fill(
|
|
attn_mask == 0, float(0.0))
|
|
else:
|
|
shifted_query = query
|
|
attn_mask = None
|
|
|
|
# nW*B, window_size, window_size, C
|
|
query_windows = self.window_partition(shifted_query)
|
|
# nW*B, window_size*window_size, C
|
|
query_windows = query_windows.view(-1, self.window_size**2, C)
|
|
|
|
# W-MSA/SW-MSA (nW*B, window_size*window_size, C)
|
|
attn_windows = self.w_msa(query_windows, mask=attn_mask)
|
|
|
|
# merge windows
|
|
attn_windows = attn_windows.view(-1, self.window_size,
|
|
self.window_size, C)
|
|
|
|
# B H' W' C
|
|
shifted_x = self.window_reverse(attn_windows, H_pad, W_pad)
|
|
# reverse cyclic shift
|
|
if self.shift_size > 0:
|
|
x = torch.roll(
|
|
shifted_x,
|
|
shifts=(self.shift_size, self.shift_size),
|
|
dims=(1, 2))
|
|
else:
|
|
x = shifted_x
|
|
|
|
if pad_r > 0 or pad_b:
|
|
x = x[:, :H, :W, :].contiguous()
|
|
|
|
x = x.view(B, H * W, C)
|
|
|
|
x = self.drop(x)
|
|
return x
|
|
|
|
def window_reverse(self, windows, H, W):
|
|
"""
|
|
Args:
|
|
windows: (num_windows*B, window_size, window_size, C)
|
|
window_size (int): Window size
|
|
H (int): Height of image
|
|
W (int): Width of image
|
|
Returns:
|
|
x: (B, H, W, C)
|
|
"""
|
|
window_size = self.window_size
|
|
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
|
x = windows.view(B, H // window_size, W // window_size, window_size,
|
|
window_size, -1)
|
|
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
|
return x
|
|
|
|
def window_partition(self, x):
|
|
"""
|
|
Args:
|
|
x: (B, H, W, C)
|
|
window_size (int): window size
|
|
Returns:
|
|
windows: (num_windows*B, window_size, window_size, C)
|
|
"""
|
|
B, H, W, C = x.shape
|
|
window_size = self.window_size
|
|
x = x.view(B, H // window_size, window_size, W // window_size,
|
|
window_size, C)
|
|
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous()
|
|
windows = windows.view(-1, window_size, window_size, C)
|
|
return windows
|
|
|
|
|
|
class SwinBlock(BaseModule):
|
|
""""
|
|
Args:
|
|
embed_dims (int): The feature dimension.
|
|
num_heads (int): Parallel attention heads.
|
|
feedforward_channels (int): The hidden dimension for FFNs.
|
|
window size (int, optional): The local window scale. Default: 7.
|
|
shift (bool): whether to shift window or not. Default False.
|
|
qkv_bias (int, optional): 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.
|
|
drop_rate (float, optional): Dropout rate. Default: 0.
|
|
attn_drop_rate (float, optional): Attention dropout rate. Default: 0.
|
|
drop_path_rate (float, optional): Stochastic depth rate. Default: 0.2.
|
|
act_cfg (dict, optional): The config dict of activation function.
|
|
Default: dict(type='GELU').
|
|
norm_cfg (dict, optional): The config dict of nomalization.
|
|
Default: dict(type='LN').
|
|
init_cfg (dict | list | None, optional): The init config.
|
|
Default: None.
|
|
"""
|
|
|
|
def __init__(self,
|
|
embed_dims,
|
|
num_heads,
|
|
feedforward_channels,
|
|
window_size=7,
|
|
shift=False,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop_rate=0.,
|
|
attn_drop_rate=0.,
|
|
drop_path_rate=0.,
|
|
act_cfg=dict(type='GELU'),
|
|
norm_cfg=dict(type='LN'),
|
|
init_cfg=None):
|
|
|
|
super(SwinBlock, self).__init__()
|
|
|
|
self.init_cfg = init_cfg
|
|
|
|
self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
|
|
self.attn = ShiftWindowMSA(
|
|
embed_dims=embed_dims,
|
|
num_heads=num_heads,
|
|
window_size=window_size,
|
|
shift_size=window_size // 2 if shift else 0,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
attn_drop_rate=attn_drop_rate,
|
|
proj_drop_rate=drop_rate,
|
|
dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
|
|
init_cfg=None)
|
|
|
|
self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
|
|
self.ffn = FFN(
|
|
embed_dims=embed_dims,
|
|
feedforward_channels=feedforward_channels,
|
|
num_fcs=2,
|
|
ffn_drop=drop_rate,
|
|
dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
|
|
act_cfg=act_cfg,
|
|
add_identity=True,
|
|
init_cfg=None)
|
|
|
|
def forward(self, x, hw_shape):
|
|
identity = x
|
|
x = self.norm1(x)
|
|
x = self.attn(x, hw_shape)
|
|
|
|
x = x + identity
|
|
|
|
identity = x
|
|
x = self.norm2(x)
|
|
x = self.ffn(x, identity=identity)
|
|
|
|
return x
|
|
|
|
|
|
class SwinBlockSequence(BaseModule):
|
|
"""Implements one stage in Swin Transformer.
|
|
|
|
Args:
|
|
embed_dims (int): The feature dimension.
|
|
num_heads (int): Parallel attention heads.
|
|
feedforward_channels (int): The hidden dimension for FFNs.
|
|
depth (int): The number of blocks in this stage.
|
|
window size (int): The local window scale. Default: 7.
|
|
qkv_bias (int): 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.
|
|
drop_rate (float, optional): Dropout rate. Default: 0.
|
|
attn_drop_rate (float, optional): Attention dropout rate. Default: 0.
|
|
drop_path_rate (float, optional): Stochastic depth rate. Default: 0.2.
|
|
downsample (BaseModule | None, optional): The downsample operation
|
|
module. Default: None.
|
|
act_cfg (dict, optional): The config dict of activation function.
|
|
Default: dict(type='GELU').
|
|
norm_cfg (dict, optional): The config dict of nomalization.
|
|
Default: dict(type='LN').
|
|
init_cfg (dict | list | None, optional): The init config.
|
|
Default: None.
|
|
"""
|
|
|
|
def __init__(self,
|
|
embed_dims,
|
|
num_heads,
|
|
feedforward_channels,
|
|
depth,
|
|
window_size=7,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop_rate=0.,
|
|
attn_drop_rate=0.,
|
|
drop_path_rate=0.,
|
|
downsample=None,
|
|
act_cfg=dict(type='GELU'),
|
|
norm_cfg=dict(type='LN'),
|
|
init_cfg=None):
|
|
super().__init__()
|
|
|
|
self.init_cfg = init_cfg
|
|
|
|
drop_path_rate = drop_path_rate if isinstance(
|
|
drop_path_rate,
|
|
list) else [deepcopy(drop_path_rate) for _ in range(depth)]
|
|
|
|
self.blocks = ModuleList()
|
|
for i in range(depth):
|
|
block = SwinBlock(
|
|
embed_dims=embed_dims,
|
|
num_heads=num_heads,
|
|
feedforward_channels=feedforward_channels,
|
|
window_size=window_size,
|
|
shift=False if i % 2 == 0 else True,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop_rate=drop_rate,
|
|
attn_drop_rate=attn_drop_rate,
|
|
drop_path_rate=drop_path_rate[i],
|
|
act_cfg=act_cfg,
|
|
norm_cfg=norm_cfg,
|
|
init_cfg=None)
|
|
self.blocks.append(block)
|
|
|
|
self.downsample = downsample
|
|
|
|
def forward(self, x, hw_shape):
|
|
for block in self.blocks:
|
|
x = block(x, hw_shape)
|
|
|
|
if self.downsample:
|
|
x_down, down_hw_shape = self.downsample(x, hw_shape)
|
|
return x_down, down_hw_shape, x, hw_shape
|
|
else:
|
|
return x, hw_shape, x, hw_shape
|
|
|
|
|
|
@BACKBONES.register_module()
|
|
class SwinTransformer(BaseModule):
|
|
""" Swin Transformer
|
|
A PyTorch implement of : `Swin Transformer:
|
|
Hierarchical Vision Transformer using Shifted Windows` -
|
|
https://arxiv.org/abs/2103.14030
|
|
|
|
Inspiration from
|
|
https://github.com/microsoft/Swin-Transformer
|
|
|
|
Args:
|
|
pretrain_img_size (int | tuple[int]): The size of input image when
|
|
pretrain. Defaults: 224.
|
|
in_channels (int): The num of input channels.
|
|
Defaults: 3.
|
|
embed_dims (int): The feature dimension. Default: 96.
|
|
patch_size (int | tuple[int]): Patch size. Default: 4.
|
|
window_size (int): Window size. Default: 7.
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
|
Default: 4.
|
|
depths (tuple[int]): Depths of each Swin Transformer stage.
|
|
Default: (2, 2, 6, 2).
|
|
num_heads (tuple[int]): Parallel attention heads of each Swin
|
|
Transformer stage. Default: (3, 6, 12, 24).
|
|
strides (tuple[int]): The patch merging or patch embedding stride of
|
|
each Swin Transformer stage. (In swin, we set kernel size equal to
|
|
stride.) Default: (4, 2, 2, 2).
|
|
out_indices (tuple[int]): Output from which stages.
|
|
Default: (0, 1, 2, 3).
|
|
qkv_bias (bool, optional): If True, add a learnable bias to query, key,
|
|
value. Default: True
|
|
qk_scale (float | None, optional): Override default qk scale of
|
|
head_dim ** -0.5 if set. Default: None.
|
|
patch_norm (bool): If add a norm layer for patch embed and patch
|
|
merging. Default: True.
|
|
drop_rate (float): Dropout rate. Defaults: 0.
|
|
attn_drop_rate (float): Attention dropout rate. Default: 0.
|
|
drop_path_rate (float): Stochastic depth rate. Defaults: 0.1.
|
|
use_abs_pos_embed (bool): If True, add absolute position embedding to
|
|
the patch embedding. Defaults: False.
|
|
act_cfg (dict): Config dict for activation layer.
|
|
Default: dict(type='LN').
|
|
norm_cfg (dict): Config dict for normalization layer at
|
|
output of backone. Defaults: dict(type='LN').
|
|
pretrain_style (str): Choose to use official or mmcls pretrain weights.
|
|
Default: official.
|
|
pretrained (str, optional): model pretrained path. Default: None.
|
|
init_cfg (dict, optional): The Config for initialization.
|
|
Defaults to None.
|
|
"""
|
|
|
|
def __init__(self,
|
|
pretrain_img_size=224,
|
|
in_channels=3,
|
|
embed_dims=96,
|
|
patch_size=4,
|
|
window_size=7,
|
|
mlp_ratio=4,
|
|
depths=(2, 2, 6, 2),
|
|
num_heads=(3, 6, 12, 24),
|
|
strides=(4, 2, 2, 2),
|
|
out_indices=(0, 1, 2, 3),
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
patch_norm=True,
|
|
drop_rate=0.,
|
|
attn_drop_rate=0.,
|
|
drop_path_rate=0.1,
|
|
use_abs_pos_embed=False,
|
|
act_cfg=dict(type='GELU'),
|
|
norm_cfg=dict(type='LN'),
|
|
pretrain_style='official',
|
|
pretrained=None,
|
|
init_cfg=None):
|
|
super(SwinTransformer, self).__init__()
|
|
|
|
if isinstance(pretrain_img_size, int):
|
|
pretrain_img_size = to_2tuple(pretrain_img_size)
|
|
elif isinstance(pretrain_img_size, tuple):
|
|
if len(pretrain_img_size) == 1:
|
|
pretrain_img_size = to_2tuple(pretrain_img_size[0])
|
|
assert len(pretrain_img_size) == 2, \
|
|
f'The size of image should have length 1 or 2, ' \
|
|
f'but got {len(pretrain_img_size)}'
|
|
|
|
assert pretrain_style in ['official', 'mmcls'], 'We only support load '
|
|
'official ckpt and mmcls ckpt.'
|
|
|
|
if isinstance(pretrained, str) or pretrained is None:
|
|
warnings.warn('DeprecationWarning: pretrained is a deprecated, '
|
|
'please use "init_cfg" instead')
|
|
else:
|
|
raise TypeError('pretrained must be a str or None')
|
|
|
|
num_layers = len(depths)
|
|
self.out_indices = out_indices
|
|
self.use_abs_pos_embed = use_abs_pos_embed
|
|
self.pretrain_style = pretrain_style
|
|
self.pretrained = pretrained
|
|
self.init_cfg = init_cfg
|
|
|
|
assert strides[0] == patch_size, 'Use non-overlapping patch embed.'
|
|
|
|
self.patch_embed = PatchEmbed(
|
|
in_channels=in_channels,
|
|
embed_dims=embed_dims,
|
|
conv_type='Conv2d',
|
|
kernel_size=patch_size,
|
|
stride=strides[0],
|
|
pad_to_patch_size=True,
|
|
norm_cfg=norm_cfg if patch_norm else None,
|
|
init_cfg=None)
|
|
|
|
if self.use_abs_pos_embed:
|
|
patch_row = pretrain_img_size[0] // patch_size
|
|
patch_col = pretrain_img_size[1] // patch_size
|
|
num_patches = patch_row * patch_col
|
|
self.absolute_pos_embed = nn.Parameter(
|
|
torch.zeros((1, num_patches, embed_dims)))
|
|
|
|
self.drop_after_pos = nn.Dropout(p=drop_rate)
|
|
|
|
# stochastic depth
|
|
total_depth = sum(depths)
|
|
dpr = [
|
|
x.item() for x in torch.linspace(0, drop_path_rate, total_depth)
|
|
] # stochastic depth decay rule
|
|
|
|
self.stages = ModuleList()
|
|
in_channels = embed_dims
|
|
for i in range(num_layers):
|
|
if i < num_layers - 1:
|
|
downsample = PatchMerging(
|
|
in_channels=in_channels,
|
|
out_channels=2 * in_channels,
|
|
stride=strides[i + 1],
|
|
norm_cfg=norm_cfg if patch_norm else None,
|
|
init_cfg=None)
|
|
else:
|
|
downsample = None
|
|
|
|
stage = SwinBlockSequence(
|
|
embed_dims=in_channels,
|
|
num_heads=num_heads[i],
|
|
feedforward_channels=mlp_ratio * in_channels,
|
|
depth=depths[i],
|
|
window_size=window_size,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop_rate=drop_rate,
|
|
attn_drop_rate=attn_drop_rate,
|
|
drop_path_rate=dpr[:depths[i]],
|
|
downsample=downsample,
|
|
act_cfg=act_cfg,
|
|
norm_cfg=norm_cfg,
|
|
init_cfg=None)
|
|
self.stages.append(stage)
|
|
|
|
dpr = dpr[depths[i]:]
|
|
if downsample:
|
|
in_channels = downsample.out_channels
|
|
|
|
self.num_features = [int(embed_dims * 2**i) for i in range(num_layers)]
|
|
# Add a norm layer for each output
|
|
for i in out_indices:
|
|
layer = build_norm_layer(norm_cfg, self.num_features[i])[1]
|
|
layer_name = f'norm{i}'
|
|
self.add_module(layer_name, layer)
|
|
|
|
def init_weights(self):
|
|
if self.pretrained is None:
|
|
super().init_weights()
|
|
if self.use_abs_pos_embed:
|
|
trunc_normal_init(self.absolute_pos_embed, std=0.02)
|
|
for m in self.modules():
|
|
if isinstance(m, Linear):
|
|
trunc_normal_init(m.weight, std=.02)
|
|
if m.bias is not None:
|
|
constant_init(m.bias, 0)
|
|
elif isinstance(m, LayerNorm):
|
|
constant_init(m.bias, 0)
|
|
constant_init(m.weight, 1.0)
|
|
elif isinstance(self.pretrained, str):
|
|
logger = get_root_logger()
|
|
ckpt = _load_checkpoint(
|
|
self.pretrained, logger=logger, map_location='cpu')
|
|
if 'state_dict' in ckpt:
|
|
state_dict = ckpt['state_dict']
|
|
elif 'model' in ckpt:
|
|
state_dict = ckpt['model']
|
|
else:
|
|
state_dict = ckpt
|
|
|
|
if self.pretrain_style == 'official':
|
|
state_dict = swin_convert(state_dict)
|
|
|
|
# strip prefix of state_dict
|
|
if list(state_dict.keys())[0].startswith('module.'):
|
|
state_dict = {k[7:]: v for k, v in state_dict.items()}
|
|
|
|
# reshape absolute position embedding
|
|
if state_dict.get('absolute_pos_embed') is not None:
|
|
absolute_pos_embed = state_dict['absolute_pos_embed']
|
|
N1, L, C1 = absolute_pos_embed.size()
|
|
N2, C2, H, W = self.absolute_pos_embed.size()
|
|
if N1 != N2 or C1 != C2 or L != H * W:
|
|
logger.warning('Error in loading absolute_pos_embed, pass')
|
|
else:
|
|
state_dict['absolute_pos_embed'] = absolute_pos_embed.view(
|
|
N2, H, W, C2).permute(0, 3, 1, 2).contiguous()
|
|
|
|
# interpolate position bias table if needed
|
|
relative_position_bias_table_keys = [
|
|
k for k in state_dict.keys()
|
|
if 'relative_position_bias_table' in k
|
|
]
|
|
for table_key in relative_position_bias_table_keys:
|
|
table_pretrained = state_dict[table_key]
|
|
table_current = self.state_dict()[table_key]
|
|
L1, nH1 = table_pretrained.size()
|
|
L2, nH2 = table_current.size()
|
|
if nH1 != nH2:
|
|
logger.warning(f'Error in loading {table_key}, pass')
|
|
else:
|
|
if L1 != L2:
|
|
S1 = int(L1**0.5)
|
|
S2 = int(L2**0.5)
|
|
table_pretrained_resized = F.interpolate(
|
|
table_pretrained.permute(1, 0).reshape(
|
|
1, nH1, S1, S1),
|
|
size=(S2, S2),
|
|
mode='bicubic')
|
|
state_dict[table_key] = table_pretrained_resized.view(
|
|
nH2, L2).permute(1, 0).contiguous()
|
|
|
|
# load state_dict
|
|
self.load_state_dict(state_dict, False)
|
|
|
|
def forward(self, x):
|
|
x = self.patch_embed(x)
|
|
|
|
hw_shape = (self.patch_embed.DH, self.patch_embed.DW)
|
|
if self.use_abs_pos_embed:
|
|
x = x + self.absolute_pos_embed
|
|
x = self.drop_after_pos(x)
|
|
|
|
outs = []
|
|
for i, stage in enumerate(self.stages):
|
|
x, hw_shape, out, out_hw_shape = stage(x, hw_shape)
|
|
if i in self.out_indices:
|
|
norm_layer = getattr(self, f'norm{i}')
|
|
out = norm_layer(out)
|
|
out = out.view(-1, *out_hw_shape,
|
|
self.num_features[i]).permute(0, 3, 1,
|
|
2).contiguous()
|
|
outs.append(out)
|
|
|
|
return outs
|