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# -*- coding: utf-8 -*-
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from einops.layers.torch import Rearrange
from timm.models.layers import trunc_normal_, DropPath
class WMSA(nn.Module):
""" Self-attention module in Swin Transformer
"""
def __init__(self, input_dim, output_dim, head_dim, window_size, type):
super(WMSA, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.head_dim = head_dim
self.scale = self.head_dim ** -0.5
self.n_heads = input_dim // head_dim
self.window_size = window_size
self.type = type
self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
self.relative_position_params = nn.Parameter(
torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
self.linear = nn.Linear(self.input_dim, self.output_dim)
trunc_normal_(self.relative_position_params, std=.02)
self.relative_position_params = torch.nn.Parameter(
self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
2).transpose(
0, 1))
def generate_mask(self, h, w, p, shift):
""" generating the mask of SW-MSA
Args:
shift: shift parameters in CyclicShift.
Returns:
attn_mask: should be (1 1 w p p),
"""
# supporting square.
attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
if self.type == 'W':
return attn_mask
s = p - shift
attn_mask[-1, :, :s, :, s:, :] = True
attn_mask[-1, :, s:, :, :s, :] = True
attn_mask[:, -1, :, :s, :, s:] = True
attn_mask[:, -1, :, s:, :, :s] = True
attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
return attn_mask
def forward(self, x):
""" Forward pass of Window Multi-head Self-attention module.
Args:
x: input tensor with shape of [b h w c];
attn_mask: attention mask, fill -inf where the value is True;
Returns:
output: tensor shape [b h w c]
"""
if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
h_windows = x.size(1)
w_windows = x.size(2)
# square validation
# assert h_windows == w_windows
x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
qkv = self.embedding_layer(x)
q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
# Adding learnable relative embedding
sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
# Using Attn Mask to distinguish different subwindows.
if self.type != 'W':
attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
sim = sim.masked_fill_(attn_mask, float("-inf"))
probs = nn.functional.softmax(sim, dim=-1)
output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
output = rearrange(output, 'h b w p c -> b w p (h c)')
output = self.linear(output)
output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
dims=(1, 2))
return output
def relative_embedding(self):
cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
# negative is allowed
return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
class Block(nn.Module):
def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
""" SwinTransformer Block
"""
super(Block, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
assert type in ['W', 'SW']
self.type = type
if input_resolution <= window_size:
self.type = 'W'
self.ln1 = nn.LayerNorm(input_dim)
self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.ln2 = nn.LayerNorm(input_dim)
self.mlp = nn.Sequential(
nn.Linear(input_dim, 4 * input_dim),
nn.GELU(),
nn.Linear(4 * input_dim, output_dim),
)
def forward(self, x):
x = x + self.drop_path(self.msa(self.ln1(x)))
x = x + self.drop_path(self.mlp(self.ln2(x)))
return x
class ConvTransBlock(nn.Module):
def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
""" SwinTransformer and Conv Block
"""
super(ConvTransBlock, self).__init__()
self.conv_dim = conv_dim
self.trans_dim = trans_dim
self.head_dim = head_dim
self.window_size = window_size
self.drop_path = drop_path
self.type = type
self.input_resolution = input_resolution
assert self.type in ['W', 'SW']
if self.input_resolution <= self.window_size:
self.type = 'W'
self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
self.type, self.input_resolution)
self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
self.conv_block = nn.Sequential(
nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
nn.ReLU(True),
nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
)
def forward(self, x):
conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
conv_x = self.conv_block(conv_x) + conv_x
trans_x = Rearrange('b c h w -> b h w c')(trans_x)
trans_x = self.trans_block(trans_x)
trans_x = Rearrange('b h w c -> b c h w')(trans_x)
res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
x = x + res
return x
class SCUNet(nn.Module):
# def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
super(SCUNet, self).__init__()
if config is None:
config = [2, 2, 2, 2, 2, 2, 2]
self.config = config
self.dim = dim
self.head_dim = 32
self.window_size = 8
# drop path rate for each layer
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
begin = 0
self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution)
for i in range(config[0])] + \
[nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
begin += config[0]
self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 2)
for i in range(config[1])] + \
[nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
begin += config[1]
self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 4)
for i in range(config[2])] + \
[nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
begin += config[2]
self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 8)
for i in range(config[3])]
begin += config[3]
self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
[ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 4)
for i in range(config[4])]
begin += config[4]
self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
[ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 2)
for i in range(config[5])]
begin += config[5]
self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
[ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution)
for i in range(config[6])]
self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
self.m_head = nn.Sequential(*self.m_head)
self.m_down1 = nn.Sequential(*self.m_down1)
self.m_down2 = nn.Sequential(*self.m_down2)
self.m_down3 = nn.Sequential(*self.m_down3)
self.m_body = nn.Sequential(*self.m_body)
self.m_up3 = nn.Sequential(*self.m_up3)
self.m_up2 = nn.Sequential(*self.m_up2)
self.m_up1 = nn.Sequential(*self.m_up1)
self.m_tail = nn.Sequential(*self.m_tail)
# self.apply(self._init_weights)
def forward(self, x0):
h, w = x0.size()[-2:]
paddingBottom = int(np.ceil(h / 64) * 64 - h)
paddingRight = int(np.ceil(w / 64) * 64 - w)
x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
x1 = self.m_head(x0)
x2 = self.m_down1(x1)
x3 = self.m_down2(x2)
x4 = self.m_down3(x3)
x = self.m_body(x4)
x = self.m_up3(x + x4)
x = self.m_up2(x + x3)
x = self.m_up1(x + x2)
x = self.m_tail(x + x1)
x = x[..., :h, :w]
return x
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if 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) |