265 lines
11 KiB
Python
265 lines
11 KiB
Python
# -*- coding: utf-8 -*-
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import numpy as np
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import torch
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import torch.nn as nn
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from einops import rearrange
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from einops.layers.torch import Rearrange
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from timm.models.layers import trunc_normal_, DropPath
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class WMSA(nn.Module):
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""" Self-attention module in Swin Transformer
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"""
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def __init__(self, input_dim, output_dim, head_dim, window_size, type):
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super(WMSA, self).__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.head_dim = head_dim
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self.scale = self.head_dim ** -0.5
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self.n_heads = input_dim // head_dim
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self.window_size = window_size
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self.type = type
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self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
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self.relative_position_params = nn.Parameter(
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torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
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self.linear = nn.Linear(self.input_dim, self.output_dim)
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trunc_normal_(self.relative_position_params, std=.02)
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self.relative_position_params = torch.nn.Parameter(
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self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
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2).transpose(
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0, 1))
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def generate_mask(self, h, w, p, shift):
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""" generating the mask of SW-MSA
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Args:
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shift: shift parameters in CyclicShift.
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Returns:
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attn_mask: should be (1 1 w p p),
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"""
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# supporting square.
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attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
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if self.type == 'W':
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return attn_mask
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s = p - shift
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attn_mask[-1, :, :s, :, s:, :] = True
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attn_mask[-1, :, s:, :, :s, :] = True
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attn_mask[:, -1, :, :s, :, s:] = True
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attn_mask[:, -1, :, s:, :, :s] = True
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attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
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return attn_mask
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def forward(self, x):
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""" Forward pass of Window Multi-head Self-attention module.
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Args:
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x: input tensor with shape of [b h w c];
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attn_mask: attention mask, fill -inf where the value is True;
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Returns:
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output: tensor shape [b h w c]
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"""
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if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
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x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
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h_windows = x.size(1)
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w_windows = x.size(2)
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# square validation
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# assert h_windows == w_windows
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x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
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qkv = self.embedding_layer(x)
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q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
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sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
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# Adding learnable relative embedding
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sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
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# Using Attn Mask to distinguish different subwindows.
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if self.type != 'W':
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attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
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sim = sim.masked_fill_(attn_mask, float("-inf"))
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probs = nn.functional.softmax(sim, dim=-1)
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output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
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output = rearrange(output, 'h b w p c -> b w p (h c)')
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output = self.linear(output)
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output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
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if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
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dims=(1, 2))
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return output
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def relative_embedding(self):
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cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
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relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
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# negative is allowed
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return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
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class Block(nn.Module):
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def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
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""" SwinTransformer Block
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"""
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super(Block, self).__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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assert type in ['W', 'SW']
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self.type = type
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if input_resolution <= window_size:
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self.type = 'W'
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self.ln1 = nn.LayerNorm(input_dim)
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self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.ln2 = nn.LayerNorm(input_dim)
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self.mlp = nn.Sequential(
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nn.Linear(input_dim, 4 * input_dim),
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nn.GELU(),
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nn.Linear(4 * input_dim, output_dim),
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)
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def forward(self, x):
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x = x + self.drop_path(self.msa(self.ln1(x)))
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x = x + self.drop_path(self.mlp(self.ln2(x)))
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return x
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class ConvTransBlock(nn.Module):
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def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
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""" SwinTransformer and Conv Block
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"""
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super(ConvTransBlock, self).__init__()
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self.conv_dim = conv_dim
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self.trans_dim = trans_dim
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self.head_dim = head_dim
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self.window_size = window_size
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self.drop_path = drop_path
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self.type = type
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self.input_resolution = input_resolution
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assert self.type in ['W', 'SW']
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if self.input_resolution <= self.window_size:
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self.type = 'W'
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self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
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self.type, self.input_resolution)
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self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
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self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
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self.conv_block = nn.Sequential(
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nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
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nn.ReLU(True),
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nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
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)
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def forward(self, x):
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conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
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conv_x = self.conv_block(conv_x) + conv_x
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trans_x = Rearrange('b c h w -> b h w c')(trans_x)
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trans_x = self.trans_block(trans_x)
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trans_x = Rearrange('b h w c -> b c h w')(trans_x)
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res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
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x = x + res
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return x
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class SCUNet(nn.Module):
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# def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
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def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
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super(SCUNet, self).__init__()
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if config is None:
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config = [2, 2, 2, 2, 2, 2, 2]
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self.config = config
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self.dim = dim
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self.head_dim = 32
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self.window_size = 8
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# drop path rate for each layer
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
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self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
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begin = 0
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self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution)
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for i in range(config[0])] + \
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[nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
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begin += config[0]
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self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution // 2)
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for i in range(config[1])] + \
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[nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
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begin += config[1]
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self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution // 4)
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for i in range(config[2])] + \
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[nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
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begin += config[2]
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self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution // 8)
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for i in range(config[3])]
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begin += config[3]
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self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
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[ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution // 4)
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for i in range(config[4])]
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begin += config[4]
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self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
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[ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution // 2)
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for i in range(config[5])]
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begin += config[5]
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self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
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[ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution)
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for i in range(config[6])]
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self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
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self.m_head = nn.Sequential(*self.m_head)
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self.m_down1 = nn.Sequential(*self.m_down1)
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self.m_down2 = nn.Sequential(*self.m_down2)
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self.m_down3 = nn.Sequential(*self.m_down3)
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self.m_body = nn.Sequential(*self.m_body)
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self.m_up3 = nn.Sequential(*self.m_up3)
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self.m_up2 = nn.Sequential(*self.m_up2)
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self.m_up1 = nn.Sequential(*self.m_up1)
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self.m_tail = nn.Sequential(*self.m_tail)
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# self.apply(self._init_weights)
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def forward(self, x0):
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h, w = x0.size()[-2:]
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paddingBottom = int(np.ceil(h / 64) * 64 - h)
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paddingRight = int(np.ceil(w / 64) * 64 - w)
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x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
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x1 = self.m_head(x0)
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x2 = self.m_down1(x1)
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x3 = self.m_down2(x2)
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x4 = self.m_down3(x3)
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x = self.m_body(x4)
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x = self.m_up3(x + x4)
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x = self.m_up2(x + x3)
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x = self.m_up1(x + x2)
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x = self.m_tail(x + x1)
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x = x[..., :h, :w]
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return x
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0) |