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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) |