868 lines
36 KiB
Python
868 lines
36 KiB
Python
# -----------------------------------------------------------------------------------
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# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
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# Originally Written by Ze Liu, Modified by Jingyun Liang.
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# -----------------------------------------------------------------------------------
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as checkpoint
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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class Mlp(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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def window_partition(x, window_size):
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"""
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Args:
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x: (B, H, W, C)
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window_size (int): window size
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Returns:
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windows: (num_windows*B, window_size, window_size, C)
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"""
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B, H, W, C = x.shape
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x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
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return windows
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def window_reverse(windows, window_size, H, W):
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"""
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Args:
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windows: (num_windows*B, window_size, window_size, C)
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window_size (int): Window size
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H (int): Height of image
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W (int): Width of image
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Returns:
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x: (B, H, W, C)
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"""
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B = int(windows.shape[0] / (H * W / window_size / window_size))
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x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
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return x
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class WindowAttention(nn.Module):
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r""" Window based multi-head self attention (W-MSA) module with relative position bias.
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It supports both of shifted and non-shifted window.
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Args:
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dim (int): Number of input channels.
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window_size (tuple[int]): The height and width of the window.
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num_heads (int): Number of attention heads.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
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attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
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proj_drop (float, optional): Dropout ratio of output. Default: 0.0
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"""
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def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
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super().__init__()
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self.dim = dim
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self.window_size = window_size # Wh, Ww
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim ** -0.5
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# define a parameter table of relative position bias
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(self.window_size[0])
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coords_w = torch.arange(self.window_size[1])
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coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += self.window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
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relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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self.register_buffer("relative_position_index", relative_position_index)
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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trunc_normal_(self.relative_position_bias_table, std=.02)
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self.softmax = nn.Softmax(dim=-1)
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def forward(self, x, mask=None):
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"""
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Args:
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x: input features with shape of (num_windows*B, N, C)
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mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
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"""
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B_, N, C = x.shape
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qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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attn = attn + relative_position_bias.unsqueeze(0)
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if mask is not None:
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nW = mask.shape[0]
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attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
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attn = attn.view(-1, self.num_heads, N, N)
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attn = self.softmax(attn)
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else:
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attn = self.softmax(attn)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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def extra_repr(self) -> str:
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return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
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def flops(self, N):
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# calculate flops for 1 window with token length of N
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flops = 0
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# qkv = self.qkv(x)
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flops += N * self.dim * 3 * self.dim
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# attn = (q @ k.transpose(-2, -1))
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flops += self.num_heads * N * (self.dim // self.num_heads) * N
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# x = (attn @ v)
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flops += self.num_heads * N * N * (self.dim // self.num_heads)
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# x = self.proj(x)
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flops += N * self.dim * self.dim
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return flops
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class SwinTransformerBlock(nn.Module):
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r""" Swin Transformer Block.
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Args:
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dim (int): Number of input channels.
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input_resolution (tuple[int]): Input resolution.
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num_heads (int): Number of attention heads.
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window_size (int): Window size.
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shift_size (int): Shift size for SW-MSA.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float, optional): Stochastic depth rate. Default: 0.0
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act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
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mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
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act_layer=nn.GELU, norm_layer=nn.LayerNorm):
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super().__init__()
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self.dim = dim
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self.input_resolution = input_resolution
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self.num_heads = num_heads
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self.window_size = window_size
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self.shift_size = shift_size
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self.mlp_ratio = mlp_ratio
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if min(self.input_resolution) <= self.window_size:
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# if window size is larger than input resolution, we don't partition windows
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self.shift_size = 0
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self.window_size = min(self.input_resolution)
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assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
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self.norm1 = norm_layer(dim)
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self.attn = WindowAttention(
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dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
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qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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if self.shift_size > 0:
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attn_mask = self.calculate_mask(self.input_resolution)
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else:
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attn_mask = None
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self.register_buffer("attn_mask", attn_mask)
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def calculate_mask(self, x_size):
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# calculate attention mask for SW-MSA
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H, W = x_size
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img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
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h_slices = (slice(0, -self.window_size),
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slice(-self.window_size, -self.shift_size),
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slice(-self.shift_size, None))
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w_slices = (slice(0, -self.window_size),
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slice(-self.window_size, -self.shift_size),
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slice(-self.shift_size, None))
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cnt = 0
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for h in h_slices:
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for w in w_slices:
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img_mask[:, h, w, :] = cnt
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cnt += 1
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mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
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mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
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attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
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attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
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return attn_mask
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def forward(self, x, x_size):
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H, W = x_size
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B, L, C = x.shape
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# assert L == H * W, "input feature has wrong size"
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shortcut = x
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x = self.norm1(x)
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x = x.view(B, H, W, C)
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# cyclic shift
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if self.shift_size > 0:
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shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
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else:
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shifted_x = x
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# partition windows
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x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
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x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
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# W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
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if self.input_resolution == x_size:
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attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
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else:
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attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
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# merge windows
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attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
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shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
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# reverse cyclic shift
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if self.shift_size > 0:
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x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
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else:
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x = shifted_x
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x = x.view(B, H * W, C)
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# FFN
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x = shortcut + self.drop_path(x)
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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def extra_repr(self) -> str:
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return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
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f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
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def flops(self):
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flops = 0
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H, W = self.input_resolution
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# norm1
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flops += self.dim * H * W
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# W-MSA/SW-MSA
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nW = H * W / self.window_size / self.window_size
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flops += nW * self.attn.flops(self.window_size * self.window_size)
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# mlp
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flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
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# norm2
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flops += self.dim * H * W
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return flops
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class PatchMerging(nn.Module):
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r""" Patch Merging Layer.
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Args:
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input_resolution (tuple[int]): Resolution of input feature.
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dim (int): Number of input channels.
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
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super().__init__()
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self.input_resolution = input_resolution
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self.dim = dim
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self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
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self.norm = norm_layer(4 * dim)
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def forward(self, x):
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"""
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x: B, H*W, C
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"""
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H, W = self.input_resolution
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B, L, C = x.shape
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assert L == H * W, "input feature has wrong size"
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assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
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x = x.view(B, H, W, C)
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x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
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x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
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x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
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x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
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x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
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x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
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x = self.norm(x)
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x = self.reduction(x)
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return x
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def extra_repr(self) -> str:
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return f"input_resolution={self.input_resolution}, dim={self.dim}"
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def flops(self):
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H, W = self.input_resolution
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flops = H * W * self.dim
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flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
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return flops
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class BasicLayer(nn.Module):
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""" A basic Swin Transformer layer for one stage.
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Args:
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dim (int): Number of input channels.
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input_resolution (tuple[int]): Input resolution.
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depth (int): Number of blocks.
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num_heads (int): Number of attention heads.
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window_size (int): Local window size.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
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use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
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"""
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def __init__(self, dim, input_resolution, depth, num_heads, window_size,
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mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
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super().__init__()
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self.dim = dim
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self.input_resolution = input_resolution
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self.depth = depth
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self.use_checkpoint = use_checkpoint
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# build blocks
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self.blocks = nn.ModuleList([
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SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
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num_heads=num_heads, window_size=window_size,
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shift_size=0 if (i % 2 == 0) else window_size // 2,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop, attn_drop=attn_drop,
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drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
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norm_layer=norm_layer)
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for i in range(depth)])
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# patch merging layer
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if downsample is not None:
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self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
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else:
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self.downsample = None
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def forward(self, x, x_size):
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for blk in self.blocks:
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if self.use_checkpoint:
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x = checkpoint.checkpoint(blk, x, x_size)
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else:
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x = blk(x, x_size)
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if self.downsample is not None:
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x = self.downsample(x)
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return x
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def extra_repr(self) -> str:
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return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
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def flops(self):
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flops = 0
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for blk in self.blocks:
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flops += blk.flops()
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if self.downsample is not None:
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flops += self.downsample.flops()
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return flops
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class RSTB(nn.Module):
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"""Residual Swin Transformer Block (RSTB).
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Args:
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dim (int): Number of input channels.
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input_resolution (tuple[int]): Input resolution.
|
|
depth (int): Number of blocks.
|
|
num_heads (int): Number of attention heads.
|
|
window_size (int): Local window size.
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
|
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.
|
|
drop (float, optional): Dropout rate. Default: 0.0
|
|
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
|
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
|
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
|
img_size: Input image size.
|
|
patch_size: Patch size.
|
|
resi_connection: The convolutional block before residual connection.
|
|
"""
|
|
|
|
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
|
|
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
|
|
drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
|
|
img_size=224, patch_size=4, resi_connection='1conv'):
|
|
super(RSTB, self).__init__()
|
|
|
|
self.dim = dim
|
|
self.input_resolution = input_resolution
|
|
|
|
self.residual_group = BasicLayer(dim=dim,
|
|
input_resolution=input_resolution,
|
|
depth=depth,
|
|
num_heads=num_heads,
|
|
window_size=window_size,
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias, qk_scale=qk_scale,
|
|
drop=drop, attn_drop=attn_drop,
|
|
drop_path=drop_path,
|
|
norm_layer=norm_layer,
|
|
downsample=downsample,
|
|
use_checkpoint=use_checkpoint)
|
|
|
|
if resi_connection == '1conv':
|
|
self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
|
|
elif resi_connection == '3conv':
|
|
# to save parameters and memory
|
|
self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
|
nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
|
|
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
|
nn.Conv2d(dim // 4, dim, 3, 1, 1))
|
|
|
|
self.patch_embed = PatchEmbed(
|
|
img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
|
|
norm_layer=None)
|
|
|
|
self.patch_unembed = PatchUnEmbed(
|
|
img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
|
|
norm_layer=None)
|
|
|
|
def forward(self, x, x_size):
|
|
return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
|
|
|
|
def flops(self):
|
|
flops = 0
|
|
flops += self.residual_group.flops()
|
|
H, W = self.input_resolution
|
|
flops += H * W * self.dim * self.dim * 9
|
|
flops += self.patch_embed.flops()
|
|
flops += self.patch_unembed.flops()
|
|
|
|
return flops
|
|
|
|
|
|
class PatchEmbed(nn.Module):
|
|
r""" Image to Patch Embedding
|
|
|
|
Args:
|
|
img_size (int): Image size. Default: 224.
|
|
patch_size (int): Patch token size. Default: 4.
|
|
in_chans (int): Number of input image channels. Default: 3.
|
|
embed_dim (int): Number of linear projection output channels. Default: 96.
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
|
"""
|
|
|
|
def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
|
|
super().__init__()
|
|
img_size = to_2tuple(img_size)
|
|
patch_size = to_2tuple(patch_size)
|
|
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
|
|
self.img_size = img_size
|
|
self.patch_size = patch_size
|
|
self.patches_resolution = patches_resolution
|
|
self.num_patches = patches_resolution[0] * patches_resolution[1]
|
|
|
|
self.in_chans = in_chans
|
|
self.embed_dim = embed_dim
|
|
|
|
if norm_layer is not None:
|
|
self.norm = norm_layer(embed_dim)
|
|
else:
|
|
self.norm = None
|
|
|
|
def forward(self, x):
|
|
x = x.flatten(2).transpose(1, 2) # B Ph*Pw C
|
|
if self.norm is not None:
|
|
x = self.norm(x)
|
|
return x
|
|
|
|
def flops(self):
|
|
flops = 0
|
|
H, W = self.img_size
|
|
if self.norm is not None:
|
|
flops += H * W * self.embed_dim
|
|
return flops
|
|
|
|
|
|
class PatchUnEmbed(nn.Module):
|
|
r""" Image to Patch Unembedding
|
|
|
|
Args:
|
|
img_size (int): Image size. Default: 224.
|
|
patch_size (int): Patch token size. Default: 4.
|
|
in_chans (int): Number of input image channels. Default: 3.
|
|
embed_dim (int): Number of linear projection output channels. Default: 96.
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
|
"""
|
|
|
|
def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
|
|
super().__init__()
|
|
img_size = to_2tuple(img_size)
|
|
patch_size = to_2tuple(patch_size)
|
|
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
|
|
self.img_size = img_size
|
|
self.patch_size = patch_size
|
|
self.patches_resolution = patches_resolution
|
|
self.num_patches = patches_resolution[0] * patches_resolution[1]
|
|
|
|
self.in_chans = in_chans
|
|
self.embed_dim = embed_dim
|
|
|
|
def forward(self, x, x_size):
|
|
B, HW, C = x.shape
|
|
x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
|
|
return x
|
|
|
|
def flops(self):
|
|
flops = 0
|
|
return flops
|
|
|
|
|
|
class Upsample(nn.Sequential):
|
|
"""Upsample module.
|
|
|
|
Args:
|
|
scale (int): Scale factor. Supported scales: 2^n and 3.
|
|
num_feat (int): Channel number of intermediate features.
|
|
"""
|
|
|
|
def __init__(self, scale, num_feat):
|
|
m = []
|
|
if (scale & (scale - 1)) == 0: # scale = 2^n
|
|
for _ in range(int(math.log(scale, 2))):
|
|
m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
|
|
m.append(nn.PixelShuffle(2))
|
|
elif scale == 3:
|
|
m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
|
|
m.append(nn.PixelShuffle(3))
|
|
else:
|
|
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
|
|
super(Upsample, self).__init__(*m)
|
|
|
|
|
|
class UpsampleOneStep(nn.Sequential):
|
|
"""UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
|
|
Used in lightweight SR to save parameters.
|
|
|
|
Args:
|
|
scale (int): Scale factor. Supported scales: 2^n and 3.
|
|
num_feat (int): Channel number of intermediate features.
|
|
|
|
"""
|
|
|
|
def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
|
|
self.num_feat = num_feat
|
|
self.input_resolution = input_resolution
|
|
m = []
|
|
m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
|
|
m.append(nn.PixelShuffle(scale))
|
|
super(UpsampleOneStep, self).__init__(*m)
|
|
|
|
def flops(self):
|
|
H, W = self.input_resolution
|
|
flops = H * W * self.num_feat * 3 * 9
|
|
return flops
|
|
|
|
|
|
class SwinIR(nn.Module):
|
|
r""" SwinIR
|
|
A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
|
|
|
|
Args:
|
|
img_size (int | tuple(int)): Input image size. Default 64
|
|
patch_size (int | tuple(int)): Patch size. Default: 1
|
|
in_chans (int): Number of input image channels. Default: 3
|
|
embed_dim (int): Patch embedding dimension. Default: 96
|
|
depths (tuple(int)): Depth of each Swin Transformer layer.
|
|
num_heads (tuple(int)): Number of attention heads in different layers.
|
|
window_size (int): Window size. Default: 7
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
|
|
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
|
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
|
|
drop_rate (float): Dropout rate. Default: 0
|
|
attn_drop_rate (float): Attention dropout rate. Default: 0
|
|
drop_path_rate (float): Stochastic depth rate. Default: 0.1
|
|
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
|
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
|
|
patch_norm (bool): If True, add normalization after patch embedding. Default: True
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
|
|
upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
|
|
img_range: Image range. 1. or 255.
|
|
upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
|
|
resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
|
|
"""
|
|
|
|
def __init__(self, img_size=64, patch_size=1, in_chans=3,
|
|
embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
|
|
window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
|
|
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
|
|
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
|
|
use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
|
|
**kwargs):
|
|
super(SwinIR, self).__init__()
|
|
num_in_ch = in_chans
|
|
num_out_ch = in_chans
|
|
num_feat = 64
|
|
self.img_range = img_range
|
|
if in_chans == 3:
|
|
rgb_mean = (0.4488, 0.4371, 0.4040)
|
|
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
|
|
else:
|
|
self.mean = torch.zeros(1, 1, 1, 1)
|
|
self.upscale = upscale
|
|
self.upsampler = upsampler
|
|
self.window_size = window_size
|
|
|
|
#####################################################################################################
|
|
################################### 1, shallow feature extraction ###################################
|
|
self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
|
|
|
|
#####################################################################################################
|
|
################################### 2, deep feature extraction ######################################
|
|
self.num_layers = len(depths)
|
|
self.embed_dim = embed_dim
|
|
self.ape = ape
|
|
self.patch_norm = patch_norm
|
|
self.num_features = embed_dim
|
|
self.mlp_ratio = mlp_ratio
|
|
|
|
# split image into non-overlapping patches
|
|
self.patch_embed = PatchEmbed(
|
|
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
|
|
norm_layer=norm_layer if self.patch_norm else None)
|
|
num_patches = self.patch_embed.num_patches
|
|
patches_resolution = self.patch_embed.patches_resolution
|
|
self.patches_resolution = patches_resolution
|
|
|
|
# merge non-overlapping patches into image
|
|
self.patch_unembed = PatchUnEmbed(
|
|
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
|
|
norm_layer=norm_layer if self.patch_norm else None)
|
|
|
|
# absolute position embedding
|
|
if self.ape:
|
|
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
|
|
trunc_normal_(self.absolute_pos_embed, std=.02)
|
|
|
|
self.pos_drop = nn.Dropout(p=drop_rate)
|
|
|
|
# stochastic depth
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
|
|
|
# build Residual Swin Transformer blocks (RSTB)
|
|
self.layers = nn.ModuleList()
|
|
for i_layer in range(self.num_layers):
|
|
layer = RSTB(dim=embed_dim,
|
|
input_resolution=(patches_resolution[0],
|
|
patches_resolution[1]),
|
|
depth=depths[i_layer],
|
|
num_heads=num_heads[i_layer],
|
|
window_size=window_size,
|
|
mlp_ratio=self.mlp_ratio,
|
|
qkv_bias=qkv_bias, qk_scale=qk_scale,
|
|
drop=drop_rate, attn_drop=attn_drop_rate,
|
|
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
|
|
norm_layer=norm_layer,
|
|
downsample=None,
|
|
use_checkpoint=use_checkpoint,
|
|
img_size=img_size,
|
|
patch_size=patch_size,
|
|
resi_connection=resi_connection
|
|
|
|
)
|
|
self.layers.append(layer)
|
|
self.norm = norm_layer(self.num_features)
|
|
|
|
# build the last conv layer in deep feature extraction
|
|
if resi_connection == '1conv':
|
|
self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
|
|
elif resi_connection == '3conv':
|
|
# to save parameters and memory
|
|
self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
|
|
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
|
nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
|
|
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
|
nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
|
|
|
|
#####################################################################################################
|
|
################################ 3, high quality image reconstruction ################################
|
|
if self.upsampler == 'pixelshuffle':
|
|
# for classical SR
|
|
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
|
|
nn.LeakyReLU(inplace=True))
|
|
self.upsample = Upsample(upscale, num_feat)
|
|
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
|
|
elif self.upsampler == 'pixelshuffledirect':
|
|
# for lightweight SR (to save parameters)
|
|
self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
|
|
(patches_resolution[0], patches_resolution[1]))
|
|
elif self.upsampler == 'nearest+conv':
|
|
# for real-world SR (less artifacts)
|
|
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
|
|
nn.LeakyReLU(inplace=True))
|
|
self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
|
|
if self.upscale == 4:
|
|
self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
|
|
self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
|
|
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
else:
|
|
# for image denoising and JPEG compression artifact reduction
|
|
self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
|
|
|
|
self.apply(self._init_weights)
|
|
|
|
def _init_weights(self, m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=.02)
|
|
if isinstance(m, nn.Linear) and 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)
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
return {'absolute_pos_embed'}
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay_keywords(self):
|
|
return {'relative_position_bias_table'}
|
|
|
|
def check_image_size(self, x):
|
|
_, _, h, w = x.size()
|
|
mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
|
|
mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
|
|
x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
|
|
return x
|
|
|
|
def forward_features(self, x):
|
|
x_size = (x.shape[2], x.shape[3])
|
|
x = self.patch_embed(x)
|
|
if self.ape:
|
|
x = x + self.absolute_pos_embed
|
|
x = self.pos_drop(x)
|
|
|
|
for layer in self.layers:
|
|
x = layer(x, x_size)
|
|
|
|
x = self.norm(x) # B L C
|
|
x = self.patch_unembed(x, x_size)
|
|
|
|
return x
|
|
|
|
def forward(self, x):
|
|
H, W = x.shape[2:]
|
|
x = self.check_image_size(x)
|
|
|
|
self.mean = self.mean.type_as(x)
|
|
x = (x - self.mean) * self.img_range
|
|
|
|
if self.upsampler == 'pixelshuffle':
|
|
# for classical SR
|
|
x = self.conv_first(x)
|
|
x = self.conv_after_body(self.forward_features(x)) + x
|
|
x = self.conv_before_upsample(x)
|
|
x = self.conv_last(self.upsample(x))
|
|
elif self.upsampler == 'pixelshuffledirect':
|
|
# for lightweight SR
|
|
x = self.conv_first(x)
|
|
x = self.conv_after_body(self.forward_features(x)) + x
|
|
x = self.upsample(x)
|
|
elif self.upsampler == 'nearest+conv':
|
|
# for real-world SR
|
|
x = self.conv_first(x)
|
|
x = self.conv_after_body(self.forward_features(x)) + x
|
|
x = self.conv_before_upsample(x)
|
|
x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
|
|
if self.upscale == 4:
|
|
x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
|
|
x = self.conv_last(self.lrelu(self.conv_hr(x)))
|
|
else:
|
|
# for image denoising and JPEG compression artifact reduction
|
|
x_first = self.conv_first(x)
|
|
res = self.conv_after_body(self.forward_features(x_first)) + x_first
|
|
x = x + self.conv_last(res)
|
|
|
|
x = x / self.img_range + self.mean
|
|
|
|
return x[:, :, :H*self.upscale, :W*self.upscale]
|
|
|
|
def flops(self):
|
|
flops = 0
|
|
H, W = self.patches_resolution
|
|
flops += H * W * 3 * self.embed_dim * 9
|
|
flops += self.patch_embed.flops()
|
|
for i, layer in enumerate(self.layers):
|
|
flops += layer.flops()
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flops += H * W * 3 * self.embed_dim * self.embed_dim
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flops += self.upsample.flops()
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return flops
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|
|
|
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if __name__ == '__main__':
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upscale = 4
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|
window_size = 8
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|
height = (1024 // upscale // window_size + 1) * window_size
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width = (720 // upscale // window_size + 1) * window_size
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model = SwinIR(upscale=2, img_size=(height, width),
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|
window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
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embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
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print(model)
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print(height, width, model.flops() / 1e9)
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|
|
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x = torch.randn((1, 3, height, width))
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x = model(x)
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|
print(x.shape)
|