Commit bff8d0ce authored by d8ahazard's avatar d8ahazard

Fix swinir arch name

parent 78d6aef3
# ----------------------------------------------------------------------------------- # -----------------------------------------------------------------------------------
# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 # SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
# Originally Written by Ze Liu, Modified by Jingyun Liang. # Originally Written by Ze Liu, Modified by Jingyun Liang.
# ----------------------------------------------------------------------------------- # -----------------------------------------------------------------------------------
import math import math
import torch import torch
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from timm.models.layers import DropPath, to_2tuple, trunc_normal_
class Mlp(nn.Module): class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__() super().__init__()
out_features = out_features or in_features out_features = out_features or in_features
hidden_features = hidden_features or in_features hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features) self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer() self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features) self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop) self.drop = nn.Dropout(drop)
def forward(self, x): def forward(self, x):
x = self.fc1(x) x = self.fc1(x)
x = self.act(x) x = self.act(x)
x = self.drop(x) x = self.drop(x)
x = self.fc2(x) x = self.fc2(x)
x = self.drop(x) x = self.drop(x)
return x return x
def window_partition(x, window_size): def window_partition(x, window_size):
""" """
Args: Args:
x: (B, H, W, C) x: (B, H, W, C)
window_size (int): window size window_size (int): window size
Returns: Returns:
windows: (num_windows*B, window_size, window_size, C) windows: (num_windows*B, window_size, window_size, C)
""" """
B, H, W, C = x.shape B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) 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().view(-1, window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows return windows
def window_reverse(windows, window_size, H, W): def window_reverse(windows, window_size, H, W):
""" """
Args: Args:
windows: (num_windows*B, window_size, window_size, C) windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size window_size (int): Window size
H (int): Height of image H (int): Height of image
W (int): Width of image W (int): Width of image
Returns: Returns:
x: (B, H, W, C) x: (B, H, W, C)
""" """
B = int(windows.shape[0] / (H * W / window_size / 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 = 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) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x return x
class WindowAttention(nn.Module): class WindowAttention(nn.Module):
r""" Window based multi-head self attention (W-MSA) module with relative position bias. r""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window. It supports both of shifted and non-shifted window.
Args: Args:
dim (int): Number of input channels. dim (int): Number of input channels.
window_size (tuple[int]): The height and width of the window. window_size (tuple[int]): The height and width of the window.
num_heads (int): Number of attention heads. num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True 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 qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional): Dropout ratio of output. Default: 0.0 proj_drop (float, optional): Dropout ratio of output. Default: 0.0
""" """
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__() super().__init__()
self.dim = dim self.dim = dim
self.window_size = window_size # Wh, Ww self.window_size = window_size # Wh, Ww
self.num_heads = num_heads self.num_heads = num_heads
head_dim = dim // num_heads head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5 self.scale = qk_scale or head_dim ** -0.5
# define a parameter table of relative position bias # define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter( 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 torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
# get pair-wise relative position index for each token inside the window # get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.window_size[0]) coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1]) coords_w = torch.arange(self.window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1 relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index", relative_position_index) self.register_buffer("relative_position_index", relative_position_index)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop) self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim) self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop) self.proj_drop = nn.Dropout(proj_drop)
trunc_normal_(self.relative_position_bias_table, std=.02) trunc_normal_(self.relative_position_bias_table, std=.02)
self.softmax = nn.Softmax(dim=-1) self.softmax = nn.Softmax(dim=-1)
def forward(self, x, mask=None): def forward(self, x, mask=None):
""" """
Args: Args:
x: input features with shape of (num_windows*B, N, C) x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
""" """
B_, N, C = x.shape 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) 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, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
q = q * self.scale q = q * self.scale
attn = (q @ k.transpose(-2, -1)) attn = (q @ k.transpose(-2, -1))
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( 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 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 relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0) attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None: if mask is not None:
nW = mask.shape[0] nW = mask.shape[0]
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(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 = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn) attn = self.softmax(attn)
else: else:
attn = self.softmax(attn) attn = self.softmax(attn)
attn = self.attn_drop(attn) attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B_, N, C) x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
x = self.proj(x) x = self.proj(x)
x = self.proj_drop(x) x = self.proj_drop(x)
return x return x
def extra_repr(self) -> str: def extra_repr(self) -> str:
return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
def flops(self, N): def flops(self, N):
# calculate flops for 1 window with token length of N # calculate flops for 1 window with token length of N
flops = 0 flops = 0
# qkv = self.qkv(x) # qkv = self.qkv(x)
flops += N * self.dim * 3 * self.dim flops += N * self.dim * 3 * self.dim
# attn = (q @ k.transpose(-2, -1)) # attn = (q @ k.transpose(-2, -1))
flops += self.num_heads * N * (self.dim // self.num_heads) * N flops += self.num_heads * N * (self.dim // self.num_heads) * N
# x = (attn @ v) # x = (attn @ v)
flops += self.num_heads * N * N * (self.dim // self.num_heads) flops += self.num_heads * N * N * (self.dim // self.num_heads)
# x = self.proj(x) # x = self.proj(x)
flops += N * self.dim * self.dim flops += N * self.dim * self.dim
return flops return flops
class SwinTransformerBlock(nn.Module): class SwinTransformerBlock(nn.Module):
r""" Swin Transformer Block. r""" Swin Transformer Block.
Args: Args:
dim (int): Number of input channels. dim (int): Number of input channels.
input_resolution (tuple[int]): Input resulotion. input_resolution (tuple[int]): Input resulotion.
num_heads (int): Number of attention heads. num_heads (int): Number of attention heads.
window_size (int): Window size. window_size (int): Window size.
shift_size (int): Shift size for SW-MSA. shift_size (int): Shift size for SW-MSA.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. 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 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. qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0 drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0 drop_path (float, optional): Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
""" """
def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
act_layer=nn.GELU, norm_layer=nn.LayerNorm): act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__() super().__init__()
self.dim = dim self.dim = dim
self.input_resolution = input_resolution self.input_resolution = input_resolution
self.num_heads = num_heads self.num_heads = num_heads
self.window_size = window_size self.window_size = window_size
self.shift_size = shift_size self.shift_size = shift_size
self.mlp_ratio = mlp_ratio self.mlp_ratio = mlp_ratio
if min(self.input_resolution) <= self.window_size: if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows # if window size is larger than input resolution, we don't partition windows
self.shift_size = 0 self.shift_size = 0
self.window_size = min(self.input_resolution) self.window_size = min(self.input_resolution)
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
self.norm1 = norm_layer(dim) self.norm1 = norm_layer(dim)
self.attn = WindowAttention( self.attn = WindowAttention(
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim) self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio) mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
if self.shift_size > 0: if self.shift_size > 0:
attn_mask = self.calculate_mask(self.input_resolution) attn_mask = self.calculate_mask(self.input_resolution)
else: else:
attn_mask = None attn_mask = None
self.register_buffer("attn_mask", attn_mask) self.register_buffer("attn_mask", attn_mask)
def calculate_mask(self, x_size): def calculate_mask(self, x_size):
# calculate attention mask for SW-MSA # calculate attention mask for SW-MSA
H, W = x_size H, W = x_size
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
h_slices = (slice(0, -self.window_size), h_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size), slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None)) slice(-self.shift_size, None))
w_slices = (slice(0, -self.window_size), w_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size), slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None)) slice(-self.shift_size, None))
cnt = 0 cnt = 0
for h in h_slices: for h in h_slices:
for w in w_slices: for w in w_slices:
img_mask[:, h, w, :] = cnt img_mask[:, h, w, :] = cnt
cnt += 1 cnt += 1
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
mask_windows = mask_windows.view(-1, self.window_size * self.window_size) mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) 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)) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
return attn_mask return attn_mask
def forward(self, x, x_size): def forward(self, x, x_size):
H, W = x_size H, W = x_size
B, L, C = x.shape B, L, C = x.shape
# assert L == H * W, "input feature has wrong size" # assert L == H * W, "input feature has wrong size"
shortcut = x shortcut = x
x = self.norm1(x) x = self.norm1(x)
x = x.view(B, H, W, C) x = x.view(B, H, W, C)
# cyclic shift # cyclic shift
if self.shift_size > 0: if self.shift_size > 0:
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
else: else:
shifted_x = x shifted_x = x
# partition windows # partition windows
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
if self.input_resolution == x_size: if self.input_resolution == x_size:
attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
else: else:
attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
# merge windows # merge windows
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
# reverse cyclic shift # reverse cyclic shift
if self.shift_size > 0: if self.shift_size > 0:
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
else: else:
x = shifted_x x = shifted_x
x = x.view(B, H * W, C) x = x.view(B, H * W, C)
# FFN # FFN
x = shortcut + self.drop_path(x) x = shortcut + self.drop_path(x)
x = x + self.drop_path(self.mlp(self.norm2(x))) x = x + self.drop_path(self.mlp(self.norm2(x)))
return x return x
def extra_repr(self) -> str: def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
def flops(self): def flops(self):
flops = 0 flops = 0
H, W = self.input_resolution H, W = self.input_resolution
# norm1 # norm1
flops += self.dim * H * W flops += self.dim * H * W
# W-MSA/SW-MSA # W-MSA/SW-MSA
nW = H * W / self.window_size / self.window_size nW = H * W / self.window_size / self.window_size
flops += nW * self.attn.flops(self.window_size * self.window_size) flops += nW * self.attn.flops(self.window_size * self.window_size)
# mlp # mlp
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
# norm2 # norm2
flops += self.dim * H * W flops += self.dim * H * W
return flops return flops
class PatchMerging(nn.Module): class PatchMerging(nn.Module):
r""" Patch Merging Layer. r""" Patch Merging Layer.
Args: Args:
input_resolution (tuple[int]): Resolution of input feature. input_resolution (tuple[int]): Resolution of input feature.
dim (int): Number of input channels. dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
""" """
def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
super().__init__() super().__init__()
self.input_resolution = input_resolution self.input_resolution = input_resolution
self.dim = dim self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
self.norm = norm_layer(4 * dim) self.norm = norm_layer(4 * dim)
def forward(self, x): def forward(self, x):
""" """
x: B, H*W, C x: B, H*W, C
""" """
H, W = self.input_resolution H, W = self.input_resolution
B, L, C = x.shape B, L, C = x.shape
assert L == H * W, "input feature has wrong size" assert L == H * W, "input feature has wrong size"
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
x = x.view(B, H, W, C) x = x.view(B, H, W, C)
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
x = self.norm(x) x = self.norm(x)
x = self.reduction(x) x = self.reduction(x)
return x return x
def extra_repr(self) -> str: def extra_repr(self) -> str:
return f"input_resolution={self.input_resolution}, dim={self.dim}" return f"input_resolution={self.input_resolution}, dim={self.dim}"
def flops(self): def flops(self):
H, W = self.input_resolution H, W = self.input_resolution
flops = H * W * self.dim flops = H * W * self.dim
flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
return flops return flops
class BasicLayer(nn.Module): class BasicLayer(nn.Module):
""" A basic Swin Transformer layer for one stage. """ A basic Swin Transformer layer for one stage.
Args: Args:
dim (int): Number of input channels. dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution. input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks. depth (int): Number of blocks.
num_heads (int): Number of attention heads. num_heads (int): Number of attention heads.
window_size (int): Local window size. window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. 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 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. qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0 drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention 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 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm 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 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. use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
""" """
def __init__(self, dim, input_resolution, depth, num_heads, window_size, 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., 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): drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
super().__init__() super().__init__()
self.dim = dim self.dim = dim
self.input_resolution = input_resolution self.input_resolution = input_resolution
self.depth = depth self.depth = depth
self.use_checkpoint = use_checkpoint self.use_checkpoint = use_checkpoint
# build blocks # build blocks
self.blocks = nn.ModuleList([ self.blocks = nn.ModuleList([
SwinTransformerBlock(dim=dim, input_resolution=input_resolution, SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
num_heads=num_heads, window_size=window_size, num_heads=num_heads, window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2, shift_size=0 if (i % 2 == 0) else window_size // 2,
mlp_ratio=mlp_ratio, mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop, attn_drop=attn_drop, drop=drop, attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer) norm_layer=norm_layer)
for i in range(depth)]) for i in range(depth)])
# patch merging layer # patch merging layer
if downsample is not None: if downsample is not None:
self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
else: else:
self.downsample = None self.downsample = None
def forward(self, x, x_size): def forward(self, x, x_size):
for blk in self.blocks: for blk in self.blocks:
if self.use_checkpoint: if self.use_checkpoint:
x = checkpoint.checkpoint(blk, x, x_size) x = checkpoint.checkpoint(blk, x, x_size)
else: else:
x = blk(x, x_size) x = blk(x, x_size)
if self.downsample is not None: if self.downsample is not None:
x = self.downsample(x) x = self.downsample(x)
return x return x
def extra_repr(self) -> str: def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
def flops(self): def flops(self):
flops = 0 flops = 0
for blk in self.blocks: for blk in self.blocks:
flops += blk.flops() flops += blk.flops()
if self.downsample is not None: if self.downsample is not None:
flops += self.downsample.flops() flops += self.downsample.flops()
return flops return flops
class RSTB(nn.Module): class RSTB(nn.Module):
"""Residual Swin Transformer Block (RSTB). """Residual Swin Transformer Block (RSTB).
Args: Args:
dim (int): Number of input channels. dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution. input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks. depth (int): Number of blocks.
num_heads (int): Number of attention heads. num_heads (int): Number of attention heads.
window_size (int): Local window size. window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. 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 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. qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0 drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention 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 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm 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 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. use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
img_size: Input image size. img_size: Input image size.
patch_size: Patch size. patch_size: Patch size.
resi_connection: The convolutional block before residual connection. resi_connection: The convolutional block before residual connection.
""" """
def __init__(self, dim, input_resolution, depth, num_heads, window_size, 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., 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, drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
img_size=224, patch_size=4, resi_connection='1conv'): img_size=224, patch_size=4, resi_connection='1conv'):
super(RSTB, self).__init__() super(RSTB, self).__init__()
self.dim = dim self.dim = dim
self.input_resolution = input_resolution self.input_resolution = input_resolution
self.residual_group = BasicLayer(dim=dim, self.residual_group = BasicLayer(dim=dim,
input_resolution=input_resolution, input_resolution=input_resolution,
depth=depth, depth=depth,
num_heads=num_heads, num_heads=num_heads,
window_size=window_size, window_size=window_size,
mlp_ratio=mlp_ratio, mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop, attn_drop=attn_drop, drop=drop, attn_drop=attn_drop,
drop_path=drop_path, drop_path=drop_path,
norm_layer=norm_layer, norm_layer=norm_layer,
downsample=downsample, downsample=downsample,
use_checkpoint=use_checkpoint) use_checkpoint=use_checkpoint)
if resi_connection == '1conv': if resi_connection == '1conv':
self.conv = nn.Conv2d(dim, dim, 3, 1, 1) self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
elif resi_connection == '3conv': elif resi_connection == '3conv':
# to save parameters and memory # 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), 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.Conv2d(dim // 4, dim // 4, 1, 1, 0),
nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(dim // 4, dim, 3, 1, 1)) nn.Conv2d(dim // 4, dim, 3, 1, 1))
self.patch_embed = PatchEmbed( self.patch_embed = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
norm_layer=None) norm_layer=None)
self.patch_unembed = PatchUnEmbed( self.patch_unembed = PatchUnEmbed(
img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
norm_layer=None) norm_layer=None)
def forward(self, x, x_size): def forward(self, x, x_size):
return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
def flops(self): def flops(self):
flops = 0 flops = 0
flops += self.residual_group.flops() flops += self.residual_group.flops()
H, W = self.input_resolution H, W = self.input_resolution
flops += H * W * self.dim * self.dim * 9 flops += H * W * self.dim * self.dim * 9
flops += self.patch_embed.flops() flops += self.patch_embed.flops()
flops += self.patch_unembed.flops() flops += self.patch_unembed.flops()
return flops return flops
class PatchEmbed(nn.Module): class PatchEmbed(nn.Module):
r""" Image to Patch Embedding r""" Image to Patch Embedding
Args: Args:
img_size (int): Image size. Default: 224. img_size (int): Image size. Default: 224.
patch_size (int): Patch token size. Default: 4. patch_size (int): Patch token size. Default: 4.
in_chans (int): Number of input image channels. Default: 3. in_chans (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96. embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Module, optional): Normalization layer. Default: None 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): def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
super().__init__() super().__init__()
img_size = to_2tuple(img_size) img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size) patch_size = to_2tuple(patch_size)
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
self.img_size = img_size self.img_size = img_size
self.patch_size = patch_size self.patch_size = patch_size
self.patches_resolution = patches_resolution self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1] self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans self.in_chans = in_chans
self.embed_dim = embed_dim self.embed_dim = embed_dim
if norm_layer is not None: if norm_layer is not None:
self.norm = norm_layer(embed_dim) self.norm = norm_layer(embed_dim)
else: else:
self.norm = None self.norm = None
def forward(self, x): def forward(self, x):
x = x.flatten(2).transpose(1, 2) # B Ph*Pw C x = x.flatten(2).transpose(1, 2) # B Ph*Pw C
if self.norm is not None: if self.norm is not None:
x = self.norm(x) x = self.norm(x)
return x return x
def flops(self): def flops(self):
flops = 0 flops = 0
H, W = self.img_size H, W = self.img_size
if self.norm is not None: if self.norm is not None:
flops += H * W * self.embed_dim flops += H * W * self.embed_dim
return flops return flops
class PatchUnEmbed(nn.Module): class PatchUnEmbed(nn.Module):
r""" Image to Patch Unembedding r""" Image to Patch Unembedding
Args: Args:
img_size (int): Image size. Default: 224. img_size (int): Image size. Default: 224.
patch_size (int): Patch token size. Default: 4. patch_size (int): Patch token size. Default: 4.
in_chans (int): Number of input image channels. Default: 3. in_chans (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96. embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Module, optional): Normalization layer. Default: None 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): def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
super().__init__() super().__init__()
img_size = to_2tuple(img_size) img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size) patch_size = to_2tuple(patch_size)
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
self.img_size = img_size self.img_size = img_size
self.patch_size = patch_size self.patch_size = patch_size
self.patches_resolution = patches_resolution self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1] self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans self.in_chans = in_chans
self.embed_dim = embed_dim self.embed_dim = embed_dim
def forward(self, x, x_size): def forward(self, x, x_size):
B, HW, C = x.shape 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 x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
return x return x
def flops(self): def flops(self):
flops = 0 flops = 0
return flops return flops
class Upsample(nn.Sequential): class Upsample(nn.Sequential):
"""Upsample module. """Upsample module.
Args: Args:
scale (int): Scale factor. Supported scales: 2^n and 3. scale (int): Scale factor. Supported scales: 2^n and 3.
num_feat (int): Channel number of intermediate features. num_feat (int): Channel number of intermediate features.
""" """
def __init__(self, scale, num_feat): def __init__(self, scale, num_feat):
m = [] m = []
if (scale & (scale - 1)) == 0: # scale = 2^n if (scale & (scale - 1)) == 0: # scale = 2^n
for _ in range(int(math.log(scale, 2))): for _ in range(int(math.log(scale, 2))):
m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
m.append(nn.PixelShuffle(2)) m.append(nn.PixelShuffle(2))
elif scale == 3: elif scale == 3:
m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
m.append(nn.PixelShuffle(3)) m.append(nn.PixelShuffle(3))
else: else:
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
super(Upsample, self).__init__(*m) super(Upsample, self).__init__(*m)
class UpsampleOneStep(nn.Sequential): class UpsampleOneStep(nn.Sequential):
"""UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
Used in lightweight SR to save parameters. Used in lightweight SR to save parameters.
Args: Args:
scale (int): Scale factor. Supported scales: 2^n and 3. scale (int): Scale factor. Supported scales: 2^n and 3.
num_feat (int): Channel number of intermediate features. num_feat (int): Channel number of intermediate features.
""" """
def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
self.num_feat = num_feat self.num_feat = num_feat
self.input_resolution = input_resolution self.input_resolution = input_resolution
m = [] m = []
m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1)) m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
m.append(nn.PixelShuffle(scale)) m.append(nn.PixelShuffle(scale))
super(UpsampleOneStep, self).__init__(*m) super(UpsampleOneStep, self).__init__(*m)
def flops(self): def flops(self):
H, W = self.input_resolution H, W = self.input_resolution
flops = H * W * self.num_feat * 3 * 9 flops = H * W * self.num_feat * 3 * 9
return flops return flops
class SwinIR(nn.Module): class SwinIR(nn.Module):
r""" SwinIR r""" SwinIR
A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
Args: Args:
img_size (int | tuple(int)): Input image size. Default 64 img_size (int | tuple(int)): Input image size. Default 64
patch_size (int | tuple(int)): Patch size. Default: 1 patch_size (int | tuple(int)): Patch size. Default: 1
in_chans (int): Number of input image channels. Default: 3 in_chans (int): Number of input image channels. Default: 3
embed_dim (int): Patch embedding dimension. Default: 96 embed_dim (int): Patch embedding dimension. Default: 96
depths (tuple(int)): Depth of each Swin Transformer layer. depths (tuple(int)): Depth of each Swin Transformer layer.
num_heads (tuple(int)): Number of attention heads in different layers. num_heads (tuple(int)): Number of attention heads in different layers.
window_size (int): Window size. Default: 7 window_size (int): Window size. Default: 7
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 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 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 qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
drop_rate (float): Dropout rate. Default: 0 drop_rate (float): Dropout rate. Default: 0
attn_drop_rate (float): Attention dropout rate. Default: 0 attn_drop_rate (float): Attention dropout rate. Default: 0
drop_path_rate (float): Stochastic depth rate. Default: 0.1 drop_path_rate (float): Stochastic depth rate. Default: 0.1
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False 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 patch_norm (bool): If True, add normalization after patch embedding. Default: True
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False 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 upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
img_range: Image range. 1. or 255. img_range: Image range. 1. or 255.
upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
resi_connection: The convolutional block before residual connection. '1conv'/'3conv' resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
""" """
def __init__(self, img_size=64, patch_size=1, in_chans=3, 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], 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, window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
norm_layer=nn.LayerNorm, ape=False, patch_norm=True, norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
**kwargs): **kwargs):
super(SwinIR, self).__init__() super(SwinIR, self).__init__()
num_in_ch = in_chans num_in_ch = in_chans
num_out_ch = in_chans num_out_ch = in_chans
num_feat = 64 num_feat = 64
self.img_range = img_range self.img_range = img_range
if in_chans == 3: if in_chans == 3:
rgb_mean = (0.4488, 0.4371, 0.4040) rgb_mean = (0.4488, 0.4371, 0.4040)
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
else: else:
self.mean = torch.zeros(1, 1, 1, 1) self.mean = torch.zeros(1, 1, 1, 1)
self.upscale = upscale self.upscale = upscale
self.upsampler = upsampler self.upsampler = upsampler
self.window_size = window_size self.window_size = window_size
##################################################################################################### #####################################################################################################
################################### 1, shallow feature extraction ################################### ################################### 1, shallow feature extraction ###################################
self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
##################################################################################################### #####################################################################################################
################################### 2, deep feature extraction ###################################### ################################### 2, deep feature extraction ######################################
self.num_layers = len(depths) self.num_layers = len(depths)
self.embed_dim = embed_dim self.embed_dim = embed_dim
self.ape = ape self.ape = ape
self.patch_norm = patch_norm self.patch_norm = patch_norm
self.num_features = embed_dim self.num_features = embed_dim
self.mlp_ratio = mlp_ratio self.mlp_ratio = mlp_ratio
# split image into non-overlapping patches # split image into non-overlapping patches
self.patch_embed = PatchEmbed( self.patch_embed = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, 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) norm_layer=norm_layer if self.patch_norm else None)
num_patches = self.patch_embed.num_patches num_patches = self.patch_embed.num_patches
patches_resolution = self.patch_embed.patches_resolution patches_resolution = self.patch_embed.patches_resolution
self.patches_resolution = patches_resolution self.patches_resolution = patches_resolution
# merge non-overlapping patches into image # merge non-overlapping patches into image
self.patch_unembed = PatchUnEmbed( self.patch_unembed = PatchUnEmbed(
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, 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) norm_layer=norm_layer if self.patch_norm else None)
# absolute position embedding # absolute position embedding
if self.ape: if self.ape:
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
trunc_normal_(self.absolute_pos_embed, std=.02) trunc_normal_(self.absolute_pos_embed, std=.02)
self.pos_drop = nn.Dropout(p=drop_rate) self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth # stochastic depth
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
# build Residual Swin Transformer blocks (RSTB) # build Residual Swin Transformer blocks (RSTB)
self.layers = nn.ModuleList() self.layers = nn.ModuleList()
for i_layer in range(self.num_layers): for i_layer in range(self.num_layers):
layer = RSTB(dim=embed_dim, layer = RSTB(dim=embed_dim,
input_resolution=(patches_resolution[0], input_resolution=(patches_resolution[0],
patches_resolution[1]), patches_resolution[1]),
depth=depths[i_layer], depth=depths[i_layer],
num_heads=num_heads[i_layer], num_heads=num_heads[i_layer],
window_size=window_size, window_size=window_size,
mlp_ratio=self.mlp_ratio, mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, 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 drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
norm_layer=norm_layer, norm_layer=norm_layer,
downsample=None, downsample=None,
use_checkpoint=use_checkpoint, use_checkpoint=use_checkpoint,
img_size=img_size, img_size=img_size,
patch_size=patch_size, patch_size=patch_size,
resi_connection=resi_connection resi_connection=resi_connection
) )
self.layers.append(layer) self.layers.append(layer)
self.norm = norm_layer(self.num_features) self.norm = norm_layer(self.num_features)
# build the last conv layer in deep feature extraction # build the last conv layer in deep feature extraction
if resi_connection == '1conv': if resi_connection == '1conv':
self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
elif resi_connection == '3conv': elif resi_connection == '3conv':
# to save parameters and memory # to save parameters and memory
self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), 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.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
##################################################################################################### #####################################################################################################
################################ 3, high quality image reconstruction ################################ ################################ 3, high quality image reconstruction ################################
if self.upsampler == 'pixelshuffle': if self.upsampler == 'pixelshuffle':
# for classical SR # for classical SR
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
nn.LeakyReLU(inplace=True)) nn.LeakyReLU(inplace=True))
self.upsample = Upsample(upscale, num_feat) self.upsample = Upsample(upscale, num_feat)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
elif self.upsampler == 'pixelshuffledirect': elif self.upsampler == 'pixelshuffledirect':
# for lightweight SR (to save parameters) # for lightweight SR (to save parameters)
self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
(patches_resolution[0], patches_resolution[1])) (patches_resolution[0], patches_resolution[1]))
elif self.upsampler == 'nearest+conv': elif self.upsampler == 'nearest+conv':
# for real-world SR (less artifacts) # for real-world SR (less artifacts)
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
nn.LeakyReLU(inplace=True)) nn.LeakyReLU(inplace=True))
self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
if self.upscale == 4: if self.upscale == 4:
self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) 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_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 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) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
else: else:
# for image denoising and JPEG compression artifact reduction # for image denoising and JPEG compression artifact reduction
self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
self.apply(self._init_weights) self.apply(self._init_weights)
def _init_weights(self, m): def _init_weights(self, m):
if isinstance(m, nn.Linear): if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02) trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None: if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0) nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm): elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0) nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0) nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore @torch.jit.ignore
def no_weight_decay(self): def no_weight_decay(self):
return {'absolute_pos_embed'} return {'absolute_pos_embed'}
@torch.jit.ignore @torch.jit.ignore
def no_weight_decay_keywords(self): def no_weight_decay_keywords(self):
return {'relative_position_bias_table'} return {'relative_position_bias_table'}
def check_image_size(self, x): def check_image_size(self, x):
_, _, h, w = x.size() _, _, h, w = x.size()
mod_pad_h = (self.window_size - h % self.window_size) % self.window_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 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') x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
return x return x
def forward_features(self, x): def forward_features(self, x):
x_size = (x.shape[2], x.shape[3]) x_size = (x.shape[2], x.shape[3])
x = self.patch_embed(x) x = self.patch_embed(x)
if self.ape: if self.ape:
x = x + self.absolute_pos_embed x = x + self.absolute_pos_embed
x = self.pos_drop(x) x = self.pos_drop(x)
for layer in self.layers: for layer in self.layers:
x = layer(x, x_size) x = layer(x, x_size)
x = self.norm(x) # B L C x = self.norm(x) # B L C
x = self.patch_unembed(x, x_size) x = self.patch_unembed(x, x_size)
return x return x
def forward(self, x): def forward(self, x):
H, W = x.shape[2:] H, W = x.shape[2:]
x = self.check_image_size(x) x = self.check_image_size(x)
self.mean = self.mean.type_as(x) self.mean = self.mean.type_as(x)
x = (x - self.mean) * self.img_range x = (x - self.mean) * self.img_range
if self.upsampler == 'pixelshuffle': if self.upsampler == 'pixelshuffle':
# for classical SR # for classical SR
x = self.conv_first(x) x = self.conv_first(x)
x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_after_body(self.forward_features(x)) + x
x = self.conv_before_upsample(x) x = self.conv_before_upsample(x)
x = self.conv_last(self.upsample(x)) x = self.conv_last(self.upsample(x))
elif self.upsampler == 'pixelshuffledirect': elif self.upsampler == 'pixelshuffledirect':
# for lightweight SR # for lightweight SR
x = self.conv_first(x) x = self.conv_first(x)
x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_after_body(self.forward_features(x)) + x
x = self.upsample(x) x = self.upsample(x)
elif self.upsampler == 'nearest+conv': elif self.upsampler == 'nearest+conv':
# for real-world SR # for real-world SR
x = self.conv_first(x) x = self.conv_first(x)
x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_after_body(self.forward_features(x)) + x
x = self.conv_before_upsample(x) x = self.conv_before_upsample(x)
x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
if self.upscale == 4: if self.upscale == 4:
x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) 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))) x = self.conv_last(self.lrelu(self.conv_hr(x)))
else: else:
# for image denoising and JPEG compression artifact reduction # for image denoising and JPEG compression artifact reduction
x_first = self.conv_first(x) x_first = self.conv_first(x)
res = self.conv_after_body(self.forward_features(x_first)) + x_first res = self.conv_after_body(self.forward_features(x_first)) + x_first
x = x + self.conv_last(res) x = x + self.conv_last(res)
x = x / self.img_range + self.mean x = x / self.img_range + self.mean
return x[:, :, :H*self.upscale, :W*self.upscale] return x[:, :, :H*self.upscale, :W*self.upscale]
def flops(self): def flops(self):
flops = 0 flops = 0
H, W = self.patches_resolution H, W = self.patches_resolution
flops += H * W * 3 * self.embed_dim * 9 flops += H * W * 3 * self.embed_dim * 9
flops += self.patch_embed.flops() flops += self.patch_embed.flops()
for i, layer in enumerate(self.layers): for i, layer in enumerate(self.layers):
flops += layer.flops() flops += layer.flops()
flops += H * W * 3 * self.embed_dim * self.embed_dim flops += H * W * 3 * self.embed_dim * self.embed_dim
flops += self.upsample.flops() flops += self.upsample.flops()
return flops return flops
if __name__ == '__main__': if __name__ == '__main__':
upscale = 4 upscale = 4
window_size = 8 window_size = 8
height = (1024 // upscale // window_size + 1) * window_size height = (1024 // upscale // window_size + 1) * window_size
width = (720 // upscale // window_size + 1) * window_size width = (720 // upscale // window_size + 1) * window_size
model = SwinIR(upscale=2, img_size=(height, width), model = SwinIR(upscale=2, img_size=(height, width),
window_size=window_size, img_range=1., depths=[6, 6, 6, 6], window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect') embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
print(model) print(model)
print(height, width, model.flops() / 1e9) print(height, width, model.flops() / 1e9)
x = torch.randn((1, 3, height, width)) x = torch.randn((1, 3, height, width))
x = model(x) x = model(x)
print(x.shape) print(x.shape)
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