Spaces:

OAOA
/

DifFace

Runtime error

DifFace / basicsr /archs /swinir_arch.py

Zongsheng

first upload

06f26d7 over 1 year ago

No virus

37.3 kB

	# Modified from https://github.com/JingyunLiang/SwinIR
	# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
	# Originally Written by Ze Liu, Modified by Jingyun Liang.

	import math
	import torch
	import torch.nn as nn
	import torch.utils.checkpoint as checkpoint

	from basicsr.utils.registry import ARCH_REGISTRY
	from .arch_util import to_2tuple, trunc_normal_


	def drop_path(x, drop_prob: float = 0., training: bool = False):
	"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

	From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
	"""
	if drop_prob == 0. or not training:
	return x
	keep_prob = 1 - drop_prob
	shape = (x.shape[0], ) + (1, ) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
	random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
	random_tensor.floor_() # binarize
	output = x.div(keep_prob) * random_tensor
	return output


	class DropPath(nn.Module):
	"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

	From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
	"""

	def __init__(self, drop_prob=None):
	super(DropPath, self).__init__()
	self.drop_prob = drop_prob

	def forward(self, x):
	return drop_path(x, self.drop_prob, self.training)


	class Mlp(nn.Module):

	def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
	super().__init__()
	out_features = out_features or in_features
	hidden_features = hidden_features or in_features
	self.fc1 = nn.Linear(in_features, hidden_features)
	self.act = act_layer()
	self.fc2 = nn.Linear(hidden_features, out_features)
	self.drop = nn.Dropout(drop)

	def forward(self, x):
	x = self.fc1(x)
	x = self.act(x)
	x = self.drop(x)
	x = self.fc2(x)
	x = self.drop(x)
	return x


	def window_partition(x, window_size):
	"""
	Args:
	x: (b, h, w, c)
	window_size (int): window size

	Returns:
	windows: (num_windows*b, window_size, window_size, c)
	"""
	b, h, w, c = x.shape
	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)
	return windows


	def window_reverse(windows, window_size, h, w):
	"""
	Args:
	windows: (num_windows*b, window_size, window_size, c)
	window_size (int): Window size
	h (int): Height of image
	w (int): Width of image

	Returns:
	x: (b, h, w, c)
	"""
	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 = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1)
	return x


	class WindowAttention(nn.Module):
	r""" Window based multi-head self attention (W-MSA) module with relative position bias.
	It supports both of shifted and non-shifted window.

	Args:
	dim (int): Number of input channels.
	window_size (tuple[int]): The height and width of the window.
	num_heads (int): Number of attention heads.
	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
	attn_drop (float, optional): Dropout ratio of attention weight. 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.):

	super().__init__()
	self.dim = dim
	self.window_size = window_size # Wh, Ww
	self.num_heads = num_heads
	head_dim = dim // num_heads
	self.scale = qk_scale or head_dim**-0.5

	# define a parameter table of relative position bias
	self.relative_position_bias_table = nn.Parameter(
	torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2Wh-1 2*Ww-1, nH

	# get pair-wise relative position index for each token inside the window
	coords_h = torch.arange(self.window_size[0])
	coords_w = torch.arange(self.window_size[1])
	coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
	coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
	relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, WhWw, WhWw
	relative_coords = relative_coords.permute(1, 2, 0).contiguous() # WhWw, WhWw, 2
	relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
	relative_coords[:, :, 1] += self.window_size[1] - 1
	relative_coords[:, :, 0] = 2 self.window_size[1] - 1
	relative_position_index = relative_coords.sum(-1) # WhWw, WhWw
	self.register_buffer('relative_position_index', relative_position_index)

	self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
	self.attn_drop = nn.Dropout(attn_drop)
	self.proj = nn.Linear(dim, dim)

	self.proj_drop = nn.Dropout(proj_drop)

	trunc_normal_(self.relative_position_bias_table, std=.02)
	self.softmax = nn.Softmax(dim=-1)

	def forward(self, x, mask=None):
	"""
	Args:
	x: input features with shape of (num_windows*b, n, c)
	mask: (0/-inf) mask with shape of (num_windows, WhWw, WhWw) or None
	"""
	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)
	q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)

	q = q * self.scale
	attn = (q @ k.transpose(-2, -1))

	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) # WhWw,WhWw,nH
	relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, WhWw, WhWw
	attn = attn + relative_position_bias.unsqueeze(0)

	if mask is not None:
	nw = mask.shape[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 = self.softmax(attn)
	else:
	attn = self.softmax(attn)

	attn = self.attn_drop(attn)

	x = (attn @ v).transpose(1, 2).reshape(b_, n, c)
	x = self.proj(x)
	x = self.proj_drop(x)
	return x

	def extra_repr(self) -> str:
	return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'

	def flops(self, n):
	# calculate flops for 1 window with token length of n
	flops = 0
	# qkv = self.qkv(x)
	flops += n * self.dim * 3 * self.dim
	# attn = (q @ k.transpose(-2, -1))
	flops += self.num_heads * n * (self.dim // self.num_heads) * n
	# x = (attn @ v)
	flops += self.num_heads * n * n * (self.dim // self.num_heads)
	# x = self.proj(x)
	flops += n * self.dim * self.dim
	return flops


	class SwinTransformerBlock(nn.Module):
	r""" Swin Transformer Block.

	Args:
	dim (int): Number of input channels.
	input_resolution (tuple[int]): Input resolution.
	num_heads (int): Number of attention heads.
	window_size (int): Window size.
	shift_size (int): Shift size for SW-MSA.
	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, optional): Stochastic depth rate. Default: 0.0
	act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
	norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
	"""

	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.,
	act_layer=nn.GELU,
	norm_layer=nn.LayerNorm):
	super().__init__()
	self.dim = dim
	self.input_resolution = input_resolution
	self.num_heads = num_heads
	self.window_size = window_size
	self.shift_size = shift_size
	self.mlp_ratio = mlp_ratio
	if min(self.input_resolution) <= self.window_size:
	# if window size is larger than input resolution, we don't partition windows
	self.shift_size = 0
	self.window_size = min(self.input_resolution)
	assert 0 <= self.shift_size < self.window_size, 'shift_size must in 0-window_size'

	self.norm1 = norm_layer(dim)
	self.attn = WindowAttention(
	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)

	self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
	self.norm2 = norm_layer(dim)
	mlp_hidden_dim = int(dim * mlp_ratio)
	self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

	if self.shift_size > 0:
	attn_mask = self.calculate_mask(self.input_resolution)
	else:
	attn_mask = None

	self.register_buffer('attn_mask', attn_mask)

	def calculate_mask(self, x_size):
	# calculate attention mask for SW-MSA
	h, w = x_size
	img_mask = torch.zeros((1, h, w, 1)) # 1 h w 1
	h_slices = (slice(0, -self.window_size), slice(-self.window_size,
	-self.shift_size), slice(-self.shift_size, None))
	w_slices = (slice(0, -self.window_size), slice(-self.window_size,
	-self.shift_size), slice(-self.shift_size, None))
	cnt = 0
	for h in h_slices:
	for w in w_slices:
	img_mask[:, h, w, :] = cnt
	cnt += 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)
	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))

	return attn_mask

	def forward(self, x, x_size):
	h, w = x_size
	b, _, c = x.shape
	# assert seq_len == h * w, "input feature has wrong size"

	shortcut = x
	x = self.norm1(x)
	x = x.view(b, h, w, c)

	# cyclic shift
	if self.shift_size > 0:
	shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
	else:
	shifted_x = x

	# partition windows
	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) # nwb, window_sizewindow_size, c

	# 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:
	attn_windows = self.attn(x_windows, mask=self.attn_mask) # nwb, window_sizewindow_size, c
	else:
	attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))

	# merge windows
	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

	# reverse cyclic shift
	if self.shift_size > 0:
	x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
	else:
	x = shifted_x
	x = x.view(b, h * w, c)

	# FFN
	x = shortcut + self.drop_path(x)
	x = x + self.drop_path(self.mlp(self.norm2(x)))

	return x

	def extra_repr(self) -> str:
	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}')

	def flops(self):
	flops = 0
	h, w = self.input_resolution
	# norm1
	flops += self.dim * h * w
	# W-MSA/SW-MSA
	nw = h * w / self.window_size / self.window_size
	flops += nw * self.attn.flops(self.window_size * self.window_size)
	# mlp
	flops += 2 * h * w * self.dim * self.dim * self.mlp_ratio
	# norm2
	flops += self.dim * h * w
	return flops


	class PatchMerging(nn.Module):
	r""" Patch Merging Layer.

	Args:
	input_resolution (tuple[int]): Resolution of input feature.
	dim (int): Number of input channels.
	norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
	"""

	def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
	super().__init__()
	self.input_resolution = input_resolution
	self.dim = dim
	self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
	self.norm = norm_layer(4 * dim)

	def forward(self, x):
	"""
	x: b, h*w, c
	"""
	h, w = self.input_resolution
	b, seq_len, c = x.shape
	assert seq_len == h * w, 'input feature has wrong size'
	assert h % 2 == 0 and w % 2 == 0, f'x size ({h}*{w}) are not even.'

	x = x.view(b, h, w, 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
	x2 = x[:, 0::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 = x.view(b, -1, 4 * c) # b h/2w/2 4c

	x = self.norm(x)
	x = self.reduction(x)

	return x

	def extra_repr(self) -> str:
	return f'input_resolution={self.input_resolution}, dim={self.dim}'

	def flops(self):
	h, w = self.input_resolution
	flops = h * w * self.dim
	flops += (h // 2) * (w // 2) * 4 * self.dim * 2 * self.dim
	return flops


	class BasicLayer(nn.Module):
	""" A basic Swin Transformer layer for one stage.

	Args:
	dim (int): Number of input channels.
	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.
	"""

	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):

	super().__init__()
	self.dim = dim
	self.input_resolution = input_resolution
	self.depth = depth
	self.use_checkpoint = use_checkpoint

	# build blocks
	self.blocks = nn.ModuleList([
	SwinTransformerBlock(
	dim=dim,
	input_resolution=input_resolution,
	num_heads=num_heads,
	window_size=window_size,
	shift_size=0 if (i % 2 == 0) else window_size // 2,
	mlp_ratio=mlp_ratio,
	qkv_bias=qkv_bias,
	qk_scale=qk_scale,
	drop=drop,
	attn_drop=attn_drop,
	drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
	norm_layer=norm_layer) for i in range(depth)
	])

	# patch merging layer
	if downsample is not None:
	self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
	else:
	self.downsample = None

	def forward(self, x, x_size):
	for blk in self.blocks:
	if self.use_checkpoint:
	x = checkpoint.checkpoint(blk, x)
	else:
	x = blk(x, x_size)
	if self.downsample is not None:
	x = self.downsample(x)
	return x

	def extra_repr(self) -> str:
	return f'dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}'

	def flops(self):
	flops = 0
	for blk in self.blocks:
	flops += blk.flops()
	if self.downsample is not None:
	flops += self.downsample.flops()
	return flops


	class RSTB(nn.Module):
	"""Residual Swin Transformer Block (RSTB).

	Args:
	dim (int): Number of input channels.
	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):
	x = x.transpose(1, 2).view(x.shape[0], 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


	@ARCH_REGISTRY.register()
	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

	# ------------------------- 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)
	assert self.upscale == 4, 'only support x4 now.'
	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)
	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 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 seq_len c
	x = self.patch_unembed(x, x_size)

	return x

	def forward(self, 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')))
	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

	def flops(self):
	flops = 0
	h, w = self.patches_resolution
	flops += h * w * 3 * self.embed_dim * 9
	flops += self.patch_embed.flops()
	for layer in self.layers:
	flops += layer.flops()
	flops += h * w * 3 * self.embed_dim * self.embed_dim
	flops += self.upsample.flops()
	return flops


	if __name__ == '__main__':
	upscale = 4
	window_size = 8
	height = (1024 // upscale // window_size + 1) * window_size
	width = (720 // upscale // window_size + 1) * window_size
	model = SwinIR(
	upscale=2,
	img_size=(height, width),
	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')
	print(model)
	print(height, width, model.flops() / 1e9)

	x = torch.randn((1, 3, height, width))
	x = model(x)
	print(x.shape)