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import torch
import torch.nn as nn
import torch.nn.functional as F
import numbers
from einops import rearrange
from huggingface_hub import PyTorchModelHubMixin
#########################################################################
Conv2d = nn.Conv2d
##########################################################################
## Layer Norm
def to_2d(x):
return rearrange(x, 'b c h w -> b (h w c)')
def to_3d(x):
# return rearrange(x, 'b c h w -> b c (h w)')
return rearrange(x, 'b c h w -> b (h w) c')
def to_4d(x,h,w):
# return rearrange(x, 'b c (h w) -> b c h w',h=h,w=w)
return rearrange(x, 'b (h w) c -> b c h w',h=h,w=w)
class BiasFree_LayerNorm(nn.Module):
def __init__(self, normalized_shape):
super(BiasFree_LayerNorm, self).__init__()
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
normalized_shape = torch.Size(normalized_shape)
assert len(normalized_shape) == 1
# self.weight = nn.Parameter(torch.ones(normalized_shape))
self.normalized_shape = normalized_shape
def forward(self, x):
sigma = x.var(-1, keepdim=True, unbiased=False)
return x / torch.sqrt(sigma+1e-5) #* self.weight
class WithBias_LayerNorm(nn.Module):
def __init__(self, normalized_shape):
super(WithBias_LayerNorm, self).__init__()
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
normalized_shape = torch.Size(normalized_shape)
assert len(normalized_shape) == 1
# self.weight = nn.Parameter(torch.ones(normalized_shape))
# self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.normalized_shape = normalized_shape
def forward(self, x):
mu = x.mean(-1, keepdim=True)
sigma = x.var(-1, keepdim=True, unbiased=False)
return (x - mu) / torch.sqrt(sigma+1e-5) #* self.weight + self.bias
class LayerNorm(nn.Module):
def __init__(self, dim, LayerNorm_type="WithBias"):
super(LayerNorm, self).__init__()
if LayerNorm_type =='BiasFree':
self.body = BiasFree_LayerNorm(dim)
else:
self.body = WithBias_LayerNorm(dim)
def forward(self, x):
h, w = x.shape[-2:]
return to_4d(self.body(to_3d(x)), h, w)
##########################################################################
## Dual-scale Gated Feed-Forward Network (DGFF)
class FeedForward(nn.Module):
def __init__(self, dim, ffn_expansion_factor, bias):
super(FeedForward, self).__init__()
hidden_features = int(dim*ffn_expansion_factor)
self.project_in = Conv2d(dim, hidden_features*2, kernel_size=1, bias=bias)
self.dwconv_5 = Conv2d(hidden_features//4, hidden_features//4, kernel_size=5, stride=1, padding=2, groups=hidden_features//4, bias=bias)
self.dwconv_dilated2_1 = Conv2d(hidden_features//4, hidden_features//4, kernel_size=3, stride=1, padding=2, groups=hidden_features//4, bias=bias, dilation=2)
self.p_unshuffle = nn.PixelUnshuffle(2)
self.p_shuffle = nn.PixelShuffle(2)
self.project_out = Conv2d(hidden_features, dim, kernel_size=1, bias=bias)
def forward(self, x):
x = self.project_in(x)
x = self.p_shuffle(x)
x1, x2 = x.chunk(2, dim=1)
# x2_1, x2_2 = x2.chunk(2, dim=1)
x1 = self.dwconv_5(x1)
x2 = self.dwconv_dilated2_1( x2 )
# x2_2 = self.dwconv_dilated3_1( x2_2 )
# x2 = torch.cat([x2_1, x2_2], dim=1)
x = F.mish( x2 ) * x1
x = self.p_unshuffle(x)
x = self.project_out(x)
# x1 = self.dwconv_5(x)
# x2 = self.dwconv_dilated_2(x)
# x = F.mish(x2) * x1 + x
return x
##########################################################################
## Dynamic-range Histogram Self-Attention (DHSA)
class Attention_histogram(nn.Module):
def __init__(self, dim, num_heads, bias, ifBox=True):
super(Attention_histogram, self).__init__()
self.factor = num_heads
self.ifBox = ifBox
self.num_heads = num_heads
self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
self.qkv = Conv2d(dim, dim*5, kernel_size=1, bias=bias)
self.qkv_dwconv = Conv2d(dim*5, dim*5, kernel_size=3, stride=1, padding=1, groups=dim*5, bias=bias)
self.project_out = Conv2d(dim, dim, kernel_size=1, bias=bias)
def pad(self, x, factor):
hw = x.shape[-1]
t_pad = [0, 0] if hw % factor == 0 else [0, (hw//factor+1)*factor-hw]
x = F.pad(x, t_pad, 'constant', 0)
return x, t_pad
def unpad(self, x, t_pad):
_, _, hw = x.shape
return x[:,:,t_pad[0]:hw-t_pad[1]]
def softmax_1(self, x, dim=-1):
logit = x.exp()
logit = logit / (logit.sum(dim, keepdim=True) + 1)
return logit
def normalize(self, x):
mu = x.mean(-2, keepdim=True)
sigma = x.var(-2, keepdim=True, unbiased=False)
return (x - mu) / torch.sqrt(sigma+1e-5) #* self.weight + self.bias
def reshape_attn(self, q, k, v, ifBox):
b, c = q.shape[:2]
q, t_pad = self.pad(q, self.factor)
k, t_pad = self.pad(k, self.factor)
v, t_pad = self.pad(v, self.factor)
hw = q.shape[-1] // self.factor
shape_ori = "b (head c) (factor hw)" if ifBox else "b (head c) (hw factor)"
shape_tar = "b head (c factor) hw"
q = rearrange(q, '{} -> {}'.format(shape_ori, shape_tar), factor=self.factor, hw=hw, head=self.num_heads)
k = rearrange(k, '{} -> {}'.format(shape_ori, shape_tar), factor=self.factor, hw=hw, head=self.num_heads)
v = rearrange(v, '{} -> {}'.format(shape_ori, shape_tar), factor=self.factor, hw=hw, head=self.num_heads)
q = torch.nn.functional.normalize(q, dim=-1)
k = torch.nn.functional.normalize(k, dim=-1)
attn = (q @ k.transpose(-2, -1)) * self.temperature
attn = self.softmax_1(attn, dim=-1)
out = (attn @ v)
out = rearrange(out, '{} -> {}'.format(shape_tar, shape_ori), factor=self.factor, hw=hw, b=b, head=self.num_heads)
out = self.unpad(out, t_pad)
return out
def forward(self, x):
b,c,h,w = x.shape
x_sort, idx_h = x[:,:c//2].sort(-2)
x_sort, idx_w = x_sort.sort(-1)
x[:,:c//2] = x_sort
qkv = self.qkv_dwconv(self.qkv(x))
q1,k1,q2,k2,v = qkv.chunk(5, dim=1) # b,c,x,x
v, idx = v.view(b,c,-1).sort(dim=-1)
q1 = torch.gather(q1.view(b,c,-1), dim=2, index=idx)
k1 = torch.gather(k1.view(b,c,-1), dim=2, index=idx)
q2 = torch.gather(q2.view(b,c,-1), dim=2, index=idx)
k2 = torch.gather(k2.view(b,c,-1), dim=2, index=idx)
out1 = self.reshape_attn(q1, k1, v, True)
out2 = self.reshape_attn(q2, k2, v, False)
out1 = torch.scatter(out1, 2, idx, out1).view(b,c,h,w)
out2 = torch.scatter(out2, 2, idx, out2).view(b,c,h,w)
out = out1 * out2
out = self.project_out(out)
out_replace = out[:,:c//2]
out_replace = torch.scatter(out_replace, -1, idx_w, out_replace)
out_replace = torch.scatter(out_replace, -2, idx_h, out_replace)
out[:,:c//2] = out_replace
return out
##########################################################################
class TransformerBlock(nn.Module):
def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):
super(TransformerBlock, self).__init__()
self.attn_g = Attention_histogram(dim, num_heads, bias, True)
self.norm_g = LayerNorm(dim, LayerNorm_type)
self.ffn = FeedForward(dim, ffn_expansion_factor, bias)
self.norm_ff1 = LayerNorm(dim, LayerNorm_type)
def forward(self, x):
x = x + self.attn_g(self.norm_g(x))
x_out = x + self.ffn(self.norm_ff1(x))
return x_out
##########################################################################
## Overlapped image patch embedding with 3x3 Conv
class OverlapPatchEmbed(nn.Module):
def __init__(self, in_c=3, embed_dim=48, bias=False):
super(OverlapPatchEmbed, self).__init__()
self.proj = Conv2d(in_c, embed_dim, kernel_size=3, stride=1, padding=1, bias=bias)
def forward(self, x):
x = self.proj(x)
return x
class SkipPatchEmbed(nn.Module):
def __init__(self, in_c=3, dim=48, bias=False):
super(SkipPatchEmbed, self).__init__()
self.proj = nn.Sequential(
nn.AvgPool2d( 2, stride=2, padding=0 , ceil_mode=False , count_include_pad=True , divisor_override=None ),
Conv2d(in_c, dim, kernel_size=1, bias=bias),
Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim, bias=bias)
)
def forward(self, x, ):
x = self.proj(x)
return x
##########################################################################
## Resizing modules
class Downsample(nn.Module):
def __init__(self, n_feat):
super(Downsample, self).__init__()
self.body = nn.Sequential(Conv2d(n_feat, n_feat//2, kernel_size=3, stride=1, padding=1, bias=False),
nn.PixelUnshuffle(2))
def forward(self, x):
return self.body(x)
class Upsample(nn.Module):
def __init__(self, n_feat):
super(Upsample, self).__init__()
self.body = nn.Sequential(Conv2d(n_feat, n_feat*2, kernel_size=3, stride=1, padding=1, bias=False),
nn.PixelShuffle(2))
def forward(self, x):
return self.body(x)
##########################################################################
class Histoformer(nn.Module, PyTorchModelHubMixin, ):
def __init__(self,
inp_channels=3,
out_channels=3,
dim = 36,
num_blocks = [4,4,6,8],
num_refinement_blocks = 4,
heads = [1,2,4,8],
ffn_expansion_factor = 2.667,
bias = False,
LayerNorm_type = 'WithBias', ## Other option 'BiasFree'
dual_pixel_task = False ## True for dual-pixel defocus deblurring only. Also set inp_channels=6
):
super(Histoformer, self).__init__()
self.patch_embed = OverlapPatchEmbed(inp_channels, dim)
self.encoder_level1 = nn.Sequential(*[TransformerBlock(dim=dim, num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
self.down1_2 = Downsample(dim) ## From Level 1 to Level 2
self.encoder_level2 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
self.down2_3 = Downsample(int(dim*2**1)) ## From Level 2 to Level 3
self.encoder_level3 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])
self.down3_4 = Downsample(int(dim*2**2)) ## From Level 3 to Level 4
self.latent = nn.Sequential(*[TransformerBlock(dim=int(dim*2**3), num_heads=heads[3], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[3])])
self.up4_3 = Upsample(int(dim*2**3)) ## From Level 4 to Level 3
self.reduce_chan_level3 = Conv2d(int(dim*2**3), int(dim*2**2), kernel_size=1, bias=bias)
self.decoder_level3 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])
self.up3_2 = Upsample(int(dim*2**2)) ## From Level 3 to Level 2
self.reduce_chan_level2 = Conv2d(int(dim*2**2), int(dim*2**1), kernel_size=1, bias=bias)
self.decoder_level2 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
self.up2_1 = Upsample(int(dim*2**1)) ## From Level 2 to Level 1 (NO 1x1 conv to reduce channels)
self.decoder_level1 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
self.refinement = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_refinement_blocks)])
self.skip_patch_embed1 = SkipPatchEmbed(3, 3)
self.skip_patch_embed2 = SkipPatchEmbed(3, 3)
self.skip_patch_embed3 = SkipPatchEmbed(3, 3)
self.reduce_chan_level_1 = Conv2d(int(dim*2**1)+3, int(dim*2**1), kernel_size=1, bias=bias)
self.reduce_chan_level_2 = Conv2d(int(dim*2**2)+3, int(dim*2**2), kernel_size=1, bias=bias)
self.reduce_chan_level_3 = Conv2d(int(dim*2**3)+3, int(dim*2**3), kernel_size=1, bias=bias)
#### For Dual-Pixel Defocus Deblurring Task ####
self.dual_pixel_task = dual_pixel_task
if self.dual_pixel_task:
self.skip_conv = Conv2d(dim, int(dim*2**1), kernel_size=1, bias=bias)
###########################
self.output = Conv2d(int(dim*2**1), out_channels, kernel_size=3, stride=1, padding=1, bias=bias)
def forward(self, inp_img, ):
inp_enc_level1 = self.patch_embed(inp_img)
out_enc_level1 = self.encoder_level1(inp_enc_level1) # c,h,w
inp_enc_level2 = self.down1_2(out_enc_level1) # 2c, h/2, w/2
skip_enc_level1 = self.skip_patch_embed1(inp_img)
inp_enc_level2 = self.reduce_chan_level_1(torch.cat([inp_enc_level2, skip_enc_level1], 1))
out_enc_level2 = self.encoder_level2(inp_enc_level2)
inp_enc_level3 = self.down2_3(out_enc_level2)
skip_enc_level2 = self.skip_patch_embed2(skip_enc_level1)
inp_enc_level3 = self.reduce_chan_level_2(torch.cat([inp_enc_level3, skip_enc_level2], 1))
out_enc_level3 = self.encoder_level3(inp_enc_level3)
inp_enc_level4 = self.down3_4(out_enc_level3)
skip_enc_level3 = self.skip_patch_embed3(skip_enc_level2)
inp_enc_level4 = self.reduce_chan_level_3(torch.cat([inp_enc_level4, skip_enc_level3], 1))
latent = self.latent(inp_enc_level4)
inp_dec_level3 = self.up4_3(latent)
inp_dec_level3 = torch.cat([inp_dec_level3, out_enc_level3], 1)
inp_dec_level3 = self.reduce_chan_level3(inp_dec_level3)
out_dec_level3 = self.decoder_level3(inp_dec_level3)
inp_dec_level2 = self.up3_2(out_dec_level3)
inp_dec_level2 = torch.cat([inp_dec_level2, out_enc_level2], 1)
inp_dec_level2 = self.reduce_chan_level2(inp_dec_level2)
out_dec_level2 = self.decoder_level2(inp_dec_level2)
inp_dec_level1 = self.up2_1(out_dec_level2)
inp_dec_level1 = torch.cat([inp_dec_level1, out_enc_level1], 1)
out_dec_level1 = self.decoder_level1(inp_dec_level1)
out_dec_level1 = self.refinement(out_dec_level1)
###########################
out_dec_level1 = self.output(out_dec_level1)
return out_dec_level1 + inp_img
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