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import torch
from torch import nn as nn
from torch.nn import functional as F
from basicsr.utils.registry import ARCH_REGISTRY
from .arch_util import DCNv2Pack, ResidualBlockNoBN, make_layer
class PCDAlignment(nn.Module):
"""Alignment module using Pyramid, Cascading and Deformable convolution
(PCD). It is used in EDVR.
``Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks``
Args:
num_feat (int): Channel number of middle features. Default: 64.
deformable_groups (int): Deformable groups. Defaults: 8.
"""
def __init__(self, num_feat=64, deformable_groups=8):
super(PCDAlignment, self).__init__()
# Pyramid has three levels:
# L3: level 3, 1/4 spatial size
# L2: level 2, 1/2 spatial size
# L1: level 1, original spatial size
self.offset_conv1 = nn.ModuleDict()
self.offset_conv2 = nn.ModuleDict()
self.offset_conv3 = nn.ModuleDict()
self.dcn_pack = nn.ModuleDict()
self.feat_conv = nn.ModuleDict()
# Pyramids
for i in range(3, 0, -1):
level = f'l{i}'
self.offset_conv1[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
if i == 3:
self.offset_conv2[level] = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
else:
self.offset_conv2[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
self.offset_conv3[level] = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.dcn_pack[level] = DCNv2Pack(num_feat, num_feat, 3, padding=1, deformable_groups=deformable_groups)
if i < 3:
self.feat_conv[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
# Cascading dcn
self.cas_offset_conv1 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
self.cas_offset_conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.cas_dcnpack = DCNv2Pack(num_feat, num_feat, 3, padding=1, deformable_groups=deformable_groups)
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
def forward(self, nbr_feat_l, ref_feat_l):
"""Align neighboring frame features to the reference frame features.
Args:
nbr_feat_l (list[Tensor]): Neighboring feature list. It
contains three pyramid levels (L1, L2, L3),
each with shape (b, c, h, w).
ref_feat_l (list[Tensor]): Reference feature list. It
contains three pyramid levels (L1, L2, L3),
each with shape (b, c, h, w).
Returns:
Tensor: Aligned features.
"""
# Pyramids
upsampled_offset, upsampled_feat = None, None
for i in range(3, 0, -1):
level = f'l{i}'
offset = torch.cat([nbr_feat_l[i - 1], ref_feat_l[i - 1]], dim=1)
offset = self.lrelu(self.offset_conv1[level](offset))
if i == 3:
offset = self.lrelu(self.offset_conv2[level](offset))
else:
offset = self.lrelu(self.offset_conv2[level](torch.cat([offset, upsampled_offset], dim=1)))
offset = self.lrelu(self.offset_conv3[level](offset))
feat = self.dcn_pack[level](nbr_feat_l[i - 1], offset)
if i < 3:
feat = self.feat_conv[level](torch.cat([feat, upsampled_feat], dim=1))
if i > 1:
feat = self.lrelu(feat)
if i > 1: # upsample offset and features
# x2: when we upsample the offset, we should also enlarge
# the magnitude.
upsampled_offset = self.upsample(offset) * 2
upsampled_feat = self.upsample(feat)
# Cascading
offset = torch.cat([feat, ref_feat_l[0]], dim=1)
offset = self.lrelu(self.cas_offset_conv2(self.lrelu(self.cas_offset_conv1(offset))))
feat = self.lrelu(self.cas_dcnpack(feat, offset))
return feat
class TSAFusion(nn.Module):
"""Temporal Spatial Attention (TSA) fusion module.
Temporal: Calculate the correlation between center frame and
neighboring frames;
Spatial: It has 3 pyramid levels, the attention is similar to SFT.
(SFT: Recovering realistic texture in image super-resolution by deep
spatial feature transform.)
Args:
num_feat (int): Channel number of middle features. Default: 64.
num_frame (int): Number of frames. Default: 5.
center_frame_idx (int): The index of center frame. Default: 2.
"""
def __init__(self, num_feat=64, num_frame=5, center_frame_idx=2):
super(TSAFusion, self).__init__()
self.center_frame_idx = center_frame_idx
# temporal attention (before fusion conv)
self.temporal_attn1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.temporal_attn2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.feat_fusion = nn.Conv2d(num_frame * num_feat, num_feat, 1, 1)
# spatial attention (after fusion conv)
self.max_pool = nn.MaxPool2d(3, stride=2, padding=1)
self.avg_pool = nn.AvgPool2d(3, stride=2, padding=1)
self.spatial_attn1 = nn.Conv2d(num_frame * num_feat, num_feat, 1)
self.spatial_attn2 = nn.Conv2d(num_feat * 2, num_feat, 1)
self.spatial_attn3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.spatial_attn4 = nn.Conv2d(num_feat, num_feat, 1)
self.spatial_attn5 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.spatial_attn_l1 = nn.Conv2d(num_feat, num_feat, 1)
self.spatial_attn_l2 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
self.spatial_attn_l3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.spatial_attn_add1 = nn.Conv2d(num_feat, num_feat, 1)
self.spatial_attn_add2 = nn.Conv2d(num_feat, num_feat, 1)
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
def forward(self, aligned_feat):
"""
Args:
aligned_feat (Tensor): Aligned features with shape (b, t, c, h, w).
Returns:
Tensor: Features after TSA with the shape (b, c, h, w).
"""
b, t, c, h, w = aligned_feat.size()
# temporal attention
embedding_ref = self.temporal_attn1(aligned_feat[:, self.center_frame_idx, :, :, :].clone())
embedding = self.temporal_attn2(aligned_feat.view(-1, c, h, w))
embedding = embedding.view(b, t, -1, h, w) # (b, t, c, h, w)
corr_l = [] # correlation list
for i in range(t):
emb_neighbor = embedding[:, i, :, :, :]
corr = torch.sum(emb_neighbor * embedding_ref, 1) # (b, h, w)
corr_l.append(corr.unsqueeze(1)) # (b, 1, h, w)
corr_prob = torch.sigmoid(torch.cat(corr_l, dim=1)) # (b, t, h, w)
corr_prob = corr_prob.unsqueeze(2).expand(b, t, c, h, w)
corr_prob = corr_prob.contiguous().view(b, -1, h, w) # (b, t*c, h, w)
aligned_feat = aligned_feat.view(b, -1, h, w) * corr_prob
# fusion
feat = self.lrelu(self.feat_fusion(aligned_feat))
# spatial attention
attn = self.lrelu(self.spatial_attn1(aligned_feat))
attn_max = self.max_pool(attn)
attn_avg = self.avg_pool(attn)
attn = self.lrelu(self.spatial_attn2(torch.cat([attn_max, attn_avg], dim=1)))
# pyramid levels
attn_level = self.lrelu(self.spatial_attn_l1(attn))
attn_max = self.max_pool(attn_level)
attn_avg = self.avg_pool(attn_level)
attn_level = self.lrelu(self.spatial_attn_l2(torch.cat([attn_max, attn_avg], dim=1)))
attn_level = self.lrelu(self.spatial_attn_l3(attn_level))
attn_level = self.upsample(attn_level)
attn = self.lrelu(self.spatial_attn3(attn)) + attn_level
attn = self.lrelu(self.spatial_attn4(attn))
attn = self.upsample(attn)
attn = self.spatial_attn5(attn)
attn_add = self.spatial_attn_add2(self.lrelu(self.spatial_attn_add1(attn)))
attn = torch.sigmoid(attn)
# after initialization, * 2 makes (attn * 2) to be close to 1.
feat = feat * attn * 2 + attn_add
return feat
class PredeblurModule(nn.Module):
"""Pre-dublur module.
Args:
num_in_ch (int): Channel number of input image. Default: 3.
num_feat (int): Channel number of intermediate features. Default: 64.
hr_in (bool): Whether the input has high resolution. Default: False.
"""
def __init__(self, num_in_ch=3, num_feat=64, hr_in=False):
super(PredeblurModule, self).__init__()
self.hr_in = hr_in
self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
if self.hr_in:
# downsample x4 by stride conv
self.stride_conv_hr1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
self.stride_conv_hr2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
# generate feature pyramid
self.stride_conv_l2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
self.stride_conv_l3 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
self.resblock_l3 = ResidualBlockNoBN(num_feat=num_feat)
self.resblock_l2_1 = ResidualBlockNoBN(num_feat=num_feat)
self.resblock_l2_2 = ResidualBlockNoBN(num_feat=num_feat)
self.resblock_l1 = nn.ModuleList([ResidualBlockNoBN(num_feat=num_feat) for i in range(5)])
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
def forward(self, x):
feat_l1 = self.lrelu(self.conv_first(x))
if self.hr_in:
feat_l1 = self.lrelu(self.stride_conv_hr1(feat_l1))
feat_l1 = self.lrelu(self.stride_conv_hr2(feat_l1))
# generate feature pyramid
feat_l2 = self.lrelu(self.stride_conv_l2(feat_l1))
feat_l3 = self.lrelu(self.stride_conv_l3(feat_l2))
feat_l3 = self.upsample(self.resblock_l3(feat_l3))
feat_l2 = self.resblock_l2_1(feat_l2) + feat_l3
feat_l2 = self.upsample(self.resblock_l2_2(feat_l2))
for i in range(2):
feat_l1 = self.resblock_l1[i](feat_l1)
feat_l1 = feat_l1 + feat_l2
for i in range(2, 5):
feat_l1 = self.resblock_l1[i](feat_l1)
return feat_l1
@ARCH_REGISTRY.register()
class EDVR(nn.Module):
"""EDVR network structure for video super-resolution.
Now only support X4 upsampling factor.
``Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks``
Args:
num_in_ch (int): Channel number of input image. Default: 3.
num_out_ch (int): Channel number of output image. Default: 3.
num_feat (int): Channel number of intermediate features. Default: 64.
num_frame (int): Number of input frames. Default: 5.
deformable_groups (int): Deformable groups. Defaults: 8.
num_extract_block (int): Number of blocks for feature extraction.
Default: 5.
num_reconstruct_block (int): Number of blocks for reconstruction.
Default: 10.
center_frame_idx (int): The index of center frame. Frame counting from
0. Default: Middle of input frames.
hr_in (bool): Whether the input has high resolution. Default: False.
with_predeblur (bool): Whether has predeblur module.
Default: False.
with_tsa (bool): Whether has TSA module. Default: True.
"""
def __init__(self,
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_frame=5,
deformable_groups=8,
num_extract_block=5,
num_reconstruct_block=10,
center_frame_idx=None,
hr_in=False,
with_predeblur=False,
with_tsa=True):
super(EDVR, self).__init__()
if center_frame_idx is None:
self.center_frame_idx = num_frame // 2
else:
self.center_frame_idx = center_frame_idx
self.hr_in = hr_in
self.with_predeblur = with_predeblur
self.with_tsa = with_tsa
# extract features for each frame
if self.with_predeblur:
self.predeblur = PredeblurModule(num_feat=num_feat, hr_in=self.hr_in)
self.conv_1x1 = nn.Conv2d(num_feat, num_feat, 1, 1)
else:
self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
# extract pyramid features
self.feature_extraction = make_layer(ResidualBlockNoBN, num_extract_block, num_feat=num_feat)
self.conv_l2_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
self.conv_l2_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_l3_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
self.conv_l3_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
# pcd and tsa module
self.pcd_align = PCDAlignment(num_feat=num_feat, deformable_groups=deformable_groups)
if self.with_tsa:
self.fusion = TSAFusion(num_feat=num_feat, num_frame=num_frame, center_frame_idx=self.center_frame_idx)
else:
self.fusion = nn.Conv2d(num_frame * num_feat, num_feat, 1, 1)
# reconstruction
self.reconstruction = make_layer(ResidualBlockNoBN, num_reconstruct_block, num_feat=num_feat)
# upsample
self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1)
self.pixel_shuffle = nn.PixelShuffle(2)
self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1)
self.conv_last = nn.Conv2d(64, 3, 3, 1, 1)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
def forward(self, x):
b, t, c, h, w = x.size()
if self.hr_in:
assert h % 16 == 0 and w % 16 == 0, ('The height and width must be multiple of 16.')
else:
assert h % 4 == 0 and w % 4 == 0, ('The height and width must be multiple of 4.')
x_center = x[:, self.center_frame_idx, :, :, :].contiguous()
# extract features for each frame
# L1
if self.with_predeblur:
feat_l1 = self.conv_1x1(self.predeblur(x.view(-1, c, h, w)))
if self.hr_in:
h, w = h // 4, w // 4
else:
feat_l1 = self.lrelu(self.conv_first(x.view(-1, c, h, w)))
feat_l1 = self.feature_extraction(feat_l1)
# L2
feat_l2 = self.lrelu(self.conv_l2_1(feat_l1))
feat_l2 = self.lrelu(self.conv_l2_2(feat_l2))
# L3
feat_l3 = self.lrelu(self.conv_l3_1(feat_l2))
feat_l3 = self.lrelu(self.conv_l3_2(feat_l3))
feat_l1 = feat_l1.view(b, t, -1, h, w)
feat_l2 = feat_l2.view(b, t, -1, h // 2, w // 2)
feat_l3 = feat_l3.view(b, t, -1, h // 4, w // 4)
# PCD alignment
ref_feat_l = [ # reference feature list
feat_l1[:, self.center_frame_idx, :, :, :].clone(), feat_l2[:, self.center_frame_idx, :, :, :].clone(),
feat_l3[:, self.center_frame_idx, :, :, :].clone()
]
aligned_feat = []
for i in range(t):
nbr_feat_l = [ # neighboring feature list
feat_l1[:, i, :, :, :].clone(), feat_l2[:, i, :, :, :].clone(), feat_l3[:, i, :, :, :].clone()
]
aligned_feat.append(self.pcd_align(nbr_feat_l, ref_feat_l))
aligned_feat = torch.stack(aligned_feat, dim=1) # (b, t, c, h, w)
if not self.with_tsa:
aligned_feat = aligned_feat.view(b, -1, h, w)
feat = self.fusion(aligned_feat)
out = self.reconstruction(feat)
out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
out = self.lrelu(self.conv_hr(out))
out = self.conv_last(out)
if self.hr_in:
base = x_center
else:
base = F.interpolate(x_center, scale_factor=4, mode='bilinear', align_corners=False)
out += base
return out