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 ResidualBlockNoBN, flow_warp, make_layer
from .edvr_arch import PCDAlignment, TSAFusion
from .spynet_arch import SpyNet
@ARCH_REGISTRY.register()
class BasicVSR(nn.Module):
"""A recurrent network for video SR. Now only x4 is supported.
Args:
num_feat (int): Number of channels. Default: 64.
num_block (int): Number of residual blocks for each branch. Default: 15
spynet_path (str): Path to the pretrained weights of SPyNet. Default: None.
"""
def __init__(self, num_feat=64, num_block=15, spynet_path=None):
super().__init__()
self.num_feat = num_feat
# alignment
self.spynet = SpyNet(spynet_path)
# propagation
self.backward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block)
self.forward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block)
# reconstruction
self.fusion = nn.Conv2d(num_feat * 2, num_feat, 1, 1, 0, bias=True)
self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1, bias=True)
self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1, bias=True)
self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1)
self.conv_last = nn.Conv2d(64, 3, 3, 1, 1)
self.pixel_shuffle = nn.PixelShuffle(2)
# activation functions
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
def get_flow(self, x):
b, n, c, h, w = x.size()
x_1 = x[:, :-1, :, :, :].reshape(-1, c, h, w)
x_2 = x[:, 1:, :, :, :].reshape(-1, c, h, w)
flows_backward = self.spynet(x_1, x_2).view(b, n - 1, 2, h, w)
flows_forward = self.spynet(x_2, x_1).view(b, n - 1, 2, h, w)
return flows_forward, flows_backward
def forward(self, x):
"""Forward function of BasicVSR.
Args:
x: Input frames with shape (b, n, c, h, w). n is the temporal dimension / number of frames.
"""
flows_forward, flows_backward = self.get_flow(x)
b, n, _, h, w = x.size()
# backward branch
out_l = []
feat_prop = x.new_zeros(b, self.num_feat, h, w)
for i in range(n - 1, -1, -1):
x_i = x[:, i, :, :, :]
if i < n - 1:
flow = flows_backward[:, i, :, :, :]
feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))
feat_prop = torch.cat([x_i, feat_prop], dim=1)
feat_prop = self.backward_trunk(feat_prop)
out_l.insert(0, feat_prop)
# forward branch
feat_prop = torch.zeros_like(feat_prop)
for i in range(0, n):
x_i = x[:, i, :, :, :]
if i > 0:
flow = flows_forward[:, i - 1, :, :, :]
feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))
feat_prop = torch.cat([x_i, feat_prop], dim=1)
feat_prop = self.forward_trunk(feat_prop)
# upsample
out = torch.cat([out_l[i], feat_prop], dim=1)
out = self.lrelu(self.fusion(out))
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)
base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False)
out += base
out_l[i] = out
return torch.stack(out_l, dim=1)
class ConvResidualBlocks(nn.Module):
"""Conv and residual block used in BasicVSR.
Args:
num_in_ch (int): Number of input channels. Default: 3.
num_out_ch (int): Number of output channels. Default: 64.
num_block (int): Number of residual blocks. Default: 15.
"""
def __init__(self, num_in_ch=3, num_out_ch=64, num_block=15):
super().__init__()
self.main = nn.Sequential(
nn.Conv2d(num_in_ch, num_out_ch, 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.1, inplace=True),
make_layer(ResidualBlockNoBN, num_block, num_feat=num_out_ch))
def forward(self, fea):
return self.main(fea)
@ARCH_REGISTRY.register()
class IconVSR(nn.Module):
"""IconVSR, proposed also in the BasicVSR paper.
Args:
num_feat (int): Number of channels. Default: 64.
num_block (int): Number of residual blocks for each branch. Default: 15.
keyframe_stride (int): Keyframe stride. Default: 5.
temporal_padding (int): Temporal padding. Default: 2.
spynet_path (str): Path to the pretrained weights of SPyNet. Default: None.
edvr_path (str): Path to the pretrained EDVR model. Default: None.
"""
def __init__(self,
num_feat=64,
num_block=15,
keyframe_stride=5,
temporal_padding=2,
spynet_path=None,
edvr_path=None):
super().__init__()
self.num_feat = num_feat
self.temporal_padding = temporal_padding
self.keyframe_stride = keyframe_stride
# keyframe_branch
self.edvr = EDVRFeatureExtractor(temporal_padding * 2 + 1, num_feat, edvr_path)
# alignment
self.spynet = SpyNet(spynet_path)
# propagation
self.backward_fusion = nn.Conv2d(2 * num_feat, num_feat, 3, 1, 1, bias=True)
self.backward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block)
self.forward_fusion = nn.Conv2d(2 * num_feat, num_feat, 3, 1, 1, bias=True)
self.forward_trunk = ConvResidualBlocks(2 * num_feat + 3, num_feat, num_block)
# reconstruction
self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1, bias=True)
self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1, bias=True)
self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1)
self.conv_last = nn.Conv2d(64, 3, 3, 1, 1)
self.pixel_shuffle = nn.PixelShuffle(2)
# activation functions
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
def pad_spatial(self, x):
"""Apply padding spatially.
Since the PCD module in EDVR requires that the resolution is a multiple
of 4, we apply padding to the input LR images if their resolution is
not divisible by 4.
Args:
x (Tensor): Input LR sequence with shape (n, t, c, h, w).
Returns:
Tensor: Padded LR sequence with shape (n, t, c, h_pad, w_pad).
"""
n, t, c, h, w = x.size()
pad_h = (4 - h % 4) % 4
pad_w = (4 - w % 4) % 4
# padding
x = x.view(-1, c, h, w)
x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect')
return x.view(n, t, c, h + pad_h, w + pad_w)
def get_flow(self, x):
b, n, c, h, w = x.size()
x_1 = x[:, :-1, :, :, :].reshape(-1, c, h, w)
x_2 = x[:, 1:, :, :, :].reshape(-1, c, h, w)
flows_backward = self.spynet(x_1, x_2).view(b, n - 1, 2, h, w)
flows_forward = self.spynet(x_2, x_1).view(b, n - 1, 2, h, w)
return flows_forward, flows_backward
def get_keyframe_feature(self, x, keyframe_idx):
if self.temporal_padding == 2:
x = [x[:, [4, 3]], x, x[:, [-4, -5]]]
elif self.temporal_padding == 3:
x = [x[:, [6, 5, 4]], x, x[:, [-5, -6, -7]]]
x = torch.cat(x, dim=1)
num_frames = 2 * self.temporal_padding + 1
feats_keyframe = {}
for i in keyframe_idx:
feats_keyframe[i] = self.edvr(x[:, i:i + num_frames].contiguous())
return feats_keyframe
def forward(self, x):
b, n, _, h_input, w_input = x.size()
x = self.pad_spatial(x)
h, w = x.shape[3:]
keyframe_idx = list(range(0, n, self.keyframe_stride))
if keyframe_idx[-1] != n - 1:
keyframe_idx.append(n - 1) # last frame is a keyframe
# compute flow and keyframe features
flows_forward, flows_backward = self.get_flow(x)
feats_keyframe = self.get_keyframe_feature(x, keyframe_idx)
# backward branch
out_l = []
feat_prop = x.new_zeros(b, self.num_feat, h, w)
for i in range(n - 1, -1, -1):
x_i = x[:, i, :, :, :]
if i < n - 1:
flow = flows_backward[:, i, :, :, :]
feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))
if i in keyframe_idx:
feat_prop = torch.cat([feat_prop, feats_keyframe[i]], dim=1)
feat_prop = self.backward_fusion(feat_prop)
feat_prop = torch.cat([x_i, feat_prop], dim=1)
feat_prop = self.backward_trunk(feat_prop)
out_l.insert(0, feat_prop)
# forward branch
feat_prop = torch.zeros_like(feat_prop)
for i in range(0, n):
x_i = x[:, i, :, :, :]
if i > 0:
flow = flows_forward[:, i - 1, :, :, :]
feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))
if i in keyframe_idx:
feat_prop = torch.cat([feat_prop, feats_keyframe[i]], dim=1)
feat_prop = self.forward_fusion(feat_prop)
feat_prop = torch.cat([x_i, out_l[i], feat_prop], dim=1)
feat_prop = self.forward_trunk(feat_prop)
# upsample
out = self.lrelu(self.pixel_shuffle(self.upconv1(feat_prop)))
out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
out = self.lrelu(self.conv_hr(out))
out = self.conv_last(out)
base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False)
out += base
out_l[i] = out
return torch.stack(out_l, dim=1)[..., :4 * h_input, :4 * w_input]
class EDVRFeatureExtractor(nn.Module):
"""EDVR feature extractor used in IconVSR.
Args:
num_input_frame (int): Number of input frames.
num_feat (int): Number of feature channels
load_path (str): Path to the pretrained weights of EDVR. Default: None.
"""
def __init__(self, num_input_frame, num_feat, load_path):
super(EDVRFeatureExtractor, self).__init__()
self.center_frame_idx = num_input_frame // 2
# extract pyramid features
self.conv_first = nn.Conv2d(3, num_feat, 3, 1, 1)
self.feature_extraction = make_layer(ResidualBlockNoBN, 5, 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=8)
self.fusion = TSAFusion(num_feat=num_feat, num_frame=num_input_frame, center_frame_idx=self.center_frame_idx)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
if load_path:
self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params'])
def forward(self, x):
b, n, c, h, w = x.size()
# extract features for each frame
# L1
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, n, -1, h, w)
feat_l2 = feat_l2.view(b, n, -1, h // 2, w // 2)
feat_l3 = feat_l3.view(b, n, -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(n):
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)
# TSA fusion
return self.fusion(aligned_feat)