Swap-Face-Model / basicsr /archs /basicvsrpp_arch.py
Tzktz's picture
Upload 202 files
c59c099 verified
raw
history blame
16.5 kB
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import warnings
from basicsr.archs.arch_util import flow_warp
from basicsr.archs.basicvsr_arch import ConvResidualBlocks
from basicsr.archs.spynet_arch import SpyNet
from basicsr.ops.dcn import ModulatedDeformConvPack
from basicsr.utils.registry import ARCH_REGISTRY
@ARCH_REGISTRY.register()
class BasicVSRPlusPlus(nn.Module):
"""BasicVSR++ network structure.
Support either x4 upsampling or same size output. Since DCN is used in this
model, it can only be used with CUDA enabled. If CUDA is not enabled,
feature alignment will be skipped. Besides, we adopt the official DCN
implementation and the version of torch need to be higher than 1.9.
``Paper: BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment``
Args:
mid_channels (int, optional): Channel number of the intermediate
features. Default: 64.
num_blocks (int, optional): The number of residual blocks in each
propagation branch. Default: 7.
max_residue_magnitude (int): The maximum magnitude of the offset
residue (Eq. 6 in paper). Default: 10.
is_low_res_input (bool, optional): Whether the input is low-resolution
or not. If False, the output resolution is equal to the input
resolution. Default: True.
spynet_path (str): Path to the pretrained weights of SPyNet. Default: None.
cpu_cache_length (int, optional): When the length of sequence is larger
than this value, the intermediate features are sent to CPU. This
saves GPU memory, but slows down the inference speed. You can
increase this number if you have a GPU with large memory.
Default: 100.
"""
def __init__(self,
mid_channels=64,
num_blocks=7,
max_residue_magnitude=10,
is_low_res_input=True,
spynet_path=None,
cpu_cache_length=100):
super().__init__()
self.mid_channels = mid_channels
self.is_low_res_input = is_low_res_input
self.cpu_cache_length = cpu_cache_length
# optical flow
self.spynet = SpyNet(spynet_path)
# feature extraction module
if is_low_res_input:
self.feat_extract = ConvResidualBlocks(3, mid_channels, 5)
else:
self.feat_extract = nn.Sequential(
nn.Conv2d(3, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True),
nn.Conv2d(mid_channels, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True),
ConvResidualBlocks(mid_channels, mid_channels, 5))
# propagation branches
self.deform_align = nn.ModuleDict()
self.backbone = nn.ModuleDict()
modules = ['backward_1', 'forward_1', 'backward_2', 'forward_2']
for i, module in enumerate(modules):
if torch.cuda.is_available():
self.deform_align[module] = SecondOrderDeformableAlignment(
2 * mid_channels,
mid_channels,
3,
padding=1,
deformable_groups=16,
max_residue_magnitude=max_residue_magnitude)
self.backbone[module] = ConvResidualBlocks((2 + i) * mid_channels, mid_channels, num_blocks)
# upsampling module
self.reconstruction = ConvResidualBlocks(5 * mid_channels, mid_channels, 5)
self.upconv1 = nn.Conv2d(mid_channels, mid_channels * 4, 3, 1, 1, bias=True)
self.upconv2 = nn.Conv2d(mid_channels, 64 * 4, 3, 1, 1, bias=True)
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)
self.img_upsample = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
# check if the sequence is augmented by flipping
self.is_mirror_extended = False
if len(self.deform_align) > 0:
self.is_with_alignment = True
else:
self.is_with_alignment = False
warnings.warn('Deformable alignment module is not added. '
'Probably your CUDA is not configured correctly. DCN can only '
'be used with CUDA enabled. Alignment is skipped now.')
def check_if_mirror_extended(self, lqs):
"""Check whether the input is a mirror-extended sequence.
If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the (t-1-i)-th frame.
Args:
lqs (tensor): Input low quality (LQ) sequence with shape (n, t, c, h, w).
"""
if lqs.size(1) % 2 == 0:
lqs_1, lqs_2 = torch.chunk(lqs, 2, dim=1)
if torch.norm(lqs_1 - lqs_2.flip(1)) == 0:
self.is_mirror_extended = True
def compute_flow(self, lqs):
"""Compute optical flow using SPyNet for feature alignment.
Note that if the input is an mirror-extended sequence, 'flows_forward'
is not needed, since it is equal to 'flows_backward.flip(1)'.
Args:
lqs (tensor): Input low quality (LQ) sequence with
shape (n, t, c, h, w).
Return:
tuple(Tensor): Optical flow. 'flows_forward' corresponds to the flows used for forward-time propagation \
(current to previous). 'flows_backward' corresponds to the flows used for backward-time \
propagation (current to next).
"""
n, t, c, h, w = lqs.size()
lqs_1 = lqs[:, :-1, :, :, :].reshape(-1, c, h, w)
lqs_2 = lqs[:, 1:, :, :, :].reshape(-1, c, h, w)
flows_backward = self.spynet(lqs_1, lqs_2).view(n, t - 1, 2, h, w)
if self.is_mirror_extended: # flows_forward = flows_backward.flip(1)
flows_forward = flows_backward.flip(1)
else:
flows_forward = self.spynet(lqs_2, lqs_1).view(n, t - 1, 2, h, w)
if self.cpu_cache:
flows_backward = flows_backward.cpu()
flows_forward = flows_forward.cpu()
return flows_forward, flows_backward
def propagate(self, feats, flows, module_name):
"""Propagate the latent features throughout the sequence.
Args:
feats dict(list[tensor]): Features from previous branches. Each
component is a list of tensors with shape (n, c, h, w).
flows (tensor): Optical flows with shape (n, t - 1, 2, h, w).
module_name (str): The name of the propgation branches. Can either
be 'backward_1', 'forward_1', 'backward_2', 'forward_2'.
Return:
dict(list[tensor]): A dictionary containing all the propagated \
features. Each key in the dictionary corresponds to a \
propagation branch, which is represented by a list of tensors.
"""
n, t, _, h, w = flows.size()
frame_idx = range(0, t + 1)
flow_idx = range(-1, t)
mapping_idx = list(range(0, len(feats['spatial'])))
mapping_idx += mapping_idx[::-1]
if 'backward' in module_name:
frame_idx = frame_idx[::-1]
flow_idx = frame_idx
feat_prop = flows.new_zeros(n, self.mid_channels, h, w)
for i, idx in enumerate(frame_idx):
feat_current = feats['spatial'][mapping_idx[idx]]
if self.cpu_cache:
feat_current = feat_current.cuda()
feat_prop = feat_prop.cuda()
# second-order deformable alignment
if i > 0 and self.is_with_alignment:
flow_n1 = flows[:, flow_idx[i], :, :, :]
if self.cpu_cache:
flow_n1 = flow_n1.cuda()
cond_n1 = flow_warp(feat_prop, flow_n1.permute(0, 2, 3, 1))
# initialize second-order features
feat_n2 = torch.zeros_like(feat_prop)
flow_n2 = torch.zeros_like(flow_n1)
cond_n2 = torch.zeros_like(cond_n1)
if i > 1: # second-order features
feat_n2 = feats[module_name][-2]
if self.cpu_cache:
feat_n2 = feat_n2.cuda()
flow_n2 = flows[:, flow_idx[i - 1], :, :, :]
if self.cpu_cache:
flow_n2 = flow_n2.cuda()
flow_n2 = flow_n1 + flow_warp(flow_n2, flow_n1.permute(0, 2, 3, 1))
cond_n2 = flow_warp(feat_n2, flow_n2.permute(0, 2, 3, 1))
# flow-guided deformable convolution
cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1)
feat_prop = torch.cat([feat_prop, feat_n2], dim=1)
feat_prop = self.deform_align[module_name](feat_prop, cond, flow_n1, flow_n2)
# concatenate and residual blocks
feat = [feat_current] + [feats[k][idx] for k in feats if k not in ['spatial', module_name]] + [feat_prop]
if self.cpu_cache:
feat = [f.cuda() for f in feat]
feat = torch.cat(feat, dim=1)
feat_prop = feat_prop + self.backbone[module_name](feat)
feats[module_name].append(feat_prop)
if self.cpu_cache:
feats[module_name][-1] = feats[module_name][-1].cpu()
torch.cuda.empty_cache()
if 'backward' in module_name:
feats[module_name] = feats[module_name][::-1]
return feats
def upsample(self, lqs, feats):
"""Compute the output image given the features.
Args:
lqs (tensor): Input low quality (LQ) sequence with
shape (n, t, c, h, w).
feats (dict): The features from the propagation branches.
Returns:
Tensor: Output HR sequence with shape (n, t, c, 4h, 4w).
"""
outputs = []
num_outputs = len(feats['spatial'])
mapping_idx = list(range(0, num_outputs))
mapping_idx += mapping_idx[::-1]
for i in range(0, lqs.size(1)):
hr = [feats[k].pop(0) for k in feats if k != 'spatial']
hr.insert(0, feats['spatial'][mapping_idx[i]])
hr = torch.cat(hr, dim=1)
if self.cpu_cache:
hr = hr.cuda()
hr = self.reconstruction(hr)
hr = self.lrelu(self.pixel_shuffle(self.upconv1(hr)))
hr = self.lrelu(self.pixel_shuffle(self.upconv2(hr)))
hr = self.lrelu(self.conv_hr(hr))
hr = self.conv_last(hr)
if self.is_low_res_input:
hr += self.img_upsample(lqs[:, i, :, :, :])
else:
hr += lqs[:, i, :, :, :]
if self.cpu_cache:
hr = hr.cpu()
torch.cuda.empty_cache()
outputs.append(hr)
return torch.stack(outputs, dim=1)
def forward(self, lqs):
"""Forward function for BasicVSR++.
Args:
lqs (tensor): Input low quality (LQ) sequence with
shape (n, t, c, h, w).
Returns:
Tensor: Output HR sequence with shape (n, t, c, 4h, 4w).
"""
n, t, c, h, w = lqs.size()
# whether to cache the features in CPU
self.cpu_cache = True if t > self.cpu_cache_length else False
if self.is_low_res_input:
lqs_downsample = lqs.clone()
else:
lqs_downsample = F.interpolate(
lqs.view(-1, c, h, w), scale_factor=0.25, mode='bicubic').view(n, t, c, h // 4, w // 4)
# check whether the input is an extended sequence
self.check_if_mirror_extended(lqs)
feats = {}
# compute spatial features
if self.cpu_cache:
feats['spatial'] = []
for i in range(0, t):
feat = self.feat_extract(lqs[:, i, :, :, :]).cpu()
feats['spatial'].append(feat)
torch.cuda.empty_cache()
else:
feats_ = self.feat_extract(lqs.view(-1, c, h, w))
h, w = feats_.shape[2:]
feats_ = feats_.view(n, t, -1, h, w)
feats['spatial'] = [feats_[:, i, :, :, :] for i in range(0, t)]
# compute optical flow using the low-res inputs
assert lqs_downsample.size(3) >= 64 and lqs_downsample.size(4) >= 64, (
'The height and width of low-res inputs must be at least 64, '
f'but got {h} and {w}.')
flows_forward, flows_backward = self.compute_flow(lqs_downsample)
# feature propgation
for iter_ in [1, 2]:
for direction in ['backward', 'forward']:
module = f'{direction}_{iter_}'
feats[module] = []
if direction == 'backward':
flows = flows_backward
elif flows_forward is not None:
flows = flows_forward
else:
flows = flows_backward.flip(1)
feats = self.propagate(feats, flows, module)
if self.cpu_cache:
del flows
torch.cuda.empty_cache()
return self.upsample(lqs, feats)
class SecondOrderDeformableAlignment(ModulatedDeformConvPack):
"""Second-order deformable alignment module.
Args:
in_channels (int): Same as nn.Conv2d.
out_channels (int): Same as nn.Conv2d.
kernel_size (int or tuple[int]): Same as nn.Conv2d.
stride (int or tuple[int]): Same as nn.Conv2d.
padding (int or tuple[int]): Same as nn.Conv2d.
dilation (int or tuple[int]): Same as nn.Conv2d.
groups (int): Same as nn.Conv2d.
bias (bool or str): If specified as `auto`, it will be decided by the
norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
False.
max_residue_magnitude (int): The maximum magnitude of the offset
residue (Eq. 6 in paper). Default: 10.
"""
def __init__(self, *args, **kwargs):
self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10)
super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs)
self.conv_offset = nn.Sequential(
nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1),
nn.LeakyReLU(negative_slope=0.1, inplace=True),
nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),
nn.LeakyReLU(negative_slope=0.1, inplace=True),
nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),
nn.LeakyReLU(negative_slope=0.1, inplace=True),
nn.Conv2d(self.out_channels, 27 * self.deformable_groups, 3, 1, 1),
)
self.init_offset()
def init_offset(self):
def _constant_init(module, val, bias=0):
if hasattr(module, 'weight') and module.weight is not None:
nn.init.constant_(module.weight, val)
if hasattr(module, 'bias') and module.bias is not None:
nn.init.constant_(module.bias, bias)
_constant_init(self.conv_offset[-1], val=0, bias=0)
def forward(self, x, extra_feat, flow_1, flow_2):
extra_feat = torch.cat([extra_feat, flow_1, flow_2], dim=1)
out = self.conv_offset(extra_feat)
o1, o2, mask = torch.chunk(out, 3, dim=1)
# offset
offset = self.max_residue_magnitude * torch.tanh(torch.cat((o1, o2), dim=1))
offset_1, offset_2 = torch.chunk(offset, 2, dim=1)
offset_1 = offset_1 + flow_1.flip(1).repeat(1, offset_1.size(1) // 2, 1, 1)
offset_2 = offset_2 + flow_2.flip(1).repeat(1, offset_2.size(1) // 2, 1, 1)
offset = torch.cat([offset_1, offset_2], dim=1)
# mask
mask = torch.sigmoid(mask)
return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding,
self.dilation, mask)
# if __name__ == '__main__':
# spynet_path = 'experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth'
# model = BasicVSRPlusPlus(spynet_path=spynet_path).cuda()
# input = torch.rand(1, 2, 3, 64, 64).cuda()
# output = model(input)
# print('===================')
# print(output.shape)