admin/LC_NeoRefacer
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Add codeformer and update license

Browse Source
This commit is contained in:
Felipe Daragon
2025-04-09 23:03:26 +01:00
parent f40ffde905
commit 3549b7e50c
84 changed files with 12545 additions and 19 deletions
Download Patch File Download Diff File Expand all files Collapse all files

318
basicsr/archs/arch_util.py Normal file
View File

@@ -0,0 +1,318 @@
import collections.abc
import math
import torch
import torchvision
import warnings
from distutils.version import LooseVersion
from itertools import repeat
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv
from basicsr.utils import get_root_logger
@torch.no_grad()
def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
"""Initialize network weights.
Args:
module_list (list[nn.Module] | nn.Module): Modules to be initialized.
scale (float): Scale initialized weights, especially for residual
blocks. Default: 1.
bias_fill (float): The value to fill bias. Default: 0
kwargs (dict): Other arguments for initialization function.
"""
if not isinstance(module_list, list):
module_list = [module_list]
for module in module_list:
for m in module.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, **kwargs)
m.weight.data *= scale
if m.bias is not None:
m.bias.data.fill_(bias_fill)
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight, **kwargs)
m.weight.data *= scale
if m.bias is not None:
m.bias.data.fill_(bias_fill)
elif isinstance(m, _BatchNorm):
init.constant_(m.weight, 1)
if m.bias is not None:
m.bias.data.fill_(bias_fill)
def make_layer(basic_block, num_basic_block, **kwarg):
"""Make layers by stacking the same blocks.
Args:
basic_block (nn.module): nn.module class for basic block.
num_basic_block (int): number of blocks.
Returns:
nn.Sequential: Stacked blocks in nn.Sequential.
"""
layers = []
for _ in range(num_basic_block):
layers.append(basic_block(**kwarg))
return nn.Sequential(*layers)
class ResidualBlockNoBN(nn.Module):
"""Residual block without BN.
It has a style of:
---Conv-ReLU-Conv-+-
|________________|
Args:
num_feat (int): Channel number of intermediate features.
Default: 64.
res_scale (float): Residual scale. Default: 1.
pytorch_init (bool): If set to True, use pytorch default init,
otherwise, use default_init_weights. Default: False.
"""
def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
super(ResidualBlockNoBN, self).__init__()
self.res_scale = res_scale
self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
self.relu = nn.ReLU(inplace=True)
if not pytorch_init:
default_init_weights([self.conv1, self.conv2], 0.1)
def forward(self, x):
identity = x
out = self.conv2(self.relu(self.conv1(x)))
return identity + out * self.res_scale
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)
def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
"""Warp an image or feature map with optical flow.
Args:
x (Tensor): Tensor with size (n, c, h, w).
flow (Tensor): Tensor with size (n, h, w, 2), normal value.
interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
padding_mode (str): 'zeros' or 'border' or 'reflection'.
Default: 'zeros'.
align_corners (bool): Before pytorch 1.3, the default value is
align_corners=True. After pytorch 1.3, the default value is
align_corners=False. Here, we use the True as default.
Returns:
Tensor: Warped image or feature map.
"""
assert x.size()[-2:] == flow.size()[1:3]
_, _, h, w = x.size()
# create mesh grid
grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
grid = torch.stack((grid_x, grid_y), 2).float() # W(x), H(y), 2
grid.requires_grad = False
vgrid = grid + flow
# scale grid to [-1,1]
vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)
# TODO, what if align_corners=False
return output
def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
"""Resize a flow according to ratio or shape.
Args:
flow (Tensor): Precomputed flow. shape [N, 2, H, W].
size_type (str): 'ratio' or 'shape'.
sizes (list[int | float]): the ratio for resizing or the final output
shape.
1) The order of ratio should be [ratio_h, ratio_w]. For
downsampling, the ratio should be smaller than 1.0 (i.e., ratio
< 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
ratio > 1.0).
2) The order of output_size should be [out_h, out_w].
interp_mode (str): The mode of interpolation for resizing.
Default: 'bilinear'.
align_corners (bool): Whether align corners. Default: False.
Returns:
Tensor: Resized flow.
"""
_, _, flow_h, flow_w = flow.size()
if size_type == 'ratio':
output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
elif size_type == 'shape':
output_h, output_w = sizes[0], sizes[1]
else:
raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')
input_flow = flow.clone()
ratio_h = output_h / flow_h
ratio_w = output_w / flow_w
input_flow[:, 0, :, :] *= ratio_w
input_flow[:, 1, :, :] *= ratio_h
resized_flow = F.interpolate(
input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners)
return resized_flow
# TODO: may write a cpp file
def pixel_unshuffle(x, scale):
""" Pixel unshuffle.
Args:
x (Tensor): Input feature with shape (b, c, hh, hw).
scale (int): Downsample ratio.
Returns:
Tensor: the pixel unshuffled feature.
"""
b, c, hh, hw = x.size()
out_channel = c * (scale**2)
assert hh % scale == 0 and hw % scale == 0
h = hh // scale
w = hw // scale
x_view = x.view(b, c, h, scale, w, scale)
return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
class DCNv2Pack(ModulatedDeformConvPack):
"""Modulated deformable conv for deformable alignment.
Different from the official DCNv2Pack, which generates offsets and masks
from the preceding features, this DCNv2Pack takes another different
features to generate offsets and masks.
Ref:
Delving Deep into Deformable Alignment in Video Super-Resolution.
"""
def forward(self, x, feat):
out = self.conv_offset(feat)
o1, o2, mask = torch.chunk(out, 3, dim=1)
offset = torch.cat((o1, o2), dim=1)
mask = torch.sigmoid(mask)
offset_absmean = torch.mean(torch.abs(offset))
if offset_absmean > 50:
logger = get_root_logger()
logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.')
if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'):
return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding,
self.dilation, mask)
else:
return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding,
self.dilation, self.groups, self.deformable_groups)
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
# From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):
# Computes standard normal cumulative distribution function
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn(
'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
'The distribution of values may be incorrect.',
stacklevel=2)
with torch.no_grad():
# Values are generated by using a truncated uniform distribution and
# then using the inverse CDF for the normal distribution.
# Get upper and lower cdf values
low = norm_cdf((a - mean) / std)
up = norm_cdf((b - mean) / std)
# Uniformly fill tensor with values from [low, up], then translate to
# [2l-1, 2u-1].
tensor.uniform_(2 * low - 1, 2 * up - 1)
# Use inverse cdf transform for normal distribution to get truncated
# standard normal
tensor.erfinv_()
# Transform to proper mean, std
tensor.mul_(std * math.sqrt(2.))
tensor.add_(mean)
# Clamp to ensure it's in the proper range
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
r"""Fills the input Tensor with values drawn from a truncated
normal distribution.
From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
# From PyTorch
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable):
return x
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple