import math
import torch
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
@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)