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common.py
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common.py
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import torch.nn as nn
import math
import torch
import torch.nn.functional as F
def default_conv(in_channels, out_channels, kernel_size, stride=1, bias=True, dilation=1):
if dilation==1:
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2), bias=bias)
elif dilation==2:
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=2, bias=bias, dilation=dilation)
else:
padding = int((kernel_size - 1) / 2) * dilation
return nn.Conv2d(
in_channels, out_channels, kernel_size,
stride, padding=padding, bias=bias, dilation=dilation)
class CALayer(nn.Module):
def __init__(self, channel, reduction=16):
super(CALayer, self).__init__()
# global average pooling: feature --> point
self.avg_pool = nn.AdaptiveAvgPool2d(1)
# feature channel downscale and upscale --> channel weight
self.conv_du = nn.Sequential(
nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True),
nn.Sigmoid()
)
def forward(self, x):
y = self.avg_pool(x)
y = self.conv_du(y)
return x * y
def mean_channels(F):
assert(F.dim() == 4)
spatial_sum = F.sum(3, keepdim=True).sum(2, keepdim=True)
return spatial_sum / (F.size(2) * F.size(3))
def stdv_channels(F):
assert(F.dim() == 4)
F_mean = mean_channels(F)
F_variance = (F - F_mean).pow(2).sum(3, keepdim=True).sum(2, keepdim=True) / (F.size(2) * F.size(3))
return F_variance.pow(0.5)
class ResBlock(nn.Module):
def __init__(self, conv, n_feats, kernel_size, bias=True, bn=False, act=nn.ReLU(True), res_scale=1):
super(ResBlock, self).__init__()
m = []
for i in range(2):
m.append(conv(n_feats, n_feats, kernel_size, bias=bias))
if bn:
m.append(nn.BatchNorm2d(n_feats))
if i == 0:
m.append(act)
self.body = nn.Sequential(*m)
self.res_scale = res_scale
def forward(self, x):
res = self.body(x).mul(self.res_scale)
res += x
return res
class ResAttentionBlock(nn.Module):
def __init__(self, conv, n_feats, kernel_size, bias=True, bn=False, act=nn.ReLU(True), res_scale=1):
super(ResAttentionBlock, self).__init__()
m = []
for i in range(2):
m.append(conv(n_feats, n_feats, kernel_size, bias=bias))
if bn:
m.append(nn.BatchNorm2d(n_feats))
if i == 0:
m.append(act)
m.append(CALayer(n_feats, 16))
self.body = nn.Sequential(*m)
self.res_scale = res_scale
def forward(self, x):
res = self.body(x).mul(self.res_scale)
res += x
return res
class Upsampler(nn.Sequential):
def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True):
m = []
if (scale & (scale - 1)) == 0: # Is scale = 2^n?
for _ in range(int(math.log(scale, 2))):
m.append(conv(n_feats, 4 * n_feats, 3, bias))
m.append(nn.PixelShuffle(2))
if bn:
m.append(nn.BatchNorm2d(n_feats))
if act == 'relu':
m.append(nn.ReLU(True))
elif act == 'prelu':
m.append(nn.PReLU(n_feats))
elif scale == 3:
m.append(conv(n_feats, 9 * n_feats, 3, bias))
m.append(nn.PixelShuffle(3))
if bn:
m.append(nn.BatchNorm2d(n_feats))
if act == 'relu':
m.append(nn.ReLU(True))
elif act == 'prelu':
m.append(nn.PReLU(n_feats))
else:
raise NotImplementedError
super(Upsampler, self).__init__(*m)
class DeformConv2d(nn.Module):
def __init__(self, inc, outc, kernel_size=3, padding=1, stride=1, bias=None, modulation=False):
"""
Args:
modulation (bool, optional): If True, Modulated Defomable Convolution (Deformable ConvNets v2).
"""
super(DeformConv2d, self).__init__()
self.kernel_size = kernel_size
self.padding = padding
self.stride = stride
self.zero_padding = nn.ZeroPad2d(padding)
self.conv = nn.Conv2d(inc, outc, kernel_size=kernel_size, stride=kernel_size, bias=bias)
self.p_conv = nn.Conv2d(inc, 2*kernel_size*kernel_size, kernel_size=3, padding=1, stride=stride)
nn.init.constant_(self.p_conv.weight, 0)
self.p_conv.register_backward_hook(self._set_lr)
self.modulation = modulation
if modulation:
self.m_conv = nn.Conv2d(inc, kernel_size*kernel_size, kernel_size=3, padding=1, stride=stride)
nn.init.constant_(self.m_conv.weight, 0)
self.m_conv.register_backward_hook(self._set_lr)
@staticmethod
def _set_lr(module, grad_input, grad_output):
grad_input = (grad_input[i] * 0.1 for i in range(len(grad_input)))
grad_output = (grad_output[i] * 0.1 for i in range(len(grad_output)))
def forward(self, x):
offset = self.p_conv(x)
if self.modulation:
m = torch.sigmoid(self.m_conv(x))
dtype = offset.data.type()
ks = self.kernel_size
N = offset.size(1) // 2
if self.padding:
x = self.zero_padding(x)
# (b, 2N, h, w)
p = self._get_p(offset, dtype)
# (b, h, w, 2N)
p = p.contiguous().permute(0, 2, 3, 1)
q_lt = p.detach().floor()
q_rb = q_lt + 1
q_lt = torch.cat([torch.clamp(q_lt[..., :N], 0, x.size(2)-1), torch.clamp(q_lt[..., N:], 0, x.size(3)-1)], dim=-1).long()
q_rb = torch.cat([torch.clamp(q_rb[..., :N], 0, x.size(2)-1), torch.clamp(q_rb[..., N:], 0, x.size(3)-1)], dim=-1).long()
q_lb = torch.cat([q_lt[..., :N], q_rb[..., N:]], dim=-1)
q_rt = torch.cat([q_rb[..., :N], q_lt[..., N:]], dim=-1)
# clip p
p = torch.cat([torch.clamp(p[..., :N], 0, x.size(2)-1), torch.clamp(p[..., N:], 0, x.size(3)-1)], dim=-1)
# bilinear kernel (b, h, w, N)
g_lt = (1 + (q_lt[..., :N].type_as(p) - p[..., :N])) * (1 + (q_lt[..., N:].type_as(p) - p[..., N:]))
g_rb = (1 - (q_rb[..., :N].type_as(p) - p[..., :N])) * (1 - (q_rb[..., N:].type_as(p) - p[..., N:]))
g_lb = (1 + (q_lb[..., :N].type_as(p) - p[..., :N])) * (1 - (q_lb[..., N:].type_as(p) - p[..., N:]))
g_rt = (1 - (q_rt[..., :N].type_as(p) - p[..., :N])) * (1 + (q_rt[..., N:].type_as(p) - p[..., N:]))
# (b, c, h, w, N)
x_q_lt = self._get_x_q(x, q_lt, N)
x_q_rb = self._get_x_q(x, q_rb, N)
x_q_lb = self._get_x_q(x, q_lb, N)
x_q_rt = self._get_x_q(x, q_rt, N)
# (b, c, h, w, N)
x_offset = g_lt.unsqueeze(dim=1) * x_q_lt + \
g_rb.unsqueeze(dim=1) * x_q_rb + \
g_lb.unsqueeze(dim=1) * x_q_lb + \
g_rt.unsqueeze(dim=1) * x_q_rt
# modulation
if self.modulation:
m = m.contiguous().permute(0, 2, 3, 1)
m = m.unsqueeze(dim=1)
m = torch.cat([m for _ in range(x_offset.size(1))], dim=1)
x_offset *= m
x_offset = self._reshape_x_offset(x_offset, ks)
out = self.conv(x_offset)
return out
def _get_p_n(self, N, dtype):
p_n_x, p_n_y = torch.meshgrid(
torch.arange(-(self.kernel_size-1)//2, (self.kernel_size-1)//2+1),
torch.arange(-(self.kernel_size-1)//2, (self.kernel_size-1)//2+1))
# (2N, 1)
p_n = torch.cat([torch.flatten(p_n_x), torch.flatten(p_n_y)], 0)
p_n = p_n.view(1, 2*N, 1, 1).type(dtype)
return p_n
def _get_p_0(self, h, w, N, dtype):
p_0_x, p_0_y = torch.meshgrid(
torch.arange(1, h*self.stride+1, self.stride),
torch.arange(1, w*self.stride+1, self.stride))
p_0_x = torch.flatten(p_0_x).view(1, 1, h, w).repeat(1, N, 1, 1)
p_0_y = torch.flatten(p_0_y).view(1, 1, h, w).repeat(1, N, 1, 1)
p_0 = torch.cat([p_0_x, p_0_y], 1).type(dtype)
return p_0
def _get_p(self, offset, dtype):
N, h, w = offset.size(1)//2, offset.size(2), offset.size(3)
# (1, 2N, 1, 1)
p_n = self._get_p_n(N, dtype)
# (1, 2N, h, w)
p_0 = self._get_p_0(h, w, N, dtype)
p = p_0 + p_n + offset
return p
def _get_x_q(self, x, q, N):
b, h, w, _ = q.size()
padded_w = x.size(3)
c = x.size(1)
# (b, c, h*w)
x = x.contiguous().view(b, c, -1)
# (b, h, w, N)
index = q[..., :N]*padded_w + q[..., N:] # offset_x*w + offset_y
# (b, c, h*w*N)
index = index.contiguous().unsqueeze(dim=1).expand(-1, c, -1, -1, -1).contiguous().view(b, c, -1)
x_offset = x.gather(dim=-1, index=index).contiguous().view(b, c, h, w, N)
return x_offset
@staticmethod
def _reshape_x_offset(x_offset, ks):
b, c, h, w, N = x_offset.size()
x_offset = torch.cat([x_offset[..., s:s+ks].contiguous().view(b, c, h, w*ks) for s in range(0, N, ks)], dim=-1)
x_offset = x_offset.contiguous().view(b, c, h*ks, w*ks)
return x_offset