/
rn.py
130 lines (99 loc) · 4.62 KB
/
rn.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class RN_binarylabel(nn.Module):
def __init__(self, feature_channels):
super(RN_binarylabel, self).__init__()
self.bn_norm = nn.BatchNorm2d(feature_channels, affine=False, track_running_stats=False)
def forward(self, x, label):
'''
input: x: (B,C,M,N), features
label: (B,1,M,N), 1 for foreground regions, 0 for background regions
output: _x: (B,C,M,N)
'''
label = label.detach()
rn_foreground_region = self.rn(x * label, label)
rn_background_region = self.rn(x * (1 - label), 1 - label)
return rn_foreground_region + rn_background_region
def rn(self, region, mask):
'''
input: region: (B,C,M,N), 0 for surroundings
mask: (B,1,M,N), 1 for target region, 0 for surroundings
output: rn_region: (B,C,M,N)
'''
shape = region.size()
sum = torch.sum(region, dim=[0,2,3]) # (B, C) -> (C)
Sr = torch.sum(mask, dim=[0,2,3]) # (B, 1) -> (1)
Sr[Sr==0] = 1
mu = (sum / Sr) # (B, C) -> (C)
return self.bn_norm(region + (1 - mask) * mu[None,:,None,None]) * \
(torch.sqrt(Sr / (shape[0] * shape[2] * shape[3])))[None,:,None,None]
class RN_B(nn.Module):
def __init__(self, feature_channels):
super(RN_B, self).__init__()
'''
input: tensor(features) x: (B,C,M,N)
condition Mask: (B,1,H,W): 0 for background, 1 for foreground
return: tensor RN_B(x): (N,C,M,N)
---------------------------------------
args:
feature_channels: C
'''
# RN
self.rn = RN_binarylabel(feature_channels) # need no external parameters
# gamma and beta
self.foreground_gamma = nn.Parameter(torch.zeros(feature_channels), requires_grad=True)
self.foreground_beta = nn.Parameter(torch.zeros(feature_channels), requires_grad=True)
self.background_gamma = nn.Parameter(torch.zeros(feature_channels), requires_grad=True)
self.background_beta = nn.Parameter(torch.zeros(feature_channels), requires_grad=True)
def forward(self, x, mask):
# mask = F.adaptive_max_pool2d(mask, output_size=x.size()[2:])
mask = F.interpolate(mask, size=x.size()[2:], mode='nearest') # after down-sampling, there can be all-zero mask
rn_x = self.rn(x, mask)
rn_x_foreground = (rn_x * mask) * (1 + self.foreground_gamma[None,:,None,None]) + self.foreground_beta[None,:,None,None]
rn_x_background = (rn_x * (1 - mask)) * (1 + self.background_gamma[None,:,None,None]) + self.background_beta[None,:,None,None]
return rn_x_foreground + rn_x_background
class SelfAware_Affine(nn.Module):
def __init__(self, kernel_size=7):
super(SelfAware_Affine, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
self.sigmoid = nn.Sigmoid()
self.gamma_conv = nn.Conv2d(1, 1, kernel_size, padding=padding)
self.beta_conv = nn.Conv2d(1, 1, kernel_size, padding=padding)
def forward(self, x):
avg_out = torch.mean(x, dim=1, keepdim=True)
max_out, _ = torch.max(x, dim=1, keepdim=True)
x = torch.cat([avg_out, max_out], dim=1)
x = self.conv1(x)
importance_map = self.sigmoid(x)
gamma = self.gamma_conv(importance_map)
beta = self.beta_conv(importance_map)
return importance_map, gamma, beta
class RN_L(nn.Module):
def __init__(self, feature_channels, threshold=0.8):
super(RN_L, self).__init__()
'''
input: tensor(features) x: (B,C,M,N)
return: tensor RN_L(x): (B,C,M,N)
---------------------------------------
args:
feature_channels: C
'''
# SelfAware_Affine
self.sa = SelfAware_Affine()
self.threshold = threshold
# RN
self.rn = RN_binarylabel(feature_channels) # need no external parameters
def forward(self, x):
sa_map, gamma, beta = self.sa(x) # (B,1,M,N)
# m = sa_map.detach()
if x.is_cuda:
mask = torch.zeros_like(sa_map).cuda()
else:
mask = torch.zeros_like(sa_map)
mask[sa_map.detach() >= self.threshold] = 1
rn_x = self.rn(x, mask.expand(x.size()))
rn_x = rn_x * (1 + gamma) + beta
return rn_x