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Running
on
Zero
File size: 3,874 Bytes
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import torch
import torch.nn as nn
from models.modules.aspp import ASPP, ASPPDeformable
from models.modules.attentions import PSA, SGE
from config import Config
config = Config()
class BasicDecBlk(nn.Module):
def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
super(BasicDecBlk, self).__init__()
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
self.relu_in = nn.ReLU(inplace=True)
if config.dec_att == 'ASPP':
self.dec_att = ASPP(in_channels=inter_channels)
elif config.dec_att == 'ASPPDeformable':
self.dec_att = ASPPDeformable(in_channels=inter_channels)
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
self.bn_in = nn.BatchNorm2d(inter_channels)
self.bn_out = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv_in(x)
x = self.bn_in(x)
x = self.relu_in(x)
if hasattr(self, 'dec_att'):
x = self.dec_att(x)
x = self.conv_out(x)
x = self.bn_out(x)
return x
class ResBlk(nn.Module):
def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
super(ResBlk, self).__init__()
if out_channels is None:
out_channels = in_channels
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
self.bn_in = nn.BatchNorm2d(inter_channels)
self.relu_in = nn.ReLU(inplace=True)
if config.dec_att == 'ASPP':
self.dec_att = ASPP(in_channels=inter_channels)
elif config.dec_att == 'ASPPDeformable':
self.dec_att = ASPPDeformable(in_channels=inter_channels)
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
self.bn_out = nn.BatchNorm2d(out_channels)
self.conv_resi = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
def forward(self, x):
_x = self.conv_resi(x)
x = self.conv_in(x)
x = self.bn_in(x)
x = self.relu_in(x)
if hasattr(self, 'dec_att'):
x = self.dec_att(x)
x = self.conv_out(x)
x = self.bn_out(x)
return x + _x
class HierarAttDecBlk(nn.Module):
def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
super(HierarAttDecBlk, self).__init__()
if out_channels is None:
out_channels = in_channels
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
self.split_y = 8 # must be divided by channels of all intermediate features
self.split_x = 8
self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, 1)
self.psa = PSA(inter_channels*self.split_y*self.split_x, S=config.batch_size)
self.sge = SGE(groups=config.batch_size)
if config.dec_att == 'ASPP':
self.dec_att = ASPP(in_channels=inter_channels)
elif config.dec_att == 'ASPPDeformable':
self.dec_att = ASPPDeformable(in_channels=inter_channels)
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1)
def forward(self, x):
x = self.conv_in(x)
N, C, H, W = x.shape
x_patchs = x.reshape(N, -1, H//self.split_y, W//self.split_x)
# Hierarchical attention: group attention X patch spatial attention
x_patchs = self.psa(x_patchs) # Group Channel Attention -- each group is a single image
x_patchs = self.sge(x_patchs) # Patch Spatial Attention
x = x.reshape(N, C, H, W)
if hasattr(self, 'dec_att'):
x = self.dec_att(x)
x = self.conv_out(x)
return x
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