File size: 4,050 Bytes
2a41a22 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | 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) if config.batch_size > 1 else nn.Identity()
self.bn_out = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
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) if config.batch_size > 1 else nn.Identity()
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) if config.batch_size > 1 else nn.Identity()
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
|