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import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
import math
class SelfAttention(nn.Module):
def __init__(self, ip_dim, att_dim):
super(SelfAttention, self).__init__()
# Note, does not encode position information (absolute or realtive)
self.temperature = 1.0
self.att_dim = att_dim
self.key_fun = nn.Linear(ip_dim, att_dim)
self.val_fun = nn.Linear(ip_dim, att_dim)
self.que_fun = nn.Linear(ip_dim, att_dim)
self.pro_fun = nn.Linear(att_dim, ip_dim)
def forward(self, x):
x = x.squeeze(2).permute(0,2,1)
kk = torch.matmul(x, self.key_fun.weight.T) + self.key_fun.bias.unsqueeze(0).unsqueeze(0)
qq = torch.matmul(x, self.que_fun.weight.T) + self.que_fun.bias.unsqueeze(0).unsqueeze(0)
vv = torch.matmul(x, self.val_fun.weight.T) + self.val_fun.bias.unsqueeze(0).unsqueeze(0)
kk_qq = torch.bmm(kk, qq.permute(0,2,1)) / (self.temperature*self.att_dim)
att_weights = F.softmax(kk_qq, 1) # each col of each attention matrix sums to 1
att = torch.bmm(vv.permute(0,2,1), att_weights)
op = torch.matmul(att.permute(0,2,1), self.pro_fun.weight.T) + self.pro_fun.bias.unsqueeze(0).unsqueeze(0)
op = op.permute(0,2,1).unsqueeze(2)
return op
class ConvBlockDownCoordF(nn.Module):
def __init__(self, in_chn, out_chn, ip_height, k_size=3, pad_size=1, stride=1):
super(ConvBlockDownCoordF, self).__init__()
self.coords = nn.Parameter(torch.linspace(-1, 1, ip_height)[None, None, ..., None], requires_grad=False)
self.conv = nn.Conv2d(in_chn+1, out_chn, kernel_size=k_size, padding=pad_size, stride=stride)
self.conv_bn = nn.BatchNorm2d(out_chn)
def forward(self, x):
freq_info = self.coords.repeat(x.shape[0],1,1,x.shape[3])
x = torch.cat((x, freq_info), 1)
x = F.max_pool2d(self.conv(x), 2, 2)
x = F.relu(self.conv_bn(x), inplace=True)
return x
class ConvBlockDownStandard(nn.Module):
def __init__(self, in_chn, out_chn, ip_height=None, k_size=3, pad_size=1, stride=1):
super(ConvBlockDownStandard, self).__init__()
self.conv = nn.Conv2d(in_chn, out_chn, kernel_size=k_size, padding=pad_size, stride=stride)
self.conv_bn = nn.BatchNorm2d(out_chn)
def forward(self, x):
x = F.max_pool2d(self.conv(x), 2, 2)
x = F.relu(self.conv_bn(x), inplace=True)
return x
class ConvBlockUpF(nn.Module):
def __init__(self, in_chn, out_chn, ip_height, k_size=3, pad_size=1, up_mode='bilinear', up_scale=(2,2)):
super(ConvBlockUpF, self).__init__()
self.up_scale = up_scale
self.up_mode = up_mode
self.coords = nn.Parameter(torch.linspace(-1, 1, ip_height*up_scale[0])[None, None, ..., None], requires_grad=False)
self.conv = nn.Conv2d(in_chn+1, out_chn, kernel_size=k_size, padding=pad_size)
self.conv_bn = nn.BatchNorm2d(out_chn)
def forward(self, x):
op = F.interpolate(x, size=(x.shape[-2]*self.up_scale[0], x.shape[-1]*self.up_scale[1]), mode=self.up_mode, align_corners=False)
freq_info = self.coords.repeat(op.shape[0],1,1,op.shape[3])
op = torch.cat((op, freq_info), 1)
op = self.conv(op)
op = F.relu(self.conv_bn(op), inplace=True)
return op
class ConvBlockUpStandard(nn.Module):
def __init__(self, in_chn, out_chn, ip_height=None, k_size=3, pad_size=1, up_mode='bilinear', up_scale=(2,2)):
super(ConvBlockUpStandard, self).__init__()
self.up_scale = up_scale
self.up_mode = up_mode
self.conv = nn.Conv2d(in_chn, out_chn, kernel_size=k_size, padding=pad_size)
self.conv_bn = nn.BatchNorm2d(out_chn)
def forward(self, x):
op = F.interpolate(x, size=(x.shape[-2]*self.up_scale[0], x.shape[-1]*self.up_scale[1]), mode=self.up_mode, align_corners=False)
op = self.conv(op)
op = F.relu(self.conv_bn(op), inplace=True)
return op
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