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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 | |