| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from .layers import * | |
| class PAA_kernel(nn.Module): | |
| def __init__(self, in_channel, out_channel, receptive_size, stage_size=None): | |
| super(PAA_kernel, self).__init__() | |
| self.conv0 = Conv2d(in_channel, out_channel, 1) | |
| self.conv1 = Conv2d(out_channel, out_channel, kernel_size=(1, receptive_size)) | |
| self.conv2 = Conv2d(out_channel, out_channel, kernel_size=(receptive_size, 1)) | |
| self.conv3 = Conv2d(out_channel, out_channel, 3, dilation=receptive_size) | |
| self.Hattn = SelfAttention(out_channel, 'h', stage_size[0] if stage_size is not None else None) | |
| self.Wattn = SelfAttention(out_channel, 'w', stage_size[1] if stage_size is not None else None) | |
| def forward(self, x): | |
| x = self.conv0(x) | |
| x = self.conv1(x) | |
| x = self.conv2(x) | |
| Hx = self.Hattn(x) | |
| Wx = self.Wattn(x) | |
| x = self.conv3(Hx + Wx) | |
| return x | |
| class PAA_e(nn.Module): | |
| def __init__(self, in_channel, out_channel, base_size=None, stage=None): | |
| super(PAA_e, self).__init__() | |
| self.relu = nn.ReLU(True) | |
| if base_size is not None and stage is not None: | |
| self.stage_size = (base_size[0] // (2 ** stage), base_size[1] // (2 ** stage)) | |
| else: | |
| self.stage_size = None | |
| self.branch0 = Conv2d(in_channel, out_channel, 1) | |
| self.branch1 = PAA_kernel(in_channel, out_channel, 3, self.stage_size) | |
| self.branch2 = PAA_kernel(in_channel, out_channel, 5, self.stage_size) | |
| self.branch3 = PAA_kernel(in_channel, out_channel, 7, self.stage_size) | |
| self.conv_cat = Conv2d(4 * out_channel, out_channel, 3) | |
| self.conv_res = Conv2d(in_channel, out_channel, 1) | |
| def forward(self, x): | |
| x0 = self.branch0(x) | |
| x1 = self.branch1(x) | |
| x2 = self.branch2(x) | |
| x3 = self.branch3(x) | |
| x_cat = self.conv_cat(torch.cat((x0, x1, x2, x3), 1)) | |
| x = self.relu(x_cat + self.conv_res(x)) | |
| return x | |