| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from .layers import * |
| class PAA_d(nn.Module): |
| def __init__(self, in_channel, out_channel=1, depth=64, base_size=None, stage=None): |
| super(PAA_d, self).__init__() |
| self.conv1 = Conv2d(in_channel ,depth, 3) |
| self.conv2 = Conv2d(depth, depth, 3) |
| self.conv3 = Conv2d(depth, depth, 3) |
| self.conv4 = Conv2d(depth, depth, 3) |
| self.conv5 = Conv2d(depth, out_channel, 3, bn=False) |
| |
| self.base_size = base_size |
| self.stage = stage |
| |
| 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, None] |
|
|
| self.Hattn = SelfAttention(depth, 'h', self.stage_size[0]) |
| self.Wattn = SelfAttention(depth, 'w', self.stage_size[1]) |
|
|
| self.upsample = lambda img, size: F.interpolate(img, size=size, mode='bilinear', align_corners=True) |
|
|
| def forward(self, fs): |
| fx = fs[0] |
| for i in range(1, len(fs)): |
| fs[i] = self.upsample(fs[i], fx.shape[-2:]) |
| fx = torch.cat(fs[::-1], dim=1) |
|
|
| fx = self.conv1(fx) |
|
|
| Hfx = self.Hattn(fx) |
| Wfx = self.Wattn(fx) |
|
|
| fx = self.conv2(Hfx + Wfx) |
| fx = self.conv3(fx) |
| fx = self.conv4(fx) |
| out = self.conv5(fx) |
|
|
| return fx, out |
