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import torch |
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from torch import nn |
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import math |
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import torch.nn.functional as F |
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class SelfAttention(nn.Module): |
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def __init__(self, chs, num_heads=1, ffn_expansion=4, dropout=0.1): |
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super().__init__() |
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self.norm = nn.LayerNorm(chs) |
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self.attn = nn.MultiheadAttention(embed_dim=chs, num_heads=num_heads, batch_first=True) |
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self.ffn = nn.Sequential( |
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nn.Linear(chs, chs*ffn_expansion), |
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nn.GELU(), |
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nn.Linear(chs*ffn_expansion, chs) |
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) |
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self.norm2 = nn.LayerNorm(chs) |
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self.dropout = nn.Dropout(dropout) |
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def forward(self, x): |
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b,c,h,w = x.shape |
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x_reshaped = x.view(b,c,h*w).transpose(1,2) |
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attn_out, _ = self.attn(self.norm(x_reshaped), self.norm(x_reshaped), self.norm(x_reshaped)) |
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x_attn = x_reshaped + self.dropout(attn_out) |
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ffn_out = self.ffn(self.norm2(x_attn)) |
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x_out = x_attn + self.dropout(ffn_out) |
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x_out = x_out.transpose(1,2).view(b,c,h,w) |
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return x_out |
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class CBAM(nn.Module): |
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def __init__(self,chs, reduction=16): |
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super().__init__() |
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self.channel_attn = nn.Sequential( |
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nn.AdaptiveAvgPool2d(1), |
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nn.Conv2d(chs, chs//reduction, 1), |
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nn.ReLU(), |
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nn.Conv2d(chs//reduction, chs, 1), |
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nn.Sigmoid() |
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) |
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self.spatial_attn = nn.Sequential( |
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nn.Conv2d(2,1,kernel_size=7,padding=3), |
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nn.Sigmoid() |
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) |
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def forward(self,x): |
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ch_wt = self.channel_attn(x) |
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x = x*ch_wt |
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avg_pool = torch.mean(x, dim=1, keepdim=True) |
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max_pool, _ = torch.max(x, dim=1, keepdim=True) |
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sp_wt = self.spatial_attn(torch.cat([avg_pool, max_pool], dim=1)) |
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x = x* sp_wt |
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return x |
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class Block_CBAM(nn.Module): |
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def __init__(self, in_ch, out_ch, time_emb_dim, up=False): |
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super().__init__() |
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self.time_mlp = nn.Linear(time_emb_dim, out_ch) |
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if up: |
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self.conv1 = nn.Conv2d(2*in_ch, out_ch, 3, padding=1) |
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self.transform = nn.ConvTranspose2d(out_ch, out_ch, 4, 2, 1) |
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else: |
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self.conv1 = nn.Conv2d(in_ch, out_ch, 3, padding=1) |
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self.transform = nn.Conv2d(out_ch, out_ch, 4,2,1) |
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self.conv2 = nn.Conv2d(out_ch, out_ch, 3, padding=1) |
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self.relu = nn.ReLU() |
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self.batch_norm1 = nn.BatchNorm2d(out_ch) |
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self.batch_norm2 = nn.BatchNorm2d(out_ch) |
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self.cbam = CBAM(out_ch) |
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def forward(self, x, t, ): |
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h = self.batch_norm1(self.relu(self.conv1(x))) |
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time_emb = self.relu(self.time_mlp(t)) |
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time_emb = time_emb[(..., ) + (None, ) * 2] |
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h = h + time_emb |
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h = self.batch_norm2(self.relu(self.conv2(h))) |
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h = self.cbam(h) |
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return self.transform(h) |
