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import torch |
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from torch import nn |
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from torch.nn import functional as F |
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from collections import OrderedDict |
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class SimpleDecoding(nn.Module): |
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def __init__(self, c4_dims, factor=2): |
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super(SimpleDecoding, self).__init__() |
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hidden_size = c4_dims//factor |
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c4_size = c4_dims |
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c3_size = c4_dims//(factor**1) |
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c2_size = c4_dims//(factor**2) |
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c1_size = c4_dims//(factor**3) |
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self.conv1_4 = nn.Conv2d(c4_size+c3_size, hidden_size, 3, padding=1, bias=False) |
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self.bn1_4 = nn.BatchNorm2d(hidden_size) |
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self.relu1_4 = nn.ReLU() |
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self.conv2_4 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1, bias=False) |
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self.bn2_4 = nn.BatchNorm2d(hidden_size) |
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self.relu2_4 = nn.ReLU() |
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self.conv1_3 = nn.Conv2d(hidden_size + c2_size, hidden_size, 3, padding=1, bias=False) |
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self.bn1_3 = nn.BatchNorm2d(hidden_size) |
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self.relu1_3 = nn.ReLU() |
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self.conv2_3 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1, bias=False) |
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self.bn2_3 = nn.BatchNorm2d(hidden_size) |
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self.relu2_3 = nn.ReLU() |
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self.conv1_2 = nn.Conv2d(hidden_size + c1_size, hidden_size, 3, padding=1, bias=False) |
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self.bn1_2 = nn.BatchNorm2d(hidden_size) |
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self.relu1_2 = nn.ReLU() |
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self.conv2_2 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1, bias=False) |
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self.bn2_2 = nn.BatchNorm2d(hidden_size) |
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self.relu2_2 = nn.ReLU() |
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self.conv1_1 = nn.Conv2d(hidden_size, 2, 1) |
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def forward(self, x_c4, x_c3, x_c2, x_c1): |
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if x_c4.size(-2) < x_c3.size(-2) or x_c4.size(-1) < x_c3.size(-1): |
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x_c4 = F.interpolate(input=x_c4, size=(x_c3.size(-2), x_c3.size(-1)), mode='bilinear', align_corners=True) |
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x = torch.cat([x_c4, x_c3], dim=1) |
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x = self.conv1_4(x) |
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x = self.bn1_4(x) |
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x = self.relu1_4(x) |
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x = self.conv2_4(x) |
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x = self.bn2_4(x) |
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x = self.relu2_4(x) |
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if x.size(-2) < x_c2.size(-2) or x.size(-1) < x_c2.size(-1): |
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x = F.interpolate(input=x, size=(x_c2.size(-2), x_c2.size(-1)), mode='bilinear', align_corners=True) |
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x = torch.cat([x, x_c2], dim=1) |
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x = self.conv1_3(x) |
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x = self.bn1_3(x) |
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x = self.relu1_3(x) |
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x = self.conv2_3(x) |
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x = self.bn2_3(x) |
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x = self.relu2_3(x) |
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if x.size(-2) < x_c1.size(-2) or x.size(-1) < x_c1.size(-1): |
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x = F.interpolate(input=x, size=(x_c1.size(-2), x_c1.size(-1)), mode='bilinear', align_corners=True) |
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x = torch.cat([x, x_c1], dim=1) |
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x = self.conv1_2(x) |
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x = self.bn1_2(x) |
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x = self.relu1_2(x) |
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x = self.conv2_2(x) |
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x = self.bn2_2(x) |
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x = self.relu2_2(x) |
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return self.conv1_1(x) |
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