98 lines
2.8 KiB
Python
98 lines
2.8 KiB
Python
import torch.nn as nn
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import torch.nn.functional as F
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import torch
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class ResNet(nn.Module):
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def __init__(self, n_in, n_classes):
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super(ResNet, self).__init__()
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self.n_in = n_in
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self.n_classes = n_classes
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blocks = [1, 64, 128, 128]
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self.blocks = nn.ModuleList()
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for b, _ in enumerate(blocks[:-1]):
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self.blocks.append(ResidualBlock(*blocks[b:b + 2], self.n_in))
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self.fc1 = nn.Linear(blocks[-1], self.n_classes)
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def forward(self, x: torch.Tensor):
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for block in self.blocks:
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x = block(x)
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x = F.adaptive_avg_pool2d(x, 1)
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x = x.view(-1, 1, 128)
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x = self.fc1(x)
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# x = F.log_softmax(x,1)
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return x.view(-1, self.n_classes)
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class ResidualBlock(nn.Module):
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def __init__(self, in_maps, out_maps, time_steps):
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super(ResidualBlock, self).__init__()
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self.in_maps = in_maps
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self.out_maps = out_maps
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self.time_steps = time_steps
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self.conv1 = nn.Conv2d(self.in_maps, self.out_maps, (7, 1), 1, (3, 0))
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self.bn1 = nn.BatchNorm2d(self.out_maps)
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self.conv2 = nn.Conv2d(self.out_maps, self.out_maps, (5, 1), 1, (2, 0))
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self.bn2 = nn.BatchNorm2d(self.out_maps)
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self.conv3 = nn.Conv2d(self.out_maps, self.out_maps, (3, 1), 1, (1, 0))
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self.bn3 = nn.BatchNorm2d(self.out_maps)
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def forward(self, x):
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x = x.view(-1, self.in_maps, self.time_steps, 1)
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x = F.relu(self.bn1(self.conv1(x)))
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inx = x
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x = F.relu(self.bn2(self.conv2(x)))
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x = F.relu(self.bn3(self.conv3(x)) + inx)
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return x
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# FCN model
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class ConvNet(nn.Module):
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def __init__(self, n_in, n_classes):
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super(ConvNet, self).__init__()
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self.n_in = n_in
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self.n_classes = n_classes
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self.conv1 = nn.Conv2d(1, 128, (7, 1), 1, (3, 0))
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self.bn1 = nn.BatchNorm2d(128)
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self.conv2 = nn.Conv2d(128, 256, (5, 1), 1, (2, 0))
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self.bn2 = nn.BatchNorm2d(256)
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self.conv3 = nn.Conv2d(256, 128, (3, 1), 1, (1, 0))
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self.bn3 = nn.BatchNorm2d(128)
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self.fc4 = nn.Linear(128, self.n_classes)
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def forward(self, x: torch.Tensor):
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x = x.view(-1, 1, self.n_in, 1)
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x = F.relu(self.bn1(self.conv1(x)))
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x = F.relu(self.bn2(self.conv2(x)))
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x = F.relu(self.bn3(self.conv3(x)))
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x = F.adaptive_avg_pool2d(x, 1)
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x = x.view(-1, 128)
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x = self.fc4(x)
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# return F.log_softmax(x,1)
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return x
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if __name__ == '__main__':
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# resNet = ResNet(n_in=96, n_classes=3)
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# input = torch.randn(32, 96, 1)
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# out = resNet(input)
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# print(out.shape)
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torch.manual_seed(3)
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fcn = ConvNet(n_in=112, n_classes=3)
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input = torch.randn(32, 112, 1)
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out = fcn(input)
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print(out.size())
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