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import torch
import torch.nn as nn
import torch.nn.functional as F
# https://github.com/samcw/ResNet18-Pytorch
class ResBlock(nn.Module):
def __init__(self, inchannel, outchannel, stride=1):
super(ResBlock, self).__init__()
self.left = nn.Sequential(
nn.Conv1d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False),
nn.BatchNorm1d(outchannel),
nn.ReLU(inplace=True),
nn.Conv1d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm1d(outchannel)
)
self.shortcut = nn.Sequential()
if stride != 1 or inchannel != outchannel:
self.shortcut = nn.Sequential(
nn.Conv1d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm1d(outchannel)
)
def forward(self, x):
out = self.left(x)
out = out + self.shortcut(x)
out = F.relu(out)
return out
class ResNet18(nn.Module):
def __init__(self, args):
super(ResNet18, self).__init__()
self.inchannel = 64
self.conv1 = nn.Sequential(
nn.Conv1d(1, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm1d(64),
nn.ReLU()
)
self.layer1 = self.make_layer(ResBlock, 64, 2, stride=1)
self.layer2 = self.make_layer(ResBlock, 128, 2, stride=2)
self.layer3 = self.make_layer(ResBlock, 256, 2, stride=2)
self.layer4 = self.make_layer(ResBlock, 512, 2, stride=2)
self.pred_layer = nn.Sequential(
nn.Linear(512, 512),
nn.SiLU(),
nn.Dropout(p=0.3),
nn.Linear(512, 1),
)
if getattr(args, 'mean_label', False):
self.pred_layer[3].bias.data.fill_(args.mean_label)
def make_layer(self, block, channels, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.inchannel, channels, stride))
self.inchannel = channels
return nn.Sequential(*layers)
def forward(self, x):
x = x.unsqueeze(1)
out = self.conv1(x)
out = F.max_pool1d(out, 3, 2, 1)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = out.mean(-1)
out = self.pred_layer(out)
return out |