TCN pytorch 代码

以下是一个TCN的PyTorch版本代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class TemporalBlock(nn.Module):
    def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding):
        super(TemporalBlock, self).__init__()
        self.conv1 = nn.Conv1d(n_inputs, n_outputs, kernel_size,
                               stride=stride, padding=padding, dilation=dilation)
        self.chomp1 = nn.ConstantPad1d((0, 1), 0)
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout(0.5)

        self.conv2 = nn.Conv1d(n_outputs, n_outputs, kernel_size,
                               stride=stride, padding=padding, dilation=dilation)
        self.chomp2 = nn.ConstantPad1d((0, 1), 0)
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout(0.5)

        self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1,
                                  self.conv2, self.chomp2, self.relu2, self.dropout2)
        self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
        self.relu = nn.ReLU()

    def forward(self, x):
        out = self.net(x)
        res = x if self.downsample is None else self.downsample(x)
        return self.relu(out + res)


class TemporalConvNet(nn.Module):
    def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
        super(TemporalConvNet, self).__init__()
        layers = []
        num_levels = len(num_channels)
        for i in range(num_levels):
            dilation_size = 2 ** i
            in_channels = num_inputs if i == 0 else num_channels[i-1]
            out_channels = num_channels[i]
            layers += [TemporalBlock(in_channels, out_channels, kernel_size, stride=1, dilation=dilation_size,
                                     padding=(kernel_size-1) * dilation_size)]
        self.network = nn.Sequential(*layers)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        return self.dropout(self.network(x))


class TCN(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size, dropout):
        super(TCN, self).__init__()
        self.tcn = TemporalConvNet(input_size, num_channels, kernel_size=kernel_size, dropout=dropout)
        self.linear = nn.Linear(num_channels[-1], output_size)

    def forward(self, x):
        y1 = self.tcn(x.transpose(1, 2))
        return self.linear(y1[:, :, -1])