models_.py

import torch
import torch.nn as nn
from torch.nn.utils import weight_norm


class Chomp1d(nn.Module):
    def __init__(self, chomp_size):
        super(Chomp1d, self).__init__()
        self.chomp_size = chomp_size

    def forward(self, x):
        return x[:, :, :-self.chomp_size].contiguous()


class TemporalBlock(nn.Module):
    def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout=0.2):
        super(TemporalBlock, self).__init__()
        self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size,
                                           stride=stride, padding=padding, dilation=dilation))
        self.chomp1 = Chomp1d(padding)
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout(dropout)

        self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size,
                                           stride=stride, padding=padding, dilation=dilation))
        self.chomp2 = Chomp1d(padding)
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout(dropout)

        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()
        self.init_weights()

    def init_weights(self):
        self.conv1.weight.data.normal_(0, 0.01)
        self.conv2.weight.data.normal_(0, 0.01)
        if self.downsample is not None:
            self.downsample.weight.data.normal_(0, 0.01)

    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=3, dropout=0.1):
        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, dropout=dropout)]

        self.network = nn.Sequential(*layers)

    def forward(self, x):
        return 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, dropout=dropout)
        self.linear1 = nn.Linear(646, 64)
        self.rl= nn.ReLU()
        self.linear2 = nn.Linear(64, output_size)
        self.sof = nn.Softmax()

    def forward(self, x):
        # x needs to have dimension (N, C, L) in order to be passed into CNN
        output = self.tcn(x)
        output=output[:,-1,:]
        output = self.rl(self.linear1(output))
        output = self.linear2(output).double()
        return self.sof(output)


class LSTM(nn.Module):
    """Long Short Term Memory"""
    def __init__(self, input_size, hidden_size, num_layers, output_size, bidirectional=False):
        super(LSTM, self).__init__()

        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.output_size = output_size
        self.bidirectional = bidirectional

        self.lstm = nn.LSTM(input_size=input_size,
                            hidden_size=hidden_size,
                            num_layers=num_layers,
                            batch_first=True,
                            bidirectional=bidirectional)

        if self.bidirectional:
            self.fc = nn.Linear(hidden_size * 2, output_size)
        else:
            self.fc = nn.Linear(hidden_size, output_size)