Delete tcn.py

tcn.py

@@ -1,83 +0,0 @@
-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) -> object:
-        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(inplace=False)
-        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(inplace=False)
-        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(inplace=False)
-        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) -> object:
-        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, dropout=dropout)]
-
-        self.network = nn.Sequential(*layers)
-
-    def forward(self, x) -> object:
-        return self.network(x)
-
-
-class TCN(nn.Module):
-    def __init__(self, input_size, output_size, num_channels, kernel_size=2, dropout=0):
-        super(TCN, self).__init__()
-        self.tcn = TemporalConvNet(num_inputs=input_size,
-                                   num_channels=num_channels,
-                                   kernel_size=kernel_size,
-                                   dropout=dropout)
-        self.linear = nn.Linear(num_channels[-1], output_size)
-        self.init_weights()
-
-    def init_weights(self):
-        self.linear.weight.data.normal_(0, 0.01)
-        self.linear.bias.data.fill_(0)
-
-    def forward(self, inputs) -> object:
-        y = self.tcn.forward(inputs)
-        output = self.linear(y[:, :, -1])
-        return output