first commit

models/TiDE.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LayerNorm(nn.Module):
+    """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
+
+    def __init__(self, ndim, bias):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(ndim))
+        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
+
+    def forward(self, input):
+        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
+
+
+
+class ResBlock(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim, dropout=0.1, bias=True): 
+        super().__init__()
+
+        self.fc1 = nn.Linear(input_dim, hidden_dim, bias=bias) 
+        self.fc2 = nn.Linear(hidden_dim, output_dim, bias=bias)
+        self.fc3 = nn.Linear(input_dim, output_dim, bias=bias)
+        self.dropout = nn.Dropout(dropout)
+        self.relu = nn.ReLU()
+        self.ln = LayerNorm(output_dim, bias=bias)
+        
+    def forward(self, x):
+
+        out = self.fc1(x)
+        out = self.relu(out)
+        out = self.fc2(out)
+        out = self.dropout(out)
+        out = out + self.fc3(x)
+        out = self.ln(out)
+        return out
+
+
+#TiDE
+class Model(nn.Module):  
+    """
+    paper: https://arxiv.org/pdf/2304.08424.pdf 
+    """
+    def __init__(self, configs, bias=True, feature_encode_dim=2): 
+        super(Model, self).__init__()
+        self.configs = configs
+        self.task_name = configs.task_name
+        self.seq_len = configs.seq_len  #L 
+        self.label_len = configs.label_len
+        self.pred_len = configs.pred_len  #H 
+        self.hidden_dim=configs.d_model
+        self.res_hidden=configs.d_model 
+        self.encoder_num=configs.e_layers
+        self.decoder_num=configs.d_layers
+        self.freq=configs.freq
+        self.feature_encode_dim=feature_encode_dim
+        self.decode_dim = configs.c_out
+        self.temporalDecoderHidden=configs.d_ff
+        dropout=configs.dropout
+
+        
+        freq_map = {'h': 4, 't': 5, 's': 6,
+                    'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3}
+        
+        self.feature_dim=freq_map[self.freq]
+
+
+        flatten_dim = self.seq_len + (self.seq_len + self.pred_len) * self.feature_encode_dim
+
+        self.feature_encoder = ResBlock(self.feature_dim, self.res_hidden, self.feature_encode_dim, dropout, bias)
+        self.encoders = nn.Sequential(ResBlock(flatten_dim, self.res_hidden, self.hidden_dim, dropout, bias),*([ ResBlock(self.hidden_dim, self.res_hidden, self.hidden_dim, dropout, bias)]*(self.encoder_num-1)))
+        if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
+            self.decoders = nn.Sequential(*([ ResBlock(self.hidden_dim, self.res_hidden, self.hidden_dim, dropout, bias)]*(self.decoder_num-1)),ResBlock(self.hidden_dim, self.res_hidden, self.decode_dim * self.pred_len, dropout, bias))
+            self.temporalDecoder = ResBlock(self.decode_dim + self.feature_encode_dim, self.temporalDecoderHidden, 1, dropout, bias)
+            self.residual_proj = nn.Linear(self.seq_len, self.pred_len, bias=bias)
+        if self.task_name == 'imputation':
+            self.decoders = nn.Sequential(*([ ResBlock(self.hidden_dim, self.res_hidden, self.hidden_dim, dropout, bias)]*(self.decoder_num-1)),ResBlock(self.hidden_dim, self.res_hidden, self.decode_dim * self.seq_len, dropout, bias))
+            self.temporalDecoder = ResBlock(self.decode_dim + self.feature_encode_dim, self.temporalDecoderHidden, 1, dropout, bias)
+            self.residual_proj = nn.Linear(self.seq_len, self.seq_len, bias=bias)
+        if self.task_name == 'anomaly_detection':
+            self.decoders = nn.Sequential(*([ ResBlock(self.hidden_dim, self.res_hidden, self.hidden_dim, dropout, bias)]*(self.decoder_num-1)),ResBlock(self.hidden_dim, self.res_hidden, self.decode_dim * self.seq_len, dropout, bias))
+            self.temporalDecoder = ResBlock(self.decode_dim + self.feature_encode_dim, self.temporalDecoderHidden, 1, dropout, bias)
+            self.residual_proj = nn.Linear(self.seq_len, self.seq_len, bias=bias)
+            
+        
+    def forecast(self, x_enc, x_mark_enc, x_dec, batch_y_mark):
+        # Normalization
+        means = x_enc.mean(1, keepdim=True).detach()
+        x_enc = x_enc - means
+        stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
+        x_enc /= stdev
+        
+        feature = self.feature_encoder(batch_y_mark)
+        hidden = self.encoders(torch.cat([x_enc, feature.reshape(feature.shape[0], -1)], dim=-1))
+        decoded = self.decoders(hidden).reshape(hidden.shape[0], self.pred_len, self.decode_dim)
+        dec_out = self.temporalDecoder(torch.cat([feature[:,self.seq_len:], decoded], dim=-1)).squeeze(-1) + self.residual_proj(x_enc)
+        
+        
+        # De-Normalization 
+        dec_out = dec_out * (stdev[:, 0].unsqueeze(1).repeat(1, self.pred_len))
+        dec_out = dec_out + (means[:, 0].unsqueeze(1).repeat(1, self.pred_len))
+        return dec_out
+    
+    def imputation(self, x_enc, x_mark_enc, x_dec, batch_y_mark, mask):
+        # Normalization
+        means = x_enc.mean(1, keepdim=True).detach()
+        x_enc = x_enc - means
+        stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
+        x_enc /= stdev
+
+        feature = self.feature_encoder(x_mark_enc)
+        hidden = self.encoders(torch.cat([x_enc, feature.reshape(feature.shape[0], -1)], dim=-1))
+        decoded = self.decoders(hidden).reshape(hidden.shape[0], self.seq_len, self.decode_dim)
+        dec_out = self.temporalDecoder(torch.cat([feature[:,:self.seq_len], decoded], dim=-1)).squeeze(-1) + self.residual_proj(x_enc)
+    
+        # De-Normalization 
+        dec_out = dec_out * (stdev[:, 0].unsqueeze(1).repeat(1, self.seq_len))
+        dec_out = dec_out + (means[:, 0].unsqueeze(1).repeat(1, self.seq_len))
+        return dec_out
+    
+    
+    def forward(self, x_enc, x_mark_enc, x_dec, batch_y_mark, mask=None):
+        '''x_mark_enc is the exogenous dynamic feature described in the original paper'''
+        if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
+            if batch_y_mark is None:
+                batch_y_mark = torch.zeros((x_enc.shape[0], self.seq_len+self.pred_len, self.feature_dim)).to(x_enc.device).detach()
+            else:
+                batch_y_mark = torch.concat([x_mark_enc, batch_y_mark[:, -self.pred_len:, :]],dim=1)
+            dec_out = torch.stack([self.forecast(x_enc[:, :, feature], x_mark_enc, x_dec, batch_y_mark) for feature in range(x_enc.shape[-1])],dim=-1)
+            return dec_out # [B, L, D]
+        if self.task_name == 'imputation':
+            dec_out = torch.stack([self.imputation(x_enc[:, :, feature], x_mark_enc, x_dec, batch_y_mark, mask) for feature in range(x_enc.shape[-1])],dim=-1)
+            return dec_out  # [B, L, D]
+        if self.task_name == 'anomaly_detection':
+            raise NotImplementedError("Task anomaly_detection for Tide is temporarily not supported")
+        if self.task_name == 'classification':
+            raise NotImplementedError("Task classification for Tide is temporarily not supported")
+        return None
+    
+    
+
+
+