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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 | |