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| import torch | |
| from torch import nn | |
| from layers.Transformer_EncDec import Encoder, EncoderLayer | |
| from layers.SelfAttention_Family import FullAttention, AttentionLayer | |
| from layers.Embed import PatchEmbedding | |
| class Transpose(nn.Module): | |
| def __init__(self, *dims, contiguous=False): | |
| super().__init__() | |
| self.dims, self.contiguous = dims, contiguous | |
| def forward(self, x): | |
| if self.contiguous: return x.transpose(*self.dims).contiguous() | |
| else: return x.transpose(*self.dims) | |
| class FlattenHead(nn.Module): | |
| def __init__(self, n_vars, nf, target_window, head_dropout=0): | |
| super().__init__() | |
| self.n_vars = n_vars | |
| self.flatten = nn.Flatten(start_dim=-2) | |
| self.linear = nn.Linear(nf, target_window) | |
| self.dropout = nn.Dropout(head_dropout) | |
| def forward(self, x): # x: [bs x nvars x d_model x patch_num] | |
| x = self.flatten(x) | |
| x = self.linear(x) | |
| x = self.dropout(x) | |
| return x | |
| class Model(nn.Module): | |
| """ | |
| Paper link: https://arxiv.org/pdf/2211.14730.pdf | |
| """ | |
| def __init__(self, configs, patch_len=16, stride=8): | |
| """ | |
| patch_len: int, patch len for patch_embedding | |
| stride: int, stride for patch_embedding | |
| """ | |
| super().__init__() | |
| self.task_name = configs.task_name | |
| self.seq_len = configs.seq_len | |
| self.pred_len = configs.pred_len | |
| padding = stride | |
| # patching and embedding | |
| self.patch_embedding = PatchEmbedding( | |
| configs.d_model, patch_len, stride, padding, configs.dropout) | |
| # Encoder | |
| self.encoder = Encoder( | |
| [ | |
| EncoderLayer( | |
| AttentionLayer( | |
| FullAttention(False, configs.factor, attention_dropout=configs.dropout, | |
| output_attention=False), configs.d_model, configs.n_heads), | |
| configs.d_model, | |
| configs.d_ff, | |
| dropout=configs.dropout, | |
| activation=configs.activation | |
| ) for l in range(configs.e_layers) | |
| ], | |
| norm_layer=nn.Sequential(Transpose(1,2), nn.BatchNorm1d(configs.d_model), Transpose(1,2)) | |
| ) | |
| # Prediction Head | |
| self.head_nf = configs.d_model * \ | |
| int((configs.seq_len - patch_len) / stride + 2) | |
| if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast': | |
| self.head = FlattenHead(configs.enc_in, self.head_nf, configs.pred_len, | |
| head_dropout=configs.dropout) | |
| elif self.task_name == 'imputation' or self.task_name == 'anomaly_detection': | |
| self.head = FlattenHead(configs.enc_in, self.head_nf, configs.seq_len, | |
| head_dropout=configs.dropout) | |
| elif self.task_name == 'classification': | |
| self.flatten = nn.Flatten(start_dim=-2) | |
| self.dropout = nn.Dropout(configs.dropout) | |
| self.projection = nn.Linear( | |
| self.head_nf * configs.enc_in, configs.num_class) | |
| def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec): | |
| # Normalization from Non-stationary Transformer | |
| 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 | |
| # do patching and embedding | |
| x_enc = x_enc.permute(0, 2, 1) | |
| # u: [bs * nvars x patch_num x d_model] | |
| enc_out, n_vars = self.patch_embedding(x_enc) | |
| # Encoder | |
| # z: [bs * nvars x patch_num x d_model] | |
| enc_out, attns = self.encoder(enc_out) | |
| # z: [bs x nvars x patch_num x d_model] | |
| enc_out = torch.reshape( | |
| enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1])) | |
| # z: [bs x nvars x d_model x patch_num] | |
| enc_out = enc_out.permute(0, 1, 3, 2) | |
| # Decoder | |
| dec_out = self.head(enc_out) # z: [bs x nvars x target_window] | |
| dec_out = dec_out.permute(0, 2, 1) | |
| # De-Normalization from Non-stationary Transformer | |
| dec_out = dec_out * \ | |
| (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1)) | |
| dec_out = dec_out + \ | |
| (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1)) | |
| return dec_out | |
| def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask): | |
| # Normalization from Non-stationary Transformer | |
| means = torch.sum(x_enc, dim=1) / torch.sum(mask == 1, dim=1) | |
| means = means.unsqueeze(1).detach() | |
| x_enc = x_enc - means | |
| x_enc = x_enc.masked_fill(mask == 0, 0) | |
| stdev = torch.sqrt(torch.sum(x_enc * x_enc, dim=1) / | |
| torch.sum(mask == 1, dim=1) + 1e-5) | |
| stdev = stdev.unsqueeze(1).detach() | |
| x_enc /= stdev | |
| # do patching and embedding | |
| x_enc = x_enc.permute(0, 2, 1) | |
| # u: [bs * nvars x patch_num x d_model] | |
| enc_out, n_vars = self.patch_embedding(x_enc) | |
| # Encoder | |
| # z: [bs * nvars x patch_num x d_model] | |
| enc_out, attns = self.encoder(enc_out) | |
| # z: [bs x nvars x patch_num x d_model] | |
| enc_out = torch.reshape( | |
| enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1])) | |
| # z: [bs x nvars x d_model x patch_num] | |
| enc_out = enc_out.permute(0, 1, 3, 2) | |
| # Decoder | |
| dec_out = self.head(enc_out) # z: [bs x nvars x target_window] | |
| dec_out = dec_out.permute(0, 2, 1) | |
| # De-Normalization from Non-stationary Transformer | |
| dec_out = dec_out * \ | |
| (stdev[:, 0, :].unsqueeze(1).repeat(1, self.seq_len, 1)) | |
| dec_out = dec_out + \ | |
| (means[:, 0, :].unsqueeze(1).repeat(1, self.seq_len, 1)) | |
| return dec_out | |
| def anomaly_detection(self, x_enc): | |
| # Normalization from Non-stationary Transformer | |
| 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 | |
| # do patching and embedding | |
| x_enc = x_enc.permute(0, 2, 1) | |
| # u: [bs * nvars x patch_num x d_model] | |
| enc_out, n_vars = self.patch_embedding(x_enc) | |
| # Encoder | |
| # z: [bs * nvars x patch_num x d_model] | |
| enc_out, attns = self.encoder(enc_out) | |
| # z: [bs x nvars x patch_num x d_model] | |
| enc_out = torch.reshape( | |
| enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1])) | |
| # z: [bs x nvars x d_model x patch_num] | |
| enc_out = enc_out.permute(0, 1, 3, 2) | |
| # Decoder | |
| dec_out = self.head(enc_out) # z: [bs x nvars x target_window] | |
| dec_out = dec_out.permute(0, 2, 1) | |
| # De-Normalization from Non-stationary Transformer | |
| dec_out = dec_out * \ | |
| (stdev[:, 0, :].unsqueeze(1).repeat(1, self.seq_len, 1)) | |
| dec_out = dec_out + \ | |
| (means[:, 0, :].unsqueeze(1).repeat(1, self.seq_len, 1)) | |
| return dec_out | |
| def classification(self, x_enc, x_mark_enc): | |
| # Normalization from Non-stationary Transformer | |
| 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 | |
| # do patching and embedding | |
| x_enc = x_enc.permute(0, 2, 1) | |
| # u: [bs * nvars x patch_num x d_model] | |
| enc_out, n_vars = self.patch_embedding(x_enc) | |
| # Encoder | |
| # z: [bs * nvars x patch_num x d_model] | |
| enc_out, attns = self.encoder(enc_out) | |
| # z: [bs x nvars x patch_num x d_model] | |
| enc_out = torch.reshape( | |
| enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1])) | |
| # z: [bs x nvars x d_model x patch_num] | |
| enc_out = enc_out.permute(0, 1, 3, 2) | |
| # Decoder | |
| output = self.flatten(enc_out) | |
| output = self.dropout(output) | |
| output = output.reshape(output.shape[0], -1) | |
| output = self.projection(output) # (batch_size, num_classes) | |
| return output | |
| def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None): | |
| if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast': | |
| dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec) | |
| return dec_out[:, -self.pred_len:, :] # [B, L, D] | |
| if self.task_name == 'imputation': | |
| dec_out = self.imputation( | |
| x_enc, x_mark_enc, x_dec, x_mark_dec, mask) | |
| return dec_out # [B, L, D] | |
| if self.task_name == 'anomaly_detection': | |
| dec_out = self.anomaly_detection(x_enc) | |
| return dec_out # [B, L, D] | |
| if self.task_name == 'classification': | |
| dec_out = self.classification(x_enc, x_mark_enc) | |
| return dec_out # [B, N] | |
| return None | |