Feat/time net model #2538
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| """ | ||||||
| TimesNet Model | ||||||
| ------- | ||||||
| The implementation is built upon the Time Series Library's TimesNet model | ||||||
| <https://github.com/thuml/Time-Series-Library/blob/main/layers/Embed.py> | ||||||
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| ------- | ||||||
| MIT License | ||||||
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| Copyright (c) 2021 THUML @ Tsinghua University | ||||||
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| Permission is hereby granted, free of charge, to any person obtaining a copy | ||||||
| of this software and associated documentation files (the "Software"), to deal | ||||||
| in the Software without restriction, including without limitation the rights | ||||||
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||||||
| copies of the Software, and to permit persons to whom the Software is | ||||||
| furnished to do so, subject to the following conditions: | ||||||
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| The above copyright notice and this permission notice shall be included in all | ||||||
| copies or substantial portions of the Software. | ||||||
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| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||||||
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||||||
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||||||
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||||||
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||||||
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||||||
| SOFTWARE. | ||||||
| """ | ||||||
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| import math | ||||||
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| import torch | ||||||
| import torch.nn as nn | ||||||
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| from darts.utils.torch import MonteCarloDropout | ||||||
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| class PositionalEmbedding(nn.Module): | ||||||
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| def __init__(self, d_model, max_len=5000): | ||||||
| super().__init__() | ||||||
| # Compute the positional encodings once in log space. | ||||||
| pe = torch.zeros(max_len, d_model).float() | ||||||
| pe.require_grad = False | ||||||
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| position = torch.arange(0, max_len).float().unsqueeze(1) | ||||||
| div_term = ( | ||||||
| torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model) | ||||||
| ).exp() | ||||||
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| pe[:, 0::2] = torch.sin(position * div_term) | ||||||
| pe[:, 1::2] = torch.cos(position * div_term) | ||||||
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| pe = pe.unsqueeze(0) | ||||||
| self.register_buffer("pe", pe) | ||||||
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| def forward(self, x): | ||||||
| return self.pe[:, : x.size(1)] | ||||||
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| class TokenEmbedding(nn.Module): | ||||||
| def __init__(self, c_in, d_model): | ||||||
| super().__init__() | ||||||
| padding = 1 if torch.__version__ >= "1.5.0" else 2 | ||||||
| self.tokenConv = nn.Conv1d( | ||||||
| in_channels=c_in, | ||||||
| out_channels=d_model, | ||||||
| kernel_size=3, | ||||||
| padding=padding, | ||||||
| padding_mode="circular", | ||||||
| bias=False, | ||||||
| ) | ||||||
| for m in self.modules(): | ||||||
| if isinstance(m, nn.Conv1d): | ||||||
| nn.init.kaiming_normal_( | ||||||
| m.weight, mode="fan_in", nonlinearity="leaky_relu" | ||||||
| ) | ||||||
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| def forward(self, x): | ||||||
| x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2) | ||||||
| return x | ||||||
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| class FixedEmbedding(nn.Module): | ||||||
| def __init__(self, c_in, d_model): | ||||||
| super().__init__() | ||||||
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| w = torch.zeros(c_in, d_model).float() | ||||||
| w.require_grad = False | ||||||
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| position = torch.arange(0, c_in).float().unsqueeze(1) | ||||||
| div_term = ( | ||||||
| torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model) | ||||||
| ).exp() | ||||||
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| w[:, 0::2] = torch.sin(position * div_term) | ||||||
| w[:, 1::2] = torch.cos(position * div_term) | ||||||
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| self.emb = nn.Embedding(c_in, d_model) | ||||||
| self.emb.weight = nn.Parameter(w, requires_grad=False) | ||||||
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| def forward(self, x): | ||||||
| return self.emb(x).detach() | ||||||
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| class TemporalEmbedding(nn.Module): | ||||||
| def __init__(self, d_model, embed_type="fixed", freq="h"): | ||||||
| super().__init__() | ||||||
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| minute_size = 4 | ||||||
| hour_size = 24 | ||||||
| weekday_size = 7 | ||||||
| day_size = 32 | ||||||
| month_size = 13 | ||||||
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| Embed = FixedEmbedding if embed_type == "fixed" else nn.Embedding | ||||||
| if freq == "t": | ||||||
| self.minute_embed = Embed(minute_size, d_model) | ||||||
| self.hour_embed = Embed(hour_size, d_model) | ||||||
| self.weekday_embed = Embed(weekday_size, d_model) | ||||||
| self.day_embed = Embed(day_size, d_model) | ||||||
| self.month_embed = Embed(month_size, d_model) | ||||||
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| def forward(self, x): | ||||||
| x = x.long() | ||||||
| minute_x = ( | ||||||
| self.minute_embed(x[:, :, 4]) if hasattr(self, "minute_embed") else 0.0 | ||||||
| ) | ||||||
| hour_x = self.hour_embed(x[:, :, 3]) | ||||||
| weekday_x = self.weekday_embed(x[:, :, 2]) | ||||||
| day_x = self.day_embed(x[:, :, 1]) | ||||||
| month_x = self.month_embed(x[:, :, 0]) | ||||||
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| return hour_x + weekday_x + day_x + month_x + minute_x | ||||||
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| class TimeFeatureEmbedding(nn.Module): | ||||||
| def __init__(self, d_model, embed_type="timeF", freq="h"): | ||||||
| super().__init__() | ||||||
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| freq_map = {"h": 4, "t": 5, "s": 6, "m": 1, "a": 1, "w": 2, "d": 3, "b": 3} | ||||||
| d_inp = freq_map[freq] | ||||||
| self.embed = nn.Linear(d_inp, d_model, bias=False) | ||||||
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| def forward(self, x): | ||||||
| return self.embed(x) | ||||||
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| class DataEmbedding(nn.Module): | ||||||
| def __init__(self, c_in, d_model, embed_type="fixed", freq="h", dropout=0.1): | ||||||
| super().__init__() | ||||||
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| self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model) | ||||||
| self.position_embedding = PositionalEmbedding(d_model=d_model) | ||||||
| self.temporal_embedding = ( | ||||||
| TemporalEmbedding(d_model=d_model, embed_type=embed_type, freq=freq) | ||||||
| if embed_type != "timeF" | ||||||
| else TimeFeatureEmbedding(d_model=d_model, embed_type=embed_type, freq=freq) | ||||||
| ) | ||||||
| self.dropout = MonteCarloDropout(p=dropout) | ||||||
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| def forward(self, x, x_mark): | ||||||
| if x_mark is None: | ||||||
| x = self.value_embedding(x) + self.position_embedding(x) | ||||||
| else: | ||||||
| x = ( | ||||||
| self.value_embedding(x) | ||||||
| + self.temporal_embedding(x_mark) | ||||||
| + self.position_embedding(x) | ||||||
| ) | ||||||
| return self.dropout(x) | ||||||
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| class DataEmbedding_inverted(nn.Module): | ||||||
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Suggested change
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| def __init__(self, c_in, d_model, embed_type="fixed", freq="h", dropout=0.1): | ||||||
| super().__init__() | ||||||
| self.value_embedding = nn.Linear(c_in, d_model) | ||||||
| self.dropout = MonteCarloDropout(p=dropout) | ||||||
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| def forward(self, x, x_mark): | ||||||
| x = x.permute(0, 2, 1) | ||||||
| # x: [Batch Variate Time] | ||||||
| if x_mark is None: | ||||||
| x = self.value_embedding(x) | ||||||
| else: | ||||||
| x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1)) | ||||||
| # x: [Batch Variate d_model] | ||||||
| return self.dropout(x) | ||||||
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| class DataEmbedding_wo_pos(nn.Module): | ||||||
|
There was a problem hiding this comment. The logic is so similar to |
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| def __init__(self, c_in, d_model, embed_type="fixed", freq="h", dropout=0.1): | ||||||
| super().__init__() | ||||||
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| self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model) | ||||||
| self.position_embedding = PositionalEmbedding(d_model=d_model) | ||||||
| self.temporal_embedding = ( | ||||||
| TemporalEmbedding(d_model=d_model, embed_type=embed_type, freq=freq) | ||||||
| if embed_type != "timeF" | ||||||
| else TimeFeatureEmbedding(d_model=d_model, embed_type=embed_type, freq=freq) | ||||||
| ) | ||||||
| self.dropout = MonteCarloDropout(p=dropout) | ||||||
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| def forward(self, x, x_mark): | ||||||
| if x_mark is None: | ||||||
| x = self.value_embedding(x) | ||||||
| else: | ||||||
| x = self.value_embedding(x) + self.temporal_embedding(x_mark) | ||||||
| return self.dropout(x) | ||||||
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| class PatchEmbedding(nn.Module): | ||||||
| def __init__(self, d_model, patch_len, stride, padding, dropout): | ||||||
| super().__init__() | ||||||
| # Patching | ||||||
| self.patch_len = patch_len | ||||||
| self.stride = stride | ||||||
| self.padding_patch_layer = nn.ReplicationPad1d((0, padding)) | ||||||
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| # Backbone, Input encoding: projection of feature vectors onto a d-dim vector space | ||||||
| self.value_embedding = nn.Linear(patch_len, d_model, bias=False) | ||||||
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| # Positional embedding | ||||||
| self.position_embedding = PositionalEmbedding(d_model) | ||||||
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| # Residual dropout | ||||||
| self.dropout = MonteCarloDropout(p=dropout) | ||||||
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| def forward(self, x): | ||||||
| # do patching | ||||||
| n_vars = x.shape[1] | ||||||
| x = self.padding_patch_layer(x) | ||||||
| x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride) | ||||||
| x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) | ||||||
| # Input encoding | ||||||
| x = self.value_embedding(x) + self.position_embedding(x) | ||||||
| return self.dropout(x), n_vars | ||||||
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Incorrect link