├── revin
├── __init__.py
├── revin_torch.py
└── revin_keras.py
├── figures
├── __init__.py
└── revin.png
├── .idea
├── vcs.xml
├── misc.xml
├── .gitignore
├── inspectionProfiles
│ ├── profiles_settings.xml
│ └── Project_Default.xml
├── modules.xml
├── Reversible-Instance-Normalization.iml
└── deployment.xml
├── README.md
└── LICENSE
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1 | # Default ignored files
2 | /shelf/
3 | /workspace.xml
4 | # Datasource local storage ignored files
5 | /dataSources/
6 | /dataSources.local.xml
7 | # Editor-based HTTP Client requests
8 | /httpRequests/
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/revin/revin_torch.py:
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1 | import torch
2 | import torch.nn as nn
3 |
4 |
5 | class RevIN(nn.Module):
6 | def __init__(self, num_features: int, eps=1e-5, affine=True):
7 | """Reversible Instance Normalization for Accurate Time-Series Forecasting
8 | against Distribution Shift, ICLR2021.
9 |
10 | Parameters
11 | ----------
12 | num_features: int, the number of features or channels.
13 | eps: float, a value added for numerical stability, default 1e-5.
14 | affine: bool, if True(default), RevIN has learnable affine parameters.
15 | """
16 | super().__init__()
17 | self.num_features = num_features
18 | self.eps = eps
19 | self.affine = affine
20 | if self.affine:
21 | self._init_params()
22 |
23 | def forward(self, x, mode:str):
24 | if mode == 'norm':
25 | self._get_statistics(x)
26 | x = self._normalize(x)
27 | elif mode == 'denorm':
28 | x = self._denormalize(x)
29 | else:
30 | raise NotImplementedError('Only modes norm and denorm are supported.')
31 | return x
32 |
33 | def _init_params(self):
34 | self.affine_weight = nn.Parameter(torch.ones(self.num_features))
35 | self.affine_bias = nn.Parameter(torch.zeros(self.num_features))
36 |
37 | def _get_statistics(self, x):
38 | dim2reduce = tuple(range(1, x.ndim - 1))
39 | self.mean = torch.mean(x, dim=dim2reduce, keepdim=True).detach()
40 | self.stdev = torch.sqrt(torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps).detach()
41 |
42 | def _normalize(self, x):
43 | x = x - self.mean
44 | x = x / self.stdev
45 | if self.affine:
46 | x = x * self.affine_weight
47 | x = x + self.affine_bias
48 | return x
49 |
50 | def _denormalize(self, x):
51 | if self.affine:
52 | x = x - self.affine_bias
53 | x = x / (self.affine_weight + self.eps*self.eps)
54 | x = x * self.stdev
55 | x = x + self.mean
56 | return x
57 |
58 |
59 | if __name__ == '__main__':
60 | x = torch.reshape(torch.range(0, 23), shape=(4, 3, 2))/24
61 | layer = RevIN(2)
62 | y = layer(x, mode='norm')
63 | z = layer(y, mode='denorm')
64 |
65 | print(x)
66 | print(y)
67 | print(z)
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/README.md:
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1 | # Reversible-Instance-Normalization
2 | Implementation of RevIN is based on TF2.Keras and PyTorch.
3 |
4 | ### Reference
5 | RevIN is proposed by this paper: [Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift](https://openreview.net/forum?id=cGDAkQo1C0p).
6 |
7 | ### Abstract
8 | Statistical properties such as mean and variance often change over time in time series, i.e., time-series data suffer from a distribution shift problem. This change in temporal distribution is one of the main challenges that prevent accurate time-series forecasting. To address this issue, RevIN proposes a simple yet effective normalization method called reversible instance normalization (RevIN), a generally-applicable normalization-and-denormalization method with learnable affine transformation. The proposed method is symmetrically structured to remove and restore the statistical information of a time-series instance, leading to significant performance improvements in time-series forecasting, as shown in Figs below.
9 |
10 | 
11 |
12 | ### Quick Start
13 |
14 | keras
15 | ```python
16 | import tensorflow as tf
17 | from tensorflow.keras import layers
18 | from revin.revin_keras import RevIN
19 |
20 | data = tf.reshape(tf.range(0, 24), shape=(4, 3, 2))/24
21 |
22 | revinlayer = RevIN()
23 | inputs = layers.Input(shape=(3, 2))
24 | x = revinlayer(inputs, mode='norm')
25 | x = layers.Conv1D(2, kernel_size=1, activation='relu')(x)
26 | outputs = revinlayer(x, mode='denorm')
27 |
28 | model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
29 | ```
30 |
31 | pytorch
32 | ```python
33 | import torch
34 | import torch.nn as nn
35 | from revin.revin_torch import RevIN
36 |
37 | x = torch.reshape(torch.range(0, 23), shape=(4, 3, 2))/24
38 |
39 | revinlayer = RevIN(2)
40 |
41 | class Net(nn.Module):
42 | def __init__(self):
43 | super(Net, self).__init__()
44 | self.revinlayer = RevIN(num_features=2)
45 | self.conv1d = nn.Conv1d(in_channels=2, out_channels=2, kernel_size=1)
46 |
47 | def forward(self, x):
48 | x = self.revinlayer(x, mode='norm')
49 | x = self.conv1d(x)
50 | x = nn.ReLU(x)
51 | x = self.revinlayer(x, mode='denorm')
52 | return x
53 | ```
54 |
55 | ### Conclusion
56 |
57 | Just interested in the first implementation and welcome to test and ask questions.
58 |
59 | ### Acknowledgments
60 |
61 | * [HyperTS](https://github.com/DataCanvasIO/HyperTS): A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.
62 | * [RevIN](https://github.com/ts-kim/RevIN): The official PyTorch implementation.
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/revin/revin_keras.py:
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1 | from tensorflow.keras import layers
2 | import tensorflow.keras.backend as K
3 |
4 |
5 | class RevIN(layers.Layer):
6 | """Reversible Instance Normalization for Accurate Time-Series Forecasting
7 | against Distribution Shift, ICLR2022.
8 |
9 | Parameters
10 | ----------
11 | eps: float, a value added for numerical stability, default 1e-5.
12 | affine: bool, if True(default), RevIN has learnable affine parameters.
13 | """
14 | def __init__(self, eps=1e-5, affine=True, **kwargs):
15 | super(RevIN, self).__init__(**kwargs)
16 | self.eps = eps
17 | self.affine = affine
18 |
19 | def build(self, input_shape):
20 | self.affine_weight = self.add_weight(name='affine_weight',
21 | shape=(1, input_shape[-1]),
22 | initializer='ones',
23 | trainable=True)
24 |
25 | self.affine_bias = self.add_weight(name='affine_bias',
26 | shape=(1, input_shape[-1]),
27 | initializer='zeros',
28 | trainable=True)
29 | super(RevIN, self).build(input_shape)
30 |
31 | def call(self, inputs, **kwargs):
32 | mode = kwargs.get('mode', None)
33 | if mode == 'norm':
34 | self._get_statistics(inputs)
35 | x = self._normalize(inputs)
36 | elif mode == 'denorm':
37 | x = self._denormalize(inputs)
38 | else:
39 | raise NotImplementedError('Only modes norm and denorm are supported.')
40 | return x
41 |
42 | def _get_statistics(self, x):
43 | dim2reduce = tuple(range(1, len(x.shape) - 1))
44 | self.mean = K.stop_gradient(K.mean(x, axis=dim2reduce, keepdims=True))
45 | self.stdev = K.stop_gradient(K.sqrt(K.var(x, axis=dim2reduce, keepdims=True) + self.eps))
46 |
47 | def _normalize(self, x):
48 | x = x - self.mean
49 | x = x / self.stdev
50 | if self.affine:
51 | x = x * self.affine_weight
52 | x = x + self.affine_bias
53 | return x
54 |
55 | def _denormalize(self, x):
56 | if self.affine:
57 | x = x - self.affine_bias
58 | x = x / (self.affine_weight + self.eps*self.eps)
59 | x = x * self.stdev
60 | x = x + self.mean
61 | return x
62 |
63 | def get_config(self):
64 | config = {'eps': self.eps,
65 | 'affine': self.affine}
66 | base_config = super(RevIN, self).get_config()
67 | return dict(list(base_config.items()) + list(config.items()))
68 |
69 |
70 | if __name__ == '__main__':
71 | import tensorflow as tf
72 |
73 | x = tf.reshape(tf.range(0, 24), shape=(4, 3, 2))/24
74 | layer = RevIN()
75 | y = layer(x, mode='norm')
76 | z = layer(y, mode='denorm')
77 |
78 | print(x)
79 | print(y)
80 | print(z)
81 | print(x.numpy() == z.numpy())
82 |
83 | # import numpy as np
84 | # from tensorflow.keras.layers import Input, LSTM, Dense
85 | # from tensorflow.keras.models import Model
86 | #
87 | # revin_layer = RevIN()
88 | #
89 | # x=Input(shape=(12, 2))
90 | # model=revin_layer(x,mode="norm")
91 | #
92 | # model2=LSTM(32, return_sequences=False)(model)
93 | # output_layer=Dense(2)(model2)
94 | # output_layer1=revin_layer(output_layer,mode="denorm")
95 | # model1 = Model(inputs=x, outputs=output_layer1)
96 | # model1.summary()
97 | #
98 | # model1.compile(optimizer='Adam', loss='mse')
99 | #
100 | # x = np.random.randn(16, 12, 2)
101 | # y = np.random.randn(16, 2)
102 | # model1.fit(x=x, y=y, epochs=10, batch_size=1)
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