t = torch.randn(8, 50, 128)
assert tAPE(128, 50)(t).shape == t.shapeConvTranPlus
ConvTran: Improving Position Encoding of Transformers for Multivariate Time Series Classification
This is a Pytorch implementation of ConvTran adapted by Ignacio Oguiza and based on:
Foumani, N. M., Tan, C. W., Webb, G. I., & Salehi, M. (2023). Improving Position Encoding of Transformers for Multivariate Time Series Classification. arXiv preprint arXiv:2305.16642.
Pre-print: https://arxiv.org/abs/2305.16642v1
Original repository: https://github.com/Navidfoumani/ConvTran
tAPE
def tAPE(
d_model:int, # the embedding dimension
seq_len:int=1024, # the max. length of the incoming sequence
dropout:float=0.1, # dropout value
scale_factor:float=1.0
):
time Absolute Position Encoding
AbsolutePositionalEncoding
def AbsolutePositionalEncoding(
d_model:int, # the embedding dimension
seq_len:int=1024, # the max. length of the incoming sequence
dropout:float=0.1, # dropout value
scale_factor:float=1.0
):
Absolute positional encoding
t = torch.randn(8, 50, 128)
assert AbsolutePositionalEncoding(128, 50)(t).shape == t.shapeLearnablePositionalEncoding
def LearnablePositionalEncoding(
d_model:int, # the embedding dimension
seq_len:int=1024, # the max. length of the incoming sequence
dropout:float=0.1, # dropout value
):
Learnable positional encoding
t = torch.randn(8, 50, 128)
assert LearnablePositionalEncoding(128, 50)(t).shape == t.shapeAttention
def Attention(
d_model:int, # Embedding dimension
n_heads:int=8, # number of attention heads
dropout:float=0.01, # dropout
):
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.
.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool
t = torch.randn(8, 50, 128)
assert Attention(128)(t).shape == t.shapeAttention_Rel_Scl
def Attention_Rel_Scl(
d_model:int, # Embedding dimension
seq_len:int, # sequence length
n_heads:int=8, # number of attention heads
dropout:float=0.01, # dropout
):
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.
.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool
t = torch.randn(8, 50, 128)
assert Attention_Rel_Scl(128, 50)(t).shape == t.shapeAttention_Rel_Vec
def Attention_Rel_Vec(
d_model:int, # Embedding dimension
seq_len:int, # sequence length
n_heads:int=8, # number of attention heads
dropout:float=0.01, # dropout
):
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.
.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool
t = torch.randn(8, 50, 128)
assert Attention_Rel_Vec(128, 50)(t).shape == t.shapeConvTranBackbone
def ConvTranBackbone(
c_in:int, seq_len:int, d_model:int=16, # Internal dimension of transformer embeddings
n_heads:int=8, # Number of multi-headed attention heads
dim_ff:int=256, # Dimension of dense feedforward part of transformer layer
abs_pos_encode:str='tAPE', # Absolute Position Embedding. choices={'tAPE', 'sin', 'learned', None}
rel_pos_encode:str='eRPE', # Relative Position Embedding. choices={'eRPE', 'vector', None}
dropout:float=0.01, # Droupout regularization ratio
):
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.
.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool
t = torch.randn(8, 5, 20)
assert ConvTranBackbone(5, 20)(t).shape, (8, 16, 20)ConvTranPlus
def ConvTranPlus(
c_in:int, # Number of channels in input
c_out:int, # Number of channels in output
seq_len:int, # Number of input sequence length
d:tuple=None, # output shape (excluding batch dimension).
d_model:int=16, # Internal dimension of transformer embeddings
n_heads:int=8, # Number of multi-headed attention heads
dim_ff:int=256, # Dimension of dense feedforward part of transformer layer
abs_pos_encode:str='tAPE', # Absolute Position Embedding. choices={'tAPE', 'sin', 'learned', None}
rel_pos_encode:str='eRPE', # Relative Position Embedding. choices={'eRPE', 'vector', None}
encoder_dropout:float=0.01, # Droupout regularization ratio for the encoder
fc_dropout:float=0.1, # Droupout regularization ratio for the head
use_bn:bool=True, # indicates if batchnorm will be applied to the model head.
flatten:bool=True, # this will flatten the output of the encoder before applying the head if True.
custom_head:Any=None, # custom head that will be applied to the model head (optional).
):
A sequential container.
Modules will be added to it in the order they are passed in the constructor. Alternatively, an OrderedDict of modules can be passed in. The forward() method of [Sequential](https://timeseriesAI.github.io/tsai/models.tabfusiontransformer.html#sequential) accepts any input and forwards it to the first module it contains. It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.
The value a [Sequential](https://timeseriesAI.github.io/tsai/models.tabfusiontransformer.html#sequential) provides over manually calling a sequence of modules is that it allows treating the whole container as a single module, such that performing a transformation on the [Sequential](https://timeseriesAI.github.io/tsai/models.tabfusiontransformer.html#sequential) applies to each of the modules it stores (which are each a registered submodule of the [Sequential](https://timeseriesAI.github.io/tsai/models.tabfusiontransformer.html#sequential)).
What’s the difference between a [Sequential](https://timeseriesAI.github.io/tsai/models.tabfusiontransformer.html#sequential) and a :class:torch.nn.ModuleList? A ModuleList is exactly what it sounds like–a list for storing Module s! On the other hand, the layers in a [Sequential](https://timeseriesAI.github.io/tsai/models.tabfusiontransformer.html#sequential) are connected in a cascading way.
Example::
# Using Sequential to create a small model. When `model` is run,
# input will first be passed to `Conv2d(1,20,5)`. The output of
# `Conv2d(1,20,5)` will be used as the input to the first
# `ReLU`; the output of the first `ReLU` will become the input
# for `Conv2d(20,64,5)`. Finally, the output of
# `Conv2d(20,64,5)` will be used as input to the second `ReLU`
model = nn.Sequential(
nn.Conv2d(1, 20, 5), nn.ReLU(), nn.Conv2d(20, 64, 5), nn.ReLU()
)
# Using Sequential with OrderedDict. This is functionally the
# same as the above code
model = nn.Sequential(
OrderedDict(
[
("conv1", nn.Conv2d(1, 20, 5)),
("relu1", nn.ReLU()),
("conv2", nn.Conv2d(20, 64, 5)),
("relu2", nn.ReLU()),
]
)
)xb = torch.randn(16, 5, 20)
model = ConvTranPlus(5, 3, 20, d=None)
output = model(xb)
assert output.shape == (16, 3)xb = torch.randn(16, 5, 20)
model = ConvTranPlus(5, 3, 20, d=5)
output = model(xb)
assert output.shape == (16, 5, 3)xb = torch.randn(16, 5, 20)
model = ConvTranPlus(5, 3, 20, d=(2, 10))
output = model(xb)
assert output.shape == (16, 2, 10, 3)