Multimodal

Functionality used for multiple data modalities.

A common scenario in time-series related tasks is the use of multiple types of inputs:

At the same time, these different modalities may contain:

Based on that, there are situations where we have up to 6 different types of input features:

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get_feat_idxs

 get_feat_idxs (c_in, s_cat_idxs=None, s_cont_idxs=None, o_cat_idxs=None,
                o_cont_idxs=None)

Calculate the indices of the features used for training.

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get_o_cont_idxs

 get_o_cont_idxs (c_in, s_cat_idxs=None, s_cont_idxs=None,
                  o_cat_idxs=None)

Calculate the indices of the observed continuous features.

c_in = 7
s_cat_idxs = 3
s_cont_idxs = [1, 4, 5]
o_cat_idxs = None
o_cont_idxs = None

s_cat_idxs, s_cont_idxs, o_cat_idxs, o_cont_idxs = get_feat_idxs(c_in, s_cat_idxs=s_cat_idxs, s_cont_idxs=s_cont_idxs, o_cat_idxs=o_cat_idxs, o_cont_idxs=o_cont_idxs)

test_eq(s_cat_idxs, [3])
test_eq(s_cont_idxs, [1, 4, 5])
test_eq(o_cat_idxs, [])
test_eq(o_cont_idxs, [0, 2, 6])

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TensorSplitter

 TensorSplitter (s_cat_idxs:list=None, s_cont_idxs:list=None,
                 o_cat_idxs:list=None, o_cont_idxs:list=None,
                 k_cat_idxs:list=None, k_cont_idxs:list=None,
                 horizon:int=None)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them 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):
        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 have their parameters converted too 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
Type Default Details
s_cat_idxs list None list of indices for static categorical variables
s_cont_idxs list None list of indices for static continuous variables
o_cat_idxs list None list of indices for observed categorical variables
o_cont_idxs list None list of indices for observed continuous variables
k_cat_idxs list None list of indices for known categorical variables
k_cont_idxs list None list of indices for known continuous variables
horizon int None number of time steps to predict ahead 
# Example usage
bs = 4
s_cat_idxs = 1
s_cont_idxs = [0, 2]
o_cat_idxs =[ 3, 4, 5]
o_cont_idxs = None
k_cat_idxs = None
k_cont_idxs = None
horizon=None
input_tensor = torch.randn(bs, 6, 10)  # 3D input tensor
splitter = TensorSplitter(s_cat_idxs=s_cat_idxs, s_cont_idxs=s_cont_idxs,
                          o_cat_idxs=o_cat_idxs, o_cont_idxs=o_cont_idxs)
slices = splitter(input_tensor)
for i, slice_tensor in enumerate(slices):
    print(f"Slice {i+1}: {slice_tensor.shape} {slice_tensor.dtype}")
Slice 1: torch.Size([4, 1]) torch.int64
Slice 2: torch.Size([4, 2]) torch.int64
Slice 3: torch.Size([4, 3, 10]) torch.float32
Slice 4: torch.Size([4, 0, 10]) torch.float32
# Example usage
bs = 4
s_cat_idxs = 1
s_cont_idxs = [0, 2]
o_cat_idxs =[ 3, 4, 5]
o_cont_idxs = None
k_cat_idxs = [6,7]
k_cont_idxs = 8
horizon=3
input_tensor = torch.randn(4, 9, 10)  # 3D input tensor
splitter = TensorSplitter(s_cat_idxs=s_cat_idxs, s_cont_idxs=s_cont_idxs,
                          o_cat_idxs=o_cat_idxs, o_cont_idxs=o_cont_idxs,
                          k_cat_idxs=k_cat_idxs, k_cont_idxs=k_cont_idxs, horizon=horizon)
slices = splitter(input_tensor)
for i, slice_tensor in enumerate(slices):
    print(f"Slice {i+1}: {slice_tensor.shape} {slice_tensor.dtype}")
Slice 1: torch.Size([4, 1]) torch.int64
Slice 2: torch.Size([4, 2]) torch.int64
Slice 3: torch.Size([4, 3, 7]) torch.float32
Slice 4: torch.Size([4, 0, 7]) torch.float32
Slice 5: torch.Size([4, 2, 10]) torch.float32
Slice 6: torch.Size([4, 1, 10]) torch.float32

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Embeddings

 Embeddings (n_embeddings:list, embedding_dims:list=None,
             padding_idx:int=0, embed_dropout:float=0.0, **kwargs)

Embedding layers for each categorical variable in a 2D or 3D tensor

Type Default Details
n_embeddings list List of num_embeddings for each categorical variable
embedding_dims list None List of embedding dimensions for each categorical variable
padding_idx int 0 Embedding padding_idx
embed_dropout float 0.0 Dropout probability for Embedding layer
kwargs 
t1 = torch.randint(0, 7, (16, 1))
t2 = torch.randint(0, 5, (16, 1))
t = torch.cat([t1, t2], 1).float()
emb = Embeddings([7, 5], None, embed_dropout=0.1)
test_eq(emb(t).shape, (16, 12))
t1 = torch.randint(0, 7, (16, 1))
t2 = torch.randint(0, 5, (16, 1))
t = torch.cat([t1, t2], 1).float()
emb = Embeddings([7, 5], [4, 3])
test_eq(emb(t).shape, (16, 12))
t1 = torch.randint(0, 7, (16, 1, 10))
t2 = torch.randint(0, 5, (16, 1, 10))
t = torch.cat([t1, t2], 1).float()
emb = Embeddings([7, 5], None)
test_eq(emb(t).shape, (16, 12, 10))

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StaticBackbone

 StaticBackbone (c_in, c_out, seq_len, d=None, layers=[200, 100],
                 dropouts=[0.1, 0.2], act=ReLU(inplace=True),
                 use_bn=False, lin_first=False)

Static backbone model to embed static features

# Example usage
bs = 4
c_in = 6
c_out = 8
seq_len = 10
input_tensor = torch.randn(bs, c_in, seq_len)  # 3D input tensor
backbone = StaticBackbone(c_in, c_out, seq_len)
output_tensor = backbone(input_tensor)
print(f"Input shape: {input_tensor.shape} Output shape: {output_tensor.shape}")
backbone
Input shape: torch.Size([4, 6, 10]) Output shape: torch.Size([4, 100])
StaticBackbone(
  (flatten): Reshape(bs)
  (mlp): ModuleList(
    (0): LinBnDrop(
      (0): Dropout(p=0.1, inplace=False)
      (1): Linear(in_features=60, out_features=200, bias=True)
      (2): ReLU(inplace=True)
    )
    (1): LinBnDrop(
      (0): Dropout(p=0.2, inplace=False)
      (1): Linear(in_features=200, out_features=100, bias=True)
      (2): ReLU(inplace=True)
    )
  )
)
# class MultInputWrapper(nn.Module):
#     "Model wrapper for input tensors with static and/ or observed, categorical and/ or numerical features."

#     def __init__(self,
#         arch,
#         c_in:int=None, # number of input variables
#         c_out:int=None, # number of output variables
#         seq_len:int=None, # input sequence length
#         d:tuple=None, # shape of the output tensor
#         dls:TSDataLoaders=None, # TSDataLoaders object
#         s_cat_idxs:list=None, # list of indices for static categorical variables
#         s_cat_embeddings:list=None, # list of num_embeddings for each static categorical variable
#         s_cat_embedding_dims:list=None, # list of embedding dimensions for each static categorical variable
#         s_cont_idxs:list=None, # list of indices for static continuous variables
#         o_cat_idxs:list=None, # list of indices for observed categorical variables
#         o_cat_embeddings:list=None, # list of num_embeddings for each observed categorical variable
#         o_cat_embedding_dims:list=None, # list of embedding dimensions for each observed categorical variable
#         o_cont_idxs:list=None, # list of indices for observed continuous variables. All features not in s_cat_idxs, s_cont_idxs, o_cat_idxs are considered observed continuous variables.
#         patch_len:int=None, # Number of time steps in each patch.
#         patch_stride:int=None, # Stride of the patch.
#         flatten:bool=False, # boolean indicating whether to flatten bacbone's output tensor
#         use_bn:bool=False, # boolean indicating whether to use batch normalization in the head
#         fc_dropout:float=0., # dropout probability for the fully connected layer in the head
#         custom_head=None, # custom head to replace the default head
#         **kwargs
#     ):
#         super().__init__()

#         # attributes
#         c_in = c_in or dls.vars
#         c_out = c_out or dls.c
#         seq_len = seq_len or dls.len
#         d = d or (dls.d if dls is not None else None)
#         self.c_in, self.c_out, self.seq_len, self.d = c_in, c_out, seq_len, d

#         # tensor splitter
#         if o_cont_idxs is None:
#             o_cont_idxs = get_o_cont_idxs(c_in, s_cat_idxs=s_cat_idxs, s_cont_idxs=s_cont_idxs, o_cat_idxs=o_cat_idxs)
#         self.splitter = TensorSplitter(s_cat_idxs, s_cont_idxs, o_cat_idxs, o_cont_idxs)
#         s_cat_idxs, s_cont_idxs, o_cat_idxs, o_cont_idxs = self.splitter.s_cat_idxs, self.splitter.s_cont_idxs, self.splitter.o_cat_idxs, self.splitter.o_cont_idxs
#         assert c_in == sum([len(s_cat_idxs), len(s_cont_idxs), len(o_cat_idxs), len(o_cont_idxs)])

#         # embeddings
#         self.s_embeddings = Embeddings(s_cat_embeddings, s_cat_embedding_dims)
#         self.o_embeddings = Embeddings(o_cat_embeddings, o_cat_embedding_dims)

#         # patch encoder
#         if patch_len is not None:
#             patch_stride = patch_stride or patch_len
#             self.patch_encoder = PatchEncoder(patch_len, patch_stride, seq_len=seq_len)
#             c_mult = patch_len
#             seq_len = (seq_len + self.patch_encoder.pad_size - patch_len) // patch_stride + 1
#         else:
#             self.patch_encoder = nn.Identity()
#             c_mult = 1

#         # backbone
#         n_s_features = len(s_cont_idxs) + self.s_embeddings.embedding_dims
#         n_o_features = (len(o_cont_idxs) + self.o_embeddings.embedding_dims) * c_mult
#         s_backbone = StaticBackbone(c_in=n_s_features, c_out=c_out, seq_len=1, **kwargs)
#         if isinstance(arch, str):
#             arch = get_arch(arch)
#         if isinstance(arch, nn.Module):
#             o_model = arch
#         else:
#             o_model = build_ts_model(arch, c_in=n_o_features, c_out=c_out, seq_len=seq_len, d=d, **kwargs)
#         assert hasattr(o_model, "backbone"), "the selected arch must have a backbone"
#         o_backbone = getattr(o_model, "backbone")

#         # head
#         o_head_nf = output_size_calculator(o_backbone, n_o_features, seq_len)[0]
#         s_head_nf = s_backbone.head_nf
#         self.backbone = nn.ModuleList([o_backbone, s_backbone])
#         self.head_nf = o_head_nf + s_head_nf
#         if custom_head is not None:
#             if isinstance(custom_head, nn.Module): self.head = custom_head
#             else:self. head = custom_head(self.head_nf, c_out, seq_len, d=d)
#         else:
#             if "rocket" in o_model.__name__.lower():
#                 self.head = rocket_nd_head(self.head_nf, c_out, seq_len=seq_len, d=d, use_bn=use_bn, fc_dropout=fc_dropout)
#             else:
#                 self.head = lin_nd_head(self.head_nf, c_out, seq_len=seq_len, d=d, flatten=flatten, use_bn=use_bn, fc_dropout=fc_dropout)

#     def forward(self, x):
#         # split x into static cat, static cont, observed cat, and observed cont
#         s_cat, s_cont, o_cat, o_cont = self.splitter(x)

#         # create categorical embeddings
#         s_cat = self.s_embeddings(s_cat)
#         o_cat = self.o_embeddings(o_cat)

#         # contatenate static and observed features
#         s_x = torch.cat([s_cat, s_cont], 1)
#         o_x = torch.cat([o_cat, o_cont], 1)

#         # patch encoder
#         o_x = self.patch_encoder(o_x)

#         # pass static and observed features through their respective backbones
#         for i,(b,xi) in enumerate(zip(self.backbone, [o_x, s_x])):
#             if i == 0:
#                 x = b(xi)
#                 if x.ndim == 2:
#                     x = x[..., None]
#             else:
#                 x = torch.cat([x,  b(xi)[..., None].repeat(1, 1, x.shape[-1])], 1)

#         # head
#         x = self.head(x)
#         return x
# from tsai.models.InceptionTimePlus import InceptionTimePlus
# c_in = 6
# c_out = 3
# seq_len = 97
# d = None

# s_cat_idxs=2
# s_cont_idxs=4
# o_cat_idxs=[0, 3]
# o_cont_idxs=None
# s_cat_embeddings = 5
# s_cat_embedding_dims = None
# o_cat_embeddings = [7, 3]
# o_cat_embedding_dims = [3, None]

# t0 = torch.randint(0, 7, (16, 1, seq_len)) # cat
# t1 = torch.randn(16, 1, seq_len)
# t2 = torch.randint(0, 5, (16, 1, seq_len)) # cat
# t3 = torch.randint(0, 3, (16, 1, seq_len)) # cat
# t4 = torch.randn(16, 1, seq_len)
# t5 = torch.randn(16, 1, seq_len)

# t = torch.cat([t0, t1, t2, t3, t4, t5], 1).float()

# patch_lens = [None, 5, 5, 5, 5]
# patch_strides = [None, None, 1, 3, 5]
# for patch_len, patch_stride in zip(patch_lens, patch_strides):
#     for arch in ["InceptionTimePlus", InceptionTimePlus, "MultiRocketPlus"]:
#         print(f"arch: {arch}, patch_len: {patch_len}, patch_stride: {patch_stride}")

#         model = MultInputWrapper(
#             arch=arch,
#             c_in=c_in,
#             c_out=c_out,
#             seq_len=seq_len,
#             d=d,
#             s_cat_idxs=s_cat_idxs, s_cat_embeddings=s_cat_embeddings, s_cat_embedding_dims=s_cat_embedding_dims,
#             s_cont_idxs=s_cont_idxs,
#             o_cat_idxs=o_cat_idxs, o_cat_embeddings=o_cat_embeddings, o_cat_embedding_dims=o_cat_embedding_dims,
#             o_cont_idxs=o_cont_idxs,
#             patch_len=patch_len,
#             patch_stride=patch_stride,
#         )

#         test_eq(model(t).shape, (16,3))

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FusionMLP

 FusionMLP (comb_dim, layers, act='relu', dropout=0.0, use_bn=True)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them 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):
        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 have their parameters converted too 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

bs = 16
emb_dim = 128
seq_len = 20
cat_dim = 24
cont_feat = 3

comb_dim = emb_dim + cat_dim + cont_feat
emb = torch.randn(bs, emb_dim, seq_len)
cat = torch.randn(bs, cat_dim)
cont = torch.randn(bs, cont_feat)
fusion_mlp = FusionMLP(comb_dim, layers=comb_dim, act='relu', dropout=.1)
output = fusion_mlp(cat, cont, emb)
test_eq(output.shape, (bs, comb_dim))
bs = 16
emb_dim = 50000
cat_dim = 24
cont_feat = 3

comb_dim = emb_dim + cat_dim + cont_feat
emb = torch.randn(bs, emb_dim)
cat = torch.randn(bs, cat_dim)
cont = torch.randn(bs, cont_feat)
fusion_mlp = FusionMLP(comb_dim, layers=[128], act='relu', dropout=.1)
output = fusion_mlp(cat, cont, emb)
test_eq(output.shape, (bs, 128))

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MultInputBackboneWrapper

 MultInputBackboneWrapper (arch, c_in:int=None, seq_len:int=None,
                           d:tuple=None,
                           dls:tsai.data.core.TSDataLoaders=None,
                           s_cat_idxs:list=None,
                           s_cat_embeddings:list=None,
                           s_cat_embedding_dims:list=None,
                           s_cont_idxs:list=None, o_cat_idxs:list=None,
                           o_cat_embeddings:list=None,
                           o_cat_embedding_dims:list=None,
                           o_cont_idxs:list=None, patch_len:int=None,
                           patch_stride:int=None,
                           fusion_layers:list=[128],
                           fusion_act:str='relu',
                           fusion_dropout:float=0.0,
                           fusion_use_bn:bool=True, **kwargs)

Model backbone wrapper for input tensors with static and/ or observed, categorical and/ or numerical features.

Type Default Details
arch
c_in int None number of input variables
seq_len int None input sequence length
d tuple None shape of the output tensor
dls TSDataLoaders None TSDataLoaders object
s_cat_idxs list None list of indices for static categorical variables
s_cat_embeddings list None list of num_embeddings for each static categorical variable
s_cat_embedding_dims list None list of embedding dimensions for each static categorical variable
s_cont_idxs list None list of indices for static continuous variables
o_cat_idxs list None list of indices for observed categorical variables
o_cat_embeddings list None list of num_embeddings for each observed categorical variable
o_cat_embedding_dims list None list of embedding dimensions for each observed categorical variable
o_cont_idxs list None list of indices for observed continuous variables. All features not in s_cat_idxs, s_cont_idxs, o_cat_idxs are considered observed continuous variables.
patch_len int None Number of time steps in each patch.
patch_stride int None Stride of the patch.
fusion_layers list [128] list of layer dimensions for the fusion MLP
fusion_act str relu activation function for the fusion MLP
fusion_dropout float 0.0 dropout probability for the fusion MLP
fusion_use_bn bool True boolean indicating whether to use batch normalization in the fusion MLP
kwargs

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MultInputWrapper

 MultInputWrapper (arch, c_in:int=None, c_out:int=1, seq_len:int=None,
                   d:tuple=None, dls:tsai.data.core.TSDataLoaders=None,
                   s_cat_idxs:list=None, s_cat_embeddings:list=None,
                   s_cat_embedding_dims:list=None, s_cont_idxs:list=None,
                   o_cat_idxs:list=None, o_cat_embeddings:list=None,
                   o_cat_embedding_dims:list=None, o_cont_idxs:list=None,
                   patch_len:int=None, patch_stride:int=None,
                   fusion_layers:list=128, fusion_act:str='relu',
                   fusion_dropout:float=0.0, fusion_use_bn:bool=True,
                   custom_head=None, **kwargs)

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/models.layers.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/models.layers.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/models.layers.html#sequential) applies to each of the modules it stores (which are each a registered submodule of the [Sequential](https://timeseriesAI.github.io/models.layers.html#sequential)).

What’s the difference between a [Sequential](https://timeseriesAI.github.io/models.layers.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/models.layers.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())
        ]))
Type Default Details
arch
c_in int None number of input variables
c_out int 1 number of output variables
seq_len int None input sequence length
d tuple None shape of the output tensor
dls TSDataLoaders None TSDataLoaders object
s_cat_idxs list None list of indices for static categorical variables
s_cat_embeddings list None list of num_embeddings for each static categorical variable
s_cat_embedding_dims list None list of embedding dimensions for each static categorical variable
s_cont_idxs list None list of indices for static continuous variables
o_cat_idxs list None list of indices for observed categorical variables
o_cat_embeddings list None list of num_embeddings for each observed categorical variable
o_cat_embedding_dims list None list of embedding dimensions for each observed categorical variable
o_cont_idxs list None list of indices for observed continuous variables. All features not in s_cat_idxs, s_cont_idxs, o_cat_idxs are considered observed continuous variables.
patch_len int None Number of time steps in each patch.
patch_stride int None Stride of the patch.
fusion_layers list 128 list of layer dimensions for the fusion MLP
fusion_act str relu activation function for the fusion MLP
fusion_dropout float 0.0 dropout probability for the fusion MLP
fusion_use_bn bool True boolean indicating whether to use batch normalization in the fusion MLP
custom_head NoneType None custom head to replace the default head
kwargs 
from tsai.models.InceptionTimePlus import InceptionTimePlus
bs = 8
c_in = 6
c_out = 3
seq_len = 97
d = None

s_cat_idxs=2
s_cont_idxs=4
o_cat_idxs=[0, 3]
o_cont_idxs=None
s_cat_embeddings = 5
s_cat_embedding_dims = None
o_cat_embeddings = [7, 3]
o_cat_embedding_dims = [3, None]

fusion_layers = 128

t0 = torch.randint(0, 7, (bs, 1, seq_len)) # cat
t1 = torch.randn(bs, 1, seq_len)
t2 = torch.randint(0, 5, (bs, 1, seq_len)) # cat
t3 = torch.randint(0, 3, (bs, 1, seq_len)) # cat
t4 = torch.randn(bs, 1, seq_len)
t5 = torch.randn(bs, 1, seq_len)

t = torch.cat([t0, t1, t2, t3, t4, t5], 1).float().to(default_device())

patch_lens = [None, 5, 5, 5, 5]
patch_strides = [None, None, 1, 3, 5]
for patch_len, patch_stride in zip(patch_lens, patch_strides):
    for arch in ["InceptionTimePlus", InceptionTimePlus, "TSiTPlus"]:
        print(f"arch: {arch}, patch_len: {patch_len}, patch_stride: {patch_stride}")

        model = MultInputWrapper(
            arch=arch,
            c_in=c_in,
            c_out=c_out,
            seq_len=seq_len,
            d=d,
            s_cat_idxs=s_cat_idxs, s_cat_embeddings=s_cat_embeddings, s_cat_embedding_dims=s_cat_embedding_dims,
            s_cont_idxs=s_cont_idxs,
            o_cat_idxs=o_cat_idxs, o_cat_embeddings=o_cat_embeddings, o_cat_embedding_dims=o_cat_embedding_dims,
            o_cont_idxs=o_cont_idxs,
            patch_len=patch_len,
            patch_stride=patch_stride,
            fusion_layers=fusion_layers,
        ).to(default_device())

        test_eq(model(t).shape, (bs, c_out))
arch: InceptionTimePlus, patch_len: None, patch_stride: None
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: None, patch_stride: None
arch: TSiTPlus, patch_len: None, patch_stride: None
arch: InceptionTimePlus, patch_len: 5, patch_stride: None
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: None
arch: TSiTPlus, patch_len: 5, patch_stride: None
arch: InceptionTimePlus, patch_len: 5, patch_stride: 1
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 1
arch: TSiTPlus, patch_len: 5, patch_stride: 1
arch: InceptionTimePlus, patch_len: 5, patch_stride: 3
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 3
arch: TSiTPlus, patch_len: 5, patch_stride: 3
arch: InceptionTimePlus, patch_len: 5, patch_stride: 5
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 5
arch: TSiTPlus, patch_len: 5, patch_stride: 5
bs = 8
c_in = 6
c_out = 3
seq_len = 97
d = None

s_cat_idxs=None
s_cont_idxs=4
o_cat_idxs=[0, 3]
o_cont_idxs=None
s_cat_embeddings = None
s_cat_embedding_dims = None
o_cat_embeddings = [7, 3]
o_cat_embedding_dims = [3, None]

fusion_layers = 128

t0 = torch.randint(0, 7, (bs, 1, seq_len)) # cat
t1 = torch.randn(bs, 1, seq_len)
t2 = torch.randint(0, 5, (bs, 1, seq_len)) # cat
t3 = torch.randint(0, 3, (bs, 1, seq_len)) # cat
t4 = torch.randn(bs, 1, seq_len)
t5 = torch.randn(bs, 1, seq_len)

t = torch.cat([t0, t1, t2, t3, t4, t5], 1).float().to(default_device())

patch_lens = [None, 5, 5, 5, 5]
patch_strides = [None, None, 1, 3, 5]
for patch_len, patch_stride in zip(patch_lens, patch_strides):
    for arch in ["InceptionTimePlus", InceptionTimePlus, "TSiTPlus"]:
        print(f"arch: {arch}, patch_len: {patch_len}, patch_stride: {patch_stride}")

        model = MultInputWrapper(
            arch=arch,
            c_in=c_in,
            c_out=c_out,
            seq_len=seq_len,
            d=d,
            s_cat_idxs=s_cat_idxs, s_cat_embeddings=s_cat_embeddings, s_cat_embedding_dims=s_cat_embedding_dims,
            s_cont_idxs=s_cont_idxs,
            o_cat_idxs=o_cat_idxs, o_cat_embeddings=o_cat_embeddings, o_cat_embedding_dims=o_cat_embedding_dims,
            o_cont_idxs=o_cont_idxs,
            patch_len=patch_len,
            patch_stride=patch_stride,
            fusion_layers=fusion_layers,
        ).to(default_device())

        test_eq(model(t).shape, (bs, c_out))
arch: InceptionTimePlus, patch_len: None, patch_stride: None
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: None, patch_stride: None
arch: TSiTPlus, patch_len: None, patch_stride: None
arch: InceptionTimePlus, patch_len: 5, patch_stride: None
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: None
arch: TSiTPlus, patch_len: 5, patch_stride: None
arch: InceptionTimePlus, patch_len: 5, patch_stride: 1
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 1
arch: TSiTPlus, patch_len: 5, patch_stride: 1
arch: InceptionTimePlus, patch_len: 5, patch_stride: 3
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 3
arch: TSiTPlus, patch_len: 5, patch_stride: 3
arch: InceptionTimePlus, patch_len: 5, patch_stride: 5
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 5
arch: TSiTPlus, patch_len: 5, patch_stride: 5
bs = 8
c_in = 6
c_out = 3
seq_len = 97
d = None

s_cat_idxs=2
s_cont_idxs=4
o_cat_idxs=None
o_cont_idxs=None
s_cat_embeddings = 5
s_cat_embedding_dims = None
o_cat_embeddings = None
o_cat_embedding_dims = None

fusion_layers = 128

t0 = torch.randint(0, 7, (bs, 1, seq_len)) # cat
t1 = torch.randn(bs, 1, seq_len)
t2 = torch.randint(0, 5, (bs, 1, seq_len)) # cat
t3 = torch.randint(0, 3, (bs, 1, seq_len)) # cat
t4 = torch.randn(bs, 1, seq_len)
t5 = torch.randn(bs, 1, seq_len)

t = torch.cat([t0, t1, t2, t3, t4, t5], 1).float().to(default_device())

patch_lens = [None, 5, 5, 5, 5]
patch_strides = [None, None, 1, 3, 5]
for patch_len, patch_stride in zip(patch_lens, patch_strides):
    for arch in ["InceptionTimePlus", InceptionTimePlus, "TSiTPlus"]:
        print(f"arch: {arch}, patch_len: {patch_len}, patch_stride: {patch_stride}")

        model = MultInputWrapper(
            arch=arch,
            c_in=c_in,
            c_out=c_out,
            seq_len=seq_len,
            d=d,
            s_cat_idxs=s_cat_idxs, s_cat_embeddings=s_cat_embeddings, s_cat_embedding_dims=s_cat_embedding_dims,
            s_cont_idxs=s_cont_idxs,
            o_cat_idxs=o_cat_idxs, o_cat_embeddings=o_cat_embeddings, o_cat_embedding_dims=o_cat_embedding_dims,
            o_cont_idxs=o_cont_idxs,
            patch_len=patch_len,
            patch_stride=patch_stride,
            fusion_layers=fusion_layers,
        ).to(default_device())

        test_eq(model(t).shape, (bs, c_out))
arch: InceptionTimePlus, patch_len: None, patch_stride: None
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: None, patch_stride: None
arch: TSiTPlus, patch_len: None, patch_stride: None
arch: InceptionTimePlus, patch_len: 5, patch_stride: None
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: None
arch: TSiTPlus, patch_len: 5, patch_stride: None
arch: InceptionTimePlus, patch_len: 5, patch_stride: 1
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 1
arch: TSiTPlus, patch_len: 5, patch_stride: 1
arch: InceptionTimePlus, patch_len: 5, patch_stride: 3
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 3
arch: TSiTPlus, patch_len: 5, patch_stride: 3
arch: InceptionTimePlus, patch_len: 5, patch_stride: 5
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 5
arch: TSiTPlus, patch_len: 5, patch_stride: 5
bs = 8
c_in = 6
c_out = 3
seq_len = 97
d = None

s_cat_idxs=None
s_cont_idxs=None
o_cat_idxs=None
o_cont_idxs=None
s_cat_embeddings = None
s_cat_embedding_dims = None
o_cat_embeddings = None
o_cat_embedding_dims = None

fusion_layers = 128

t0 = torch.randint(0, 7, (bs, 1, seq_len)) # cat
t1 = torch.randn(bs, 1, seq_len)
t2 = torch.randint(0, 5, (bs, 1, seq_len)) # cat
t3 = torch.randint(0, 3, (bs, 1, seq_len)) # cat
t4 = torch.randn(bs, 1, seq_len)
t5 = torch.randn(bs, 1, seq_len)

t = torch.cat([t0, t1, t2, t3, t4, t5], 1).float().to(default_device())

patch_lens = [None, 5, 5, 5, 5]
patch_strides = [None, None, 1, 3, 5]
for patch_len, patch_stride in zip(patch_lens, patch_strides):
    for arch in ["InceptionTimePlus", InceptionTimePlus, "TSiTPlus"]:
        print(f"arch: {arch}, patch_len: {patch_len}, patch_stride: {patch_stride}")

        model = MultInputWrapper(
            arch=arch,
            c_in=c_in,
            c_out=c_out,
            seq_len=seq_len,
            d=d,
            s_cat_idxs=s_cat_idxs, s_cat_embeddings=s_cat_embeddings, s_cat_embedding_dims=s_cat_embedding_dims,
            s_cont_idxs=s_cont_idxs,
            o_cat_idxs=o_cat_idxs, o_cat_embeddings=o_cat_embeddings, o_cat_embedding_dims=o_cat_embedding_dims,
            o_cont_idxs=o_cont_idxs,
            patch_len=patch_len,
            patch_stride=patch_stride,
            fusion_layers=fusion_layers,
        ).to(default_device())

        test_eq(model(t).shape, (bs, c_out))
arch: InceptionTimePlus, patch_len: None, patch_stride: None
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: None, patch_stride: None
arch: TSiTPlus, patch_len: None, patch_stride: None
arch: InceptionTimePlus, patch_len: 5, patch_stride: None
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: None
arch: TSiTPlus, patch_len: 5, patch_stride: None
arch: InceptionTimePlus, patch_len: 5, patch_stride: 1
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 1
arch: TSiTPlus, patch_len: 5, patch_stride: 1
arch: InceptionTimePlus, patch_len: 5, patch_stride: 3
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 3
arch: TSiTPlus, patch_len: 5, patch_stride: 3
arch: InceptionTimePlus, patch_len: 5, patch_stride: 5
arch: <class 'tsai.models.InceptionTimePlus.InceptionTimePlus'>, patch_len: 5, patch_stride: 5
arch: TSiTPlus, patch_len: 5, patch_stride: 5