--- title: XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification keywords: fastai sidebar: home_sidebar summary: "This is an unofficial PyTorch implementation of XCM created by Ignacio Oguiza." description: "This is an unofficial PyTorch implementation of XCM created by Ignacio Oguiza." nb_path: "nbs/114b_models.XCMPlus.ipynb" ---
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class XCMPlus[source]

XCMPlus(c_in:int, c_out:int, seq_len:Optional[int]=None, nf:int=128, window_perc:float=1.0, flatten:bool=False, custom_head:callable=None, concat_pool:bool=False, fc_dropout:float=0.0, bn:bool=False, y_range:tuple=None, **kwargs) :: Sequential

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 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 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 applies to each of the modules it stores (which are each a registered submodule of the Sequential).

What's the difference between a 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 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())
        ]))
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from tsai.data.all import *
from tsai.models.XCM import *

dsid = 'NATOPS'
X, y, splits = get_UCR_data(dsid, split_data=False)
tfms = [None, Categorize()]
dls = get_ts_dls(X, y, splits=splits, tfms=tfms)
model =  XCMPlus(dls.vars, dls.c, dls.len)
learn = Learner(dls, model, metrics=accuracy)
xb, yb = dls.one_batch()

bs, c_in, seq_len = xb.shape
c_out = len(np.unique(yb.cpu().numpy()))

model = XCMPlus(c_in, c_out, seq_len, fc_dropout=.5)
test_eq(model.to(xb.device)(xb).shape, (bs, c_out))
model = XCMPlus(c_in, c_out, seq_len, concat_pool=True)
test_eq(model.to(xb.device)(xb).shape, (bs, c_out))
model = XCMPlus(c_in, c_out, seq_len)
test_eq(model.to(xb.device)(xb).shape, (bs, c_out))
test_eq(count_parameters(XCMPlus(c_in, c_out, seq_len)), count_parameters(XCM(c_in, c_out, seq_len)))
model
XCMPlus(
  (backbone): _XCMPlus_Backbone(
    (conv2dblock): Sequential(
      (0): Unsqueeze(dim=1)
      (1): Conv2dSame(
        (conv2d_same): Conv2d(1, 128, kernel_size=(1, 51), stride=(1, 1))
      )
      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): ReLU()
    )
    (conv2d1x1block): Sequential(
      (0): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))
      (1): ReLU()
      (2): Squeeze(dim=1)
    )
    (conv1dblock): Sequential(
      (0): Conv1d(24, 128, kernel_size=(51,), stride=(1,), padding=(25,))
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
    )
    (conv1d1x1block): Sequential(
      (0): Conv1d(128, 1, kernel_size=(1,), stride=(1,))
      (1): ReLU()
    )
    (concat): Concat(dim=1)
    (conv1d): Sequential(
      (0): Conv1d(25, 128, kernel_size=(51,), stride=(1,), padding=(25,))
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
    )
  )
  (head): Sequential(
    (0): GAP1d(
      (gap): AdaptiveAvgPool1d(output_size=1)
      (flatten): Flatten(full=False)
    )
    (1): LinBnDrop(
      (0): Linear(in_features=128, out_features=6, bias=True)
    )
  )
)
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model.show_gradcam(xb[0], yb[0])
/Users/nacho/opt/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/module.py:974: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.
  warnings.warn("Using a non-full backward hook when the forward contains multiple autograd Nodes "
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bs = 16
n_vars = 3
seq_len = 12
c_out = 1
xb = torch.rand(bs, n_vars, seq_len)
new_head = partial(conv_lin_nd_head, d=(5, 2))
net = XCMPlus(n_vars, c_out, seq_len, custom_head=new_head)
print(net.to(xb.device)(xb).shape)
net.head
torch.Size([16, 5, 2])
create_conv_lin_nd_head(
  (0): Conv1d(128, 1, kernel_size=(1,), stride=(1,))
  (1): Linear(in_features=12, out_features=10, bias=True)
  (2): Transpose(-1, -2)
  (3): Reshape(bs, 5, 2)
)
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bs = 16
n_vars = 3
seq_len = 12
c_out = 2
xb = torch.rand(bs, n_vars, seq_len)
net = XCMPlus(n_vars, c_out, seq_len)
change_model_head(net, create_pool_plus_head, concat_pool=False)
print(net.to(xb.device)(xb).shape)
net.head
torch.Size([16, 2])
Sequential(
  (0): AdaptiveAvgPool1d(output_size=1)
  (1): Flatten(full=False)
  (2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (3): Linear(in_features=128, out_features=512, bias=False)
  (4): ReLU(inplace=True)
  (5): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (6): Linear(in_features=512, out_features=2, bias=False)
)
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