magnet.utils

magnet.utils.summarize(module, x, parameters='trainable', arguments=False, batch=False, max_width=120)[source]

Prints a pretty picture of how a one-input one output sequential model works.

Similar to Model.summarize found in Keras.

Parameters:
  • module (nn.Module) – The module to summarize
  • x (torch.Tensor) – A sample tensor sent as input to the module.
  • parameters (str or True) – Which kind of parameters to enumerate. Default: 'trainable'
  • arguments (bool) – Whether to show the arguments to a node. Default: False
  • batch (bool) – Whether to show the batch dimension in the shape. Default: False
  • max_width (int) – The maximum width of the table. Default: 120

  • parameters is one of ['trainable', 'non-trainable', 'all', True].

    ‘trainable’ parameters are the ones which require gradients and can be optimized by SGD.

    Setting this to True will print both types as a tuple.

magnet.utils.images

magnet.utils.images.show_images(images, **kwargs)[source]

A nifty helper function to show images represented by tensors. :param images: The images

to show
  • images can be anything which from you could conceivable harvest an image. If it’s a torch.Tensor, it is converted to a numpy.ndarray. The first dimension of the tensor is treated as a batch dimension. If it’s a str, it is treated as a glob path from which all images are extracted. More commonly, a list of numpy arrays can be given.
Keyword Arguments:
 
  • pixel_range (tuple or 'auto') – The range of pixel values to be expected. Default: 'auto'
  • cmap (str or None) – The color map for the plots. Default: 'gray'
  • merge (bool) – If True, all images are merged into one giant image. Default: True
  • titles (list or None) – The titles for each image. Default: None
  • shape (str) – The shape of the merge tile. Default: 'square'
  • resize (str) – The common shape to which images are resized. Default: 'smean'
  • retain (bool) – If True, the plot is retained. Default: False
  • savepath (str or None) – If given, the image is saved to this path. Default: None
  • pixel_range default to the range in the image.
  • titles should only be given if merge is True.

Note

The merge shape is controlled by shape which can be either 'square', 'row', 'column' or a tuple which explicitly specifies this shape. 'square' automatically finds a shape with least difference between the number of rows and columns. This is aesthetically pleasing. In the explicit case, the product of the tuple needs to equal the number of images.

magnet.utils.plot

magnet.utils.plot.smooth_plot(*args, **kwargs)[source]

Same as the plot function from matplotlib… only smoother!

This function plots a modified, smoothened version of the data. Useful when data is jagged and one is interested in the average trends.

Keyword Arguments:
 
  • window_fraction (float) – The fraction of the data to use as window to the smoothener. Default: \(0.3\)
  • gain (float) – The amount of artificial datapoints inserted per raw datapoint. Default: \(10\)
  • replace_outliers (bool) – If True, replaces outlier datapoints by a sensible value. Default: True
  • ax (Pyplot axes object) – The axis to plot onto. Default: None

Note

Uses a Savitzky Golay filter to smoothen out the data.

magnet.utils.varseq

magnet.utils.varseq.pack(sequences, lengths=None)[source]

Packs a list of variable length Tensors

Parameters:
  • sequences (list or torch.Tensor) – The list of Tensors to pack
  • lengths (list) – list of lengths of each tensor. Default: None

Note

If sequences is a tensor, lengths needs to be provided.

Note

The packed sequence that is returned has a convinient unpack() method as well as shape and order attributes. The order attribute stores the sorting order which should be used for unpacking.

Shapes:
sequences should be a list of Tensors of size L x *, where L is the length of a sequence and * is any number of trailing dimensions, including zero.
magnet.utils.varseq.unpack(sequence, as_list=False)[source]

Unpacks a PackedSequence object.

Parameters:
  • sequence (PackedSequence) – The tensor to unpack.
  • as_list (bool) – If True, returns a list of tensors. Default: False

Note

The sequence should have an order attribute that stores the sorting order.

magnet.utils.varseq.sort(sequences, order, dim=0)[source]

Sorts a tensor in a certain order along a certain dimension.

Parameters:
  • sequences (torch.Tensor) – The tensor to sort
  • order (numpy.ndarray) – The sorting order
  • dim (int) – The dimension to sort. Default 0
magnet.utils.varseq.unsort(sequences, order, dim=0)[source]

Unsorts a tensor in a certain order along a certain dimension.

Parameters:
  • sequences (torch.Tensor) – The tensor to unsort
  • order (numpy.ndarray) – The sorting order
  • dim (int) – The dimension to unsort. Default 0