True => Dont include module functions in graphviz False => Include modules function in graphviz Default: True hide_inner_tensors (bool): Inner tensor is all the tensors of computation graph but input and output tensors True => Does not show inner tensors in graphviz False => Shows inner tensors in graphviz Default: True roll (bool): If true, rolls recursive modules. Some modules consist only of torch_functions (no submodule), e.g. when there are tensors from module node to module node and hiding those tensors Default: True expand_nested(bool): if true shows nested modules with dashed borders graph_dir (str): Sets the direction of visual graph 'TB' -> Top to Bottom 'LR' -> Left to Right 'BT' -> Bottom to Top 'RL' -> Right to Left Default: None -> TB hide_module_function (bool): Determines whether to hide module torch_functions. Defaults to Eval mode if not given Default: None strict (bool): if true, graphviz visual does not allow multiple edges between nodes. mode (str): Mode of model to use for forward prop. Default: None dtypes (list of torch.dtype): Uses dtypes to set the types of input tensor if input size is given. Defaults to gpu if cuda is seen by pytorch, otherwise to cpu. Default: 3 device (str or vice): Device to place and input tensors. For instance, main module has depth=0, whereas submodule of main module has depth=1, and so on. Depth is measured how far is module/tensor inside the module hierarchy. Also default name graphviz file of Graph Visualization Default: 'model' depth (int): Upper limit for depth of nodes to be shown in visualization. Default: None graph_name (str): Name for graphviz.Digraph object. Wrap it in a list for multiple args or in a dict or kwargs input_size (Sequence of Sizes): Shape of input data as a List/Tuple/torch.Size (dtypes must match model input, default is FloatTensors). input_data (data structure containing torch.Tensor): input for forward method of model. Args: model (nn.Module): Pytorch model to represent visually. This graph visual shows modules/ module hierarchy, torch_functions, shapes and tensors recorded during forward prop, for examples see documentation, and colab notebooks. 2) graphviz.Digraph object that contains visual representation of computation graph of pytorch module.

ComputationGraph object contains: 1) Root nodes (usually tensor node for input tensors) which connect to all the other nodes of computation graph of pytorch module recorded during forward propagation. '''Returns visual representation of the input Pytorch Module with ComputationGraph object.

device | str | None = None,ĭtypes: list | None = None, Input_size: INPUT_SIZE_TYPE | None = None,ĭevice: torch. Input_data: INPUT_DATA_TYPE | None = None,

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