fix doc model part 2
This commit is contained in:
giovanni
@@ -7,30 +7,27 @@ from .integral import Integral
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class ContinuousConvBlock(BaseContinuousConv):
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"""
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Implementation of Continuous Convolutional operator.
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The algorithm expects input to be in the form:
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:math:`[B, N_{in}, N, D]`
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where :math:`B` is the batch_size, :math:`N_{in}` is the number of input
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fields, :math:`N` the number of points in the mesh, :math:`D` the dimension
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of the problem. In particular:
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r"""
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Continuous Convolutional block.
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The class expects the input to be in the form:
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:math:`[B \times N_{in} \times N \times D]`, where :math:`B` is the
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batch_size, :math:`N_{in}` is the number of input fields, :math:`N`
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the number of points in the mesh, :math:`D` the dimension of the problem.
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In particular:
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* :math:`D` is the number of spatial variables + 1. The last column must
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contain the field value. For example for 2D problems :math:`D=3` and
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the tensor will be something like ``[first coordinate, second
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coordinate, field value]``.
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* :math:`N_{in}` represents the number of vectorial function presented.
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For example a vectorial function :math:`f = [f_1, f_2]` will have
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contain the field value.
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* :math:`N_{in}` represents the number of function components.
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For instance, a vectorial function :math:`f = [f_1, f_2]` has
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:math:`N_{in}=2`.
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.. seealso::
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**Original reference**: Coscia, D., Meneghetti, L., Demo, N. et al.
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*A continuous convolutional trainable filter for modelling
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unstructured data*. Comput Mech 72, 253–265 (2023).
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**Original reference**:
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Coscia, D., Meneghetti, L., Demo, N. et al.
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*A continuous convolutional trainable filter for modelling unstructured
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data*. Comput Mech 72, 253-265 (2023).
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DOI `<https://doi.org/10.1007/s00466-023-02291-1>`_
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"""
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def __init__(
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@@ -44,53 +41,48 @@ class ContinuousConvBlock(BaseContinuousConv):
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no_overlap=False,
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):
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"""
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:param input_numb_field: Number of fields :math:`N_{in}` in the input.
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:type input_numb_field: int
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:param output_numb_field: Number of fields :math:`N_{out}` in the
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output.
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:type output_numb_field: int
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:param filter_dim: Dimension of the filter.
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:type filter_dim: tuple(int) | list(int)
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:param stride: Stride for the filter.
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:type stride: dict
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:param model: Neural network for inner parametrization,
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defaults to ``None``. If None, a default multilayer perceptron
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of width three and size twenty with ReLU activation is used.
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:type model: torch.nn.Module
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:param optimize: Flag for performing optimization on the continuous
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filter, defaults to False. The flag `optimize=True` should be
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used only when the scatter datapoints are fixed through the
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training. If torch model is in ``.eval()`` mode, the flag is
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automatically set to False always.
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:type optimize: bool
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:param no_overlap: Flag for performing optimization on the transpose
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continuous filter, defaults to False. The flag set to `True` should
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be used only when the filter positions do not overlap for different
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strides. RuntimeError will raise in case of non-compatible strides.
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:type no_overlap: bool
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Initialization of the :class:`ContinuousConvBlock` class.
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:param int input_numb_field: The number of input fields.
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:param int output_numb_field: The number of input fields.
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:param filter_dim: The shape of the filter.
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:type filter_dim: list[int] | tuple[int]
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:param dict stride: The stride of the filter.
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:param torch.nn.Module model: The neural network for inner
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parametrization. Default is ``None``.
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:param bool optimize: If ``True``, optimization is performed on the
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continuous filter. It should be used only when the training points
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are fixed. If ``model`` is in ``eval`` mode, it is reset to
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``False``. Default is ``False``.
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:param bool no_overlap: If ``True``, optimization is performed on the
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transposed continuous filter. It should be used only when the filter
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positions do not overlap for different strides.
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Default is ``False``.
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.. note::
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Using `optimize=True` the filter can be use either in `forward`
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or in `transpose` mode, not both. If `optimize=False` the same
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filter can be used for both `transpose` and `forward` modes.
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If ``optimize=True``, the filter can be use either in ``forward``
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or in ``transpose`` mode, not both.
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:Example:
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>>> class MLP(torch.nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self. model = torch.nn.Sequential(
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torch.nn.Linear(2, 8),
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torch.nn.ReLU(),
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torch.nn.Linear(8, 8),
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torch.nn.ReLU(),
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torch.nn.Linear(8, 1))
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def forward(self, x):
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return self.model(x)
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... def __init__(self) -> None:
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... super().__init__()
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... self. model = torch.nn.Sequential(
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... torch.nn.Linear(2, 8),
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... torch.nn.ReLU(),
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... torch.nn.Linear(8, 8),
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... torch.nn.ReLU(),
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... torch.nn.Linear(8, 1)
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... )
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... def forward(self, x):
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... return self.model(x)
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>>> dim = [3, 3]
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>>> stride = {"domain": [10, 10],
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"start": [0, 0],
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"jumps": [3, 3],
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"direction": [1, 1.]}
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>>> stride = {
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... "domain": [10, 10],
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... "start": [0, 0],
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... "jumps": [3, 3],
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... "direction": [1, 1.]
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... }
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>>> conv = ContinuousConv2D(1, 2, dim, stride, MLP)
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>>> conv
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ContinuousConv2D(
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@@ -116,7 +108,6 @@ class ContinuousConvBlock(BaseContinuousConv):
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)
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)
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"""
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super().__init__(
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input_numb_field=input_numb_field,
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output_numb_field=output_numb_field,
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@@ -143,13 +134,13 @@ class ContinuousConvBlock(BaseContinuousConv):
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def _spawn_networks(self, model):
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"""
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Private method to create a collection of kernels
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Create a collection of kernels
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:param model: A :class:`torch.nn.Module` model in form of Object class.
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:type model: torch.nn.Module
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:return: List of :class:`torch.nn.Module` models.
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:param torch.nn.Module model: A neural network model.
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:raises ValueError: If the model is not a subclass of
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``torch.nn.Module``.
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:return: A list of models.
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:rtype: torch.nn.ModuleList
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"""
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nets = []
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if self._net is None:
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@@ -176,13 +167,11 @@ class ContinuousConvBlock(BaseContinuousConv):
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def _extract_mapped_points(self, batch_idx, index, x):
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"""
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Priviate method to extract mapped points in the filter
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Extract mapped points in the filter.
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:param x: Input tensor of shape ``[channel, N, dim]``
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:type x: torch.Tensor
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:param torch.Tensor x: Input tensor of shape ``[channel, N, dim]``
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:return: Mapped points and indeces for each channel,
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:rtype: torch.Tensor, list
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:rtype: tuple
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"""
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mapped_points = []
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indeces_channels = []
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@@ -218,11 +207,9 @@ class ContinuousConvBlock(BaseContinuousConv):
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def _find_index(self, X):
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"""
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Private method to extract indeces for convolution.
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:param X: Input tensor, as in ContinuousConvBlock ``__init__``.
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:type X: torch.Tensor
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Extract indeces for convolution.
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:param torch.Tensor X: The input tensor.
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"""
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# append the index for each stride
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index = []
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@@ -236,11 +223,9 @@ class ContinuousConvBlock(BaseContinuousConv):
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def _make_grid_forward(self, X):
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"""
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Private method to create forward convolution grid.
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:param X: Input tensor, as in ContinuousConvBlock docstring.
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:type X: torch.Tensor
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Create forward convolution grid.
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:param torch.Tensor X: The input tensor.
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"""
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# filter dimension + number of points in output grid
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filter_dim = len(self._dim)
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@@ -264,12 +249,9 @@ class ContinuousConvBlock(BaseContinuousConv):
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def _make_grid_transpose(self, X):
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"""
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Private method to create transpose convolution grid.
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:param X: Input tensor, as in ContinuousConvBlock docstring.
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:type X: torch.Tensor
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Create transpose convolution grid.
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:param torch.Tensor X: The input tensor.
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"""
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# initialize to all zeros
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tmp = torch.zeros_like(X).as_subclass(torch.Tensor)
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@@ -280,14 +262,12 @@ class ContinuousConvBlock(BaseContinuousConv):
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def _make_grid(self, X, type_):
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"""
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Private method to create convolution grid.
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:param X: Input tensor, as in ContinuousConvBlock docstring.
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:type X: torch.Tensor
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:param type: Type of convolution, ``['forward', 'inverse']`` the
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possibilities.
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:type type: str
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Create convolution grid.
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:param torch.Tensor X: The input tensor.
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:param str type_: The type of convolution.
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Available options are: ``forward`` and ``inverse``.
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:raises TypeError: If the type is not in the available options.
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"""
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# choose the type of convolution
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if type_ == "forward":
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@@ -300,15 +280,12 @@ class ContinuousConvBlock(BaseContinuousConv):
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def _initialize_convolution(self, X, type_="forward"):
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"""
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Private method to intialize the convolution.
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The convolution is initialized by setting a grid and
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calculate the index for finding the points inside the
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filter.
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Initialize the convolution by setting a grid and computing the index to
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find the points inside the filter.
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:param X: Input tensor, as in ContinuousConvBlock docstring.
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:type X: torch.Tensor
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:param str type: type of convolution, ``['forward', 'inverse'] ``the
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possibilities.
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:param torch.Tensor X: The input tensor.
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:param str type_: The type of convolution. Available options are:
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``forward`` and ``inverse``. Default is ``forward``.
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"""
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# variable for the convolution
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@@ -319,11 +296,10 @@ class ContinuousConvBlock(BaseContinuousConv):
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def forward(self, X):
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"""
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Forward pass in the convolutional layer.
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Forward pass.
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:param x: Input data for the convolution :math:`[B, N_{in}, N, D]`.
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:type x: torch.Tensor
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:return: Convolution output :math:`[B, N_{out}, N, D]`.
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:param torch.Tensor x: The input tensor.
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:return: The output tensor.
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:rtype: torch.Tensor
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"""
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@@ -381,25 +357,14 @@ class ContinuousConvBlock(BaseContinuousConv):
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def transpose_no_overlap(self, integrals, X):
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"""
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Transpose pass in the layer for no-overlapping filters
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Transpose pass in the layer for no-overlapping filters.
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:param integrals: Weights for the transpose convolution. Shape
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:math:`[B, N_{in}, N]`
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where B is the batch_size, :math`N_{in}` is the number of input
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fields, :math:`N` the number of points in the mesh, D the dimension
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of the problem.
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:type integral: torch.tensor
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:param X: Input data. Expect tensor of shape
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:math:`[B, N_{in}, M, D]` where :math:`B` is the batch_size,
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:math`N_{in}`is the number of input fields, :math:`M` the number of
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points
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in the mesh, :math:`D` the dimension of the problem.
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:type X: torch.Tensor
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:return: Feed forward transpose convolution. Tensor of shape
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:math:`[B, N_{out}, M, D]` where :math:`B` is the batch_size,
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:math`N_{out}`is the number of input fields, :math:`M` the number of
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points
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in the mesh, :math:`D` the dimension of the problem.
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:param torch.Tensor integrals: The weights for the transpose convolution.
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Expected shape :math:`[B, N_{in}, N]`.
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:param torch.Tensor X: The input data.
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Expected shape :math:`[B, N_{in}, M, D]`.
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:return: Feed forward transpose convolution.
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Expected shape: :math:`[B, N_{out}, M, D]`.
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:rtype: torch.Tensor
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.. note::
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@@ -466,25 +431,14 @@ class ContinuousConvBlock(BaseContinuousConv):
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def transpose_overlap(self, integrals, X):
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"""
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Transpose pass in the layer for overlapping filters
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Transpose pass in the layer for overlapping filters.
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:param integrals: Weights for the transpose convolution. Shape
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:math:`[B, N_{in}, N]`
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where B is the batch_size, :math`N_{in}` is the number of input
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fields, :math:`N` the number of points in the mesh, D the dimension
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of the problem.
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:type integral: torch.tensor
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:param X: Input data. Expect tensor of shape
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:math:`[B, N_{in}, M, D]` where :math:`B` is the batch_size,
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:math`N_{in}`is the number of input fields, :math:`M` the number of
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points
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in the mesh, :math:`D` the dimension of the problem.
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:type X: torch.Tensor
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:return: Feed forward transpose convolution. Tensor of shape
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:math:`[B, N_{out}, M, D]` where :math:`B` is the batch_size,
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:math`N_{out}`is the number of input fields, :math:`M` the number of
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points
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in the mesh, :math:`D` the dimension of the problem.
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:param torch.Tensor integrals: The weights for the transpose convolution.
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Expected shape :math:`[B, N_{in}, N]`.
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:param torch.Tensor X: The input data.
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Expected shape :math:`[B, N_{in}, M, D]`.
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:return: Feed forward transpose convolution.
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Expected shape: :math:`[B, N_{out}, M, D]`.
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:rtype: torch.Tensor
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.. note:: This function is automatically called when ``.transpose()``
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