17464ceca9
* Adding Residual MLP * Adding test Residual MLP * Modified relative import Continuous Conv
548 lines
21 KiB
Python
548 lines
21 KiB
Python
"""Module for Continuous Convolution class"""
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from .convolution import BaseContinuousConv
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from .utils_convolution import check_point, map_points_
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from .integral import Integral
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import torch
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class ContinuousConvBlock(BaseContinuousConv):
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"""
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Implementation of Continuous Convolutional operator.
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.. seealso::
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**Original reference**: Coscia, D., Meneghetti, L., Demo, N.,
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Stabile, G., & Rozza, G.. (2022). A Continuous Convolutional Trainable
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Filter for Modelling Unstructured Data.
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DOI: `10.48550/arXiv.2210.13416
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<https://doi.org/10.48550/arXiv.2210.13416>`_.
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"""
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def __init__(self, input_numb_field, output_numb_field,
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filter_dim, stride, model=None, optimize=False,
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no_overlap=False):
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"""
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:param input_numb_field: Number of fields 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 N_out in the 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/ list
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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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is used, see BaseContinuousConv.DefaultKernel.
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:type model: torch.nn.Module, optional
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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, optional
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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, optional
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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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.. warning::
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The algorithm expects input to be in the form: [B x N_in x N x D]
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where B is the batch_size, N_in is the number of input
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fields, N the number of points in the mesh, D the dimension
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of the problem. In particular:
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* 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 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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* N_in represents the number of vectorial function presented.
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For example a vectorial function f = [f_1, f_2] will have
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N_in=2.
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The algorithm returns a tensor of shape: [B x N_out x N x D]
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where B is the batch_size, N_out is the number of output
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fields, N' the number of points in the mesh, D the dimension
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of the problem (coordinates + field value).
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For example, a 2-dimensional vectorial function N_in=2 of
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3-dimensionalcinput D=3+1=4 with 100 points input mesh and batch
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size of 8 is represented as a tensor `[8, 2, 100, 4]`, where the
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columnsc`[:, 0, :, -1]` and `[:, 1, :, -1]` represent the first and
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second filed value respectively.
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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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>>> 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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>>> conv = ContinuousConv2D(1, 2, dim, stride, MLP)
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>>> conv
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ContinuousConv2D(
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(_net): ModuleList(
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(0): MLP(
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(model): Sequential(
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(0): Linear(in_features=2, out_features=8, bias=True)
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(1): ReLU()
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(2): Linear(in_features=8, out_features=8, bias=True)
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(3): ReLU()
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(4): Linear(in_features=8, out_features=1, bias=True)
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)
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)
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(1): MLP(
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(model): Sequential(
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(0): Linear(in_features=2, out_features=8, bias=True)
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(1): ReLU()
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(2): Linear(in_features=8, out_features=8, bias=True)
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(3): ReLU()
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(4): Linear(in_features=8, out_features=1, bias=True)
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)
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)
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)
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)
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"""
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super().__init__(input_numb_field=input_numb_field,
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output_numb_field=output_numb_field,
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filter_dim=filter_dim,
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stride=stride,
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model=model,
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optimize=optimize,
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no_overlap=no_overlap)
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# integral routine
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self._integral = Integral('discrete')
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# create the network
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self._net = self._spawn_networks(model)
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# stride for continuous convolution overridden
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self._stride = self._stride._stride_discrete
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def _spawn_networks(self, model):
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"""Private method to create a collection of kernels
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:param model: a 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 torch.nn.Module 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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for _ in range(self._input_numb_field * self._output_numb_field):
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tmp = ContinuousConvBlock.DefaultKernel(len(self._dim), 1)
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nets.append(tmp)
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else:
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if not isinstance(model, object):
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raise ValueError("Expected a python class inheriting"
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" from torch.nn.Module")
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for _ in range(self._input_numb_field * self._output_numb_field):
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tmp = model()
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if not isinstance(tmp, torch.nn.Module):
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raise ValueError("The python class must be inherited from"
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" torch.nn.Module. See the docstring for"
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" an example.")
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nets.append(tmp)
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return torch.nn.ModuleList(nets)
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def _extract_mapped_points(self, batch_idx, index, x):
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"""Priviate method to extract mapped points in the filter
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:param x: input tensor [channel x N x dim]
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:type x: torch.tensor
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:return: mapped points and indeces for each channel
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:rtype: tuple(torch.tensor, list)
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"""
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mapped_points = []
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indeces_channels = []
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for stride_idx, current_stride in enumerate(self._stride):
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# indeces of points falling into filter range
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indeces = index[stride_idx][batch_idx]
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# how many points for each channel fall into the filter?
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numb_points_insiede = torch.sum(indeces, dim=-1).tolist()
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# extracting points for each channel
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# shape: [sum(numb_points_insiede), filter_dim + 1]
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point_stride = x[indeces]
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# mapping points in filter domain
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map_points_(point_stride[..., :-1], current_stride)
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# extracting points for each channel
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point_stride_channel = point_stride.split(numb_points_insiede)
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# appending in list for later use
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mapped_points.append(point_stride_channel)
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indeces_channels.append(numb_points_insiede)
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# stacking input for passing to neural net
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mapping = map(torch.cat, zip(*mapped_points))
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stacked_input = tuple(mapping)
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indeces_channels = tuple(zip(*indeces_channels))
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return stacked_input, indeces_channels
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def _find_index(self, X):
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"""Private method to extract indeces for convolution.
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:param X: input tensor, as in ContinuousConv2D docstring
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:type X: torch.tensor
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"""
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# append the index for each stride
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index = []
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for _, current_stride in enumerate(self._stride):
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tmp = check_point(X, current_stride, self._dim)
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index.append(tmp)
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# storing the index
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self._index = index
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def _make_grid_forward(self, X):
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"""Private method to create forward convolution grid.
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:param X: input tensor, as in ContinuousConv2D docstring
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:type X: torch.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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number_points = len(self._stride)
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# initialize the grid
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grid = torch.zeros(size=(X.shape[0],
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self._output_numb_field,
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number_points,
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filter_dim + 1),
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device=X.device,
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dtype=X.dtype)
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grid[..., :-1] = (self._stride + self._dim * 0.5)
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# saving the grid
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self._grid = grid.detach()
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def _make_grid_transpose(self, X):
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"""Private method to create transpose convolution grid.
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:param X: input tensor, as in ContinuousConv2D docstring
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:type X: torch.tensor
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"""
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# initialize to all zeros
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tmp = torch.zeros_like(X)
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tmp[..., :-1] = X[..., :-1]
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# save on tmp
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self._grid_transpose = tmp
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def _make_grid(self, X, type):
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"""Private method to create convolution grid.
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:param X: input tensor, as in ContinuousConv2D 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: string
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"""
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# choose the type of convolution
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if type == 'forward':
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return self._make_grid_forward(X)
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elif type == 'inverse':
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self._make_grid_transpose(X)
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else:
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raise TypeError
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def _initialize_convolution(self, X, type='forward'):
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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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:param X: input tensor, as in ContinuousConv2D 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: string
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"""
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# variable for the convolution
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self._make_grid(X, type)
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# calculate the index
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self._find_index(X)
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def forward(self, X):
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"""Forward pass in the layer
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:param x: input data (input_numb_field x N x filter_dim)
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:type x: torch.tensor
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:return: feed forward convolution (output_numb_field x N x filter_dim)
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:rtype: torch.tensor
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"""
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# initialize convolution
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if self.training: # we choose what to do based on optimization
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self._choose_initialization(X, type='forward')
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else: # we always initialize on testing
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self._initialize_convolution(X, 'forward')
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# create convolutional array
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conv = self._grid.clone().detach()
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# total number of fields
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tot_dim = self._output_numb_field * self._input_numb_field
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for batch_idx, x in enumerate(X):
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# extract mapped points
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stacked_input, indeces_channels = self._extract_mapped_points(
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batch_idx, self._index, x)
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# compute the convolution
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# storing intermidiate results for each channel convolution
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res_tmp = []
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# for each field
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for idx_conv in range(tot_dim):
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# index for each input field
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idx = idx_conv % self._input_numb_field
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# extract input for each channel
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single_channel_input = stacked_input[idx]
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# extract filter
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net = self._net[idx_conv]
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# calculate filter value
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staked_output = net(single_channel_input[..., :-1])
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# perform integral for all strides in one field
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integral = self._integral(staked_output,
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single_channel_input[..., -1],
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indeces_channels[idx])
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res_tmp.append(integral)
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# stacking integral results
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res_tmp = torch.stack(res_tmp)
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# sum filters (for each input fields) in groups
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# for different ouput fields
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conv[batch_idx, ..., -1] = res_tmp.reshape(self._output_numb_field,
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self._input_numb_field,
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-1).sum(1)
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return conv
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def transpose_no_overlap(self, integrals, X):
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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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[B x N_in x N]
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where B is the batch_size, N_in is the number of input
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fields, 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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[B x N_in x M x D] where B is the batch_size,
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N_in is the number of input fields, M the number of points
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in the mesh, D the dimension of the problem. Note, last column
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:type X: torch.tensor
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:return: Feed forward transpose convolution. Tensor of shape
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[B x N_out x N] where B is the batch_size,
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N_out is the number of output fields, N the number of points
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in the mesh, D the dimension of the problem.
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:rtype: torch.tensor
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.. note::
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This function is automatically called when `.transpose()`
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method is used and `no_overlap=True`
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"""
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# initialize convolution
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if self.training: # we choose what to do based on optimization
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self._choose_initialization(X, type='inverse')
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else: # we always initialize on testing
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self._initialize_convolution(X, 'inverse')
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# initialize grid
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X = self._grid_transpose.clone().detach()
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conv_transposed = self._grid_transpose.clone().detach()
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# total number of dim
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tot_dim = self._input_numb_field * self._output_numb_field
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for batch_idx, x in enumerate(X):
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# extract mapped points
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stacked_input, indeces_channels = self._extract_mapped_points(
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batch_idx, self._index, x)
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# compute the transpose convolution
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# total number of fields
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res_tmp = []
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# for each field
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for idx_conv in range(tot_dim):
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# index for each output field
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idx = idx_conv % self._output_numb_field
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# index for each input field
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idx_in = idx_conv % self._input_numb_field
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# extract input for each field
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single_channel_input = stacked_input[idx]
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rep_idx = torch.tensor(indeces_channels[idx])
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integral = integrals[batch_idx,
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idx_in, :].repeat_interleave(rep_idx)
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# extract filter
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net = self._net[idx_conv]
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# perform transpose convolution for all strides in one field
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staked_output = net(single_channel_input[..., :-1]).flatten()
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integral = staked_output * integral
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res_tmp.append(integral)
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# stacking integral results and sum
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# filters (for each input fields) in groups
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# for different output fields
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res_tmp = torch.stack(res_tmp).reshape(self._input_numb_field,
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self._output_numb_field,
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-1).sum(0)
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conv_transposed[batch_idx, ..., -1] = res_tmp
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return conv_transposed
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def transpose_overlap(self, integrals, X):
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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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[B x N_in x N]
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where B is the batch_size, N_in is the number of input
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fields, 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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[B x N_in x M x D] where B is the batch_size,
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N_in is the number of input fields, M the number of points
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in the mesh, D the dimension of the problem. Note, last column
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:type X: torch.tensor
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:return: Feed forward transpose convolution. Tensor of shape
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[B x N_out x N] where B is the batch_size,
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N_out is the number of output fields, N the number of points
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in the mesh, D the dimension of the problem.
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:rtype: torch.tensor
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.. note:: This function is automatically called when `.transpose()`
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method is used and `no_overlap=False`
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"""
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# initialize convolution
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if self.training: # we choose what to do based on optimization
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self._choose_initialization(X, type='inverse')
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else: # we always initialize on testing
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self._initialize_convolution(X, 'inverse')
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# initialize grid
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X = self._grid_transpose.clone().detach()
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conv_transposed = self._grid_transpose.clone().detach()
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# list to iterate for calculating nn output
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tmp = [i for i in range(self._output_numb_field)]
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iterate_conv = [item for item in tmp for _ in range(
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self._input_numb_field)]
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for batch_idx, x in enumerate(X):
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# accumulator for the convolution on different batches
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accumulator_batch = torch.zeros(
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size=(self._grid_transpose.shape[1],
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self._grid_transpose.shape[2]),
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requires_grad=True,
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device=X.device,
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dtype=X.dtype).clone()
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for stride_idx, current_stride in enumerate(self._stride):
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# indeces of points falling into filter range
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indeces = self._index[stride_idx][batch_idx]
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# number of points for each channel
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numb_pts_channel = tuple(indeces.sum(dim=-1))
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# extracting points for each channel
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point_stride = x[indeces]
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# if no points to upsample we just skip
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if point_stride.nelement() == 0:
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continue
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# mapping points in filter domain
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map_points_(point_stride[..., :-1], current_stride)
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# input points for kernels
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# we split for extracting number of points for each channel
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nn_input_pts = point_stride[..., :-1].split(numb_pts_channel)
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# accumulate partial convolution results for each field
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res_tmp = []
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# for each channel field compute transpose convolution
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for idx_conv, idx_channel_out in enumerate(iterate_conv):
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# index for input channels
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idx_channel_in = idx_conv % self._input_numb_field
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|
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# extract filter
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net = self._net[idx_conv]
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|
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# calculate filter value
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staked_output = net(nn_input_pts[idx_channel_out])
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|
|
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# perform integral for all strides in one field
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|
integral = staked_output * integrals[batch_idx,
|
|
idx_channel_in,
|
|
stride_idx]
|
|
# append results
|
|
res_tmp.append(integral.flatten())
|
|
|
|
# computing channel sum
|
|
channel_sum = []
|
|
start = 0
|
|
for _ in range(self._output_numb_field):
|
|
tmp = res_tmp[start:start + self._input_numb_field]
|
|
tmp = torch.vstack(tmp).sum(dim=0)
|
|
channel_sum.append(tmp)
|
|
start += self._input_numb_field
|
|
|
|
# accumulate the results
|
|
accumulator_batch[indeces] += torch.hstack(channel_sum)
|
|
|
|
# save results of accumulation for each batch
|
|
conv_transposed[batch_idx, ..., -1] = accumulator_batch
|
|
|
|
return conv_transposed
|