update GNO and PointNet
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@@ -180,7 +180,6 @@ class GatingGNO(nn.Module):
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x = blk(x, c, edge_index, edge_attr=edge_attr)
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if plot_results:
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x_ = self.dec(x)
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assert bc == x[boundary_mask]
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plot_results_fn(x_, pos, i * _, batch=batch)
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return self.dec(x)
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@@ -108,14 +108,14 @@ class MLP(torch.nn.Module):
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tmp_layers.append(self._output_dim)
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self._layers = []
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self._batchnorm = []
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self._LayerNorm = []
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for i in range(len(tmp_layers) - 1):
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self._layers.append(
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self.spect_norm(nn.Linear(tmp_layers[i], tmp_layers[i + 1]))
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)
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self._batchnorm.append(nn.LazyBatchNorm1d())
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self._LayerNorm.append(nn.LazyLayerNorm())
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if isinstance(func, list):
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self._functions = func
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@@ -124,7 +124,7 @@ class MLP(torch.nn.Module):
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unique_list = []
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for layer, func, bnorm in zip(
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self._layers[:-1], self._functions, self._batchnorm
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self._layers[:-1], self._functions, self._LayerNorm
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):
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unique_list.append(layer)
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@@ -208,7 +208,7 @@ class TNet(nn.Module):
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)
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self._function = function()
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self._bn1 = nn.LazyBatchNorm1d()
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self._bn1 = nn.LazyLayerNorm()
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def forward(self, X):
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"""Forward pass for T-Net
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@@ -299,9 +299,9 @@ class PointNet(nn.Module):
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self._tnet_feature = TNet(input_dim=64)
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self._function = function()
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self._bn1 = nn.LazyBatchNorm1d()
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self._bn2 = nn.LazyBatchNorm1d()
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self._bn3 = nn.LazyBatchNorm1d()
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self._bn1 = nn.LazyLayerNorm()
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self._bn2 = nn.LazyLayerNorm()
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self._bn3 = nn.LazyLayerNorm()
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def concat(self, embedding, input_):
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"""Returns concatenation of global and local features for Point-Net
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@@ -370,205 +370,3 @@ class PointNet(nn.Module):
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X = self._mlp4(X)
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return X
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class ConvTNet(nn.Module):
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"""T-Net base class. Implementation of T-Network with convolutional layers.
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Reference: Ali Kashefi et al. https://arxiv.org/abs/2208.13434
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"""
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def __init__(self, input_dim):
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"""T-Net block constructor
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:param input_dim: input dimension of point cloud
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:type input_dim: int
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"""
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super().__init__()
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function = nn.Tanh
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self._function = function()
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self._block1 = nn.Sequential(
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nn.Conv1d(input_dim, 64, 1),
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nn.BatchNorm1d(64),
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self._function,
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nn.Conv1d(64, 128, 1),
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nn.BatchNorm1d(128),
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self._function,
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nn.Conv1d(128, 1024, 1),
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nn.BatchNorm1d(1024),
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self._function,
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)
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self._block2 = MLP(
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input_dim=1024,
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output_dim=input_dim * input_dim,
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layers=[512, 256],
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func=function,
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batch_norm=True,
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)
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def forward(self, X):
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"""Forward pass for T-Net
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:param X: input tensor, shape [batch, $input_{dim}$, N]
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with batch the batch size, N number of points and $input_{dim}$
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the input dimension of the point cloud.
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:type X: torch.tensor
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:return: output affine matrix transformation, shape
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[batch, $input_{dim} \times input_{dim}$] with batch
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the batch size and $input_{dim}$ the input dimension
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of the point cloud.
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:rtype: torch.tensor
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"""
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batch, input_dim = X.shape[0], X.shape[1]
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# encoding using first MLP
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X = self._block1(X)
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# applying symmetric function to aggregate information (using max as default)
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X, _ = torch.max(X, dim=-1)
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# decoding using third MLP
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X = self._block2(X)
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return X.reshape(batch, input_dim, input_dim)
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class ConvPointNet(nn.Module):
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"""Point-Net base class. Implementation of Point Network for segmentation.
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Reference: Ali Kashefi et al. https://arxiv.org/abs/2208.13434
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"""
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def __init__(self, input_dim, output_dim, tnet=False):
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"""Point-Net block constructor
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:param input_dim: input dimension of point cloud
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:type input_dim: int
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:param output_dim: output dimension of point cloud
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:type output_dim: int
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:param tnet: apply T-Net transformation, defaults to False
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:type tnet: bool, optional
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"""
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super().__init__()
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self._function = nn.Tanh()
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self._use_tnet = tnet
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self._block1 = nn.Sequential(
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nn.Conv1d(input_dim, 64, 1),
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nn.BatchNorm1d(64),
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self._function,
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nn.Conv1d(64, 64, 1),
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nn.BatchNorm1d(64),
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self._function,
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)
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self._block2 = nn.Sequential(
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nn.Conv1d(64, 64, 1),
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nn.BatchNorm1d(64),
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self._function,
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nn.Conv1d(64, 128, 1),
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nn.BatchNorm1d(128),
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self._function,
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nn.Conv1d(128, 1024, 1),
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nn.BatchNorm1d(1024),
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self._function,
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)
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self._block3 = nn.Sequential(
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nn.Conv1d(1088, 512, 1),
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nn.BatchNorm1d(512),
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self._function,
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nn.Conv1d(512, 256, 1),
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nn.BatchNorm1d(256),
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self._function,
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nn.Conv1d(256, 128, 1),
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nn.BatchNorm1d(128),
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self._function,
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)
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self._block4 = nn.Conv1d(128, output_dim, 1)
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if self._use_tnet:
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self._tnet_transform = ConvTNet(input_dim=input_dim)
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self._tnet_feature = ConvTNet(input_dim=64)
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def concat(self, embedding, input_):
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"""
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Returns concatenation of global and local features for Point-Net
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:param embedding: global features of Point-Net, shape [batch, $input_{dim}$]
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with batch the batch size and $input_{dim}$ the input dimension
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of the point cloud.
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:type embedding: torch.tensor
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:param input_: local features of Point-Net, shape [batch, N, $input_{dim}$]
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with batch the batch size, N number of points and $input_{dim}$
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the input dimension of the point cloud.
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:type input_: torch.tensor
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:return: concatenation vector, shape [batch, N, $input_{dim}$]
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with batch the batch size, N number of points and $input_{dim}$
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:rtype: torch.tensor
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"""
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n_points = input_.shape[-1]
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embedding = embedding.unsqueeze(2).repeat(1, 1, n_points)
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return torch.cat([embedding, input_], dim=1)
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def forward(self, X):
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"""Forward pass for Point-Net
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:param X: input tensor, shape [batch, N, $input_{dim}$]
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with batch the batch size, N number of points and $input_{dim}$
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the input dimension of the point cloud.
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:type X: torch.tensor
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:return: segmentation vector, shape [batch, N, $output_{dim}$]
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with batch the batch size, N number of points and $output_{dim}$
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the output dimension of the point cloud.
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:rtype: torch.tensor
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"""
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# permuting indeces
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X = X.permute(0, 2, 1)
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# using transform tnet if needed
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if self._use_tnet:
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transform = self._tnet_transform(X)
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X = X.transpose(2, 1)
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X = torch.matmul(X, transform)
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X = X.transpose(2, 1)
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# encoding using first MLP
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X = self._block1(X)
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# using transform tnet if needed
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if self._use_tnet:
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transform = self._tnet_feature(X)
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X = X.transpose(2, 1)
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X = torch.matmul(X, transform)
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X = X.transpose(2, 1)
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# saving latent representation for later concatanation
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latent = X
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# encoding using second MLP
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X = self._block2(X)
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# applying symmetric function to aggregate information (using max as default)
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X, _ = torch.max(X, dim=-1)
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# concatenating with latent vector
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X = self.concat(X, latent)
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# decoding using third MLP
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X = self._block3(X)
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# decoding using fourth MLP
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X = self._block4(X)
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# permuting indeces
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X = X.permute(0, 2, 1)
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return X
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