From 59f497667a8852dc0148ed6cca5b15c1ebee3f58 Mon Sep 17 00:00:00 2001 From: Hongwei Jin Date: Fri, 12 Jul 2024 13:58:18 -0500 Subject: [PATCH 1/3] Delaunay Lifting (pointcloud2graph) with new ShapeNet dataset --- configs/datasets/ShapeNet.yaml | 14 + configs/models/graph/graphsage.yaml | 4 + .../pointcloud2graph/delaunay_lifting.yaml | 3 + modules/data/load/loaders.py | 6 + modules/models/graph/graphsage.py | 80 ++++ modules/transforms/data_transform.py | 4 + .../pointcloud2graph/delaunay_lifting.py | 77 ++++ .../pointcloud2graph/test_delaunay_lifting.py | 38 ++ .../pointcloud2graph/delaunay_lifting.ipynb | 358 ++++++++++++++++++ 9 files changed, 584 insertions(+) create mode 100644 configs/datasets/ShapeNet.yaml create mode 100644 configs/models/graph/graphsage.yaml create mode 100644 configs/transforms/liftings/pointcloud2graph/delaunay_lifting.yaml create mode 100644 modules/models/graph/graphsage.py create mode 100644 modules/transforms/liftings/pointcloud2graph/delaunay_lifting.py create mode 100644 test/transforms/liftings/pointcloud2graph/test_delaunay_lifting.py create mode 100644 tutorials/pointcloud2graph/delaunay_lifting.ipynb diff --git a/configs/datasets/ShapeNet.yaml b/configs/datasets/ShapeNet.yaml new file mode 100644 index 00000000..27440278 --- /dev/null +++ b/configs/datasets/ShapeNet.yaml @@ -0,0 +1,14 @@ +data_domain: point_cloud +data_type: ShapeNet +data_name: ShapeNet +data_dir: datasets/${data_domain}/${data_type} +#data_split_dir: ${oc.env:PROJECT_ROOT}/datasets/data_splits/${data_name} + +# Dataset parameters +num_features: 3 +num_classes: 50 +category: plane +task: classification +loss_type: cross_entropy +monitor_metric: accuracy +task_level: graph diff --git a/configs/models/graph/graphsage.yaml b/configs/models/graph/graphsage.yaml new file mode 100644 index 00000000..c0b9fdcd --- /dev/null +++ b/configs/models/graph/graphsage.yaml @@ -0,0 +1,4 @@ +in_channels: -1 # This will be set by the dataset +hidden_channels: 32 +out_channels: null # This will be set by the dataset +n_layers: 2 diff --git a/configs/transforms/liftings/pointcloud2graph/delaunay_lifting.yaml b/configs/transforms/liftings/pointcloud2graph/delaunay_lifting.yaml new file mode 100644 index 00000000..2870c5d3 --- /dev/null +++ b/configs/transforms/liftings/pointcloud2graph/delaunay_lifting.yaml @@ -0,0 +1,3 @@ +transform_type: "lifting" +transform_name: "GraphDelaunayLifting" +feature_lifting: ProjectionSum diff --git a/modules/data/load/loaders.py b/modules/data/load/loaders.py index 8ccafb11..f9e464e0 100755 --- a/modules/data/load/loaders.py +++ b/modules/data/load/loaders.py @@ -108,6 +108,12 @@ def load(self) -> torch_geometric.data.Dataset: data = load_manual_graph() dataset = CustomDataset([data], self.data_dir) + elif self.parameters.data_name in ["ShapeNet"]: + dataset = torch_geometric.datasets.ShapeNet( + root=root_data_dir, + include_normals=True, + ) + else: raise NotImplementedError( f"Dataset {self.parameters.data_name} not implemented" diff --git a/modules/models/graph/graphsage.py b/modules/models/graph/graphsage.py new file mode 100644 index 00000000..b124e3bb --- /dev/null +++ b/modules/models/graph/graphsage.py @@ -0,0 +1,80 @@ +import torch +from torch import Tensor +from torch_geometric.nn.models import GraphSAGE +from torch_geometric.utils import scatter + + +def global_mean_pool(x, batch=None, size=None) -> Tensor: + r"""Returns batch-wise graph-level-outputs by averaging node features + across the node dimension. + + For a single graph :math:`\mathcal{G}_i`, its output is computed by + + .. math:: + \mathbf{r}_i = \frac{1}{N_i} \sum_{n=1}^{N_i} \mathbf{x}_n. + + Functional method of the + :class:`~torch_geometric.nn.aggr.MeanAggregation` module. + + Parameters + ---------- + x : torch.Tensor + Node feature matrix :math:`\mathbf{X}`. + batch : torch.Tensor, optional + The batch vector :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, + which assigns each node to a specific example. + size : int, optional + The number of examples :math:`B`. Automatically calculated if not given. + """ + dim = -1 if isinstance(x, Tensor) and x.dim() == 1 else -2 + + if batch is None: + return x.mean(dim=dim, keepdim=x.dim() <= 2) + return scatter(x, batch, dim=dim, dim_size=size, reduce="mean") + + +class GraphSAGEModel(torch.nn.Module): + r"""A simple GreaphSage model that runs over graph data. + Note that some parameters are defined by the considered dataset. + + Parameters + ---------- + model_config : Dict | DictConfig + Model configuration. + dataset_config : Dict | DictConfig + Dataset configuration. + """ + + def __init__(self, model_config, dataset_config): + in_channels = ( + dataset_config["num_features"] + if isinstance(dataset_config["num_features"], int) + else dataset_config["num_features"][0] + ) + hidden_channels = model_config["hidden_channels"] + out_channels = dataset_config["num_classes"] + n_layers = model_config["n_layers"] + super().__init__() + self.base_model = GraphSAGE( + in_channels=in_channels, + hidden_channels=hidden_channels, + out_channels=out_channels, + num_layers=n_layers, + ) + self.pool = global_mean_pool + + def forward(self, data): + r"""Forward pass of the model. + + Parameters + ---------- + data : torch_geometric.data.Data + Input data. + + Returns + ------- + torch.Tensor + Output tensor. + """ + z = self.base_model(data.x, data.edge_index) + return self.pool(z) diff --git a/modules/transforms/data_transform.py b/modules/transforms/data_transform.py index 59253ecf..f362056f 100755 --- a/modules/transforms/data_transform.py +++ b/modules/transforms/data_transform.py @@ -15,6 +15,9 @@ from modules.transforms.liftings.graph2simplicial.clique_lifting import ( SimplicialCliqueLifting, ) +from modules.transforms.liftings.pointcloud2graph.delaunay_lifting import ( + GraphDelaunayLifting, +) TRANSFORMS = { # Graph -> Hypergraph @@ -31,6 +34,7 @@ "OneHotDegreeFeatures": OneHotDegreeFeatures, "NodeFeaturesToFloat": NodeFeaturesToFloat, "KeepOnlyConnectedComponent": KeepOnlyConnectedComponent, + "GraphDelaunayLifting": GraphDelaunayLifting, } diff --git a/modules/transforms/liftings/pointcloud2graph/delaunay_lifting.py b/modules/transforms/liftings/pointcloud2graph/delaunay_lifting.py new file mode 100644 index 00000000..716ce7a1 --- /dev/null +++ b/modules/transforms/liftings/pointcloud2graph/delaunay_lifting.py @@ -0,0 +1,77 @@ +import torch +import torch_geometric +from torch_geometric.transforms import Delaunay +from torch_geometric.utils import to_undirected + +from modules.transforms.liftings.pointcloud2graph.base import PointCloud2GraphLifting + + +class GraphDelaunayLifting(PointCloud2GraphLifting): + r"""Lifts point cloud to graph domain by considering k-nearest neighbors. + + Parameters + ---------- + **kwargs : optional + Additional arguments for the class. + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.transform = Delaunay() + + def face_to_edge(self, data: torch_geometric.data.Data): + r"""Converts mesh faces to edges indices for both 2D and 3D meshes. + + Parameters + ---------- + data : torch_geometric.data.Data + The input data to be converted. + + Returns + ------- + torch_geometric.data.Data + The converted data. + """ + if hasattr(data, "face"): + assert data.face is not None + face = data.face + if face.shape[0] == 3: + # 2D + edge_index = torch.cat([face[:2], face[1:], face[::2]], dim=1) + edge_index = to_undirected(edge_index, num_nodes=data.num_nodes) + elif face.shape[0] == 4: + # 3D + edge_index = torch.cat( + [face[:2], face[1:3], face[2:], face[::3]], dim=1 + ) + edge_index = to_undirected(edge_index, num_nodes=data.num_nodes) + else: + raise ValueError("Faces must be of dimension 2 or 3.") + data.edge_index = edge_index + return data + + def lift_topology(self, data: torch_geometric.data.Data) -> dict: + r"""Lifts the topology of a graph to hypergraph domain by considering k-nearest neighbors. + + Parameters + ---------- + data : torch_geometric.data.Data + The input data to be lifted. + + Returns + ------- + dict + The lifted topology. + """ + num_nodes = data.x.shape[0] + + # Step 1: Perform Delaunay Triangulation to get faces + data_delaunay = self.transform(data) + faces = data_delaunay.face + # Step 2: Create Edge List from faces + data = self.face_to_edge(data_delaunay) + + # Step 3: Convert Edge List to edge_index format + edge_index = data.edge_index + + return {"num_nodes": num_nodes, "edge_index": edge_index, "face": faces} diff --git a/test/transforms/liftings/pointcloud2graph/test_delaunay_lifting.py b/test/transforms/liftings/pointcloud2graph/test_delaunay_lifting.py new file mode 100644 index 00000000..d390f42e --- /dev/null +++ b/test/transforms/liftings/pointcloud2graph/test_delaunay_lifting.py @@ -0,0 +1,38 @@ +import torch +from torch_geometric.data import Data + +from modules.transforms.liftings.pointcloud2graph.delaunay_lifting import ( + GraphDelaunayLifting, +) + + +class TestDelaunayLifting: + """Test the GraphDelaunayLifting class.""" + + def setup_method(self): + """Set up the test.""" + # Define the data and the lifting. + pos = torch.tensor( + [ + [0.0, 0.0, 1.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.5], + [1.0, 1.0, 0.5], + [0.5, 0.5, 1.0], + ], + dtype=torch.float32, + ) + x = torch.tensor( + [[1.0, 2.0], [2.0, 3.0], [3.0, 4.0], [4.0, 5.0], [5.0, 6.0]], + dtype=torch.float32, + ) + self.data = Data(x=x, pos=pos) + self.lifting = GraphDelaunayLifting() + + def test_lift_topology(self): + """Test the lift_topology method.""" + + lifted = self.lifting.forward(self.data.clone()) + assert lifted.num_nodes == 5, "The number of nodes is incorrect." + assert lifted.edge_index.shape == (2, 14), "The number of edges is incorrect." + assert lifted.face.shape == (4, 2), "The number of faces is incorrect." diff --git a/tutorials/pointcloud2graph/delaunay_lifting.ipynb b/tutorials/pointcloud2graph/delaunay_lifting.ipynb new file mode 100644 index 00000000..5072c221 --- /dev/null +++ b/tutorials/pointcloud2graph/delaunay_lifting.ipynb @@ -0,0 +1,358 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pointcloud-to-Graph Delaunay Lifting Tutorial" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# With this cell any imported module is reloaded before each cell execution\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "from modules.data.load.loaders import GraphLoader\n", + "from modules.data.preprocess.preprocessor import PreProcessor\n", + "from modules.utils.utils import (\n", + " describe_data,\n", + " load_dataset_config,\n", + " load_model_config,\n", + " load_transform_config,\n", + ")\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the Dataset (ShapeNet)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset configuration for ShapeNet:\n", + "\n", + "{'data_domain': 'point_cloud',\n", + " 'data_type': 'ShapeNet',\n", + " 'data_name': 'ShapeNet',\n", + " 'data_dir': 'datasets/point_cloud/ShapeNet',\n", + " 'num_features': 3,\n", + " 'num_classes': 50,\n", + " 'category': 'plane',\n", + " 'task': 'classification',\n", + " 'loss_type': 'cross_entropy',\n", + " 'monitor_metric': 'accuracy',\n", + " 'task_level': 'graph'}\n" + ] + } + ], + "source": [ + "dataset_name = \"ShapeNet\"\n", + "dataset_config = load_dataset_config(dataset_name)\n", + "loader = GraphLoader(dataset_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = loader.load()\n", + "# describe_data(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([2252, 3])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[0].pos.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# import matplotlib.pyplot as plt\n", + "# import numpy as np\n", + "\n", + "# Assuming dataset[0].pos and dataset[0].y are defined\n", + "pos = dataset[1].pos\n", + "y = dataset[1].y\n", + "\n", + "# Create a 3D scatter plot\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection=\"3d\")\n", + "\n", + "# Use a colormap to map the values in data.y to colors\n", + "# 'viridis' is a good default choice for a colormap, but you can choose another\n", + "scatter = ax.scatter(pos[:, 0], pos[:, 1], pos[:, 2], c=y, cmap=\"viridis\", s=5)\n", + "\n", + "# Add a colorbar to the plot to show the mapping from data.y values to colors\n", + "plt.colorbar(scatter, ax=ax, label=\"data.y values\")\n", + "\n", + "# Set the title and remove grid, labels, and background\n", + "ax.set_title(\"Graph in 3D\")\n", + "ax.grid(False)\n", + "ax.set_xticklabels([])\n", + "ax.set_yticklabels([])\n", + "ax.set_zticklabels([]) # Also remove z labels\n", + "ax.set_facecolor(\"none\")\n", + "\n", + "# Rotate the axes with the mouse\n", + "# ax.view_init(elev=20, azim=30)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# import matplotlib.pyplot as plt\n", + "# import numpy as np\n", + "# import imageio\n", + "\n", + "# # Assuming dataset[1].pos and dataset[1].y are defined\n", + "# pos = dataset[1].pos\n", + "# y = dataset[1].y\n", + "\n", + "# # Function to create and return the 3D scatter plot as an image array\n", + "\n", + "\n", + "# def create_3d_scatter(elev=30, azim=30):\n", + "# fig = plt.figure()\n", + "# ax = fig.add_subplot(111, projection='3d')\n", + "# scatter = ax.scatter(pos[:, 0], pos[:, 1], pos[:, 2], c=y, cmap='viridis', s=5)\n", + "# ax.view_init(elev=elev, azim=azim)\n", + "# ax.grid(False)\n", + "# ax.set_xticklabels([])\n", + "# ax.set_yticklabels([])\n", + "# ax.set_zticklabels([])\n", + "# ax.set_facecolor('none')\n", + "# # Convert the Matplotlib figure to an image array\n", + "# fig.canvas.draw() # Draw the figure\n", + "# image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')\n", + "# image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n", + "# plt.close(fig) # Close the figure to free memory\n", + "# return image\n", + "\n", + "\n", + "# # Create a list to hold the image frames\n", + "# frames = []\n", + "\n", + "# # Generate frames by rotating the azimuth angle\n", + "# for azim in range(0, 360, 10): # Adjust the step and range as needed\n", + "# frame = create_3d_scatter(elev=20, azim=azim)\n", + "# frames.append(frame)\n", + "\n", + "# # Save frames as a GIF\n", + "# imageio.mimsave('3d_scatter_rotation.gif', frames, fps=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Transform configuration for pointcloud2graph/delaunay_lifting:\n", + "\n", + "{'transform_type': 'lifting',\n", + " 'transform_name': 'GraphDelaunayLifting',\n", + " 'feature_lifting': 'ProjectionSum'}\n" + ] + } + ], + "source": [ + "# Define transformation type and id\n", + "transform_type = \"liftings\"\n", + "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", + "transform_id = \"pointcloud2graph/delaunay_lifting\"\n", + "\n", + "# Read yaml file\n", + "transform_config = {\n", + " \"lifting\": load_transform_config(transform_type, transform_id)\n", + " # other transforms (e.g. data manipulations, feature liftings) can be added here\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: /home/jinh/icml24/challenge-icml-2024/datasets/point_cloud/ShapeNet/ShapeNet/lifting/1731765996\n" + ] + } + ], + "source": [ + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "# describe_data(lifted_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# from torch_geometric.utils import to_networkx\n", + "# import networkx as nx\n", + "# from mpl_toolkits.mplot3d import Axes3D\n", + "# import numpy as np\n", + "\n", + "# data = lifted_dataset[1]\n", + "# G = to_networkx(data,to_undirected=True)\n", + "# for i, node in enumerate(G.nodes()):\n", + "# G.nodes[node]['pos'] = data.pos[i].numpy()\n", + "# node_colors = data.y.numpy()\n", + "# pos = nx.get_node_attributes(G, 'pos')\n", + "# # node_xyz = np.array(pos[v] for v in sorted(G))\n", + "# node_xyz = np.array(list(pos.values()))\n", + "# edge_xyz = np.array([(pos[u], pos[v]) for u, v in G.edges()])\n", + "\n", + "# # Create the 3D figure\n", + "# fig = plt.figure()\n", + "# ax = fig.add_subplot(111, projection=\"3d\")\n", + "\n", + "# # Plot the nodes - alpha is scaled by \"depth\" automatically\n", + "# ax.scatter(*node_xyz.T, s=5, c=node_colors)\n", + "\n", + "# for vizedge in edge_xyz:\n", + "# ax.plot(*vizedge.T, color=\"tab:gray\")\n", + "\n", + "# ax.grid(False)\n", + "# # Suppress tick labels\n", + "# for dim in (ax.xaxis, ax.yaxis, ax.zaxis):\n", + "# dim.set_ticks([])\n", + "# # Set axes labels\n", + "# ax.set_xlabel(\"x\")\n", + "# ax.set_ylabel(\"y\")\n", + "# ax.set_zlabel(\"z\")\n", + "# fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create and Run a GraphSage Model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model configuration for graph GRAPHSAGE:\n", + "\n", + "{'in_channels': -1, 'hidden_channels': 32, 'out_channels': None, 'n_layers': 2}\n" + ] + } + ], + "source": [ + "from modules.models.graph.graphsage import GraphSAGEModel\n", + "\n", + "model_type = \"graph\"\n", + "model_id = \"graphsage\"\n", + "model_config = load_model_config(model_type, model_id)\n", + "\n", + "model = GraphSAGEModel(model_config, dataset_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "y_hat = model(lifted_dataset.get(0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "topox", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2bfe3bbfb7cbe23140d5418c9732d3a1a9bdf4be Mon Sep 17 00:00:00 2001 From: Hongwei Jin Date: Fri, 12 Jul 2024 14:02:53 -0500 Subject: [PATCH 2/3] fix linting --- tutorials/pointcloud2graph/delaunay_lifting.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/tutorials/pointcloud2graph/delaunay_lifting.ipynb b/tutorials/pointcloud2graph/delaunay_lifting.ipynb index 5072c221..74b80086 100644 --- a/tutorials/pointcloud2graph/delaunay_lifting.ipynb +++ b/tutorials/pointcloud2graph/delaunay_lifting.ipynb @@ -19,7 +19,6 @@ "from modules.data.load.loaders import GraphLoader\n", "from modules.data.preprocess.preprocessor import PreProcessor\n", "from modules.utils.utils import (\n", - " describe_data,\n", " load_dataset_config,\n", " load_model_config,\n", " load_transform_config,\n", From 79af79cf89313f56d94aa5f9290696d32351efba Mon Sep 17 00:00:00 2001 From: Hongwei Jin Date: Fri, 12 Jul 2024 14:10:00 -0500 Subject: [PATCH 3/3] fix linting --- tutorials/pointcloud2graph/delaunay_lifting.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tutorials/pointcloud2graph/delaunay_lifting.ipynb b/tutorials/pointcloud2graph/delaunay_lifting.ipynb index 74b80086..ec9e817e 100644 --- a/tutorials/pointcloud2graph/delaunay_lifting.ipynb +++ b/tutorials/pointcloud2graph/delaunay_lifting.ipynb @@ -16,14 +16,15 @@ "# With this cell any imported module is reloaded before each cell execution\n", "%load_ext autoreload\n", "%autoreload 2\n", + "import matplotlib.pyplot as plt\n", + "\n", "from modules.data.load.loaders import GraphLoader\n", "from modules.data.preprocess.preprocessor import PreProcessor\n", "from modules.utils.utils import (\n", " load_dataset_config,\n", " load_model_config,\n", " load_transform_config,\n", - ")\n", - "import matplotlib.pyplot as plt" + ")" ] }, {