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graphs/multivariate/g2m_edge_index.pt

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graphs/multivariate/g2m_features.pt

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graphs/multivariate/m2g_edge_index.pt

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graphs/multivariate/m2g_features.pt

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graphs/multivariate/m2m_edge_index.pt

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graphs/multivariate/m2m_features.pt

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graphs/multivariate/mesh_features.pt

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+version https://git-lfs.github.com/spec/v1
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graphs/multivariate/view_features.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "g2m_edge_index: torch.Size([2, 9009])\n",
+      "g2m_features: torch.Size([9009, 3])\n",
+      "m2g_edge_index: torch.Size([123444])\n",
+      "m2g_features: torch.Size([123444, 3])\n",
+      "m2m_edge_index: torch.Size([2, 57616])\n",
+      "m2m_features: torch.Size([57616, 3])\n",
+      "mesh_features: torch.Size([6561, 2])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# read torch pt file\n",
+    "g2m_edge_index = torch.load('/home/X-neural-lam/graphs/multivariate/g2m_edge_index.pt')\n",
+    "g2m_features = torch.load('/home/X-neural-lam/graphs/multivariate/g2m_features.pt')\n",
+    "m2g_edge_index = torch.load('/home/X-neural-lam/graphs/multivariate/m2g_edge_index.pt')\n",
+    "m2g_features = torch.load('/home/X-neural-lam/graphs/multivariate/m2g_features.pt') # (3*81*381,3)\n",
+    "m2m_edge_index = torch.load('/home/X-neural-lam/graphs/multivariate/m2m_edge_index.pt')\n",
+    "m2m_features = torch.load('/home/X-neural-lam/graphs/multivariate/m2m_features.pt')\n",
+    "mesh_features = torch.load('/home/X-neural-lam/graphs/multivariate/mesh_features.pt')\n",
+    "\n",
+    "# print shape\n",
+    "print(f'g2m_edge_index: {g2m_edge_index.shape}')\n",
+    "print(f'g2m_features: {g2m_features.shape}')\n",
+    "print(f'm2g_edge_index: {m2g_edge_index[0].shape}')\n",
+    "print(f'm2g_features: {m2g_features.shape}')\n",
+    "print(f'm2m_edge_index: {m2m_edge_index[0].shape}')\n",
+    "print(f'm2m_features: {m2m_features[0].shape}')\n",
+    "print(f'mesh_features: {mesh_features[0].shape}')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## test mlp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input = torch.randn(4,3, 10, requires_grad=True)\n",
+    "net = torch.nn.Linear(10, 5)\n",
+    "output = net(input)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}