init.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from e3nn.nn.models.v2106.gate_points_networks import SimpleNetwork, radius_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determine the average number of neighbors\n",
    "\n",
    "r_max = 1.0\n",
    "pos = torch.randn(100, 3)\n",
    "r = torch.cdist(pos, pos)\n",
    "index = ((r < r_max) & (r > 0)).nonzero().T\n",
    "\n",
    "index.shape[1] / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = SimpleNetwork(\n",
    "    irreps_in=\"32x0e\",\n",
    "    irreps_out=\"0e\",\n",
    "    max_radius=1.0,\n",
    "    num_neighbors=8.0,\n",
    "    num_nodes=100,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50x0e+50x1o+50x2e [0.9967367053031921, 0.9615816473960876, 0.9536312222480774]\n",
      "50x0e+50x1o+50x1e+50x2o+50x2e [1.0500167608261108, 0.9709723591804504, 1.241517186164856, 1.1689879894256592, 0.9532064199447632]\n",
      "50x0o+50x0e+50x1o+50x1e+50x2o+50x2e [0.9403162002563477, 1.178753137588501, 0.9169595241546631, 1.3434650897979736, 1.2201933860778809, 0.8891504406929016]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[-2.4210]], grad_fn=<DivBackward0>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network({\n",
    "    \"pos\": torch.randn(100, 3),\n",
    "    \"x\": torch.randn(100, 32),\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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