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"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a71a7958",
"metadata": {},
"outputs": [],
"source": [
"from graphviz import Digraph\n",
"from vre.utils import str_topk\n",
"\n",
"DATA = {\n",
" 'rgb': [],\n",
" 'semantic_m2f_coco_0': [],\n",
" 'semantic_m2f_mapillary_0': [],\n",
" 'semantic_m2f_mapillary_1': [],\n",
" 'depth_marigold': [],\n",
" 'semantic_m2f_swin_coco_converted': ['semantic_m2f_coco_0'],\n",
" 'semantic_m2f_swin_mapillary_converted': ['semantic_m2f_mapillary_0'],\n",
" 'semantic_m2f_r50_mapillary_converted': ['semantic_m2f_mapillary_1'],\n",
" 'buildings': ['semantic_m2f_mapillary_0', 'semantic_m2f_coco_0', 'semantic_m2f_mapillary_1'],\n",
" 'sky-and-water': ['semantic_m2f_mapillary_0', 'semantic_m2f_coco_0', 'semantic_m2f_mapillary_1'],\n",
" 'transportation': ['semantic_m2f_mapillary_0', 'semantic_m2f_coco_0', 'semantic_m2f_mapillary_1'],\n",
" 'containing': ['semantic_m2f_mapillary_0', 'semantic_m2f_coco_0', 'semantic_m2f_mapillary_1'],\n",
" 'vegetation': ['semantic_m2f_mapillary_0', 'semantic_m2f_coco_0', 'semantic_m2f_mapillary_1'],\n",
" 'normals_svd(depth_marigold)': ['depth_marigold'],\n",
" 'buildings(nearby)': ['semantic_m2f_mapillary_0', 'semantic_m2f_coco_0', 'semantic_m2f_mapillary_1', 'depth_marigold'],\n",
" 'semantic_output': ['semantic_m2f_swin_mapillary_converted', 'semantic_m2f_r50_mapillary_converted', 'semantic_m2f_swin_coco_converted'],\n",
" 'safe-landing-no-sseg': ['depth_marigold', 'normals_svd(depth_marigold)'],\n",
" 'safe-landing-semantics': ['depth_marigold', 'normals_svd(depth_marigold)', 'semantic_m2f_mapillary_0', 'semantic_m2f_coco_0', 'semantic_m2f_mapillary_1'],\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6b33d6b",
"metadata": {},
"outputs": [],
"source": [
"def f(item: str) -> str:\n",
" if item == 'buildings(nearby)':\n",
" return 'buildings\\n(nearby)'\n",
" if item == 'safe-landing-semantics':\n",
" return 'safe-landing\\n-semantics'\n",
" if item == 'semantic_output':\n",
" return 'semantic\\n_output'\n",
" if item == 'semantic_m2f_coco_0':\n",
" return 'semantic_m2f\\n_coco_0'\n",
" if item == 'safe-landing-no-sseg':\n",
" return 'safe-landing\\n-no-sseg'\n",
" if item == 'normals_svd(depth_marigold)':\n",
" return 'normals_svd\\n(depth_marigold)'\n",
" if item == 'semantic_m2f_swin_mapillary_converted':\n",
" return 'semantic_m2f_swin\\n_mapillary_converted'\n",
" if item == 'semantic_m2f_swin_coco_converted':\n",
" return 'semantic_m2f_swin\\n_coco_converted'\n",
" if item == 'semantic_m2f_r50_mapillary_converted':\n",
" return 'semantic_m2f_r50\\n_mapillary_converted'\n",
" if len(item) < 15:\n",
" return item\n",
"\n",
" k = len(item) // 2\n",
" parts = []\n",
" for i in range(len(item) // k + (len(item) % k != 0)):\n",
" parts.append(item[i*k: (i+1)*k])\n",
" return \"\\n\".join(parts)\n",
"\n",
"# print(f(\"abc\"))\n",
"# print(f(\"abcabcabcabc\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e6ef629",
"metadata": {},
"outputs": [],
"source": [
"g = Digraph()\n",
"g.attr(rankdir=\"LR\")\n",
"shape = \"box\"\n",
"edges: list[tuple[str, str]] = []\n",
"for node, node_deps in DATA.items():\n",
" for node_dep in node_deps:\n",
" edges.append((f(node), f(node_dep)))\n",
" else:\n",
" g.node(f(node), shape=shape)\n",
"for l, r in edges:\n",
" g.edge(r, l, shape=shape) # reverse?\n",
"display(g)\n",
"g.render(\"graph-test\", cleanup=True, format=\"png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "441ab171",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "vre",
"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": 5
}
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